Limits to Evolvability Define the Maximal Sustainable Niche of Generalists

物种进化最重要的方式之一就是改变它们的生态位，即它们能够繁衍生息的环境范围。生态位的宽度可以随着物种适应不断变化的环境而扩大或缩小，量化这一进化过程将有助于我们理解为什么生态系统如此丰富和多样，并预测物种如何适应不断变化的栖息地。这个项目将使用数学理论和计算机模拟来揭示自然界的基本规则，这些规则支配着生态位的进化变化。具体来说，我们的目标是模拟物种如何在其栖息地范围内适应多种变化的负担，并预测栖息地的变化何时可能将物种推向更窄的生态位。我们还将与高中教师合作，开发和推广使用计算机模拟的新方法，帮助学生了解物种在自然界中如何竞争和共存。我们的提案使用理论，包括分析和数值方法，量化种群进化能力与生态位宽度之间的联系。我们的中心假设是，即使在没有遗传权衡的情况下，对多种环境的适应性反应之间的干扰也限制了种群利用广泛生态位的能力。为了验证这一假设，我们将首先在两个环境中的通才人口的适应短期回合的生态进化反馈模型，我们假设，连接，硬选择，和健身和人口规模之间的强反馈将有利于专业化的演变。然后，我们将分析长期的进化行为的专家对健身景观，允许无成本的通才;我们假设，通才进化的障碍将有效地“锁定”的结果生态位减少，陷阱人口在次优景观高峰的专业化。最后，我们建议通过探索生态位进化模型来连接这些片段，在这些模型中，由于外部因素或对抗性共同进化，环境不断变化。总之，这些目标结合了联合收割机在短期和长期时间尺度上的演变，提供了一个可测试的，预测性的框架，将适应率与社区层面的长期成果联系起来。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.