植物病害的随机空间模型
Stochastic spatial models of plant diseases
AI总结：
本文提出三个随机空间显式的植物-病原体模型，利用成对近似或矩闭包及时间尺度分离，计算空间聚集对阈值结构的修正，并研究入侵、共存及病原体抗性进化。
摘要：
我提出了三个植物-病原体相互作用的模型。这些模型是随机的，并且在单株植物的尺度上是空间显式的。对于每个模型，我使用成对近似或矩闭包的一种版本，结合时间尺度分离的论证，来确定空间聚集对阈值结构的影响。通过计算入侵早期的空间结构，我找到了对平均场理论的显式修正。在第一章中，我提出了一个疾病对其宿主不直接致命、但影响其与邻居竞争能力的格子模型。我使用一种成对近似来确定入侵和共存的条件。在第二章中，我研究了一个在连续空间中的基本SIR流行病点过程。我实现了一个乘法矩闭包方案，以计算作为空间参数函数的阈值传播率。在最后一章中，我模拟了当两种植物物种共享一种病原体时病原体抗性的进化。进化可能导致宿主发现该疾病是一种有用的武器而选择不抵抗。我使用一个具有普通成对近似假设的格子模型，通过新菌株的重复入侵来研究表型进化。
英文摘要：
I present three models of plant--pathogen interactions. The models are stochastic and spatially explicit at the scale of individual plants. For each model, I use a version of pair approximation or moment closure along with a separation of timescales argument to determine the effects of spatial clustering on threshold structure. By computing the spatial structure early in an invasion, I find explicit corrections to mean field theory. In the first chapter, I present a lattice model of a disease that is not directly lethal to its host, but affects its ability to compete with neighbors. I use a type of pair approximation to determine conditions for invasions and coexistence. In the second chapter, I study a basic SIR epidemic point process in continuous space. I implement a multiplicative moment closure scheme to compute the threshold transmission rate as a function of spatial parameters. In the final chapter, I model the evolution of pathogen resistance when two plant species share a pathogen. Evolution may lead to non--resistance by a host that finds the disease to be a useful weapon. I use a lattice model with the ordinary pair approximation assumption to study phenotypic evolution via repeated invasions by novel strains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
