pmc.ncbi.nlm.nih.gov

Eco-evolutionary dynamics

Abstract

Evolutionary ecologists and population biologists have recently considered that ecological and evolutionary changes are intimately linked and can occur on the same time-scale. Recent theoretical developments have shown how the feedback between ecological and evolutionary dynamics can be linked, and there are now empirical demonstrations showing that ecological change can lead to rapid evolutionary change. We also have evidence that microevolutionary change can leave an ecological signature. We are at a stage where the integration of ecology and evolution is a necessary step towards major advances in our understanding of the processes that shape and maintain biodiversity. This special feature about ‘eco-evolutionary dynamics’ brings together biologists from empirical and theoretical backgrounds to bridge the gap between ecology and evolution and provide a series of contributions aimed at quantifying the interactions between these fundamental processes.

Keywords: eco-evolutionary dynamics, natural selection, contemporary evolution, evolutionary changes, population dynamics, genotype-by-environment interaction

1. Introduction

Students of biology will intuitively recognize that ecology matters for evolution—and could point to several examples. Perhaps the earliest and the best known is the case of industrial melanism in peppered moths (Biston betularia). The dark (melanic) form of the moth was first recorded in 1848 near Manchester, UK. Environmental change caused by industrial pollution then favoured survival of the dark form over the light form, leading to a steady increase in the frequency of dark moths (Tutt 1896). Eventually, the dark form constituted more than 90 per cent of the population in heavily polluted areas (Kettlewell 1973). A number of other examples of apparently adaptive phenotypic changes were then reported in the 1960s, including those experienced by mice introduced to islands (Berry 1964), plants on mine tailings (Bradshaw 1965) and sparrows introduced to North America (Johnston & Selander 1964). Missing from some of these studies was direct evidence that the observed phenotypic changes were genetically based, but a number of studies in the 1980s showed that contemporary phenotypic change could indeed be genetically based (Endler 1980; Stearns 1983). This realization precipitated an explosion of studies in the past two decades exploring phenotypic and genetic changes in natural populations (reviewed in Hendry & Kinnison 1999; Reznick & Ghalambor 2001; Bradshaw & Holzapfel 2006; Carroll et al. 2007; Gienapp et al. 2008).

If measurable changes in phenotypic distributions can occur over only a few years, then perhaps these changes can also influence ecological dynamics. Such eco-evolutionary feedbacks might occur at multiple levels, such as in demographic parameters, community composition, food webs, nutrient cycling and productivity. Over long enough time-scales, such feedbacks are obvious and undisputed, with good examples including coevolution between predators and prey or between pollinators and plants. On short time-scales, however, the influence of evolution on ecology has received relatively little attention. One reason may be the logistical challenges inherent in quantifying feedbacks between evolutionary and ecological processes. A more general problem may be that scientists have often assumed that evolution happens too slowly to leave a signature in ecological dynamics (Slobodkin 1961). The present theme issue joins other recent attempts to change this perception (Thompson 1998; Hairston et al. 2005; Coulson et al. 2006a; Saccheri & Hanski 2006; Fussmann et al. 2007; Kinnison & Hairston 2007; Kokko & Lopez-Sepulcre 2007; Hughes et al. 2008) by examining the interplay between ecology and evolution at multiple levels, i.e. ‘eco-evolutionary dynamics’.

Work on eco-evolutionary dynamics is ongoing in several natural and laboratory systems (figure 1). The specific goal of this theme issue was to bring together ecologists and evolutionary biologists from diverse backgrounds to jointly explore eco-evolutionary dynamics. As editors, we were especially interested in studies that develop new approaches, or that explore eco-evolutionary dynamics using different approaches (theory, experiments in the laboratory or nature and surveys in nature) at different levels of organization. These contributions fell broadly into three general categories: (i) effects of genotypes and phenotypes on population dynamics, (ii) interactions between environmental and genetic variation, and (iii) consequences of contemporary evolution for community composition and ecosystem processes. In this introduction, we first provide a more detailed review of the historical developments behind the specific idea that contemporary evolution can influence ecological processes. We then outline several key questions that are addressed by contributions to the theme issue. Finally, we identify future challenges and directions for studies on eco-evolutionary dynamics.

Figure 1.

Figure 1

Five study systems where eco-evolutionary dynamics have been studied empirically (reviewed in Post & Palkovacs 2009): (a) the Darwin finches (Hairston et al. 2005), (b) the Trinidadian guppies (Palkovacs et al. 2009), (c) the Alewives study (Post & Palkovacs 2009), (d) the Populus system (Bailey et al. 2009) and (e) the microcosm experiments on the interaction between rotifer and algae (Yoshida et al. 2003; Jones et al. 2009). Photo by (a) A. Hendry, (b) P. Hill, (c) E. Palkovacs, (d) T. Whitham and (e) G. Fussmann.

2. Historical developments

The idea that ecology can influence evolution, even on short time-scales, has long been an important focus of evolutionary biology. The reciprocal causal pathway (evolution to ecology) also turns out to have a long and distinguished history. For example, both Darwin (1859) and Fisher (1930) recognized the intertwined nature of ecological and evolutionary processes. Then in the 1950s and 1960s, a few ecologists started to formally hypothesize how evolution might influence ecology. Chitty (1952, 1960) argued that cycles in vole populations could be a consequence of natural selection favouring different types of life histories at different phases of a cycle. Similarly, Pimentel (1968) argued that ‘evolution provides foundation for control of herbivore, parasite, and predator numbers in nature’ (Pimentel 1968).

