Multiseason occupancy models for correlated replicate surveys

Occupancy surveys collecting data from adjacent (sometimes correlated) spatial replicates have become relatively popular for logistical reasons. Hines et al. (2010) presented one approach to modelling such data for single-season occupancy surveys. Here, we present a multiseason analogue of this model (with corresponding software) for inferences about occupancy dynamics. We include a new parameter to deal with the uncertainty associated with the first spatial replicate for both single-season and multiseason models. We use a case study, based on the brown-headed nuthatch, to assess the need for these models when analysing data from the North American Breeding Bird Survey (BBS), and we test various hypotheses about occupancy dynamics for this species in the south-eastern United States.

The new model permits inference about local probabilities of extinction, colonization and occupancy for sampling conducted over multiple seasons. The model performs adequately, based on a small simulation study and on results of the case study analysis.

The new model incorporating correlated replicates was strongly favoured by model selection for the BBS data for brown-headed nuthatch (Sitta pusilla). Latitude was found to be an important source of variation in local colonization and occupancy probabilities for brown-headed nuthatch, with both probabilities being higher near the centre of the species range, as opposed to more northern and southern areas.

We recommend this new occupancy model for detection–nondetection studies that use potentially correlated replicates.