Developing Ecosystem Indicators for Responses to Multiple Stressors
Bibliographic record
Abstract
Human activities in coastal and marine ecosystems provide a suite of benefits for people, but can also produce a number of stressors that can act additively, synergistically, or antagonistically to change ecosystem structure, function, and dynamics in ways that differ from single stressor responses. Scientific tools that can be used to evaluate the effects of multiple stressors are needed to assist decision making. In this paper, we review indicator selection methods and general approaches to assess indicator responses to multiple stressors and compare example ecosystem assessments. Recommendations are presented for choosing and assessing suites of indicators to characterize responses. Indicators should be chosen based upon defined criteria, conceptual models linking indicators to pressures and drivers, and defined strategic goals and ecological or management objectives. Indicators should be complementary and nonredundant, and they should integrate responses to multiple stressors and reflect the status of the ecosystem. An initial core set of indicators could include those that have been tested for the effects of climate and fishing and then expanded to include other pressures and ecosystem-specific, feature-pressure interactions. Identifying indicators and evaluating multiple stressors on marine ecosystems require a variety of approaches, such as empirical analyses, expert opinion, and model-based simulation. The goal is to identify a meaningful set of indicators that can be used to assist with the management of multiple types of human interactions with marine ecosystems.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".