Why is coevolution important

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Zimmer C. The tangled bank: an introduction to evolution. Greenwood Village, Colorado: Roberts and Company; Download references. I thank Niles Eldredge for helpful comments on the manuscript, Rodrigo Medel for discussions throughout the development of this special volume, and the authors of this special issue who have contributed so much to the development of our understanding of the coevolutionary process.

You can also search for this author in PubMed Google Scholar. Correspondence to John N. Reprints and Permissions. Thompson, J. Four Central Points About Coevolution. Evo Edu Outreach 3, 7—13 Download citation. Published : 21 January Issue Date : March 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. Skip to main content. Search all BMC articles Search. Download PDF. Volume 3 Supplement 1. Abstract Much of evolution is about the coevolution of species with each other. Complex organisms require coevolved interactions to survive and reproduce Our own genetic code is incomplete, as is that of most other species. Table 1 Some coevolved interactions that have been crucial to the diversification of the web of life Full size table.

Species-rich ecosystems are built on a base of coevolved interactions When we describe the organization of biological communities, we often focus on competition and predation and perhaps mention parasitism and a few instances of mutualism like pollination.

Coevolution takes multiple forms and generates a diversity of ecological outcomes The diversity of lifestyles that we see today in all ecological communities has come from the diversity of the coevolutionary process itself.

Table 2 The various ways in which local populations of species coevolve Full size table. Interactions coevolve as constantly changing geographic mosaics Perhaps one of the biggest changes to have occurred in recent years in our understanding of coevolution is that we now know that it is a relentless ecological process and not something rare and observable only over long periods of geologic time.

Full size image. Final Points This is an exciting time in coevolutionary biology as new discoveries occur each year. Google Scholar Lively CM.

Google Scholar Schluter D. Google Scholar Thompson JN. Google Scholar Toju H. Google Scholar Download references. Acknowledgments I thank Niles Eldredge for helpful comments on the manuscript, Rodrigo Medel for discussions throughout the development of this special volume, and the authors of this special issue who have contributed so much to the development of our understanding of the coevolutionary process.

Thompson Authors John N. Thompson View author publications. About this article Cite this article Thompson, J. Copy to clipboard. Contact us Submission enquiries: Access here and click Contact Us General enquiries: info biomedcentral. In general, there is much discussion about the likelihood of coevolution in cases where more than one species is involved in an evolutionary interactions.

An "Ockham's Razor" approach to proving coevolution requires that we should first disprove the simpler hypothesis of unilateral adaptation. Answers to the question "How likely is coevolution?

Various types have been proposed:. Specific coevolution may of course be short-lived, but if the interaction is very close, as in many host-parasite systems, concordant speciation or cospeciation may result; where the speciation in one form causes speciation in another.

Of course, cospeciation doesn't necessarily require coevolution. For example, a very unimportant but highly host-restricted parasite may always speciate whenever its host speciates, without the parasite causing any evolutionary reaction in the host. In diffuse coevolution , also called guild coevolution , whole groups of species interact with other groups of species, leading to changes that cannot really be identified as examples of specific, pairwise coevolution between two species.

For example, a group of plant species may be fed on by a particular family of insects, which may frequently in evolutionary time change hosts. The plants may evolve defensive adaptations, such as defensive chemistry, or physical defenses such as spines, which work against large numbers of the species. In time, some of the insects may be able to overcome the plant's defences, leading to further evolution by the plant, and so on.

Another related type of evolution is called escape-and-radiate coevolution. Here, an evolutionary innovation by either partner in a coevolutionary interaction enables an adaptive radiation , or speciation due to the availability of ecological opportunity. For example, it is easy to imagine that this could be a result of the diffuse kind of herbivore-plant coevolution described above. Phylogenies are very useful in the study of coevolution. If the phylogenies of two closely associated groups, such as host and parasite, are concordant see overhead , this may imply:.

However, as we have seen, even contemporaneous cospeciation with concordant phylogenies does not prove that two lineages have coevolved. Instead, we can look at individual adaptations of the interacting species to get an idea of whether coevolution has taken place.