The shared assertion of the Chitty and Pimentel papers was that evolutionary change on relatively short time-scales could influence population dynamics. It took considerably longer, however, for biologists to specifically test some of these influences. Although Chitty's hypothesis was later discarded as a likely mechanism for population cycles in voles (Boonstra & Boag 1987), it was shown in other systems that natural selection on life history and genetic variation can influence population dynamics (Sinervo et al. 2000; Yoshida et al. 2003). Part of the delay in obtaining empirical evidence for eco-evolutionary dynamics was caused by the need to first develop simple and robust ways to measure selection (Lande 1982; Lande & Arnold 1983), fitness (Clutton-Brock 1991; McGraw & Caswell 1996; Coulson et al. 2006b) and genetic variance for fitness-related traits (Kruuk 2004; Kruuk et al. 2008). Owing largely to these developments, many studies now routinely estimate these parameters in wild populations (Endler 1986; Kingsolver et al. 2001; Kruuk 2004). Another major advance was the accumulation of the detailed, long-term, empirical, individual-based data and pedigrees required to reliably document selection and evolution in nature (for an example of such studies, see Ezard et al. 2009). Finally, laboratory methods were developed for manipulating genetic variation and for examining evolutionary and population dynamical responses in multi-species communities (Yoshida et al. 2003). Together, these methods have allowed us to more formally consider eco-evolutionary dynamics.

3. From interactions to feedbacks

Several studies have shown that ecological change can drive evolutionary change on contemporary scales, and some studies are now showing the reciprocal pathway. Eco-evolutionary dynamics in the broad sense can thus include the unidirectional effects of ecological changes on evolutionary processes or the unidirectional effects of evolutionary changes on ecological processes. An important goal, however, should be to elucidate bidirectional eco-evolutionary interactions, referred to as ‘eco-evolutionary feedbacks’ (Post & Palkovacs 2009). Such feedbacks might arise at different scales in space and time and at different levels of biological organization (genes, individuals, populations, communities and ecosystems). Because many biological processes are hierarchical or organized in a network, changing one aspect of the chain can affect many levels (Coulson et al. 2006a). Many such interactions and feedbacks are possible and we will here provide only a few examples.

At the population level, natural selection and population dynamics are closely linked, simply because both are affected by the birth and death of individuals. Thus, if natural selection acts on a trait (e.g. body size) through survival or reproductive success, it will leave a population dynamical signature (Coulson et al. 2006b; Pelletier et al. 2007). This signature is expected to be the strongest when populations are not already density limited (Saccheri & Hanski 2006; Kinnison & Hairston 2007). As another example, landscape features can influence the distribution of genetic diversity at the individual and population levels (Jeyasingh & Weider 2007; Clark et al. 2008), thus affecting gene frequencies. If the change in gene frequencies translates into phenotypic trait changes that affect demographic rates (such as reproduction, survival or dispersal), then, ultimately, the genetic change will affect population dynamics (Hanski & Saccheri 2006; Saccheri & Hanski 2006). Effects at the community level are also possible. When two species, such as a predator and a prey, interact, evolutionary change in one can influence selection and demography of the other—and vice versa (Fussmann et al. 2007). Finally, changes in the numbers of individuals, the types of resources they use and their efficiency in doing so can influence a suite of ecosystem functions, such as decomposition, nutrient cycling and primary productivity (Thompson 1998).

There are many ways to write general expressions for eco-evolutionary dynamics (e.g. Coulson et al. 2006a; Fussmann et al. 2007; Bailey et al. 2009; Post & Palkovacs 2009). The commonality between all of these expressions is a hierarchical relationship between fitness, phenotypes and genes. As a complement to Dobzhansky's famous quote ‘nothing in biology makes sense except in the light of evolution’ (Dobzhansky 1964), we therefore suggest that nothing in evolution or ecology makes sense except in the light of the other. The present theme issue is an attempt to underline the value of this view by focusing on several key questions.

(a) How does evolution influence demography?

Understanding the relative contribution of ecological and evolutionary processes to the changes in population dynamics is essential to understand how the dynamics of heritable traits influence population processes. As reviewed previously, several studies have shown that phenotypic and microevolutionary changes can occur on ecological time-scales. These changes presumably influence population dynamics, but the extent of this influence is still generally unknown (Saccheri & Hanski 2006) because only a few studies have quantified the importance of phenotypic change to population growth and dynamics. The few examples include analyses of Darwin's finches (Geospiza fortis; Hairston et al. 2005), Soay sheep (Ovis aries; Pelletier et al. 2007) and New Zealand chinook salmon (Oncorhynchus tshawytscha; Kinnison et al. 2008). Such work is increasingly needed, however, as it may yield information as to the magnitude and direction of the influence evolution exerts on population persistence and extinction in changing environments (Burger & Lynch 1995; Gomulkiewicz & Holt 1995; Orr & Unckless 2008).

