Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species
Bibliographic record
Abstract
Abstract Nonadditive effects from multiple interacting stressors can have unpredictable outcomes on wildlife. Stressors that initially have negligible impacts may become significant if they act in synergy with novel stressors. Wildlife markers can be a source of physiological stress for animals and are ubiquitous in ecological studies. Their potential impacts on vital rates may vary over time, particularly when changing environments impose new stressors. In this study, we evaluated the temporal changes in the combined impact of two stressors, one constant (collar marking) and another one variable over time (hunting intensity), in greater snow geese ( Anser caerulescens atlantica ). Over a 30‐year period (1990–2019), hunting regulations were liberalized twice, in 1999 and 2009, with the instauration of special spring and winter hunting seasons respectively. We evaluated the effect of collars on goose survival through this period of changing hunting regulations. We compared annual survival of >20,000 adult females marked with and without neck collars using multi‐event capture–recapture models, and partitioned hunting from nonhunting mortality. Survival of geese marked with or without collars was similar in 1990–1998, before hunting regulations were liberalized (average survival [95% CI]: 0.87 [0.86, 0.89]). However, absolute survival of collared geese was 0.05 [0.03, 0.07] lower than that of noncollared geese between 1999 and 2009, and 0.12 [0.09, 0.15] lower after hunting regulations were liberalized further in 2009. Hunting and nonhunting mortality probabilities were both higher in collared birds compared to those without collars. The interaction between the effects of collars and hunting was synergistic because collars affected survival only after the hunting pressure increased significantly. These cumulated stresses probably reduced goose body condition sufficiently to increase their vulnerability to multiple sources of mortality.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".