Here are some examples:. Plants have many complex chemicals, called "secondary chemicals", which are not obviously used in normal metabolism. Ehrlich and Raven and others subsequently interpreted this "secondary chemistry" as an example of defensive adaptation by the plants. Many of these compounds for instance, tannins and other phenolic compounds, alkaloids like nicotine, cocaine, opiates and THC, or cyanogenic glycosides are highly toxic. Many animals such as insects have adapted to feeding exclusively on plants with particular defensive chemistry.

This plant is similar to other members of the genus Acacia thorn trees in the pea family , in that it has large spines which presumably protect it against mammalian herbivores another example of coevolution, presumably against mammalian browsers. However, it lacks the cyanogenic glycosides cyanide-producing chemicals found in related Acacia and the thorns in this species are particularly large and hollow, and provides shelter to a species of Pseudomyrmex ant.

The plant also provides proteinaceous food bodies on the tips of the leaflets, which sustain the ant colonies. These ants are particularly nasty I can tell you from personal experience! It has been shown experimentally that the ants will also remove any caterpillars from the leaves that they patrol.

The ants even remove vines and plants from around the base of the tree, creating a bare patch on the soil. Plants of the bullshorn Acacia which have not been occupied by ant colonies are heavily attacked by herbivores and often have vines growing in the branches.

Related Acacia species lack hollow thorns and food bodies, and do not have specific associations with ants. Spatial structure may influence the effects of climate change on coevolving species [19] , [20] ; intermediate dispersal levels may slow local adaptation by diluting locally adapted genotypes, while low dispersal levels may speed local adaptation by providing advantageous genotypes [33].

In addition, when the climate itself varies across space, intermediate dispersal levels could lead to a geographic mosaic of coevolution where selection pressures and species traits vary across space [34]. Nonetheless, to focus on local adaption, we assumed that each species is represented by a single, panmictic population. We modeled species interactions in terms of population dynamics: how the density of one species affects the population growth rate of the other.

For example, for predation a high density of the predator will lead to a decrease in the population growth rate of the prey, and a high density of the prey will lead to an increase in the population growth rate of the predator; note that this general definition of predator-prey interactions encompasses host-pathogen and plant-herbivore interactions.

While there is only a single interaction between species in the model, it is modeled as two parameters, one for the effect of the interaction on each species.

Thus, for competitive interactions, one interaction parameter measures the negative effect of the density of the first species on the population growth rate of the second, and another parameter measures the effect of the second species on the population growth rate of the first. We further assumed that each species has a trait that affects the strength of these interaction parameters.

For example, a prey has a defensive trait that simultaneously decreases the negative effect of predation it experiences and decreases the positive effect accrued by the predator; similarly, a predator has an offensive trait that increases the predation rate on prey and increases the benefits obtained by the predator.

Note that, in contrast to many models of species coevolution [35] — [37] , we did not assume that there is trait matching in which the strength of interaction depends on a match between the traits values of each species; in our model both species have traits that cause monotonic benefits to the species.

These benefits, however, have a cost that is exacted by decreases in the intrinsic rate of increase of the species. For example, a prey might increase its defensive trait and as a consequence suffer a reduced reproduction rate. Finally, we modeled trait evolution using a quantitative genetics approach, so the rate of evolution depends on the strength of selection and the additive genetic variance of the trait, where the additive genetic variance is constant.

While this assumption about evolution is unlikely to hold in the long term when mutations will be needed to maintain genetic variation , under very strong selection which will cause loss of genetic variation , and for small populations that lack large initial genetic variation and experience genetic drift , it is a reasonable starting point to investigate the short-term hundreds of generations response of species to climate change [38].

We used the models to pose the question: If the environment changes in such a way that the intrinsic rate of increase of one species rises, how will coevolution affect the equilibrium densities of both species? We made the simplifying assumption that only one of the species experiences a direct change in its intrinsic rate of increase caused by the environmental change; this just makes it easier to separate the evolutionary changes in one species that directly experiences environmentally driven demographic changes from the other species that only responds indirectly through its interactions with the first.

There is no loss of generality with this assumption, however, since the net effect of environmental changes to both species would be, to a first approximation, the simple combination of environmental changes to each species separately Box 1.

