A Fish Habitat Classification Model for the Upper and Middle Sections of the Bay of Quinte, Lake Ontario
Notice bibliographique
Résumé
A fish habitat classification model was developed and applied to the upper and middle sections of the Bay of Quinte, Lake Ontario. Available habitat inventories were assembled in a GIS database, bringing bathymetric, shoreline, substrate, and vegetation data together in a series of layers. The classification model was developed in four steps. In the first step, the Defensible Methods (DM) model developed by Minns et al. (2000) was used to estimate suitability values in all habitat patches for a set of nine fish groups each with three life stages. The fish groups were formed from the assemblage of fish species present in the Bay of Quinte by combining them according to thermal and vegetation preferences, and combinations of size and age-at-maturity. Different methods of combining the 27 suitability indices were examined to allow designation of each unique habitat patch to low, medium or high suitability categories for fish. The K-means clustering technique was selected for classifying habitat patches into three suitability categories, thereby exploiting natural breaks in the cumulative distributions of suitability values and maintaining consistency with underlying habitat features. In the second step, the spatially rare habitats for each fish group by life stage combination were used to identify habitat patches that are important for particular fish groups and life stages but which had been classified as medium or low suitability in the first classification step. Criteria for recognizing rarity were used to reassign habitat patches rated low or medium in step one to the high class. In the third step, local expert knowledge of important fish habitats gathered from anglers and fishers were used to develop an expert classification. This expert mapping of important fishing areas was compared with that obtained via suitability and rarity ratings and then, in step four, used to upgrade some areas from low or medium to high. The final habitat classification model is a mixture of suitability, rarity and expert ratings. The habitat suitability class assignments obtained in step one were not changed appreciably by steps two and three. The combined suitability-rarity ratings showed good agreement with the local expert ratings. Important fishing areas either overlapped suitable areas or were close by where fisher access would be restricted by depth or vegetation density. The final habitat classification for the Bay of Quinte provides a context for both conservation and restoration efforts. Periodic updating of the classification system will be needed as conditions change, e.g., as a result of climate change or as the effects of the zebra mussel invasion on macrophytes and substrates mature, or as data on other habitat elements becomes available, e.g., seasonal and spatially thermal habitat maps. Further effort is needed to understand the procedures used by government agencies at different levels to integrate the knowledge embodied in habitat maps into on-going fisheries and fish habitat management.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».