Grizzly bears without borders: Spatially explicit capture–recapture in southwestern Alberta
Notice bibliographique
Résumé
ABSTRACT Local perceptions of grizzly bear ( Ursus arctos ) numbers in southwestern Alberta, Canada are incongruent with their threatened status. We used non‐invasive genetic sampling to estimate grizzly bear density and abundance in southwestern Alberta. We established 899 bear rub objects (e.g., tree, power pole, fence post) for hair sample collection across the study area by surveying trail networks, using geographic information system layers, and working with >70 landowners to identify priority sampling areas. The study area included 2 management zones: the Recovery Zone where the objective was to recover the grizzly bear population, and a Support Zone intended to maintain those bears not exclusively within the Recovery Zone. We visited rub objects every 3 weeks from late May through early November for 8 visits (7 sampling occasions) per field season. We also allowed for opportunistically collected hair samples (e.g., trapped bears, hair at agricultural bear‐conflict sites). We identified species, individual identity, and sex based on nuclear DNA extracted from hair follicles. From 2013 through 2014, we identified 164 individual grizzly bears. Using spatially explicit capture–recapture models (SECR), we estimated density in 2 ways. First, we estimated density for each sex and year separately (2013: M = 9.2/1,000 km 2 in the Recovery Zone and 8.1/1,000 km 2 in the Support Zone, F = 14.9/1,000 km 2 in the Recovery Zone and 13.6/1,000 km 2 in the Support Zone; 2014: M = 7.2/1,000 km 2 in the Recovery Zone and 5.7/1,000 km 2 in the Support Zone, F = 9.0/1,000 km 2 in the Recovery Zone and 8.5/1,000 km 2 in the Support Zone). Second, we did not allow density to vary across years and instead estimated a single density for the study area (M = 8.0/1,000 km 2 in the Recovery Zone and 7.1/1,000 km 2 in the Support Zone, F = 12.4/1,000 km 2 in the Recovery Zone and 10.0/1,000 km 2 in the Support Zone). Though yearly variation occurred, we derived from our density estimates an expected abundance of approximately 67.4 resident grizzly bears, indicating a 4% per year increase since a 2007 estimate of 51 bears. These SECR density estimates pertain only to bears with home ranges that were centered within the study area. Using traditional capture‐mark‐recapture (CMR) models with the same data yielded a higher estimate of bears because it included all bears that were using the study area (2013: F = 68.9, M = 102.6; 2014: F = 63.0, M = 108.6), including >50% of bears previously genotyped in Montana or British Columbia. In contrast with the SECR estimates, the CMR estimates represent the number of bears that southwestern Alberta residents could have encountered (i.e., the population of bears that had potential to have been involved in conflict). Shifts in grizzly bear distribution resulted in large changes in our SECR density estimates between years, whereas our estimate of the number of bears using the area remained constant. We recommend increased inter‐jurisdictional monitoring and management of this international grizzly bear population. © 2016 The Wildlife Society.
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 | 0,001 | 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) | 0,001 | 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 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 ».