DOI: 10.5061/dryad.31zcrjdxg
title: Are human-altered landscapes reshaping carnivore niche spaces in the trans-Himalaya?

description: Understanding carnivore interactions under growing human pressures is
 crucial for conservation. We examined spatial and temporal niche
 structuring among snow leopards (Panthera uncia), Himalayan wolves (Canis
 lupus chanco), and red foxes (Vulpes vulpes), while also incorporating
 free-ranging dogs (Canis lupus familiaris) as a human-subsidized
 mesopredator whose presence reflects and amplifies anthropogenic
 pressures. Using camera-trap data from the trans-Himalayan landscape of
 Spiti, we applied multi-species occupancy, spatial co-occurrence, and diel
 activity analyses to study how human influence and interspecific
 interactions shape carnivore coexistence. Spatial analyses revealed
 positive associations between red fox occupancy and other carnivores,
 influenced by elevation, prey availability, and distance to human
 settlements, likely reflecting facilitation via scavenging opportunities.
 In contrast, interactions among dogs, wolves, and snow leopards were more
 variable, with both positive and negative spatial associations depending
 on environmental covariates. Temporal partitioning was a key strategy for
 both red foxes and snow leopards to avoid dogs; however, foxes showed no
 significant diel shifts when co-occurring with snow leopards, indicating
 limited temporal avoidance. Snow leopards increased activity but delayed
 peaks with a greater human footprint, likely exploiting livestock or prey
 drawn to such areas. Red foxes, by contrast, delayed peaks but maintained
 activity, and dogs maintained stable activity patterns regardless of human
 footprint. Together, these results demonstrate that carnivores actively
 structure their niches in response to both human pressures and
 interspecific interactions, facilitating coexistence despite overlap in
 resource use. Free-ranging dogs represent an emerging conservation concern
 in the region, underscoring the need for their management and reducing
 human disturbance in critical areas. Integrating insights from multiple
 niche axes can inform more holistic and effective strategies to promote
 carnivore coexistence in human-influenced mountain landscapes.
description: # Are human-altered landscapes reshaping carnivore niche spaces in the
 trans-Himalaya?
 [https://doi.org/10.5061/dryad.31zcrjdxg](https://doi.org/10.5061/dryad.31zcrjdxg) ## Description of the data and file structure This dataset was collected to investigate niche partitioning among carnivores in the Spiti Valley, northern India, under increasing human pressures. The study focused on four species: snow leopards (*Panthera uncia*), Himalayan wolves (*Canis lupus chanco*), red foxes (*Vulpes vulpes*), and free-ranging dogs (*Canis lupus familiaris*). The data includes results from **Camera Trap Surveys** used to monitor species occupancy, spatio-temporal activity patterns, and interactions among carnivores. ### Files and variables #### File: WLB-2025-01494.R2.zip **Description:** It contains the following three folders: ## 1. occupancy The folder contains data and analysis scripts related to the detection and occupancy patterns of carnivore species in the Spiti Valley. #### **Dataset Files** 1. **Detection Histories**: dog (DH_dog.csv), red fox (DH_redfox.csv), snow leopard (DH_SL.csv) & Himalayan wolf (DH_wolf.csv). * These files contain a binary dataset (`1` = species detected, `0` = species not detected) collected from camera trap surveys. * Columns (`o1, o2, o3, o4...`) represent a survey occasion, where one occasion corresponds to a one-week sampling period at a given camera station. * Rows indicate observations from individual survey locations/camera traps. 2. **Scaled Covariates** (Scaled_covs.csv):  * Includes environmental and anthropogenic covariates (e.g., elevation, prey availability, distance from settlements) scaled for analysis. These covariates are used to model occupancy probabilities. * All covariates were standardized (z-scores; mean = 0, SD = 1) prior to modeling. * Covariates were calculated as mean values for the grid cell (1 × 1 km) in which each camera trap was located. Each row, therefore, represents a single grid cell containing one camera trap station. Correlated covariates were not used in the final analysis. | Variable | Description | Unit (before scaling) | | :---------- | :-------------------------------------------------------------------------------------------- | :----------------------------- | | S.No.  | Serial number of records | — | | BI\_CI | Unique camera trap ID combining block and site code (e.g., “K\_A01” = Kibber block, camera 1) | — | | Site | Name of the survey site | — | | Trapnights | Sampling effort at each camera trap | Nights (number of trap-nights) | | Dist\_Drain | Distance from the nearest drainage or stream | Meters | | Dist\_roadm | Distance from the nearest road | Meters | | Dist\_settl | Distance from the nearest human settlement | Meters | | Slopemean | Mean slope value of the grid cell | Degrees | | DEMmean | Mean elevation | Meters above sea level | | NDVImean | Mean Normalized Difference Vegetation Index | Unitless (NDVI index) | | Trimean | Mean terrain ruggedness index value | Unitless | | Small\_prey | Relative abundance index of small prey species (e.g., Pika) | Detections per 100 trap-nights | | Large\_prey | Relative abundance index of large prey species (e.g., Ibex) | Detections per 100 trap-nights | | livestock | Relative abundance index of livestock detections | Detections per 100 trap-nights | | wildprey | Combined prey abundance (small + large prey) | Detections per 100 trap-nights | **Analysis Codes**: The folder includes three R scripts used for multi-species occupancy modeling and visualization of results. All scripts are written in R and require the following packages: unmarked, AICcmodavg, ggplot2, MuMIn, and dplyr * marginal_occupancy.txt – Estimates marginal occupancy probabilities (ψ) for each species independently using site-level covariates, and computes detection probabilities (p) across all survey sites. * conditional_occupancy.txt – Performs conditional occupancy analyses that account for species co-occurrence, estimating how the occupancy of one species varies with the presence or absence of another (e.g., fox given dog presence). * marginal_graphs.txt – Generates graphical outputs of the marginal occupancy models, visualizing predicted occupancy probabilities across key environmental gradients such as elevation or distance to settlements. ## 2. spatial overlap This folder contains data and scripts related to the spatial interactions among carnivore species in the Spiti Valley, with a focus on co-occurrence patterns and the relative abundance index (RAI). #### **Dataset Files** 1. **Co-occurrence File** (All_Co-occurrence.csv): Contains binary data (`1` = species detected, `0` = species not detected) indicating the presence of species at different sampling locations/camera traps. * **Columns**: * `species`: Species names (e.g., *Canis lupus*, *Panthera uncia*, *Feral dog*, *Vulpes vulpes*). * `A_01`, `A_02`, ...: Camera trap IDs. * **Rows**: Detection results for each species at specific locations. 2. **Relative Abundance Index (RAI) File** (All_Pianka_RAI.csv): Contains the Relative Abundance Index (RAI) values for each carnivore species across different sampling locations/camera traps. The RAI represents the relative abundance of species based on detection frequency and was calculated as the number of independent detections per 100 trap-nights for each camera location. * **Columns**: Species names (e.g., *Canis lupus*, *Panthera uncia*, Feral dog, *Vulpes vulpes*). * **Rows**: RAI values calculated for each location **Analysis Codes**:  Includes R script (spatial_overlap_codes.txt) for assessing spatial overlap and co-occurrence among carnivore species. * Pianka’s Niche Overlap Index was used (via the spaa package) to measure pairwise overlap in habitat use among species based on RAI values, including bootstrapped estimates of overlap stability. * Species Co-occurrence Analysis (via the cooccur package) was performed to evaluate non-random patterns of spatial association between species using binary detection data, providing effect sizes, probability tables, and summaries of significant pairwise associations. ## 3. temporal interactions The folder contains data and scripts to analyze the temporal activity patterns of carnivore species in the Spiti Valley. The dataset helps assess temporal overlap between species by examining their activity times throughout the 24-hour cycle. #### **Dataset Files**: 1. **Camera trap data** (CameraTrapProject_CT_data_for_analysis_MASTER.csv): **Contains metadata for all camera trap stations deployed during the survey. Includes information on setup and retrieval dates, session identifiers, and a scaled measure of human influence at each station. This dataset defines the temporal range and operational details for each camera trap, used to align species detections with sampling effort and assess potential human influence. * Session: Sampling period (e.g., Summer2022). * Site: Camera trap ID. * Date_setup / Date_retr: Dates when each camera trap was installed and retrieved. * Problem1_from / Problem1_to: Indicates inactive periods, if any, during the sampling session. * ghm: Scaled Human Footprint Index (z-score), representing relative anthropogenic disturbance at each camera location.  2. **Species detection data** (Species_records.csv): Contains species-level detection records with exact timestamps for each camera trap station. These data form the basis for temporal activity pattern and overlap analyses. The file provides precise detection times used to model daily activity patterns and estimate temporal overlap among species. * Station: Camera trap ID. * Species: Species name (e.g., *Panthera uncia*, *Vulpes vulpes*, Feral dog). * DateTimeOriginal: Original timestamp of the detection event. * Date / Time: Extracted date and time components for analysis. * Session: Sampling period corresponding to the detection. **Analysis Codes**: Includes R scripts to model species-specific diel activity patterns and quantify temporal overlap between carnivore pairs using GLMM-based predictions. * **shape of activity curves.txt** – Models diel activity patterns of individual carnivore species. Uses hourly detection data and trigonometric GLMMs (*GLMMadaptive* package) to classify activity as unimodal, bimodal, or cathemeral based on best-fitting AIC models. * **interaction_code.txt** – Estimates temporal overlap between species pairs by comparing predicted activity probabilities from the best-fitting models, visualizing shared activity periods across a 24-hour cycle.