EDITORIAL: Toward Darwinian fisheries management
Bibliographic record
Abstract
Table of contents Introduction 246–259 Dunlop, E.S., K. Enberg, C. Jørgensen, and M. Heino. Toward Darwinian fisheries management Empirical evidence 260–275 Sharpe, D., and A. Hendry. Life history change in commercially exploited fish stocks: an analysis of trends across studies. 276–290 Conover, D.O. and H. Baumann. The role of experiments in understanding fishery‐induced evolution. 291–298 Pérez‐Rodríguez, A., M. Morgan, and F. Saborido‐Rey. Comparison of demographic and direct methods to calculate probabilistic maturation reaction norms for Flemish Cap cod ( Gadus morhua ). 299–311 Cooke, S.J., M.R. Donaldson, S.G. Hinch, G.T. Crossin, D.A. Patterson, K.C. Hanson, K.K. English, J.M. Shrimpton, and A.P. Farrell. Is fishing selective for physiological and energetic characteristics in migratory adult sockeye salmon? 312–323 Redpath, T.D., S.J. Cooke, R. Arlinghaus, D.H. Wahl, and D.P. Philipp. Life‐history traits and energetic status in relation to vulnerability to angling in an experimentally selected teleost fish. Theory and management 324–334 Hutchings, J.A. Avoidance of fisheries‐induced evolution: management implications for catch selectivity and limit reference points. 335–355 Arlinghaus, R., S. Matsumura, and U. Dieckmann. Quantifying selection differentials caused by recreational fishing: development of modeling framework and application to reproductive investment in pike ( Esox lucius ) 356–370 Jørgensen, C., B. Ernande, and Ø. Fiksen. Size‐selective fishing gear and life history evolution in the Northeast Arctic cod. 371–393 Dunlop, E.S., M. Baskett, M. Heino, and U. Dieckmann. Propensity of marine reserves to reduce the evolutionary effects of fishing in a migratory species. 394–414 Enberg, K., C. Jørgensen, E.S. Dunlop, M. Heino, and U. Dieckmann. Implications of fisheries‐induced evolution for stock rebuilding and recovery. 415–437 Okamoto, K., R. Whitlock, P. Magnan, and U. Dieckmann. Mitigating fisheries‐induced evolution in lacustrine brook charr ( Salvelinus fontinalis ) in southern Quebec, Canada. 438–455 Wang, H‐Y., and T.O. Höök. Eco‐genetic model to explore fishing‐induced ecological and evolutionary effects on growth and maturation schedules. Abstract There is increasing evidence that fishing may cause rapid contemporary evolution in freshwater and marine fish populations. This has led to growing concern about the possible consequences such evolutionary change might have for aquatic ecosystems and the utility of those ecosystems to society. This special issue contains contributions from a symposium on fisheries‐induced evolution held at the American Fisheries Society Annual Meeting in August 2008. Contributions include primary studies and reviews of field‐based and experimental evidence, and several theoretical modeling studies advancing life‐history theory and investigating potential management options. In this introduction we review the state of research in the field, discuss current controversies, and identify contributions made by the papers in this issue to the knowledge of fisheries‐induced evolution. We end by suggesting directions for future research.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.058 | 0.038 |
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".