essay on evolutionary biology

Volume 5 Supplement 2

Evolutionary Developmental Biology

• Open access
• Published: 08 June 2012

Evolutionary Developmental Biology (Evo-Devo): Past, Present, and Future

• Brian K. Hall 1  

Evolution: Education and Outreach volume  5 ,  pages 184–193 ( 2012 ) Cite this article

39k Accesses

35 Citations

15 Altmetric

Metrics details

Evolutionary developmental biology (evo–devo) is that part of biology concerned with how changes in embryonic development during single generations relate to the evolutionary changes that occur between generations. Charles Darwin argued for the importance of development (embryology) in understanding evolution. After the discovery in 1900 of Mendel’s research on genetics, however, any relationship between development and evolution was either regarded as unimportant for understanding the process(es) of evolution or as a black box into which it was hard to see. Research over the past two decades has opened that black box, revealing how studies in evo–devo highlight the mechanisms that link genes (the genotype) with structures (the phenotype). This is vitally important because genes do not make structures. Developmental processes make structures using road maps provided by genes, but using many other signals as well—physical forces such as mechanical stimulation, temperature of the environment, and interaction with chemical products produced by other species—often species in entirely different kingdoms as in interactions between bacteria and squid or between leaves and larvae (Greene Science 243:643–666, 1989 ). Not only do genes not make structures (the phenotype), but new properties and mechanisms emerge during embryonic development: genes are regulated differentially in different cells and places; aggregations of similar cells provide the cellular resources (modules) from which tissues and organs arise; modules and populations of differently differentiated cells interact to set development along particular tracks; and organisms interact with their environment and create their niche in that environment. Such interactions are often termed “epigenetic,” meaning that they direct gene activity using mechanisms that are not encoded in the DNA of the genes. This paper reviews the origins of evo–devo, how the field has changed over the past 30 years, evaluates the recognition of the importance for development and evolution of mechanisms that are not encoded in DNA, and evaluates what the future might bring for evo–devo. Although impossible to know, history tells us that we might expect more of the same; expansion of evo–devo into other areas of biology (ecology, physiology, behavior); absorption of evo–devo by evolution or a unification of biology in which evo–devo plays a major role.

Introduction

Evolutionary developmental biology (evo–devo) is the name for that part of biology involved in understanding how alterations in the mechanisms of embryonic development influence or direct evolutionary changes in any and all stages of the life cycle. The field has become enormous in recent years; McCain ( 2009 ) analyzed thousands of papers published between 1975 and 2004 in her study of the impact of one early practitioner of evo–devo, Conrad Hal Waddington. Consequently, what follows is my perspective on the past, present, and future of evo–devo. I apologize in advance to those researchers whose contributions are underrepresented.

Although not usually regarded as a “founding father” of the field, the term “evolutionary developmental biology” appears first to have been used in print in 1983 by the zoologist and later environmentalist, Peter Calow, of the University of Sheffield in England. In an earlier book, Calow ( 1978 ) had provided an influential treatment of life cycles from developmental, evolutionary, and physiological points of view. Calow included a chapter on evolution and development (in itself unusual at the time Footnote 1 ) in his 1983 book Evolutionary Principles , published by Blackie in their Tertiary Level Biology Series. Calow described the field of evolution and development as “evolutionary developmental biology,” noting that “The area is a relatively new and complex one so the reader should not expect to find fully comprehensive treatments in the literature” (p. 80).

In his book of only 100 pages and 105 references and contrary to much prior opinion, Calow emphasized that natural selection could and did act at any stage in the life cycle of multicellular organisms, a conclusion reached by one of the founders of evolutionary embryology 109 years earlier. In his first paper on shark development, Francis Balfour argued for the role of selection in embryonic development as perhaps even more relevant for evolution than selection on the adult:

I see no reason for doubting that the embryo in the earliest periods of development is as subject to the laws of natural selection as is the animal at any other period. Indeed, there appear to me grounds for the thinking that it is more so. (Balfour 1874 , p. 343)

Although the roots of evo–devo are deep (Gould 1977 ; Bonner 1982 ; Arthur 1988 ; Hall and Olson 2003 ; Laubichler and Maienschein 2007 ; Olsson et al. 2009 ), evo–devo is just coming into its own. In the introduction to the publication of the Kowalevsky Medal winner symposium held in January 2003 (published in 2004) to recognize the research of the first recipients of the Kowalevsky medal, Laubichler and Wagner concluded that “By all accounts ‘evo–devo’ has arrived. It is now solidly entrenched in the conceptual framework of modern biology and has all the markings of a new discipline, such as representation in professional societies, scientific journals devoted to the field, academic programs and job searches, panels at funding agencies, textbooks, etc.” ( 2004 , p. 1) Eight years later, classes, courses, workshops, postdoctoral fellowships, faculty positions, university chairs, and research grant selection panels in evo–devo are widespread.

I took “evolutionary developmental biology” as the title for my 1992 book (Hall 1992 ), which set out to summarize what was then known about the origins of the field, its history, and its role in contributing to the evolutionary process. I will do a little of the same in the paper. Why? Because we can only appreciate the future prospects of evo–devo in the context of its past. What questions were being asked of embryos and of development? To which organisms were these questions addressed? Were we seeking a broad overarching theory of evo–devo that would apply to all animal life or differences that characterized grades of biological organization—kingdoms, phyla, classes, even individual species. What was missing from prevalent approaches to evolution that necessitated this new developmental approach?

Origins of Evo-Devo

I go back to the late nineteenth century when we find the origins of evo–devo in the research of individuals in England (largely Trinity College, Cambridge) and in Continental Europe. These evolutionary morphologists/evolutionary embryologists were attracted to this research following the publications of The Origin of Species by Charles Darwin (Darwin 1859 ) and Ernst Haeckel’s theory that ontogeny recapitulates phylogeny (Haeckel 1866 ). Paradoxically, the first published study testing Darwin’s theory using embryos and larvae—Fritz Müller’s study of crustacean life histories (Müller 1864 )—showed that ontogeny could be used to understand patterns of evolutionary history (phylogeny) and that mechanisms could be sought in ontogeny. So varied were crustacean life history strategies found to be that Müller found he could use the details and varieties of life history stages to construct a phylogeny of crustacean relationships. Haeckel took exactly the opposite position. Haeckel theorized that phylogeny explains ontogeny and erected his Biogenetic Law on this basis.

Embryos provided the way to study evolution. The fossil record was incomplete. Embryos, on the other hand, recorded in their development the history of their ancestors. This history had to be read with great care; there were gaps in the record, and secondary specializations such as the placenta could confuse the unwary (Bowler 1996 ; Hall 1999 ). Nevertheless, from the late 1860s or early 1870s until the mid-1880s, evolutionary embryology was the field that attracted the brightest and best zoologists. It attracted those who wanted to study embryos in the laboratory or field station and those who wanted to seek embryos of such ‘missing links’ as the platypus (thought to link reptiles and mammals), lungfish (thought to link fish and tetrapods), and the velvet worm Peripatus (thought to link insects and arthropods) in such exotic places as Australia, South America and Africa (Hall 1999 , 2001 ; MacLeod 1994 ; Bowler 1996 ; Laubichler and Maienschein 2007 ). William Bateson, the English zoologist who coined the name “genetics,” began his career as an evolutionary embryologist. Reminiscing on his career, Bateson commented that

Morphology was studied because it was the material believed to be the most favorable for the elucidation of the problems of evolution, and we all thought that in embryology the quintessence of morphological truth was most palpably presented. Therefore every aspiring zoologist was an embryologist, and the one topic of professional conversation was evolution. (Bateson 1922 , p. 56)

Frustration with reconstructing evolutionary trees from embryonic sequences, the rise of experimental and physiological approaches to embryonic development in the 1880s, and the rediscovery of Mendelian genetics in 1900 all cast evolutionary embryology into a backwater from which it would take a century to resurface. Mendel’s principles of segregation and assortment coupled with studies on the fruit fly Drosophila provided a powerful foundation upon which the new science of genetics was built. Publication of Dobzhansky’s ( 1937 ) influential book, “ Genetics and the Origin of Species ,” provided a basis for understanding evolution through population genetics, the mathematical models for which have been developed in the 1920.

By the middle of the twentieth century, maintenance of the features of organisms, variation in those features, and the origin of new features all seemed explicable by a fusion of Mendelian and population genetics. Paradoxically, the use of Drosophila as the model organism for genetics eliminated the roles of embryonic development and of the environment from evolutionary discussion and theory; inbred laboratory organisms display none of the variation and adaptability seen in nature.

The Twentieth Century

Despite the valiant attempts of a handful of individual researchers through the first six decades of the twentieth century, linking embryos and evolution did not make a comeback until Stephen J. Gould’s book Ontogeny and Phylogeny was published in 1977. Weighing in at 501 pages, this was no easy read, especially as the first half was a detailed history of the way in which development and evolution (ontogeny and phylogeny—the terms proposed by Haeckel) had been related in the past and when the past for Gould began at 450 BC. This historical tour de force was important; Gould reminded us, for indeed we had forgotten that we had been seeking relationships between development and evolution for millennia.

The second half of Gould’s book was equally important because it revisited a concept conceived by Haeckel in the 1860s, elaborated by Gavin de Beer in the 1930s (de Beer 1930 ), but then all but abandoned. Haeckel and de Beer’s concept is summed up in the word “heterochrony”—changes in the timing of developmental processes between a descendant and its ancestors (see Zelditch 2001 ; Willmore 2010 ). De Beer’s insight was to take studies showing that genes affected the rates of physiological processes in insects (Goldschmidt 1918 ) and to see that the rates of development must change during evolution. Here was a way in which comparative embryology could be investigated in an evolutionary (phylogenetic) framework). Heterochrony as a developmental mechanism operating during individual development could be selected for and so was important in evolutionary change; heterochrony was an “evolutionary developmental mechanism” (Hall and Olson 2003 ).

Julian Huxley, one of the founders of the modern synthesis of evolution, appreciated the importance for evolution of the study of genes in development—“a study of genes during development is as essential for an understanding of evolution as are the study of mutation and selection” (Huxley 1942 , p. 8)—but even Huxley neglected to incorporate development into evolutionary theory. Other founders of the modern synthesis such as Ernst Mayr spoke of an internal biology (to which development belonged) and an external biology (evolution, ecological interactions) as if the two were separate and non-overlapping/interacting one to the other (Mayr 1982 , 1997 ).

Because it was difficult to compare ancestors and their descendants, living organisms were compared with one another, initially across broad divisions such as phyla and classes—homology of the tissue interactions that initiate the development of Meckel’s cartilage in the lower jaw of amphibians, birds, and mammals (Goodwin et al. 1983 ), for example—but increasingly across smaller evolutionary gaps, to the point that pairs of species in the same genus (congeneric species) could be compared

—loss of the larval stage from one of a pair of congeneric sea urchins (Raff 1992 ), for example—

changes in the developmental processes in inbred strains of a single species of laboratory animal could be compared as experiments in evo–devo.

—changes in the timing (heterochrony) of the tissue interactions responsible for the induction of the lower jaw in three inbred strains of mice (C57BL, C3H/He, CBA/J) by MacDonald and Hall ( 2001 ), for example—

to the present day when variation in individuals of a single species are revealing the mechanisms underlying the maintenance of natural variation in developmental processes upon which natural selection can act

—as in the correlation of genetic distance between individuals in relation to variation in the timing of developmental events in the pond snail, Radix balthica by Tills et al. ( 2011 ).

Evo–devo exploded as heterochrony was found everywhere. Along the way, heterochrony became such a pervasive term that it lost some of its explanatory power; any change in timing became heterochrony, whether evolutionarily relevant or not (Zelditch 2001 , especially pp. vii–ix).

The next major impetus to evo–devo was not the resurrection of a previously known evolutionary developmental mechanisms but the discovery that all animals (subsequently shown for all plants and fungi too) share genes that contain a 180-bp sequence known as the homeobox and that these genes, known as homeobox, homeotic, or Hox genes, are responsible for determining that animals have an anterior and a posterior, a dorsal and a ventral side, and specific regions (often as repeated segments) along the body axis—head at one end tail at the other, thorax in front of abdomen, wings on a specific pair of segments, and so forth (Lewis 1978 ; Gehring 1985 , 1998 ; Averof 1997 ; Grenier et al. 1997 ; Carroll 2008 ).

“Master” genes, also known as developmental or regulatory genes, were discovered. One of the best understood of such genes is the paired-box protein gene known as Pax-6 in vertebrates and as eyeless ( ey ) in Drosophila . As determined from its DNA sequence, orthologues of Pax-6 are present throughout the Animal Kingdom. As determined from functional studies, the role of Pax-6 in anteriorizing the embryo and in the formation of anterior sensory structures also is conserved across the Animal Kingdom. Although best known as the major gene controlling eye development, Pax-6 functions in organisms that lack eyes, reflecting its ancient developmental role. Indeed, ey from fruit flies can initiate eyes in frogs and Pax-6 from frogs can initiate eyes in Drosophila (Dahl et al. 1997 ; Suga et al. 2010 ). Here is an astonishing and previously unthought-of genetic conservation across animals whose morphology varied enormously.

Heterochrony, homeotic genes, increasingly resolved relationships between organisms (phylogenetic trees), and an appreciation during the 1980s and 1990s of the importance of ecological and species interactions led evo–devo to the position where the aims of evo–devo could be stated as understanding:

The origination and evolution of embryonic development;

The role of modifications of developmental processes in the production of novel features;

How the adaptive plasticity of development facilitates the origin and maintenance of complex life cycles with embryos, larvae, and adults; and

How developing organisms interact with their ecological environment to facilitate evolutionary change (Hall 2000 ; West-Eberhard 2003 ).

Others had similar lists. Müller ( 2007a , b ) and Collins et al. ( 2007 ) listed seven approaches and aims of evo–devo:

The origin of developmental systems;

The evolution of developmental systems;

Modifications of timing and context of developmental processes;

Environment–development interactions;

Maintenance of phenotypic variation;

Origin of phenotypic novelty; and

Integration of genetics and epigenetic mechanisms.

Research programs in evo–devo are comparative and experimental (Hall 1999 ; Raff and Love 2004 ). Increasingly, they involve what Laubichler ( 2007 ) calls “evolutionary developmental genetics.” In pursuit of their analysis of the integration of development, evolution, and ecology, Collins et al. ( 2007 ) analyzed three model systems—breeding of the domestic dog Canis familiaris , research on a sea anemone Nematostella vectensis , and research into horn development/evolution in dung beetles in the genus Onthophagus —and the development/evolution of vertebrate limbs and mammalian teeth. In a very different approach, McCain ( 2010 ) identified trends in evo–devo between 1996 and 2008 by analyzing linkages between the literature in what she identified as three core journals of evo–devo [Evolution & Development; Development, Genes & Evolution; and the Journal of Experimental Zoology (Part B)]. Her analysis is worth a close examination, as is an earlier study in which the research of Conrad Waddington was used to trace the rise of evo–devo (McCain 2009 ).

Critical to our ability to answer these questions and evaluate these model systems are a sound basis to ensure that we are comparing the same organisms/processes/genes—the central biological concept of homology (Hall 1994 , 2012 )—and our ability to determine the direction of evolutionary change because of the development of robust phylogenetic trees of relationships (Valentine 2004 ; Erwin et al. 2011 ). We have the model systems and we have the methodology but still lack the required theory. In their analysis of the status of modeling in evo–devo, Collins et al. ( 2007 ) concluded that “these models are mostly diagrammatic and functional; very few analytical and predictive models exist within EvoDevo” (p. 373). In part, they see this deficiency residing in evo–devo’s reliance on developmental genetics rather than evolutionary theory, a position that is slowly changing.

The Present

Molecular genetics has revolutionized evo–devo over the past two decades. Integrating our expanding molecular understanding with mechanisms operating at the cell or other levels (tissues, organs, whole organism, organism–environment interactions; Box 1) has been and remains a major goal and a challenge for evo–devo. Essentially, it involves opening the black box between genotype and phenotype, taking out what is found in the box and how it fits together and then determining how to put the contents back in the box (Hall 1999 , 2003a , b ; West-Eberhard 2003 ; Carroll et al. 2005 ). One of the items found in the black box is known as epigenetics.

Box 1. A sample of evolutionary developmental mechanisms operating at various levels

Epigenetics

Many of the controls on gene regulation and function are subsumed under the term “epigenetics,” a term coined by the British geneticist and embryologist Conrad Waddington for the causal factors that control gene action during development (Waddington 1940 ; and see the papers in Hall and Laubichler 2009 ). Hall ( 1992 ) defined epigenetics as “the sum of the genetic and non-genetic factors acting upon cells to control selectively the gene expression that produces increasing phenotypic complexity during development” ( 1992 , p. 89). To this, I would only add “and evolution” at the end.

Epigenetics is still used with this original meaning but has increasingly come to be applied at the molecular level for heritable changes to the DNA other than changes to the nucleotide bases. Such changes include methylation, imprinting, and regulation of chromatin—which have been known for some time (Biémont 2010 ; Hallgrímsson and Hall 2011 ; Moazed 2011 ; Molaro et al. 2011 )—and regulation of genes by small RNA molecules, especially miRNAs (Kosik 2009 ; Hallgrímsson and Hall 2011 ). This now very well-characterized second inheritance system was appreciated in its infancy by John Maynard Smith, a leading twentieth century evolutionary theorist: “There is a second inheritance system—an epigenetic inheritance system—in addition to the system based on DNA sequence that links sexual generations (Maynard Smith 1989 , p. 11).

The heritable aspect of epigenetics has shown us that organisms do not start their lives as naked nuclear DNA. They possess DNA in their mitochondria, epigenetic “marks” in their nuclear DNA, and they inherit mRNA and proteins that were produced under the control of their mother’s DNA and deposited into the egg cytoplasm. Epigenetics provides another, but not the only other, means by which heritable information operates in organismal development. The ability to learn behaviors, interact with environments, and construct niches (Laland et al. 2008 ; Gissis and Jablonka 2011 ) are three further mechanisms introduced below.

Integrated Mechanisms

Integrated studies using molecular biology, molecular genetics, developmental biology, phylogenetics, paleontology, and molecular paleobiology are revealing previously unimagined information on how features change during evolution (Erwin and Wing 2000 ; Hall 2002 ; Wilkins 2002 , 2007 ; Carroll et al. 2005 ; Peterson et al. 2007 ; Raff 2007 ; Erwin et al. 2011 ).

One instance is exemplified by the rise of paleobiology as a discipline that brought evolutionary theory back into paleontology and incorporated developmental, phylogenetic, and environmental approaches into a biological perspective of fossils. The journal Paleobiology celebrated its 25th anniversary in 2000 with a special issue, Deep Time: Paleobiology’s Perspective , published as a supplement to volume 26. An edited volume of 26 essays by leading paleontologists and published in 2009 demonstrates the breadth and depth of insight achieved by paleobiology as “The Paleobiological Revolution” (Sepkoski and Ruse 2009 ).

We have long appreciated that development is responsible for introducing variation at the level of the individual (Darwin 1859 ; Thomson 1988 ; Hallgrímsson and Hall 2005 ; Salazar-Ciudad 2006 ; Fusco and Minelli 2010 ). Much of the research in evo–devo, however, has been conducted on features of organisms that characterize particular groups and whose evolutionary origin was a major departure (transition is the term often used for the change, novelty, or innovation for the character) from prior characters (Brylski and Hall 1988a , b ; Hall and Kerney 2012 ). Important examples include feathers and the origin of birds/flight (Prum and Brush 2002 ; Xu et al. 2011 ), flowers and the origin of land plants (Niklas 1997 ; Leliaert et al. 2011 ), wings and the origin of insects (Carroll et al. 1995 ; Abouheif and Wray 2002 ), and shells and the origin of turtles (Gilbert et al. 2001 ; Rieppel 2001 ; Willmore 2010 ). Indeed, some researchers see the major contribution of evo–devo (and its major contribution to expanding the modern synthesis of evolution) as being to provide a theoretical basis from developmental biology for the origin of novelties (Müller and Wagner 1991 ; Müller 2007a , b Pigliucci and Müller 2010 ).

New groups of organisms, as represented by new classes or orders, can arise slowly through the gradual accumulation of new characters or can arise more rapidly through key innovations (the origination of wings or lungs, for example) or through coordinated changes in different characters as seen in the origin of lungs, middle ear ossicles, and the transformation of fins to limbs at the origin of the tetrapods (Thomson 1988 ; Shubin et al. 1997 ). This is how microevolutionary changes within species are linked to macroevolutionary changes, as reflected in levels of classification: “the careful analyses of the differences in pathways between organisms of known phylogenetic relationship” (Thomson 1988 , p. 138).

The list of mechanisms summarized in Box 1 implies that evolutionary developmental mechanisms are not all found in the genes, although all have a genetic basis. This is because new mechanisms emerge as development proceeds. Evolutionary developmental mechanisms may be genetic, cellular, developmental, physiological, hormonal, or any combination of these. Embryonic development is hierarchical, with new properties and mechanisms emerging as development unfolds, each dependent on the stage/processes preceding them. The single cell that is the fertilized egg cannot show any of the cell-to-cell interactions that characterize the multicellular embryo, some of which come about because cells take up new positions in the embryo through active migration. The recent elucidation of gene networks is providing the regulatory link between the genotype and cellular modules (Davidson 2006 ; Davidson and Erwin 2006 ; Wagner et al. 2007 ; Wilkins 2007 ). Embryonic inductions, tissue, organ, and functional interactions link cellular activity to the phenotype. The onset of embryonic movement ushers in a new type of process, which is interactions between developing tissues and organs such as bones and muscles (Box 1; and see Hall 1999 , 2003a , b ; Wilkins 2002 ; Hall and Olson 2003 ).

Some of the most striking information has come from the discovery that different morphological types of some single species (often referred to as morphs) arise following interactions with individuals of other species. These range from species that live in societies with different life history forms [chemical signaling between individuals determines the balance of workers and soldiers in ant colonies (Nijhout 1999 ); chemicals produced by predatory tadpole shrimp induce the formation of a protective helmet and enlargement of the head in the water shrimp Daphnia (Petrusek et al. 2009 )]; the density of tadpoles and/or the amount of food promoting the development of large cannibalistic tadpoles in such species as the New Mexico spadefoot toad Scaphiopus multiplicatus (Pfennig 1992 ; Ledón-Rettig and Pfenning 2011 ); moth larvae-mimicking leaves of catkins in response to seasonal levels of tannin produced by oak trees when either catkins or leaves are on the trees (Greene 1989 , 1999 ); and interactions of bacterial viral and eukaryotic species in the human gut with one another and the role of this microbiota in maintaining the health of their human hosts (Clemente et al. 2012 ).

Of course, it is impossible to tell what the future of evo–devo will be or what evo–devo will bring to the future. Nevertheless, given the past dramatic history of links between development and evolution, it is interesting to speculate on the future.

It could be business as usual, with evo–devo continuing to inform us of how changes in development relate to changes in evolution. Or evo–devo may change.

Evo–devo Plus Endless Prefixes/Suffixes

The past decade has seen the addition of modifiers to evo–devo to reflect its embracing of other fields. Thus:

Eco-evo–devo brings ecology into evo–devo (Hall 2003a ; Gilbert and Apel 2008 ).

Evo–devo–niche construction links development to the evolutionary role of organisms constructing essential elements of their niche such as nests or tunnels (Laland et al. 2008 ).

Behav–evo–devo is the use of evolutionary developmental mechanisms to explain the origins of behaviors, learning, and language (Lickliter 2007 ; Bertossa 2011 ; Hoang et al. 2011 ; Hall 2012 ).

Evo–devo–medicine is the application of evo–devo to medical practice (Gluckman et al. 2009 ).

The future of evo–devo may be a continuation of this trend: evo–devo–physiology; evo–devo–life history evolution.

Or it could be that evo–devo will be replaced by what has been called developmental evolution or devel-evol (Hall 2000 ; Wagner 2000 ).

What is the difference between evo–devo and devo–evo? Evo–devo seeks to situate development within the study of evolution (Carroll 2008 ). Devo–evo seeks to generate a new theory of evolution based in development. Gunter Wagner and his colleagues (Wagner 2000 ; Wagner et al. 2000 ; Wagner and Larsson 2003 ), for example, maintain that understanding (1) the origin of innovations (novelties), (2) why development is constrained along particular paths, and (3) how new properties emerge in evolution requires an approach that is fundamentally evolutionary (devo–evo) rather than developmental (evo–devo).

Devo–evo would provide a new theory of the origin of novel structures and behaviors with evolution based on population genetics continuing to explain variation in existing phenotypes and different theories to explain the evolution of the new and the maintenance of the old (Hallgrímsson and Hall 2005 , 2011 ). Ken Weiss emphasized this in a discussion of the importance of niche construction and behavior-driven evolution when he concluded that “local divergence can be achieved by behavioral sorting by which organisms find or modify local niches according to their abilities, but this need not involve fitness differences nor even be based on genetic inheritance” (Weiss 2004 , p. 203).

Evo–devo = Evo

Or, it could be, as Scott Gilbert mused, “that sooner or later, the term ‘evo–devo’ will be abandoned, because at that time it will have become synonymous with ‘evolutionary biology” (Gilbert 2009 , p. 332). The Modern Synthesis forged in the 1930s and 1940s will become the “Expanded Modern Synthesis” or the “Extended Synthesis” of the twenty-first century. Although there were calls in the past for the replacement of the modern synthesis, the expectation now is an expanded synthesis that incorporates development (Müller 2007a , b ; Pigliucci and Müller 2010 ). Sommer ( 2009 ), who evaluated the various likely fates of evo–devo, demonstrated that “a synthesis of evo–devo with population genetics and evolutionary ecology is needed to meet future challenges” (p. 416), those challenges being to understand phenotypic change and novelty (his call for researchers to choose a limited number of model organisms goes against a major trend in evo–devo, which is to expand the numbers of organisms under investigation).

Unification

An expanded synthesis will be more than the modern synthesis plus evo–devo.

Laubichler ( 2010 ) argues that “the revolutionary nature of evo–devo lies precisely in its return to a more inclusive conception of phenotypic evolution, one that more closely resembles the conceptual framework of Darwin and the first few generations of evolutionists than the more narrowly focused interpretation of the Modern Synthesis” (p. 199). Laubichler is referring to the separation of the study of heredity, development, and evolution early in the twentieth century (Allen 1975 ; Laubichler and Maienschein 2007 ; Deichman 2011 ) and the need to reunify these three before a full account of development, evolution, or evo–devo can be given. Biology was integrated in the late nineteenth century. Evo–devo will be at center stage when we forge the integrated biology of the twenty-first century.

Conceptual unification will require much more than the addition of more and more suffixes and prefixes until all of biology is ensnared by evo–devo. As Laubichler ( 2007 ) points out, “any future synthesis of evo–devo will be conceptual rather than simply data driven…[and allow] the integration of developmental mechanisms into evolutionary explanations at a higher level of resolution than the current ideas about regulatory evolution and the evolution of the genetic toolkit suggest” (pp. 343, 359). At the very least, unification will involve molecular genetics, development, paleontology, systematics, the nature of heredity, and the role of ecology and the environment.

Experience from the past teaches us that unification could arise from the development of new techniques and methods, as occurred with the ability to identify biological species, decipher the genetic code in DNA, sequence genomes, and create phylogenetic trees. Experience also teaches us that unification may (is more likely to?) require unthought-of and perhaps revolutionary concepts and/or new ways to integrate signaling mechanisms operating at different levels (genes, cells, organs, organisms–environment). The gene regulatory networks discussed above provide such a conceptual and integrative advance at the level of gene action, but so far, they are not sufficient to link the activity of genes to the activities of cells. The phenotypic plasticity that links environment to phenotypic response is integrative at the level of environment–organism, but not at the level of genes–cells. We have traveled an enormously long way, but have an even longer uncharted road ahead.

As an exception, one of the acknowledged founders of evo-devo, N.J. Berrill (1903–1996), included a chapter on evolution and development in an undergraduate biology textbook (Berrill 1966 ) in which he saw the importance of development for understanding phylogenetic relationships, not because of recapitulation (see Origins of Evo-Devo) but because of developmental mechanisms such as neoteny.

Abouheif E, Wray G. Evolution of the gene network underlying wing polyphenism in ants. Science. 2002;297:249–52.

CAS   PubMed   Google Scholar  

Allen G. Life science in the twentieth century. New York: Wiley; 1975.

Google Scholar  

Arthur W. A theory of the evolution of development. Chichester: Wiley; 1988.

Averof M. Arthropod evolution: same Hox genes, different body plans. Curr Biol. 1997;7:R634–6.

Balfour FM. A preliminary account of the development of the elasmobranch fishes. Q J Microsc Sci. 1874;14:323–64.

Bateson W. Evolutionary faith and modern doubts. Science. 1922;55:53–61.

Berrill NJ. Biology in action: a beginning college textbook. New York: Dodd, Mead & Co.; 1966.

Bertossa RC. Morphology and behaviour: functional links in development and evolution. Philos Trans R Soc B. 2011;366:2056–68.

Biémont C. From genotype to phenotype. What do epigenetics and epigenomics tell us? Heredity. 2010;105:1–3.

PubMed   Google Scholar  

Bonner JT. Evolution and development. Report of the Dahlem Workshop on Evolution and Development, Berlin 1981, May 10–15. Life Sciences Research Report 22. Berlin: Springer; 1982.

Bowler PJ. Life's splendid drama. Evolutionary biology and the reconstruction of life's ancestry 1860–1940. Chicago: The University of Chicago Press; 1996.

Brylski P, Hall BK. Ontogeny of a macroevolutionary phenotype: the external cheek pouches of geomyoid rodents. Evolution. 1988a;42:391–5.

Brylski P, Hall BK. Epithelial behaviour and threshold effects in the development of external and internal cheek pouches in rodents. Zool Syst Evolutionsforsch. 1988b;26:144–54.

Calow P. Life cycles: an evolutionary approach to the physiology of reproduction, development and ageing. London: Wiley; 1978.

Calow P. Evolutionary principles. Blackie: Glasgow and London; 1983.

Carroll SB. Evo–devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell. 2008;134:25–36.

Carroll SB, Weatherbee SD, Langeland JA. Homeotic genes and the regulation and evolution of insect wing number. Nature. 1995;375:58–61.

Carroll SB, Grenier JK, Weatherbee SD. From DNA to diversity. Molecular genetics and the evolution of animal design. 2nd ed. Malden: Blackwell Science; 2005.

Clemente JC, Ursell LK, Palfrey LW, Knight B. The impact of the gut microbiota on human health: an integrative view. Cell. 2012;148:1258–70.

Collins JP, Gilbert S, Laubichler MD, Müller GB. Modeling in EvoDevo: how to integrate development, evolution, and ecology. In: Laubichler MD, editor. Roots of theoretical biology: the Prater Vivarium centenary. Cambridge: MIT Press; 2007. p. 355–78.

Dahl E, Koseki H, Balling R. Pax genes and organogenesis. BioEssays. 1997;19:755–65.

Darwin CR. The origin of species by means of natural selection. London: John Murray; 1859.

Davidson EH. The regulatory genome: gene regulatory networks in development and evolution. San Diego: Academic; 2006.

Davidson EH, Erwin DH. Gene regulatory networks and the evolution of animal body plans. Science. 2006;311:796–800.

de Beer GR. Embryos and evolution. Oxford: Clarendon; 1930.

Deichman U. Early 20th-century research at the interface of genetics, development, and evolution: reflections on progress and dead ends. Dev Biol. 2011;347:3–12.

Dobzhansky Th. Genetics and the origin of species. New York: Columbia University Press; 1937.

Erwin DH, Wing SL. Deep time: paleobiology’s perspective. Paleobiology (Suppl). 2000;26(4):1–371.

Erwin DH, Laflamme M, Tweedt SM, Sperling EA, Pisani D, Peterson KJ. The Cambrian conundrum: early divergence and later ecological success in the early history of animals. Science. 2011;334:1091–7.

Fusco G, Minelli A. Phenotypic plasticity in development and evolution: facts and concepts. Philos Trans R Soc B. 2010;365:547–56.

Gehring WJ. The homeobox: a key to the understanding of development? Cell. 1985;40:3–5.

Gehring WJ. Master control genes in development and evolution: the homeobox story. New Haven: Yale University Press; 1998.

Gilbert SF. 2009 BIO. Evol Dev. 2009;11:331–2.

Gilbert SF, Apel D. Ecological developmental biology: integrating epigenetics, medicine, and evolution. Sunderland: Sinauer Associates; 2008.

Gilbert SF, Loredo GA, Brukman A, Burke AC. Morphogenesis of the turtle shell: the development of a novel structure in tetrapod evolution. Evol Dev. 2001;3:47–58.

Gissis SB, Jablonka E. Transformations of Lamarckism: from subtle fluids to molecular biology. Cambridge: MIT Press; 2011.

