Conserving ecosystem integrity: Ecological theory as a guide for marine protected area monitoring
Bibliographic record
Abstract
Global policies increasingly focus on the importance of maintaining or improving the integrity of ecosystems, but defining, assessing, and monitoring integrity in marine protected areas (MPAs) remains a challenge. In this paper, we conceptualized ecological integrity along dimensions of heterogeneity and stability containing seven components: physical structure, diversity, function, persistence, resistance, resilience, and natural variability. Through a structured literature search, we identified indicators and metrics used for quantifying ecosystem status components in the marine environment, then reviewed MPA management plans worldwide for inclusion of these components. We evaluated 202 papers applying 83 ecological indicators built from 72 metrics. Ecosystem components were most comprehensively addressed by metrics of taxa presence, organisms count, and area occupied by benthic organisms, and community structure, biomass, and percent cover indicators. Of the 557 MPA management plans we reviewed globally, 93% used at least one ecosystem status term or its synonym in an ecologically relevant context, but 39% did not address any components of stability. In particular, resistance was mentioned in only 1% of management plans, but in some cases it may be inferred from indicators and metrics used to track the best addressed component in management plans, diversity. Plans for MPAs with both an ecological/biological purpose and a research and education purpose contained ecosystem status terms more frequently than other plans, suggesting that engagement with the scientific community may have improved the application of these terms. An improved understanding of how to operationalize and measure ecological integrity can help MPA monitoring and 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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".