The Behavior Of Endangered Populations In A Randomly Fluctuating Environment

Author : Jaime Diamond Ashander
Publisher :
ISBN 13 : 9781369310627
Total Pages : pages
Book Rating : 4.3/5 (16 download)

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Download or read book Causes and Consequences of Evolutionary Rescue in Noisy Environments written by Jaime Diamond Ashander and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Anthropogenic global change poses a grand challenge for management of wild populations. Environmental change affects two fundamental processes—selection and phenotypic plasticity—that shape variation within populations Because variation within populations in turn affects population dynamics, incorporating understanding of how these processes shape trait dynamics into forecasts and management planning is an urgent need. In this dissertation I address that need, focussing on phenotypic plasticity and varying selection and their implications for evolutionary rescue, which occurs when adaptive evolution enables population persistence in a changed environment. A key element in both of these processes is stochasticity. Population dynamics exhibit random-seeming fluctuations that can be caused by random changes in the phenotype-fitness relationship, i.e., fluctuating phenotypic selection. The resulting eco-evolutionary dynamics must be understood if we are to forecast population responses to rapid environmental change. A particularly important context is evolutionary rescue, where evo-evolutionary change affects population persistence. Yet, current theories of evolutionary rescue for selection on continuous phenotypes do not incorporate stochasticity, and thus cannot provide predictions for the effects of fluctuating selection on the distribution of population size. In Chapter 1, I provide such theory, studying evolutionary rescue when population growth depends on adaptation to a fluctuating optimum phenotype and approximating the population size distribution. To make the analytical progress, I ignore effects of genotype-by-environment interactions (plasticity), but they are the focus of the next chapter. Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables evolution of increased plasticity. Heightened plasticity, however, might interact with environmental stochasticity ubiquitous in natural systems (and necessary for plasticity to evolve in the first place) to increase variation in phenotype-environment mismatches in the new environment. The resulting variation in growth rate could increase extinction risk.In Chapter 2, I quantify the adaptive role of plasticity given environmental stochasticity by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. I develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. I find that lower predictability increases extinction risk and reduces persistence, because it increases stochastic load during rescue. This understanding of how stochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests. In Chapter 3, I turn to implications for management. Even if people successfully mitigate global-scale change, many local populations may still be faced with “decelerating change”, where the local environment they experience changes rapidly at first, but this change slows over time. Biological responses to global-scale change likely include evolutionary change. In theory, current knowledge of evolutionary processes can inform optimal allocation of conservation spending. I explore this potential for the case of decelerating change, adapting models from evolutionary biology of a population adapting to a moving optimum. To make progress on this question, I once again ignore the genotype-by-environment interactions that were my focus in Chapter 2. I first show that without intervention, the “decelerating optimum” faced by many populations can still result in strong declines. Assuming that such declines would occur absent an intervention, I examine the optimal control path of an intervention (i) that must avoid extinction and (ii) has minimum costs. In my subsequent analysis, I find that the optimal path of intervention starts out above zero, but ramps up quickly as the population declines. Upon population growth reaching zero, or near-persistence, the intervention ramps down. Both of these patterns are less strong when the population is well below carrying capacity at first. I also find that yearly budgets can constrain action—but when extinction is still avoidable they will result in a more-rapid ramp-up in spending. The results of Chapter 3 show several interesting things. First, it can be optimal to wind down investment, with a combination of evolution and global-scale action (deceleration of the optimum) taking over for local management. Second, an optimal strategy may also result in increasing investment, even in the face of a crashing population. Third, one must monitor the right signals. Although hard to measure, the empirical growth rate (after accounting for the effect of management) provides the best information about whether the control effort can end without jeopardizing the population’s future.