  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.
