Using Bayesian hierarchical models and random forest algorithm for habitat use studies: a case of nest site selection of the crested ibis at regional scales

The association of species distribution and environmental variables is often complex, with nonlinear and interacting effects. Bayesian hierarchical models can quantify linear and high order effects of the variables, as well as their interaction effects. Their strength is to take into account the uncertainties in observation, models, and parameters. However, the model selection process is usually time-consuming, especially when the number of environmental variables is large. Random forest, an efficient machine learning algorithm, can rank the environmental variables so as to facilitate the model selection process. We analyzed the nest site selection of the crested ibis (Nipponia nippon) at watersheds in its distribution range. The crested ibis has attracted much attention in the past 30 years due to its extremely low population level, and now it has recovered to over 1,000 individuals in the wild. We built Bayesian hierarchical models to quantify the association between the number of nests in 95 watersheds and nine environmental variables of these watersheds. We applied random forest to check the effect of every variable and removed the unimportant variables from the hierarchical models. Unlike our previous studies, we found that the interaction between the area of rice paddy and the area of water bodies (i.e. rivers, lakes and ponds) had most contribution to the nest site selection, whereas the linear terms of either rice paddy or water body had little effects. The detection probability of the nests during the surveys was inversely associated with elevation and the standard deviation of elevation (i.e. roughness of the landscape) in the watershed. Our models provide the insight that the crested ibis need both rice paddies and water bodies in their annual life cycle. Habitat protection practices should cover not only rice paddies, but also water bodies to ensure long term survival of this endangered bird.