(c) Statistical analyses
To compare among models, the model with the lowest Akaike’s Information Criterion (AIC) was considered the best or most parsimonious model (Burnham & Anderson 2002). Within each model, to identify which variables most influenced patterns of genetic diversity, we plotted marginal effects with the R package sjPlot (Lüdecke 2021) and additionally examined the p -values of variable coefficients. Model fits and spatial autocorrelation in the residuals were checked with DHARMa v.0.4.3 (Hartig 2021). Moran’s I was near zero for all models, and significant spatial autocorrelation (p< 0.5) was only found in 2 of the 42 possible model combinations (Table S1). This is effectively the same proportion (0.048) as the expected false positive rate (0.05), so we did not consider spatial autocorrelation to be an issue. Finally, to assess sensitivity to missing and rare data, all models were bootstrapped 1000x with the boot package v.1.3.28 (Canty & Ripley 2021). All analyses were performed in R v.4.2.3 (R Core Team 2023).