The first paper in this section of the theme issue (Ezard et al. 2009) extends the above analyses by quantifying the influence of phenotypic distributions on population dynamics in five natural systems. Specifically, the authors partition the effects of phenotypic and environmental changes on population growth (Hairston et al. 2005) using five detailed long-term time series of marked ungulate populations. Ezard et al. (2009) find that juvenile mass (a key heritable life-history trait in ungulates; Albon et al. 1987; Wilson et al. 2005) can influence population growth to a similar extent as can environmental variables such as climate and food availability. In short, Ezard et al. (2009) show that neither phenotypic changes nor ecology can be ignored when considering population growth.

Life histories emerge from transitions between life stages and can therefore influence population dynamics (Saether & Bakker 2000; Oli & Dobson 2003). Moreover, life histories can clearly evolve on short time-scales (Stearns 1983; Reznick et al. 1990), thus providing a possible conduit for eco-evolutionary dynamics. Several theoretical studies have tried to understand how the variety of life histories observed in nature might have evolved. These models have generally considered the fitness of alternative life histories in stable environments, which may be an unrealistic assumption. In the present theme issue, Tuljapurkar et al. (2009) explore the fitness landscape of various life histories under varying environmental conditions. Their work suggests that ‘optimal phenotypes’ in a fluctuating environment are likely to differ from those in a constant environment.

Central to all studies of eco-evolutionary dynamics is the concept of ‘fitness’. In a constant environment with no density dependence and no age structure, classic theoretical models hold that evolution maximizes the mean relative fitness (Fisher 1930; Crow & Kimura 1970). Natural populations, however, frequently experience variable environments (Engen et al. 2005) and density dependence (Bonenfant et al. 2009). Lande et al. (2009) apply diffusion theory to analyse a system of coupled ecological and evolutionary dynamics to derive an evolutionary maximum principle under these more realistic conditions. They show how ‘the magnitude of environmental stochasticity and the form of density dependence govern the trade-off between r and K selection’.

(b) How do ecological variables influence the potential for evolution?

The recognition that ecological conditions can influence evolution is not new. In fact, genotype-by-environment interactions have long been integrated into evolutionary quantitative genetic theory (Bradshaw 1965; Falconer & Mackay 1996). To date, most evidence for such interactions has come from controlled laboratory experiments. For natural populations, some studies have revealed that habitat quality can influence genetic variance (reviewed in Hoffmann & Merilä 1999; Charmantier & Garant 2005) and covariance (reviewed in Sgrò & Hoffmann 2004). However, relatively few attempts have been made to assess the influence of environmental variability on the distribution of genetic diversity at different levels of biological organization. Studies in this section of the present theme issue used a variety of approaches to determine how ecological processes influence evolutionary parameters, as well as their links to short- and long-term population dynamics.

One factor that can influence genetic variation, adaptation and population dynamics is dispersal (reviewed in Garant et al. 2007). Zheng et al. (2009) emphasize the need to study these interactions in theoretical models tailored to specific empirical systems. For example, evolutionary models do not usually formalize the actual genetic mechanisms underlying dispersal. Zheng et al. (2009) do so with an individual-based model of Glanville fritillary butterflies (Melitaea cinxia) in a fragmented landscape. The authors find a strong coupling between metapopulation dynamics and genetic dynamics at a locus (Pgi) affecting dispersal and other life-history traits. The strength of the coupling depends on spatial scale and is asymmetric; demography has a greater impact on genetics than vice versa. Zheng et al. (2009) consider the positive role of dispersal on population dynamics, whereas Moore & Hendry (2009) consider the possible negative role. They specifically test whether gene flow between three-spine stickleback (Gasterosteus aculeatus L.) populations in different environments (lake versus stream) can cause maladaptation that limits population size and constrains species ranges (suggested by Kirkpatrick & Barton 1997). Finding only mixed evidence in support of the hypothesis despite a simplified system with high gene flow and trait maladaptation, Moore & Hendry (2009) discuss how any negative effects of gene flow on population dynamics might be difficult to demonstrate in nature.

Another factor that might influence genetic variation, adaptation and population dynamics is spatial habitat heterogeneity. The integration of geographical information system methods and landscape genetic approaches is well suited to explore this topic, as shown by one contribution to the special issue. Porlier et al. (2009) explore how the spatial distribution of habitat types influences population structure and individual neutral genetic variability in wild tree swallows (Tachycineta bicolor). The authors show that individual genetic diversity is greater in poorer habitats, at least partly due to non-random settlement patterns of swallows across the landscape. Dionne et al. (2009) then provide a direct assessment of how spatial variation in selection influences genetic variability of selected loci. The authors first document differences in parasites and pathogens for Atlantic salmon (Salmo salar) from distinct genetic groups and ecological regions. They then identify specific major histocompatibility complex alleles that influence resistance to pathogen infection in their wild populations. Finally, they show a decrease in the frequency of the susceptibility allele through time, suggesting that pathogen-driven selection is acting in their systems.

Ultimately, many investigators would like to predict evolutionary patterns among populations as a function of selection and genetic variation within populations. This effort has thus far been hampered by the lack of a precise way to quantify and compare complex multivariate genetic variance–covariance (G) matrices. To fill this gap, Hine et al. (2009) present a new method for comparing G matrices and then ask whether the G matrix within Drosophila serrata populations can explain variation in the same traits among those populations.

(c) When will evolution influence community and ecosystem variables?