To analyze the effects on densities and species traits that climate-driven changes in intrinsic rates of increase can have, we used an analytical approach akin to loop analysis [82]. This approach is complementary to the simulations used in the text and provides more general results that do not depend on the details of simulation models.

For the cases of competition and mutualism, we can use Equation B1 to analyze the effects of conflicting versus nonconflicting evolutionary interests encapsulated in the term d. Formally, d is a partial derivative giving the change in per capita interaction strength between species with respect to the change in the other species' trait value Text S1. Nonetheless, for simplicity we represent this partial derivative as a single term d that we assume is the same for both species.

The values C 1 and C 2 are positive constants such that in the absence of coevolution,. In addition to generalizing the results from the simulations, approximation B2 identifies the positive and negative feedback loops underlying the effects of coevolution, which are given by strings of a ij 's.

The first coevolutionary loop containing a 32 a 23 links the evolution of the trait of species 1 with changes in the density of species 2. The second coevolutionary feedback loop containing a 41 a 14 links the effect of evolution of the trait of species 2 to changes in the density of species 1. Thus, the effects of evolution explicitly involve coevolution of a species trait in response to changes in the density of the interacting species.

This is emphasized by the result of the approximation that if the evolution of one species has no direct effect on the density of the other i. In other words, even if species evolve in response to climate change, and even if this evolutionary response changes the impact they experience from other species, this is not sufficient for evolution to change the response of their abundance to climate change.

In addition, it is necessary for evolution of a species to affect its impact on other species it interacts with. Thus, understanding the case where both species are affected by climate change can be easily determined by combining the cases where a single species is affected. There is a rich history of studies that show the effects of environmental change on demographic factors that affect the intrinsic rates of increase of species. For example, higher temperatures often lead to increased development rates in ectotherms, a relationship that is easily quantified [39].

Similarly, increased environmental carbon dioxide generally leads to increased plant growth, although the strength of this effect varies from species to species [40]. In addition to broad-scale climatic changes such as these, our models have implications for environmental changes on a more local scale.

For example, increased nitrogen and phosphorus runoff and land management regimes can each alter growth or mortality rates, and significantly degrade the structure of ecological communities [41] — [43]. We intentionally did not specify a particular type of environmental effect in order to retain the general applicability of the models, although we recognize that there are a myriad of different effects that environmental changes can bring, and climate change will likely affect multiple environmental factors that will directly impact species' population growth rates.

A key issue in our models is how changes in the trait value of one species affects the fitness of the other species. For example, suppose that selection on the trait of a competitor to decrease the strength of competition it experiences simultaneously decreased the strength of competition experienced by the second species. This could occur if the trait reduced competition by reducing the feeding niche overlap between competitors, so the second species would benefit from selection on the first.

We refer to this case as nonconflicting coevolution. Conversely, if the trait were to make the first competitor more aggressive and hence better able to defend itself against the second competitor, then the second competitor would suffer from the evolution of the first. We refer to this as conflicting coevolution. As we will show, the consequences of coevolution for the abundance of species depend on whether changes in the trait of one species is beneficial or detrimental to its interacting partner—that is, whether coevolution is nonconflicting or conflicting.

Competitors and mutualistic partners could experience either conflicting or nonconflicting coevolution, and different types of models have been used to describe each coevolutionary pathway. For example, competition models where competitors can reduce competition by shifting traits away from competitors e. In contrast, models focused on competitive arms races e. Although coevolution of mutualists is traditionally modeled as nonconflicting e. For example, yucca moths pollinate yucca plants while ovipositing in yucca flowers, and evolution of increased egg production within each flower leads to greater benefit received by the yucca moth, while negatively impacting yucca plants [46].

Thus, conflicting coevolution will occur for mutualists whenever there is the possibility of one partner cheating and reducing the benefit it provides [7] , [46] , [47].

For predator-prey interactions, evolution of prey to decrease the predation rate will generally be detrimental to the predator, whereas evolution of the predator to increase the predation rate will likely be detrimental to the prey. Therefore, coevolution of predator-prey interactions will generally be comparable to conflicting types of competition and mutualism, although as we discuss later, this might not strictly be the case for host-pathogen interactions.

