Can waterfowl buffer the mortality risk induced by GPS tags? A cautionary tale for applied inference across species
Bibliographic record
Abstract
GPS tags have become a common tool in ecological studies of animal behaviour and demography despite previous research indicating negative impacts on vital rates across a variety of taxa. Many researchers face tradeoffs when deciding whether they are an appropriate tool because GPS tags may impact vital rates, but they provide detailed data on movements and behaviour that often cannot be obtained in other ways. Using band recovery data, we evaluated the strength of effects induced by GPS tags on annual mortality of adult females across 13 waterfowl taxa, and examined whether taxa with a slower life-history strategy and larger body size were more resilient to GPS tag effects than their fast-lived counterparts with small body size. All species were exposed to hunting, which may interact with underlying processes affecting the impact of GPS tags on mortality, but also allowed for robust analysis of overall annual mortality. Hazard ratios, indicating the risk of death for individuals wearing GPS tags compared to those wearing only metal bands, ranged from 1.13 to 3.25 and the mean proportional difference in survival between marker types across species was 0.33. The magnitude of tag effects was surprisingly consistent across life-history tempo and body size, indicating that slower-lived species did not buffer the effect of wearing GPS tags. Our results highlight that even large, long-lived species, which are generally better at buffering their mortality against environmental adversity, are not immune to the effects GPS tags can have on survival and mortality. The results of our study emphasize the importance of testing for such effects across taxa in future research as technology advances.
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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 | 0.131 | 0.358 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".