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Enregistrement W4402915289 · doi:10.24124/2024/59543

Spatiotemporal overlap of sympatric mesocarnivores in central British Columbia, Canada

2024· dissertation· en· W4402915289 sur OpenAlexaboutno aff
Lauren Wheelhouse

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueWildlife Ecology and Conservation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSympatric speciationGeographyEcologyBiology

Résumé

récupéré en direct d'OpenAlex

,Many wildlife species use similar resources, leading to the potential for overlapping niches. These overlaps can create negative interspecific interactions, including different forms of competition. Niche overlap can be experienced on several different axes, including spatial, temporal, and dietary. There are many factors that may affect species co-occurrence patterns, including population cycles, natural and anthropogenic landscape change, harvest mortality, and changes in resource availability. Effective wildlife management is dependent on an understanding of the interaction between community dynamics and competition. Many mesocarnivores in central British Columbia overlap spatially, temporally, and dietarily. This high degree of overlap means that understanding the mechanisms facilitating their coexistence is particularly important. I used five years of data from remote cameras and fine-scale habitat data from LiDAR to assess patterns in the spatial and temporal co-occurrence of short-tailed weasels (Mustela erminea), American mink (Neogale vison), American marten (Martes americana), fishers (Pekannia pennanti), wolverines (Gulo gulo), and Canada lynx (Lynx canadensis). During this study, there were fluctuations in snowshoe hare (Lepus americanus) abundance, as well as many predators, specifically decreases in lynx and increases in fisher. Habitat features, like structural complexity, can facilitate species co-occurrence by allowing for fine-scale niche partitioning. I used multi-species occupancy models to test hypotheses about the relationships between mesocarnivore co-occurrence and habitat. Mesocarnivores were more likely to co-occur at sites with greater complexity of vertical forest structure and at sites closer to riparian zones. Short-tailed weasels, however, did not co-occur with other mustelids in riparian zones. Importantly, I found that habitat covariates associated with co-occurrence were relatively similar over time despite notable changes in the abundance of predators and prey. My findings highlight the importance of riparian habitats and forest complexity in facilitating species co-occurrence in harvested forests. Temporal niche partitioning is a second mechanism that allows species to co-exist in space and may occur if one species shifts its temporal activity patterns to avoid interactions with another. I tested the hypothesis that smaller-bodied species would shift their activity in the presence of larger-bodied species. I found partial support for this hypothesis in that marten activity differed in the presence of larger-bodied lynx when lynx were abundant but not when lynx were rare. Furthermore, the activity patterns of the largest mesocarnivores in our study, lynx and wolverine, were unaffected by the presence of smaller species. In contrast with my hypothesis, weasel activity was similar in the presence of larger-bodied species. Collectively, these findings suggest that mesocarnivores may alter their temporal use of habitat to avoid co-occurrence in response to the presence of other species. Combined, my research provides insight into the mechanisms by which mesocarnivores—species with overlap in diet and habitat—share space. My findings highlight the importance of forest management practices that retain structural complexity and riparian areas to promote the co-existence of sympatric mesocarnivores. Further, my results emphasize the responses of sympatric species to changes in community dynamics, which is important for understanding the effects of population cycles on species co-occurrence.

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)0,002
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 enseignants0,022
Score d'incertitude au seuil0,055

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,001
Communication savante0,0010,000
Science ouverte0,0010,001
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 Opus0,003
Tête enseignante GPT0,180
Écart entre enseignants0,177 · 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
Publié2024
Routes d'admission1
Résumé présentoui

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