Combining single- and repeated-visit occupancy models to make the best of monitoring surveys

Abstract

A major challenge in statistical ecology consists of integrating knowledge from different datasets to produce robust ecological indicators. To estimate species distribution, occupancy models are a flexible framework that can accommodate several datasets obtained from different sampling methods. However, repeating visits at sampling sites is a prerequisite for using standard occupancy models. Occupancy models were recently developed to analyze detection/non-detection data collected during a single visit. To date, single-visit occupancy models have never been used to integrate several different datasets. Here, we showcase an approach that combines two datasets into an integrated single-visit occupancy model. As a case study, we estimated the distribution of Bottlenose dolphins (Tursiops truncatus) over the North-western Mediterranean Sea by combining 24,624 km of aerial surveys and 21,464 km of at-sea monitoring. We compared the outputs of single- vs. repeated-visit occupancy models into integrated occupancy models. Integrated models allowed a better sampling coverage of species home-range, which provided a better precision for occupancy estimates than occupancy models using datasets in isolation. Overall, single- and repeated-visit integrated occupancy models produced similar inference about the distribution of bottlenose dolphins. We suggest that single-visit occupancy models open promising perspectives for the use of existing ecological datasets.