Variation in mineral levels and immune responses relative to environmental and individual conditions in adult female moose in central British Columbia
Notice bibliographique
Résumé
,Environmental change can compromise the health and fitness of individual wildlife, leading to negative consequences for populations. Understanding how environmental change relates to wildlife health and fitness is therefore crucial for informing effective conservation and management strategies. Mineral status and immune function are key components of animal health that are sensitive to changes in habitat, climate, and disturbance regimes, and may therefore serve as useful biomarkers for examining how environmental variation corresponds with health and population resilience in wildlife. Moose (Alces alces) are one species whose health may be affected by environmental change. Over the past two decades, moose populations in central British Columbia (BC) have declined dramatically following a severe mountain pine beetle epidemic and subsequent timber salvage logging, which resulted in a heavily altered landscape. In response to these declines, the Province of BC initiated a long-term research project on adult female moose. This research documented cases of starvation and health-related mortalities, along with suboptimal pregnancy rates, which suggests that bottom-up factors may have contributed to the observed declines. My thesis draws on and supplements information collected as part of the BC Provincial Moose Research Project to investigate associations between bottom-up factors and moose health. Specifically, I examined environmental and individual correlates of essential mineral concentrations and immune responses in female moose to better characterize patterns linking environmental variation and moose health. First, I examined whether mineral concentrations in the hair of adult female moose were associated with environmental factors in their summer–autumn habitat. I used hair samples collected during winter captures to quantify the concentrations of 15 macro and trace minerals. Using generalized linear mixed-effects models, I tested whether variation in mineral concentrations may have reflected differences in habitat composition, landscape disturbance, and climatic conditions. I found that precipitation was an important predictor of selenium and zinc concentrations, suggesting that mineral uptake could be influenced by climate-driven effects on vegetation. Moose that spent more time in deciduous forests had greater concentrations of potassium and magnesium, possibly reflecting the nutritional value of these forest stands. Furthermore, moose with access to recent wildfire burns had greater zinc levels, suggesting that fire could enhance forage quality or availability. Collectively, these findings reveal patterns in moose nutritional health in relation to environmental conditions. Second, I measured concentrations of multiple immune biomarkers in the serum of female moose and investigated how these markers related to individual condition and parasite exposure. Moose with greater fat reserves had higher concentrations of interleukin-12, suggesting that individuals in better condition may be able to allocate more resources toward immune function. Total globulin concentrations were elevated in moose exposed to both microand macro-parasites, reflecting immune activation in response to parasitic challenges. I also found correlations between zinc levels and both IL-12 and total globulin, whereas copper concentrations were associated with haptoglobin, indicating a potential role of trace minerals in modulating immune responses. Combined, my results highlight connections between nutrition, immune function, and parasite exposure in moose. Collectively, my findings offer novel insights into patterns of variation in moose health in relation to environmental conditions. Moreover, my findings provide baseline data on a range of health biomarkers in female moose and highlight the importance of future monitoring to assess the effects of environmental change on wildlife health.
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 | 0,001 | 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) | 0,001 | 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 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 ».