Ecology of harvest‐driven trait changes and implications for ecosystem management
Bibliographic record
Abstract
Harvest of wild animals and plants is pervasive, exerts ecological and evolutionary pressure on populations, and is known to drive rapid changes in organismal traits. Although the factors that lead to rapid trait changes have received increased attention, the ecological consequences of harvest‐driven trait changes are less appreciated. We review recent evidence that harvest‐driven trait changes can affect community and ecosystem processes. Growing experimental evidence, modeling studies, and field observations have revealed that common responses to harvest include changes in life‐history and behavioral traits, which have the potential to reshape the ecology of harvested systems. On the basis of existing evidence, we propose a set of general mechanisms that link harvest‐driven trait changes to ecological processes, including trophic cascades, nutrient dynamics, keystone interactions, ecosystem stability, and habitat use. Managing harvested ecosystems sustainably may require strategies that account for harvest‐driven trait changes. We recommend that trait changes be monitored closely as part of ecosystem‐based management plans, especially in cases where targeted traits are known to affect important aspects of ecosystem function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".