Locating and prioritizing areas with high conservation value in the Saint John River watershed
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Notice bibliographique
Résumé
Information on the distribution of species-at-risk habitat facilitates conservation efforts of those species, and enables the development of a more accurate landscape-scale conservation plan.Geographical information system (GIS)-based predictive habitat mapping can greatly improve this process by reducing the required amount of time-and resource-consuming field surveys.The purpose of this study was to explore the possibilities of providing a semi-automated GIS-based approach to predictive at-risk species habitat distribution modeling.The study area was the New Brunswick portion of the upper and middle Saint John River watershed in western New Brunswick, Canada.First, the most important habitat factors were identified for 175 terrestrial species-at-risk by comparing point observation data to selected habitat characteristic features.The results were then used to locate areas with similar habitat characteristics, and -thereby -potential habitat of the species.These steps were performed using ArcGIS software, where a series of models were built to automate the process, in order to facilitate the processing of large amounts of data.Four species from different species groups were selected to illustrate the developed method: Bi cknell's thrush (Catharus bicknelli) from birds, the spine-crowned clubtail (Gomphus abbreviatus) from insects, the wood turtle (Glyptemys insculpta) from reptiles, and the little bluestem (Schizachyrium scoparium) from plants.The results of the study indicate a correspondence between model-generated habitat characteristics and those defined in literature.A series of habitat characteristics match those expressed in literature for the selected species, but some key habitat characteristics, most notably water vicinity, were not allocated a sufficient preference value.The results highlight the need for precise species observation point data, as well as a set of habitat factors that accurately describe the habitat quality for each individual species.The resulting potential habitat distribution maps of individual species illustrate areas with varying degrees of habitat quality.This data on either individual species or species groups can be used for a variety of planning or research projects.Based on the results of the analyses performed in this thesis, the feasibility of spatially optimizing the most important habitat areas for conservation was assessed.The habitat distribution data created with this method can be used to produce a strategic conservation plan, identifying priority locations for conservation and providing an insight into the feasibility of their proposed conservation.A number of software can be used to carry out the spatial optimization.This would support important conservation efforts in the upper and middle Saint John River watershed area.However, since high value potential habitat does not as such indicate species presence or abundance, any management decisions based on the results of these analyses should be supported by on-site surveys.
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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 découle