A data‐driven approach to multiple‐stressor impact assessment for a marine protected area
Bibliographic record
Abstract
The coastal environment is not managed in a way that considers the impact of cumulative threats, despite being subject to threats from all realms (marine, land, and atmosphere). Relationships between threats and species are often nonlinear; thus, current (linear) approaches to estimating the impact of threats may be misleading. We developed a data-driven approach to assessing cumulative impacts on ecosystems and applied it to explore nonlinear relationships between threats and a temperate reef fish community. We used data on water quality, commercial fishing, climate change, and indicators of recreational fishing and urbanization to build a cumulative threat map of the northern region in New South Wales, Australia. We used statistical models of fish abundance to quantify associations among threats and biophysical covariates and predicted where cumulative impacts are likely to have the greatest impact on fish. We also assessed the performance of no-take zones (NTZs), to protect fish from cumulative threats across 2 marine protected area networks (marine parks). Fishing had a greater impact on fish than water quality threats (i.e., percent increase above the mean for invertivores was 337% when fishing was removed and was 11% above the mean when water quality was removed inside NTZs), and fishing outside NTZs affected fish abundances inside NTZs. Quantifying the spatial influence of multiple threats enables managers to understand the multitude of management actions required to address threats.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".