Integrating Prey Resource into the Analysis of Snow Leopards Corridor and Conflict Risk Zone for Sustainable Conservation

README - Replication Data and Code Manuscript Title: Integrating Prey Resource into the Analysis of Snow Leopards Corridor and Conflict Risk Zone for Sustainable ConservationRepository Content: Data and R scripts for reproducing MaxEnt habitat suitability models. =========================================================================DIRECTORY STRUCTURE AND FILE DESCRIPTIONS========================================================================= This repository is organized into two main folders: 1. 01RawData Contains the spatially rarefied (thinned) occurrence records for the Snow Leopard and its primary prey, as well as the study area boundary. - SnowLeopardOccurrencesThinned. csv - BlueSheepOccurrencesThinned. csv - SiberianIbexOccurrencesThinned. csv - study area/ (Folder containing the shapefile of the study area boundary, used to clip environmental variables) 2. 02RScripts Contains the R code used for data pre-processing and parameter optimization. - Step1GenerateBackgroundPoints. R: Code to generate geographically and environmentally stratified pseudo-absence points based on the occurrence data. - Step2ParameterOptimizationENMeval. R: Code to perform model parameter tuning using the ENMeval package. *IMPORTANT NOTE ON USAGE*: When running these scripts, users must manually adjust species-specific parameters (e. g. , buffer distances, slope thresholds) as detailed in the "Methods" section of the manuscript. The default values in the scripts may need to be updated depending on which species is being analyzed. =========================================================================WORKFLOW SUMMARY========================================================================= 1. Pre-processing: Use `Step1GenerateBackgroundPoints. R` with occurrence data from `01RawData` to generate species-specific pseudo-absence points. 2. Optimization: Use `Step2ParameterOptimizationENMeval. R` to determine optimal feature classes (FC) and regularization multipliers (RM). 3. Final Modeling: The final habitat suitability models were constructed using MaxEnt software (version 3. 4. 3) based on the optimized parameters and generated points. *Note on Post-Processing*: Subsequent spatial analyses mentioned in the manuscript, specifically Core Area Extraction (via MSPA/GuidosToolbox) and Corridor Analysis (via Omniscape. jl in Julia), were conducted using standalone software tools and thus are not included in these R scripts.