MétaCan
Menu
Back to cohort
Record W4402904458 · doi:10.24124/2024/59544

Effects of landscape change on cow moose body fat and physiology in central British Columbia

2024· dissertation· en· W4402904458 on OpenAlexaboutno aff
Carl-Evan Jefferies

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicEffects of Environmental Stressors on Livestock
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyPhysiologyBiology

Abstract

fetched live from OpenAlex

,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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.005
GPT teacher head0.199
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicEffects of Environmental Stressors on LivestockFrench-language works237,207