Gluckman P, Beedle A, Hanson M. Principles of evolutionary medicine. Oxford: Oxford University Press; 2009.

Goldschmidt R. A preliminary report on some genetic experiments concerning evolution. Am Nat. 1918;52:28–50.

Goodwin BC, Holder NJ, Wylie CC, editors. Development and evolution. Cambridge: Cambridge University Press; 1983.

Gould SJ. Ontogeny and phylogeny. Cambridge: The Belknap Press of Harvard University Press; 1977.

Greene E. A diet-induced developmental polymorphism in a caterpillar. Science. 1989;243:643–66.

Greene E. Phenotypic variation in larval development and evolution: polymorphism, polyphenism, and developmental reaction norms. In: Hall BK, Wake MH, editors. The origin and evolution of larval form. San Diego: Academic; 1999. p. 379–410.

Grenier JK, Garber TL, Warren R, Whitington PM, Carroll SB. Evolution of the entire arthropod Hox gene set predated the origin and radiation of the onychophoran/arthropod clade. Curr Biol. 1997;7:547–53.

Haeckel E. Generelle morphologie der organismen: Allgemeine grundzüge der organischen formen-wissenschaft, mechanisch begründet durch die von Charles Darwin reformite descendenz-theorie (2 volumes). Berlin: Georg Reimer; 1866.

Hall BK. Evolutionary developmental biology. London: Chapman and Hall; 1992.

Hall BK. Homology: the hierarchical basis of comparative biology. San Diego: Academic; 1994.

Hall BK. Evolutionary developmental biology. 2nd ed. Dordrecht: Kluwer Academic; 1999.

Hall BK. Evo–devo or devo–evo—does it matter? Evol Dev. 2000;2:177–88.

Hall BK. John Samuel Budgett (1872–1904): in pursuit of Polypterus . BioScience. 2001;51:399–407.

Hall BK. Palaeontology and evolutionary developmental biology: a science of the 19th and 21st centuries. Palaeontology. 2002;45:647–69.

Hall BK. Evolution as the control of development by ecology. In: Hall BK, Pearson R, Müller GB, editors. Environment, evolution and development: towards a synthesis. Cambridge: MIT Press; 2003a. p. ix–xxiii.

Hall BK. Unlocking the black box between genotype and phenotype: cells and cell condensations as morphogenetic (modular) units. Biol Philos. 2003b;18:219–27.

Hall BK. Homology, homoplasy, novelty and behavior. Dev Psychobiol. 2012 (in press).

Hall BK, Kerney R. Levels of biological organization and the origin of novelty. J Exp Zool (Mol Dev Evol). 2012;314B. doi: 10.1002/jez.b.21425 .

Hall K, Olson WM (eds). Keywords and concepts in evolutionary developmental biology . Cambridge, MA: Harvard University Press; 2003.

Hall BK, Laubichler MD, (eds). Conrad Hal Waddington, theoretical biology, and evo–devo. Biol. Theory. 2009;3(3):185–289.

Hallgrîmsson B, Hall BK, editors. Epigenetics: linking genotype and phenotype in development and evolution. Berkeley: University of California Press; 2011.

Hallgrímsson B, Hall BK (eds). Variation: a central concept in biology . New York: Elsevier/Academic Press; 2005

Hoang T-H, McKay RI, Essam D, Hoai NX. On synergistic interactions between evolution, development and layered learning. IEEE Trans Evol Comput. 2011;15:287–311.

Huxley JS. Evolution: the modern synthesis. London: Allen and Unwin; 1942.

Kosik KS. MicroRNAs tell an evo–devo story. Nat Rev Neurosci. 2009;10:754–9.

Laland KN, Odling-Smee J, Gilbert SF. EvoDevo and niche construction: building bridges. J Exp Zool (Mol Dev Evol). 2008;310B:549–66.

Laubichler MD. Evolutionary developmental biology. In: Hull DL, Ruse M, editors. The Cambridge companion to the philosophy of biology. Cambridge: Cambridge University Press; 2007. p. 342–60.

Laubichler MD. Evolutionary developmental biology offers a significant challenge to the neo-Darwinian paradigm. In: Ayala FJ, Arp R, editors. Contemporary debates in philosophy of biology. Malden: Wiley-Blackwell; 2010. p. 199–212.

Laubichler MD, Maienschein J, editors. From Embryology to evo–devo: a history of developmental evolution. Cambridge: MIT Press; 2007.

Laubichler MD, Wagner GP. Introduction to the papers of the 2001 Kowalevsky Medal winner symposium. J Exp Zool (Mol Dev Evol). 2004;302B:1–4.

Ledón-Rettig CC, Pfenning DW. Emerging model systems in eco–evo–devo: the environmentally responsive spadefoot toad. Evol Dev. 2011;13:391–400.

Leliaert F, Verbruggen H, Zechman FW. Into the deep: new discoveries at the base of the green plant phylogeny. BioEssays. 2011;33:683–92.

Lewis EB. A gene complex controlling segmentation in Drosophila . Nature. 1978;276:565–70.

Lickliter R. The dynamics of development and evolution: insights from behavioral embryology. Dev Psychobiol. 2007;49:749–57.

MacDonald ME, Hall BK. Altered timing of the extracellular-matrix-mediated epithelial–mesenchymal interaction that initiates mandibular skeletogenesis in three inbred strains of mice: development, heterochrony, and evolutionary change in morphology. J Exp Zool. 2001;291:258–73.

Macleod R. Embryology and empire. In: Macleod R, Rehbock PF, editors. Darwin's laboratory. Evolutionary theory and natural history in the Pacific. Honolulu: University of Hawaii Press; 1994. p. 140–65.

Maynard Smith J. Weismann and modern biology. In: Harvey PH, Partridge L, editors. Oxford surveys in evolutionary biology, vol. 6. Oxford: Oxford University Press; 1989. p. 1–12.

Mayr E. The growth of biological thought. Diversity, evolution, and inheritance. Cambridge: The Belknap Press of Harvard University Press; 1982.

Mayr E. The establishment of evolutionary biology as a discrete biological discipline. BioEssays. 1997;19:263–6.

McCain KW. Using tricitation to dissect the citation image: Conrad Hal Waddington and the rise of evolutionary developmental biology. J Am Soc Inf Sci Technol. 2009;60:1301–19.

McCain KW. Core journal literatures and persistent research themes in an emerging interdisciplinary field: exploring the literature of evolutionary development biology. J Informet. 2010;4:157–65.

Moazed D. Mechanisms for the inheritance of chromatin states. Cell. 2011;146:510–7.

Molaro A, Hodges E, Fang F, Song Q, McCombie WR, Hannon GJ. Sperm methylation profiles reveal features of epigenetic inheritance and evolution in primates. Cell. 2011;146:1029–41.

Müller F. Für Darwin. Leipzig: Engelman; 1864 (Translated by W. S. Dallas as Facts and Arguments for Darwin , London: John Murray, 1869).

Müller GB. Six memos for EvoDevo. In: Laubichler MD, Maienschein J, editors. From Embryology to evo–devo: a history of developmental evolution. Cambridge: MIT Press; 2007a. p. 499–524.

Müller GB. Evo–devo” extending the evolutionary synthesis. Nat Rev Genet. 2007b;8:943–9.

Müller GB, Wagner GP. Novelty in evolution: restructuring the concept. Annu Rev Ecol Syst. 1991;22:229–56.

Nijhout HF. Hormonal control in larval development and evolution—insects. In: Hall BK, Wake MH, editors. The origin and evolution of larval form. San Diego: Academic; 1999. p. 217–54.

Niklas KJ. The evolutionary biology of plants. Chicago: The University of Chicago Press; 1997.

Olsson L, Hoßfeld U, Breidbach O. Preface. Between Ernst Haeckel and the homeobox: the role of developmental biology in evolution. Theory Biosci. 2009;128:1–5.

Peterson KJ, Summons RE, Donoghue PCJ. Molecular paleobiology. Palaeontology. 2007;50:775–809.

Petrusek A, Tollrian R, Schwenk K, Haas A, Laforsch C. A “crown of thorns” is an inducible defense that protects Daphnia against an ancient predator. Proc Natl Acad Sci USA. 2009;106:2248–52.

Pfennig DW. Polyphenism in spadefoot toad tadpoles as a locally adjusted evolutionarily stable strategy. Evolution. 1992;46:1408–20.

Pigliucci M, Müller GB, editors. Evolution—the extended synthesis. Cambridge: The MIT Press; 2010.

Prum RO, Brush AH. The evolutionary origin of diversification of feathers. Q Rev Biol. 2002;77:261–95.

Raff RA. Direct-developing sea urchins and the evolutionary reorganization of early development. BioEssays. 1992;14:211–8.

Raff RA. Written in stone: fossils, genes and evo–devo. Nat Rev Genet. 2007;8:911–20.

Raff RA, Love AC. Kowalevsky, comparative evolutionary embryology, and the intellectual lineage of evo–devo. J Exp Zool (Mol Dev Evol). 2004;302B:19–34.

Rieppel O. Turtles as hopeful monsters. BioEssays. 2001;23:987–91.

Salazar-Ciudad I. Developmental constraints vs. variational properties: how pattern formation can help to understand evolution and development. J Exp Zool (Mol Dev Evol). 2006;306B:107–25.

Sepkoski D, Ruse M. (eds). The paleobiological revolution: essays on the growth of modern paleontology. Chicago: The University of Chicago Press; 2009.

Shubin N, Tabin C, Carroll S. Fossils, genes, and the evolution of animal limbs. Nature. 1997;388:639–48.

Sommer RJ. The future of evo–devo: model systems and evolutionary theory. Nat Rev Genet. 2009;10:416–22.

Suga H, Tschopp P, Graziussi DF, Stierwald M, Schmid V, Gehring WJ. Flexibly deployed Pax genes in eye development at the early evolution of animals demonstrated by studies on a hydrozoan jellyfish. Proc Natl Acad Sci USA. 2010;107:14263–8.

Thomson KS. Morphogenesis and evolution. Oxford: Oxford University Press; 1988.

Tills O, Rundle SD, Salinger M, Haun T, Pfenninger M, Spicer JI. A genetic basis for intraspecific differences in developmental timing? Evol Dev. 2011;13:542–8.

Valentine JW. On the origin of phyla. Chicago: The university of Chicago Press; 2004.

Waddington CH. Organizers and genes. Cambridge: Cambridge University Press; 1940.

Wagner GP. What is the promise of developmental evolution? Part I: Why is developmental biology necessary to explain evolutionary innovations? J Exp Zool (Mol Dev Evol). 2000;288:95–8.

CAS   Google Scholar  

Wagner GP, Larsson HCE. What is the promise of developmental evolution? III. The crucible of developmental evolution. J Exp Zool (Mol Dev Evol). 2003;300B:1–4.

Wagner GP, Chiu C-H, Laubichler M. Developmental evolution as a mechanistic science: the inference from developmental mechanisms to evolutionary processes. Am Zool. 2000;40:819–31.

Wagner GP, Pavlicev M, Cheverud JM. The road to modularity. Nat Rev Genet. 2007;8:921–31.

Weiss KM. Thomas Henry Huxley (1825–1895) puts us in our place. J Exp Zool (Mol Dev Evol). 2004;302B:196–206.

West-Eberhard MJ. Developmental plasticity and evolution. Oxford: Oxford University Press; 2003.

Wilkins AS. The evolution of developmental pathways. Sunderland: Sinauer Associates; 2002.

Wilkins AS. Between “design” and “bricolage”: genetic networks, levels of selection, and adaptive evolution. Proc Natl Acad Sci USA. 2007;104:8590–6.

Willmore KE. Development influences evolution. Am Sci. 2010;98:220–7.

Xu X, You H, Du K, Han F. An Archaeopteryx -like theropod from China and the origin of Avialae. Nature. 2011;475:465–70.

Zelditch M. Beyond heterochrony: the evolution of development. New York: Wiley; 2001.

Download references

Acknowledgments

Research in my laboratory is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant A5056). I thank all past laboratory colleagues—students, postdoctoral fellows, sabbaticants, and collaborators—for discussions from which I have benefited enormously over the past 44 years. Jane Maienschein and an anonymous referee provided important comments on the manuscript.

Author information

Authors and affiliations.

Department of Biology, Dalhousie University, Halifax, NS, Canada, B3H 4R2

Brian K. Hall

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Brian K. Hall .

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article.

Hall, B.K. Evolutionary Developmental Biology (Evo-Devo): Past, Present, and Future. Evo Edu Outreach 5 , 184–193 (2012). https://doi.org/10.1007/s12052-012-0418-x

Download citation

Published : 08 June 2012

Issue Date : June 2012

DOI : https://doi.org/10.1007/s12052-012-0418-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Evolutionary developmental biology
• Gene regulation
• Evolutionary developmental mechanisms
• Modern synthesis
• Expanded modern synthesis

Evolution: Education and Outreach

ISSN: 1936-6434

• Submission enquiries: [email protected]

• Subject List
• Take a Tour
• For Authors
• Subscriber Services
• Publications
• African American Studies
• African Studies
• American Literature
• Anthropology
• Architecture Planning and Preservation
• Art History
• Atlantic History
• Biblical Studies
• British and Irish Literature
• Childhood Studies
• Chinese Studies
• Cinema and Media Studies
• Communication
• Criminology
• Environmental Science

Evolutionary Biology

• International Law
• International Relations
• Islamic Studies
• Jewish Studies
• Latin American Studies
• Latino Studies
• Linguistics
• Literary and Critical Theory
• Medieval Studies
• Military History
• Political Science
• Public Health
• Renaissance and Reformation
• Social Work
• Urban Studies
• Victorian Literature
• Browse All Subjects

How to Subscribe

• Free Trials

In This Article Expand or collapse the "in this article" section Evolution

Introduction, general overviews.

• The Evolution of Evolutionary Thinking
• Foundations of the Modern Theory: The Mendelian/Biometrician Controversy
• The Modern Synthesis
• Population Genetics and Molecular Evolution
• Evolution and Development
• Behavioral Ecology
• Evolutionary Ecology
• Phylogenetics and the Comparative Method
• Punctuated Equilibria

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

• Ecological Genetics
• Evolution and Development: Genes and Mutations Underlying Phenotypic Variation
• Evolution and Development of Individual Behavioral Variation
• Evolution of Animal Mating Systems
• Evolution of Mutualism
• Evolution of Reaction Norms
• Evolutionary Developmental Biology
• Genome Evolution
• History of Evolutionary Thought, 1860–1925
• History of Evolutionary Thought before Darwin
• Mutation Rate and Spectrum
• Neutral Theory
• Phylogenetic Comparative Methods and Tests of Macroevolutionary Questions
• Population Genetics

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

• Bacterial Species Concepts
• Recombination and Evolution
• Selective Sweeps
• Find more forthcoming articles...
• Export Citations
• Share This Facebook LinkedIn Twitter

Evolution by Andrew Berry LAST REVIEWED: 12 August 2022 LAST MODIFIED: 26 April 2018 DOI: 10.1093/obo/9780199941728-0100

The term “evolution” is widely applied to a huge range of biological phenomena—from grand patterns in the history of life to the specifics of population genetic process. Instead of trying to define the term in some kind of all-encompassing way that embraces these many ideas and many scales of analysis, we prefer to start with a narrow formalism—a focus on process—from which the grander and more nebulous applications of the term may be derived. Evolution is change in the genetic composition of a population over time. The word is also sometimes applied loosely (and incorrectly) to consistent generation-to-generation change that is environmentally, rather than genetically, induced. Typically, evolution involves changes in a population’s allele and/or genotype frequencies between generations. Of the several factors that may cause evolution, natural selection is the most important, both in eliminating deleterious mutations that compromise an organism and in fixing adaptive mutations. Evolution has traditionally been parsed into micro-evolution (processes that occur within a population) and macro-evolution (processes that occur in the formation of new species or in the formation of new higher taxa). It is a basic premise of the standard theory of evolution that macro-evolution can be explained in terms of micro-evolution: macro-evolution is simply an extrapolation of micro-evolution over long periods of time. Some, however, have argued that micro- and macro-evolutionary processes are fundamentally different and that the two are decoupled. The standard extrapolationist perspective predicts both what Darwin termed “descent with modification”—change from generation to generation that sustained over time has produced the extraordinary diversity of life that we see around us—and the hierarchical pattern of phylogenetic relationship seen among those living forms. The term “evolution” is therefore frequently applied to the natural world’s grand patterns of similarity, difference, and relatedness. Although the word “evolution” (and its cognates) is ineradicably associated with Darwin, it was barely used by Darwin. The final word of the first edition of On the Origin of Species sees its only use in the book: “from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved.” Evolution remains controversial in areas in the world with poor access to secular education, and, also, in the United States. While there are plenty of disputes about the specifics of mechanism and about the interpretation of particular instances, there is, however, no controversy within the scientific community about the fact that evolution has occurred.

Evolution is a challenging field to summarize because it is inherently interdisciplinary, embracing as it does a wide range of subfields ranging from the mathematical minutiae of population genetics to the geochemical inferences underpinning interpretation of the early fossil record. Charles Darwin’s Origin of Species is of course the field’s founding document, and, despite its frequently prolix Victorian prose, it remains surprisingly fresh and readable. Reading it provides an opportunity that is perhaps unique in the history of science: it is possible to experience the unveiling of a truly seismic scientific revolution in the (relatively) user-friendly words of the architect of that revolution. There are several estimable textbooks that introduce the field: Futuyma and Kirkpatrick 2017 is arguably the industry standard; Zimmer and Emlen 2016 is the newest; and Freeman and Herron 2014 also provides an up-to-date and comprehensive treatment. Barton, et al. 2007 ( Evolution ) has not been updated since its 2007 first edition, but it remains a valuable resource in being somewhat more advanced in its treatment than the others. For more technical, multi-authored overviews, see the encyclopedic treatments (both of which run to nearly a thousand pages) of Ruse and Travis 2009 and Losos 2014 . Dobzhansky’s famous 1973 essay is a reminder of the power and reach of an idea, which as Miller, et al. 2006 shows, remains astonishingly unaccepted in the United States.

Barton, Nicholas H., Derek E. G. Briggs, Jonathan A. Eisen, David Goldstein, and Nipam H. Patel. 2007. Evolution . Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press.

At ten years old, this book is now somewhat out of date, especially in fast-moving areas such as genomics. However, it has the virtue of an author team with a variety of expertise (for example, Barton is a population geneticist; Briggs a palaeontologist; Patel an expert in evolution and development). This means that each section is written with real authority. The result is a text that is more advanced than the others listed here.

Costa, James C. 2009. The annotated origin of species . Cambridge, MA: Harvard Univ. Press.

This is a reprint of the first edition of The Origin of Species (1859), which is the most direct statement of Darwin’s ideas. In the subsequent five editions put out during his lifetime, Darwin’s message was diluted as he responded to criticism. This version provides a facsimile reproduction of each page of the text along with helpful annotations.

Coyne, Jerry A. 2009. Why evolution is true . Oxford: Oxford Univ. Press.

A popular but rigorous and reasonably up-to-date presentation of the evidence, some of it stemming from Darwin’s own observations, in support of evolution. The book was written with Creationists in mind, but it is a useful compendium for working biologists as well.

Dobzhansky, Theodosius. 1973. Nothing in biology makes sense except in the light of evolution. American Biology Teacher 35.3: 125–129.

DOI: 10.2307/4444260

A famous statement about the power and reach of the theory of evolution by natural selection. We can analyze biological phenomena in, for example, purely engineering terms. But we will not understand the precise relationship between form and function without an appreciation of the historical dimension—the previous states from which the current state is derived.

Freeman, Scott, and Jon C. Herron. 2014. Evolutionary analysis . 5th ed. San Francisco: Benjamin Cummings.

This has long been the main competition to Futuyma. Its approach is less traditional, with greater emphasis on case study and experiment. For example, the first chapter focuses on the evolutionary lessons that can be learned from studies of HIV.

Futuyma, Douglas J., and Mark Kirkpatrick. 2017. Evolution . 4th ed. Sunderland, MA: Sinauer Associates.

Evolution is a slimmed-down version of Futuyma’s earlier text, Evolutionary Biology , which first appeared in 1979 and signed off in 1998 with a hefty, encyclopedic third edition. The first edition of Evolution came out in 2005. Densely written, Evolution is nevertheless eminently readable and provides a balanced mix of classical ideas and experiments and new material.

Losos, Jonathan, ed. 2014. The Princeton guide to evolution . Princeton, NJ: Princeton Univ. Press.

Currently the best single source on evolution. With 107 chapters (divided into eight sections, such as “Natural Selection and Adaptation”) written by an army of experts, this is an excellent synopsis of the state of the field.

Miller, J. D., E. C. Scott, and S. Okamoto. 2006. Public acceptance of evolution. Science 313:765–766.

DOI: 10.1126/science.1126746

A paper that generated the league table, by nation, of support for evolution. In a survey of thirty-four countries, mostly European (though Japan and Turkey were included), the United States was found to rank thirty-third (only Turkey ranked lower) for acceptance of evolutionary ideas. Consistently over years of surveying, about 40 percent of Americans are anti-evolution.

Ruse, Michael, and Joseph Travis, eds. 2009. Evolution: The first four billion years . Cambridge, MA: Harvard Univ. Press.

The first half of this book offers sixteen essays on evolutionary topics and the second half an alphabetic compilation of topics. The alphabetic organization is a little confusing (if you are interested in the Ediacaran fauna, for example, you have to turn to “O” for “Organismic evolution and radiation before the Cambrian”) but makes for an enjoyably unpredictable “dip in” read (“Leslie Orgel” is the entry after “Organismic evolution and radiation before the Cambrian”). Again, there are many contributors.

Zimmer, Carl, and Douglas J. Emlen. 2016. Evolution: Making sense of life . 2d ed. Colorado: Roberts.

This collaboration between Zimmer, a New York Times science writer, and Emlen, an evolutionary biologist, has yielded a superbly produced and wonderfully accessible book.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

• About Evolutionary Biology »
• Meet the Editorial Board »
• Adaptive Radiation
• Amniotes, Diversification of
• Ancient DNA
• Canalization and Robustness
• Cancer, Evolutionary Processes in
• Character Displacement
• Coevolution
• Cognition, Evolution of
• Constraints, Evolutionary
• Contemporary Evolution
• Convergent Evolution
• Cooperation and Conflict: Microbes to Humans
• Cooperative Breeding in Insects and Vertebrates
• Creationism
• Cryptic Female Choice
• Darwin, Charles
• Disease Virulence, Evolution of
• Diversification, Diversity-Dependent
• Ecological Speciation
• Endosymbiosis
• Epigenetics and Behavior
• Epistasis and Evolution
• Eusocial Insects as a Model for Understanding Altruism, Co...
• Eusociality
• Evidence of Evolution, The
• Evolution and Development: Genes and Mutations Underlying ...
• Evolution and Development of Individual Behavioral Variati...
• Evolution, Cultural
• Evolution of Antibiotic Resistance
• Evolution of New Genes
• Evolution of Plant Mating Systems
• Evolution of Specialization
• Evolutionary Biology of Aging
• Evolutionary Biomechanics
• Evolutionary Computation
• Evolutionary Ecology of Communities
• Experimental Evolution
• Field Studies of Natural Selection
• Founder Effect Speciation
• Frequency-Dependent Selection
• Fungi, Evolution of
• Gene Duplication
• Gene Expression, Evolution of
• Genetics, Ecological
• Geographic Variation
• Group Selection
• Heterochrony
• Heterozygosity
• History of Evolutionary Thought Since 1930
• Human Behavioral Ecology
• Human Evolution
• Hybrid Speciation
• Hybrid Zones
• Hybridization and Diversification
• Identifying the Genomic Basis Underlying Phenotypic Variat...
• Inbreeding and Inbreeding Depression
• Inclusive Fitness
• Innovation, Evolutionary
• Islands as Evolutionary Laboratories
• Kin Selection
• Land Plants, Evolution of
• Landscape Genetics
• Landscapes, Adaptive
• Language, Evolution of
• Latitudinal Diversity Gradient, The
• Macroevolution
• Macroevolution, Clade-Level Interactions and
• Macroevolutionary Rates
• Male-Male Competition
• Mass Extinction
• Mate Choice
• Maternal Effects
• Mating Tactics and Strategies
• Medicine, Evolutionary
• Meiotic Drive
• Modern Synthesis, The
• Molecular Clocks
• Molecular Phylogenetics
• Mutualism, Evolution of
• Natural Selection in Human Populations
• Natural Selection in the Genome, Detecting
• New Zealand, Evolutionary Biogeography of
• Niche Construction
• Niche Evolution
• Non-Human Animals, Cultural Evolution in
• Origin and Early Evolution of Animals
• Origin of Amniotes and the Amniotic Egg
• Origin of Eukaryotes
• Origin of Life, The
• Paradox of Sex
• Parental Care, Evolution of
• Parthenogenesis
• Personality Differences, Evolution of
• Pest Management, Evolution and
• Phenotypic Plasticity
• Phylogenetic Comparative Methods and Tests of Macroevoluti...
• Phylogenetic Trees, Interpretation of
• Phylogeography
• Polyploid Speciation
• Population Structure
• Post-Copulatory Sexual Selection
• Psychology, Evolutionary
• Quantitative Genetic Variation and Heritability
• Reaction Norms, Evolution of
• Reinforcement
• Reproductive Proteins, Evolution of
• Selection, Directional
• Selection, Disruptive
• Selection Gradients
• Selection, Natural
• Selection, Sexual
• Selfish Genes
• Sequential Speciation and Cascading Divergence
• Sexual Conflict
• Sexual Selection and Speciation
• Sexual Size Dimorphism
• Speciation Continuum
• Speciation Genetics and Genomics
• Speciation, Geography of
• Speciation, Sympatric
• Species Concepts
• Species Delimitation
• Sperm Competition
• Systems Biology
• Taxonomy and Classification
• Tetrapod Evolution
• The Philosophy of Evolutionary Biology
• Theory, Coalescent
• Trends, Evolutionary
• Wallace, Alfred Russel
• Privacy Policy
• Cookie Policy
• Legal Notice
• Accessibility

Powered by:

• [66.249.64.20|185.66.15.189]
• 185.66.15.189

Loading metrics

Open Access

Essays articulate a specific perspective on a topic of broad interest to scientists.

See all article types »

Evolutionary Biology for the 21st Century

* E-mail: [email protected]

Affiliation Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America

Affiliation Department of Zoology, Oregon State University, Corvallis, Oregon, United States of America

Affiliation Departments of Developmental Biology and Computer Science, Stanford University, Stanford, California, United States of America

Affiliation Department of Biology, University of Virginia, Charlottesville, Virginia, United States of America

Affiliation Department of Biology, Clark University, Worcester, Massachusetts, United States of America

Affiliations Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America

Affiliation Department of Biochemistry and Biophysics, University of California, San Francisco, California, United States of America

Affiliation Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America

Affiliations Museum of Vertebrate Zoology, University of California, Berkeley, California, United States of America, The Australian National University, Canberra, Australia

Affiliation Department of Biology, University of Rochester, Rochester, New York, United States of America

Affiliation Department of Biology, Stanford University, Stanford, California, United States of America

Affiliation Department of Biology, University of Munich, Munich, Germany

Affiliation Department of Biology, University of Missouri, St. Louis, Missouri, United States of America

Affiliation Florida Museum of Natural History, University of Florida, Gainesville, Florida, United States of America

Affiliation Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, California, United States of America

• Jonathan B. Losos, 
• Stevan J. Arnold, 
• Gill Bejerano, 
• E. D. Brodie III, 
• David Hibbett, 
• Hopi E. Hoekstra, 
• David P. Mindell, 
• Antónia Monteiro, 
• Craig Moritz, 

Published: January 8, 2013

• https://doi.org/10.1371/journal.pbio.1001466
• Reader Comments

Citation: Losos JB, Arnold SJ, Bejerano G, Brodie ED III, Hibbett D, Hoekstra HE, et al. (2013) Evolutionary Biology for the 21st Century. PLoS Biol 11(1): e1001466. https://doi.org/10.1371/journal.pbio.1001466

Copyright: © 2013 Losos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The workshop that led to this report was funded by the National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

We live in an exciting time for biology. Technological advances have made data collection easier and cheaper than we could ever have imagined just 10 years ago. We can now synthesize and analyze large data sets containing genomes, transcriptomes, proteomes, and multivariate phenotypes. At the same time, society's need for the results of biological research has never been greater. Solutions to many of the world's most pressing problems—feeding a global population, coping with climate change, preserving ecosystems and biodiversity, curing and preventing genetically based diseases—will rely heavily on biologists, collaborating across disciplines.

Theodosius Dobzhansky famously proclaimed that “nothing makes sense in biology except in the light of evolution." Though Dobzhansky's statement is sometimes dismissed by biologists in other fields as self-promotion, recent advances in many areas of biology have shown it to be prophetic. For example, genomics, which emerged mostly from molecular biology, is now steeped in evolutionary biology. Evolutionary theory helps to explain our origins, our history, and how we function as organisms and interact with other life forms, all of which are crucial to understanding our future (e.g., [1] – [5] ). Evolutionary approaches have helped reconstruct the history of human culture, including, for example, the history of human populations and languages [6] – [11] . And the impact of evolutionary biology is extending further and further into biomedical research and nonbiological fields such as engineering, computer sciences, and even the criminal justice system.

The pervasive relevance of evolution can be seen in the 2009 report commissioned by the National Research Council of the National Academies, A New Biology for the 21 st Century [12] , which identified four broad challenges for biology: develop better crops to feed the world, understand and sustain ecosystem function and biodiversity in a changing world, expand sustainable alternative energy sources, and understand individual health. In each of these areas, the report noted, evolutionary concepts and analyses have played—and will continue to play—an integral role.

It's hard to overstate evolutionary biology's power to explain the living world and our place in it. Many applications of evolutionary theory and methods—from animal and plant breeding to vaccine development to management of biological reserves and endangered species—affect society and promote human well-being [13] , [14] . Much human activity, however, is changing Earth's climate and habitats, with uncertain but potentially severe environmental stresses on many other species [15] – [18] , and the solutions to the many resulting problems may well require understanding evolutionary interactions among species and their mutual dependencies.

Our ability to apply evolutionary concepts to a wide range of problems has never been greater. Changes in the availability of data and an emerging scientific culture that embraces rapid, open access to many kinds of data (genomic, phenotypic, and environmental), along with a computational infrastructure that can connect these rich sources of data ( [19] , Figure 1 ), will transform the nature and scale of problems that can be addressed by evolutionary biology.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

All this can be connected to the Tree of Life (phylogeny), from populations to entire clades, and is enabled by new protocols and networks in biodiversity informatics.

https://doi.org/10.1371/journal.pbio.1001466.g001

Periodically, groups of scientists meet to identify new opportunities in evolutionary biology and associated disciplines (e.g., [12] , [20] – [23] ). Rather than set a specific research agenda for the future—clearly the charge of individual investigators—the aim has been to identify new themes and research directions that are already emerging in the field and to focus on the intersection of fundamental problems with new technologies and methods. In the following sections, we briefly highlight some key applications of evolutionary biology, provide examples of emerging research areas, and identify infrastructure and training needs.

Evolutionary Applications

Evolutionary medicine.

The new field of “evolutionary medicine" [24] – [26] posits that understanding our evolutionary past can inform us of the causes of perplexing common diseases. For instance, diabetes and autoimmune diseases such as asthma may represent mismatches between evolutionary adaptation to the environments in which humans evolved and current conditions. In addition, some age-related conditions, such as cancer, can be understood as the outcome of selection for early reproduction, when humans faced dying of disease or predation at an early age. This long-term selection on the cellular machinery to optimize growth and survival through early reproduction may now explain the prevalence of cancer late in life, a modern malaise that emerges because of the recent, rapid extension of postreproductive lifespan [27] . Aside from providing explanations for the occurrence of diseases, the field of evolutionary medicine is also concerned with suggesting strategies for slowing the evolution of resistance in pathogen populations [28] – [30] ; strategies to improve public health and reduce the incidence of common diseases [31] , [32] ; prediction of diseases that may emerge from recent host-shifts to humans [33] ; discovery, design, and enhancement of drugs and vaccines (e.g., [34] ); and understanding the role of the microbiome in human health [35] .

Feeding the Human Population

Feeding the rapidly growing human population, especially with increasing stress on agricultural systems from climate change, continues to be a major challenge. The green revolution, from the 1950s onwards, rested on selective plant breeding for larger yields and was underpinned by evolutionary theory [36] . Currently, the trend is to rely on biotechnology to introduce either herbicide resistance genes or herbivore-directed toxins, such as Bt, to combat crop competitors and herbivores, respectively, and thus promote increasing yields [37] . Unfortunately, genetically modified crops are genetically uniform and so do not represent a long-term solution against the evolution of either herbicide or Bt resistance. In addition, these herbicide resistance or toxin genes can be transferred to other nontarget species through pollen-mediated hybridization, rendering them resistant or toxic as well [38] . The agriculture of the future must incorporate evolutionary thinking to reduce the evolution of resistance and the risk of pathogen outbreaks. Maintaining genetic diversity in crop and animal production systems considerably reduces these risks [38] .

