SEM analysis
To construct the SEM, we used generalized linear mixed models with a negative binomial distribution to model overdispersion, using the Bayesian brms package (Bürkner, 2017) in R version 3.6.1 (R-Core-Team, 2019). We used the number of species encounters (number of days in which an animal was detected by a camera per year and season) as the response variable, with the log-transformed number of active camera trap days as an offset. Depending on the species, explanatory variables included: i) large carnivores (wolf and lynx) encounters and human disturbance, and ii) land cover variables (proportion of agricultural land and EVI). We allowed the model to estimate different values for each pathway for winter and summer by adding the interaction of season with all pathways. We also included a varying intercept by study site and another by camera location by including these two variables as random effects.
We included the default flat priors of brms and fitted the models using 3000 iterations on 3 chains. We used LOO values as indicators of goodness of fit, and for model selection. We checked convergence by looking at the trace plots of the MCMC chains, and with the Gelman and Rubin convergence diagnostic Ȓ (Gelman and Rubin, 1992). We also calculated a Bayesian R 2 (Gelman et al., 2018) for the best fitting model using the bayes_R2 function in the brms package (Bürkner, 2017) to assess the variance explained by the main factors. Below, we present the posterior median and associated 90% credible interval (CRI) for all parameters, which we discuss in terms of non-overlapping CRI for convenience. The whole posterior distributions can be found in Appendix 1 and 2. We also present the Probability of Direction (pd ) as defined in the bayestestR package (Makowski et al., 2019), which represents the (un)certainty with which an effect is either positive or negative, as well as the Region of Practical Equivalence (ROPE; e.g. Kruschke, 2014), which assesses the magnitude and importance of an effect (i.e., its “significance”) (Makowski et al., 2019). These values are in Appendix 3.