Modeling Species Distribution and Change Using Random Forest
Modeling Species Distribution and Change Using Random Forest
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DOI：
10.1007/978-1-4419-7390-0_8
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发表时间：
2011-01-01
期刊：
影响因子：
--
通讯作者：
Cushman, Samuel A.
中科院分区：
文献类型：
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作者：
Evans, Jeffrey S.;Murphy, Melanie A.;Cushman, Samuel A.
尽管推理是生态建模中的关键组成部分，但准确的预测和推理之间的平衡是生态学研究的最终目标(Peter 1991；De‘ath 2007)。生态学在保护规划、生态系统评估和生物多样性方面的实际应用高度依赖于对生态过程和空间格局的非常准确的空间预测(Millar等人。2007)。然而，生态系统的复杂性阻碍了我们使用传统的频率主义数据模型生成准确模型的能力(Breiman 2001a；Austin 2007)。生态建模中明确定义的问题，如复杂的非线性相互作用、空间自相关、高维、非平稳、历史信号、各向异性和尺度，导致频率数据模型难以解决的问题(Olden等人。2008年)。当人们批判性地评估生态模型中使用的数据时，很少有数据满足独立性、同方差和多变量正态分布的假设(Breiman 2001a)。这导致了对建模方法和空间自相关等反复出现的问题的影响的不断重新评估。
Although inference is a critical component in ecological modeling, the balance between accurate predictions and inference is the ultimate goal in ecological studies (Peters 1991; De’ath 2007). Practical applications of ecology in conservation planning, ecosystem assessment, and bio-diversity are highly dependent on very accurate spatial predictions of ecological process and spatial patterns (Millar et al. 2007). However, the complex nature of ecological systems hinders our ability to generate accurate models using the traditional frequentist data model (Breiman 2001a; Austin 2007). Well-defined issues in ecological modeling, such as complex non-linear interactions, spatial autocorrelation, high-dimensionality, non-stationary, historic signal, anisotropy, and scale contribute to problems that the frequentist data model has difficulty addressing (Olden et al. 2008). When one critically evaluates data used in ecological models, rarely do the data meet assumptions of independence, homoscedasticity, and multivariate normality (Breiman 2001a). This has caused constant reevaluation of modeling approaches and the effects of reoccurring issues such as spatial autocorrelation.