Sustaining Biological Diversity

Evolutionary approaches have often been applied to the conservation of species and ecosystems [13] , [39] – [42] . Linking spatial data on phenotypes, genomes and environments in a phylogenetic context allows us to identify and name Earth's diverse life forms. This linkage, in turn, helps to provide the basic units needed to quantify taxonomic diversity and to pursue its conservation. Determining phylogenetic relationships among species allows us to identify their unique adaptations and provides the historical context to understand how they arose [43] – [45] . Evolutionary approaches also can be used to determine the origins of invasive species [46] – [48] and to help design effective remediation [49] , [50] . Collectively, understanding the distribution of current biodiversity and its evolutionary response to past environmental change is fundamental to mitigating effects of ongoing habitat loss and climate change [51] . Given the rate of anthropogenic climate change, evolutionary theory and experiments can help predict vulnerability (i.e., inability to adapt) of species and thus improve conservation strategies [52] .

Computation and Design

Models of mutation, inheritance, and selection have inspired the development of computational evolutionary algorithms that are used to solve complex problems in many fields [53] , [54] . In particular, engineering and design processes have incorporated evolutionary computation, leading to improvements in design of cars, bridges, traffic systems robots, and wind turbine energy, among other applications [55] – [59] .

Evolution and Justice

Genealogical relationships bear on many court cases. Is the defendant really the parent of the plaintiff? Does the evidence (e.g., blood, semen, or skin cells) at the crime scene tend to exonerate or implicate a suspect? Evolutionary methods, particularly population genetics, are now used frequently in forensics and court cases to test the link of crime scene evidence to individuals [60] , and phylogenetic analyses have been vetted and accepted as valid and appropriate methods for determining facts of history in the United States criminal court system [61] .

Emerging Research and Future Challenges in Evolutionary Biology

Divining the direction of future scientific research is always fraught with difficulty. Nonetheless, we feel that it is possible to identify some general themes that will be important in coming years. We also present some examples of classic research problems that remain unsolved and that might well be the focus of future work, as well as new and important questions for which evolutionary approaches may be key.

The flood of data in all areas of evolutionary biology poses important theoretical challenges: new kinds of theory are sometimes required to make sense of new kinds of data. We can already point to certain broad areas of evolutionary biology that will likely demand sustained theoretical work. These include the elaboration of more formal theories for evolutionary developmental biology (e.g., analysis of gene network evolution and modification); the more complete incorporation of the roles of epigenetics, behavior, and plasticity in models of trait evolution; analysis of units of selection; and attempts to construct a quantitative and predictive theory that describes the genetic basis of adaptation. In other areas, problems will likely be more statistical than theoretical. Indeed, the enormous quantity of genome data poses serious statistical challenges even for fields that already possess strong theoretical foundations, such as evolutionary genetics.

The Explosion and Diversity of Data

DNA sequencing can now generate whole-genome data not only for single representatives of a few species but for multiple individuals from multiple conspecific populations and even from entire communities. Such multilevel data are giving rise to whole new fields of study (e.g., population genomics and metagenomics) as well as to new theoretical, computational, and data management challenges.

One particularly exciting avenue of research afforded by new genomic technology is the possibility of directly observing the dynamics of evolution. In the last few years, genomic analyses of experimental evolution have yielded new understanding of how RNA molecules, viruses, and bacteria evolve (bacteria: [62] , [63] ; virus: [64] ; RNA molecules: [65] ). This approach is now being applied to eukaryotic model systems such as C. elegans and yeast [66] – [68] . These efforts will continue to expand and will surely involve natural systems in field settings. Past evolution, for example, can be inferred from samples derived from ancient specimens, archived material in museum collections, lake sediments, and glacier cores. Contemporary evolution can be inferred from genomic sampling across seasons and years and can be detected in response to climatic disturbances such as El Niño events and to manmade environmental changes such as oil spills. In parallel with long-term ecological data (e.g., species abundance and distributions through time), we can now track genomic variation through ecological and evolutionary time. This capability, together with the realization that evolutionary change can occur on ecological timescales [69] , provides an important new window on real-time evolution. Evolution on contemporary time scales is likely to be especially important in the context of evolving pathogens, pest resistance, and the response of organisms to rapid environmental change.

While the explosion of data on genome sequences has received the most attention, supplementing these data with information on the natural history of individuals, species, and their environments will be important. Core information from field-collected specimens always includes species identity and place and time of collection, but increasingly, this information is being enriched with links to field notes and phenotypic (e.g., images), behavioral (e.g., sounds), and genomic data in a variety of databases (e.g., Morphbank— http://www.morphbank.net/ , Barcode of Life— http://www.barcodeoflife.org/ , Macaulay Library— http://macaulaylibrary.org/ ). Precise information on place, time, and reproductive stage can be integrated with data on local environmental conditions, often obtained from remote sensing [70] . The key is to connect information across repositories, such as natural history museums and genomic databases ( Figure 2 ). Such efforts will also include observational data provided by the broader public [71] .

Photo by Jeremiah Trimble, Department of Ornithology, Museum of Comparative Zoology, Harvard University.

https://doi.org/10.1371/journal.pbio.1001466.g002

Evolutionary Processes That Shape Genomic and Phenotypic Variation

The availability of genomic data from a remarkable range of species has allowed the alignment and comparison of whole genomes. These comparative approaches have been used to characterize the relative importance of fundamental evolutionary processes that cause genomic evolution and to identify particular regions of the genome that have experienced recent positive selection, recurrent adaptive evolution, or extreme sequence conservation [72] – [75] . Yet more recently, resequencing of additional individuals or populations is also allowing genome-wide population genetic analyses within species [76] – [82] . Such population-level comparisons will allow even more powerful study of the relative importance of particular evolutionary processes in molecular evolution as well as the identification of candidate genomic regions that are responsible for key evolutionary changes (e.g., sticklebacks [83] , butterflies [84] , Arabidopsis [85] ). These data, combined with theoretical advances, should provide insight into long-standing questions such as the prevalence of balancing selection, the relative frequency of strong versus weak directional selection, the role of hybridization, and the importance of genetic drift. A key challenge will be to move beyond documenting the action of natural selection on the genome to understanding the importance of particular selective agents. For example, what proportion of selection on genomes results from adaptation to the abiotic environment, coevolution of species, sexual selection, or genetic conflict? Finally, as sequencing costs continue to drop and analytical tools improve, these same approaches may be applied to organisms that present intriguing evolutionary questions but were not tractable methodologically just a few years ago. The nonmodel systems of today may well become the model systems of tomorrow [86] .

Earth–Biosphere Interactions Over Vast Stretches of Time and Space

We are in the midst of a massive perturbation of natural communities as species respond to human-driven changes in climate and land cover. Beyond the challenge of understanding the capacity of species to respond (e.g., [51] , [87] ) and the potential for dramatic state-shifts in the biosphere [17] lies the daunting problem of understanding the many interactions between community-scale ecological dynamics and evolution of traits within populations.

We now can also ask if evolution matters for ecosystem functioning. To date, most ecosystem studies have assumed that all individuals that compose a population within a community are equivalent ecologically. But individuals within a population are variable, and this variation may lead to ecological interactions that are in a continual state of evolutionary flux as ecologically driven evolutionary change feedbacks to alter the ongoing ecological interactions [88] – [90] . This evolutionary perspective on communities is an emerging area that will require the acquisition and analysis of large, temporal samples of genomic and phenotypic data, as well as the direct measurement of fitness. Samples that include paleo/historical DNA as well as contemporary DNA might be especially valuable by providing a temporal view on such questions.

Understanding Biological Diversification

A major and urgent challenge is to improve knowledge of the identity and distribution of species globally. While we need to retain the traditional focus on phenotypes, powerful new capabilities to obtain and interpret both genomic and spatial data can and should revolutionize the science of biodiversity. Building on momentum from single-locus “barcoding" efforts, new genome-level data can build bridges from population biology to systematics [91] . By establishing a comprehensive and robust “Tree of Life," we will improve understanding of both the distribution of diversity and the nature and timing of the evolutionary processes that have shaped it.

Studies of the biodiversity of Bacteria and Archaea are complicated by the widespread occurrence of lateral gene transfer. However, the phylogeny of these organisms and their genes remains critical to understanding their scope, origins, distributions, and change over time [92] . The advent of metagenomic sequencing of environmental microbial communities has revealed greater diversity and flux of genotypes than ever imagined, defying conventional species concepts and presenting a major challenge to applying traditional evolutionary and ecological theory to understanding microbial diversity [93] , [94] . Addressing this challenge will be necessary to advance microbial ecology beyond the descriptive stage. Moreover, it is only with such understanding that a natural history of microbes can be developed, leading to more meaningful exploration of genomic structure and function, the origin of novel genes, and increased knowledge of microbial influences at both the global and individual (microbiome) levels.

In addition to documenting biodiversity, more research is needed on the processes that produce this diversity. While research on speciation has seen a resurgence over the last two decades [95] – [97] , new tools—including genomic data—can support new approaches for a number of important questions, including discovering genomic signatures underlying the evolution of prezygotic reproductive isolation, and describing how hybridization, contact between incipient species, genome reorganization, and genome duplication, affect speciation.

Understanding the diversification of species and the origin of adaptations poses a number of challenges for evolutionary biologists, including integration of the fossil record with diversification inferred from the relationships among contemporary species; determining the relationship between lineage and phenotypic diversification; understanding the factors that lead to the replacement of clades over time; understanding the occupancy of ecological niche space through evolutionary diversification, adaptive radiation, and extinction; and assessing the role that evolving species interactions play in diversification.

All evolution has an ecological context that is essential to the interpretation of diversification. Consequently, we need to incorporate analyses of the environmental context of evolution, particularly species interactions that are likely to both set limits to diversification and promote evolutionary novelty. For all these reasons, further integration of paleontology with other fields of evolutionary biology, as well as development of genetic-evolutionary research programs on clades with excellent fossil records (e.g., foraminifera, diatoms, mollusks; Figure 3 ), will be important. More generally, uniting understanding of evolutionary pattern and process will require reductionist studies on evolutionary mechanisms of species formation and phenotypic change, as well as broadly historical studies that incorporate phylogenetic, paleontological, and geological data.

Genomic sequence data for stickleback fish is now providing insight into evolutionary patterns, such as the reduction in the pelvic skeleton, manifest both in the fossil record and in extant populations [83] . Photograph courtesy Peter J. Park.

https://doi.org/10.1371/journal.pbio.1001466.g003

As we address these challenges, the importance of natural history data cannot be overemphasized. Observations on the natural history of organisms, the basic building blocks of more detailed studies of ecology and evolution, are critical if we are to preserve and understand biological diversity [98] . Though few would argue against this point in principle, natural history research is rarely encouraged or supported. Making the acquisition of natural history data an integral part of hypothesis-driven science is now more important than ever.

Logistical Issues and Opportunities

To take full advantage of technological advances, especially the availability of new data types and databases, we must confront several challenges that involve community resources and how we use them. Some challenges concern infrastructure, while others involve aspects of scientific culture. Still others involve how we train the next generation of evolutionary biologists, who will need a better grasp of diverse disciplines—from natural history to developmental biology—as well as bioinformatics skills to handle immense datasets across multiple fields (see Text S1 and also Figure S2 ).

The infrastructure challenges center on creation of new kinds of databases—for instance, ones that focus on (continuous) phenotypic and not merely (discrete) DNA sequence data—as well as on integration across databases to allow synthesis of very different kinds of data (see Text S2 ). The cultural challenges center on the need for supporting a climate of scientific openness. Maintaining openness will require evolutionary biologists to make the results of their research available rapidly and in a form that is most useful to their colleagues. The scientific community has already made great strides in this direction (for instance by requiring deposition of data as a condition for publication and by founding open access journals), but additional steps are necessary. We strongly support the movement toward open access for the scientific literature to accelerate research and allow more investigators to participate. We also encourage provision of open software, data and databases, as well as their computational reuse and distillation, as outlined by Lathrop et al. (2011) [99] . These individual and community efforts will be increasingly necessary for development of new research programs and insights.

As noted at the outset, we live in an exciting time for evolutionary biology. The field has embraced the “omic" revolution, and answers to many classic questions, which have motivated research for a century, are now within reach. The study of evolution, which in the past was often equated with changes in gene frequencies in populations, has become more holistic and integrative. Researchers are increasingly interested in exploring how interactions among genes, individuals, and environments have shaped the evolutionary process, both at micro- and macrolevels. At the same time, large challenges such as global warming, novel infectious diseases, and threats to biodiversity are increasing, and the opportunity for evolutionary biologists to contribute to their resolution has never been greater.

Realizing the full potential inherent in evolutionary biology is, however, far from assured. The task of integrating evolutionary knowledge within and across scales of biological organization, as discussed above, requires development of many comparative databases and analytical tools. We would do well to collaborate broadly, cultivating new expertise, and to watch out for the unexpected, as analyses of new kinds of data can reveal that preconceptions are unfounded.

Because most of our science is supported by limited public funds, evolutionary biologists and ecologists should support and participate in efforts to help the public understand the issues and the value of scientific understanding. Science in general and evolutionary science in particular are often politicized, exactly because of their fundamental importance to human society. The next 20 years hold the promise of a golden age for evolutionary biology. Whether we realize that promise depends in part on how effectively we communicate that message.

Cyberinfrastructure —The research environments that support advanced data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services distributed over the Internet beyond the scope of a single institution. In scientific usage, cyberinfrastructure is a technological solution to the problem of efficiently connecting laboratories, data, computers, and people.

Evolutionary developmental biology —The study of the evolution of development, often by the comparative study of gene expression patterns through the course of development in different species.

Evolutionary genetics —Population and quantitative genetics.

Gene network —A flow diagram describing the interactions among genes during development that affect a particular phenotype or set of phenotypes.

Genomics —The study of the entire complement of DNA in organisms (Genome), including is sequence and organization.

GMO —Genetically modified organisms in which the genome has been deliberately changed; transgenic organisms resulting from DNA manipulations.

Lateral (horizontal) gene transfer —Genetic transfer between species, as opposed to vertical gene transmission from parents to offspring in a lineage.

Metadata —Data associated with individual DNA sequences or organismal specimens (e.g., the date and locality where the sample originated, its ecological context, etc.).

Model organism —Organisms whose genome has been sequenced and for which sophisticated tools for genetic manipulation are available.

Natural history —The entire description of an organism, including its phenotype, genome, and ecological context (i.e., abiotic niche as well as its biotic interactions with other species).

Nonmodel organism —Organisms whose genome has not been sequenced and/or for which sophisticated tools for genetic manipulation are not available.

Ontology —The naming of categories, especially of the functions of genes.

Population genetics —The study of the evolutionary forces that change the genetic composition of a population; the discipline is often concerned with evolution at one or a few genetic loci.

Quantitative genetics —The study of the inheritance and evolution of traits that are typically affected by many genetic loci.

Transgenic tools —Tools that enable the deliberate transfer of DNA sequences from one organism to another or the deletion or modification of DNA sequences, in every cell, in one organism.

Supporting Information

An example of the enormous phylogenetic trees that soon will represent the norm in phylogenetic analyses. This is the consensus tree of the maximum likelihood phylogenies for 55,473 species of seed plants with the location of significant shifts in species diversification rates marked in red across the tree. Adapted from [4] .

https://doi.org/10.1371/journal.pbio.1001466.s001

The Phenomobile, a remote sensing field buggy, and the Blimp, for remotely imaging an entire field. The Phenomobile integrates a variety of remote sensing technologies for measuring phenotypic variables on many plants simultaneously. The buggy straddles a plot and collects measurements of plant temperature, stress, chemistry, color, size and shape, as well as measures of senescence. The Blimp is designed to image all the plants in an entire field from a height of 30–80 m using both infrared and digital color cameras. These technologies were developed by David Deery of the High Resolution Plant Phenomics Centre at the Commonwealth Scientific and Industrial Research Organisation in Australia. Photo credit: Carl Davies, CSIRO Plant Industry.

https://doi.org/10.1371/journal.pbio.1001466.s002

Training to sustain evolutionary biology.

https://doi.org/10.1371/journal.pbio.1001466.s003

Infrastructure needs and opportunities in evolutionary biology.

https://doi.org/10.1371/journal.pbio.1001466.s004

Acknowledgments

The workshop that led to this report was funded by the National Science Foundation. We thank the American Society of Naturalists, the Society for the Study of Evolution, and the Society of Systematic Biologists for organizational and planning assistance. Many thanks to Melissa Woolley for invaluable assistance with logistics and manuscript preparation and to M. Bell, J. Borewitz, D. Jablonski, P. Parks, K. Roy, S. Smith, J. Trimble, and A. Weirman for help procuring images.

• 1. Wilson EO (2002) The future of life. New York: Alfred A. Knopf.
• 2. Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: synthesis. Washington, DC: Island Press.
• 3. Mindell DP (2006) The evolving world: evolution in everyday life. Cambridge, MA: Harvard University Press.
• 4. Chivian E, Bernstein A (2008) Sustaining life: how human health depends on biodiversity. Oxford; New York: Oxford University Press.
• 5. Held LI Jr (2009) Quirks of human anatomy: an evo-devo look at the human body. Cambridge: Cambridge University Press.
• View Article
• Google Scholar
• 11. Pagel M (2012) Wired for culture: origins of the human social mind. New York: W.W. Norton & Company.
• 12. National Research Council (US) Committee on a New Biology for the 21st Century: Ensuring the United States Leads the Coming Biology Revolution (2009) A new biology for the 21st century: ensuring the United States leads the coming biology revolution. Washington, DC: National Academies Press. Available: https://download.nap.edu/catalog.php?record_id=12764 . Accessed May 25, 2012.
• 20. National Science Foundation (1998) Frontiers in population biology: report of a population biology task force. Arlington, VA: National Science Foundation.
• 21. National Science Foundation (2005) Frontiers in evolutionary biology. Arlington, VA: National Science Foundation.
• 23. National Research Council. 2010. Research at the intersection of the physical and life sciences. Washington, DC: National Academies Press.
• 25. Gluckman P, Beedle A, Hanson M (2009) Principles of evolutionary medicine. Oxford; New York: Oxford University Press.
• 36. Kingsbury N (2009) Hybrid: the history and science of plant breeding. Chicago: University of Chicago Press. 512 p.
• 53. Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Available: http://dces.essex.ac.uk/staff/rpoli/gp-field-guide/A_Field_Guide_to_Genetic_Programming.pdf . Accessed May 22, 2012.
• 54. Chiong R, Weise T, Michalewicz Z (2011) Variants of evolutionary algorithms for real-world applications. New York: Springer-Verlag.
• 58. Byrne J, Fenton M, Hemberg E, McDermott J, O'Neill M, et al.. (2011) Combining structural analysis and multi-objective criteria for evolutionary architectural design. In: Di Chio C, et al.. editors. Applications of evolutionary computation: EvoApplications 2011: EvoCOMNET, EvoFIN, EvoHOT, EvoMUSART, EvoSTIM, and EvoTRANSLOG, Torino, Italy, April 27–29, 2011, Proceedings, Part II.
• 95. Howard DJ, Berlocher SH, eds (1998) Endless forms: species and speciation. New York; Oxford: Oxford University Press.
• 96. Coyne JA, Orr HA (2004) Speciation. Sunderland: Sinauer Press.
• 97. Dieckmann U, Doebeli M, Metz JAJ, Tautz D, editors (2004) Adaptive speciation. Cambridge studies in adaptive dynamics. Cambridge, UK: Cambridge University Press.

• Search Menu
• Advance articles
• Editor's Choice
• Special Issues and Sections
• Methods Articles
• Trending Articles
• Cover Archive
• Author Guidelines

Submission Site

• Open Access Policy
• Self-Archiving Policy
• About Journal of Evolutionary Biology
• About the European Society for Evolutionary Biology
• Editorial Board
• Abstracting and Indexing
• Advertising & Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Editor-in-Chief

Max Reuter, University College London, UK 

Editorial Board 

Managing Editor

Nicola Cook, University of St Andrews, UK

Editorial Office

About the journal 

JEB is an international, peer-reviewed journal that covers diverse research areas in evolutionary biology. The journal prioritizes publishing significant advances from a broad taxonomic perspective.

Latest articles

Editorial team 

Meet the editorial board for the Journal of Evolutionary Biology . 

Learn more 

Submit your work

Submit your manuscript for consideration to be included in a 2024 issue of the Journal of Evolutionary Biology .  

 Review Author Guidelines   

Email alerts

Register to receive table of contents email alerts as soon as new issues of Journal of Evolutionary Biology are published online.

Keep up to date with JEB  

Keep up with latest from the  Journal of Evolutionary Biology  and the wider evolutionary biology community.

Follow @jevbio.bsky.social on Bluesky

Follow @JEvBio on X

Read the JEB Blog

More from ESEB

Join us from 17-22 August 2025, in Barcelona, Spain, for the 2025 Congress of the European Society of Evolutionary Biology.

Visit the conference website

John Maynard Smith Prize 

Every year the European Society for Evolutionary Biology distinguishes an outstanding young evolutionary biologist with the John Maynard Smith (JMS) Prize.  In 2023, the winner is Dr Paul Jay, postdoctoral fellow at Copenhagen and Berkeley. Paul will give the 2023 JMS prize lecture at the ESEB Congress in 2025. 

Visit the JMS Prize website 

ESEB Social media 

Never miss a call, deadline or news announcement from the Society!  

Bluesky: @eseb.bsky.social

X:  @eseb_org 

Join our society!

ESEB brings together thousands of evolutionary biologists worldwide via our initiatives and international congresses. 

Being an ESEB member qualifies you for benefits such as: 

Reduced registration fee at ESEB congresses 

30% discount on the Article Publication Charge for J ournal of Evolutionary Biology  

ESEB travel awards, mobility grants, and prizes 

Join now! 

Author resources

Learn about how to submit your article, our publishing process, and tips on how to promote your article.

Find out more

Read and Publish deals

Authors interested in publishing in Journal of Evolutionary Biology may be able to publish their paper Open Access using funds available through their institution’s agreement with OUP.

Find out if your institution is participating

Open Access options for authors 

The Journal of Evolutionary Biology welcomes submissions from authors wishing to submit open access papers. 

Related Titles

• Advertising & Corporate Services
• Journals Career Network

Affiliations

• Online ISSN 1420-9101
• Print ISSN 1010-061X
• Copyright © 2024 European Society for Evolutionary Biology
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

18.1 Understanding Evolution

Learning objectives.

In this section, you will explore the following questions:

• How was the present-day theory of evolution developed?
• What is adaptation, and how does adaptation relate to natural selection?
• What are the differences between convergent and divergent evolution, and what are examples of each that support evolution by natural selection?
• What are examples of homologous and vestigial structures, and what evidence do these structures provide to support patterns of evolution?
• What are common misconceptions about the theory of evolution?

Connection for AP ® Courses

Millions of species, from bacteria to blueberries to baboons, currently call Earth their home, but these organisms evolved from different species. Furthermore, scientists estimate that several million more species will become extinct before they have been classified and studied. But why don’t polar bears naturally inhabit deserts or rain forests, except, perhaps, in movies? Why do humans possess traits, such as opposable thumbs, that are unique to primates but not other mammals? How did observations of finches by Charles Darwin visiting the Galapagos Islands in the 1800s provide the foundation for our modern understanding of evolution?

The theory of evolution as proposed by Darwin is the unifying theory of biology. The tenet that all life has evolved and diversified from a common ancestor is the foundation from which we approach all questions in biology. As we learned in our exploration of the structure and function of DNA, variations in individuals within a population occur through mutation, allowing more desirable traits to be passed to the next generation. Due to competition for resources and other environmental pressures, individuals possessing more favorable adaptive characteristics are more likely to survive and reproduce, passing those characteristics to the next generation with increased frequency. When environments change, what was once an unfavorable trait may become a favorable one. Organisms may evolve in response to their changing environment by the accumulation of favorable traits in succeeding generations. Thus, evolution by natural selection explains both the unity and diversity of life.

Convergent evolution occurs when similar traits with the same function evolve in multiple species exposed to similar selection pressure, such as the wings of bats and insects. In divergent evolution , two species evolve in different directions from a common point, such as the forelimbs of humans, dogs, birds, and whales. Although Darwin’s theory was revolutionary for its time because it contrasted with long-held ideas (for example, Lamarck proposed the inheritance of acquired characteristics ), evidence drawn from many scientific disciplines, including the fossil record, the existence of homologous and vestigial structures, mathematics, and DNA analysis supports evolution through natural selection. It is also important to understand that evolution continues to occur; for example, bacteria that evolve resistance to antibiotics or plants that become resistant to pesticides provide evidence for continuing change.

Information presented and the examples highlighted in this section support concepts outlined in Big Idea 1 of the AP ® Biology Curriculum Framework. The AP ® Learning Objectives listed in the Curriculum Framework provide a transparent foundation for the AP ® Biology course, an inquiry-based laboratory experience, instructional activities, and AP ® exam questions. A learning objective merges required content with one or more of the seven science practices.

Teacher Support

The chapter talks about embryology, so it might be important to mention Ernst Haeckel (1834–1919) and his famous principle "ontogeny recapitulates phylogeny." Please see this PBS website for more information.

The Science Practice Challenge Questions contain additional test questions for this section that will help you prepare for the AP exam. These questions address the following standards: [APLO 1.10][APLO 1.12][APLO 1.13][APLO 1.31][APLO 1.32][APLO 1.27][APLO 1.28][APLO 1.30][APLO 1.14][APLO 1.29][APLO 1.26][APLO 4.8]

The Origin of Life

Humans have adopted many theories regarding the origin of life over the course of our time on Earth. Early civilizations believed that life was created by supernatural forces. Organisms were “hand-made” to be perfectly adapted to their environment and, therefore, did not change over time. Some early thinkers, such as the Greek philosopher Aristotle, believed that organisms belonged to a ladder of increasing complexity. Based on this understanding, scientists such as Carolus Linnaeus attempted to organize all living things into classification schemes that demonstrated an increasing complexity of life.

Over time, however, scientists came to understand that life was constantly evolving on Earth. Georges Cuvier found that fossilized remains or organisms changed as he dug into deeper rock layers (strata), indicating that the organisms present in the area had changed over time. This observation led Jean-Baptiste de Lamarck to hypothesize that organisms adapted to their environment by changing over time. As organisms used different parts of their body, those parts improved, and these changes were passed down to their offspring. Ultimately, these theories were disproven by scientists, but their development contributed to the theory of evolution that was finally formulated by Charles Darwin.

Charles Darwin and Natural Selection

In the mid-nineteenth century, the actual mechanism for evolution was independently conceived of and described by two naturalists: Charles Darwin and Alfred Russel Wallace. Importantly, each naturalist spent time exploring the natural world on expeditions to the tropics. From 1831 to 1836, Darwin traveled around the world on H.M.S. Beagle , including stops in South America, Australia, and the southern tip of Africa. Wallace traveled to Brazil to collect insects in the Amazon rainforest from 1848 to 1852 and to the Malay Archipelago from 1854 to 1862. Darwin’s journey, like Wallace’s later journeys to the Malay Archipelago, included stops at several island chains, the last being the Galápagos Islands west of Ecuador. On these islands, Darwin observed species of organisms on different islands that were clearly similar, yet had distinct differences. For example, the ground finches inhabiting the Galápagos Islands comprised several species with a unique beak shape ( Figure 18.2 ). The species on the islands had a graded series of beak sizes and shapes with very small differences between the most similar. He observed that these finches closely resembled another finch species on the mainland of South America. Darwin imagined that the island species might be species modified from one of the original mainland species. Upon further study, he realized that the varied beaks of each finch helped the birds acquire a specific type of food. For example, seed-eating finches had stronger, thicker beaks for breaking seeds, and insect-eating finches had spear-like beaks for stabbing their prey.

Wallace and Darwin both observed similar patterns in other organisms and they independently developed the same explanation for how and why such changes could take place. Darwin called this mechanism natural selection. Natural selection , also known as “survival of the fittest,” is the more prolific reproduction of individuals with favorable traits that survive environmental change because of those traits; this leads to evolutionary change.

For example, a population of giant tortoises found in the Galapagos Archipelago was observed by Darwin to have longer necks than those that lived on other islands with dry lowlands. These tortoises were “selected” because they could reach more leaves and access more food than those with short necks. In times of drought when fewer leaves would be available, those that could reach more leaves had a better chance to eat and survive than those that couldn’t reach the food source. Consequently, long-necked tortoises would be more likely to be reproductively successful and pass the long-necked trait to their offspring. Over time, only long-necked tortoises would be present in the population.

Natural selection, Darwin argued, was an inevitable outcome of three principles that operated in nature. First, most characteristics of organisms are inherited, or passed from parent to offspring. Although no one, including Darwin and Wallace, knew how this happened at the time, it was a common understanding. Second, more offspring are produced than are able to survive, so resources for survival and reproduction are limited. The capacity for reproduction in all organisms outstrips the availability of resources to support their numbers. Thus, there is competition for those resources in each generation. Both Darwin and Wallace’s understanding of this principle came from reading an essay by the economist Thomas Malthus who discussed this principle in relation to human populations. Third, offspring vary among each other in regard to their characteristics and those variations are inherited. Darwin and Wallace reasoned that offspring with inherited characteristics which allow them to best compete for limited resources will survive and have more offspring than those individuals with variations that are less able to compete. Because characteristics are inherited, these traits will be better represented in the next generation. This will lead to change in populations over generations in a process that Darwin called descent with modification. Ultimately, natural selection leads to greater adaptation of the population to its local environment; it is the only mechanism known for adaptive evolution.

Papers by Darwin and Wallace ( Figure 18.3 ) presenting the idea of natural selection were read together in 1858 before the Linnean Society in London. The following year Darwin’s book, On the Origin of Species, was published. His book outlined in considerable detail his arguments for evolution by natural selection.

Demonstrations of evolution by natural selection are time consuming and difficult to obtain. One of the best examples has been demonstrated in the very birds that helped to inspire Darwin’s theory: the Galápagos finches. Peter and Rosemary Grant and their colleagues have studied Galápagos finch populations every year since 1976 and have provided important demonstrations of natural selection. The Grants found changes from one generation to the next in the distribution of beak shapes with the medium ground finch on the Galápagos island of Daphne Major. The birds have inherited variation in the bill shape with some birds having wide deep bills and others having thinner bills. During a period in which rainfall was higher than normal because of an El Niño, the large hard seeds that large-billed birds ate were reduced in number; however, there was an abundance of the small soft seeds which the small-billed birds ate. Therefore, survival and reproduction were much better in the following years for the small-billed birds. In the years following this El Niño, the Grants measured beak sizes in the population and found that the average bill size was smaller. Since bill size is an inherited trait, parents with smaller bills had more offspring and the size of bills had evolved to be smaller. As conditions improved in 1987 and larger seeds became more available, the trend toward smaller average bill size ceased.

Career Connection

Field biologist.

Many people hike, explore caves, scuba dive, or climb mountains for recreation. People often participate in these activities hoping to see wildlife. Experiencing the outdoors can be incredibly enjoyable and invigorating. What if your job was to be outside in the wilderness? Field biologists by definition work outdoors in the “field.” The term field in this case refers to any location outdoors, even under water. A field biologist typically focuses research on a certain species, group of organisms, or a single habitat ( Figure 18.4 ).

One objective of many field biologists includes discovering new species that have never been recorded. Not only do such findings expand our understanding of the natural world, but they also lead to important innovations in fields such as medicine and agriculture. Plant and microbial species, in particular, can reveal new medicinal and nutritive knowledge. Other organisms can play key roles in ecosystems or be considered rare and in need of protection. When discovered, these important species can be used as evidence for environmental regulations and laws.

Processes and Patterns of Evolution

Natural selection can only take place if there is variation , or differences, among individuals in a population. Importantly, these differences must have some genetic basis; otherwise, the selection will not lead to change in the next generation. This is critical because variation among individuals can be caused by non-genetic reasons such as an individual being taller because of better nutrition rather than different genes.

Genetic diversity in a population comes from two main mechanisms: mutation and sexual reproduction. Mutation, a change in DNA, is the ultimate source of new alleles, or new genetic variation in any population. The genetic changes caused by mutation can have one of three outcomes on the phenotype. A mutation can affect the phenotype of the organism in a way that gives it reduced fitness—lower likelihood of survival or fewer offspring. Alternatively, a mutation may produce a phenotype with a beneficial effect on fitness. And, many mutations will also have no effect on the fitness of the phenotype; these are called neutral mutations. Mutations may also have a whole range of effect sizes on the fitness of the organism that expresses them in their phenotype, from a small effect to a great effect. Sexual reproduction also leads to genetic diversity: when two parents reproduce, unique combinations of alleles assemble to produce the unique genotypes and thus phenotypes in each of the offspring.

