Center for Conservation Biology, Department of Biology, University of Washington, Seattle, Washington, United States of America.
U.S. Environmental Protection Agency, Pacific Ecological Systems Division, Corvallis, Oregon, United States of America.
PLoS One. 2023 Mar 9;18(3):e0282535. doi: 10.1371/journal.pone.0282535. eCollection 2023.
Eco-evolutionary dynamics result when interacting biological forces simultaneously produce demographic and genetic population responses. Eco-evolutionary simulators traditionally manage complexity by minimizing the influence of spatial pattern on process. However, such simplifications can limit their utility in real-world applications. We present a novel simulation modeling approach for investigating eco-evolutionary dynamics, centered on the driving role of landscape pattern. Our spatially-explicit, individual-based mechanistic simulation approach overcomes existing methodological challenges, generates new insights, and paves the way for future investigations in four focal disciplines: Landscape Genetics, Population Genetics, Conservation Biology, and Evolutionary Ecology. We developed a simple individual-based model to illustrate how spatial structure drives eco-evo dynamics. By making minor changes to our landscape's structure, we simulated continuous, isolated, and semi-connected landscapes, and simultaneously tested several classical assumptions of the focal disciplines. Our results exhibit expected patterns of isolation, drift, and extinction. By imposing landscape change on otherwise functionally-static eco-evolutionary models, we altered key emergent properties such as gene-flow and adaptive selection. We observed demo-genetic responses to these landscape manipulations, including changes in population size, probability of extinction, and allele frequencies. Our model also demonstrated how demo-genetic traits, including generation time and migration rate, can arise from a mechanistic model, rather than being specified a priori. We identify simplifying assumptions common to four focal disciplines, and illustrate how new insights might be developed in eco-evolutionary theory and applications by better linking biological processes to landscape patterns that we know influence them, but that have understandably been left out of many past modeling studies.
当相互作用的生物力量同时产生人口统计学和遗传种群反应时，就会产生生态进化动态。生态进化模拟器传统上通过最小化空间模式对过程的影响来管理复杂性。然而，这种简化可能会限制它们在实际应用中的效用。我们提出了一种新的模拟建模方法，用于研究生态进化动态，其核心是景观格局的驱动作用。我们的空间显式、基于个体的机械模拟方法克服了现有方法学的挑战，产生了新的见解，并为景观遗传学、种群遗传学、保护生物学和进化生态学这四个焦点学科的未来研究铺平了道路。我们开发了一个简单的基于个体的模型来说明空间结构如何驱动生态进化动态。通过对我们的景观结构进行微小的改变，我们模拟了连续的、隔离的和半连接的景观，并同时测试了几个焦点学科的经典假设。我们的结果表现出了预期的隔离、漂移和灭绝模式。通过对原本功能静态的生态进化模型施加景观变化，我们改变了关键的涌现属性，如基因流和适应性选择。我们观察到了这些景观操作对演示-遗传响应，包括种群大小、灭绝概率和等位基因频率的变化。我们的模型还展示了演示-遗传特征（如世代时间和迁移率）如何从机械模型中产生，而不是预先指定。我们确定了四个焦点学科中常见的简化假设，并说明了如何通过更好地将生物过程与我们知道会影响它们但在许多过去的建模研究中被忽略的景观模式联系起来，在生态进化理论和应用中产生新的见解。
