Sampling effects on the effectiveness of ecological indicators in detecting fishery-induced community changes
Bibliographic record
Abstract
Effective ecological indicators (EI) should reflect changes to ecosystem status in a timely manner to guide fisheries management; however, the robustness of EIs in the face of sampling uncertainty is not well understood and sampling errors may result in delayed or even unhelpful actions for management. In this study, we use a size-spectrum model to evaluate the effectiveness of EIs in detecting fishery-induced ecosystem changes given various levels of sampling uncertainty. We demonstrate that there is a time-lag that exists between changes in fishing pressure and EI response. The selectivity of survey gears can strongly determine the level of EI responses within certain size ranges. EIs may lose statistical power once sampling errors exceed a certain level, implying that several decades of monitoring data may be needed to be sure of detecting even a large change. Multivariate methods can strengthen the statistical powers of EIs, but only when the level of sampling noises is low. This study suggests the need for considering the impact of sampling uncertainty on the use of EIs in fisheries management.
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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 | 0.059 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".