A heritable trait that helps the survival and reproduction of an organism in its present environment is called an adaptation . Scientists describe groups of organisms becoming adapted to their environment when a change in the range of genetic variation occurs over time that increases or maintains the “fit” of the population to its environment. The webbed feet of platypuses are an adaptation for swimming. The snow leopards’ thick fur is an adaptation for living in the cold. The cheetahs’ fast speed is an adaptation for catching prey.

These adaptations can occur through the rearrangements of entire genomes or can be caused by the mutation of a single gene. For example, dogs have 78 chromosomes while cats have 38. A large number of the characteristics that distinguish dogs from cats arose from chromosomal rearrangements that have occurred since both groups diverged from their last common ancestor. On the other hand, certain mice are white and other mice are black. The difference in fur color occurs through the mutation of a single gene. Thus, as a result of a single mutation, a mouse population can become more adapted to survive in snowy environments versus a dark, forest floor.

Whether or not a trait is favorable depends on the environmental conditions at the time. The same traits are not always selected because environmental conditions can change. For example, consider a species of plant that grew in a moist climate and did not need to conserve water. Large leaves were selected because they allowed the plant to obtain more energy from the sun. Large leaves require more water to maintain than small leaves, and the moist environment provided favorable conditions to support large leaves. After thousands of years, the climate changed, and the area no longer had excess water. The direction of natural selection shifted so that plants with small leaves were selected because those populations were able to conserve water to survive the new environmental conditions.

The evolution of species has resulted in enormous variation in form and function. Sometimes, evolution gives rise to groups of organisms that become tremendously different from each other. When two species evolve in diverse directions from a common point, it is called divergent evolution. Such divergent evolution can be seen in the forms of the reproductive organs of flowering plants which share the same basic anatomies; however, they can look very different as a result of selection in different physical environments and adaptation to different kinds of pollinators ( Figure 18.5 ).

In other cases, similar phenotypes evolve independently in distantly related species. For example, flight has evolved in both bats and insects, and they both have structures we refer to as wings, which are adaptations to flight. However, the wings of bats and insects have evolved from very different original structures. This phenomenon is called convergent evolution, where similar traits evolve independently in species that do not share a recent common ancestry. The two species came to the same function, flying, but did so separately from each other.

These physical changes occur over enormous spans of time and help explain how evolution occurs. Natural selection acts on individual organisms, which in turn can shape an entire species. Although natural selection may work in a single generation on an individual, it can take thousands or even millions of years for the genotype of an entire species to evolve. It is over these large time spans that life on earth has changed and continues to change.

Evidence of Evolution

The evidence for evolution is compelling and extensive. Looking at every level of organization in living systems, biologists see the signature of past and present evolution. Darwin dedicated a large portion of his book, On the Origin of Species , to identifying patterns in nature that were consistent with evolution, and since Darwin, our understanding has become clearer and broader.

Fossils provide solid evidence that organisms from the past are not the same as those found today, and fossils show the gradual evolutionary changes over time. Scientists determine the age of fossils and categorize them from all over the world to determine when the organisms lived relative to each other. The resulting fossil record tells the story of the past and shows the evolution of form over millions of years ( Figure 18.6 ). For example, scientists have recovered highly detailed records showing the evolution of humans and horses.

Anatomy and Embryology

Another type of evidence for evolution is the presence of structures in organisms that share the same basic form. For example, the bones in the appendages of a human, dog, bird, and whale all share the same overall construction ( Figure 18.7 ) resulting from their origin in the appendages of a common ancestor. Over time, evolution led to changes in the shapes and sizes of these bones in different species, but they have maintained the same overall layout. Scientists call these synonymous parts homologous structures .

Some structures exist in organisms that have no apparent function at all, and appear to be residual parts from a past common ancestor. These unused structures without function are called vestigial structures . Examples of vestigial structures include wings on flightless birds, leaves on some cacti, and hind leg bones in whales.

Link to Learning

Visit this interactive site to guess which bones structures are homologous and which are analogous, and see examples of evolutionary adaptations to illustrate these concepts.

• Things that are analogous look similar and things that are homologous do not.
• Things that are analogous have the same function and things that are homologous have different functions.
• Things that are analogous are not a result of evolution, whereas things that are homologous are.
• Things that are analogous result from convergence and things that are homologous result from common ancestry

Another piece of evidence of evolution is the convergence of form in organisms that share similar environments. For example, species of unrelated animals, such as the arctic fox and ptarmigan, living in the arctic region have been selected for seasonal white phenotypes during winter to blend with the snow and ice ( Figure 18.8 ab ). These similarities occur not because of common ancestry, but because of similar selection pressures—the benefits of not being seen by predators.

Embryology, the study of the development of the anatomy of an organism to its adult form, also provides evidence of relatedness between now widely divergent groups of organisms. Mutational tweaking in the embryo can have such magnified consequences in the adult that embryo formation tends to be conserved. As a result, structures that are absent in some groups often appear in their embryonic forms and disappear by the time the adult or juvenile form is reached. For example, all vertebrate embryos, including humans, exhibit gill slits and tails at some point in their early development. These disappear in the adults of terrestrial groups but are maintained in adult forms of aquatic groups such as fish and some amphibians. Great ape embryos, including humans, have a tail structure during their development that is lost by the time of birth.

Biogeography

The geographic distribution of organisms on the planet follows patterns that are best explained by evolution in conjunction with the movement of tectonic plates over geological time. Broad groups that evolved before the breakup of the supercontinent Pangaea (about 200 million years ago) are distributed worldwide. Groups that evolved since the breakup appear uniquely in regions of the planet, such as the unique flora and fauna of northern continents that formed from the supercontinent Laurasia and of the southern continents that formed from the supercontinent Gondwana. The presence of members of the plant family Proteaceae in Australia, southern Africa, and South America, for example, is best explained by their presence prior to the southern supercontinent Gondwana breaking up.

The great diversification of marsupials in Australia and the absence of other mammals reflect Australia’s long isolation. Australia has an abundance of endemic species—species found nowhere else—which is typical of islands whose isolation by expanses of water prevents species from migrating. Over time, these species diverge evolutionarily into new species that look very different from their ancestors that may exist on the mainland. The marsupials of Australia, the finches on the Galápagos, and many species on the Hawaiian Islands are all unique to their one point of origin, yet they display distant relationships to ancestral species on mainlands.

Molecular Biology

Like anatomical structures, the structures of the molecules of life reflect descent with modification. Evidence of a common ancestor for all of life is reflected in the universality of DNA as the genetic material and in the near universality of the genetic code and the machinery of DNA replication and expression. Fundamental divisions in life between the three domains are reflected in major structural differences in otherwise conservative structures such as the components of ribosomes and the structures of membranes. In general, the relatedness of groups of organisms is reflected in the similarity of their DNA sequences—exactly the pattern that would be expected from descent and diversification from a common ancestor.

DNA sequences have also shed light on some of the mechanisms of evolution. For example, it is clear that the evolution of new functions for proteins commonly occurs after gene duplication events that allow the free modification of one copy by mutation, selection, or drift (changes in a population’s gene pool resulting from chance), while the second copy continues to produce a functional protein.

Direct Observations

Scientists have also observed evolution occurring in both the laboratory and in the wild. A common example of this is the spread of antibiotic resistant genes in a population of bacteria. When bacteria are exposed to antibiotics, alleles that help the organism survive increase in frequency Figure 18.9 . This is because individuals that cannot resist the antibacterial die off, leaving only individuals with the resistance gene to reproduce.

Adaptations for homeostasis

One major reason that organisms adapt is to maintain homeostasis, one of the main characteristics of life. All organisms have likely descended from a single common ancestor, which is why so many organisms share anatomical, morphological, and molecular features. However, each organism has adapted these similar features to suit their environment and adapt to environmental changes over time. For example, all organisms use DNA polymerase to replicate their genomes. However, whereas organisms with small genomes can get away with just a single polymerase molecule working at one point in the genome at time, organisms with larger genomes replicate many points of the genome simultaneously. Other organisms that live in extremely hot environments, such as deep-sea thermal vents, have specialized polymerase molecules that can withstand the heat that would quickly denature the polymerases in land-based animals. Although the basis for each of these different DNA polymerase molecules is the same, each one has been adapted to function in the organism’s environmental niche.

Misconceptions of Evolution

Although the theory of evolution generated some controversy when it was first proposed, it was almost universally accepted by biologists, particularly younger biologists, within 20 years after publication of On the Origin of Species . Nevertheless, the theory of evolution is a difficult concept and misconceptions about how it works abound.

This site addresses some of the main misconceptions associated with the theory of evolution.

• Misconception: Evolution is not a well-founded theory. Correction: Although evolution cannot be observed occurring today, there is strong evidence in the fossil record and in shared DNA sequences to support the theory
• Misconception: Humans are not currently evolving. Correction: The environmental pressures humans face are different than the ones they faced several thousands of years ago, but they are still there, and they are still producing (slowly!) evolutionary change.
• Misconception: Evolution produces individuals that are perfectly fit to their environment. Correction: Evolution produces random changes in the genetic code that sometimes lead to adaptations
• Misconception: Evolution is a random process. Correction: evolution is a force that makes animals adapt to perfectly fit the environment they are living in

Evolution Is Just a Theory

Critics of the theory of evolution dismiss its importance by purposefully confounding the everyday usage of the word “theory” with the way scientists use the word. In science, a “theory” is understood to be a body of thoroughly tested and verified explanations for a set of observations of the natural world. Scientists have a theory of the atom, a theory of gravity, and the theory of relativity, each of which describes understood facts about the world. In the same way, the theory of evolution describes facts about the living world. As such, a theory in science has survived significant efforts to discredit it by scientists. In contrast, a “theory” in common vernacular is a word meaning a guess or suggested explanation; this meaning is more akin to the scientific concept of “hypothesis.” When critics of evolution say evolution is “just a theory,” they are implying that there is little evidence supporting it and that it is still in the process of being rigorously tested. This is a mischaracterization.

Individuals Evolve

Evolution is the change in genetic composition of a population over time, specifically over generations, resulting from differential reproduction of individuals with certain alleles. Individuals do change over their lifetime, obviously, but this is called development and involves changes programmed by the set of genes the individual acquired at birth in coordination with the individual’s environment. When thinking about the evolution of a characteristic, it is probably best to think about the change of the average value of the characteristic in the population over time. For example, when natural selection leads to bill-size change in medium-ground finches in the Galápagos, this does not mean that individual bills on the finches are changing. If one measures the average bill size among all individuals in the population at one time and then measures the average bill size in the population several years later, this average value will be different as a result of evolution. Although some individuals may survive from the first time to the second, they will still have the same bill size; however, there will be many new individuals that contribute to the shift in average bill size.

Evolution Explains the Origin of Life

It is a common misunderstanding that evolution includes an explanation of life’s origins. Some of the theory’s critics believe that it cannot explain the origin of life. The theory does not try to explain the origin of life. The theory of evolution explains how populations change over time and how life diversifies the origin of species. It does not shed light on the beginnings of life including the origins of the first cells, which is how life is defined. The mechanisms of the origin of life on Earth are a particularly difficult problem because it occurred a very long time ago, and presumably it just occurred once. Importantly, biologists believe that the presence of life on Earth precludes the possibility that the events that led to life on Earth can be repeated because the intermediate stages would immediately become food for existing living things.

However, once a mechanism of inheritance was in place in the form of a molecule like DNA either within a cell or pre-cell, these entities would be subject to the principle of natural selection. More effective reproducers would increase in frequency at the expense of inefficient reproducers. So while evolution does not explain the origin of life, it may have something to say about some of the processes operating once pre-living entities acquired certain properties.

Organisms Evolve on Purpose

Statements such as “organisms evolve in response to a change in an environment” are quite common, but such statements can lead to two types of misunderstandings. First, the statement must not be understood to mean that individual organisms evolve. The statement is shorthand for “a population evolves in response to a changing environment.” However, a second misunderstanding may arise by interpreting the statement to mean that the evolution is somehow intentional. A changed environment results in some individuals in the population, those with particular phenotypes, benefiting and therefore producing proportionately more offspring than other phenotypes. This results in change in the population if the characteristics are genetically determined.

It is also important to understand that the variation that natural selection works on is already in a population and does not arise in response to an environmental change. For example, applying antibiotics to a population of bacteria will, over time, select a population of bacteria that are resistant to antibiotics. The resistance, which is caused by a gene, did not arise by mutation because of the application of the antibiotic. The gene for resistance was already present in the gene pool of the bacteria, likely at a low frequency. The antibiotic, which kills the bacterial cells without the resistance gene, strongly selects individuals that are resistant, since these would be the only ones that survived and divided. Experiments have demonstrated that mutations for antibiotic resistance do not arise as a result of antibiotic.

In a larger sense, evolution is not goal directed. Species do not become “better” over time; they simply track their changing environment with adaptations that maximize their reproduction in a particular environment at a particular time. Evolution has no goal of making faster, bigger, more complex, or even smarter species, despite the commonness of this kind of language in popular discourse. What characteristics evolve in a species are a function of the variation present and the environment, both of which are constantly changing in a non-directional way. What trait is fit in one environment at one time may well be fatal at some point in the future. This holds equally well for a species of insect as it does the human species.

Science Practice Connection for AP® Courses

Using information from a book or online resource such as Jonathan Weiner’s The Beak of the Finch , explain how contemporary evidence drawn from multiple scientific disciplines supports the observations of Charles Darwin regarding evolution by natural selection. Then, in small groups or as a whole class discussion or debate, present an argument to dispel misconceptions about evolution and how it works.

AP ® Biology Investigative Labs: Inquiry-Based, Investigation 8: Biotechnology: Bacterial Transformation . You will explore how genetic engineering techniques can be used to manipulate heritable information by inserting plasmids into bacterial cells.

Think About It

What selection pressures may affect the survival and reproduction of a group of pea seeds scattered by a person along the ground?

• The activity is an application of all of the AP ® Learning Objectives and Science Practices listed above because students are constructing an argument based on scientific evidence and data that support Darwin’s model of evolution through natural selection.
• The lab investigation is an application of AP ® Learning Objective 1.2 and Science Practices 2.2 and 5.3, Learning Objective 1.4 and Science Practice 5.3, and Learning Objective 1.26 and Science Practice 5.3 because students are performing experiments and collecting and analyzing data to confirm that the development of resistance to antibiotics by bacteria is an example of evolution by natural selection and that evolution continues to occur. (Note: This lab investigation also connects to concepts studied in the Biotechnology chapter and is a link between genetic variation and evolution.)
• The Think About It question is an application of Learning Objective 1.25 and Science Practice 1.2 because students are describing a model that represents evolution within a population.
• Think About It sample answer: The survival and reproduction of the pea seeds would likely face selection pressure imposed by the fertility of the ground on which they land, how often the ground is disturbed (such as by people walking on it), and the amount of water and light the plants receive.
• Biointeractive activities contain more evolution activities that generate population statistics which students can analyze.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/biology-ap-courses/pages/1-introduction

• Authors: Julianne Zedalis, John Eggebrecht
• Publisher/website: OpenStax
• Book title: Biology for AP® Courses
• Publication date: Mar 8, 2018
• Location: Houston, Texas
• Book URL: https://openstax.org/books/biology-ap-courses/pages/1-introduction
• Section URL: https://openstax.org/books/biology-ap-courses/pages/18-1-understanding-evolution

© Jan 8, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Evolutionary biology articles within Nature Reviews Genetics

Journal Club | 19 March 2024

The evolution of modifier genes

In this Journal Club, Yoav Ram recalls how he reconciled results from his own research with the reduction principle through the help of a paper published in PNAS by Altenberg et al.

Perspective | 31 January 2024

Selection on synonymous sites: the unwanted transcript hypothesis

Multiple mechanisms have evolved to prevent or trap deleterious unwanted transcripts. The unwanted transcript hypothesis proposes that selection at synonymous sites favours mutations that prevent the generation of unwanted transcripts or that make native transcripts appear ‘wanted’ by being GC-rich.

• Sofia Radrizzani
• , Grzegorz Kudla
•  &  Laurence D. Hurst

Research Highlight | 26 January 2024

Top predator decline has evolutionary as well as ecological effects

Beer et al. use multiple complementary approaches to show that declining densities of the Tasmanian devil have had evolutionary effects on gene flow and selection in the subordinate predator, the spotted-tail quoll.

• Kirsty Minton

Review Article | 23 January 2024

Horizontal gene transfer in eukaryotes: aligning theory with data

In this Review, Patrick Keeling proposes that the eukaryotic-specific processes of phagocytosis and endosymbiosis are unlikely to increase the frequency of horizontal gene transfers, because most of the transferred genes will be non-essential and will thus not be selected for the long term.

• Patrick J. Keeling

Journal Club | 20 December 2023

Swapping genes within and beyond our bodies

Mashaal Sohail reflects on a 2011 Nature study by Smillie et al., which analysed human microbiome data to show that microbial ecology, rather than phylogeny or geography, is a key driver of horizontal gene transfer.

• Mashaal Sohail

Journal Club | 25 September 2023

The evolutionary tale of lactase persistence in humans

Luis Barreiro highlights a 2007 paper by Tishkoff et al. that identified genetic variants associated with lactose persistence in East African populations, representing one of the first examples of convergent evolution in humans.

• Luis B. Barreiro

Review Article | 18 September 2023

More than a decade of genetic research on the Denisovans

Ancient DNA studies over the past decade have yielded a plethora of insights into the Denisovan archaic hominin group. The authors review our understanding of Denisovan population history and their interactions with other human groups, insights from studies of Denisovan ancestry in modern humans, what we know about the Denisovan phenotype and their impact on our own evolutionary history.

• Stéphane Peyrégne
• , Viviane Slon
•  &  Janet Kelso

Review Article | 03 July 2023

Steering and controlling evolution — from bioengineering to fighting pathogens

Control can be applied to alter the ecological or evolutionary trajectory of a target system towards a predefined objective. In this Review, the authors discuss the aims, applications, mechanisms and dynamics of eco-evolutionary control across different biological systems.

• Michael Lässig
• , Ville Mustonen
•  &  Armita Nourmohammad

Journal Club | 31 May 2023

Cell by cell: population genetics in the wild

Tanja Woyke highlights a 2014 study by Kashtan et al., who applied single-cell genomics to populations of the marine cyanobacterium Prochlorococcus , revealing hundreds of subpopulations with distinct genomic backbones of this wild uncultured microorganism.

• Tanja Woyke

Review Article | 04 January 2023

Current advances in primate genomics: novel approaches for understanding evolution and disease

In this Review, the authors describe how advances in comparative primate genomics — complemented by multi-layered omic resources and primate cell systems — are providing insights into the evolution of primates and the genetic underpinnings of key traits of developmental and biomedical importance.

• , Gabriel Santpere
•  &  Tomas Marques-Bonet

Research Highlight | 13 December 2022

Social shifts in spiders

A comparative genomics study published in Nature Communications provides new insight into the genomic changes underlying the convergent evolution of sociality in spiders.

• Dorothy Clyde

Journal Club | 05 December 2022

The Neanderthal inside us

Luis Saraiva recalls a 1997 paper by Krings et al., which reports the sequencing of mitochondrial Neanderthal DNA extracted from a 40,000-year-old bone, enabling the direct study of the relationship between ancient and modern humans.

• Luis R. Saraiva

Journal Club | 02 December 2022

Live long & prosper: evidence of evolutionary forces on lifespan

In this Journal Club, Morgan Levine discusses a publication by Rose and Charlesworth that provided direct evidence of the impact of natural selection on differential ageing rates.

• Morgan Levine

Research Highlight | 27 September 2022

Mapping vertebrate brain evolution

Four papers in Science use single-cell, single-nucleus and spatial transcriptomic profiling of reptilian and amphibian brain tissue to provide insights into the evolution of vertebrate forebrains.

Review Article | 15 August 2022

Sex-specific morphs: the genetics and evolution of intra-sexual variation

Sex-specific morphs exhibit phenotypes that differ between the sexes and typically include many different traits. Here, the author reviews recent genomic and transcriptomic studies that are yielding new insights into the evolutionary origin and development of sex-specific morphs in a wide range of animal species.

• Judith E. Mank

Perspective | 12 July 2022

Alternative splicing as a source of phenotypic diversity

In this Perspective, the authors discuss how regulated alternative splicing can generate phenotypic diversity and outline emerging evidence that alternative splicing contributes to adaptation and species divergence.

• Charlotte J. Wright
• , Christopher W. J. Smith
•  &  Chris D. Jiggins

Journal Club | 17 March 2022

The genome that fuelled a Mexican scientific revolution

Carla Daniela Robles-Espinoza celebrates a paper that inspired a new generation of Mexican scientists.

• Carla Daniela Robles-Espinoza

Journal Club | 10 March 2022

Genome-wide insights into human population structure

Irene Gallego Romero recalls a landmark publication by Rosenberg et al., which reported on the fine-scale structure within and between human populations.

• Irene Gallego Romero

Review Article | 08 February 2022

Genetic load: genomic estimates and applications in non-model animals

The reduction in individual and mean population fitness induced by novel deleterious genetic variation is known as the genetic load. Bertorelle et al. review the definition of the genetic load and its components as well as the impact of whole-genome sequencing on the theoretical and applied study of the genetic load.

• Giorgio Bertorelle
• , Francesca Raffini
•  &  Cock van Oosterhout

Expert Recommendation | 26 January 2022

Rethinking nomenclature for interspecies cell fusions

Cell fusion models containing genomes from different cell types or species in a single nucleus offer unique research benefits. Here, Pavlovic et al. advocate against describing such models as hybrids and propose a new nomenclature for interspecies cell fusions that lacks reproductive connotations.

• Bryan J. Pavlovic
•  &  Alex A. Pollen

Review Article | 21 October 2021

Disentangling host–microbiota complexity through hologenomics

Hologenomic studies aim to further our understanding of host–microbiota interactions through the integrated analysis of host genomes and microbiota metagenomes. Here, Alberdi and colleagues discuss key considerations for designing optimal hologenomic studies and outline important biological questions that these studies can address.

• Antton Alberdi
• , Sandra B. Andersen
•  &  M. Thomas P. Gilbert

Review Article | 18 August 2021

Opportunities and challenges of macrogenetic studies

Leigh and colleagues describe the potential of the emerging field of macrogenetics to improve conservation and biodiversity management. Challenges preventing the field from reaching its full promise are highlighted and possible solutions and a framework for future macrogenetic studies are proposed.

• Deborah M. Leigh
• , Charles B. van Rees
•  &  Ivan Paz-Vinas

Review Article | 13 August 2021

Genetic innovations in animal–microbe symbioses

The evolutionary persistence of animal symbioses depends on both host and symbiont innovations. Perreau and Moran review how genome sequencing and related experiments have clarified how these innovations arise under different symbiont population structures, categorized here as open, closed and mixed.

• Julie Perreau
•  &  Nancy A. Moran

Research Highlight | 28 April 2021

Bone-free ancient DNA

A new study in Science reports the extraction and analysis of ancient hominid nuclear DNA from Paleolithic sediments. This advance paves the way to a fuller picture of human evolution by bypassing the dependency on rare skeletal remains.

Review Article | 29 May 2020

From molecules to populations: appreciating and estimating recombination rate variation

Genetic recombination is a fundamental biological process generating genetic variation by shuffling combinations of alleles. In this Review, Peñalba and Wolf focus on how sequencing-based approaches are providing diverse insights into recombination rate variation across levels of biological organization and timescales, from individual gametes of single individuals to populations through evolutionary history.

• Joshua V. Peñalba
•  &  Jochen B. W. Wolf

Review Article | 16 April 2020

Harnessing genomics to fast-track genetic improvement in aquaculture

Genetic improvement of production traits in aquaculture has great potential to help meet the rising seafood demands driven by human population growth. The authors review how genomics is being applied to aquaculture species at all stages of the domestication process to optimize selective breeding.

• Ross D. Houston
• , Tim P. Bean
•  &  Diego Robledo

Research Highlight | 22 October 2019

Single-cell genomics illuminates human forebrain development

In a new study published in Nature , Kanton et al. shed light on the unique genetic features of human forebrain development and how they diverged from those in great apes.

• Conor A. Bradley

Research Highlight | 06 August 2019

Decoding dragon DNA

A study in Nature Ecology and Evolution reports the genome sequence of the Komodo dragon and describes genomic features that may underlie its distinct physiology.

Research Highlight | 10 May 2019

Ancient genomes shed light on dark horses

A study of ancient horse genomes, described in Cell , reveals the existence of two now-extinct horse lineages and shows that modern breeding practices reduced genetic diversity in horses.

• Katharine H. Wrighton

Research Highlight | 24 April 2019

Bug battles end in compromise

An experimental evolution study published in Science demonstrates that non-additive interactions between pollinators (bumblebees) and herbivores (caterpillars) drive rapid evolution in plants.

Perspective | 18 March 2019

The genetics of convergent evolution: insights from plant photosynthesis

Using the example of carbon concentrating mechanisms in plants, the authors of this Perspective provide evidence that broad comparative genomic analyses likely overestimate the genetic complexity underlying convergent evolution of complex traits.

• Karolina Heyduk
• , Jose J. Moreno-Villena
•  &  Erika J. Edwards

Review Article | 01 November 2018

The causes of evolvability and their evolution

In this article, Payne and Wagner discuss how recent experimental studies are complementing theoretical work to enhance our understanding of the evolvability of diverse biological systems. They highlight phenotypic heterogeneity, robustness and adaptive landscape topography as causes of evolvability, and they additionally discuss evidence for whether evolvability itself can evolve.

• Joshua L. Payne
•  &  Andreas Wagner

Review Article | 24 October 2017

Population genetics of sexual conflict in the genomic era

Sexual conflict is thought to increase population genetic diversity though balancing selection, which has important evolutionary implications. This Review discusses how population genomic approaches are contributing to a deeper understanding of sexual conflict and how it is resolved.

Review Article | 25 September 2017

Demographic history, selection and functional diversity of the canine genome

Despite being a single species, dogs represent nearly 400 breeds with substantial genetic, morphological and behavioural diversity. In this Review, Ostrander et al . discuss how genomics studies of dogs have enhanced our understanding of dog and human population history, the desired and unintended consequences of trait-based selective breeding, and potentially human-applicable insights into cancer, ageing, behaviour and neurological diseases.

• Elaine A. Ostrander
• , Robert K. Wayne
•  &  Brian W. Davis

Review Article | 15 May 2017

The evolutionary significance of polyploidy

Polyploidy occurs frequently but is usually detrimental to survival; thus, few polyploids survive in the long term. Here, evidence linking the short-term evolutionary success of polyploids to environmental upheaval is reviewed and possible longer-term evolutionary benefits of polyploidy are discussed.

• Yves Van de Peer
• , Eshchar Mizrachi
•  &  Kathleen Marchal

Review Article | 14 November 2016

Making sense of genomic islands of differentiation in light of speciation

To characterize the genetic underpinnings of speciation, genome scans can identify genomic regions that differ between divergent populations of wild organisms. In this Review, Wolf and Ellegren describe the methodological details of these approaches and how genomic islands of differentiation should be interpreted cautiously in the search for 'speciation genes'. They also discuss methodological best practice that takes into consideration genomic differentiation occurring through speciation-independent evolutionary processes.

• Jochen B. W. Wolf
•  &  Hans Ellegren

Review Article | 03 October 2016

Epigenetic inheritance of acquired traits through sperm RNAs and sperm RNA modifications

Studies have demonstrated that paternal traits acquired in response to environmental conditions can be inherited by the offspring, sometimes persisting for multiple generations. In this Review, the authors discuss the accumulating evidence of a major role for sperm RNAs and RNA modifications in the inheritance of acquired traits and the mechanisms that may underlie this.

•  &  Enkui Duan

Review Article | 06 June 2016

Determinants of genetic diversity

The degree of genetic diversity differs greatly among species and across genomic loci within genomes. The wide ranges in genetic diversity have important implications, including for evolution, conservation and management of wild and domesticated species. In this Review, the authors discuss how genome-scale sequencing strategies are providing insight into the varied determinants of genetic variation both among species and across genomic regions.

• Hans Ellegren
•  &  Nicolas Galtier

Review Article | 30 November 2015

Evolution of vertebrate sex chromosomes and dosage compensation

The differentiation of sex chromosomes in vertebrates created a need for mechanisms that compensate for differences in dosage of gene expression between the sexes. The author reviews the diversity of these mechanisms, their effects on gene expression, and their origin and evolution across the major vertebrate groups.

• Jennifer A. Marshall Graves

Review Article | 10 November 2015

Methods and models for unravelling human evolutionary history

The rapid accumulation and increasing quality of human DNA sequence-variation data brought about by advances in genome-scale sequencing present opportunities to investigate human evolution. The authors discuss the statistical methods and models that can be used to gain insight into the evolution of human populations from analyses of large-scale genomic data sets, as well as the challenges associated with these approaches.

• Joshua G. Schraiber
•  &  Joshua M. Akey

Review Article | 09 June 2015

Reconstructing ancient genomes and epigenomes

Sequencing genomes of ancient specimens, including human ancestors, can provide rich insights into evolutionary histories. However, ancient DNA samples are frequently degraded, damaged and contaminated with ancient and modern DNA from various sources. This Review describes the methodological and bioinformatic advances that allow these challenges to be overcome in order to process and sequence ancient samples for genome reconstruction, as well as recent progress in characterizing ancient epigenomes.

• Ludovic Orlando
• , M. Thomas P. Gilbert
•  &  Eske Willerslev

Review Article | 11 November 2014

The RNA World: molecular cooperation at the origins of life

The RNA World concept is the idea that billions of years ago — before current life based on DNA, RNA and proteins — the primary living substance was RNA or something chemically similar. This Review highlights the challenges and solutions of this point of view, particularly for the synthesis and replication of RNA, and how various types of molecular cooperation probably had important roles.

• Paul G. Higgs
•  &  Niles Lehman

Review Article | 09 October 2014

Evolutionary dynamics of coding and non-coding transcriptomes

This Review provides insights obtained from comparative transcriptomic studies of mammalian species. The dynamics of gene expression evolution in coding and non-coding genes, as well as the regulatory basis of transcriptome evolution and future research avenues, are discussed.

• Anamaria Necsulea
•  &  Henrik Kaessmann

In Brief | 12 August 2014

Homing in on anthropoid evolution

• Bryony Jones

Sexual conflict in nematodes

• Isabel Lokody

Review Article | 11 March 2014

Cryptic genetic variation: evolution's hidden substrate

This Review discusses cryptic genetic variation and focuses particularly on empirical support for widespread cryptic genetic variation in natural populations, its potential role in human diseases and its contribution to evolution.

• Annalise B. Paaby
•  &  Matthew V. Rockman

Review Article | 18 February 2014

Genomics and the origin of species

Genomic approaches are an increasingly important aspect of speciation research. The authors review recent insights from speciation genomics and propose a roadmap for this field, which is aimed at addressing both long-standing and emerging questions about speciation.

• Ole Seehausen
• , Roger K. Butlin
•  &  Alex Widmer

Review Article | 09 October 2013

The genetic causes of convergent evolution

This Review distinguishes between three distinct routes by which similar genetic changes contribute to convergent evolution and discusses examples from diverse taxa. Convergent genetic evolution might result from the fact that some mutations both minimize pleiotropic effects and maximize adaptation.

• David L. Stern

Research Highlight | 11 September 2013

A complex case of resource management

• Louisa Flintoft

In Brief | 06 August 2013

Genetic hitch-hiking prevalence

• Hannah Stower

Browse broader subjects

Quick links.

• Explore articles by subject
• Guide to authors
• Editorial policies

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.4(2); 2011 Mar

Evolutionary principles and their practical application

Andrew p hendry.