To illustrate our theoretical results that are shared by all interactions—competition, mutualism, and predation—we used simple simulation models that share the characteristics discussed above. To aid the illustration, we selected parameter values intentionally to give coexistence of species at least under some environmental conditions and simple dynamics with stable equilibrium points.

A theoretically more general, yet conceptually more challenging, approach to the same type of model is presented in Box 1 ; this general approach confirms that the qualitative patterns illustrated by our simulations are in fact found much more broadly under the general assumptions we have described.

We modeled coevolution of two competitors using a discrete-time, modified Lotka-Volterra competition model. The density of species i at time t , N i,t , is given by 1 in which F i gives the per capita population growth rate or, equivalently, the fitness of species i.

The trait values that govern the strength of competition experienced by each species at time t are denoted i , t and j , t. The parameter d determines whether coevolution is conflicting or nonconflicting, and hence is key to the model. The trait value i , t affects not only competition experienced by species i but also its intrinsic rate of increase r i E , i , t.

In the model, i , t gives the mean value of a quantitative genetic trait whose distribution among individuals in the population is symmetric with additive genetic variance V i. Provided the magnitude of the variance is not too large [38] , [48] , [49] , selection for changes in the mean value i , t is equal to the derivative of fitness with respect to the trait divided by mean fitness [50].

For our model: 2. The model for two mutualists has the same structure as the competition model 1. As in the competition model, d determines whether coevolution is conflicting or nonconflicting.

The other components of the model are the same as described for the competition model, and evolutionary change is described by Equation 2.

For predator-prey interactions we used a discrete-time version of a model in which the predator attack rate is determined by traits of both prey, 1, t , and predator, 2, t [51]. Prey trait 1, t represents antipredator defense behavior, whereas predator trait 2, t represents the ability of the predator to overcome prey defenses. Although we assumed q E is independent of prey density for simplicity, preliminary analyses showed that incorporating a nonlinear type II functional response [52] does not qualitatively alter the results.

Finally, if V N and V P are the additive genetic variances for prey and predators, respectively, evolution is given by: 4. To illustrate the importance of coevolution—especially the contrast between conflicting and nonconflicting coevolution—for the response of populations to environmental changes, we conducted two types of simulations. For each type, we assumed that the populations begin at eco-evolutionary equilibrium i.

For mutualism and competition models, the two species were initially identical in every way except in their response to environmental change. For the first type of simulation, we tracked the trajectories of population densities and traits through time as the intrinsic rate of increase of one of the species increases with the environment, E.

We compared the trajectories for different levels of genetic variance, because the lower the genetic variance, the slower the rate of evolution. The second type of simulation involved evaluating how environmental changes alter the ecological and coevolutionary equilibriums.

To find these equilibriums, after changing the environment we simulated the models for an additional 1, generations to allow population densities and trait values to stabilize. We did not find alternative stable states, and thus present the single equilibrium for each scenario.

These two types of simulations proved to give the same conclusions, with the simulations of trajectories giving only one additional piece of information: that trait values and densities moved uniformly to the equilibriums given by the second type of simulations.

The correspondence between the two types of simulations results from the fact that the level of genetic variance determines the rate of approach to equilibrium but does not alter the equilibrium itself, which is a joint optimization of fitness in each species. To avoid redundancy, we only present the trajectories for the conflicting competition case, and subsequently focus solely on the equilibrium simulations.

We refer the reader to Box 1 for a full mathematical treatment that does not depend on the specific equations we used for the simulation models. Finally, although we only considered two interacting species here, we have found qualitatively similar results in simulations of larger communities results not shown.

To illustrate the competition model, we began by simulating the consequences of raising the environmental quality for species 1 increasing E through time while varying the rate of coevolution. When the additive genetic variances for the traits expressed by both species, V 1 and V 2 , are zero, evolution cannot occur, whereas increasing V 1 and V 2 increases the rate of evolution. For this illustration we assumed competition is conflicting.

Increasing E increases the density of species 1 and decreases the density of species 2, yet allowing evolution moderates both effects Figure 1A. The effects of coevolution are largely driven by changes in trait values for species 2, with less change in species 1 Figure 1B. This occurs because species 2 evolves to invest heavily in the competitive arms race, limiting the decline in investment by species 1.

