Migratory connectivity of Swan Geese based on species' distribution models, feather stable isotope assignment and satellite tracking

Abstract

AimUnderstanding connectivity between avian breeding and non‐breeding areas is essential to understand processes affecting threatened migrants throughout their annual cycle. We attempted to establish migratory connectivity and flyway structure of the IUCN vulnerable Swan Geese (Anser cygnoides) by combining citizen science species' distribution models (SDMs) and feather stable isotope analysis.LocationsRussia, Mongolia and China.MethodsWe established migratory origins and movements of 46 Swan Geese from five wintering locations by integrating citizen science SDMs and feather stable hydrogen isotope (δ2H) measurements by linking feathers to precipitation isoscapes in a Bayesian probability framework.ResultsWe determined multiple summering origins among Swan Geese from the most important wintering location, Poyang Lake, Jiangxi Province, China. As predicted, we found no evidence for sex‐biased differences in δ2H measurements. Four geese tracked with GPS/GSM loggers all migrated to moulting areas, which confirmed the accuracy of our predictions from δ2H assignments. Differences between summering ranges inferred from historical and modern samples coincided with major wetland loss in northeastern China since the 1950s. Despite limited historical data, we contend that this supports the hypothesis that the summering range prior to 1950 was much wider than that of the current population.Main conclusionsThis was the first Asian study to establish migratory origins of wintering Anatidae based on stable isotopes and citizen science SDMs. We advocate the wider combined application of SDMs, telemetry studies and stable isotopes to investigate effectively avian migratory connectivity, as the results from this study provided important contributions to the development of conservation measures for this threatened and declining species in East Asia.

Increasing Trust in New Data Sources: Crowdsourcing Image Classification for Ecology

“…Geographical, spatial recording, and preferential sampling bias may arise from opportunistically collected data, with more observations from frequently visited locations such as around roads, with irregular frequencies across time, or as a result of preferential sampling spots with certain habitat types or where specific species are more likely to be found. See the discussion in Chevalier et al (2021); Fournier et al (2017); Zhu et al (2020). In addition, observer bias may arise as the result of inexperience, or incorrect understanding or perception of a variable of interest.…”

Potential Effects of Habitat Change on Migratory Bird Movements and Avian Influenza Transmission in the East Asian-Australasian Flyway

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Modeling current and future species distribution of breeding birds as regional essential biodiversity variables (SD EBVs): A bird perspective in Swiss Alps

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