The idea that contemporary evolution can influence demographic parameters is relatively straightforward as are some links between contemporary evolution and community structure. For instance, the evolution of one species often improves its ability to compete with, or to exploit, another species (Yoshida et al. 2003; Fussmann et al. 2007). This adaptation of one species then selects for countermeasures in the other species, initiating a coevolutionary arms race with a variety of possible outcomes (Thompson 2005). Coevolutionary feedbacks are also particularly obvious for tight mutualistic interactions such as those between ants and aphids, and flowering plants and pollinators. Less straightforward, however, are local links between evolutionary change and higher level ecological variables, such as community composition, food webs, nutrient dynamics and primary productivity. In comparison with population dynamics, these variables are further removed from the genetic variation on which selection acts, and may therefore be less sensitive to evolutionary change. What, then, are the conditions under which these higher level eco-evolutionary dynamics might be important?

One of the earliest formal demonstrations of evolutionary effects, above the population level, was chemostat experiments showing that evolutionary change influenced population cycles of predators (rotifers) and prey (algae) (Yoshida et al. 2003). Jones et al. (2009) further explore that system by modelling factors influencing its eco-evolutionary dynamics. The authors show that ecological and genetic ‘details’ matter. For example, changes in environmental variables (chemostat flow rates) or cost/effectiveness trade-offs in prey defence can shift the identity of the surviving prey types. An even more complex range of outcomes follows from a consideration of heritable variation in both multiple predator types and multiple prey types with resulting dynamics ranging from equilibrium to chaos (Jones et al. 2009).

Chemostat experiments have proven extremely informative but experiments in nature are also needed. Johnson et al. (2009) take steps in this direction with a combined theoretical–experimental approach. They first develop a theoretical framework for understanding when natural selection should drive community dynamics. They then demonstrate these conditions in their experimental plant–arthropod system, i.e. natural selection acts on plant biomass, which is heritable and influences arthropod abundance and species richness. These findings are then integrated back into the theoretical framework to predict evolutionary changes in the plants, as well as ecological responses in arthropod communities.

Ecosystem variables should be less affected by genetic variation than are population and community variables. Bailey et al. (2009) therefore predict that genetic variation will have its greatest effect on population variables, a weaker effect on community variables and the weakest effect on ecosystem variables. Using a meta-analytical approach, the authors confirm that genetic variation in plant populations is most closely associated with plant traits, less associated with community variables and least associated with ecosystem variables. Critically, however, strong effects of genetic variation were sometimes present at all of these levels, highlighting the need for future studies to consider additional factors that might influence the strength of eco-evolutionary dynamics.

Many studies of eco-evolutionary dynamics at the community and ecosystem levels focus on genetic variation in plants as the evolutionary driver. Here, Palkovacs et al. (2009) instead focus on the potential effects of genetic variation in animals. The authors use mesocosms to study the ecological effects of population-level adaptive differences in guppies (Poecilia reticulata) and killifish (Rivulus hartii). They find that guppies adapted to different predation environments (high versus low) have differential effects on the biomass of aquatic invertebrates and algae. Killifish adapted to the presence or absence of guppies were also found to have differential effects on aquatic invertebrates. These results suggest the possible importance of top-down drivers of eco-evolutionary dynamics.

Post & Palkovacs (2009) provide a capstone by specifically considering the conditions under which eco-evolutionary feedbacks are most likely. They then review five empirical systems in which indirect evidence suggests such feedbacks are present (figure 1). Each of these systems shows considerable promise for documenting eco-evolutionary dynamics in nature. It also seems certain that new systems will soon emerge as excellent candidates for such kind of study.

4. Challenges

One of the key steps for future research will be to develop a general theoretical framework for eco-evolutionary dynamics—and then to quantify these dynamics in natural populations. When it comes to population dynamics, new methods have been advanced for estimating the contribution of trait distributions to population growth and dynamics (Coulson et al. 2006b; Pelletier et al. 2007). Now we also have good statistical methods to decompose trait distributions into environmental and genetic components (Kruuk 2004). At this stage, however, we still have a very limited understanding of the dynamics of phenotypes and genotypes in natural settings (Merilä et al. 2001; Kruuk et al. 2008). Furthermore, we have no studies quantifying how ongoing evolution influences community or ecosystem variables in nature. To directly demonstrate eco-evolutionary dynamics, we should now strive for more studies that track the ongoing evolution of traits and their ecological consequences. Long-term studies can be helpful in this matter but, in some cases, stochastic temporal and environmental trends will be intertwined with evolutionary change and population dynamics. Disentangling this complexity might require further theoretical and statistical developments, as well as manipulative experiments in microcosms and nature.

As emphasized by many contributors to this theme issue, another key challenge is to identify and formally confirm situations where eco-evolutionary dynamics and feedbacks might be particularly important. This effort will require examining multi-way interactions and transition phases, as well as potential steady states in various systems. Crespi (2004) argued that eco-evolutionary feedbacks are hard to measure because they are episodic. Thus, although feedbacks might be common in nature, they might be measurable only during transition periods. This might explain why most of the examples of eco-evolutionary feedbacks come from species recently experiencing an abrupt environmental shift such as invasion of a new habitat (Kinnison et al. 2008), exposure to novel predators or pathogens (Facon et al. 2006) or extreme weather conditions (Grant & Grant 2002).

