Spatial heterogeneity of habitat selection of large carnivores and their ungulate prey in proximity to roads

Geographic heterogeneity, encompassing both species-environment interactions and interspecific relationships, significantly influences the ecological attributes of wildlife habitat selection and population distribution. However, the impact of geographic heterogeneity on the distribution of target species within predator-prey systems, particularly in human-dominated landscapes, remains unclear. By conducting line transect surveys, utilizing a monitoring network, and applying logistic Geographically Weighted Regression (GWR) in conjunction with generalized linear models (GLM), we examined the spatial heterogeneity of habitat selection by the Amur tiger, Amur leopard, and their main ungulate prey, wild boar and roe deer, in Northeast China. Our results suggest that the factors affecting the spatial distribution of predators are more complex than those for prey. More significantly, the selection coefficients of roe deer and wild boar for certain habitat factors serve as crucial explanatory ..., , , # Data from: Spatial heterogeneity of habitat selection of large carnivores and their ungulate prey in proximity to roads

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[Access this dataset on Dryad] https://doi.org/10.5061/dryad.47d7wm3p6
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## 1. Dataset Overview

This dataset supports the publication titled *\"Spatial heterogeneity of habitat selection of large carnivores and their ungulate prey in proximity to roads\"*. The data were collected and processed to analyze how habitat variables and anthropogenic disturbances affect habitat selection patterns. The dataset includes spatial environmental variables at a 200-meter resolution, field survey results, and outputs from geographically weighted regression models.

## 2. File Inventory

* original data. xlsx: Primary dataset with spatial environmental predictors.
* Source code files of the models. R: The dataset includes R scripts used to perform data preprocessing, model fitting, and spatial prediction using GLM methods.

All scripts are written in R (tested in R 4.3.2) ...,