Differential population trends align with migratory connectivity in an endangered shorebird
Bibliographic record
Abstract
Abstract Migratory connectivity describes the extent to which migratory species' populations are connected throughout the annual cycle. While recognized as critical for understanding the population dynamics of migratory species and conserving them, empirical evidence of links between migratory connectivity and population dynamics are uncommon. We analyzed associations between spatiotemporal connectivity and differential population trends in a declining and endangered migratory shorebird, the far eastern curlew ( Numenius madagascariensis ), with multiyear tracking data from across the Australian nonbreeding grounds. We found evidence of temporal and spatial segregation during migration and breeding: curlew from southeast Australia initiated northward migration earlier, arrived at breeding sites earlier, and bred at lower latitudes than curlew from northwest Australia. Analysis of land modification intensity revealed that populations from southeast Australia face greater human impacts compared to those from northwest Australia at both the breeding and nonbreeding grounds, a pattern that aligns with steeper population declines in southeast Australia. This alignment between migratory connectivity, human impacts, and differential population change highlights the importance of a full annual cycle approach to conservation that includes mitigating threats on the breeding grounds and better protecting nonbreeding habitats in Australia where far eastern curlew spend over half of each year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".