Ecosystem effects of invertebrate fisheries
Bibliographic record
Abstract
Abstract Since the 1950s, invertebrate fisheries catches have rapidly expanded globally to more than 10 million tonnes annually, with twice as many target species, and are now significant contributors to global seafood provision, export, trade and local livelihoods. Invertebrates play important and diverse functional roles in marine ecosystems, yet the ecosystem effects of their exploitation are poorly understood. Using 12 ecosystem models distributed worldwide, we analysed the trade‐offs of various invertebrate fisheries and their ecosystem effects as well as ecological indicators. Although less recognized for their contributions to marine food webs, our results show that the magnitude of trophic impacts of invertebrates on other species of commercial and conservation interest is comparable with those of forage fish. Generally, cephalopods showed the strongest ecosystem effects and were characterized by a strong top‐down predatory role. Lobster, and to a lesser extent, crabs, shrimp and prawns, also showed strong ecosystem effects, but at lower trophic levels. Benthic invertebrates, including epifauna and infauna, also showed considerable ecosystem effects, but with strong bottom‐up characteristics. In contrast, urchins, bivalves, and gastropods showed generally lower ecosystem effects in our simulations. Invertebrates also strongly contributed to benthic–pelagic coupling, with exploitation of benthic invertebrates impacting pelagic fishes and vice versa. Finally, on average, invertebrates produced maximum sustainable yield at lower levels of depletion (~45%) than forage fish (~65%), highlighting the need for management targets that avoid negative consequences for target species and marine ecosystems as a whole.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".