Model for growth and survival of mussels Mytilus edulis reared in Prince Edward Island, Canada
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 323:171-183 (2006) - doi:10.3354/meps323171 Model for growth and survival of mussels Mytilus edulis reared in Prince Edward Island, Canada Jean-Sébastien Lauzon-Guay1,*, Myriam A. Barbeau1, James Watmough2, Diana J. Hamilton1,3 1Biology Department, University of New Brunswick, Bag Service 45111, Fredericton, New Brunswick E3B 6E1, Canada 2Department of Mathematics and Statistics, University of New Brunswick, PO Box 4400, Fredericton, New Brunswick E3B 5A3, Canada 3Present address: Department of Biology, Mount Allison University, 63B York Street, Sackville, New Brunswick E4L 1G7, Canada *Email: js.lauzon@unb.ca ABSTRACT: Mathematical models of commercially important species enable one to integrate the diversity of information on these species, understand mechanisms responsible for observed population dynamics, and assess management scenarios. We present a population model for blue mussels Mytilus edulis grown in suspended culture in 2 bays in Prince Edward Island, Canada. The model incorporates a number of ecological processes, namely allometric growth of individual mussels, temperature-dependent growth rates (based on the Lassiter-Kearns equation), and survival of mussels based on self-thinning. Analysis of our model suggests that the optimal temperature for mussel growth is 15.8°C. Also, survival does not depend on site or year, indicating that self-thinning is probably due to competition for space rather than food or other site-specific conditions. Based on sensitivity analyses, growth predictions are robust to changes in parameter values, while survival predictions are quite sensitive to changes in the strength of the effect of initial mussel density and of self-thinning. Evaluation of management scenarios over one grow-out period indicates that date of deployment strongly affects time for seeds to reach commercial size. Optimal initial mussel density depends on whether one wants to maximise the proportion of mussels surviving to harvest or the number of mussels available at harvest; this decision depends on whether seed availability or lease area is limiting. KEY WORDS: Aquaculture · Blue mussel · Mytilus edulis · Growth · Mathematical model · Survival Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 323. Online publication date: October 05, 2006 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2006 Inter-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 | 0.000 | 0.001 |
| 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 | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".