Linking alternative food sources to winter habitat selection of herbivores in overbrowsed landscapes
Notice bibliographique
Résumé
Abstract During winter, ungulates in boreal forests must cope with high energetic costs related to locomotion in deep snow and reduced forage abundance and quality. At high density, ungulates face additional constraints, because heavy browsing reduces availability of woody browse, the main source of forage during winter. Under these severe conditions, large herbivores might forage on alternative food sources likely independent of browsing pressure, such as litterfall or windblown trees. We investigated the influence of alternative food sources on winter habitat selection, by studying female white‐tailed deer ( Odocoileus virginianus ) living in 2 landscapes with contrasted browse abundance, recently logged and regenerated landscapes, in a population at high density and on a large island free of predators. We fitted 21 female white‐tailed deer with Global Positioning System (GPS) collars and delineated winter home ranges and core areas. We measured snow conditions in different habitat categories and sampled vegetation in the core areas and in the rest of the home ranges to determine how forage abundance, protective cover, and snow conditions influenced habitat selection within the home range. In both landscapes, deer were less likely to use open habitat categories as snow accumulated on the ground. At a finer scale, deer inhabiting the regenerated landscape intensively used areas where balsam fir cover was intermediate with greater balsam fir browse density than in the rest of the home range. In the recently logged landscape, deer were more likely to be found near edges between clear‐cuts and balsam fir stands and in areas where windblown balsam fir trees were present; the latter being the most influential variable. Although balsam fir browse was sparse and mainly out of reach in this landscape, deer increased the use of areas where it was present. Our results offer novel insights into the resource selection processes of northern ungulates, as we showed that access to winter forage, such as woody browse and alternative food sources, depends on climatic conditions and stochastic events, such as abundant compacted snow or windthrows. To compensate for these scarce and unpredictable food supplies, deer selected habitat categories, but mostly areas within those habitat categories, where the likelihood of finding browse, litterfall, and windblown trees was greatest. © 2011 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 | 0,000 | 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 ».