Effects of landscape change on cow moose body fat and physiology in central British Columbia
Notice bibliographique
Résumé
,Forest harvesting affects wildlife in complex ways, including through changes in the nutritional, thermal, and security properties of habitat. Understanding the mechanisms by which wildlife respond to changes in habitat is therefore necessary to identify beneficial characteristics to be protected or restored. This is important given compounding effects of climate change, which can interact with habitat to alter the thermal and nutritional environment experienced by wildlife. Physiological bioindicators that reflect endocrine activity and nutrition are one approach to identify the importance of multiple mechanisms by which wildlife respond to environmental change. I used multiple bioindicators measured in adult cow moose (Alces alces) at capture and through non-invasive sampling to evaluate moose response to forest harvesting in central British Columbia (BC). Over the past two decades, moose in central BC have experienced dramatic declines in abundance. The BC Provincial Moose Research Project provide evidence of nutritional deficiencies in moose, potentially associated with increased forest harvesting related to the mountain pine beetle epidemic that has occurred over the same period. I focused on the knowledge gap related to specific mechanisms linking forest harvesting with poor nutritional condition in moose. First, I tested hypotheses that hair cortisol and body fat in cow moose would be associated with nutritional, thermal, and predation risk characteristics of summer-autumn habitat. I used hair samples and body fat measurements collected at capture and general linear mixed-effects models. Hair cortisol was higher in moose that experienced warmer summer home range temperatures and decreased when summer home ranges had a higher proportion of mature conifer forest, providing thermoregulatory refuge. I found that 50% of moose had body fat levels below 9% at the time of winter captures, which is considered indicative of poor nutrition. Cows with calves, had lower body fat, compared to those without, suggesting that the energetic costs of lactation influence moose physiology more than nutrition, predation risk, thermal conditions, or anthropogenic disturbances. Second, I used non-invasive measures of physiology to understand how cow moose respond to disturbance, predation, habitat characteristics, and environmental conditions in winter. I found that temperature was the most important predictor of urea nitrogen: creatine concentrations, supporting evidence that warm thermal conditions in winter limit the energy profile of moose. Fecal cortisol was higher in moose that used mature conifer forests, reflecting the low nutritional value of this habitat type. Fecal triiodothyronine levels were higher in moose that recently used mid-seral stands, indicating increased energetic intake. My findings highlight that for a cold-adapted species such as moose, physiological processes alone may prove inadequate to withstand both nutritional deficits and thermal energetic costs endured within fragmented landscapes. The physiological capacity to withstand these pressures is likely linked to heterogenous habitats that provide both forage and thermal relief. I conclude that thermal stress is the most important parameter influencing the physiological state of moose and that it is one mechanism contributing to health-related mortalities. Moose stand to benefit from forest management that maintains thermal shelter and implements silviculture prescriptions that promotes static foraging opportunities in perpetuity.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 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) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».