Alien species stabilize local fisheries catch in a highly invaded ecosystem
Bibliographic record
Abstract
Alien species may be a valuable resource for marine fisheries, yet their contribution to the catch might be balanced by negative effects on indigenous species. In this study, we explored a unique high-resolution time series of catch data from a highly invaded ecosystem in the eastern Mediterranean. We analyzed over 5000 fishing hauls digitalizing from fishers’ logbooks. We found that the catch per unit effort (CPUE) of alien species increased over time, while for indigenous species, CPUE remained relatively stable between 1996 and 2013. This suggests a lack of competitive exclusion of indigenous target species due to the proliferation of alien species. From the perspective of the fishers’ revenues, alien species gradually became a more important part of the catch, while overall fishers’ revenues showed temporal stability. This was the combined result of alien species increasing CPUE and fishers shifting their effort toward shallower water where alien species were dominant. Our findings demonstrate that alien species can become a valuable resource for a local fishing industry with little effect on indigenous species, which is highly relevant to worldwide fisheries experiencing range redistribution of commercial species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".