Limits to Evolvability Define the Maximal Sustainable Niche of Generalists

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One of the most important ways that species can evolve is by changing their niche—the range of environments in which they can thrive and reproduce. The breadth of a niche can expand or contract as a species adapts to changing environments, and quantifying this evolutionary process will help us to understand why ecosystems are so rich and diverse, and to predict how species might adapt to changing habitats. This project will use mathematical theory and computer simulations to uncover basic rules of nature that govern evolutionary change of the niche. Specifically, we aim to model how species might juggle the burdens of adapting to multiple changes across their range of habitats, and to predict when habitat change might drive a species toward a narrower niche. We will also work with high-school teachers to develop and distribute new ways of using computer simulations to help students learn how species compete and coexist in nature.Our proposal uses theory, including both analytical and numerical methods, to quantify connections between a population’s capacity to evolve and its niche breadth. Our central hypothesis is that interference among the adaptive responses to multiple environments limits a population’s ability to exploit a broad niche, even in the absence of genetic trade-offs. To test this hypothesis, we will first model eco-evolutionary feedbacks during a short-term bout of adaptation in a generalist population in two environments; we hypothesize that linkage, hard selection, and strong feedbacks between fitness and population size will favor the evolution of specialization. We will then analyze the long-term evolutionary behavior of specialists on fitness landscapes that permit cost-free generalists; we hypothesize that barriers to evolution of generalism will effectively ‘lock in’ the results of niche reduction, trapping populations at the suboptimal landscape peak of specialism. Finally, we propose to connect these pieces by exploring models of niche evolution in scenarios in which environments continually change due to either extrinsic factors or antagonistic coevolution. Together, these aims combine evolution at short and long time-scales to provide a testable, predictive framework connecting the rate of adaptation to long-term outcomes at the community level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.