A penalized likelihood for multispecies occupancy models improves predictions of species interactions.

Multispecies occupancy models estimate dependence among multiple species of interest from patterns of co-occurrence, but problems associated with separation and boundary estimates can lead to unreasonably large estimates of parameters and associated standard errors when species are rarely observed at the same site or when data are sparse. In this paper, we overcome these issues by implementing a penalized likelihood, which introduces a small bias in parameter estimates in exchange for a potentially large reduction in variance. We compare parameter estimates obtained from both penalized and unpenalized multispecies occupancy models fit to simulated data that exhibit various degrees of separation and to a real-word data set of bird surveys with little apparent overlap between potentially interacting species.