Logging, linear features, and human infrastructure shape the spatial dynamics of wolf predation on an ungulate neonate
Notice bibliographique
Résumé
Humans are increasingly recognized as important players in predator-prey dynamics by modifying landscapes. This trend has been well-documented for large mammal communities in North American boreal forests: logging creates early seral forests that benefit ungulates such as white-tailed deer (Odocoileus virginianus), while the combination of infrastructure development and resource extraction practices generate linear features that allow predators such as wolves (Canis lupus) to travel and forage more efficiently throughout the landscape. Disturbances from recreational activities and residential development are other major sources of human activity in boreal ecosystems that may further alter wolf-ungulate dynamics. Here, we evaluate the influence that several major types of anthropogenic landscape modifications (timber harvest, linear features, and residential infrastructure) have on where and how wolves hunt ungulate neonates in a southern boreal forest ecosystem in Minnesota, USA. We demonstrate that each major anthropogenic disturbance significantly influences wolf predation of white-tailed deer fawns (n = 427 kill sites). In contrast with the "human shield hypothesis" that posits prey use human-modified areas as refuge, wolves killed fawns closer to residential buildings than expected based on spatial availability. Fawns were also killed within recently-logged areas more than expected. Concealment cover was higher at kill sites than random sites, suggesting wolves use senses other than vision, probably olfaction, to detect hidden fawns. Wolves showed strong selection for hunting along linear features, and kill sites were also closer to linear features than expected. We hypothesize that linear features facilitated wolf predation on fawns by allowing wolves to travel efficiently among high-quality prey patches (recently logged areas, near buildings), and also increase encounter rates with olfactory cues that allow them to detect hidden fawns. These findings provide novel insight into the strategies predators use to hunt ungulate neonates and the many ways human activity alters wolf-ungulate neonate predator-prey dynamics, which have remained elusive due to the challenges of locating sites where predators kill small prey. Our research has important management and conservation implications for wolf-ungulate systems subjected to anthropogenic pressures, particularly as the range of overlap between wolves and deer expands and appears to be altering food web dynamics in boreal ecosystems.
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) | 0,001 | 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 ».