A theoretical quantitative genetic study of negative ecological interactions and extinction times in changing environments - PubMed (original) (raw)
A theoretical quantitative genetic study of negative ecological interactions and extinction times in changing environments
Adam G Jones. BMC Evol Biol. 2008.
Abstract
Background: Rapid human-induced changes in the environment at local, regional and global scales appear to be contributing to population declines and extinctions, resulting in an unprecedented biodiversity crisis. Although in the short term populations can respond ecologically to environmental alterations, in the face of persistent change populations must evolve or become extinct. Existing models of evolution and extinction in changing environments focus only on single species, even though the dynamics of extinction almost certainly depend upon the nature of species interactions.
Results: Here, I use a model of quantitative trait evolution in a two-species community to show that negative ecological interactions, such as predation and competition, can produce unexpected results regarding time to extinction. Under some circumstances, negative interactions can be expected to hasten the extinction of species declining in numbers. However, under other circumstances, negative interactions can actually increase times to extinction. This effect occurs across a wide range of parameter values and can be substantial, in some cases allowing a population to persist for 40 percent longer than it would in the absence of the species interaction.
Conclusion: This theoretical study indicates that negative species interactions can have unexpected positive effects on times to extinction. Consequently, detailed studies of selection and demographics will be necessary to predict the consequences of species interactions in changing environments for any particular ecological community.
Figures
Figure 1
The effects of a predator-prey interaction on mean extinction times of the two interacting species. The top panel shows the effect under different rates of environmental change, whereas the bottom panel shows the effect for different strengths of selection. In both panels, extinction times of the prey are shown by the red symbols and lines, and those of the predator are shown by the blue symbols and lines. The solid symbols joined by solid lines represent results from experimental runs of the model with the predator-prey interaction intact, whereas the open symbols joined by broken lines show the control runs (in which the predator-prey interaction is removed). Over a wide range of rates of environmental change and strengths of selection, both the predator and prey persist longer when the two species interact than when they do not. Each point represents the mean of 40 runs of the simulation. In the top panel _ω_2 is 49, and in the bottom panel k is 0.20. The values of k are in units of environmental standard deviations, which are slightly smaller than phenotypic standard deviations in this study. See methods for other parameters used to generate these data.
Figure 2
An extensive exploration of the average extinction times in the predator-prey model under different rates of environmental change and strengths of selection. The top panels show the average extinction times of the control populations (i.e., no interaction between predator and prey). The prey is on the left and the predator is on the right. The bottom panels show the percentage change in persistence times for the prey (left) and predator (right) when the predator-prey interaction is included in the model. Blue colors indicate combinations of parameters under which the population persisted longer due to the predator-prey interaction. See methods for the parameter values. I used values of _ω_2 ranging from 5 to 95, in increments of 10, and values of k ranging from 0.15 to 0.35 in increments of 0.025. The values of k are units of environmental standard deviations for the traits, which are slightly smaller than phenotypic standard deviations in this study. For each combination of parameters, I averaged across 200 runs of the simulation to generate the means depicted in this figure.
Figure 3
Results of the competition model under different strengths of selection and rates of environmental change. The top panels show average extinction times for the first species to go extinct (left) versus the last species to go extinct (right) with no interaction between the species. The bottom panels show the percentage change in expected extinction times due to the competitive interaction. Note that the first species to go extinct (which lags the greatest distance from the optimum) always goes extinct more rapidly when competition is present than when it is absent (bottom left). However, under most combinations of parameters, the species closest to the moving optimum persists for longer periods of time when competition is present than when it is absent (bottom right). See Methods and Figure 1 for parameter values.
Figure 4
Mechanistic details of the predator-prey model for a sample set of parameter values. The top two panels show the lag and additive genetic variance over time in the predator-prey model for the two species in typical control (a) and experimental (b) runs of the simulation. When the predator-prey interaction is absent, the lag for both species increases rapidly (a). However, when the predator-prey interaction is present, the rate of increase of the lag is reduced for both species, permitting both species to persist for longer periods of time before extinction (b). Under these parameter combinations, the reduced lag occurs for both the predator (solid blue lines) and the prey (solid red lines). The additive genetic variances of the predator (broken blue lines) and prey (broken red lines) do not differ dramatically between the experimental and control runs, except that there is a slightly more rapid loss of genetic variance in the prey during the experimental runs. The bottom panel (c) shows the tendency for the predator to eat the most maladapted individuals as the optimum moves. When the optimum does not move (black lines), the difference between mean predator gape size and mean prey body size reaches a steady-state expected value. A similar situation occurs for the difference between the mean size of prey in the population and the mean size of prey that are actually eaten. As the optimum moves (red lines), however, the gape size of the predator decreases relative to the mean prey size, and the difference in size between mean prey size and predated individuals becomes even greater than it is in the population experiencing a stationary optimum. The results depicted in this figure used the standard set of parameter values (see methods), and k and _ω_2 were set to 0.15 and 50, respectively. Each point on each graph is a mean from 50 replicate runs of the simulation.
References
- Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE. Extinction risk from climate change. Nature. 2004;427:145–148. doi: 10.1038/nature02121. -DOI -PubMed
- Lynch M, Lande R. In: Biotic Interactions and Global Change. Kareiva PM, Kingsolver JG, Huey RB, editor. Sinauer, Sunderland, Massachusetts; 1993. Evolution and extinction in response to environmental change; pp. 234–250.
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