This README describes the R scripts and datasets used in the manuscript titled:
"Taxonomic, spatiotemporal patterns and ecological correlates of new mammal distribution records in China"
Submitted to Global Ecology and Biogeography (2025)
Implements Bayesian Phylogenetic Generalized Linear Mixed Models (BPGLMM) using the brms package. Assesses the effects of ecological, environmental, and anthropogenic predictors on the probability of new mammal distribution records at the species level.
Performs chi-square tests and calculates standardized residuals for categorical traits (e.g., activity pattern, endemism, IUCN status, human use) to examine associations with new records.
Conducts multivariate regression models at the provincial level to assess the roles of historical/current survey effort, GDP, species richness, area, and habitat heterogeneity in explaining spatial variation in new records. Includes partial regression analysis and hierarchical partitioning.
Creates radar plots visualizing the compass directionality of new records relative to species' known ranges.
Runs chi-square tests to detect non-random compass direction patterns in the spatial occurrence of new records.
All scripts are compatible with R version 4.4.1 and require standard spatial and Bayesian modeling packages (brms, phytools, ggplot2, etc.).
New Mammal Records Dataset
Bibliographic dataset of 192 studies documenting newly recorded mammal occurrences in China (2001–2023), including metadata on record source, species, location, and detection context.
Species Traits and Environmental Data
Comprehensive dataset of 555 terrestrial mammal species in China, including biological traits (e.g., body mass, activity pattern), range size, elevation metrics, climate niche parameters, and human disturbance indicators.
GBIF Occurrence-based Niche Estimates
Used in sensitivity analysis to validate climatic niche estimates derived from IUCN ranges. Contains cleaned GBIF occurrence points and corresponding climate values used to recalculate species-level climatic means and variances.
- Chenchen Ding (Postdoctoral Fellow, Peking University)
- Jiale Ding, Huijie Qiao, Zhigang Jiang, Zhiheng Wang Contributors to data compilation, statistical modeling, and manuscript preparation.
For questions regarding code or data, please contact the corresponding author:
📧 chenchen.ding@pku.edu.cn; dccpanther@163.com
Last updated: 2025-08-02