Modelling migratory waterfowl stopover habitat while accounting for ephemeral environmental conditions

2025

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.1111/1365-2664.70169
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Abstract Migratory species depend on ephemeral environmental conditions; thus, species distribution modelling (SDM) must incorporate phenological changes along migratory routes. Our overarching goal was to model habitats for three waterfowl species migrating through Eurasian grasslands (red‐breasted goose [ Branta ruficollis ], taiga bean goose [ Anser fabalis fabalis ] and Bewick's swan [ Cygnus columbianus bewickii ]) while accounting for ephemeral environmental conditions. Our objectives were (a) to develop a workflow of mapping ephemeral environmental conditions, (b) model habitats for the three species and (c) evaluate the protection status of habitats in natural and agricultural landscapes. We expected water availability, particularly ephemeral spring waterbodies, to strongly influence these species' distributions. We utilized MODIS data for phenological synchronization of Landsat images to create species‐ and season‐specific metrics and land cover maps. We used Landsat‐derived environmental variables, elevation and bird GPS locations in Maxent SDM. We compared locations of modelled habitats, protected areas and Ramsar sites. Our land cover maps had an overall accuracy of 0.92–0.95 and captured ephemeral water extent during these species' migrations. All models had AUC scores of 0.89–0.94; distance to water, land cover and elevation were the most important variables. Modelled habitats were distributed unevenly and occurred in both natural and agricultural landscapes; 40%–76% fell within croplands. Although most croplands provide a rich food supply, their value as waterfowl habitat critically depended on water availability. Approximately 22% of potential habitat in the natural landscape, but only 3% in croplands, had some level of protection. Synthesis and applications . We demonstrated the potential of phenological synchronization to estimate fine‐scale ephemeral environmental conditions crucial for migratory species. Modelled habitats revealed key stopover sites in both natural and agricultural landscapes. The maps showed gaps in the protected area network of Eurasian grasslands, especially in agriculture. Our workflow could be easily adapted for other species and is broadly relevant for conservation.