Bycatch mitigation could prevent strong changes in the ecological strategies of seabird communities across the globe
Bibliographic record
Abstract
Abstract Fisheries bycatch, the incidental mortality of non-target species, is a major threat to seabirds worldwide. Mitigating bycatch is an important factor to reduce seabird population declines and consequent changes in ocean trophic dynamics and ecosystem functioning. However, it remains an open question how and where mitigating bycatch at a global scale may conserve seabird traits and the ecological strategies that traits represent. Here we combine a dataset of species’ traits and distribution ranges for 341 seabirds with spatially resolved fishing effort data for gillnet, longline, trawl, and purse seine gears to: (1) understand spatial variation in seabird community traits; and (2) test whether mitigating fisheries bycatch may prevent shifts in traits of seabird communities and loss of ecological strategies. We find distinct spatial variation in the community weighted mean of five seabird traits (clutch size, body mass, generation length, foraging guild, and diet guild). Furthermore, our analysis suggests that successful bycatch mitigation could prevent strong shifts in the traits of seabird communities across the globe particularly in the North Atlantic and Southern Oceans. Specifically, changes in dominant foraging and diet guilds, and shifts towards communities with faster reproductive speeds (larger clutch sizes and shorter generation lengths) and smaller body masses could be avoided. Therefore, bycatch mitigation may have important indirect benefits for sustaining ecosystem functioning, as mediated by species traits. Incorporating species traits into management actions will provide valuable tools for marine spatial planning and when evaluating the success of conservation initiatives.
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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) | 0.002 | 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".