Human activities change marine ecosystems by altering predation risk
Bibliographic record
Abstract
In ocean ecosystems, many of the changes in predation risk - both increases and decreases - are human-induced. These changes are occurring at scales ranging from global to local and across variable temporal scales. Indirect, risk-based effects of human activity are known to be important in structuring some terrestrial ecosystems, but these impacts have largely been neglected in oceans. Here, we synthesize existing literature and data to explore multiple lines of evidence that collectively suggest diverse human activities are changing marine ecosystems, including carbon storage capacity, in myriad ways by altering predation risk. We provide novel, compelling evidence that at least one key human activity, overfishing, can lead to distinct, cascading risk effects in natural ecosystems whose magnitude exceeds that of presumed lethal effects and may account for previously unexplained findings. We further discuss the conservation implications of human-caused indirect risk effects. Finally, we provide a predictive framework for when human alterations of risk in oceans should lead to cascading effects and outline a prospectus for future research. Given the speed and extent with which human activities are altering marine risk landscapes, it is crucial that conservation and management policy considers the indirect effects of these activities in order to increase the likelihood of success and avoid unfortunate surprises.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".