1 Redpath Museum and Department of Biology, McGill University, Montreal, QC, Canada

Michael T Kinnison

2 School of Biology and Ecology, University of Maine, Orono, ME, USA

Mikko Heino

3 Department of Biology, University of Bergen, Bergen, Norway

4 International Institute for Applied Systems Analysis, Laxenburg, Austria

20 Institute of Marine Research, Bergen, Norway

5 Departments of Mathematics and Statistics and Biology, Queen's University, Kingston, ON, Canada

Thomas B Smith

6 Center for Tropical Research, Institute of the Environment, University of California, Los Angeles, CA, USA

7 Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

8 CSIRO Entomology and Cotton Catchment Communities CRC, Long Pocket Laboratories, Indooroopilly, Qld, Australia

Carl T Bergstrom

9 Department of Biology, University of Washington, Seattle, WA, USA

John Oakeshott

10 CSIRO Entomology, Black Mountain, Canberra, ACT, Australia

Peter S Jørgensen

11 Center for Macroecology, Evolution and Climate, Department of Biology, University of Copenhagen, Copenhagen, Denmark

Myron P Zalucki

12 School of Biological Sciences, The University of Queensland, Brisbane, Qld, Australia

George Gilchrist

13 Division of Environmental Biology, National Science Foundation, Arlington, VA, USA

Simon Southerton

14 CSIRO Plant Industry, Canberra, ACT, Australia

15 Department of Environmental Science and Policy, University of California, Davis, CA, USA

Sharon Strauss

16 Section of Evolution and Ecology, University of California, Davis, CA, USA

Robert F Denison

17 Ecology Evolution and Behavior, University of Minnesota, Saint Paul, MN, USA

Scott P Carroll

18 Institute for Contemporary Evolution, Davis, CA, USA

19 Department of Entomology, University of California, Davis, CA, USA

Evolutionary principles are now routinely incorporated into medicine and agriculture. Examples include the design of treatments that slow the evolution of resistance by weeds, pests, and pathogens, and the design of breeding programs that maximize crop yield or quality. Evolutionary principles are also increasingly incorporated into conservation biology, natural resource management, and environmental science. Examples include the protection of small and isolated populations from inbreeding depression, the identification of key traits involved in adaptation to climate change, the design of harvesting regimes that minimize unwanted life-history evolution, and the setting of conservation priorities based on populations, species, or communities that harbor the greatest evolutionary diversity and potential. The adoption of evolutionary principles has proceeded somewhat independently in these different fields, even though the underlying fundamental concepts are the same. We explore these fundamental concepts under four main themes: variation, selection, connectivity, and eco-evolutionary dynamics. Within each theme, we present several key evolutionary principles and illustrate their use in addressing applied problems. We hope that the resulting primer of evolutionary concepts and their practical utility helps to advance a unified multidisciplinary field of applied evolutionary biology.

Introduction

A basic goal of biology is to understand and predict the diversity and function of life, and to intervene when necessary to achieve desired outcomes. Evolution provides an essential framework for these endeavors because only in its light can we understand fundamental questions about our world and ourselves. Why do we get sick? What determines antibiotic and pesticide effectiveness? How much and in what ways can crops be improved? Why are life histories changing in harvested populations? Can natural populations adapt to environmental change? With this recognition, decision makers are increasingly called on to incorporate evolutionary thinking into environmental science, conservation biology, human health, agriculture, and natural resource exploitation ( Futuyma 1995 ; Nesse and Williams 1998 ; Palumbi 2001 ; Ashley et al. 2003 ; Jørgensen et al. 2007 ; Smith and Bernatchez 2008 ; Dunlop et al. 2009 ; Gluckman et al. 2009a ; Neve et al. 2009 ; Hendry et al. 2010 ; Omenn 2010 ).

The incorporation of evolutionary thinking has been largely independent in different areas of applied biology, and yet the relevant principles should be the same. It is important to explore and illustrate this common ground for several reasons. First, evolutionary principles routinely applied in one discipline might not be considered in other disciplines. Through exposure to how these principles play out in different disciplines, investigators might be inspired toward new applications. Second, particular evolutionary principles might not be equally important in all disciplines. The recognition of these differences can help us to understand how evolutionary interventions should be implemented differently in different contexts. Following from these two main reasons, and perhaps most important of all, we need to foster a unified multidisciplinary field of Applied Evolutionary Biology. Such a field would benefit from a primer of evolutionary biology couched in a common framework that can be considered across its various sub-fields. This primer might also facilitate understanding and acceptance by decision makers, who have traditionally been slow to incorporate evolutionary principles into the decision-making process ( Smith and Bernatchez 2008 ; Hendry et al. 2010 ). Our hope is to provide some steps in this direction.

As an introductory example, one unifying concept that recurs throughout applied biology is the mismatch between the current phenotypes of organisms and the phenotypes that would be best suited for a given environment ( Fig. 1 ). Examples include breeding times under climate warming ( Both et al. 2006 ; Phillimore et al. 2010 ), antipredator behavior when exposed to exotic predators ( Sih et al. 2010 ), human nutrition under current high-food conditions ( Gluckman et al. 2009a ), the traits of insects exposed to new pesticides ( Carrière and Tabashnik 2001 ; Beckie and Reboud 2009 ), and the traits of bacteria exposed to new antibiotics ( Bergstrom and Feldgarden 2007 ). When the mismatches are slight, populations should be well adapted and robust. When the mismatches are large, populations should be poorly adapted and could decline. In applied biology, we sometimes want these mismatches to be small, such as for threatened species facing environmental change. At other times, we want them to be large, such as when imposing treatments to reduce the impact of unwanted pests, pathogens, or invasive species. Or we may wish to maintain traits that reduce the fitness of individuals because these same traits are useful to us, as in the case of domesticated species (e.g., greater allocation to grain or seed set; Denison et al. 2003 ) or harvested wild species (e.g., big horns in game animals and large size in fish; Heino 1998 ; Harris et al. 2002 ). Evolutionary principles are fundamental to achieving these goals because they help us to understand current mismatches and potential responses, as well as how we might manipulate environments or organisms to achieve the desired mismatch.

Applied biology often considers mismatches between current phenotypes and those that would be best suited for a given environment. The graph shows the fitness of individuals with a given phenotype (fitness function: blue dashed line) and the distribution of phenotypes in a population under those conditions (numbers of individuals: black curve). The degree of the current mismatch is the distance between the peak of the fitness function and the peak of the frequency distribution. In Panel A, the mismatch is high and so average fitness in the population is low and the population size is small. In Panel B, the mismatch is small and so the average fitness is high and the population size is large. In some cases, we might wish a large mismatch to be smaller (e.g., conservation biology). In Panel A, then, we might manipulate phenotypes or the environment to decrease the mismatch (horizontal arrows). We might also find a way to increase fitness for a given phenotype (thin vertical arrow). The expected outcome is an increase in population size (thick vertical arrow). In other cases, we might wish the mismatch to be larger (e.g., pathogens or pests). In Panel B, then, we might manipulate phenotypes or the environment to increase the mismatch (horizontal arrows). We might also find a way to decrease fitness for a given phenotype (thin vertical arrow). The expected outcome is a decrease in population size (thick vertical arrow).

Our goal in the present paper is to summarize some basic evolutionary principles and illustrate their practical utility across multiple areas of applied biology. These principles are organized under four main themes: variation, selection, connectivity, and eco-evolutionary dynamics. (A similar categorization appears in Lankau et al. 2011 .) Within each theme, we present basic evolutionary principles and describe how they have been used in environmental science, conservation biology, human health, agriculture, and natural resource management. We do not have the space to treat all evolutionary principles, nor all pertinent applications and examples. Moreover, we will often have to provide generalizations that will have exceptions, which we try to highlight and explain. Many more examples are provided in the other papers of this special issue and we show where these ideas fit into the current framework.

Before proceeding, we need to clarify several terms and concepts. First, we follow the standard definition of evolution as changes in allele frequency within a population across generations. Any force causing such changes, including artificial selection, is an evolutionary force. Phenotypic change confirmed to have a genetic basis is also evolution, even if the underlying allele frequencies are not known. Second, when we discuss mismatches (as introduced above), we are usually referring to the average properties of a population, such as mean phenotypic trait values or allele frequencies and the resulting mean absolute fitness of the population (e.g., population size or rate of increase). Adaptation that improves the fitness of individuals within a population (i.e., relative fitness) is also important and might sometimes run counter to population mean fitness, as we will later describe. For individual relative fitness, no particular definition is universally accepted, but lifetime reproductive success is one of the better operational fitness surrogates ( Clutton-Brock 1999 ; Benton and Grant 2000 ). Even this metric is hard to quantify, however, and so investigators often turn to major fitness components, such as survival or fecundity. Third, when we say that a particular phenotypic change is adaptive , we mean that it improves fitness in a given environment (often reducing a mismatch), but this does not necessarily require genetic change – it instead could be environmentally induced plasticity (see the following paragraphs for details). In contrast, we reserve the term adaptation for adaptive genetic change. By complement, we use the term ‘maladaptive’ to refer to phenotypic changes that reduce fitness. Fourth, we will use the term ‘contemporary evolution’ when referring to evolution occurring on the time frame of less than a few hundred years ( Hendry and Kinnison 1999 ).

Phenotypic variation determines how organisms interact with their environment and respond to the resulting selection pressures. This variation can come in the form of genetic differences, individual phenotypic plasticity (potential for an organism to produce different phenotypes in different environments), epigenetic changes (gene expression regulated by modification of DNA or histones), maternal effects (phenotype of the mother influences the phenotype of her offspring), and several other forms of nongenetic inheritance ( Bonduriansky and Day 2009 ). Understanding the origins, nature, and maintenance of this variation provides an important foundation for predicting and interpreting responses to changing environmental conditions.

Phenotypes matter

Modern genetic tools have revolutionized the information available to biologists, but this has caused an increasing tendency to forget that phenotypes, rather than just genotypes, matter ( Houle 2010 ). Phenotypes matter because they are the direct interface with the environment, which is critical in two major respects. First, selection acts directly on phenotypes, with genetic change potentially occurring as an indirect consequence. Second, phenotypes have ecological effects, for example, on population dynamics, on community structure, and on ecosystem function (see section on Eco-evolutionary dynamics). An understanding of phenotypes therefore should precede an understanding of genotypes.

Additional compelling reasons exist to study phenotypes. First, adaptation to a given set of environmental conditions will usually involve many genes, as well as interactions among them (more details are given in the following paragraphs), and so examining only a few genes will not be sufficient for understanding adaptive potential or evolutionary responses. Second, phenotypic variation is structured not only by genes, but also by nongenetic effects. As a result, populations that are phenotypically different for a given trait might be genetically similar with respect to that trait, whereas groups that are phenotypically similar for a given trait might be genetically different with respect to that trait ( Conover and Schultz 1995 ). In addition, adaptive responses to changing conditions can be genetic (‘adaptation’ in the strict sense), nongenetic (e.g., plasticity), or some combination of the two. For instance, the effect of a given genotype can differ between environments, yielding a genotype-by-environment interaction. A recent example at the genomic level is the demonstration that a particular allele can have opposite phenotypic effects in different Eucalyptus nitens populations ( Southerton et al. 2010 ). Phenotypic traits should thus be considered as ‘reaction norms,’ which depict the phenotypes expressed by a given genotype (or population or species) across a range of different environmental conditions ( Stearns 1989 ; Gluckman et al. 2009b ; Fig. 2 ). Importantly, these reaction norms can evolve in response to selection ( Stearns and Koella 1986 ; Olsen et al. 2004 ; Lande 2009 ; Crispo et al. 2010 ).

Phenotypic variation can be described by reaction norms. (A) Reaction norms depict the phenotypes a single genotype (or individual or population) expressed in different environments. Differences between reaction norms represent genetic differences. (B) Examples of reaction norms: shown are the adult male body sizes from different populations of male speckled wood butterflies ( Pararge aegeria ) when their larvae are raised at different temperatures (redrawn from Sibly et al. 1997 ). Temperature has plastic effects on body size in all populations, but the degree of its plasticity differs among populations. Genetic differences among the populations become more evident with decreasing temperature.

Different phenotypic traits will differ in their relevance to both fitness and ecological processes. An important task is therefore to identify ‘key’ traits or trait complexes – in broad analogy with the search for limiting factors in ecology ( Sih and Gleeson 1995 ). A standard approach is to measure a set of phenotypic traits (e.g., body size and shape) and to relate variation in these traits to (i) some measure of fitness, such as survival, fecundity, or lifetime reproductive success ( Lande and Arnold 1983 ; Brodie et al. 1995 ); and (ii) some ecological response (e.g., population growth rate, community richness, and nutrient cycling). These methods can identify traits under strong selection and traits that might have large ecological effects.

Notwithstanding the value of the above trait-based approach, ‘phenotype’ must sometimes be considered as an integrated unit. For example, the ability of a population to persist in the face of environmental change is ultimately determined by the complex multivariate interaction of all traits that contribute to population growth. One way to assess this overall adaptation (i.e., population mean fitness) is to transfer groups of individuals between environments and then measure differences in survival or reproductive success ( Kawecki and Ebert 2004 ; Hereford 2009 ). Similar methods can be used to estimate how quickly individual or population mean fitness can improve through adaptive change following environmental disturbances (e.g., Kinnison et al. 2008 ; Gordon et al. 2009 ).

Nongenetic changes can be very important – especially on short time scales

Organisms poorly suited for their local environment can respond adaptively by altering their location to better suit their phenotype (e.g., habitat choice) or by altering their phenotype to better suit their location (e.g., plasticity or evolution). For the former, individuals often avoid newly disturbed areas (e.g., Frid and Dill 2002 ) and can select areas for which their phenotypes are better suited (review: Edelaar et al. 2008 ). In many cases, however, such movement is not feasible or sufficient, and so populations must respond in situ . In this latter case, the quickest route to adaptive change will often be individual phenotypic plasticity, particularly behavioral plasticity, or maternal effects ( Stearns 1989 ; Price et al. 2003 ; West-Eberhard 2003 ; Ghalambor et al. 2007 ; Räsänen and Kruuk 2007 ; Sih et al. 2011 ). Congruent with this suggestion, a meta-analysis of phenotypic change in natural populations experiencing environmental change concluded that plasticity was probably very important ( Hendry et al. 2008 ). As a specific example, populations of many species experiencing climate warming now reproduce at earlier dates ( Parmesan and Yohe 2003 ), and a large part of this change reflects individuals responding plastically to increased temperature ( Gienapp et al. 2008 ). This does not mean that genetic change does not contribute to these phenological shifts ( Bradshaw and Holzapfel 2008 ) – merely that plasticity certainly does.

Phenotypic plasticity is not, however, a panacea – because it is subject to a number of limits and costs ( DeWitt 1998 ). Hence, most phenotypic responses to environmental change will ultimately involve both plastic and genetic contributions ( Dieckmann and Heino 2007 ; Visser 2008 ; Phillimore et al. 2010 ; Sih et al. 2011 ; Fig. 3 ). Variation in humans and domesticated organisms provides nice examples. For instance, lightly pigmented human skin becomes darker under greater exposure to sun, and hemoglobin levels rise at high elevations. At the same time, adaptive genetic differences are present in these same traits: human populations from areas with more sun exposure have genetically darker skin ( Jablonski 2004 ) and human populations living at high elevation have evolved several mechanisms to increase oxygen uptake and transport ( Beall 2006 ). From a reaction-norm perspective, phenotypic variation in these traits reflects both genetic and plastic effects, along with possible genetic variation in this plasticity. These ideas are considered daily in agriculture, where production or quality are maximized by simultaneously seeking the best genetic strains, creating the best environmental conditions (fertilizer, water, pesticides, and herbicides), and finding the best match between genetic strains and environmental conditions.

An example of how genetic differences and plasticity are jointly considered when evaluating potential responses to climate change. Panel A shows the mean breeding times of different UK populations of the common frog ( Rana temporaria ) in relation to the mean temperature experienced by those populations. Panel B shows how the mean breeding time within each of those populations varies among years with the mean temperature in those years. The lines thus represent adaptive phenotypic plasticity, and differences between the lines adaptive genetic differences among populations. Panel C shows the breeding time changes that each population is expected to undergo as a result of plasticity in response to projected warming between 2050 and 2070. Panel D shows the difference between these adaptive plastic responses and the changes in breeding time that would be necessary to keep pace with climate change if the trends on Panel A are fully adaptive. These differences thus represent the evolutionary change that will be necessary to maintain full adaptation. Adapted from Phillimore et al. (2010) with data provided by A. Phillimore.

Overall, then, applied evolution should evaluate phenotypes and fitness in the context of both genetic and plastic effects, ideally through their integration into reaction norms. The evolution of these reaction norms might be especially important under environmental change, as suggested by recent theory ( Lande 2009 ) and meta-analyses ( Crispo et al. 2010 ).

Individual genes rarely capture overall genetic adaptation

Some phenotypic traits, most famously Gregor Mendel's wrinkled versus smooth peas, have a single-gene foundation. Other examples include some human diseases ( Roach et al. 2010 ), the evolution of insecticide resistance by some mosquitoes ( Raymond et al. 2001 ), and many instances of artificial selection by humans for particular traits. In agriculture, wheat yield nearly doubled between 1950 and 1965 ( Ortiz-Monasterio et al. 1997 ) largely because of the introduction of a dwarfing gene ( Rht ) that increased yield by 57% ( Miralles and Slafer 1995 ). In dog domestication, traits that identify particular breeds seem to have a very simple genetic basis ( Boyko et al. 2010 ). Overall, however, most traits are controlled by many genes and their interactions. This is strikingly seen in the so-called ‘missing heritability paradox,’ where genome-wide association studies (GWAS) can explain very little of the heritable variation in many traits ( Manolio et al. 2009 ; Crespi 2011 ). For instance, ‘highly significant and well-replicated single nucleotide polymorphisms (SNPs) identified to date explain only ∼5% of the phenotypic variance for height … common SNPs in total explain another ∼40% of phenotypic variance. Hence, 88% (40/45) of the variation caused by SNPs has been undetected in published GWASs, because the effects of the SNPs are too small to be statistically significant’ ( Yang et al. 2010 ).

Aside from specific traits, we are often concerned with overall adaptation to a given environment, which will be determined by multiple traits and therefore even more genes. To exemplify this point, we turn to the influential work on adaptation to fresh water in threespine stickleback fish ( Gasterosteus aculeatus ). Some large-effect genes have been discovered: EDA explains 78% of the variation in the number of bony plates ( Colosimo et al. 2004 ), PitX1 explains 65% of the variation in pelvic spine length ( Shapiro et al. 2004 ), and Kit ligand explains 56% of the variation in gill color ( Miller et al. 2007 ). Overall adaptation to fresh water in stickleback, however, involves dozens of phenotypic traits, and so the above large-effect genes for specific traits might contribute relatively little to overall adaptation. Indeed, Hohenlohe et al. (2010) used SNPs to find many chromosomal regions in stickleback that contribute to adaptation to fresh water – and even their assay remained biased toward the detection of large-effect genes found in multiple watersheds. In short, it is increasingly apparent that adaptation to a given environment will often involve many genes of small to modest effect.

The limitations of single-gene approaches are also evident in health and agriculture. For instance, the finding of specific genes that influence certain human ailments (e.g., Roach et al. 2010 ) does not change the fact that such genes often explain relatively little of the variation in that ailment ( Weiss 2008 ; Manolio et al. 2009 ; Crespi 2011 ). Likewise, the search for genomic regions of large effect in plants of commercial value (e.g., forest trees) has often been disappointing. Thumma et al. (2010) did not find any quantitative trait loci (QTL) in E. nitens that explained more than 16% of the variation in any wood trait – and most QTL explained much less. Low variance explained by individual QTL appears to be a common result across many tree traits and species ( Butcher and Southerton 2007 ). Although individual QTL can be very important to some traits in some species, and thereby of great use in selective breeding, most of the variation in most traits of most species will be influenced by multiple genes (e.g., Laurie et al. 2004 ; Manolio et al. 2009 ; Yang et al. 2010 ).

Although current adaptation is thus usually the product of many genes, adaptation to new conditions might sometimes initially proceed through only a subset of those genes – particularly those of largest effect ( Orr 1998 ; Schoustra et al. 2009 ). So the search for large-effect genes or QTL can indeed contribute to our understanding of how adaptation might proceed in changed environments – as long as we remember that those genes likely explain only a small part of overall adaptation in the long run. The future would ideally see an integration of quantitative genetic approaches, QTL approaches, and functional genomics.

Standing genetic variation will be the primary fuel for contemporary evolution

Adaptation to changing environments might proceed through standing genetic variation or new mutations. In general, the former is probably more important, at least on short time scales and for organisms that do not have very short generation lengths ( Aitken et al. 2008 ; Barrett and Schluter 2008 ; Orr and Unckless 2008 ). Humans provide an exemplar; some alleles that provide advantages under recent conditions clearly arose earlier – probably because they were favored by some other selective force in the past. One putative example is the allele that confers lactose tolerance in adult Europeans ( Myles et al. 2005 ). Another is the 32-bp deletion allele (CCR5Δ32) of the chemokine receptor gene that confers resistance to HIV ( Galvani and Novembre 2005 ).

In agriculture, evolution from standing genetic variation is particularly dramatic. After 100 years of annual selection in the Illinois maize experiment ( Fig. 4 ) ‘… responses of both protein and oil are >20 standard deviations from the original population mean in the positive direction and four standard deviations in the negative direction’ ( Moose et al. 2004 ). The reason why this dramatic change could be driven by standing genetic variation is that these traits were influenced by many alleles and many loci, such that selection on each allele was relatively weak and recombination allowed for new variation ( Moose et al. 2004 ). Of course, the favorable conditions in laboratories or agriculture may free genes formerly constrained by stabilizing selection to evolve in novel directions. Thus, responses to artificial selection for a specific end (e.g., high protein) might not be representative of evolution in general. And yet, adaptation from standing genetic variation is also prevalent in weeds or pests adapting to herbicides or pesticides. For instance, brown rats ( Rattus norvegicus ) have evolved resistance to warfarin at least partly through pre-existing variants of the gene VKORC1 ( Pelz et al. 2005 ), and the same is true for blowflies ( Lucilia cuprina ) evolving resistance to malathion ( Hartley et al. 2006 ).

Changes in protein content as a result of artificial selection in the Illinois maize ( Zea mays ) selection experiment. Initially, one line was selected for high protein (solid line trending up: IHP) and another for low protein (solid line trending down: ILP). Half way through the time series, new lines were established taking the high protein line and selecting for low protein (dashed line trending down: RHP) or taking the low protein line and selecting for high protein (dashed line trending up: RLP). Adapted from Moose et al. (2004) .

Even outside of the human sphere of influence, most recent adaptation is probably driven by standing genetic variation ( Barrett and Schluter 2008 ). In stickleback, for example, the allele at EDA that is favored in fresh water is of the same lineage in many independent watersheds, implying that this allele is present in ancestral marine populations ( Colosimo et al. 2005 ). The retention of this allele in the ocean, where it is not selectively favored, is probably possible because it is recessive and therefore partially shielded from selection (see below for more about recessive alleles). Moreover, recent population genomic analyses suggest that standing genetic variation in many gene regions is important to freshwater adaptation by stickleback ( Hohenlohe et al. 2010 ).

Standing genetic variation thus provides the best indication of evolutionary potential and ‘resilience’ of natural populations facing environmental change ( Sgrò et al. 2011 ). A common proxy for this potential is the proportion of the total genetic variation that has an additive genetic basis (i.e., heritability) ( Visscher et al. 2008 ). Heritability has now been assayed for many traits in many populations of many species, and nearly all estimates indicate substantial evolutionary potential ( Mousseau and Roff 1987 ; Houle 1992 ), although exceptions are known ( Kellermann et al. 2009 ). Heritability estimates depend on the environment in which they are assayed ( Hoffmann and Merilä 1999 ), and so will be most relevant when they are for the specific population and environmental conditions under consideration ( McGuigan and Sgrò 2009 ). This is much easier said than done and so a quick-and-dirty substitute for evolutionary potential has been sought. Neutral genetic variation was hoped to fulfill this role, but it has proven to be only weakly associated with quantitative genetic variation ( Reed and Frankham 2001 ). More recently, genome scans have been used to search for chromosome regions under selection (e.g., Hohenlohe et al. 2010 ). Perhaps variation in such regions might be used to infer adaptive potential – but whether they accurately reflect variation at the phenotypic level remains uncertain ( Latta 1998 ; Manolio et al. 2009 ; Yang et al. 2010 ).

New mutations provide fuel for longer-term evolution

Standing genetic variation is sometimes absent or can be depleted in the direction of selection. Continuing adaptive evolution will then require new mutations – or intragenic recombination in bacteria. The supply rate of these new mutations is an important determinant of ‘sustainable rates of evolution’ in theoretical models (e.g., Lynch et al. 1995 ). The contribution of new mutations to contemporary adaptation is expected to be greatest for large populations with short generation times. For example, new mutations are so important in HIV evolution that very large genetic changes are evident through time for virus populations within individual patients ( Shankarappa et al. 1999 ). Also, laboratory studies on adaptation in micro-organisms often start with single clones and reveal dramatic evolution by new mutations (review: Bell 2008 ). A particularly relevant example is the demonstration of ‘evolutionary rescue’ through new adaptive mutations in laboratory yeast populations exposed to stressful environments ( Bell and Gonzalez 2009 ).

Even for species with longer generation times than micro-organisms, new mutations sometimes contribute to adaptation. For example, resistance to pesticides has sometimes evolved through new mutations, such as diazinon resistance in blowflies ( Hartley et al. 2006 ). In stickleback, alleles at PitX1 that enable adaptive pelvic reduction in fresh water appear to have arisen independently in different watersheds following the last glaciation ( Chan et al. 2010 ). In humans, several new lactose tolerant alleles apparently arose de novo and then spread in Sub-Saharan Africa following the advent of pastoralism ( Myles et al. 2005 ). But despite these and other examples, mutational supply rates will be too low to provide major contributions to the contemporary evolution of macro-organisms facing environmental change.

Bacteria and archaea blur the lines between standing genetic variation and new mutations. First, they often undergo lateral (horizontal) gene transfer, which effectively gives individual ‘species’ access to a near limitless supply of variants ( Lerat et al. 2005 ; Dagan and Martin 2007 ; Russell et al. 2011 ). These variants are often a part of standing genetic variation in bacteria/archaea as a whole, but their incorporation into a particular species is equivalent to a new mutation. Second, different genes collectively required for a new function are often progressively condensed into operons through a series of transposon-mediated transposition events, and so they end up behaving essentially as a single, coordinated genetic unit ( Lal et al. 2010 ). In this case, both standing genetic variation and new mutations (transpositions) are used to assemble new biochemical pathways.

Small and isolated populations can have genetic problems

In addition to their demographic vulnerability, small and isolated populations can have genetic problems. One problem is that genetic variation can be lost owing to drift, low mutational inputs, and low immigration. These effects might be especially strong in the case of founder events or bottlenecks, i.e., populations founded or perpetuated by only a few individuals will have only a small portion of the initial standing genetic variation. The result might be a limited potential to respond to future environmental changes. So far, however, the empirical evidence suggests that evolutionary potential is severely compromised in only the very smallest populations ( Willi et al. 2006 ). And this can also be seen in the Illinois maize experiment, where dramatic evolution continued despite effective population sizes of only 4–12 individuals per line ( Moose et al. 2004 ). Part of the reason why genetic variation might only rarely be limiting in small populations is that bottlenecks can alter the genetic background of interacting alleles (epistatic effects) and thus potentially increase genetic variation (e.g., Cheverud and Routman 1996 ; Carroll et al. 2003 ). And, even when bottleneck effects are initially strong, they can be transitory owing to ongoing immigration ( Keller et al. 2001 ) or selection against individuals with low genetic variation ( Kaeuffer et al. 2007 ).

A related problem in small, isolated, and bottlenecked populations of eukaryotes is inbreeding; i.e., mating with close relatives ( Keller and Waller 2002 ). As examples, inbreeding appears to increase extinction risk in local populations of butterflies ( Saccheri et al. 1998 ), and appears to cause low fecundity or fertility in Florida panthers ( Puma concolor coryi ; Pimm et al. 2006 ) and Greater Prairie Chickens ( Tympanuchus cupido pinnatus ; Westemeier et al. 1998 ). Similar effects can be seen in humans. As just one example, Finland was colonized by relatively few people, who then spread rapidly within the region, forming small and isolated groups. The result has been a high frequency of more than 30 diseases that are typically masked in larger populations ( Peltonen et al. 1999 ). And yet, inbreeding will not always be associated with major problems, with agriculture providing a case in point. Crops and domesticated animals are often very inbred (because humans have attempted to fix desirable genetic variants) and yet can have very large population sizes and high reproductive output. This is possible because (i) humans provide beneficial conditions and aid reproduction, (ii) frequent polyploidy buffers crops from inbreeding problems, and (iii) deleterious mutations were likely purged through past bottlenecks and selection. This purging is also seen in natural populations ( Crnokrak and Barrett 2002 ). Despite these exceptions, the general conclusion is that inbreeding reduces population mean fitness.

Under the right conditions, genetic bottlenecks could be used to our advantage. As just one example, bottlenecked viruses show considerable fitness declines owing to the accumulation of deleterious mutations through ‘Muller's ratchet’ ( Elena et al. 2000 ). That is, high mutation rates and bottlenecks in small populations lead to the irreversible accumulation of genotypes with more mutations ( Muller 1964 ). This effect varies greatly among different viruses but fitness decreases can be quite large, such as in experimentally bottlenecked HIV-1 ( Yuste et al. 1999 ). This example serves to remind us that demographic and genetic considerations align quite nicely when it comes to population size. That is, reductions in the population size of bothersome organisms will not only reduce their immediate impact but also could limit their evolutionary potential and therefore their future impacts. And the reverse applies for beneficial organisms. Of course, changes in population density can also change selective pressures in ways that cause unexpected evolutionary change ( Lankau and Strauss 2011 ).

Evolutionary history influences current traits and future responses

The evolutionary history of a lineage influences the phenotypes and genotypes currently present, which then influences the direction and speed of contemporary evolution. Knowledge of evolutionary history thus improves our understanding of the current state of affairs and helps to craft predictions about the future ( Denison 2011 ; Crespi 2011 ; Gluckman et al. 2011 ; Thrall et al. 2011 ). As an example, we can understand our craving for high-energy foods that are rich in fat and sugar as a result of past selection to consume such foods as a buffer against times of food scarcity. Now that these foods are available in abundance, overindulgence has led to many health problems, including diabetes, heart disease, and obesity ( Schlaepfer et al. 2002 ; Gluckman et al. 2009a ). Similarly, we can understand why some human traits are prevalent in some geographical areas rather than others, such as lactose tolerance in areas where pastoralism led to milk consumption past childhood ( Tishkoff et al. 2007 ). Evolutionary history can also help us to understand the seemingly greater ease with which generalist insects evolve resistance to pesticides ( Krieger et al. 1971 ) and why relatedness to native species can influence the success of invasive species ( Ricciardi and Ward 2006 ; Strauss et al. 2006 ).

Evolutionary history also helps us to understand past extinctions and future extinction risk. In isolated habitats, such as islands, local fauna often evolved without serious predators and so lacked appropriate responses when predators later arrived ( Cox and Lima 2006 ; Sih et al. 2010 ). This evolutionary naiveté led to many extinctions, including flightless rails in the South Pacific after the arrival of Polynesians ( Steadman 1995 ) and bird species on Guam after the introduction of brown treesnakes ( Boiga irregularis ) ( Fritts and Rodda 1998 ). In plants, extinction risk in the face of environmental change might be exacerbated by reliance on specialized pollinators ( Pauw 2007 ) or the use of photoperiod rather than temperature as a cue for flowering time ( Willis et al. 2008 ). It also seems likely that species evolving low dispersal will be more vulnerable to extinction in the face of local disturbances and climate change ( Kotiaho et al. 2005 ). Conversely, species evolved for high dispersal can be in trouble if the matrix between good patches becomes inhospitable or dangerous ( Fahrig 2007 ).

Knowledge of evolutionary history is increasingly used to set conservation priorities ( Lankau et al. 2011 ; Thomassen et al. 2011 ). At the organismal level, species or populations that have a longer history of evolutionary independence are more likely to harbor unique genetic variation, including novel adaptive traits ( Waples 1991 ; Smith et al. 1993 ; Moritz 1994 ). At the regional level, communities with greater phylogenetic diversity can harbor greater genetic diversity, including novel adaptive traits with important potential services for humanity ( Forest et al. 2007 ; Faith et al. 2010 ). But current evolutionary processes should also be considered. For instance, Thomassen et al. (2011) show that areas harboring the greatest intra-specific genetic and morphological diversity, presumably reflecting contemporary evolution, are not always those that have the highest inter-specific diversity.

Some evolution is not possible

Although evolution can accomplish remarkable things, it is not omnipotent. Severe limits on adaptation might occur in several ways: (i) genetic variation might be lacking in the direction of selection ( Kellermann et al. 2009 ), (ii) some trait combinations might not be possible given biophysical constraints, and (iii) transitional states between the current phenotype and better phenotypes might have low fitness (i.e., fitness valleys). The human appendix might typify this last situation because, although its absence might be best, further size reductions would reduce blood flow, making infections more life-threatening ( Nesse and Williams 1998 ).

