Migratory connectivity of Swan Geese based on species' distribution models, feather stable isotope assignment and satellite tracking
Bibliographic record
Abstract
Abstract Aim Understanding 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. Locations Russia, Mongolia and China. Methods We established migratory origins and movements of 46 Swan Geese from five wintering locations by integrating citizen science SDMs and feather stable hydrogen isotope (δ 2 H) measurements by linking feathers to precipitation isoscapes in a Bayesian probability framework. Results We 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 δ 2 H measurements. Four geese tracked with GPS/GSM loggers all migrated to moulting areas, which confirmed the accuracy of our predictions from δ 2 H 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 conclusions This 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".