A <scp>GLMM</scp> approach for combining multiple relative abundance surfaces
نویسندگان
چکیده
Abstract Spatio‐temporal maps of organismal density or relative abundance are fundamental to many applications in conservation and ecology. There often a number data sources inform species maps, including citizen science monitoring programs, remote sensing, geolocation from satellite‐tagged animals formal scientific surveys. In these cases, it may be desirable come up with single map integrating all sources. We introduce novel two‐step method for combining inference about using multiple Log‐scale surfaces first estimated individual sets, resultant then treated as ‘data’ within generalized linear mixed model framework spatially autocorrelated mean process. also generalizations allowing misalignment predictive processes decrease computational burden. Using simulation, we show that our approach frequently outperfoms other approaches, basing on surface, taking simple arithmetic (although the performs well when there few surfaces). demonstrate satellite‐tracking Steller sea lions Alaska. this case, consisted an effort‐adjusted developed platform‐of‐opportunity sightings utilization distribution records. The resulting combined surface represented compromise between single‐data source predictions. Our should useful ecologists seeking reconcile alternative particularly prone bias, is no obvious common currency (e.g. point process), demands preclude fully integrated analysis.
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ژورنال
عنوان ژورنال: Methods in Ecology and Evolution
سال: 2022
ISSN: ['2041-210X']
DOI: https://doi.org/10.1111/2041-210x.13948