Variation in mineral levels and immune responses relative to environmental and individual conditions in adult female moose in central British Columbia
Bibliographic record
Abstract
,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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".