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Modeling Eastern Russian High Arctic Geese (Anser fabalis, A. albifrons) during moult and brood rearing with Machine Learning, Open Access GIS and 24 years-long Big Data: Good Evidence for the New Digital Arctic of 2021


 Many polar species and habitats are now affected by man-made global climate change and underlying infrastructure; it makes for a New Arctic. Arctic grazers, like Eastern Russian migratory populations of Tundra Bean Goose Anser fabalis and Greater White-fronted Goose A. albifrons, are affected along the entire flyway in East Asia, namely China, Japan and Korea. Here we present the best-available long-term 24 years digitized GIS data for the breeding study area (East Yakutia and Chukotka) and its habitats with ISO-compliant metadata. Further, we used seven publically available GIS predictor layers to predict the distribution for these two species within the tundra habitats. We are able to improve on the ecological niche prediction inference for both species by focusing for the first time specifically on biological relevant aspects: post-breeding moulting non-breeders, as well as post-breeding parent birds with broods. We then assessed it with 4 lines of evidence including alternative best-available open access field data from GBIF.org as well as compiled literature and found a good model accuracy in support of our evidence for a robust inference of these new findings. Our predictions indicate a relative index of occurrence (RIO) based on the quantified ecological niche showing more realistic gradual occurrence patterns and that are not fully in agreement with the current strictly applied parsimonious flyway and species delineations. While our predictions are to be improved further, e.g. when synergetic data are made freely available, here we offer the first open access model platform for fine-tuning and future predictions for this otherwise poorly represented region in times of a highly changing industrialized ‘new’ arctic with global repercussions.