Identify local limiting factors of species distribution using min-linear logistic regression

TL;DR

This paper introduces a min-linear logistic regression model that better captures the limiting factors of species distribution by aligning with ecological principles, demonstrated through analysis of Asiatic black bears.

Contribution

The paper presents a novel min-linear logistic regression model that incorporates the minimum structure of ecological limiting factors, improving interpretability and ecological relevance.

Findings

The min-linear model outperforms traditional methods in species distribution analysis.

It successfully identifies ecologically meaningful limiting factors for bear distribution.

The model's simplicity facilitates ecological interpretation and extension.

Abstract

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.