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）中表示的空间数据相联系的模拟模型正在成为保护生物学和景观生态学等领域的一个突出特征。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，他们的有效性是有限的，由于景观数据，栖息地建模，以及参数代表我们的理解分散动物的行为的不确定性。使用基于代理的，面向对象的建模框架的数据集成和合成，我们将探讨空间景观数据的模糊表示及其随后使用的空间显式人口模型（SEPMs）之间的相互作用。将开发基于经验的方法，用于基于档案现场数据导出栖息地的空间明确的模糊表示的策略。基于GIS的栖息地表示的进步将被纳入一个建模框架，允许探索的理论模型的分散行为，将纳入感知范围的各向异性性质，以及景观矩阵对个人的行为的影响。这扩展了最近的工作，集成了模糊表示在GIS数据库模块中的动物运动行为的模糊控制使用的概念，可探测对象。一个通用的模型构建目标是能够显式地表示行为权衡，或不同的决策策略，分散器可以使用导航的景观。它将促进我们对分散动物在不同情况下如何表现的理解，从而帮助设计空间上明确的模拟，这些模拟可以将景观表示为物种相关的空间信息和基于个体的动物运动模型。这种系统的改进应能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.