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_ijP_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.