Age-dependent response to anthropogenic habitat during migration of an endangered raptor
Bibliographic record
Abstract
Decisions made by migrating animals can impact individual fitness and population dynamics. 1 , 2 For avian migrants, these decisions can be affected by environmental 3 , 4 , 5 , 6 , 7 and anthropogenic 8 , 9 , 10 , 11 , 12 factors and by internal 13 , 14 , 15 , 16 , 17 , 18 states. However, recent reviews have pointed to multiple gaps in our understanding of these decisions. 19 , 20 , 21 , 22 We studied the decisions made by migrating endangered Steppe Eagles Aquila nipalensis by tracking individuals for up to 7 years with GPS transmitters. We used weather reanalysis models and high-resolution remote sensing to obtain environmental and anthropogenic information. 23 , 24 Using complementary statistical methods, we differentiated between two behavioral states, migratory flights and stopovers, and studied how different factors shape the birds' movements and the transition between these states. 25 , 26 Most prominently, we detected effects of experience on the birds' response to anthropogenic habitats, with juvenile eagles drawn to them, adults avoiding them, and sub-adults showing no preference. Experience also affected the choice of tailwind and flight direction during migration, with juvenile individuals choosing stronger winds and more direct routes than more experienced eagles. During stopover, experienced eagles flew greater distances than less experienced eagles, and during both stopover and migratory periods, stronger tailwinds increased the distance birds moved. Finally, winds blowing toward the migratory direction increased the probability that a bird would initiate migration after a stopover, while opposite winds had the opposite effect. Our results advance our understanding of the ontogeny of bird migration and the effects of environmental and anthropogenic factors on migratory decision-making, with implications for the conservation of migratory species.
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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 | 0.000 | 0.000 |
| 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".