Farm management practices and their associations to productivity in Prince Edward Island (PEI) mussel farms
Notice bibliographique
Résumé
The mussel industry of Prince Edward Island (PEI) has grown rapidly over the last 25 years by adapting the subsurface longline system. Presently there are limited coastal sites that can support new farming operations across PEI. It remains unclear whether variation in production is mainly related to environmental factors or longline setup. A multi-year (2002-2004) survey was conducted in Tracadie Bay; as well as across PEI in 2003 to (1) document longlines design variability and (2) quantify its potential association on productivity. In 2004, a controlled trial investigated the effect of sock spacings on productivity of mussel socks in Tracadie Bay. Results from the multi-year survey showed that longline setup varied temporally over the three years: sock spacing increased by 30% and was directly correlated to a 28% reduction in lease stocking density. This change coincided with the implementation of an adaptive bay management plan, which limited lease stocking density to 12 socks/100 m². Regression analyses from our multi-year survey showed that sock spacing was positively associated to sock weight (2002) and condition index (2004). Our models suggested that for every 10 cm increase in sock spacing, sock weight increased by 1.24 kg and condition index by 1.59 respectively. Across PEI, analyses showed a correlation between sock spacing and stocking density in association to the total farming area at the bay scale. The magnitude of the adjustment was important, with an increase in sock spacing of 2.6 cm and a decline of 5.6 socks/100 m² for each additional 100 hectares in farm coverage within a given bay. A third correlation indicated that the condition index was also negatively correlated within embayments characterized by extensive farm coverage compared to those with little farming development. Multiple regression analysis from this survey also revealed variability in sock weights across PEI leases, while longline setup was also highly variable. Results from our controlled trial showed that shell growth and survival were positively associated with higher sock spacing treatments (Tx 80) at two of the four sites. Shell growth increased by 8% and 7% respectively, while survival was 42% and 17% higher, when comparing higher sock spacing (Tx 80) to lower sock spacings (Tx 10) of cultured mussels. Sites where all management strategies (i.e. socking density and seed size) were kept constant displayed no association to sock spacing treatments. The significant differences between sock spacing treatments at two of the four sites may be due to high initial socking density and smaller seed size. These sites were characterized as having 58% and 47% more mussels per meter and initial shell lengths were on average 46% and 23% smaller in comparison to the sites that displayed no association. Our results have shown that simple management strategies such as increasing sock spacing at the lease level can have a substantial influence on productivity. These studies have also generated information for aquaculturists on the relative cost and benefits of longline design and their associations to productivity. Further development of the PEI mussel industry is dependent upon optimal usage of coastal inlets.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».