Incorporating landscape context into species distribution models improves predictions for migratory shorebirds

Dataset for: Incorporating landscape context into species distribution models improves predictions for migratory shorebirds This dataset supports the findings reported in [Incorporating landscape context into species distribution models improves predictions for migratory shorebirds]. The study evaluated the contribution of landscape-scale variables to species distribution models (SDMs) for six migratory shorebird species in the East Asian-Australasian Flyway (EAAF) during the non-breeding season. Contents: Occurrence records (CSV): Spatially filtered eBird occurrence records (November–February, 2021–2023) for six shorebird species. Species abbreviations: batgod, Bar-tailed Godwit (Limosa lapponica); bkbplo, Black-bellied Plover (Pluvialis squatarola); comgre, Common Greenshank (Tringa nebularia); eurcur, Eurasian Curlew (Numenius arquata); grekno, Great Knot (Calidris tenuirostris); grsplo, Greater Sand-Plover (Anarhynchus leschenaultia). Spatial filtering was applied using a 20 km × 20 km grid, retaining only the record with the maximum observation count per grid cell. Predicted habitat suitability maps (GeoTIFF): Relative habitat suitability maps at 100-m resolution for each species. Maps cover the EAAF coastal zone (60°N–60°S) and are clipped to species-specific non-breeding ranges from BirdLife International.