Unintended Cultivation, Shifting Baselines, and Conflict between Objectives for Fisheries and Conservation
Bibliographic record
Abstract
The effects of fisheries on marine ecosystems, and their capacity to drive shifts in ecosystem states, have been widely documented. Less well appreciated is that some commercially valuable species respond positively to fishing-induced ecosystem change and can become important fisheries resources in modified ecosystems. Thus, the ecological effects of one fishery can unintentionally increase the abundance and productivity of other fished species (i.e., cultivate). We reviewed examples of this effect in the peer-reviewed literature. We found 2 underlying ecosystem drivers of the effect: trophic release of prey species when predators are overfished and habitat change. Key ecological, social, and economic conditions required for one fishery to unintentionally cultivate another include strong top-down control of prey by predators, the value of the new fishery, and the capacity of fishers to adapt to a new fishery. These unintended cultivation effects imply strong trade-offs between short-term fishery success and conservation efforts to restore ecosystems toward baseline conditions because goals for fisheries and conservation may be incompatible. Conflicts are likely to be exacerbated if fisheries baselines shift relative to conservation baselines and there is investment in the new fishery. However, in the long-term, restoration toward ecosystem baselines may often benefit both fishery and conservation goals. Unintended cultivation can be identified and predicted using a combination of time-series data, dietary studies, models of food webs, and socioeconomic data. Identifying unintended cultivation is necessary for management to set compatible goals for fisheries and conservation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".