Territory and Group Structure of Free-Ranging Dogs in Urban–Rural India: Data for Testing the Resource Dispersion Hypothesis
Description
This dataset supports a study testing the Resource Dispersion Hypothesis (RDH) in free-ranging dogs (Canis lupus familiaris) across urban and rural habitats in India. It includes spatial and social data collected through two complementary approaches: a census-based study and a territory-based tracking study, conducted over three reproductive seasons—pre-mating, mating, and post-mating (pup emergence). The census-based dataset comprises observations from 52 rural and 41 urban sites, where surveys documented dog presence, group structure, age-sex classification, and affiliative interactions. Each survey area was defined as a polygon (1.29 to 161 hectares) encompassing human settlements and nearby accessible areas. Observers also geolocated and categorized food-related resources (e.g., waste bins, food stalls, shops, markets), recorded anthropogenic food subsidies through household surveys, and scored resource points following published protocols. The territory-based dataset includes high-resolution spatio-temporal tracking of free-ranging dog groups across multiple seasons. Territories were defined using behavioural observations (≥30 hours per group/season), focusing on aggression, scent-marking, and territorial defense. Each territory polygon includes GPS-marked boundaries and mapped food resources. Resource distribution within each territory is quantified using: Patch Richness (total food resource points), Resource Heterogeneity (types of food sources), and Resource Dispersion (mean inter-resource distances, calculated using R spatial packages). The dataset also includes metadata on dog group composition, male:female ratios, and seasonal changes in territory size. Statistical analysis scripts (GLMs and GLMMs using lme4 and glmmTMB in R) are available to reproduce the findings. The data can be used to study spatial ecology, urban animal behaviour, and effects of human-driven resource variation on free-ranging species.
Files
Steps to reproduce
1. Study Site Classification o Classify rural and urban areas using the official criteria outlined in Ravi (2023), EAC-PM, Govt. of India. o Randomly select representative sites from both categories within the chosen geographic region. 2. Survey Polygon Creation and Sampling o For each selected site, create a random polygon using Google My Maps to define the survey area. o You may generate random start points using GIS or grid overlays and then draw polygons to include surrounding accessible areas. o Each polygon should encompass human settlements and adjacent open or accessible zones where free-ranging dogs are likely to be found. o Polygons should vary in size to capture habitat heterogeneity across locations. 3. Census-Based Surveys (Dog Observations) o Conduct spot censuses between 16:00 and 18:00 hours (when dogs are most active), following Sen Majumder et al. (2014). o Traverse each road within a polygon only once to prevent double counting. o For every dog sighted, record: Time of sighting Sex (via genital observation) Age class (pup, juvenile, adult) Social status (solitary or in group) Group size (if observed interacting affiliatively within ~1.5 m) 4. Resource Mapping o Walk through each polygon and map food-related resource points using GPS. o Categories include: waste bins, dumps, food stalls, restaurants, markets, and water sources. o Score each resource for accessibility and provisioning potential, following Bhattacharjee & Bhadra (2021). 5. Anthropogenic Food Subsidy Survey o With prior consent, survey all households within the polygon to document how often and what type of food is provided to dogs. o Use these data to score individual resource points based on actual food support levels. 6. Territory-Based Tracking o Select known dog groups and observe them across three reproductive seasons: pre-mating, mating, post-mating. o Conduct ≥30 hours of observation per group per season using instantaneous scan and all-occurrence sampling (Paul et al., 2014a). o Record: Aggressive interactions Urine marking Territorial defense points o Map all GPS points and use them to draw territory polygons in Google My Maps. Note overlapping regions when applicable. 7. Resource Quantification Within Territories o Survey and score resource points inside each territory using the same method as in the census-based study. o Calculate: Patch Richness (number of food points) Resource Heterogeneity (diversity of resource types) Resource Dispersion (mean pairwise distance among points using spatstat, sf, and sp in R) 8. Statistical Analysis o Use R (v4.2.0) for all analyses. o Perform: Mann–Whitney U tests for urban–rural comparisons GLMs (Gaussian/Gamma) for dog and territory size predictors GLMMs with dog group as a random effect to assess seasonal changes o Use lme4, glmmTMB, performance, and DHARMa packages for modeling and diagnostics. o Model selection based on AIC criteria; significance threshold set at α = 0.05.
Institutions
- Indian Institute of Science Education and Research Kolkata