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

Liang, Xuankai1 ; Long, Zexu 2 ; Chen, Shiyu1 ; Qi, Jinzhe1 ; Sun, Buyi3 ; Roberts, Nathan James 1 ; Bao, Heng1 ; Jiang, Guangshun 1

Published Apr 25, 2025 on Dryad. https://doi.org/10.5061/dryad.47d7wm3p6

Data files

Apr 25, 2025 version files 606.72 KB

original_data.xlsx

595.55 KB
README.md

7.97 KB
Source_code_files_of_the_models.R

3.20 KB

Abstract

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 variables in the Amur tiger and leopard models. Our findings emphasize the importance of spatial non-stationarity in predator-prey habitat selections, and the heterogeneous selection by prey may drive dispersals of large felids across complex road landscapes. This study offers new insights into how to help apex predators cross road barriers by effectively managing prey habitat selection in a landscape dominated by roads, providing valuable guidance for future habitat conservation policies.

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

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) and are commented to support reproducibility. Users are encouraged to modify file paths and data names as needed. These scripts are provided under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.
README.md: This README file.

3. Description of Variable

The following table summarizes the environmental variables used in the dataset, their definitions, units, and sources.

Habitat Factor	Habitat Variable	Description	Source	Units
Topographic factor	Elevation	Elevation values for each 200m grid cell	SRTM 1 Arc-Second Global	m
	Aspect	Slope aspect derived from the digital elevation model	Ditto	°
	Slope	Slope degree derived from the digital elevation model	Ditto	°
Forest type	BDF	Distance to nearest closed (>40%) broadleaved deciduous forest patch	GlobCover 2009, ESA	m
	NDEF	Distance to nearest open (15–40%) needle-leaved forest patch	Ditto	m
	MBNF	Distance to nearest mixed broadleaved and needle-leaved forest patch	Ditto	m
	MFSG	Distance to nearest mosaic forest/shrubland-grassland patch	Ditto	m
	MGFS	Distance to nearest mosaic grassland-forest/shrubland patch	Ditto	m
	SV	Distance to nearest sparse (<15%) vegetation patch	Ditto	m
Human disturbance	Settlements	Distance to nearest temporary settlements	National Basic Geographic Database (1:250,000), 2015	m
	Farmland	Distance to nearest farmland area	Ditto	m
	Ginseng	Distance to nearest ginseng plantation	Ditto	m
	Mining	Distance to nearest mining area	Ditto	m
	Pasture	Distance to nearest pasture	Ditto	m
	Village	Distance to nearest village	Ditto	m
	P_Road	Distance to nearest primary road	Ditto	m
	S_Road	Distance to nearest secondary road	Ditto	m
	T_Road	Distance to nearest tertiary road	Ditto	m
Weather factor	Snow	Average snow depth per 200m grid (measured during Jan–Feb 2018)	Field surveys, 2018	cm
Other	Food	Number of new shrub branches within the 200m grid (proxy for food availability)	Field surveys, 2018	–
	River	Distance to nearest river or tributary	National Basic Geographic Database (1:250,000), 2015	m

4. Software Description: `sgwrwin.exe`

The sgwrwin.exe software used in the analysis is not distributed as part of this Dryad submission due to license constraints. For access and usage, please refer to https://gwrtools.github.io/.

5. Licensing and Data Source Compliance

All data included in this submission complies with the CC0 public domain waiver required by Dryad. Specifically:

The SRTM elevation dataset is distributed under open access with no restrictions, and is publicly available via the U.S. Geological Survey (USGS): https://doi.org/10.5066/F7PR7TFT
The GlobCover 2009 land cover dataset is publicly distributed by the European Space Agency (ESA), and made available for free public access. While not formally CC0, ESA does not restrict reuse or redistribution, thus compliant with Dryad’s data sharing principles.
All field-collected data and derived variables are original and generated by the authors.

6. Contact

For questions about the dataset, please contact:

Name:**Xuankai Liang
Affiliation:**Feline Research Center of National Forestry and Grassland Administration, College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China
Email:liangxk2023@163.com