To exclose nests or not: structured decision making for the conservation of a threatened species
Bibliographic record
Abstract
Abstract Decisions regarding endangered species recovery often face sparse data and multiple sources of uncertainty about the effects of management. Structured decision making ( SDM ) provides a framework for assembling knowledge and expert opinion and evaluating the tradeoffs between different objectives while formally incorporating uncertainty. The Atlantic Coast piping plover provides an illustrative case for the utility of SDM in endangered species management because its population growth is simple to model, most populations are monitored, decision alternatives are well defined, and many managers are open to recovery recommendations. We built a model to evaluate the decision to use nest exclosures to protect piping plover eggs from predators, where the objective was to maximize λ and the tradeoff was between nest survival and adult survival. The latter can be reduced by exclosures. We used a novel mixed multinomial logistic exposure model to predict daily nest fates and incorporated the results into a stochastic projection matrix that included renesting after nest failure, and adult mortality associated with abandonment. In our test data set ( n = 329 nests from 28 sites over four years), the mean nest survival over 34 days was markedly higher for exclosed nests (0.76 ± 0.03 SE ) than for unexclosed nests (0.37 ± 0.07). Abandonment rates were also higher for exclosed nests (0.092 ± 0.017) than for unexclosed nests (0.045 ± 0.017), but the difference was not statistically significant and the loss rate to “other sources” (mostly predators) was much lower for exclosed nests (0.15 ± 0.03) than for unexclosed nests (0.58 ± 0.07). Population growth rate (λ) was clearly improved by exclosure use at the sites with high background nest loss rates, but λ was still <1 with exclosure use. Where the background nest loss rates were low, the decision to use exclosures was ambiguous, and λ could benefit from reducing uncertainty in vital rates. Our process demonstrated that geographic and temporal variation in nest mortality determines whether exclosures will be useful in attaining positive population growth rates and that other management options must be considered where the background nest mortality rates are high.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".