Obtaining Environmental Favourability Functions from Logistic Regression
Obtaining Environmental Favourability Functions from Logistic Regression
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DOI：
10.1007/s10651-005-0003-3
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发表时间：
2006-06
影响因子：
3.8
通讯作者：
R. Real;A. Márcia Barbosa;J. M. Vargas
中科院分区：
文献类型：
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作者：
R. Real;A. Márcia Barbosa;J. M. Vargas
Logistic回归是一种统计工具，广泛用于从有无数据和一组自变量开始预测物种的潜在分布。然而，Logistic回归方程不仅根据预测变量的值计算概率值，还根据数据集中存在和不存在的相对比例来计算概率值，这不能充分描述环境对物种存在的有利或不利。已经使用了一些策略来规避这一点，但它们通常意味着更改原始数据或丢弃潜在有价值的信息。我们提出了一种从Logistic回归中获得环境有利度函数的方法，其结果不受在场和缺席比例不均匀的影响。我们测试了该方法在虚构领域中的虚拟物种分布。无论在场/缺席比率的变化如何，优选度模型都产生了相似的值。我们还以西班牙的比利牛斯德斯曼(Galemys Pyrenaicus)分布为例进行了说明。优选度模型比Logistic回归模型得到了更真实的潜在分布图。有利性值可以看作是环境条件对物种有利的地点的模糊集合的隶属度，这使得模糊逻辑的规则能够应用于分布建模。它们还允许在研究区域内具有不同存在/不存在比率的物种的模型之间进行直接比较。这使得它们更有助于估计区域的保护价值，设计生态走廊，或选择合适的区域重新引入物种。
Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes them more useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.