Evolutionary limits have frequently been invoked in agricultural contexts. For instance, rusts can attack many cereals but not rice. Perhaps rice has resistance genes that simply cannot be circumvented by rusts. Evolutionary limits also hamper attempts to improve crop yield, especially for traits, like drought tolerance, that already have been long subject to improvement by natural and artificial selection ( Denison et al. 2003 ). In the case of global warming, temperature tolerance might represent an evolutionary limit. For aquatic organisms, increasing temperature increases oxygen demand but also decreases oxygen supply, until aerobic metabolism eventually becomes impossible. Pörtner and Knust (2007) have argued that the resulting constraint explains inter-annual variation in the population size of eelpout ( Zoarces viviparus ) in the North and Baltic Seas. An outstanding question is the extent to which this constraint is a true evolutionary limit, given that other fishes are certainly very successful in much warmer waters. At the extreme, Lake Migadi Tilapia ( Oreochromis alcalicus grahami ) live in 42°C water, in part because they can breathe air ( Franklin et al. 1995 ). So temperature tolerance can clearly evolve – but perhaps not always, or not quickly enough. These issues are clearly important for modeling changes in the geographic distribution of organisms under climate change ( Skelly et al. 2007 ), but this is only rarely carried out ( Urban et al. 2007 ).

Evolutionary constraints can be used to our advantage in slowing the unwanted evolution of weeds, pests, and pathogens. For example, pheromone traps used to attract and kill pest insects might long retain their effectiveness because reduced responses to these pheromones could reduce mating success ( Witzgall et al. 2010 ). Evolutionary constraints are also a key premise of biological control programs. The choice of agents for release in these programs generally emphasizes very strong specificity for a chosen target species, and hence a hoped for inability to evolve to nontarget species. Although biocontrol agents have certainly impacted nontarget species ( Louda et al. 1997 ; Henneman and Memmott 2001 ), it is not clear how often contemporary evolution has been the reason. For instance, van Klinken and Edwards (2002) reviewed 352 intentionally released exotic biocontrol agents of weeds and concluded that none had evolved a novel propensity to use new hosts. However, rare variants might have been missed and native insects at least have evolved an increased ability to use introduced plants ( Carroll et al. 2005 ; Carroll 2011 ).

The difficulty in correctly identifying evolutionary limits and constraints is well illustrated in medicine. Ribosomally synthesized antimicrobial peptides (RAMPs) are a natural part of the human immune system and act on negatively charged phospholipid head groups on the outer surface of bacterial membranes. Given that this is a fundamental property of prokaryotic but not eukaryotic cells, it was argued that bacteria would have difficulty evolving resistance to RAMPs even if they were synthesized and used as topical antibiotics ( Zasloff 2002 ). However, Perron et al. (2005) showed that resistance to synthesized RAMPs evolved rapidly in many lines of two bacteria species ( Fig. 5 ). It is not clear how the bacteria solved the supposed problem, but their success in doing so raised alarms that widespread application of synthesized RAMPs could lead to bacteria also evolving around an important component of the human immune system ( Bell and Gouyon 2003 ). Incorrectly identified evolutionary limits could also be very dangerous in the case of biocontrol agents evolving to use nontarget species.

The evolution of resistance to the cationic antimicrobial peptide pexiganan by Escherichia coli (Panel A) and Pseudomonas fluorescens (Panel B). Shown is growth rate ( y -axis) in relation to the test concentration of pexiganan at the end of the selection experiment. The different colored lines in each panel represent different strains (each the average of multiple lines) selected for resistance (solid lines) and the same strains not selected for resistance (dashed lines). Adapted from Perron et al. (2005) with data provided by G. Perron.

One way that organisms might circumvent some evolutionary limits is through the big leaps that can attend hybridization or polyploid events. Another way is to acquire new genes from a divergent source, such as lateral gene transfer in prokaryotes. And humans get into the act through the introduction of transgenes (genes taken from one species and inserted into another) that enable evolutionary jumps that by chance or design land organisms on new adaptive peaks. The insertion of insect-resistant genes from the bacterium Bacillus thuringiensis ( Bt ) into cotton and corn has certainly revolutionized their agriculture ( Tabashnik et al. 2008 ). However, pests do at least sometimes evolve resistance even to genetically engineered plants ( Hilder and Boulter 1999 ; Tabashnik et al. 2008 ). In short, the consideration of evolutionary limits and how they might or might not be circumvented is a major area of evolutionary applications.

Traits are correlated and so do not evolve independently

Different phenotypic traits are often correlated with one another. This can occur if the same genes influence multiple traits, if genes for different traits are closely linked on chromosomes, or if environmental effects (e.g., temperature or diet) simultaneously influence multiple traits. If traits are phenotypically correlated, direct selection on one will lead to indirect selection on the others ( Lande and Arnold 1983 ). If traits are genetically correlated, the evolution of one will lead to the evolution of others ( Hansen and Houle 2008 ). The upshot is that trait correlations will influence evolutionary potential, sometimes called ‘evolvability’ ( Hansen and Houle 2008 ). In the ‘evo-devo’ literature, the ties between trait correlations and evolvability are often discussed in the context of ‘modularity,’ where correlations are strong between traits within a given module but weak between traits in different modules ( Wagner and Altenberg 1996 ; Hansen 2003 ).

In applied biology, trait correlations have been considered in several contexts. One context occurs when correlations between traits act in opposition to the direction of selection ( Hellmann and Pineda-Krch 2007 ). For example, climate change is expected to favor more and thicker leaves in the annual legume Chamaecrista fasciculata , but these two traits are negatively genetically correlated, which will slow their joint adaptive evolution ( Etterson and Shaw 2001 ). Another example is when behaviors expressed in different contexts (e.g., mating versus foraging) are tied together into behavioral ‘types,’‘personalities,’ or ‘syndromes’ (e.g., bold versus shy) ( Sih et al. 2004 ). Even if selection favors different behaviors in different contexts (e.g., bold while mating but shy while foraging), the shared systems that determine behavior may limit their independent expression. Another context is trade-offs, where beneficial changes in one trait (e.g., increasing egg size) necessarily cause detrimental changes in another trait (e.g., decreasing egg number). In wild salmon, the balance between egg size and number evolves under conflicting selection pressures for high fecundity versus large juvenile size ( Einum and Fleming 2000 ). In hatcheries, selection still favors high fecundity but no longer strongly favors large eggs. The result can be the evolution of higher fecundity and smaller egg size, which might have maladaptive effects when hatchery fish interbreed with wild populations ( Heath et al. 2003 ). In agriculture, Denison et al. (2003) have emphasized that trade-offs, including constraints from conservation of matter, limit our ability to improve crop genetics through traditional breeding or biotechnology. For example, molecular biologists suggested that we might increase the photosynthetic efficiency of crops by replacing a key photosynthetic enzyme with its equivalent from red algae ( Mann 1999 ), but it turns out that more efficient versions of this enzyme have slower reaction rates ( Tcherkez et al. 2006 ). And, of course, trade-offs are also an important part of the human condition. A classic example is pelvic width: bipedal locomotion generally favors a narrow pelvis, but large neonate head size generally favors a wide pelvis. The compromise is a pelvis that is narrower than optimal for child birth but wider than optimal for locomotion ( Hogervorst et al. 2009 ).

Trade-offs can be used in a proactive way to manipulate evolutionary trajectories, such as in the design of drug treatments that slow resistance evolution ( Levin et al. 2000 ; Normark and Normark 2002 ). For example, the periodic cessation of a drug treatment can lead to a decline in the prevalence of resistance when the resistance genes are costly in the absence of the treatment. Unfortunately, this trade-off can be circumvented by the evolution of compensatory mutations that mask resistance costs ( Davies et al. 1996 ; Levin et al. 2000 ; Normark and Normark 2002 ). The consideration of trade-offs is a valuable part of applied evolutionary biology but evolutionarily unbreakable trade-offs, if they exist, can be difficult to confirm.

Natural selection is the engine that converts variation into evolutionary change. Selection occurs when particular phenotypes/genotypes have higher fitness than others. In well-adapted populations, selection may be relatively weak because most individuals will be near a local fitness peak. As environments change, however, maladaptation is expected to increase and the result can be strong selection and contemporary evolution.

Selection and adaptation can occur at multiple levels

Evolution by natural selection can occur at any level of biological organization, so long as the requisite ingredients are in place: heritable variation among entities that differ in fitness ( Keller 1999 ). These entities can be species, populations (groups), families, individuals, genes, or alleles. Sometimes selection acts in different directions at different levels, for example, traits that improve individual fitness can arise at the expense of overall population fitness. The tension between levels of selection can play out in a number of ways depending on selection and variation present at each level. These factors often combine in ways that make individual-level selection the most influential for evolution, but this does not mean that the other levels should be ignored.

The relevance of higher-level selection is particularly clear in agriculture and natural resource management. In these contexts, humans often strive to maximize yield, but this can run counter to selection for increased individual competitiveness ( Donald 1968 ; Denison et al. 2003 ). For instance, competition among individual plants favors larger root systems and larger leaves, but productivity at the population level is maximized at intermediate root and leaf sizes ( Schieving and Poorter 1999 ; Zhang et al. 1999 ). Cognizance of these trade-offs can improve the design of breeding programs and cultivation methods for evolutionary improvements in yield ( Donald 1968 ; Harper 1977 ; Denison et al. 2003 ; Denison 2011 ). In fisheries, the frequent evolution of smaller size or earlier maturation under intensive harvesting leads to the evolution of life histories that can decrease yield ( Conover and Munch 2002 ; Olsen et al. 2004 ). The challenge is to design harvest programs that slow, avert, or reverse this yield-impairing evolution ( Law and Grey 1989 ).

In the context of virulence (pathogen-induced host mortality), natural selection at the between-host level (different infected individuals) can favor reduced virulence because, all else being equal, killing the host often reduces transmission to new hosts. But if infections are genetically diverse (infection of an individual host by multiple strains), competitive interactions among strains within a host can also be evolutionarily important ( Frank 1996 ; Brown et al. 2002 ). In some cases, the evolution of increased within-host competitiveness can lead to higher virulence, as in some malaria parasites ( de Roode et al. 2005 ). This trade-off between competitiveness and virulence can generate antagonistic selection at the between-host versus within-host levels. In principle, this could result in a level of virulence that is higher (or lower) than expected solely from between (or within)-host competition ( Brown et al. 2002 ). In the case of malaria, it has been argued that the host's immune response, which also damages host tissue (immunopathology), disrupts the virulence-transmission trade-off, and so medical interventions to deal with immunopathology can influence virulence evolution ( Long et al. 2011 ). Bringing gene-level selection into the picture, it has been argued that meiotic drive, which subverts meiosis in favor of a particular gamete, can be used to artificially increase the frequency of anti-pathogen transgenes in mosquito disease vectors ( Cha et al. 2006 ; Huang et al. 2007 ).

Theory suggests that individual-level selection can be so detrimental to population growth that it can lead to extinction ( Webb 2003 ; Rankin and López-Sepulcre 2005 ). The idea here is that benefits to individuals can spread even if they are costly to population size and therefore persistence. Empirical confirmation of this ‘evolutionary suicide’ or ‘Darwinian extinction’ is currently lacking, but it is certainly true that intra-specific competition, including resource monopolization (e.g., territoriality), can reduce population size to the point that extinction risk increases. Management practices that enhance phenotypic diversity (e.g., polymorphisms that reduce intra-specific competition) may increase the carrying capacity of a habitat and thus the population densities it can sustain ( Carroll and Watters 2008 ). In short, the consideration of multi-level selection can help us to better attain desired population-level traits.

Selection overwhelms drift

Populations frequently differ from each other in a number of phenotypic traits and genes. If these differences are the result of genetic drift, they indicate restricted gene flow but little else. If the differences are adaptive, however, they are more likely to (i) trigger protection in conservation efforts ( Waples 1991 ; Smith et al. 1993 ; Moritz 1994 ), (ii) influence productivity in agricultural settings ( Denison et al. 2003 ), and (iii) suggest ways to combat pathogens or invasive species. At one level are inferences about whether populations are adaptively divergent in general (i.e., local adaptation). At another level are inferences about whether and why particular phenotypic differences or changes are adaptive.

Many, perhaps most, overt phenotypic differences among populations are likely adaptive. For example, human populations clearly show adaptive differences in skin color, body size and shape, oxygen use, lactose tolerance, disease resistance, and many other traits ( Jablonski 2004 ; Balter 2005 ; Beall 2006 ; Tishkoff et al. 2007 ; Gluckman et al. 2009a ). And the same is true for natural populations of other organisms. First, different populations or species in similar environments tend to have similar phenotypes: i.e., convergent or parallel evolution ( Endler 1986 ; Schluter 2000 ). Second, populations introduced to new environments often evolve phenotypes expected for those environments ( Reznick and Ghalambor 2005 ). Third, reciprocal transplants show that individual fitness is usually higher for local individuals than for foreign individuals ( Hereford 2009 ), even when those populations diverged only recently ( Kinnison et al. 2008 ; Gordon et al. 2009 ). Fourth, selection in wild populations is usually quite weak ( Kingsolver et al. 2001 ; Hersch and Phillips 2004 ) and variable ( Siepielski et al. 2009 ) – consistent with the idea that most populations are relatively well adapted to local conditions ( Estes and Arnold 2007 ; Hendry and Gonzalez 2008 ).

Also, phenotypic changes through time will be adaptive in many cases. Perhaps most obvious is the repeated evolution of resistance to chemicals by weeds, pests, and pathogens – as discussed in more detail later. And the same is true for mechanical weed control: barnyard grass that grows in hand-weeded rice fields has evolved to be more cryptic by morphologically converging on rice ( Barrett 1983 ; Fig. 6 ). In fisheries, intensively harvested wild populations have repeatedly evolved smaller body size, younger age at first reproduction, and higher reproductive allocation ( Jørgensen et al. 2007 ; Dunlop et al. 2009 ; Sharpe and Hendry 2009 ). These parallel phenotypic changes in response to parallel shifts in selection strongly implicate selection and adaptation – although the genetic basis for temporal change is hard to confirm.

Crop mimicry in barnyard grass. Panel A shows rice ( Oryza sativa ) on the left, a barnyard grass ( Echinochloa crus-galli var. oryzicola ) that mimics rice in the center, and a very closely related barnyard grass ( Echinochloa crus-galli var. crus-galli ) that does not mimic rice on the right. Panel B is a discriminant functions plot that shows the morphological similarity of multiple individuals (points) in these three groups. Centuries of hand weeding is thought to have led to the close similarity of rice and its barnyard grass mimic. Adapted from Barrett (1983) with a photograph and data provided by S. Barrett.

Not all phenotypic differences and changes will be adaptive, and so it is prudent to also consider alternatives. Nonadaptive or maladaptive differences might sometimes arise through genetic drift, although probably only for very small populations and traits under very weak selection. Maladaptive variation can also be caused by high gene flow ( Hendry and Taylor 2004 ; Bolnick and Nosil 2007 ) or ongoing environmental change ( Grant and Grant 2006 ), with the latter increasingly important in a human dominated world (see next section). In addition, past selection pressures that led to the evolution of particular traits might no longer be present (‘relaxed selection’), but the traits might take a long time to decay if they are now selectively neutral ( Lahti et al. 2009 ). The remnant pelvic bones of whales and some snakes are not adaptive per se , but have persisted because they have not been strongly selected against. In addition, nutritional limitation can cause trait change in ways that are not necessarily adaptive ( Grether 2005 ). Regardless, it is safest to start from the premise that phenotypic differences are adaptive (remembering that they might be plastic or genetic), because this will often be true and it is the precautionary approach while awaiting confirmation.

Human activities impose particularly strong selection

Humans cause dramatic environmental changes and should therefore impose particularly strong selection. Especially obvious are the many examples of bacteria evolving resistance to antibiotics ( Palumbi 2001 ). As noted by Bergstrom and Feldgarden (2007) : ‘The evolution of resistance to a clinical antibiotic occurs with near certainty after several years of widespread use’. Human viruses, such as HIV, also evolve resistance to a variety of treatments ( Little et al. 2002 ), as does cancer in response to chemotherapy ( Pepper et al. 2009 ). Insects that are vectors of human diseases, particularly mosquitoes, frequently evolve resistance to insecticides ( Hemingway and Ranson 2000 ; Raymond et al. 2001 ). Agriculture is rife with analogous situations. Heap (1997) reports ‘183 herbicide-resistant weed biotypes (124 different species) in 42 countries’. Whalon et al. (2008) list 7747 cases of resistance evolution to 331 compounds in 553 pest arthropod species.

For wild populations of vertebrates, meta-analyses have revealed that phenotypic changes are greatest when environmental changes are the result of human activities, including pollution, translocations, invasive species, hunting, and harvesting ( Hendry et al. 2008 ). An example described in this special issue is the difference in morphological traits of Anolis sagrei lizards in urban areas versus natural habitats ( Marnocha et al. 2011 ). Phenotypic changes are especially strong when they involve the hunting/harvesting of wild populations ( Darimont et al. 2009 ) – probably because humans here directly select on the population, rather than having an indirect effect acting through human-induced environmental change. Some of the observed phenotypic changes are probably the immediate result of phenotypic plasticity, whereas others will represent genetic change ( Dieckmann and Heino 2007 ; Bradshaw and Holzapfel 2008 ; Gienapp et al. 2008 ; Hendry et al. 2008 ; Crispo et al. 2010 ). In short, humans cause particularly dramatic changes in organisms – and these changes are probably often adaptive.

Selection can be manipulated to help or harm populations

Selection is a demographic process that can alter birth and death rates, and so it can have an immediate influence on population dynamics. Selection also drives adaptation, and so it can have future influences on population dynamics. We discuss these points further in the section on Eco-evolutionary dynamics. For now, we focus on how selection can be manipulated to achieve desired population consequences. On the one hand, selection on pests, weeds, or pathogens might be designed to exceed their adaptive potential, thus decreasing population sizes and potentially causing eradication. On the other hand, selection in conservation situations might be eased in order to give populations a more gradual (and therefore achievable) route to adaptation.

Altering the intensity of selection is one possible manipulation. For example, one can apply more, or more powerful, herbicides, pesticides, antibiotics, or antivirals in the hope of causing severe population declines. But if eradication does not occur, this stronger selection can lead to increased rates of adaptation, effectively undoing any progress initially achieved. On the flip side, decreasing the intensity of selection can be problematic if one wishes to speed adaptation in threatened species. Other strategies might therefore be implemented to manipulate selection so as to promote desired demographic consequences while reducing undesired evolutionary consequences. One strategy is to increase the dimensionality of selection by altering the environment in multiple ways. Another is to alter the timing of selection by changing the life stage when selection acts.

An example of altering the dimensionality of selection comes from HIV treatment, where the initial problem was that resistance quickly evolved to single drugs ( Little et al. 2002 ). The advance was to use multiple drugs specifically designed to act in different ways that require independent mutations for the virus to circumvent. These ‘highly active anti-retroviral therapy’ treatments can include a combination of nucleoside/nucleotide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, and aspartic protease inhibitors, sometimes combined with fusion inhibitors ( Barbaro et al. 2005 ). Resistance to these drug cocktails does evolve more slowly, not only in HIV but also in tuberculosis ( Bonhoeffer et al. 1997 ). Unfortunately, multi-drug resistance does still ultimately evolve in many cases ( Coker 2004 ). Selection dimensionality plays into agriculture through ‘toxin stacking’ or ‘pyramiding’ in pest control – essentially layering one selective force on top of another. For instance, rotations or mixtures of herbicides or pesticides with ‘discrete modes of action’ are a common strategy to slow the evolution of resistance ( Beckie and Reboud 2009 ). Likewise, multiple insecticidal toxin genes from bacterial sources can be incorporated into transgenic crop plants ( Roush 1998 ). Although this method is not yet widely deployed, two-gene transgenic Bt cotton is being used in Australia ( Fitt 2008 ).

Altering the timing of selection has its motivation in the evolutionary theory of senescence ( Medawar 1952 ; Williams 1957 ). This theory argues that unavoidable extrinsic mortality (from predators, pathogens, starvation, or accidents) dictates that few individuals reach advanced ages, and so selection against deleterious alleles that act late in life will be relatively weak. Applying this idea to infectious diseases, Read et al. (2009) proposed that insecticides targeting older mosquitoes could reduce malaria transmission without imposing strong selection for resistance in the mosquitoes. The reason is that malaria transmission becomes more likely late in the life of mosquitoes after they have already reproduced at least once (see also Koella et al. 2009 ). Because relatively few mosquitoes make it to this age, late-life-acting insecticides might be ‘evolution proof’ ( Read et al. 2009 ), in contrast to the current early-life-acting insecticides to which mosquitoes have so routinely evolved resistance ( Raymond et al. 2001 ). Although experience teaches that ‘evolution proof’ is a long shot for organisms with short generation times and large population sizes, late-life treatments might be at least ‘evolution resistant.’

Selection is influenced by allelic interactions (e.g., recessivity)

Interactions between alleles at a given locus can dramatically alter selection and evolutionary responses. Of particular relevance, some alleles are recessive, having phenotypic effects only (or mainly) in homozygous form. When these alleles are rare, it is hard to change their frequency because they primarily occur in heterozygous form and so are shielded from selection. Recessive alleles have played an important role in management strategies to slow the evolution of resistance. One strategy has been to promote interbreeding between resistant and nonresistant individuals, the latter often coming from reserves or from controlled releases (see the Connectivity section). If the genes for resistance are recessive, they will be selected against when the resulting heterozygotes are exposed to the control strategy ( Carrière and Tabashnik 2001 ). Recessive alleles are also relevant in the conservation of small populations. Breeding between close relatives in these situations can increase the frequency of homozygotes and thereby increase the expression of recessive deleterious mutations ( Lynch et al. 1995 ), which can decrease fitness ( Keller and Waller 2002 ). These effects are also recognized in human populations, as codified in social norms that discourage marriage between close relatives.

A different type of allelic interaction occurs with ‘imprinted genes’ in mammals ( Wilkins and Haig 2003 ), where the allele from only one parent is expressed in the offspring, often as a result of methylation of the other allele. This imprinting has several effects. First, it shelters one allele from selection in each generation – and this might, by chance, be the same allele across several successive generations. Second, and in counterpoint, it is unlikely that the same allele will be sheltered for a number of successive generations, and so recessive deleterious mutations are less likely to escape selection for long. Imprinted genes represent only about 1% of autosomal genes – but they nevertheless have important effects and are related to a number of developmental disorders ( Wilkins and Haig 2003 ; Jirtle and Skinner 2007 ).

Connectivity

Connectivity determines the movement of individuals and gametes across a landscape. Connectivity is influenced by organismal attributes (e.g., behavior and body size), by population densities and distributions, and by natural and man-made structures (e.g., mountains, oceans, roads, dams, canals, corridors, and currents). From an ecological perspective, increased connectivity can have consequences that are either positive (demographic rescue) or negative (spread of diseases or invasive species). From a genetic perspective, increased connectivity increases gene flow, which generally increases genetic variation within populations (by bringing it from elsewhere) and decreases genetic variation among populations (by mixing their gene pools). These genetic effects can either enhance or constrain adaptive evolution, depending on the circumstances (review: Garant et al. 2007 ). Some potential enhancing effects include reduced inbreeding and increased genetic variation for future adaptation. A potential constraining effect is the erosion of local adaptation by the influx of locally maladaptive genes. Although connectivity plays into many of the topics and examples discussed elsewhere in this article, we treat it separately here because the manipulation of connectivity has played an important role in applied evolution.

Gene flow can be manipulated to achieve desired outcomes

Increased gene flow is commonly considered in efforts genetically ‘rescue’ small and isolated populations from inbreeding depression ( Keller and Waller 2002 ). Famous examples include the aforementioned Greater Prairie Chickens ( Westemeier et al. 1998 ) and Florida panthers ( Hedrick 1995 ; Pimm et al. 2006 ). Less commonly, increased gene flow has been considered in efforts to enhance the adaptive potential of populations facing environmental change. For instance, a population threatened by maladaptation to increasing temperatures might be rescued by gene flow from populations adapted to warmer conditions. As a possible natural analog, the lineage of field mice ( Peromyscus leucopus ) present in the Chicago area prior to the 1980s has been completely replaced by a different lineage that appears better adapted to the new conditions ( Pergams and Lacy 2008 ).

Increased gene flow has been intentionally used in agriculture to either enhance adaptation or to constrain it. Toward the first purpose, considerable success has been achieved by crossing cultivars with their wild relatives to ‘pyramid’ independent genomic regions that increase yield ( Gur and Zamir 2004 ). Toward the second purpose, gene flow is often used to slow the evolution of resistance to pesticides. A common approach is to match fields of transgenic Bt crops, where insects are under selection to evolve resistance, with adjacent ‘reserves’ of non- Bt crops, where insects are not under this selection ( Carrière and Tabashnik 2001 ). Mating between insects from the two areas then hampers the evolution of resistance – to a degree that depends on recessive inheritance, incomplete resistance, fitness costs, and the degree of assortative mating ( Carrière and Tabashnik 2001 ; Tabashnik et al. 2005 , 2008 ). The refuge strategy does hinder resistance evolution in at least some systems, such as pink bollworm ( Pectinophora gossypiella ) in the USA ( Tabashnik et al. 2005 ) and cotton bollworm ( Helicoverpa armigera ) in Australia ( Downes et al. 2010 ). An alternative to crop reserves as a source of nonresistant genotypes is their mass culture and release ( Alphey et al. 2007 ). Alternatively, sterile individuals can be released that reduce the reproductive success of wild individuals with which they mate ( Benedict and Robinson 2003 ). Or mosquitoes can be released that have been bred to be less able to transmit malaria ( Ito et al. 2002 ) or to have heritable life-shortening Wolbachia infections ( McMeniman et al. 2009 ).

Decreased gene flow is also a management tool. Theory suggests that high gene flow between populations in different environments can compromise adaptation, leading to population declines and possible extirpation ( Boulding and Hay 2001 ). This concern has become pervasive when considering the effects of cultured organisms on wild populations ( Tufto 2010 ). Hatcheries and fish farms often use nonlocal genotypes or cause the evolution of traits that are maladaptive in the wild ( Araki et al. 2008 ). Frequent releases or escapes from such facilities can cause maladaptive gene flow that compromises adaptation in wild populations ( Hindar et al. 2006 ). Attempts have therefore been made to reduce gene flow from captive to wild populations ( Cotter et al. 2000 ). Related to this, concerns surround the possible spread of transgenes from genetically modified organisms (GMOs) into wild populations ( Ellstrand 2001 ; Andow and Zwahlen 2006 ). These transgenes could cause problems for wild populations or, alternatively, enhance the fitness of potentially weedy species ( Marvier 2008 ). For example, gene flow among canola ( Brassica napus ) crops with different insecticide resistance genes has resulted in multiple-herbicide resistance in ‘volunteer’ canola plants growing as weeds in other crops ( Beckie et al. 2003 ).

Gene flow can evolve when organismal traits influencing connectivity or movement experience altered selection. As possible examples, marine reserves that provide refuges from fishermen ( Baskett et al. 2007 ), or roads that kill migrants traveling between habitats, might favor the evolution of reduced dispersal. Reduced dispersal can then decrease gene flow between populations (although not under all circumstances, Heino and Hanski 2001 ), and thus alter the aforementioned effects.

Eco-evolutionary dynamics

In our consideration of how variation, selection, and connectivity influence the evolution of phenotypic traits, we have sometimes discussed how this evolution can have consequences for population dynamics. These effects fall under the umbrella of interactions between ecology and evolution, wherein ecological change drives evolutionary change and evolutionary change can feed back to influence ecological change, i.e., ‘eco-evolutionary dynamics’ ( Fussmann et al. 2007 ; Kinnison and Hairston 2007 ; Palkovacs and Post 2009 ; Pelletier et al. 2009 ). We now expand on the second part of this dynamic (evo-to-eco) by more explicitly discussing the effects of phenotypic/genetic change on population dynamics (e.g., numbers of individuals and population persistence), community structure (e.g., species richness or diversity), and ecosystem function (e.g., nutrient cycling, decomposition, and primary productivity).

Evolution influences population dynamics

Evolution can influence population dynamics in two basic ways ( Saccheri and Hanski 2006 ; Kinnison and Hairston 2007 ), roughly corresponding to so-called ‘hard’ versus ‘soft’ selection ( Wallace 1975 ). In the first instance, evolution can alter the rate of increase of a population in the absence of density dependence: better-adapted populations have higher birth rates or lower death rates. In the second instance, evolution can alter the population size at which density dependence becomes limiting: better-adapted populations sustain more individuals at a given resource level. These population parameters can be depressed when organisms are poorly adapted for their local environments – and extinction can be the result. However, contemporary adaptation to those changed environments can boost these parameters and thereby aid population recovery ( Gomulkiewicz and Holt 1995 ; Bell and Gonzalez 2009 ; Enberg et al. 2009 ). As obvious examples, new treatments to which weeds, pests, or pathogens are not well adapted can cause initial population declines, but adaptation can then allow their recovery.

Will similar dynamics be important for wild populations facing environmental change ( Lakau et al. 2011 )? Some evidence has certainly accumulated that environmental change can generate maladaptation that causes population declines ( Both et al. 2006 ; Pörtner and Knust 2007 ). Evidence also exists that contemporary adaptive change improves the fitness of individuals or populations facing environmental change. For example, survival and reproductive output increase through time as fish populations adapt following an abrupt environmental change ( Kinnison et al. 2008 ; Gordon et al. 2009 ). And changes in the population size of ungulates from 1 year to the next are influenced by phenotypic changes on the same time scale ( Pelletier et al. 2007 ; Ezard et al. 2009 ). In most cases, it is not clear to what extent these improvements are the result of plasticity or genetic change or both. However, genetic effects have been confirmed for improvements in the individual fitness of chinook salmon ( Oncorhynchus tshawytscha ) introduced to new environments ( Kinnison et al. 2008 ) and the growth rate of local populations within a metapopulation of Glanville Fritillary butterflies ( Melitaea cinxia ) ( Saccheri and Hanski 2006 ).

In the case of invasive species, demographic costs of initial maladaptation are implied in the observation that introduced species usually fail to become established ( Sax and Brown 2000 ). And the demographic benefits of contemporary adaptation are implied in the observation that introduced species that become invasive often do so only after a lag period or repeated introductions, which are often accompanied by phenotypic changes ( Facon et al. 2006 ). Weese et al. (2011) provide an experimental example wherein population recovery following disturbance was mostly driven by locally adapted individuals rather than maladapted immigrants.

Evolution influences communities and ecosystems

Phenotypic variation can have direct or indirect consequences for community structure and ecosystem function. Direct effects can occur if specific phenotypes influence ecological variables, such as different foraging traits influencing consumption patterns that then alter food web structure ( Palkovacs and Post 2009 ). Indirect effects could occur if phenotypes influence population size (as described earlier) and population size then has ecological effects. For example, adaptive evolution that increases the size of a predator population could have cascading effects on other trophic levels. These direct and indirect effects can be considered in the context of standing variation within or between populations, or dynamic changes in the composition of populations through time. These ecological consequences of phenotypic variation/change are expected to be particularly important in species with large per capita ecological roles (e.g., keystone species and foundation species) or that are very abundant or rapidly evolving (e.g., some pathogens or pests).

One approach to eco-evolutionary effects is to examine how genetic variation among individual plants influences their ecological effects, such as on attendant arthropod or plant communities. These effects can be quite strong ( Whitham et al. 2006 ; Bailey et al. 2009 ; Johnson et al. 2009 ), implying that dynamic changes in the composition of populations should cause dynamic changes in ecological variables. These changes are much harder to study than are the effects of standing variation. One approach has been to use mesocosms to compare the ecological effects of fish populations that recently diverged from a common ancestor ( Fig. 7 ). Guppy ( Poecilia reticulata ) and killifish ( Rivulus hartii ) populations that colonized divergent environments thousands to millions of years ago now differentially influence algal biomass, algal accrual, aquatic invertebrates, decomposition rates, and nutrient fluxes ( Palkovacs et al. 2009 ; Bassar et al. 2010 ). Stickleback populations that colonized divergent environments thousands of years ago now differentially influence zooplankton communities, primary productivity, dissolved organic materials, and light transmission ( Harmon et al. 2009 ). Alewife ( Alosa pseudoharengus ) populations that colonized divergent environments hundreds of years ago now differentially influence zooplankton communities, with potential feedbacks to alewife evolution ( Palkovacs and Post 2009 ).

Examples of the community and ecosystem effects of phenotypic differences between fish populations. Panel A shows that mesocosms with guppies ( Poecilia reticulata ) from high-predation (HP) populations have more periphyton and fewer benthic macroinvertebrates than do mesocosms with guppies from low-predation (LP) populations. These data are adapted from Palkovacs and Post (2009) – see also Bassar et al. (2010) . Panel B shows that mesocosms with alewife ( Alosa pseudoharengus ) from anadromous (ANAD) populations have fewer zooplankton and more phytoplankton than do mesocosms with alewife from resident freshwater (FW) populations. These data are for the first sampling date after fish were added to the mesocosms and are from Palkovacs et al. (2009) for zooplankton and from E. Palkovacs (unpublished) for phytoplankton. Panel C shows that mesocosms with benthic threespine stickleback ( Gasterosteus aculeatus ) have greater light extinction coefficients and greater UV absorption than do mesocosms with limnetic threespine stickleback. These data are adapted from Harmon et al. (2009) with data provided by L. Harmon. In all panels, the bars are standard errors around the mean value across replicate mesocosms.

