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Enregistrement W2187285270

10.3 RESOURCE SELECTION BY FEMALE GRIZZLY BEARS WITH CONSIDERATION TO HETEROGENEOUS LANDSCAPE PATTERN AND SCALE

2005· article· en· W2187285270 sur OpenAlexaboutno aff
Jeannette Theberge, Stephen Herrero, Scott Jevons

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueWildlife Ecology and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésVegetation (pathology)TerrainGeographyDominance (genetics)Resource (disambiguation)Scale (ratio)Selection (genetic algorithm)EcologyGrizzly BearsPhysical geographyEnvironmental scienceUrsusCartographyPopulationBiologyDemographyComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

research suggests that many species perceive multiple scales, few studies have included a range of scale-dependent variables in studies regarding resource selection. We investigate the selection of such features for grizzly bears - a mobile species whose landscape selection could be influenced by landscape pattern. We investigate whether female grizzly bears in the eastern slopes region of the Alberta portion of the Central Rockies Ecosystem select resource characteristics and heterogeneous landscape patterns differently than available within home ranges when landscape patterns are measured at multiple scales simultaneously. Resource characteristics were measured in the 300-m diameter immediate-vicinity of bears, specifically vegetation, slope, aspect, elevation, proximity to edge, proximity to water, and proximity to human activity. Heterogeneous landscape patterns were measured in 300-m, 1.5-km, 3.0-km diameter windows, specifically vegetation diversity, vegetation dominance, terrain ruggedness, density of motorized access, and density of non-motorized access. We used logistic regression to calculate resource selection functions. Female grizzly bears responded to environmental conditions beyond the immediate vicinity of 300 metres, frequently selecting heterogeneous landscape patterns at different scales, and simultaneously at several scales. We describe results for wary individuals during 2 seasons. All female bears selected pockets of low- density non-motorized access by humans at the 1.5-km scale, within larger 3.0-km areas of high-density non- motorized access by humans. For all female bears, relatively high diversity of vegetation types was selected at the 300-m scale in the preberry season, and selection for high diversity at the 1.5-km scale in the berry season. Homogeneous vegetation within the 300-m scale was never selected. Close proximity to edge was consistently selected. Wary females selected high levels of ruggedness at the broadest scales during both seasons. Also commonly selected were general-shrub, graminoid meadows, and avalanche paths, suggesting their general importance to female grizzlies. We recommend that resource selection studies incorporate variables at multiple scales. Management along the eastern slopes should maintain vegetation edge and diversity of vegetation communities through the maintenance of disturbance regimes. Furthermore, management should attempt to minimize human disturbance in areas that have any or all of the following characteristics: are within 60 metres of vegetation edges, have high levels of vegetation diversity within 300-m and 1.5-km windows, consist of rugged terrain within broad 3.0-km areas, contain graminoid meadows and avalanche paths, or are close to riparian areas. To take into account habitat selected by grizzly bears levels of human access should be minimal in contiguous 1.5-km diameter areas that contain these habitat attributes. We recognize that competing land use pressures will often exist. In applying seasonal resource selection functions to the eastern slopes landscape, we identified 4 geographic areas containing a concentration of high probability of adult female occurrence. These areas were: 1) around Lake Louise, 2) from the Red Deer River/Ya Ha Tinda area, south to and including the Burnt Timber drainage, 3) around Banff townsite, and 4) along the Canmore/Bow River corridor as far east as the Kananaskis River drainage and the Old Fort Creek drainage, and extending south to include the Wind Valley and the Evan-Thomas Recreation Area. We also identified numerous smaller pockets of high probability of female grizzly occurrence distributed throughout the study area but especially south of the Trans Canada Highway. Each of the 4 areas with a concentration of high probability of adult female use is a candidate for management that will allow for grizzly bear habitat use with minimal human-caused mortality risk. This will be challenging because of extensive human use in these areas.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,188
Écart entre enseignants0,183 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2005
Routes d'admission1
Résumé présentoui

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