Experimental harvest regulations reveal that water availability during spring, not harvest, affects change in a waterfowl population
Bibliographic record
Abstract
Population change is regulated by vital rates that are influenced by environmental conditions, demographic stochasticity, and, increasingly, anthropogenic effects. Habitat destruction and climate change threaten the future of many wildlife populations, and there are additional concerns regarding the effects of harvest rates on demographic components of harvested organisms. Further, many population managers strictly manage harvest of wild organisms to mediate population trends of these populations. The goal of our study was to decouple harvest and environmental variability in a closely monitored population of wild ducks in North America, where we experimentally regulated harvest independently of environmental variation over a period of 4 years. We used 9 years of capture-mark-recapture data to estimate breeding population size during the spring for a population of wood ducks in Nevada. We then assessed the effect of one environmental variable and harvest pressure on annual changes in the breeding population size. Climatic conditions influencing water availability were strongly positively related to population growth rates of wood ducks in our study system. In contrast, harvest regulations and harvest rates did not affect population growth rates. We suggest efforts to conserve waterfowl should focus on the effects of habitat loss in breeding areas and climate change, which will likely affect precipitation regimes in the future. We demonstrate the utility of capture-mark-recapture methods to estimate abundance of species which are difficult to survey and test the impacts of anthropogenic harvest and climate on populations. Finally, our results continue to add to the importance of experimentation in applied conservation biology, where we believe that continued experiments on nonthreatened species will be critically important as researchers attempt to understand how to quantify and mitigate direct anthropogenic impacts in a changing world.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".