Effects of contemporary evolution on communities and ecosystems are likely common on even shorter time scales – given the ample evidence that adaptive traits can evolve on the time scale of just a few generations ( Hendry et al. 2008 ). Such effects are obvious in medical and agricultural settings, where the evolution of resistance clearly has consequences for human populations and crops. In more natural settings, a putative example is the rabbit–myxoma interaction in Australia: introduced rabbits ( Oryctolagus cuniculus ) had dramatic ecological consequences, which abated when myxomatosis was introduced to kill the rabbits, but increased again when mortality rates declined owing to co-evolution of the rabbits and myxomatosis ( Dwyer et al. 1990 ). In addition, selective herbivory has been shown to alter the chemical composition of tree communities, which has consequences for other ecological processes. For example, beaver ( Castor canadensis ) avoid Populus trees with high tannin content, the increasing frequency of which then reduces nitrogen mineralization ( Whitham et al. 2006 ). We suspect that many more ecological effects of contemporary evolution will be revealed as more investigators turn to this problem.

Take home summary

• Understanding phenotypes (as opposed to just genotypes) is important because phenotypes interact with the environment, come under direct selection, and have ecological effects.
• Individual and population mean fitness can improve more rapidly through plasticity than through genetic change – at least in the short term. Genetic change, however, will often be necessary to finish any recovery.
• In the study of adaptation, the examination of specific genes is often insufficient. Adaptation will usually involve many genes, which highlights the importance of a quantitative genetic approach.
• Standing genetic variation in fitness-related traits is nearly ubiquitous, and so is likely to be the initial fuel for evolutionary change in response to environmental change.
• New mutations become important when standing genetic variation is absent or depleted. New mutations will be particularly important for organisms with short generation times and large population sizes (e.g., viruses, bacteria, and some insects and plants).
• Small population sizes, and especially bottlenecks, can lead to genetic problems. These problems will apply more often to current fitness (e.g., inbreeding depression) than to future evolutionary potential.
• Current trait distributions are a product of past selection. Evolutionary history can therefore help to understand the current state of affairs and to predict responses to future environmental change.
• Some evolutionary change is not possible because of limited genetic variation, trade-offs, or physiological constraints. Identifying these limits is difficult but can aid attempts to slow unwanted evolution.
• The phenotypes of organisms are an integrated complex of traits in association with each other. These associations influence the rate and trajectory of evolution.
• Natural selection generally favors traits that improve individual-level fitness, whereas humans often care about population-level traits, such as productivity or yield. Cognizance of these different levels of selection can be used to tailor evolutionary trajectories as desired.
• Phenotypic differences among populations or through time are usually adaptive, rather than the product of genetic drift. Exceptions do exist, particularly for very small populations or for traits under relaxed selection.
• Human activities impose particularly strong selection. Adaptive phenotypic change will be the result, and at least some of this change will be genetically based.
• Selection can be manipulated to help or harm organisms, but the resulting contemporary evolution can hamper these goals. Manipulating the dimensionality or timing of selection can have desired demographic effects while reducing undesired evolutionary effects.
• Allelic interactions alter natural selection in important ways. For example, recessive alleles are often sheltered from selection, which can be exploited to slow the evolution of resistance.
• Manipulations of connectivity that alter gene flow are an important management tool. Gene flow can be increased to reduce inbreeding or increase evolutionary potential. Gene flow can be decreased to reduce impacts of cultured organisms on wild populations.
• Adaptive evolution influences population dynamics and sometimes allows evolutionary rescue. Such effects are not inevitably large, and so an important topic becomes the conditions under which they will be important.
• Adaptive evolution will alter how organisms interact with their environment and can therefore influence community structure and ecosystem function. These effects are particularly pronounced for organisms that have large ecological effects (e.g., keystone species, foundation species) or that are very numerous (e.g., pathogens, pests, and weeds).

This listing is only a starting point. As additional knowledge and experience accumulate, some of the above points will need to be deleted or altered – and new ones added. Nevertheless, we are struck by how each of the aforementioned principles has clear existing or envisioned applications in multiple areas of biology, ranging across health, medicine, agriculture, conservation biology, natural resource management, and environmental science. This cross-disciplinary relevance serves to illustrate the unifying aspect of evolution and its ramifications across the applied biological sciences. Hopefully, this illustration will inspire practitioners within a given applied discipline to consider evolutionary principles currently applied in other disciplines.

Applied evolutionary biology is on the cusp of coming into its own as a discipline, and we hope that it will eventually be so seamlessly integrated into ‘applied biology’ that this more general term will immediately evoke a strong evolutionary foundation. This integration will not always be smooth sailing. Some ecologists still do not think evolution is relevant on short time scales. Some manipulations of connectivity might be ruled out based on ethical issues. Some theoretically sensible evolutionary interventions might be ruled out owing to their initial cost or environmental effects ( Thrall et al. 2011) . And, of course, a surprising fraction of humans still ‘don't believe in evolution.’ However, it seems to us that the benefits of applying evolutionary principles will eventually be so obvious that their widespread application will be insidiously inevitable. Thus, while doctors or farmers might euphemistically talk about acquired resistance, when they really mean the evolution of resistance, they nonetheless think about and apply some evolutionary principles on a daily basis.

Acknowledgments

For supporting the summit at which this paper was born, we thank University of Queensland School of Biological Sciences, CSIRO, Institute for Contemporary Evolution, Australian-American Fulbright Commission, NSF and the bioGENESIS core project of DIVERSITAS.

Literature cited

• Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evolutionary Applications. 2008; 1 :95–111. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Alphey N, Coleman PG, Donnelly CA, Alphey L. Managing insecticide resistance by mass release of engineered insects. Journal of Economic Entomology. 2007; 100 :1642–1649. [ PubMed ] [ Google Scholar ]
• Andow DA, Zwahlen C. Assessing environmental risks of transgenic plants. Ecology Letters. 2006; 9 :196–214. [ PubMed ] [ Google Scholar ]
• Araki H, Berejikian BA, Ford MJ, Blouin MS. Fitness of hatchery-reared salmonids in the wild. Evolutionary Applications. 2008; 1 :342–355. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ashley MV, Willson MF, Pergams ORW, O'Dowd DJ, Gende SM, Brown JS. Evolutionarily enlightened management. Biological Conservation. 2003; 111 :115–123. [ Google Scholar ]
• Bailey JK, Schweitzer JA, Úbeda F, Koricheva J, LeRoy CJ, Madritch MD, Rehill BJ, et al. From genes to ecosystems: a synthesis of the effects of plant genetic factors across levels of organization. Philosophical Transactions of the Royal Society B-Biological Sciences. 2009; 364 :1607–1616. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Balter M. Are humans still evolving? Science. 2005; 309 :234–237. [ PubMed ] [ Google Scholar ]
• Barbaro G, Scozzafava A, Mastrolorenzo A, Supuran CT. Highly active antiretroviral therapy: current state of the art, new agents and their pharmacological interactions useful for improving therapeutic outcome. Current Pharmaceutical Design. 2005; 11 :1805–1843. [ PubMed ] [ Google Scholar ]
• Barrett SCH. Crop mimicry in weeds. Economic Botany. 1983; 37 :255–282. [ Google Scholar ]
• Barrett RDH, Schluter D. Adaptation from standing genetic variation. Trends in Ecology & Evolution. 2008; 23 :38–44. [ PubMed ] [ Google Scholar ]
• Baskett ML, Weitz JS, Levin SA. The evolution of dispersal in reserve networks. American Naturalist. 2007; 170 :59–78. [ PubMed ] [ Google Scholar ]
• Bassar RD, Marshall MC, López-Sepulcre A, Zandonà E, Auer SK, Travis J, Pringle CM, et al. Local adaptation in Trinidadian guppies alters ecosystem processes. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107 :3616–3621. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Beall CM. Andean, Tibetan, and Ethiopian patterns of adaptation to high-altitude hypoxia. Integrative and Comparative Biology. 2006; 46 :18–24. [ PubMed ] [ Google Scholar ]
• Beckie HJ, Reboud X. Selecting for weed resistance: herbicide rotation and mixture. Weed Technology. 2009; 23 :363–370. [ Google Scholar ]
• Beckie HJ, Warwick SI, Nair H, Séguin-Swartz GS. Gene flow in commercial fields of herbicide-resistant canola ( Brassica napus . Ecological Applications. 2003; 13 :1276–1294. [ Google Scholar ]
• Bell G. Selection: The Mechanism of Evolution. Oxford: Oxford University Press; 2008. [ Google Scholar ]
• Bell G, Gonzalez A. Evolutionary rescue can prevent extinction following environmental change. Ecology Letters. 2009; 12 :942–948. [ PubMed ] [ Google Scholar ]
• Bell G, Gouyon PH. Arming the enemy: the evolution of resistance to self-proteins. Microbiology. 2003; 149 :1367–1375. [ PubMed ] [ Google Scholar ]
• Benedict MQ, Robinson AS. The first releases of transgenic mosquitoes: an argument for the sterile insect technique. Trends in Parasitology. 2003; 19 :349–355. [ PubMed ] [ Google Scholar ]
• Benton TG, Grant A. Evolutionary fitness in ecology: comparing measures of fitness in stochastic, density-dependent environments. Evolutionary Ecology Research. 2000; 2 :769–789. [ Google Scholar ]
• Bergstrom CT, Feldgarden M. The ecology and evolution of antibiotic-resistant bacteria. In: Stearns SC, Koella JC, editors. Evolution in Health and Disease. Oxford: Oxford University Press; 2007. pp. 124–138. [ Google Scholar ]
• Bolnick DI, Nosil P. Natural selection in populations subject to a migration load. Evolution. 2007; 61 :2229–2243. [ PubMed ] [ Google Scholar ]
• Bonduriansky R, Day T. Nongenetic inheritance and its evolutionary implications. Annual Review of Ecology, Evolution, and Systematics. 2009; 40 :103–125. [ Google Scholar ]
• Bonhoeffer S, Lipsitch M, Levin BR. Evaluating treatment protocols to prevent antibiotic resistance. Proceedings of the National Academy of Sciences of the United States of America. 1997; 94 :12106–12111. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Both C, Bouwhuis S, Lessells CM, Visser ME. Climate change and population declines in a long-distance migratory bird. Nature. 2006; 441 :81–83. [ PubMed ] [ Google Scholar ]
• Boulding EG, Hay T. Genetic and demographic parameters determining population persistence after a discrete change in the environment. Heredity. 2001; 86 :313–324. [ PubMed ] [ Google Scholar ]
• Boyko AR, Quignon P, Li L, Schoenebeck JJ, Degenhardt JD, Lohmueller KE, Zhao K, et al. A simple genetic architecture underlies morphological variation in dogs. PLoS Biology. 2010; 8 :e1000451. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Bradshaw WE, Holzapfel CM. Genetic response to rapid climate change: it's seasonal timing that matters. Molecular Ecology. 2008; 17 :157–166. [ PubMed ] [ Google Scholar ]
• Brodie ED, Moore AJ, Janzen FJ. Visualizing and quantifying natural selection. Trends in Ecology & Evolution. 1995; 10 :313–318. [ PubMed ] [ Google Scholar ]
• Brown SP, Hochberg ME, Grenfell BT. Does multiple infection select for raised virulence? Trends in Microbiology. 2002; 10 :401–405. [ PubMed ] [ Google Scholar ]
• Butcher P, Southerton S. Marker-assisted selection in forestry species. In: Guimaraes EP, Ruane J, Scherf BD, Sonnino A, Dargie JD, editors. Marker-Assisted Selection, Current Status and Future Perspectives in Crops, Livestock, Forestry and Fish. Rome: Food and Agriculture Organization of the United Nations; 2007. pp. 283–305. [ Google Scholar ]
• Carrière Y, Tabashnik BE. Reversing insect adaptation to transgenic insecticidal plants. Proceedings of the Royal Society of London Series B-Biological Sciences. 2001; 268 :1475–1480. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Carroll SP, Watters JV. Managing phenotypic variability with genetic and environmental heterogeneity: adaptation as a first principle of conservation practice. In: Carroll SP, Fox CW, editors. Conservation Biology: Evolution in Action. Oxford: Oxford University Press; 2008. pp. 181–198. [ Google Scholar ]
• Carroll SP, Dingle H, Famula TR. Rapid appearance of epistasis during adaptive divergence following colonization. Proceedings of the Royal Society of London Series B-Biological Sciences. 2003; 270 :S80–S83. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Carroll SP, Loye JE, Dingle H, Mathieson M, Famula TR, Zalucki MP. And the beak shall inherit – evolution in response to invasion. Ecology Letters. 2005; 8 :944–951. [ PubMed ] [ Google Scholar ]
• Carroll SP. Conciliation Biology: on the eco-evolutionary management of permanently invaded biotic systems. Evolutionary Applications. 2011; 4 :184–199. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cha SJ, Mori A, Chadee DD, Severson DW. Cage trials using an endogenous meiotic drive gene in the mosquito Aedes aegypti to promote population replacement. American Journal of Tropical Medicine and Hygiene. 2006; 74 :62–68. [ PubMed ] [ Google Scholar ]
• Chan YF, Marks ME, Jones FC, Villarreal G, Shapiro MD, Brady SD, Southwick AM, et al. Adaptive evolution of pelvic reduction in sticklebacks by recurrent deletion of a Pitx1 enhancer. Science. 2010; 327 :302–305. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cheverud JM, Routman EJ. Epistasis as a source of increased additive genetic variance at population bottlenecks. Evolution. 1996; 50 :1042–1051. [ PubMed ] [ Google Scholar ]
• Clutton-Brock T. Reproductive Success. Chicago: Chicago University Press; 1999. [ Google Scholar ]
• Coker RJ. Multidrug-resistant tuberculosis: public health challenges. Tropical Medicine & International Health. 2004; 9 :25–40. [ PubMed ] [ Google Scholar ]
• Colosimo PF, Peichel CL, Nereng K, Blackman BK, Shapiro MD, Schluter D, Kingsley DM. The genetic architecture of parallel armor plate reduction in threespine sticklebacks. PLoS Biology. 2004; 2 :635–641. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Colosimo PF, Hosemann KE, Balabhadra S, Villarreal G, Dickson M, Grimwood J, Schmutz J, et al. Widespread parallel evolution in sticklebacks by repeated fixation of ectodysplasin alleles. Science. 2005; 307 :1928–1933. [ PubMed ] [ Google Scholar ]
• Conover DO, Munch SB. Sustaining fisheries yields over evolutionary time scales. Science. 2002; 297 :94–96. [ PubMed ] [ Google Scholar ]
• Conover DO, Schultz ET. Phenotypic similarity and the evolutionary significance of countergradient variation. Trends in Ecology & Evolution. 1995; 10 :248–252. [ PubMed ] [ Google Scholar ]
• Cotter D, O'Donovan V, O'Maoiléidigh N, Rogan G, Roche N, Wilkins NP. An evaluation of the use of triploid Atlantic salmon ( Salmo salar L.) in minimising the impact of escaped farmed salmon on wild populations. Aquaculture. 2000; 186 :61–75. [ Google Scholar ]
• Cox JG, Lima SL. Naiveté and an aquatic-terrestrial dichotomy in the effects of introduced predators. Trends in Ecology & Evolution. 2006; 21 :674–680. [ PubMed ] [ Google Scholar ]
• Crespi BJ. The emergence of human-evolutionary medical genomics. Evolutionary Applications. 2011; 4 :292–314. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Crispo E, DiBattista JD, Correa C, Thibert-Plante X, McKellar AE, Schwartz AK, Berner D, et al. The evolution of phenotypic plasticity in response to anthropogenic disturbance. Evolutionary Ecology Research. 2010; 12 :47–66. [ Google Scholar ]
• Crnokrak P, Barrett SCH. Purging the genetic load: a review of the experimental evidence. Evolution. 2002; 56 :2347–2358. [ PubMed ] [ Google Scholar ]
• Dagan T, Martin W. Ancestral genome sizes specify the minimum rate of lateral gene transfer during prokaryote evolution. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104 :870–875. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Darimont CT, Carlson SM, Kinnison MT, Paquet PC, Reimchen TE, Wilmers CC. Human predators outpace other agents of trait change in the wild. Proceedings of the National Academy of Sciences of the United States of America. 2009; 106 :952–954. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Davies AG, Game AY, Chen ZZ, Williams TJ, Goodall S, Yen JL, McKenzie JA, et al. Scalloped wings is the Lucilia cuprina Notch homologue and a candidate for the Modifier of fitness and asymmetry of diazinon resistance. Genetics. 1996; 143 :1321–1337. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Denison RF, Kiers ET, West SA. Darwinian agriculture: when can humans find solutions beyond the reach of natural selection? Quarterly Review of Biology. 2003; 78 :145–168. [ PubMed ] [ Google Scholar ]
• Denison RF. Past evolutionary tradeoffs represent opportunities for crop genetic improvement and increased human lifespan. Evolutionary Applications. 2011; 4 :216–224. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• DeWitt TJ. Costs and limits of phenotypic plasticity: tests with predator-induced morphology and life history in a freshwater snail. Journal of Evolutionary Biology. 1998; 11 :465–480. [ Google Scholar ]
• Dieckmann U, Heino M. Probabilistic maturation reaction norms: their history, strengths, and limitations. Marine Ecology Progress Series. 2007; 335 :253–269. [ Google Scholar ]
• Donald CM. Breeding of crop ideotypes. Euphytica. 1968; 17 :385–403. [ Google Scholar ]
• Downes S, Mahon RJ, Rossiter L, Kauter G, Leven T, Fitt G, Baker G. Adaptive management of pest resistance by Helicoverpa species (Noctuidae) in Australia to the Cry2Ab Bt toxin in Bollgard II® cotton. Evolutionary Applications. 2010; 3 :574–584. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dunlop ES, Enberg K, Jørgensen C, Heino M. Toward Darwinian fisheries management. Evolutionary Applications. 2009; 2 :246–259. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Dwyer G, Levin SA, Buttel L. A simulation model of the population dynamics and evolution of myxomatosis. Ecological Monographs. 1990; 60 :423–447. [ Google Scholar ]
• Edelaar P, Siepielski AM, Clobert J. Matching habitat choice causes directed gene flow: a neglected dimension in evolution and ecology. Evolution. 2008; 62 :2462–2472. [ PubMed ] [ Google Scholar ]
• Einum S, Fleming IA. Highly fecund mothers sacrifice offspring survival to maximize fitness. Nature. 2000; 405 :565–567. [ PubMed ] [ Google Scholar ]
• Elena SF, Miralles RF, Cuevas JM, Turner PE, Moya A. The two faces of mutation: extinction and adaptation in RNA viruses. IUBMB Life. 2000; 49 :5–9. [ PubMed ] [ Google Scholar ]
• Ellstrand NC. When transgenes wander, should we worry? Plant Physiology. 2001; 125 :1543–1545. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Enberg K, Jørgensen C, Dunlop ES, Heino M, Dieckmann U. Implications of fisheries-induced evolution for stock rebuilding and recovery. Evolutionary Applications. 2009; 2 :394–414. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Endler AJ. Natural Selection in the Wild. Princeton, NJ: Princeton University Press; 1986. [ Google Scholar ]
• Estes S, Arnold SJ. Resolving the paradox of stasis: models with stabilizing selection explain evolutionary divergence on all timescales. American Naturalist. 2007; 169 :227–244. [ PubMed ] [ Google Scholar ]
• Etterson JR, Shaw RG. Constraint to adaptive evolution in response to global warming. Science. 2001; 294 :151–154. [ PubMed ] [ Google Scholar ]
• Ezard THG, Côté SD, elletier F. Eco-evolutionary dynamics: disentangling phenotypic, environmental and population fluctuations. Philosophical Transactions of the Royal Society B-Biological Sciences. 2009; 364 :1491–1498. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Facon B, Genton BJ, Shykoff J, Jarne P, Estoup A, David P. A general eco-evolutionary framework for understanding bioinvasions. Trends in Ecology and Evolution. 2006; 21 :130–135. [ PubMed ] [ Google Scholar ]
• Fahrig L. Non-optimal animal movement in human-altered landscapes. Functional Ecology. 2007; 21 :1003–1015. [ Google Scholar ]
• Faith DP, Magallón S, Hendry AP, Conti E, Yahara T, Donoghue MJ. Evosystem services: an evolutionary perspective on the links between biodiversity and human well-being. Current Opinion in Environmental Sustainability. 2010; 2 :1–9. [ Google Scholar ]
• Fitt GP. Have Bt crops led to changes in insecticide use patterns and impacted IPM? In: Romeis J, Shelton AM, Kennedy GG, editors. Integration of Insect-Resistant GM crops within IPM Programs. New York: Springer; 2008. pp. 303–328. [ Google Scholar ]
• Forest F, Grenyer R, Rouget M, Davies TJ, Cowling RM, Faith DP, Balmford A, et al. Preserving the evolutionary potential of floras in biodiversity hotspots. Nature. 2007; 445 :757–760. [ PubMed ] [ Google Scholar ]
• Frank SA. Models of parasite virulence. Quarterly Review of Biology. 1996; 71 :37–78. [ PubMed ] [ Google Scholar ]
• Franklin CE, Johnston IA, Crockford T, Kamunde C. Scaling of oxygen consumption of Lake Magadi tilapia, a fish living at 37°C. Journal of Fish Biology. 1995; 46 :829–834. [ Google Scholar ]
• Frid A, Dill L. Human-caused disturbance stimuli as a form of predation risk. Conservation Ecology. 2002; 6 :11. [ Google Scholar ]
• Fritts TH, Rodda GH. The role of introduced species in the degradation of island ecosystems: a case history of Guam. Annual Review of Ecology and Systematics. 1998; 29 :113–140. [ Google Scholar ]
• Fussmann GF, Loreau M, Abrams PA. Eco-evolutionary dynamics of communities and ecosystems. Functional Ecology. 2007; 21 :465–477. [ Google Scholar ]
• Futuyma DJ. The uses of evolutionary biology. Science. 1995; 267 :41–42. [ PubMed ] [ Google Scholar ]
• Galvani AP, Novembre J. The evolutionary history of the CCR5-Delta 32 HIV-resistance mutation. Microbes and Infection. 2005; 7 :302–309. [ PubMed ] [ Google Scholar ]
• Garant D, Forde SE, Hendry AP. The multifarious effects of dispersal and gene flow on contemporary adaptation. Functional Ecology. 2007; 21 :434–443. [ Google Scholar ]
• Ghalambor CK, McKay JK, Carroll SP, Reznick DN. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Functional Ecology. 2007; 21 :394–407. [ Google Scholar ]
• Gienapp P, Teplitsky C, Alho JS, Mills JA, Merilä J. Climate change and evolution: disentangling environmental and genetic responses. Molecular Ecology. 2008; 17 :167–178. [ PubMed ] [ Google Scholar ]
• Gluckman P, Beedle A, Hanson M. Principles of Evolutionary Medicine. Oxford: Oxford University Press; 2009a. [ Google Scholar ]
• Gluckman PD, Hanson MA, Bateson P, Beedle AS, Law CM, Bhutta ZA, Anokhin KV, et al. Towards a new developmental synthesis: adaptive developmental plasticity and human disease. Lancet. 2009b; 373 :1654–1657. [ PubMed ] [ Google Scholar ]
• Gluckman PD, Low FM, Buklijas T, Hanson MA, Beedle AS. How evolutionary principles improve the understanding of human health and disease. Evolutionary Applications. 2011; 4 :249–263. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gomulkiewicz R, Holt RD. When does evolution by natural selection prevent extinction. Evolution. 1995; 49 :201–207. [ PubMed ] [ Google Scholar ]
• Gordon SP, Reznick DN, Kinnison MT, Bryant MJ, Weese DJ, Räsänen K, Millar NP, et al. Adaptive changes in life history and survival following a new guppy introduction. American Naturalist. 2009; 174 :34–45. [ PubMed ] [ Google Scholar ]
• Grant PR, Grant BR. Evolution of character displacement in Darwin's finches. Science. 2006; 313 :224–226. [ PubMed ] [ Google Scholar ]
• Grether GF. Environmental change, phenotypic plasticity, and genetic compensation. American Naturalist. 2005; 166 :E115–E123. [ PubMed ] [ Google Scholar ]
• Gur A, Zamir D. Unused natural variation can lift yield barriers in plant breeding. PLoS Biology. 2004; 2 :1610–1615. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hansen TF. Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. BioSystems. 2003; 69 :83–94. [ PubMed ] [ Google Scholar ]
• Hansen TF, Houle D. Measuring and comparing evolvability and constraint in multivariate characters. Journal of Evolutionary Biology. 2008; 21 :1201–1219. [ PubMed ] [ Google Scholar ]
• Harmon LJ, Matthews B, Des Roches S, Chase JM, Shurin JB, Schluter D. Evolutionary diversification in stickleback affects ecosystem functioning. Nature. 2009; 458 :1167–1170. [ PubMed ] [ Google Scholar ]
• Harper JL. Population Biology of Plants. London: Academic Press; 1977. [ Google Scholar ]
• Harris RB, Wall WA, Allendorf FW. Genetic consequences of hunting: what do we know and what should we do? Wildlife Society Bulletin. 2002; 30 :634–643. [ Google Scholar ]
• Hartley CJ, Newcomb RD, Russell RJ, Yong CG, Stevens JR, Yeates DK, La Salle J, et al. Amplification of DNA from preserved specimens shows blowflies were preadapted for the rapid evolution of insecticide resistance. Proceedings of the National Academy of Sciences of the United States of America. 2006; 103 :8757–8762. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Heap IM. The occurrence of herbicide-resistant weeds worldwide. Pesticide Science. 1997; 51 :235–243. [ Google Scholar ]
• Heath DD, Heath JW, Bryden CA, Johnson RM, Fox CW. Rapid evolution of egg size in captive salmon. Science. 2003; 299 :1738–1740. [ PubMed ] [ Google Scholar ]
• Hedrick PW. Gene flow and genetic restoration – the Florida Panther as a case study. Conservation Biology. 1995; 9 :996–1007. [ PubMed ] [ Google Scholar ]
• Heino M. Management of evolving fish stocks. Canadian Journal of Fisheries and Aquatic Sciences. 1998; 55 :1971–1982. [ Google Scholar ]
• Heino M, Hanski I. Evolution of migration rate in a spatially realistic metapopulation model. American Naturalist. 2001; 157 :495–511. [ PubMed ] [ Google Scholar ]
• Hellmann JJ, Pineda-Krch M. Constraints and reinforcement on adaptation under climate change: selection of genetically correlated traits. Biological Conservation. 2007; 137 :599–609. [ Google Scholar ]
• Hemingway J, Ranson H. Insecticide resistance in insect vectors of human disease. Annual Review of Entomology. 2000; 45 :371–391. [ PubMed ] [ Google Scholar ]
• Hendry AP, Gonzalez A. Whither adaptation? Biology & Philosophy. 2008; 23 :673–699. [ Google Scholar ]
• Hendry AP, Kinnison MT. The pace of modern life: measuring rates of contemporary microevolution. Evolution. 1999; 53 :1637–1653. [ PubMed ] [ Google Scholar ]
• Hendry AP, Taylor EB. How much of the variation in adaptive divergence can be explained by gene flow? An evaluation using lake-stream stickleback pairs. Evolution. 2004; 58 :2319–2331. [ PubMed ] [ Google Scholar ]
• Hendry AP, Farrugia TJ, Kinnison MT. Human influences on rates of phenotypic change in wild animal populations. Molecular Ecology. 2008; 17 :20–29. [ PubMed ] [ Google Scholar ]
• Hendry AP, Lohmann LG, Conti E, Cracraft J, Crandall KA, Faith DP, Hauser C, et al. Evolutionary biology in biodiversity science, conservation, and policy: a call to action. Evolution. 2010; 64 :1517–1528. [ PubMed ] [ Google Scholar ]
• Henneman ML, Memmott J. Infiltration of a Hawaiian community by introduced biological control agents. Science. 2001; 293 :1314–1316. [ PubMed ] [ Google Scholar ]
• Hereford J. A quantitative survey of local adaptation and fitness trade-offs. American Naturalist. 2009; 173 :579–588. [ PubMed ] [ Google Scholar ]
• Hersch EI, Phillips PC. Power and potential bias in field studies of natural selection. Evolution. 2004; 58 :479–485. [ PubMed ] [ Google Scholar ]
• Hilder VA, Boulter D. Genetic engineering of crop plants for insect resistance – a critical review. Crop Protection. 1999; 18 :177–191. [ Google Scholar ]
• Hindar K, Fleming IA, McGinnity P, Diserud A. Genetic and ecological effects of salmon farming on wild salmon: modelling from experimental results. ICES Journal of Marine Science. 2006; 63 :1234–1247. [ Google Scholar ]
• Hoffmann AA, Merilä J. Heritable variation and evolution under favourable and unfavourable conditions. Trends in Ecology & Evolution. 1999; 14 :96–101. [ PubMed ] [ Google Scholar ]
• Hogervorst T, Bouma HW, de Vos J. Evolution of the hip and pelvis. Acta Orthopaedica. 2009; 80 :1–39. [ PubMed ] [ Google Scholar ]
• Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA, Cresko WA. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genetics. 2010; 6 :e10000862. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Houle D. Comparing evolvability and variability of quantitative traits. Genetics. 1992; 130 :195–204. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Houle D. Numbering the hairs on our heads: the shared challenge and promise of phenomics. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107 :1793–1799. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Huang YX, Magori K, Lloyd AL, Gould F. Introducing transgenes into insect populations using combined gene-drive strategies: modeling and analysis. Insect Biochemistry and Molecular Biology. 2007; 37 :1054–1063. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ito J, Ghosh A, Moreira LA, Wimmer EA, Jacobs-Lorena M. Transgenic anopheline mosquitoes impaired in transmission of a malaria parasite. Nature. 2002; 417 :452–455. [ PubMed ] [ Google Scholar ]
• Jablonski NG. The evolution of human skin and skin color. Annual Review of Anthropology. 2004; 33 :585–623. [ Google Scholar ]
• Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nature Reviews Genetics. 2007; 8 :253–262. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Johnson MTJ, Vellend M, Stinchcombe JR. Evolution in plant populations as a driver of ecological changes in arthropod communities. Philosophical Transactions of the Royal Society B-Biological Sciences. 2009; 364 :1593–1605. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jørgensen C, Enberg K, Dunlop ES, Arlinghaus R, Boukal DS, Brander K, Ernande B, et al. Managing evolving fish stocks. Science. 2007; 318 :1247–1248. [ PubMed ] [ Google Scholar ]
• Kaeuffer R, Coltman DW, Chapuis JL, Pontier D, Realé D. Unexpected heterozygosity in an island mouflon population founded by a single pair of individuals. Proceedings of the Royal Society B-Biological Sciences. 2007; 274 :527–533. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecology Letters. 2004; 7 :1225–1241. [ Google Scholar ]
• Keller L. Levels of Selection in Evolution: Monographs in Behavior and Ecology. Princeton: Princeton University Press; 1999. [ Google Scholar ]
• Keller LF, Waller DM. Inbreeding effects in wild populations. Trends in Ecology & Evolution. 2002; 17 :230–241. [ Google Scholar ]
• Keller LF, Jeffery KJ, Arcese P, Beaumont MA, Hochachka WM, Smith JNM, Bruford MW. Immigration and the ephemerality of a natural population bottleneck: evidence from molecular markers. Proceedings of the Royal Society of London Series B-Biological Sciences. 2001; 268 :1387–1394. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kellermann V, van Heerwaarden B, Sgrò CM, Hoffmann AA. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science. 2009; 325 :1244–1246. [ PubMed ] [ Google Scholar ]
• Kingsolver JG, Hoekstra HE, Hoekstra JM, Berrigan D, Vignieri SN, Hill CE, Hoang A, et al. The strength of phenotypic selection in natural populations. American Naturalist. 2001; 157 :245–261. [ PubMed ] [ Google Scholar ]
• Kinnison MT, Hairston NG., Jr Eco-evolutionary conservation biology: contemporary evolution and the dynamics of persistence. Functional Ecology. 2007; 21 :444–454. [ Google Scholar ]
• Kinnison MT, Unwin MJ, Quinn TP. Eco-evolutionary vs. habitat contributions to invasion in salmon: experimental evaluation in the wild. Molecular Ecology. 2008; 17 :405–414. [ PubMed ] [ Google Scholar ]
• van Klinken RD, Edwards OR. Is host-specificity of weed biological control agents likely to evolve rapidly following establishment? Ecology Letters. 2002; 5 :590–596. [ Google Scholar ]
• Koella JC, Lynch PA, Thomas MB, Read AF. Towards evolution-proof malaria control with insecticides. Evolutionary Applications. 2009; 2 :469–480. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kotiaho JS, Kaitala V, Komonen A, Päivinen J. Predicting the risk of extinction from shared ecological characteristics. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102 :1963–1967. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krieger RI, Feeny PP, Wilkinson CF. Detoxication enzymes in guts of caterpillars – evolutionary answer to plant defenses. Science. 1971; 172 :579–581. [ PubMed ] [ Google Scholar ]
• Lahti DC, Johnson NA, Ajie BC, Otto SP, Hendry AP, Blumstein DT, Coss RG, et al. Relaxed selection in the wild. Trends in Ecology & Evolution. 2009; 24 :487–496. [ PubMed ] [ Google Scholar ]
• Lal R, Pandey G, Sharma P, Kumari K, Malhotra S, Pandey R, Raina V, et al. Biochemistry of microbial degradation of hexachlorocyclohexane and prospects for bioremediation. Microbiology and Molecular Biology Reviews. 2010; 74 :58–80. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lande R. Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. Journal of Evolutionary Biology. 2009; 22 :1435–1446. [ PubMed ] [ Google Scholar ]
• Lande R, Arnold SJ. The measurement of selection on correlated characters. Evolution. 1983; 37 :1210–1226. [ PubMed ] [ Google Scholar ]
• Lankau RA, Strauss SY. Newly rare or newly common: Evolutionary feedbacks through changes in population density and relative species abundance and their management implications. Evolutionary Applications. 2011; 4 :338–353. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lankau RA, Jørgensen PS, Harris DJ, Sih A. Incorporating evolutionary principles into environmental management and policy. Evolutionary Applications. 2011; 4 :315–325. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Latta RG. Differentiation of allelic frequencies at quantitative trait loci affecting locally adaptive traits. American Naturalist. 1998; 151 :283–292. [ PubMed ] [ Google Scholar ]
• Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, Hauge B, Lai CQ, et al. The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics. 2004; 168 :2141–2155. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Law R, Grey DR. Evolution of yields from populations with age-specific cropping. Evolutionary Ecology. 1989; 3 :343–359. [ Google Scholar ]
• Lerat E, Daubin V, Ochman H, Moran NA. Evolutionary origins of genomic repertoires in bacteria. PLoS Biology. 2005; 3 :807–814. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Levin BR, Perrot V, Walker N. Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics. 2000; 154 :985–997. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Little SJ, Holte S, Routy JP, Daar ES, Markowitz M, Collier AC, Koup RA, et al. Antiretroviral-drug resistance among patients recently infected with HIV. New England Journal of Medicine. 2002; 347 :385–394. [ PubMed ] [ Google Scholar ]
• Long GH, Graham AL. Consequences of immunopathology for pathogen virulence evolution and public health: malaria as a case study. Evolutionary Applications. 2011; 4 :278–291. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Louda SM, Kendall D, Connor J, Simberloff D. Ecological effects of an insect introduced for the biological control of weeds. Science. 1997; 277 :1088–1090. [ Google Scholar ]
• Lynch M, Conery J, Bürger R. Mutation accumulation and the extinction of small populations. American Naturalist. 1995; 146 :489–518. [ Google Scholar ]
• Mann CC. Future food – bioengineering – genetic engineers aim to soup up crop photosynthesis. Science. 1999; 283 :314–316. [ PubMed ] [ Google Scholar ]
• Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, et al. Finding the missing heritability of complex diseases. Nature. 2009; 461 :747–753. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marnocha E, Pollinger J, Smith TB. Human-induced morphological shifts in an island lizard. Evolutionary Applications. 2011; 4 :388–396. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marvier M. Implications of transgene escape for conservation. In: Carroll SP, Fox CW, editors. Conservation Biology: Evolution in Action. Oxford: Oxford University Press; 2008. pp. 297–307. [ Google Scholar ]
• McGuigan K, Sgrò CM. Evolutionary consequences of cryptic genetic variation. Trends in Ecology & Evolution. 2009; 24 :305–311. [ PubMed ] [ Google Scholar ]
• McMeniman CJ, Lane RV, Cass BN, Fong AWC, Sidhu M, Wang YF, O'Neill SL. Stable introduction of a life-shortening Wolbachia Infection into the mosquito Aedes aegypti . Science. 2009; 323 :141–144. [ PubMed ] [ Google Scholar ]
• Medawar PB. An Unsolved Problem in Biology. London: H.K. Lewis and Co; 1952. [ Google Scholar ]
• Miller CT, Beleza S, Pollen AA, Schluter D, Kittles RA, Shriver MD, Kingsley DM. cis-regulatory changes in kit ligand expression and parallel evolution of pigmentation in sticklebacks and humans. Cell. 2007; 131 :1179–1189. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Miralles DJ, Slafer GA. Yield, biomass and yield components in dwarf, semi-dwarf and tall isogenic lines of spring wheat under recommended and late sowing dates. Plant Breeding. 1995; 114 :392–396. [ Google Scholar ]
• Moose SP, Dudley JW, Rocheford TR. Maize selection passes the century mark: a unique resource for 21st century genomics. Trends in Plant Science. 2004; 9 :358–364. [ PubMed ] [ Google Scholar ]
• Moritz C. Defining ‘evolutionarily significant units’ for conservation. Trends in Ecology & Evolution. 1994; 9 :373–375. [ PubMed ] [ Google Scholar ]
• Mousseau TA, Roff DA. Natural selection and the heritability of fitness components. Heredity. 1987; 59 :181–197. [ PubMed ] [ Google Scholar ]
• Muller HJ. The relation of recombination to mutational advance. Mutation Research. 1964; 1 :2–9. [ PubMed ] [ Google Scholar ]
• Myles S, Bouzekri N, Haverfield E, Cherkaoui M, Dugoujon JM, Ward R. Genetic evidence in support of a shared Eurasian-North African dairying origin. Human Genetics. 2005; 117 :34–42. [ PubMed ] [ Google Scholar ]
• Nesse RM, Williams GC. Evolution and the origins of disease. Scientific American. 1998; 279 :86–93. [ PubMed ] [ Google Scholar ]
• Neve P, Vila-Aiub M, Roux F. Evolutionary-thinking in agricultural weed management. New Phytologist. 2009; 184 :783–793. [ PubMed ] [ Google Scholar ]
• Normark BH, Normark S. Evolution and spread of antibiotic resistance. Journal of Internal Medicine. 2002; 252 :91–106. [ PubMed ] [ Google Scholar ]
• Olsen EM, Heino M, Lilly GR, Morgan MJ, Brattey J, Ernande B, Dieckmann U. Maturation trends indicative of rapid evolution preceded the collapse of northern cod. Nature. 2004; 428 :932–935. [ PubMed ] [ Google Scholar ]
• Omenn GS. Evolution and public health. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107 :1702–1709. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Orr HA. The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution. 1998; 52 :935–949. [ PubMed ] [ Google Scholar ]
• Orr HA, Unckless RL. Population extinction and the genetics of adaptation. American Naturalist. 2008; 172 :160–169. [ PubMed ] [ Google Scholar ]
• Ortiz-Monasterio JI, Sayre KD, Rajaram S, McMahon M. Genetic progess in wheat yield and nitrogen use efficiency under four nitrogen rates. Crop Science. 1997; 37 :898–904. [ Google Scholar ]
• Palkovacs EP, Post DM. Experimental evidence that phenotypic divergence in predators drives community divergence in prey. Ecology. 2009; 90 :300–305. [ PubMed ] [ Google Scholar ]
• Palkovacs EP, Marshall MC, Lamphere BA, Lynch BR, Weese DJ, Fraser DF, Reznick DN, et al. Experimental evaluation of evolution and coevolution as agents of ecosystem change in Trinidadian streams. Philosophical Transactions of the Royal Society B-Biological Sciences. 2009; 364 :1617–1628. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Palumbi SR. Humans as the world's greatest evolutionary force. Science. 2001; 293 :1786–1790. [ PubMed ] [ Google Scholar ]
• Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003; 421 :37–42. [ PubMed ] [ Google Scholar ]
• Pauw A. Collapse of a pollination web in small conservation areas. Ecology. 2007; 88 :1759–1769. [ PubMed ] [ Google Scholar ]
• Pelletier F, Clutton-Brock T, Pemberton J, Tuljapurkar S, Coulson T. The evolutionary demography of ecological change: linking trait variation and population growth. Science. 2007; 315 :1571–1574. [ PubMed ] [ Google Scholar ]
• Pelletier F, Garant D, Hendry AP. Eco-evolutionary dynamics. Philosophical Transactions of the Royal Society B-Biological Sciences. 2009; 364 :1483–1489. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Peltonen L, Jalanko A, Varilo T. Molecular genetics of the Finnish disease heritage. Human Molecular Genetics. 1999; 8 :1913–1923. [ PubMed ] [ Google Scholar ]
• Pelz HJ, Rost S, Hünerberg M, Fregin A, Heiberg AC, Baert K, MacNicoll AD, et al. The genetic basis of resistance to anticoagulants in rodents. Genetics. 2005; 170 :1839–1847. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pepper JW, Findlay CS, Kassen R, Spencer SL, Maley CC. Cancer research meets evolutionary biology. Evolutionary Applications. 2009; 2 :62–70. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pergams ORW, Lacy RC. Rapid morphological and genetic change in Chicago-area Peromyscus . Molecular Ecology. 2008; 17 :450–463. [ PubMed ] [ Google Scholar ]
• Perron GG, Zasloff M, Bell G. Experimental evolution of resistance to an antimicrobial peptide. Proceedings of the Royal Society B-Biological Sciences. 2005; 273 :251–256. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Phillimore AB, Hadfield JD, Jones OR, Smithers RJ. Differences in spawning date between populations of common frog reveal local adaptation. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107 :8292–8297. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pimm SL, Dollar L, Bass OL., Jr The genetic rescue of the Florida panther. Animal Conservation. 2006; 9 :115–122. [ Google Scholar ]
• Pörtner HO, Knust R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science. 2007; 315 :95–97. [ PubMed ] [ Google Scholar ]
• Price TD, Qvarnström A, Irwin DE. The role of phenotypic plasticity in driving genetic evolution. Proceedings of the Royal Society of London Series B-Biological Sciences. 2003; 270 :1433–1440. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rankin DJ, López-Sepulcre A. Can adaptation lead to extinction? Oikos. 2005; 111 :616–619. [ Google Scholar ]
• Räsänen K, Kruuk LEB. Maternal effects and evolution at ecological time-scales. Functional Ecology. 2007; 21 :408–421. [ Google Scholar ]
• Raymond M, Berticat C, Weill M, Pasteur N, Chevillon C. Insecticide resistance in the mosquito Culex pipiens: what have we learned about adaptation? Genetica. 2001; 112 :287–296. [ PubMed ] [ Google Scholar ]
• Read AF, Lynch PA, Thomas MB. How to make evolution-proof insecticides for malaria control. PLoS Biology. 2009; 7 :e1000058. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reed DH, Frankham R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution. 2001; 55 :1095–1103. [ PubMed ] [ Google Scholar ]
• Reznick DN, Ghalambor CK. Selection in nature: experimental manipulations of natural populations. Integrative and Comparative Biology. 2005; 45 :456–462. [ PubMed ] [ Google Scholar ]
• Ricciardi A, Ward JM. Comment on “Opposing effects of native and exotic herbivores on plant invasions” Science. 2006; 313 :298a. [ PubMed ] [ Google Scholar ]
• Roach JC, Glusman G, Smit AFA, Huff CD, Hubley R, Shannon PT, Rowen L, et al. Analysis of genetic inheritance in a family quartet by whole-genome sequencing. Science. 2010; 328 :636–639. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• de Roode JC, Pansini R, Cheesman SJ, Helinski MEH, Huijben S, Wargo AR, Bell AS, et al. Virulence and competitive ability in genetically diverse malaria infections. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102 :7624–7628. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Roush RT. Two-toxin strategies for management of insecticidal transgenic crops: can pyramiding succeed where pesticide mixtures have not? Philosophical Transactions of the Royal Society of London Series B-Biological Sciences. 1998; 353 :1777–1786. [ Google Scholar ]
• Russell RJ, Scott C, Jackson CJ, Pandey R, Pandey G, Taylor MC, Coppin CW, et al. The evolution of new enzyme function: lessons from xenobiotic metabolising bacteria versus insecticide resistant insects. Evolutionary Applications. 2011; 4 :225–248. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Saccheri I, Hanski I. Natural selection and population dynamics. Trends in Ecology & Evolution. 2006; 21 :341–347. [ PubMed ] [ Google Scholar ]
• Saccheri I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I. Inbreeding and extinction in a butterfly metapopulation. Nature. 1998; 392 :491–494. [ Google Scholar ]
• Sax DF, Brown JH. The paradox of invasion. Global Ecology and Biogeography. 2000; 9 :363–371. [ Google Scholar ]
• Schieving F, Poorter H. Carbon gain in a multispecies canopy: the role of specific leaf area and photosynthetic nitrogen-use efficiency in the tragedy of the commons. New Phytologist. 1999; 143 :201–211. [ Google Scholar ]
• Schlaepfer MA, Runge MC, Sherman PW. Ecological and evolutionary traps. Trends in Ecology & Evolution. 2002; 17 :474–480. [ Google Scholar ]
• Schluter D. The Ecology of Adaptive Radiation. Oxford: Oxford University Press; 2000. [ Google Scholar ]
• Schoustra SE, Bataillon T, Gifford DR, Kassen R. The properties of adaptive walks in evolving populations of fungus. PLoS Biology. 2009; 7 :e10000250. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sgrò CM, Lowe AJ, Hoffmann AA. Building evolutionary resilience for conserving biodiversity under climate change. Evolutionary Applications. 2011; 4 :326–337. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shankarappa R, Margolick JB, Gange SJ, Rodrigo AG, Upchurch D, Farzadegan H, Gupta P, et al. Consistent viral evolutionary changes associated with the progression of human immunodeficiency virus type 1 infection. Journal of Virology. 1999; 73 :10489–10502. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Shapiro MD, Marks ME, Peichel CL, Blackman BK, Nereng KS, Jónsson B, Schluter D, et al. Genetic and developmental basis of evolutionary pelvic reduction in threespine sticklebacks. Nature. 2004; 428 :717–723. [ PubMed ] [ Google Scholar ]
• Sharpe DMT, Hendry AP. Life history change in commercially exploited fish stocks: an analysis of trends across studies. Evolutionary Applications. 2009; 2 :260–275. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sibly RM, Winokur L, Smith RH. Interpopulation variation in phenotypic plasticity in the speckled wood butterfly, Pararge aegeria . Oikos. 1997; 78 :323–330. [ Google Scholar ]
• Siepielski AM, DiBattista JD, Carlson SM. It's about time: the temporal dynamics of phenotypic selection in the wild. Ecology Letters. 2009; 12 :1261–1276. [ PubMed ] [ Google Scholar ]
• Sih A, Gleeson SK. A limits-oriented approach to evolutionary ecology. Trends in Ecology & Evolution. 1995; 10 :378–382. [ PubMed ] [ Google Scholar ]
• Sih A, Bell AM, Johnson JC, Ziemba RE. Behavioral syndromes: an integrative overview. Quarterly Review of Biology. 2004; 79 :241–277. [ PubMed ] [ Google Scholar ]
• Sih A, Bolnick DI, Luttbeg B, Orrock JL, Peacor SD, Pintor LM, Preisser E, et al. Predator-prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos. 2010; 119 :610–621. [ Google Scholar ]
• Sih A, Ferrari MCO, Harris DJ. Evolution and behavioural responses to human-induced rapid environmental change. Evolutionary Applications. 2011; 4 :367–387. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Skelly DK, Joseph LN, Possingham HP, Freidenburg LK, Farrugia TJ, Kinnison MT, Hendry AP. Evolutionary responses to climate change. Conservation Biology. 2007; 21 :1353–1355. [ PubMed ] [ Google Scholar ]
• Smith TB, Bernatchez L. Evolutionary change in human-altered environments. Molecular Ecology. 2008; 17 :1–8. [ PubMed ] [ Google Scholar ]
• Smith TB, Bruford MW, Wayne RK. The preservation of process: the missing element of conservation programs. Biodiversity Letters. 1993; 1 :164–167. [ Google Scholar ]
• Southerton SG, MacMillan CP, Bell JC, Bhuiyan N, Downes G, Ravenwood IC, Joyce KR, et al. Association of allelic variation in xylem genes with wood properties in Eucalyptus nitens (Deane & Maiden) Australian Forestry. 2010; 73 :259–264. [ Google Scholar ]
• Steadman DW. Prehistoric extinctions of Pacific Island birds – biodiversity meets zooarchaeology. Science. 1995; 267 :1123–1131. [ PubMed ] [ Google Scholar ]
• Stearns SC. The evolutionary significance of phenotypic plasticity – phenotypic sources of variation among organisms can be described by developmental switches and reaction norms. BioScience. 1989; 39 :436–445. [ Google Scholar ]
• Stearns SC, Koella JC. The evolution of phenotypic plasticity in life-history traits – predictions of reaction norms for age and size at maturity. Evolution. 1986; 40 :893–913. [ PubMed ] [ Google Scholar ]
• Strauss SY, Webb CO, Salamin N. Exotic taxa less related to native species are more invasive. Proceedings of the National Academy of Sciences of the United States of America. 2006; 103 :5841–5845. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tabashnik BE, Dennehy TJ, Carrière Y. Delayed resistance to transgenic cotton in pink bollworm. Proceedings of the National Academy of Sciences of the United States of America. 2005; 102 :15389–15393. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tabashnik BE, Gassmann AJ, Crowder DW, Carrière Y. Insect resistance to Bt crops: evidence versus theory. Nature Biotechnology. 2008; 26 :199–202. [ PubMed ] [ Google Scholar ]
• Tcherkez GGB, Farquhar GD, Andrews TJ. Despite slow catalysis and confused substrate specificity, all ribulose bisphosphate carboxylases may be nearly perfectly optimized. Proceedings of the National Academy of Sciences of the United States of America. 2006; 103 :7246–7251. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thomassen HA, Fuller T, Buermann W, Milá B, Kiewsetter CM, Jarrin-V P, Cameron SE, et al. Mapping evolutionary process: a multi-taxa approach to conservation prioritization. Evolutionary Applications. 2011; 4 :397–413. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thrall PT, Oakeshott JG, Fitt G, Southerton S, Burdon JJ, Sheppard A, Russell RJ, et al. Evolution in agriculture – directions and constraints on the application of evolutionary approaches to the management of agro-ecosystems. Evolutionary Applications. 2011; 4 :200–215. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Thumma BR, Southerton SG, Bell JC, Owen JV, Henery ML, Moran GF. Quantitative trait locus (QTL) analysis of wood quality traits in Eucalyptus nitens . Tree Genetics & Genomes. 2010; 6 :305–317. [ Google Scholar ]
• Tishkoff SA, Reed FA, Ranciaro A, Voight BF, Babbitt CC, Silverman JS, Powell K, et al. Convergent adaptation of human lactase persistence in Africa and Europe. Nature Genetics. 2007; 39 :31–40. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Tufto J. Gene flow from domesticated species to wild relatives: migration load in a model of multivariate selection. Evolution. 2010; 64 :180–192. [ PubMed ] [ Google Scholar ]
• Urban MC, Phillips BL, Skelly DK, Shine R. The cane toad's ( Chaunus [ Bufo ] marinus ) increasing ability to invade Australia is revealed by a dynamically updated range model. Proceedings of the Royal Society B-Biological Sciences. 2007; 274 :1413–1419. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Visscher PM, Hill WG, Wray NR. Heritability in the genomics era – concepts and misconceptions. Nature Reviews Genetics. 2008; 9 :255–266. [ PubMed ] [ Google Scholar ]
• Visser ME. Keeping up with a warming world: assessing the rate of adaptation to climate change. Proceedings of the Royal Society B-Biological Sciences. 2008; 275 :649–659. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wagner GP, Altenberg L. Complex adaptations and the evolution of evolvability. Evolution. 1996; 50 :967–976. [ PubMed ] [ Google Scholar ]
• Wallace B. Hard and soft selection revisited. Evolution. 1975; 29 :465–473. [ PubMed ] [ Google Scholar ]
• Waples RS. Pacific salmon, Oncorhynchus spp., and the definition of “species” under the endangered Species Act. Marine Fisheries Review. 1991; 53 :11–22. [ Google Scholar ]
• Webb C. A complete classification of Darwinian extinction in ecological interactions. American Naturalist. 2003; 161 :181–205. [ PubMed ] [ Google Scholar ]
• Weese DJ, Schwartz AK, Bentzen P, Hendry AP, Kinnison MT. Eco-evolutionary effects on population recovery following catastrophic disturbance. Evolutionary Applications. 2011; 4 :354–366. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Weiss KM. Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation. Genetics. 2008; 179 :1741–1756. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• West-Eberhard MJ. Developmental Plasticity and Evolution. Oxford: Oxford University Press; 2003. [ Google Scholar ]
• Westemeier RL, Brawn JD, Simpson SA, Esker TL, Jansen RW, Walk JW, Kershner EL, et al. Tracking the long-term decline and recovery of an isolated population. Science. 1998; 282 :1695–1698. [ PubMed ] [ Google Scholar ]
• Whalon ME, Mota-Sanchez D, Hollingworth RM. Global Pesticide Resistance in Arthropods. Oxfordshire: CABI; 2008. [ Google Scholar ]
• Whitham TG, Bailey JK, Schweitzer JA, Shuster SM, Bangert RK, LeRoy CJ, Lonsdorf EV, et al. A framework for community and ecosystem genetics: from genes to ecosystems. Nature Reviews Genetics. 2006; 7 :510–523. [ PubMed ] [ Google Scholar ]
• Wilkins JF, Haig D. What good is genomic imprinting: the function of parent-specific gene expression. Nature Reviews Genetics. 2003; 4 :359–368. [ PubMed ] [ Google Scholar ]
• Willi Y, Van Buskirk J, Hoffmann AA. Limits to the adaptive potential of small populations. Annual Review of Ecology, Evolution, and Systematics. 2006; 37 :433–458. [ Google Scholar ]
• Williams GC. Pleiotropy, natural selection, and the evolution of senescence. Evolution. 1957; 11 :398–411. [ Google Scholar ]
• Willis CG, Ruhfel B, Primack RB, Miller-Rushing AJ, Davis CC. Phylogenetic patterns of species loss in Thoreau's woods are driven by climate change. Proceedings of the National Academy of Sciences of the United States of America. 2008; 105 :17029–17033. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Witzgall P, Kirsch P, Cork A. Sex pheromones and their impact on pest management. Journal of Chemical Ecology. 2010; 36 :80–100. [ PubMed ] [ Google Scholar ]
• Yang JA, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, et al. Common SNPs explain a large proportion of the heritability for human height. Nature Genetics. 2010; 42 :565–569. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Yuste E, Sánchez-Palomino S, Casado C, Domingo E, López-Galíndez C. Drastic fitness loss in human immunodeficiency virus type 1 upon serial bottleneck events. Journal of Virology. 1999; 73 :2745–2751. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zasloff M. Antimicrobial peptides of multicellular organisms. Nature. 2002; 415 :389–395. [ PubMed ] [ Google Scholar ]
• Zhang DY, Sun GJ, Jiang XH. Donald's ideotype and growth redundancy: a game theoretical analysis. Field Crops Research. 1999; 61 :179–187. [ Google Scholar ]

