Sustainable exploitation of social species: a test and comparison of models
Bibliographic record
Abstract
Summary Overexploitation is a major threat to the persistence of many species. A wide variety of approaches to setting ‘sustainable’ quotas for exploitation exist but there are major discrepancies between theory and practice, and only limited integration between different branches of exploitation literature. Here, we bring together and compare the efficacy of a range of different approaches to estimating sustainability. A simulated population of a social mammal was used to provide data for, and compare the recommendations of, 10 widely used estimators for setting levels of sustainable exploitation. Estimators tested included four methods for setting sustainable levels of constant‐effort harvesting, two approaches to assessing the sustainability of constant‐yield harvesting, and four systems for setting thresholds below which harvesting should cease. The method used to fit catch per unit effort data to a stock dynamic model had an important influence on the variance of recommendations. Recommendations were also affected by the length of data set available and the frequency of changes in exploitation effort. Observation‐error estimators were more consistent and more conservative than equilibrium, effort‐averaging and process‐error approaches. Harvesting at the point of maximum productivity was found to be unstable in a noisy system, suggesting the need for considerable caution when using any of these estimators. Constant‐yield indices, developed for use in the bush meat trade, overestimated the point at which exploitation was likely to become highly unsustainable. Sociality was an important factor underlying this finding and the assessment of sustainability in constant‐yield systems should give consideration to the effects of different social systems. Overall, threshold‐harvesting systems provided the highest mean yields in relation to extinction risk. However, the introduction of error into these systems, particularly in the form of less frequent censuses, greatly increased both variance in yields and risk of extinction.
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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 | 0.000 | 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) | 0.001 | 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 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".