Projecting changes in the distribution and maximum catch potential of warm water fishes under climate change scenarios in the Yellow Sea
Bibliographic record
Abstract
Abstract Aim Ocean warming has been observed in a number of marine ecosystems and is believed to influence marine species in many ways, such as through changes in distribution range and abundance. In this study, we investigated the potential impacts of climate change on the distribution and maximum catch potential of 34 warm water fishes from 2000 to 2060. Location Yellow Sea, China. Methods We used a dynamic bioclimate envelope model under the RCP2.6 and RCP8.5 scenarios with Earth system models, including GFDL, IPSL, MPI and their ensemble average, to predict current species distributions and their relative abundance and project future species distributions and maximum catch potential (MCP). Results are subsequently summarized by indices such latitudinal centroid (LC) and mean temperature of relative abundance (MTRA). Results Our results showed that the 34 warm water fish species in the Yellow Sea will likely shift to lower latitude regions under future climate change scenarios. In particular, the average LC in the Earth system models of GFDL, IPSL and MPI from 1970 to 2060 is projected to shift at rate of −2.96 ± 1.29 ( SE ) and −3.20 ± 1.94 ( SE ) km per decade under the RCP2.6 and RCP8.5 scenarios, respectively. In addition, the corresponding maximum catch potential is decreased under the above climate change scenarios. The projected changes in the distribution may have major ecological and socio‐economic importance as well as implications for invasive species management, marine ranching construction and shifts in fishing grounds. Main conclusions The projected distribution of 34 warm water fish species in the Yellow Sea shifted to lower latitudes from 2000 to 2060 following both RCP scenarios and Earth system models. This result is contrary to the projections of previous studies suggesting that fish species can shift to higher latitudes or deeper waters under increased temperature scenarios. This difference might be due to the semi‐enclosed shelf sea of Yellow Sea, which is commonly influenced by the fluctuation of the coastal current, the warm current, the cold water mass and overfishing.
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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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".