Effects of landscape change on cow moose body fat and physiology in central British Columbia
Bibliographic record
Abstract
,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.
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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 | 0.000 | 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) | 0.002 | 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".