DOI: 10.5061/dryad.47d7wm3p6
title: Data from: Spatial heterogeneity of habitat selection of large carnivores and their ungulate prey in proximity to roads

description: 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.
description: # Data from: Spatial heterogeneity of habitat selection of large
 carnivores and their ungulate prey in proximity to roads ``` [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](https://creativecommons.org/publicdomain/zero/1.0/). *
 `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](https://doi.org/10.5066/F7PR7TFT) | 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 (&gt;40%) broadleaved deciduous forest patch | [GlobCover 2009, ESA](http://due.esrin.esa.int/page_globcover.php) | 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 (&lt;15%) vegetation patch | Ditto | m | | Human disturbance | **Settlements** | Distance to nearest temporary settlements | [National Basic Geographic Database (1:250,000), 2015](https://www.ngcc.cn/ngcc/) | 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](https://www.ngcc.cn/ngcc/) | 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/](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](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](mailto:liangxk2023@163.com "Link to email address")