2.5. a) Data sources for distribution-wide assessment of dhole pack size:
Through google scholar we searched for scientific literature on pack size of dholes, using the keywords “Cuon alpinus”, “Dhole”, “Average”, “Mean”, “Pack-size”. Our search resulted in 34 scientific assessments from 1973 to 2018 that had reported average pack size of dholes. These 34 assessments belonged to 24 unique protected areas across dhole ranging countries in South and South-east Asia. 18 of these unique sites were also a part of the recently published dhole diet review (Srivathsa, Sharma, & Oli, 2020). Subsequently, snowball sampling approach was used (Handcock & Gile, 2011), by using dhole pack size as baseline information from the previously conducted assessments. Literature cited within these assessments were referred to collate data on tiger density along with prey density (of the closest or same assessment year) and size of the protected area. We obtained data on the topography of 24 protected areas through google earth engine (Gorelick et a., 2017).
b) Analytical methods:
We used generalized linear models to examine correlates of dhole pack size reported from 24 unique sites across dhole distribution range. We used only those studies (n=29) for which data on all the predictor variables were available i.e., tiger density and ungulate density, size of the protected area (PA), elevational heterogeneity and terrain ruggedness of the PA. We checked for correlations among predictors and dropped the correlated ones (r > 0.6), prior to analysis. After screening for normal distribution of response variable, we used ”gaussian” family for the analysis. We tested for model parameters based on our hypothesis, and compared them to null model (Intercept only). Model fits were compared using Akaike’s Information Criterion corrected (AICC), and the effect of parameters was gauged based on direction and statistical significance of corresponding β-coefficients. We used ”MuMIn” package for model selection and averaging. Model selection was based on difference between AIC models, (ΔAIC < 2). Further, model selection was done using Royall’s 1/8 strength of evidence and 95% cumulative weight criteria. Model averaging was carried out for parameters based on top model selection. All analyses were performed in program R (R Development Core Team, 2014).