Finally, another important challenge will be to overcome traditional views deeply anchored in each field. The assumption that ecological and evolutionary processes occur on a very different time-scale has to be abandoned. In addition, we need to go a step further by quantifying eco-evolutionary interactions instead of only interpreting ecological studies in an evolutionary context or looking at evolutionary parameters in one or two environments. An increasing integration of ecology and evolution should provide key insights into the forces that generate and maintain biodiversity in nature.

Acknowledgments

We thank Jennifer Chambers, Denis Réale, Marco Festa-Bianchet and Tim Coulson for their comments on the introduction and all the authors for contributing such interesting papers to this special feature. We gratefully acknowledge NERC via the Centre for Population Biology (Imperial College London) for funding to F.P. to organize the workshop series entitled ‘Linking Evolutionary and Ecological Dynamics’.

Footnotes

One contribution of 14 to a Theme Issue ‘Eco-evolutionary dynamics’.

References

  1. Albon S.D., Clutton-Brock T.H., Guinness F.E. Early development and population dynamics in red deer. II. Density-independent effects of cohort variation. J. Anim. Ecol. 1987;56:69–81. doi:10.2307/4800 [Google Scholar]
  2. Bailey J.K., et al. From genes to ecosystems: a synthesis of the effects of plant genetic factors across levels of organization. Phil. Trans. R. Soc. B. 2009;364:1607–1616. doi: 10.1098/rstb.2008.0336. doi:10.1098/rstb.2008.0336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Berry R.J. The evolution of an island population of the house mouse. Evolution. 1964;18:468–483. doi:10.2307/2406357 [Google Scholar]
  4. Bonenfant C., et al. Empirical evidences of density-dependence in populations of large herbivores. Adv. Ecol. Res. 2009;41:299–338. [Google Scholar]
  5. Boonstra R., Boag P.T. A test of the Chitty hypothesis: inheritance of life-history traits in meadow voles Microtus pennsylvanicus. Evolution. 1987;41:929–947. doi: 10.1111/j.1558-5646.1987.tb05868.x. doi:10.2307/2409183 [DOI] [PubMed] [Google Scholar]
  6. Bradshaw A.D. Evolutionary significance of phenotypic plasticity in plants. Adv. Genet. 1965;13:115–155. doi:10.1016/S0065-2660(08)60048-6 [Google Scholar]
  7. Bradshaw W.E., Holzapfel C.M. Evolutionary response to rapid climate change. Science. 2006;312:1477–1478. doi: 10.1126/science.1127000. doi:10.1126/science.1127000 [DOI] [PubMed] [Google Scholar]
  8. Burger R., Lynch M. Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution. 1995;49:151–163. doi: 10.1111/j.1558-5646.1995.tb05967.x. doi:10.2307/2410301 [DOI] [PubMed] [Google Scholar]
  9. Carroll S.P., Hendry A.P., Reznick D.N., Fox C.W. Evolution on ecological time-scales. Funct. Ecol. 2007;21:387–393. doi:10.1111/j.1365-2435.2007.01289.x [Google Scholar]
  10. Charmantier A., Garant D. Environmental quality and evolutionary potential: lessons from wild populations. Proc. R. Soc. B. 2005;272:1415–1425. doi: 10.1098/rspb.2005.3117. doi:10.1098/rspb.2005.3117 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chitty D. Mortality among voles (Microtus agrestis) at Lake Vyrnwy, Montgomeryshire in 1936–1939. Phil. Trans. R. Soc. Lond. B. 1952;236:505–552. doi:10.1098/rstb.1952.0009 [Google Scholar]
  12. Chitty D. Population processes in the vole and their relevance to general theory. Can. J. Zool. 1960;38:99–113. doi:10.1139/z60-011 [Google Scholar]
  13. Clark R.W., Brown W.S., Stechert R., Zamudio K.R. Integrating individual behaviour and landscape genetics: the population structure of timber rattlesnake hibernacula. Mol. Ecol. 2008;17:719–730. doi: 10.1111/j.1365-294X.2007.03594.x. doi:10.1111/j.1365-294X.2007.03594.x [DOI] [PubMed] [Google Scholar]
  14. Clutton-Brock T.H. Lifetime data and the measurement of selection. Evolution. 1991;45:454. doi: 10.1111/j.1558-5646.1991.tb04422.x. doi:10.2307/2409682 [DOI] [PubMed] [Google Scholar]
  15. Coulson T., Benton T.G., Lundberg P., Dall S.R.X., Kendall B.E. Putting evolutionary biology back in the ecological theatre: a demographic framework mapping genes to communities. Evol. Ecol. Res. 2006a;8:1155–1171. [Google Scholar]
  16. Coulson T., Benton T.G., Lundberg P., Dall S.R.X., Kendall B.E., Gaillard J.-M. Estimating individual contributions to population growth: evolutionary fitness in ecological time. Proc. R. Soc. B. 2006b;273:547–555. doi: 10.1098/rspb.2005.3357. doi:10.1098/rspb.2005.3357 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Crespi B.J. Vicious circles: positive feedback in major evolutionary and ecological transitions. Trends Ecol. Evol. 2004;19:627–633. doi: 10.1016/j.tree.2004.10.001. doi:10.1016/j.tree.2004.10.001 [DOI] [PubMed] [Google Scholar]
