Drivers of dynamics of small pelagic fish resources: biology, management and human factors
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 617-618:1-6 (2019) - DOI: https://doi.org/10.3354/meps12985 INTRODUCTION Drivers of dynamics of small pelagic fish resources: biology, management and human factors Jürgen Alheit1,*, Myron A. Peck2 1Leibniz Institute for Baltic Sea Research, Seestraße 15, 18119 Warnemünde, Germany 2Institute for Marine Ecosystem and Fisheries Science (IMF), Center for Earth System Research and Sustainability (CEN), University of Hamburg, Große Elbstraße 133, 22767 Hamburg, Germany *Corresponding author: juergen.alheit@web.de ABSTRACT: Populations of small pelagic fish (SPF) such as sardine, anchovy, herring, capelin and mackerel provide ~25% of the global annual yield of capture fisheries, and the well-being of many human coastal communities around the world, particularly in developing countries, critically depends on these SPF resources. These fishes display large ‘boom and bust’ cycles with great ecological as well socioeconomic consequences. Despite many internationally coordinated research efforts, sufficient knowledge about the drivers of SPF population dynamics and, particularly, the interactive effects of environmental and anthropogenic factors is lacking. The ecology and management of SPF were discussed in a symposium in Victoria, BC (Canada), attracting participants from 31 countries. This Theme Section consists of 22 research contributions providing fundamental insights into (1) the biology of SPF, (2) the drivers of SPF dynamics and (3) the socioeconomic impacts of SPF fisheries. Such insights are urgently needed for effective, ecosystem-based management of these highly variable fish populations. The symposium was an important catalyst for future, internationally coordinated research efforts to further advance our knowledge on the drivers of SPF population dynamics and the effective management of SPF fisheries. KEY WORDS: Small pelagic fish · Recruitment · Growth · Food web dynamics · Management · Assessment · Socioeconomic aspects Full text in pdf format NextCite this article as: Alheit J, Peck MA (2019) Drivers of dynamics of small pelagic fish resources: biology, management and human factors. Mar Ecol Prog Ser 617-618:1-6. https://doi.org/10.3354/meps12985 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 617-618. Online publication date: May 16, 2019 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2019 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".