Evidence for synergistic cumulative impacts of marking and hunting in a wildlife species.
Bibliographic record
Abstract
Non-additive 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 multievent capture-recapture models, and partitioned hunting from non-hunting mortality. Survival of geese marked with or without collars was similar in 1990-1998, before hunting regulations were liberalized (average survival[95% C.I.]: 0.87[0.84, 0.89]). However, absolute survival of collared geese was 0.05[0.03, 0.07] lower than that of non-collared geese between 1999 and 2009, and 0.12[0.09, 0.15] lower after hunting regulations were liberalized further in 2009. Hunting and non-hunting mortality 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. Synthesis and applications: Researchers relying on long-term marking programs should evaluate the effect of markers periodically rather than solely in the beginning, as interactions with changing environmental conditions may eventually affect conclusions of studies based on marked animals. Here, we provide a rare demonstration in a natural setting that a combination of stressors can push animals beyond a threshold where vital rates are affected, even when one stressor applied alone initially had no detectable impact.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".