  18. Crow J.F., Kimura M. Harper and Row; New York, NY: 1970. An introduction to population genetics theory. [Google Scholar]
  19. Darwin C. On the origin of species. London, UK: John Murray. (Reprinted by Harvard Unviersity Press 1964) 1859 [Google Scholar]
  20. Dionne M., Miller K.M., Dodson J.J., Bernatchez L. MHC standing genetic variation and pathogen resistance in wild Atlantic salmon. Phil. Trans. R. Soc. B. 2009;364:1555–1565. doi: 10.1098/rstb.2009.0011. doi:10.1098/rstb.2009.0011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Dobzhansky T. Biology, molecular and organismic. Am. Zool. 1964;4:443–452. doi: 10.1093/icb/4.4.443. doi:10.1093/icb/4.4.443 [DOI] [PubMed] [Google Scholar]
  22. Endler J.A. Natural selection on color patterns in Poecilia reticulata. Evolution. 1980;34:76–91. doi: 10.1111/j.1558-5646.1980.tb04790.x. doi:10.2307/2408316 [DOI] [PubMed] [Google Scholar]
  23. Endler J.A. Princeton University Press; Princeton, NJ: 1986. Natural selection in the wild. [Google Scholar]
  24. Engen S., Lande R., Saether B.-E. Effective size of a fluctuating age-structured population. Genetics. 2005;170:941–954. doi: 10.1534/genetics.104.028233. doi:10.1534/genetics.104.028233 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ezard T.H.G., Côté S.D., Pelletier F. Eco-evolutionary dynamics: disentangling phenotypic, environmental and population fluctuations. Phil. Trans. R. Soc. B. 2009;364:1491–1498. doi: 10.1098/rstb.2009.0006. doi:10.1098/rstb.2009.0006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Facon B., Genton B.J., Shykoff J., Jarne P., Estoup A., David P. A general eco-evolutionary framework for understanding bioinvasions. Trends Ecol. Evol. 2006;21:130–133. doi: 10.1016/j.tree.2005.10.012. doi:10.1016/j.tree.2005.10.012 [DOI] [PubMed] [Google Scholar]
  27. Falconer D.S., Mackay T.F.C. Pearson Prentice Hall; Harlow, UK: 1996. Introduction to quantitative genetics. [Google Scholar]
  28. Fisher R.A. Clarendon Press (reprinted by Dover, 1958); Oxford, UK: 1930. The genetical theory of natural selection. [Google Scholar]
  29. Fussmann G.F., Loreau M., Abrams P.A. Eco-evolutionary dynamics of communities and ecosystems. Funct. Ecol. 2007;21:465–477. doi:10.1111/j.1365-2435.2007.01275.x [Google Scholar]
  30. Garant D., Forde S.E., Hendry A.P. The multifarious effects of dispersal and gene flow. Funct. Ecol. 2007;21:434–443. doi:10.1111/j.1365-2435.2006.01228.x [Google Scholar]
  31. Gienapp P., Teplitsky C., Alho J.S., Mills J.A., Merilä J. Climate change and evolution: disentangling environmental and genetic responses. Mol. Ecol. 2008;17:167–178. doi: 10.1111/j.1365-294X.2007.03413.x. doi:10.1111/j.1365-294X.2007.03413.x [DOI] [PubMed] [Google Scholar]
  32. Gomulkiewicz R., Holt R.D. When does evolution by natural selection prevent extinction? Evolution. 1995;49:201–207. doi: 10.1111/j.1558-5646.1995.tb05971.x. doi:10.2307/2410305 [DOI] [PubMed] [Google Scholar]
  33. Grant P.R., Grant B.R. Unpredictable evolution in a 30-year study of Darwin's finches. Science. 2002;296:707–711. doi: 10.1126/science.1070315. doi:10.1126/science.1070315 [DOI] [PubMed] [Google Scholar]
  34. Hairston N.G., Jr, Ellner S.P., Geber M., Yoshida T., Fox J.E. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 2005;8:1114–1127. doi:10.1111/j.1461-0248.2005.00812.x [Google Scholar]
  35. Hanski I., Saccheri I. Molecular-level variation affects population growth in a butterfly metapopulation. PLoS Biol. 2006;4:719–726. doi: 10.1371/journal.pbio.0040129. doi:10.1371/journal.pbio.0040129 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hendry A.P., Kinnison M.T. The pace of modern life: measuring rates of contemporary microevolution. Evolution. 1999;53:1637–1653. doi: 10.1111/j.1558-5646.1999.tb04550.x. doi:10.2307/2640428 [DOI] [PubMed] [Google Scholar]
  37. Hine E., Chenoweth S.F., Rundle H.D., Blows M.W. Characterizing the evolution of genetic variance using genetic covariance tensors. Phil. Trans. R. Soc. B. 2009;364:1567–1578. doi: 10.1098/rstb.2008.0313. doi:10.1098/rstb.2008.0313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hoffmann A.A., Merilä J. Heritable variation and evolution under favourable and unfavourable conditions. Trends Ecol. Evol. 1999;14:96–101. doi: 10.1016/s0169-5347(99)01595-5. doi:10.1016/S0169-5347(99)01595-5 [DOI] [PubMed] [Google Scholar]
