DOI: 10.5061/dryad.547d7wmd9
title: Data from: Understanding carnivore interactions in a cold arid trans-Himalayan landscape: What drives co-existence patterns within predator guild along varying resource gradients?

description: Predators compete for resources aggressively, forming trophic hierarchies
 that shape the structure of an ecosystem. Competitive interactions between
 species are modified in human-altered environments and become particularly
 important where an introduced predator can have negative effects on native
 predator and prey species. The trans-Himalayan region of northern India
 has seen significant development in tourism and associated infrastructure
 over the last two decades, resulting in many changes to the natural
 setting of the landscape. While tourism, combined with unmanaged garbage,
 can facilitate red fox (Vulpes vulpes), it also allows free-ranging dogs
 (Canis lupus familiaris), an introduced mesopredator, to thrive—possibly
 more than the native red fox. We look at the little-known competitive
 dynamics of these two mesocarnivores, as well as their intra-guild
 interactions with the region's top carnivores, the snow leopard
 (Panthera uncia) and the Himalayan wolf (Canis lupus chanco). To study
 interactions between these four carnivores, we performed multispecies
 occupancy modeling and analyzed spatiotemporal interactions between these
 predators using camera trap data. We also collected scat samples to
 calculate dietary niche overlaps and determine the extent of competition
 for food resources among these carnivores. The study found that, after
 controlling for habitat and prey covariates, red fox site use was related
 positively to snow leopard site use, but negatively to dog and wolf site
 use. In addition, site use of the dog was associated negatively with top
 predators, that is, snow leopard and Himalayan wolf, while top predators
 themselves related negatively in their site use. As anthropogenic impacts
 increase, we find that these predators coexist in this resource-scarce
 landscape through dietary or spatiotemporal segregation, implying
 competition for limited resources. Our research adds to the scant
 ecological knowledge of the predators in the region and improves our
 understanding of community dynamics in human-altered ecosystems.
description: To examine carnivore interactions, we applied multi-species
 occupancy modelling and assessed spatiotemporal interactions among
 predators using camera trap data. Additionally, scat samples were analyzed
 to estimate dietary niche overlap and evaluate potential competition for
 food resources among the carnivores.
description: # Understanding carnivore interactions in a cold arid trans-Himalayan
 landscape: What drives co-existence patterns within predator guild along
 varying resource gradients?
 [https://doi.org/10.5061/dryad.547d7wmd9](https://doi.org/10.5061/dryad.547d7wmd9) The data was analysed using Scat samples and Camera Trap Surveys to study species diet patterns, occupancy and spatio-temporal activity patterns, and interactions among carnivores in Spiti Valley.  Files and variables **File**: **Data_archive__ECE-2022-10-01556.R1.zip** Description: It contains the following four folders: 1. \******Diet ****** This folder contains data and analyses used to estimate dietary niche overlap among carnivore species across three study sites in the Spiti Valley (Chandratal, Kibber, and Pin). #### **Data Files:** * `Chandratal_dietoverlap.csv` * `Kibber_dietoverlap.csv` * `Pin_dietoverlap.csv` Each file presents the relative frequency of occurrence (RF) of different food items consumed by carnivore species pairs (Snow leopard–Red fox, Snow leopard–Wolf, and Red fox–Wolf). #### **Data Description:** Each row represents a prey or food item, and columns include: * **Food items** – Prey or food category identified in scats. * **Species (p~ij~, p~ik~)** – Relative frequency (%) of each food type consumed by the two species compared. * **p~ij~*p~ik~, p~ij~, p~ik~** – Intermediate values used in Pianka’s index computation. * **Overlap** – Final Pianka’s niche overlap index (O~jk~) between the species pair. #### **Calculation Details:** The **relative frequency (RF)** of each food item was calculated as: RF=Number of occurrences of each food type/Total occurrences of all food items×100 The **dietary niche overlap** was quantified using **Pianka’s Index**: O~jk~=∑P~ij~P~ik~/sqrt∑P~ij~∑P~ik~ where *O~jk~* is the overlap between species *j* and *k*, and *P~ij~* and *P~ik~* are the proportions of use of each resource category *i* (food item) by species *j* and *k*, respectively. 2. \******Occupancy ****** It contains data and analysis scripts related to the detection and occupancy patterns of carnivore species in the Spiti Valley. **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...) represents 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. **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. **Detection covariates** (det_covs.csv): This includes covariates that influence detection of a species.  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, while distance-based and prey-related covariates were derived for the same grid unit. Each row therefore represents a single grid cell containing one camera trap station. **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. * conditional_graphs.txt - Generates graphical outputs of the conditional occupancy models 
 
 3. \******spatial overlap ****** This folder contains data and scripts
 related to spatial interactions among carnivore species in the Spiti
 Valley, across three study sites — Kibber, Pin, and Chandratal — focusing
 on patterns of co-occurrence and species-specific relative abundance
 indices (RAI). Dataset Files **Co-occurrence File**
 (Chandratal_Co-occurrence.csv, Kibber_Co-occurrence.csv,
 Pin_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 names (e.g., Canis
 lupus, Panthera uncia, Feral dog, Vulpes vulpes). - Rows: Detection
 results for each species at specific camera trap locations. **Relative
 Abundance Index (RAI) File** (Chandratal_Pianka_RAI.csv,
 Kibber_Pianka_RAI.csv, Pin_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. 4.
 \******Temporal overlap ****** * The temporal dataset for each species
 (Dog_CT.csv, Dog_Kibber.csv, Dog_Pin.csv, RF_CT.csv, RF_Kibber.csv,
 RF_Pin.csv, SL_Kibber.csv, SL_Pin.csv, Wolf_CT.csv) includes time-stamped
 detection records formatted as two columns: Species and Time. The Time
 column represents the time of detection converted into a continuous
 24-hour scale (0–1), allowing analysis of diel activity patterns and
 temporal overlap among species. Here, CT, Kibber, and Pin refer to the
 three study regions within the Spiti Valley—Chandratal, Kibber, and Pin
 Valley, respectively. - Includes R script (**codes_temporal overlap.txt**)
 for estimating and visualizing temporal activity overlap between carnivore
 species. The code uses the *overlap* package to generate kernel density
 plots of activity patterns, calculate overlap coefficients (Δ), and derive
 bootstrap-based means, standard deviations, and 95% confidence intervals
 for pairwise temporal overlap estimates.