Skip to main content

Table 2 Akaike weights of the models including each of the non-spatial predictors in logistic regression on presence/absence of social Stegodyphus spp

From: Habitat productivity constrains the distribution of social spiders across continents – case study of the genus Stegodyphus

Variable βA ORsoc Psoc w i
GVI 1.366 3.921 0.981 1.000
PSea −0.057 0.945 0.720 0.299
Region - - - 0.525
Region × GVI - - - 0.136
Region × PSea - - - 0.023
  1. Akaike weights of all the models (Table 1) were used for calculating the multi-model coefficient estimates, the sign of which are given in the table. The sums of Akaike weights of models containing each of the variables are given (wi). Vegetation productivity (GVI, Figure 1a) of the habitats received the most support. Other abbreviations used: precipitation variation (PSea; Figure 1b), two binary regional variables (Region binary 1/2) and the interactions of the two environmental predictors with the former categorical variable for region (Region*GVI, Region*PSea; Figure 1a & b) of each of the three social species. The coefficient estimates for the main regional and interaction effects are not given, as they are difficult to interpret and only weakly supported. βA denotes the model-averaged regression coefficients; ORsoc is the odds ratio of being social; and Psoc is the probability of finding a social species with one unit increase of the respective environmental variable.