Pelagic and demersal fish population rebuilding in response to fisheries-induced evolution in exploited China Seas
Bibliographic record
Abstract
Marine ecosystems are undergoing life-history adaptations with impacts on productivity, resilience, and economic value due to Fisheries-Induced Evolution (FIE). Long-term and often intense selective commercial harvesting has led to truncations in population structure and evolutionary changes in key life-history traits. However, the consequences for different functional groups have rarely been evaluated, especially in the context of rebuilding depleted marine stocks. This study uses an individual-based eco-genetic modeling approach to investigate the effects of FIE during shifts in fishing intensity. We focus on functional groups of three types of pelagic fish and three types of demersal fish with different life histories in the China Seas, proposing and evaluating two types of evolving trait response indicators to FIE, and assessing the influence of fishing intensity during the population rebuilding phase. Our results indicate that FIE has a more pronounced impact on biomass recovery in demersal fishes compared to pelagic fishes. The recovery time ranges from 10 to 40 years and strongly correlates with length at 50% vulnerability ( L 50 ). Reductions in fishing intensity facilitate biomass recovery, particularly in demersal fishes. In conclusion, our study suggests that adopting a management approach tailored to the needs of distinct functional groups is highly beneficial for promoting the efficient recovery of declining demersal fisheries. This understanding is crucial for developing effective fishery management strategies that integrate the evolutionary responses of different functional groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".