Bibliographic record
Abstract
MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 530:223-232 (2015) - DOI: https://doi.org/10.3354/meps11135 Economic incentives and overfishing: a bioeconomic vulnerability index William W. L. Cheung1,*, U. Rashid Sumaila2 1Changing Oceans Research Unit and 2Fisheries Economics Research Unit, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada *Corresponding author: w.cheung@fisheries.ubc.ca ABSTRACT: Bioeconomic theory predicts that the trade-offs between maximization of economic benefits and conservation of vulnerable marine species can be assessed using the ratio between the discount rate of fishers and the intrinsic rate of growth of the exploited populations. In this paper, we use this theory to identify areas of the global ocean where higher vulnerability of fishes to overfishing would be expected in the absence of management. We derive an index to evaluate the level of vulnerability by comparing discount rates and fishes’ intrinsic population growth rates. Using published discount rates of countries that are reported to fish in the ocean and estimating the intrinsic population growth rate for major exploited fishes in the world, we calculate the vulnerability index for each 0.5° latitude × 0.5° longitude grid for each taxon and each fishing country. Our study shows that vulnerability is inherently high on the northeastern coast of Canada, the Pacific coast of Mexico, the Peruvian coast, in the South Pacific, on the southern and southeastern coast of Africa, and in the Antarctic region. It should be noted that this index does not account for the management regime currently in place in different areas, and thus mainly reflects the vulnerability resulting from the intrinsic life history characteristics of the fish species being targeted and the discount rates of the fishers exploiting them. Despite the uncertainties of this global-scale analysis, our study highlights the potential applications of large-scale spatial bioeconomics in identifying areas where fish stocks are more likely to be over-exploited when there is no effective fisheries management; this applies to many fisheries around the world today. KEY WORDS: Vulnerability · Discount rate · Overfishing · Fisheries · Spatial bioeconomics Full text in pdf format Supplementary material PreviousNextCite this article as: Cheung WWL, Sumaila UR (2015) Economic incentives and overfishing: a bioeconomic vulnerability index. Mar Ecol Prog Ser 530:223-232. https://doi.org/10.3354/meps11135 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 530. Online publication date: June 18, 2015 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2015 Inter-Research.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".