How ocean warming and acidification affect the life cycle of six worldwide commercialised sea urchin species: A review
Bibliographic record
Abstract
Abstract Ongoing global changes are expected to affect the worldwide production of many fisheries and aquaculture systems. Because invertebrates represent a relevant industry, it is crucial to anticipate challenges that are resulting from the current environmental alterations. In this review, we rely on the estimated physiological limits of six commercialised species of sea urchins ( Loxechinus albus , Mesocentrotus franciscanus , Paracentrotus lividus, Strongylocentrotus droebachiensis , Strongylocentrotus intermedius and Strongylocentrotus purpuratus ) to define the vulnerability (or resilience) of their populations facing ocean warming and acidification (OW&A). Considering that coastal systems do not change uniformly and that the populations’ response to stressors varies depending on their origin, we investigate the effects of OW&A by including studies that estimate future environmental mutations within their distribution areas. Cross‐referencing 79 studies, we find that several sea urchin populations are potentially vulnerable to the predicted OW&A as environmental conditions in certain regions are expected to shift beyond their estimated physiological limit of tolerance. Specifically, while upper thermal thresholds seem to be respected for L. albus along the SW American coast, M. franciscanus and S. purpuratus southern populations appear to be vulnerable in NW America. Moreover, as a result of the strong warming expected in the Arctic and sub‐Arctic regions, the local productivity of S. droebachiensis is also potentially largely affected. Finally, populations of S. intermedius and P. lividus found in northern Japan and eastern Mediterranean respectively, are supposed to decline due to large environmental changes brought about by OW&A. This review highlights the status and the potential of local adaptation of a number of sea urchin populations in response to changing environmental conditions, revealing possible future challenges for various local fishing industries.
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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 | 0.001 |
| 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) | 0.000 | 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".