Spatial Autocorrelation and Species Distribution Models: Analyzing the Effects of Spatial Structure, Sampling Strategy, Statistical Methods, and Scale Using Simulated Data

在认识到环境变化及其生态后果的背景下，利用物种分布模型(SDM)绘制和监测动植物分布已变得越来越重要。尽管在SDM中使用了越来越复杂的统计方法，但绝大多数方法都是在没有考虑数据的空间自相关性的情况下开发的。当忽略空间自相关性并使用非空间统计方法时，系数估计的精确度较低，总体而言，模型可能无法很好地描述。明确的空间统计方法不仅可以改进这些模型校准问题，而且它们还可以包含关于竞争、扩散和扰动等空间过程的信息。尽管最近明确涉及空间统计方法的SDM研究有所增加，但结果并不一致，很难综合。这些研究的地点、数据或尺度的特定性质阻碍了努力理清数据中空间结构、抽样策略、研究规模和所使用的统计方法的影响。这个项目专门用一个一般性的研究问题来解决这些问题：空间自相关性如何影响物种分布模型？研究重点是利用多分辨率模拟分布图和新的评估方法来分析空间结构、抽样策略、尺度和统计方法这四个问题上的差异是如何分别和一致地影响SDM的。这个项目的主要成果将是一个框架，当使用具有空间自相关性的二进制数据时，该框架可用于指导模型概念化和发展的所有方面(抽样策略、所使用的统计方法(S)和适当的空间尺度)。这项研究将对更好地理解空间自相关如何影响用于二元响应数据的归纳模型做出重要贡献。除了预测物种分布，这些模型已成为各种生物地理应用的重要和广泛使用的决策工具，例如研究气候变化的影响，确定潜在的保护区，确定可能易受入侵的地点，以及绘制媒介传播的疾病传播和风险地图。在生物地理学之外，类似的二元响应模型被用于医疗/健康应用(例如，诊断测试)以及解决经济和社会科学问题(例如，劳动力市场状况、信用评分和投票行为)。

The use of species distribution models (SDM) to map and monitor animal and plant distributions has become increasingly important in the context of awareness of environmental change and its ecological consequences. Although increasingly sophisticated statistical methods are being used in SDM, the vast majority has been developed without considering spatial autocorrelation in the data. When spatial autocorrelation is ignored and nonspatial statistical methods are used, coefficient estimates are less precise and overall the models can be poorly specified. Explicitly spatial statistical methods not only can improve upon these model calibration issues, but they also can incorporate information on spatial processes such as competition, dispersal, and disturbance. Although there has been a recent increase in SDM studies that address explicitly spatial statistical methods, results have been incongruous and difficult to synthesize. The location- , data-, or scale-specific nature of these studies has impeded efforts to disentangle the effects of spatial structure in the data, sampling strategy, the scale of the study, and statistical methods used. This project addresses each of these issues specifically with the general research question: how does spatial autocorrelation affect species distribution models? The research focuses on using multi-resolution simulated distribution maps and novel assessment measures in order to analyze how differences in each of the four issues- spatial structure, sampling strategy, scale, and statistical methods- impact SDM both separately and in concert. The main outcome of this project will be a framework that can be used to guide all aspects of model conceptualization and development (sampling strategy, statistical method(s) used, and appropriate spatial scale) when using binary data with spatial autocorrelation. The research will make an important contribution to a greater understanding of how spatial autocorrelation affects inductive models used with binary response data. Beyond predicting species distributions, these models have become an important and widely used decision-making tool for a variety of biogeographical applications, such as studying the effects of climate change, identifying potential protected areas, determining locations potentially susceptible to invasion, and mapping vector-borne disease spread and risk. Outside of biogeography, similar binary response models are used in medical/health applications (e.g., diagnostic tests) and to address economic and social science questions (e.g. labor market status, credit scoring, and voting behavior).