CRISPR gene drives and the future of evolution

via The Scientist

March 15, 2024

• #bioengineering
• #synthetic biology
• #microbiology
• Kevin Esvelt Associate Professor of Media Arts and Sciences; NEC Career Development Professor of Computer and Communications
• Mice Against Ticks - Preventing tick-borne disease by permanently immunizing mice
• Studying the evolution of gene drive systems
• Understanding Molecular Evolution
• Daisy Drives
• Evolving Decoy ACE2 Receptor Mimics As COVID-19 Therapeutics
• Media Lab Research Theme: Future Worlds
• Media Lab Research Theme: Connected Mind + Body

Share this article

By Hannah Thomasy, PhD

Today, Massachusetts Institute of Technology biologist Kevin Esvelt is well known for his work on guided evolution technologies—creating systems for evolving biomolecules in the lab and developing techniques to shape the evolutionary trajectories of species in the wild—as well as forging new pathways to safeguard these technologies from misuse.1,2

Esvelt’s entanglement with evolution began early. As a child, he visited the Galápagos, and was captivated by the islands’ stunning array of unique wildlife. “That sparked an interest in the evolution of creatures in the natural world,” said Esvelt. “It got me reading Darwin. And I started wondering—could we make things of comparable magnificence?”

So, when he joined David Liu’s research group at Harvard University for his graduate studies in 2004, he jumped into exploring how to put evolutionary processes to work in the lab. “I love solving problems that I am not actually smart enough to solve. And to do that, you need access to something that is effectively smarter than you, or at least can execute search strategies that you can’t,” said Esvelt. “One of the reasons I love playing with different disciplines is they all give you different lenses on the world. And my favorite lens, evolution, is really about search strategies through a hyper astronomically vast space.” 

Part 1: Gene Drive; Part 2: Gene Drive and Local Drive

iBiology features two introductory classes from CRISPR expert Kevin Esvelt, head of the Media Lab's Sculpting Evolution group.

Cracking the Genetic Code with Kevin Esvelt

Kevin Esvelt leads the Sculpting Evolution Group at MIT. Their work explores “evolutionary and ecological engineering and responsive science

The public doesn’t trust GMOs. Will it trust CRISPR?

There’s a huge opportunity to improve agriculture with gene editing. But we need to give CRISPR a chance.

Gene Editing: Last Week Tonight with John Oliver

Scientists are developing new ways to alter the genetic code of living organisms. John Oliver explores the risks and rewards.

What is evolutionary biology?

Hengpanhaleap L.

Checked : Heather M. , Emily M.

Latest Update 26 Jan, 2024

Table of content

Early days of evolutionary biology

Importance of evolutionary biology, challenges of evolutionary biology, the final words.

The evolutionary biology inspects transformative procedures (personal choice, across the board descending pattern, speciation) that have made a decent variety of life on earth. What is transformative science? During the 1930s, developmental science rose out of zones truly new to it, for example, advancement and topography, systematics and fossil science, through what Julian Huxley named the new elucidation union.

The extent of ebb and flow look into has extended to incorporate the genetic settlement engineering, atomic advancement and different powers, for example, sexual determination, hereditary float, and biogeographical improvement. Consequently, the later region of transformative formative science ("evo-devo") investigates how embryogenesis, undeveloped organism development, is dealt with, and along these lines, a more extensive union is given that coordinates developmental science with past developmental amalgamation fields of study.

After Darwin, the study of evolutions has developed profoundly. An evolutionary biologist is managing natural product flies; others with incipient organisms; some go on the field; others do all with a machine. A considerable amount of developmental science, with almost no relevant direct worth, is' open sky ' science. A portion of this is done in a down to earth way. All these assorted analyses tie us together to see how life functions. Transformative science can be depicted as a high rise that obliges the various natural territories.

The population must be educated about evolution. Maybe something looks tremendous and chose the ground, yet when you burrow further, issues and begin to rise. Of model, two developmental scientists can even possibly be as significant as how two new species will emerge in a specific situation, just because not all things are understood. Momentum inquires about in transformative science covers a scope of subjects and remembers hypotheses for different fields, including atomic hereditary qualities and software engineering.

A few parts of transformative research endeavour to clarify peculiarities severely talked about in ordinary developmental blend. The second is that organic researchers suggested a primary logical conversation starter: "what happened and when?." it included speculation, progresses in human reproduction, nature of collaboration, the formation of maturing and evolvability. It spreads paleobiology and frameworks and phylogenetics.

When nobody comprehended the sub-atomic premise of qualities, the advanced developmental combination was structured. The genomic structure for complex natural procedures, for example, adjustment and speciation are these days endeavoured by transformative scholars. We were searching for answers to addresses, the number of qualities included, the greatness of the impacts of every condition, the relationship of the outcomes between the various attributes, what the states do, the alterations which we produce (for instance, point-over-quality copies or even genome duplication). This points, by genome-wide connection, explore, to adjust the solid heritability saw in twin investigations with the intricacy of distinguishing which qualities are liable for that legacy.

One of the issues in inspecting genetic structure is that customary populace qualities that catalyze traditional transformative synthesis must be reexamined to consider present-day sub-atomic comprehension. It includes a few computational improvements in the feeling of the idea of atomic developments, which associate DNA succession information to the transformative hypothesis. The fourth component of the cutting edge transformative blend was the agreement about the variables that lead to development, however not its relative value. It is the thing that science is currently endeavouring to decide. It is the result of the study. It can be spoken to in the cutting edge developmental union. Ordinary, sexual, hereditary float, hereditary tract, formative imperatives, transformation predisposition and bioGeography structure developmental powers. Transformative power

A dynamic methodology is significant for many ebbs and flows to investigate, for example, in the idea of life history, on life form science and environment. Quality explanation and its capacity are, to a great extent, dependent on near approaches. The region of EDB ("evo-devo") investigates how transformative components work and dissect them in various species to decide if their development has advanced.

We Will Promptly Compose an Essay for You

The focal binding together rules in science is advancement. Crosswise in various ways, hereditary qualities can be isolated. The social structure, from a biochemical to a neuron, from individual to society, is one technique. The past approach is through the comprehension of an ordered classification, subjects like zoology, organic science and microbiology, which speak to what was once observed as the fundamental life divisions. The one-fifth strategy is ecological nature, diagnostic science, fossil science and lab propels. The various perspectives wherein the subject can be isolated can be blended in with transformative science to build up subfields, for example, formative nature and developmental science.

The combination of organic science with applied sciences, all the more as of late, has offered to ascend to new fields that grow transformative science, for example, versatile mechanical autonomy, engineering, innovative algorithms and architecture. The essential transformative procedures are utilized straightforwardly or in a roundabout way to grow new thoughts or take care of issues that are hard to address generally. Work in these fields, what's more, keeps on advancing, mainly on account of advances in software engineering and design, for example, mechanical building.

Each living animal is the result of an entrancing procedure of development, which began nearly a billion years back and proceeded around us unendingly. Transformative researcher looks to see how it functions. Micrograph intertwined into one photograph composition of various incipient organisms and a phylogenetic tree. The essential standards are a fundamental contrast between individuals, specific structures have more posterity than others, and their folks are indistinguishable. The physiological truth is. Subsequently, the transformative researcher will depict natural frameworks in manners that abandon numerous particulars and protect satisfactory advancement to ensure the illustrative load of our models, discoveries and tests. Realizing improvement requires cross-disciplinary discussion between the disciplines of scientific demonstrating and theoretical investigation from hereditary and formative science to nature.

Looking for an Experienced Essay Writer?

• Oak Valley College Bachelor's degree

No reviews yet, be the first to write your comment

Write your review

Thanks for review.

It will be published after moderation

Latest News

Humanistic Perspectives on the Arts

26 Jan, 2024

Bayesian Games

New historicism

HYPOTHESIS AND THEORY article

This article is part of the research topic.

The Future of the Homology Concept under Novel Evolutionary Paradigms

Synthesizing foundations in evolutionary morphology and a plea for conceptual explicitness regarding homology concepts Provisionally Accepted

• 1 Multiscale Biology, Johann Friedrich Blumenbach Institute for Zoology and Anthropology, Georg August University of Göttingen, Germany
• 2 Department of Biological Sciences, University of North Texas, United States
• 3 Allgemeine und Spezielle Zoologie, Institut für Biowissenschaften, Universität Rostock, Germany

The final, formatted version of the article will be published soon.

Morphology, the scientific discipline dealing with description and comparison of organismal form, is one of the oldest disciplines in biology and traditionally strongly linked to the concept of homology.With morphological data being used and morphological knowledge being applied in other (younger) biological disciplines, morphology has often been degraded to an only auxiliary discipline or a mere set of methods serving those other disciplines. While this notion has been wrong all along, the last decades have seen a renaissance of morphology mostly due to significant leaps in imaging techniques and the advent of 3D digital data. Modern large-scale morphological endeavors in what is called phenomics and new means of functional analyses underline the fruitfulness of morphological research.Furthermore, morphology has been revisited on a conceptual level leading to a "re-philosophication" of morphology acknowledging its nature as explanatory science. Based on Richter & Wirkner's research program of Evolutionary Morphology, this essay expands the conceptual framework to identify entities and processes vital for morphology as independent scientific discipline. With no unified homology concept in sight (and maybe not even desired), following the emergence of bioontologies in morphology, a plea is made for conceptual explicitness which acknowledges the plurality of homology concepts but enables intersubjective transfer.

Keywords: evolutionary morphology, ontology, homology, concepts, terminology

Received: 23 Nov 2023; Accepted: 29 Mar 2024.

Copyright: © 2024 Göpel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Torben Göpel, Multiscale Biology, Johann Friedrich Blumenbach Institute for Zoology and Anthropology, Georg August University of Göttingen, Göttingen, Germany

People also looked at