  39. Hughes A.R., Inouye B.D., Johnson M.T.J., Underwood N., Vellend M. Ecological consequences of genetic diversity. Ecol. Lett. 2008;11:609–623. doi: 10.1111/j.1461-0248.2008.01179.x. doi:10.1111/j.1461-0248.2008.01179.x [DOI] [PubMed] [Google Scholar]
  40. Jeyasingh P.D., Weider L.J. Fundamental links between genes and elements: evolutionary implications of ecological stoichiometry. Mol. Ecol. 2007;16:4649–4661. doi: 10.1111/j.1365-294X.2007.03558.x. doi:10.1111/j.1365-294X.2007.03558.x [DOI] [PubMed] [Google Scholar]
  41. Johnston R.F., Selander R.K. House sparrows: rapid evolution of races in North America. Science. 1964;144:548–550. doi: 10.1126/science.144.3618.548. doi:10.1126/science.144.3618.548 [DOI] [PubMed] [Google Scholar]
  42. Johnson M.T.J., Vellend M., Stinchcombe J.R. Evolution in plant populations as a driver of ecological changes in arthropod communities. Phil. Trans. R. Soc. B. 2009;364:1593–1605. doi: 10.1098/rstb.2008.0334. doi:10.1098/rstb.2008.0334 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Jones L.E., Becks L., Ellner S.P., Hairston N.G., Jr, Yoshida T., Fussmann G.F. Rapid contemporary evolution and clonal food web dynamics. Phil. Trans. R. Soc. B. 2009;364:1579–1591. doi: 10.1098/rstb.2009.0004. doi:10.1098/rstb.2009.0004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Kettlewell H.B.D. Oxford University Press; Oxford, UK: 1973. The evolution of melanism. [Google Scholar]
  45. Kingsolver J.G., Hoekstra H.E., Hoekstra J.M., Berrigan D., Vignieri S.N., Hill C.E., Hoang A., Gibert P., Beerli P. The strength of phenotypic selection in natural populations. Am. Nat. 2001;157:245–261. doi: 10.1086/319193. doi:10.1086/319193 [DOI] [PubMed] [Google Scholar]
  46. Kinnison M.T., Hairston J.N.G. Eco-evolutionary conservation biology: contemporary evolution and the dynamics of persistence. Funct. Ecol. 2007;21:444–454. doi:10.1111/j.1365-2435.2007.01278.x [Google Scholar]
  47. Kinnison M.T., Unwin M.J., Quinn T.P. Eco-evolutionary vs. habitat contributions to invasion in salmon: experimental evaluation in the wild. Mol. Ecol. 2008;17:405–414. doi: 10.1111/j.1365-294X.2007.03495.x. doi:10.1111/j.1365-294X.2007.03495.x [DOI] [PubMed] [Google Scholar]
  48. Kirkpatrick M., Barton N.H. Evolution of a species' range. Am. Nat. 1997;150:1–23. doi: 10.1086/286054. doi:10.1111/j.1365-2435.2007.01275.x [DOI] [PubMed] [Google Scholar]
  49. Kokko H., Lopez-Sepulcre A. The ecogenetic link between demography and evolution: can we bridge the gap between theory and data? Ecol. Lett. 2007;10:773–782. doi: 10.1111/j.1461-0248.2007.01086.x. doi:10.1111/j.1461-0248.2007.01086.x [DOI] [PubMed] [Google Scholar]
  50. Kruuk L.E.B. Estimating genetic parameters in natural populations using the ‘animal model’. Phil. Trans. R. Soc. Lond. B. 2004;359:873–890. doi: 10.1098/rstb.2003.1437. doi:10.1098/rstb.2003.1437 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Kruuk L.E.B., Slate J., Wilson A.J. New answers for old questions: the evolutionary quantitative genetics of wild animal populations. Annu. Rev. Ecol. Evol. Syst. 2008;39:525–548. doi:10.1146/annurev.ecolsys.39.110707.173542 [Google Scholar]
  52. Lande R. A quantitative genetic theory of life history evolution. Ecology. 1982;63:607–615. doi:10.2307/1936778 [Google Scholar]
  53. Lande R., Arnold S.J. The measurement of selection on correlated characters. Evolution. 1983;37:1210–1226. doi: 10.1111/j.1558-5646.1983.tb00236.x. doi:10.2307/2408842 [DOI] [PubMed] [Google Scholar]
  54. Lande R., Engen S., Sæther B.-E. An evolutionary maximum principle for density-dependent population dynamics in a fluctuating environment. Phil. Trans. R. Soc. B. 2009;364:1511–1518. doi: 10.1098/rstb.2009.0017. doi:10.1098/rstb.2009.0017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. McGraw J.B., Caswell H. Estimation of individual fitness from life-history data. Am. Nat. 1996;147:47–64. doi:10.1086/285839 [Google Scholar]
  56. Merilä J., Sheldon B.C., Kruuk L.E.B. Explaining stasis: microevolutionary studies in natural populations. Genetica. 2001;112–113:199–222. doi:10.1023/A:1013391806317 [PubMed] [Google Scholar]
  57. Moore J.-S., Hendry A.P. Can gene flow have negative demographic consequences? Mixed evidence from stream threespine stickleback. Phil. Trans. R. Soc. B. 2009;364:1533–1542. doi: 10.1098/rstb.2009.0007. doi:10.1098/rstb.2009.0007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Oli M.K., Dobson F.S. The relative importance of life-history variables to population growth rate in mammals: Cole's prediction revisited. Am. Nat. 2003;161:422–440. doi: 10.1086/367591. doi:10.1086/367591 [DOI] [PubMed] [Google Scholar]
