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

空间自相关和物种分布模型：使用模拟数据分析空间结构、采样策略、统计方法和规模的影响

• 批准号: 0962198
• 负责人: Jennifer Miller
• 金额: $ 26.69万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0962198&HistoricalAwards=false
• 关键词: Spatial; Autocorrelation; Species; Distribution; Models

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).

利用物种分布模型（SDM）来绘制和监测动植物分布，在认识环境变化及其生态后果的背景下变得越来越重要。 虽然SDM中使用的统计方法越来越复杂，但绝大多数方法都没有考虑数据的空间自相关性。 当忽略空间自相关并使用非空间统计方法时，系数估计值的精确度较低，并且总体上可以很好地指定模型。 显式的空间统计方法不仅可以改善这些模型的校准问题，但他们也可以纳入空间过程，如竞争，扩散和干扰的信息。 虽然最近有越来越多的SDM研究，明确解决空间统计方法，结果一直不协调，难以综合。 这些研究的位置，数据或规模的特定性质阻碍了努力解开空间结构的影响，在数据，抽样策略，研究的规模，和统计方法的使用。 本计画针对这些议题中的每一个，提出一般性的研究问题：空间自相关如何影响物种分布模式？ 研究的重点是使用多分辨率模拟分布图和新的评估措施，以分析如何在每一个四个问题的差异-空间结构，抽样策略，规模和统计方法-影响SDM分别和一致。该项目的主要成果将是一个框架，可用于指导使用具有空间自相关性的二进制数据时模型概念化和开发的各个方面（抽样策略，使用的统计方法和适当的空间尺度）。 这项研究将作出重要贡献，更好地了解空间自相关如何影响二进制响应数据使用的归纳模型。 除了预测物种分布，这些模型已成为一个重要的和广泛使用的决策工具，用于各种地理学应用，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位置，以及绘制媒介传播疾病的传播和风险。 在脑电描记术之外，类似的二进制响应模型用于医疗/健康应用（例如，诊断测试），并解决经济和社会科学问题（如劳动力市场状况，信用评分和投票行为）。