Models for Trait-Mediated Dispersal in Ecology

生物体的扩散强烈影响它们与环境和其他生物体种群的相互作用。扩散的生态效应反过来影响个体的适应性，从而影响自然选择，从而推动进化。 因此，扩散效应可以反馈影响生物体的属性和行为，包括那些影响扩散及其后果的属性和行为。 这些属性和行为被称为特质。它们包括寻找资源、躲避捕食者或季节性迁移的倾向，这些都可以被视为扩散策略，但它们也包括生物体利用环境信息来决定如何移动或如何与物种的其他成员交流这些信息的方式。 该项目将使用数学模型来深入了解环境的结构和可变性与预期会演变和持续的扩散特征之间的联系。数学模型是获得这种洞察力所必需的，因为扩散的影响及其演变中涉及的反馈过于微妙和复杂，无法用语言描述来捕捉。 在过去的研究中，研究人员已经很好地了解了在空间变化而不是时间变化的环境中，在单一种群的背景下，哪些固定的传播特征是最有利的。 该项目的一个方面是在环境也在不断变化的背景下解决类似的问题。另一个是更深入地研究扩散如何影响物种间的相互作用，特别是当它与生活史事件的时间变化相互作用时。 这两个研究方向的动机部分是与全球变化有关的生态问题。 该项目的其他方面侧重于了解与传播有关的行为特征;其中一些可能与保护/恢复工作以及了解疾病媒介的移动有关。 该项目的某些部分将与博士后研究员合作进行，因此应有助于加强科学工作队伍。生物的扩散通过决定其空间分布和与其他种群的相互作用来影响其适合度，然后对与扩散相关的性状产生选择压力。 这导致了扩散特性的影响和进化之间的复杂反馈，只能通过使用数学或计算模型来理解。 传统的扩散模型通常假设它是一个固定的行为，独立于环境条件，如简单的扩散。 然而，生物体寻求资源，避免敌人，并从事需要使用和传递信息的迁移等行为。 这些条件扩散行为是性状，可以通过进化来塑造，并且可以从适应动力学的角度被视为策略。 在以前的工作中，研究人员使用了各种模型的成对侵入性分析，包括反应-平流-扩散方程，离散扩散系统及其非局部积分-微分类似物，以表明对于空间变化但时间恒定的环境中的单个非结构化种群，进化稳定策略是那些产生理想自由分布的策略。 研究小组将使用类似的方法来了解在空间和时间都不同的环境中的种群的更现实的背景下，由阶段或内部状态结构化的种群，以及相互作用的种群系统中的扩散特征的演变。 为此，研究人员将使用其他类型的模型，包括积分差分模型以及之前使用的模型。 预计在这些情况下的详细结论往往不同于在时间恒定的环境中的非结构化群体的详细结论。 可能需要一些数值实验来表明结论应该是什么。 还将研究包括扩散和物候的混合模型，以及具有多种扩散模式的生物模型。 其中第一个与理解全球变化引起的物候变化如何与空间效应相互作用有关。 例如，时间上的隔离与空间上的隔离的影响有何相似之处，它们有何不同？ 第二个与了解各种生物体的运动模式有关，包括在寻找吸血和寻找产卵地点之间切换的雌性蚊子。 更广泛地说，了解传播特性与保护和害虫管理以及预测全球变化对自然和农业生态系统的影响有关。

The dispersal of organisms strongly influences their interactions with their environment and with populations of other organisms. The ecological effects of dispersal in turn influence the fitness of individuals, and hence influence natural selection, which drives evolution. Thus, the effects of dispersal can feed back to influence the attributes and behaviors of organisms, including those that affect dispersal and its consequences. Such attributes and behaviors are known as traits. They include tendencies to seek resources, avoid predators, or migrate seasonally, which can be viewed as dispersal strategies, but they also include the ways that organisms use information about their environment to decide how to move or how they communicate such information with other members of their species. This project will use mathematical models to gain insight into the connections between the structure and variability of the environment and the dispersal traits that can be expected to evolve and persist. Mathematical models are necessary to gain such insight because the effects of dispersal and the feedbacks involved in its evolution are too subtle and complex to capture with verbal descriptions. In past research, the investigators have gained a good understanding of which fixed dispersal traits are most favorable in the context of a single population in an environment that varies in space but not in time. One aspect of this project is to address similar issues in the context of environments that are also changing in time. Another is to study more deeply how dispersal influences species interactions, especially when it interacts with variations in timing of life history events. Both of those research directions are motivated in part by ecological problems related to global change. Other aspects of the project focus on understanding behavioral traits related to dispersal; some of those may be relevant to conservation/restoration efforts and to understanding the movement of disease vectors. Some parts of the project will be conducted in collaboration with postdoctoral fellows and thus should contribute to enhancing the scientific workforce.The dispersal of organisms affects their fitness by determining their spatial distribution and interactions with other populations, which then creates selective pressure on traits related to dispersal. This leads to complex feedbacks between the effects and evolution of dispersal traits that can only be understood by using mathematical or computational models. Traditional models of dispersal typically assume it is a fixed behavior, independent of environmental conditions, such as simple diffusion. However, organisms seek resources, avoid enemies, and engage in behaviors such as migration that require the use and transmission of information. These conditional dispersal behaviors are traits, which can be shaped by evolution and can be viewed as strategies from the viewpoint of adaptive dynamics. In previous work, the investigators used pairwise invasibility analysis of various models, including reaction-advection-diffusion equations, discrete diffusion systems, and their nonlocal integro-differential analogues, to show that for a single unstructured population in a spatially varying but temporally constant environment the evolutionarily stable strategies are those that produce an ideal free distribution. The research team will use similar methods to gain understanding of the evolution of dispersal traits in the more realistic contexts of populations in environments that vary in both space and time, populations that are structured by stage or internal state, and systems of interacting populations. To do that, the investigators will use additional types of models including integro-difference models as well as those used before. It is expected that the detailed conclusions in those cases will often be different from those for an unstructured population in a temporally constant environment. Some numerical experiments may be needed to suggest what the conclusions should be. Hybrid models that include both dispersal and phenology will also be studied, as well as models for organisms with multiple dispersal modes. The first of those is relevant to understanding how phenology shifts caused by global change could interact with spatial effects. For example, how are the effects of isolation in time similar to those of isolation in space, and how do they differ? The second is relevant to understanding the movement patterns of various organisms, including female mosquitoes that switch between seeking blood meals and seeking oviposition sites. More broadly, understanding dispersal traits is relevant to conservation and pest management and to predicting the effects of global change on natural and agricultural ecosystems.