Comparing habitat quality within and between environments using giving up densities: an example based on the winter habitat of white‐tailed deer <i>Odocoileus virginianus</i>
Notice bibliographique
Résumé
We previously developed a model, based on the precepts of optimal patch use, to compare habitat quality both within and between environments. Here we illustrate the use of this model in a study estimating quality of winter habitats (deer yards) of white‐tailed deer Odocoileus virginianus near the northern limit of their range by following their foraging behaviour. We compare giving up densities (GUDs), the amount of food remaining in a patch when a forager ceases foraging there, with and without the presence of supplemental food in order to draw inferences about the relative quality either of habitats within an environment or of distinct environments. We use our model to evaluate the impact of alterations to the winter habitat of deer at two distinct sites that differed in their level of predation risk and food availability. The first site, the Mont Rigaud deer yard, was surrounded by farm land and gradually‐expanding suburbs. Predators were rare and food was available in winter either in farm fields or around private homes but deer browsing in the past had left little food in the forest. At the second site, the Calumet deer yard, deer experienced a higher predation risk and did not have access to supplemental food from farm fields or private homes. However, past browsing by deer had not drastically reduced food in the forest. We offered food to deer in four habitats per site (forest, forest edge, clearing, clearing edge) with four to six replicates per site and measured the GUDs after 24 h. Analysis of these data, interpreted according to our model, suggests that deer are more sensitive to metabolic costs at Mont Rigaud and food availability at Calumet; predation risk does not alter deer behaviour between the two sites. Within habitats, deer at Mont Rigaud reacted to clearings as though they imposed higher metabolic costs than the forest. They also reacted to an interaction in which missed food costs influenced GUD only when metabolic costs were not too high. Thus our model appears to provide a convenient tool for comparing habitat quality both within and between environments.
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) | 0,000 | 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 ».