Data from: A multispecies occupancy model for two or more interacting species

Species occurrence is influenced by environmental conditions and the
presence of other species. Current approaches for multispecies occupancy
modelling are practically limited to two interacting species and often
require the assumption of asymmetric interactions. We propose a
multispecies occupancy model that can accommodate two or more interacting
species. We generalize the single-species occupancy model to two or more
interacting species by assuming the latent occupancy state is a
multivariate Bernoulli random variable. We propose modelling the
probability of each potential latent occupancy state with both a
multinomial logit and a multinomial probit model and present details of a
Gibbs sampler for the latter. As an example, we model co-occurrence
probabilities of bobcat (Lynx rufus), coyote (Canis latrans), grey fox
(Urocyon cinereoargenteus) and red fox (Vulpes vulpes) as a function of
human disturbance variables throughout 6 Mid-Atlantic states in the
eastern United States. We found evidence for pairwise interactions among
most species, and the probability of some pairs of species occupying the
same site varied along environmental gradients; for example, occupancy
probabilities of coyote and grey fox were independent at sites with little
human disturbance, but these two species were more likely to occur
together at sites with high human disturbance. Ecological communities are
composed of multiple interacting species. Our proposed method improves our
ability to draw inference from such communities by permitting modelling of
detection/non-detection data from an arbitrary number of species, without
assuming asymmetric interactions. Additionally, our proposed method
permits modelling the probability two or more species occur together as a
function of environmental variables. These advancements represent an
important improvement in our ability to draw community-level inference
from multiple interacting species that are subject to imperfect detection.