利用最小线性逻辑回归确定物种分布的局部限制因素
Identify local limiting factors of species distribution using min-linear logistic regression
摘要：
逻辑回归是生态建模中常用的构建模块，但其环境预测因子之间的加性结构通常假设预测因子之间存在补偿关系，这可能导致有问题的结果。事实上，根据李比希最小值定律，物种的分布通常是由最不利的因素决定的，这在建模中没有得到解决。为了解决这个问题，我们引入了最小线性逻辑回归模型，它具有内置的竞争因素的最小结构。在我们对亚洲黑熊（$\textit{Ursus thibetanus}$）分布的实证分析中，我们发现最小线性模型与其他方法相比表现良好，并具有一些优势。通过使用该模型，我们能够确定整个调查区域熊分布的生态意义的限制因素。该模型固有的简单性和可解释性，使其成为一个有前途的工具，扩展到其他广泛使用的生态模型。
英文摘要：
Logistic regression is a commonly used building block in ecological modeling, but its additive structure among environmental predictors often assumes compensatory relationships between predictors, which can lead to problematic results. In reality, the distribution of species is often determined by the least-favored factor, according to von Liebig's Law of the Minimum, which is not addressed in modeling. To address this issue, we introduced the min-linear logistic regression model, which has a built-in minimum structure of competing factors. In our empirical analysis of the distribution of Asiatic black bears ($\textit{Ursus thibetanus}$), we found that the min-linear model performs well compared to other methods and has several advantages. By using the model, we were able to identify ecologically meaningful limiting factors on bear distribution across the survey area. The model's inherent simplicity and interpretability make it a promising tool for extending into other widely used ecological models.
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