Incorporating climate changes into population dynamic modelling: an individual-based modelling approach for lobster
Bibliographic record
Abstract
One of the most challenging issues in fisheries management is the evaluation of the effects of fishing in the context of a changing environment. Using the pronghorn spiny lobster ( Panulirus penicillatus ) fishery off the eastern coast of Taiwan as an example, we developed an individual-based model (IBM) that is capable of describing the temperature-dependent life history processes and fishery practices for the spiny lobster. We then used the model to evaluate potential impacts of increased ocean temperature on the estimation of mortality-based biological reference points for fisheries management. We demonstrate that a warming temperature would increase the yield-per-recruit and eggs-per-recruit values and consequently reduce the risk of overexploitation under the current exploitation level. However, there is likely a high risk of overexploitation in the long term if higher temperatures induce extra-high natural mortality. The evaluation of effectiveness of size regulations suggests that increasing minimum legal size is proposed as a good candidate measure to reduce the risk of overexploitation for pessimistically unfavorable environmental conditions. This study suggests that an explicit incorporation of the relationships between environmental variables and biological processes can greatly improve fisheries assessment and management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".