For competition, trajectories of species 1 solid lines and species 2 dashed lines densities A and trait values B as the climate variable E increases from 0 to 5 over the course of time steps. Trajectories began at an eco-evolutionary equilibrium, and densities are scaled relative to this equilibrium. The trait value for species i dictates the strength of competition felt by species i per capita of species j.

The top x -axis represents the climatic effect on the intrinsic rate of increase of species 1, b 1 E. This occurs because an increase in the density of species 1 with environmental change leads to a decrease in the density of species 2. Because selection pressure is positively correlated with the density of the other species, species 1 experiences relatively less selection pressure from competition with species 2 compared to the selection pressure on species 2 from species 1. When competition is conflicting Figure 2A,C , the decreased selection on species 1 is beneficial to species 2, which acts to limit the decline of the population of species 2 and hence the decline of its effect on species 1.

Also, the increased selection on species 2 increases its per capita competitive effect on species 1. These two sources of selective pressures combine to help species 2 and, in turn, are detrimental to species 1. When competition is nonconflicting Figure 2B,D , the converse occurs; the decreased selection on species 1 caused by low densities of species 2 increases the effect of competition on species 2, and the increased selection on species 2 decreases its per capita competition effect on species 1.

This selective pressure benefits species 1, further increasing its density. Equilibrium population densities A, B and trait values C, D for two competing species at different climatic conditions. The intrinsic rate of increase of species 1 solid lines increases linearly with climate E , while the intrinsic rate of increase of species 2 dashed lines is unaffected.

The trait value shown on the y -axis of C, D for species i dictates the strength of competition felt by species i per capita of species j. The x -axis represents the climatic effect on the intrinsic rate of increase of species 1, b 1 E. In summary, conflicting competition sets up coevolution as a negative feedback, because selection on one species to reduce competition increases its competitive effect on the other species.

In contrast, nonconflicting competition sets up coevolution as a positive feedback, because selection to reduce the impact of competition on one species also reduces the impact of competition on the other Box 1. As with competition, the effects of coevolution on mutualists depended on the type of coevolution. This effect occurs because the increase in the density of species 1 due to the environmental change increases selection pressure on species 2 for investment in the mutualism.

When the mutualism is conflicting, this change is detrimental to species 1 and limits its increase, because the benefits of mutualism decrease with the investment of species 2 in the interaction. In contrast, in the case of nonconflicting mutualism, increased investment by species 2 is beneficial to species 1, further increasing the density of species 1.

In summary, conflicting mutualism sets up coevolution as a negative feedback, whereas nonconflicting mutualism sets up a positive feedback Box 1. The intrinsic rate of increase of species 1 solid lines increases linearly with the climate variable E , while the intrinsic rate of increase of species 2 dashed lines is unaffected. The trait value for species i shown on the y -axis of C, D dictates the benefits of mutualism accrued by species i per capita of species j.

When the intrinsic rate of increase of species 1 is high enough and species are allowed to coevolve, there is no equilibrium in the nonconflicting mutualism model B, D , as the growth of each species is unbounded.

For competition and mutualism, interacting species might have either conflicting or nonconflicting coevolutionary feedbacks. In contrast, predator and prey interactions are generally expected to exhibit conflicting coevolution and hence generate negative coevolutionary feedbacks: prey coevolution of defenses that reduce predation will be detrimental to the predator, and predator coevolution to increase the predation rate will be detrimental to prey.

To verify this expectation, we analyzed both the case in which climate change increases the prey intrinsic rate of increase and the case in which climate change increases the predation rate and hence the predator population growth rate. When climate change enhances the prey intrinsic rate of increase, the resulting increase in prey density leads to increased predator density, and in the absence of coevolution the equilibrium predator density increases dramatically Figure 4A.

In contrast, the equilibrium predator density increases more slowly when predator and prey coevolve Figure 4A. As with conflicting competition and mutualism, higher predator density strengthens selection pressure for prey investment in the coevolutionary arms race Figure 4C.

With increased prey investment, the predator density cannot increase as much due to heightened prey defense Figure 4A,C. Equilibrium prey solid lines and predator dashed lines densities A, B and trait values C, D for different climatic conditions.