Incorporating fuzzy sets in spatially explicit ecological modelling

将动物行为和种群动态与地理信息系统(GIS)中的空间数据联系起来的模拟模型正在成为保护生物学和景观生态学等领域的重要特征。这些模型在保护工作中很有价值，因为它们在对比管理方案中的实用性。然而，由于景观数据、栖息地建模以及代表我们对分散动物行为的理解的参数的不确定性，它们的有效性受到限制。使用基于主体的、面向对象的数据集成和综合建模框架，我们将探索空间景观数据的模糊表示与其在空间显式人口模型(SEPM)中的使用之间的相互作用。将制定以经验为基础的方法的战略，以档案现场数据为基础，得出栖息地的空间显式模糊表示。基于地理信息系统的栖息地表示的进展将被纳入一个建模框架，允许探索扩散行为的理论模型，该模型将结合感知范围的各向异性以及景观矩阵对个体行为的影响。这扩展了最近的工作，即将地理信息系统数据库模块中的模糊表示与使用可能对象的概念对动物运动行为的模糊控制相结合。一个通用的模型构建目标是能够明确地表示分散者可能用来导航的行为权衡或不同的决策策略。它将促进我们对分散的动物在不同环境下的行为的理解，从而帮助设计空间上明确的模拟，合理地将景观表示为与物种相关的空间信息，以及基于个体的动物运动模型。这种系统的改进应该会提高为保护工作提供的生态情报的质量。

Simulation models that connect animal behavior and population dynamics with spatial data represented in a geographic information system (GIS) are becoming a prominent feature in the fields such as conservation biology and landscape ecology. These models are valued in conservation efforts because of their utility in contrasting management scenarios. However, their validity is limited due to uncertainties concerning landscape data, habitat modeling, as well as parameters representing our understanding of the behavior of dispersing animals. Using an agent-based, object oriented modeling framework for data integration and synthesis, we will explore the interplay between the fuzzy representation of spatial landscape data and their subsequent use in spatially explicit population models (SEPMs). Strategies for an empirically based methodologies for deriving a spatially explicit fuzzy representation of habitat based on archival field data will be developed. The advance in GIS-based habitat representation will be incorporated into a modeling framework allowing exploration of theoretical models of dispersal behavior that will incorporate the anisotropic nature of perceptual range as well as landscape matrix effects on an individual's behavior. This extends recent work that integrates fuzzy representation in the GIS database module with the fuzzy control of animal movement behavior using the concept of Probeable objects. A general model building objective is to be able to explicitly represent the behavioral tradeoffs, or differing decision strategies, that dispersers may use to navigate the landscape. It will advance our understanding of how dispersing animals may behave under different circumstances, thus helping design spatially explicit simulations that plausibly represent both the landscape as species-relevant spatial information and the individual based model of animal movement. The improvements in such a system should advance the quality of ecological intelligence provided to conservation efforts.