  59. Orr H.A., Unckless R.L. Population extinction and the genetics of adaptation. Am. Nat. 2008;172:160–169. doi: 10.1086/589460. doi:10.1086/589460 [DOI] [PubMed] [Google Scholar]
  60. Palkovacs E.P., Marshall M.C., Lamphere B.A., Lynch B.R., Weese D.J., Fraser D.F., Reznick D.N., Pringle C.M., Kinnison M.T. Experimental evaluation of evolution and coevolution as agents of ecosystem change in Trinidadian streams. Phil. Trans. R. Soc. B. 2009;364:1617–1628. doi: 10.1098/rstb.2009.0016. doi:10.1098/rstb.2009.0016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. 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. doi: 10.1126/science.1139024. doi:10.1126/science.1139024 [DOI] [PubMed] [Google Scholar]
  62. Pimentel D. Population regulation and genetic feedback. Science. 1968;159:1432–1437. doi: 10.1126/science.159.3822.1432. doi:10.1126/science.159.3822.1432 [DOI] [PubMed] [Google Scholar]
  63. Porlier M., Bélisle M., Garant D. Non-random distribution of individual genetic diversity along an environmental gradient. Phil. Trans. R. Soc. B. 2009;364:1543–1554. doi: 10.1098/rstb.2009.0010. doi:10.1098/rstb.2009.0010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Post D.M., Palkovacs E.P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Phil. Trans. R. Soc. B. 2009;364:1629–1640. doi: 10.1098/rstb.2009.0012. doi:10.1098/rstb.2009.0012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Reznick D., Ghalambor C.K. The population ecology of contemporary adaptations: what empirical studies reveal about the conditions that promote adaptative evolution. Genetica. 2001;112–113:183–198. doi:10.1023/A:1013352109042 [PubMed] [Google Scholar]
  66. Reznick D.A., Bryga H., Endler J.A. Experimentally induced life-history evolution in a natural population. Nature. 1990;346:357–359. doi:10.1038/346357a0 [Google Scholar]
  67. Saccheri I., Hanski I. Natural selection and population dynamics. Trends Ecol. Evol. 2006;21:341–347. doi: 10.1016/j.tree.2006.03.018. doi:10.1016/j.tree.2006.03.018 [DOI] [PubMed] [Google Scholar]
  68. Saether B.-E., Bakker O. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology. 2000;81:642–653. doi:10.2307/177366 [Google Scholar]
  69. Sgrò C.M., Hoffmann A.A. Genetic correlations, tradeoffs and environmental variation. Heredity. 2004;93:241–248. doi: 10.1038/sj.hdy.6800532. doi:10.1038/sj.hdy.6800532 [DOI] [PubMed] [Google Scholar]
  70. Sinervo B., Svensson E., Comendant T. Density cycles and an offspring quantity and quality game driven by natural selection. Nature. 2000;406:985–988. doi: 10.1038/35023149. doi:10.1038/35023149 [DOI] [PubMed] [Google Scholar]
  71. Slobodkin L.B. Holt, Rinehart and Winston; New York, NY: 1961. Growth and regulation of animal populations. [Google Scholar]
  72. Stearns S.C. The genetic basis of differences in life-history traits among six populations of mosquitofish (Gambusia affinis) that shared ancestors in 1905. Evolution. 1983;37:618–627. doi: 10.1111/j.1558-5646.1983.tb05578.x. doi:10.2307/2408274 [DOI] [PubMed] [Google Scholar]
  73. Thompson J.M. Rapid evolution as an ecological process. Trends Ecol. Evol. 1998;13:329–332. doi: 10.1016/s0169-5347(98)01378-0. doi:10.1016/S0169-5347(98)01378-0 [DOI] [PubMed] [Google Scholar]
  74. Thompson J.N. University of Chicago Press; Chicago, IL: 2005. The geographic mosaic of coevolution. [Google Scholar]
  75. Tuljapurkar S., Gaillard J.-M., Coulson T. From stochastic environments to life histories and back. Phil. Trans. R. Soc. B. 2009;364:1499–1509. doi: 10.1098/rstb.2009.0021. doi:10.1098/rstb.2009.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Tutt J.W. George Routledge; London, UK: 1896. British moths. [Google Scholar]
  77. Wilson A.J., Pilkington J.G., Pemberton J., Coltman D.W., Overall A.D.J., Byrne K.A., Kruuk L. Selection on mothers and offspring: whose phenotype is it and does it matter? Evolution. 2005;59:451–463. doi:10.1554/04-480 [PubMed] [Google Scholar]
  78. Yoshida T., Jones L.E., Ellner S.P., Fussmann G.F., Hairston N.G., Jr Rapid evolution drives ecological dynamics in a predator–prey system. Nature. 2003;424:303–306. doi: 10.1038/nature01767. doi:10.1038/nature01767 [DOI] [PubMed] [Google Scholar]
  79. Zheng C., Ovaskainen O., Hanski I. Modelling single nucleotide effects in phosphoglucose isomerase on dispersal in the Glanville fritillary butterfly: coupling of ecological and evolutionary dynamics. Phil. Trans. R. Soc. B. 2009;364:1519–1532. doi: 10.1098/rstb.2009.0005. doi:10.1098/rstb.2009.0005 [DOI] [PMC free article] [PubMed] [Google Scholar]