Interactions among ecosystem stressors and their importance in conservation
Bibliographic record
Abstract
Interactions between multiple ecosystem stressors are expected to jeopardize biological processes, functions and biodiversity. The scientific community has declared stressor interactions-notably synergies-a key issue for conservation and management. Here, we review ecological literature over the past four decades to evaluate trends in the reporting of ecological interactions (synergies, antagonisms and additive effects) and highlight the implications and importance to conservation. Despite increasing popularity, and ever-finer terminologies, we find that synergies are (still) not the most prevalent type of interaction, and that conservation practitioners need to appreciate and manage for all interaction outcomes, including antagonistic and additive effects. However, it will not be possible to identify the effect of every interaction on every organism's physiology and every ecosystem function because the number of stressors, and their potential interactions, are growing rapidly. Predicting the type of interactions may be possible in the near-future, using meta-analyses, conservation-oriented experiments and adaptive monitoring. Pending a general framework for predicting interactions, conservation management should enact interventions that are robust to uncertainty in interaction type and that continue to bolster biological resilience in a stressful world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".