A GLMM approach for combining multiple relative abundance surfaces

Abstract

Spatio-temporal maps of organismal density or relative abundance are fundamental to many applications in conservation and ecology. There are often a number of data sources to inform species maps, including citizen science monitoring programs, remote sensing, geolocation data from satellite-tagged animals and formal scientific surveys. In these cases, it may be desirable to come up with a single map integrating all data sources. We introduce a novel two-step method for combining inference about relative abundance maps using multiple data sources. Log-scale relative abundance surfaces are first estimated from individual data sets, and resultant surfaces are then treated as ‘data’ within a generalized linear mixed model framework with a spatially autocorrelated mean process. We also introduce generalizations allowing data misalignment and predictive processes to decrease computational burden. Using simulation, we show that our approach frequently outperfoms other approaches, including basing inference on a single surface, or taking a simple arithmetic mean (although the arithmetic mean performs well when there are few surfaces). We then demonstrate our method using citizen science and satellite-tracking data of Steller sea lions in Alaska. In this case, relative abundance surfaces consisted of an effort-adjusted map developed from platform-of-opportunity sightings and a utilization distribution developed from geolocation records. The resulting combined surface represented a compromise between single-data source predictions. Our approach should be useful for ecologists seeking to reconcile alternative species distribution maps, particularly when individual surfaces are prone to bias, when there is no obvious common currency (e.g. point process), or when computational demands preclude a fully integrated analysis.