Functional landscape connectivity is vital for the conservation of wildlife species. Landscape connectivity models often overlook factors such as mortality and asymmetry in landscape resistance that can have a significant impact on functional connectivity. Individual-based models (IBMs) can be used to explore such factors through the implementation of mechanistic dispersal behavior. Furthermore, population-level patterns of animal dispersal and landscape connectivity resulting from the simulation of alternative landscapes or scenarios of animal behavior can be compared. Use an IBM to evaluate the effects of disperser mortality, asymmetrical dispersal due to landscape structure, and land-use change on the functional landscape connectivity between two populations of reintroduced American martens Martes americana. We applied a previously calibrated IBM of marten dispersal to simulate movement between two reintroduced populations in Wisconsin and Michigan, USA. We used machine learning analyses to determine how each factor affected dispersal between populations (connectivity) across five consecutive generations. Functional landscape connectivity between populations was not always correlated with more traditional dispersal metrics, such as dispersal distance. Mortality had the greatest impact on functional connectivity. Land-use change and landscape configuration affected connectivity mostly when mortality was not incorporated into simulations. These experimental factors had a stronger effect on long-distance dispersal between populations than on more local dispersal. Conservation planning for landscape connectivity may benefit from accounting for mortality risks within matrix habitat. The development of individual-based models that incorporate landscape heterogeneity and complex animal behaviors when investigating long-distance dispersal can provide unique and specific insights into both landscape connectivity and wildlife conservation.

中文翻译：

---

基于个体的建模强调了死亡率和景观结构在功能连通性测量中的重要性

功能性景观连通性对于保护野生动物物种至关重要。景观连通性模型通常会忽略诸如死亡率和景观阻力的不对称性等因素，这些因素可能对功能连通性产生重大影响。基于个体的模型 (IBM) 可用于通过实施机械分散行为来探索这些因素。此外，可以比较由模拟替代景观或动物行为场景产生的动物扩散和景观连通性的种群水平模式。使用 IBM 评估传播者死亡率、景观结构导致的不对称传播以及土地利用变化对重新引入的美洲貂的两个种群之间的功能景观连通性的影响。我们应用了先前校准的 IBM 貂散布来模拟美国威斯康星州和密歇根州两个重新引入的种群之间的运动。我们使用机器学习分析来确定每个因素如何影响连续五代人口之间的分散（连通性）。人口之间的功能景观连通性并不总是与更传统的分散指标相关，例如分散距离。死亡率对功能连接的影响最大。当死亡率没有被纳入模拟时，土地利用变化和景观配置主要影响连通性。这些实验因素对种群间远距离扩散的影响比对局部扩散的影响更大。景观连通性的保护规划可能受益于矩阵栖息地内的死亡风险。在调查长距离扩散时，开发结合景观异质性和复杂动物行为的基于个体的模型，可以为景观连通性和野生动物保护提供独特而具体的见解。