Mean temperature of the catch index can be masked by changes in catch composition unrelated to ocean warming
Bibliographic record
Abstract
Abstract Oceans are increasingly warming through climate change. Fish and invertebrate ectotherms respond to ocean warming through poleward and depth‐related migrations, a consequence of which is disruption of fisheries catch compositions. Mean temperature of the catch (MTC) is an index of change in catch composition, from colder to warmer water species. MTC is widely applied as an easily parameterised variable using readily available data (catch and species preferred temperature), but few studies underscore situations that might mask the “true” MTC trend. Here, we use fisheries catch in the Arabian‐Persian Gulf (“Gulf”) to highlight, for the first time, how abrupt changes in market demand can strongly influence catch composition and thereby mask a trend in MTC, and discuss the implications of the unmasked MTC trend to fisheries in the region. We found that a recent sharp decline in MTC from 27 to 26°C, despite a gradual increase in sea surface temperature, coincided with an escalated demand for the largehead hairtail ( Trichiurus lepturus ), a relatively cold‐water species in the Gulf, that caused catch to dramatically increase for export to overseas markets. Our findings suggest that the change in MTC reflected a fishery response to satisfy increased international market demand, rather than reflecting warming‐driven changes in catch composition. When excluding the effect of T. lepturus catch, the Gulf MTC trend was stable over time and consistent with a trend in many tropical and subtropical waters. Our findings highlight that an MTC change can be masked by factors unrelated to warming‐driven changes in catch composition, and that catch‐only MTC trends should be examined cautiously.
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 | 0.001 |
| 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 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".