Implications Of Population Genetics And Physiological Responses On The Conservation Of Moose (alces Alces Americana)
Bibliographic record
Abstract
Wildlife populations around the globe are facing numerous, complex challengesto their persistence, yet conservation efforts are hindered by limited information about these populations and the anthropogenic pressures they face. North American moose (Alces alces americana), despite being of ecological, cultural, and economical importance, inhabit remote landscapes, making population monitoring difficult. At the same time, many moose populations, including in Vermont and eastern North America, have experienced recent declines mainly due to winter tick (Dermacentor albipictus) epizootics. Anthropogenic landscape change and climate-mediated pressures pose future challenges for moose across the southern extent of their distribution. Though impacts of winter tick infestation on population vital rates have been well-studied, there has been little research on how moose population genetics and physiological responses interplay with current and future challenges posed by parasites, climate change, and increasing anthropogenic pressures. This dissertation addresses these knowledge gaps by 1) developing a novel approach for estimating wildlife abundance in cases where common abundance measures are difficult to implement, 2) describing the genetic diversity and connectivity of moose populations across the northeastern United States and Southern Quebec, and 3) identifying drivers and fitness implications of stress hormone and nutritional restriction dynamics in Vermont’s highest density moose population. Using a simulated moose population, this dissertation advanced the use of pedigree reconstruction as an abundance estimator, which appeared particularly useful for low-density populations. Genetic samples from moose across five U.S. states and a Canadian province indicated low measures of genetic diversity yet provides evidence of genetic connectivity that will likely be challenged by future climate, habitat, and population conditions. Finally, several climate, habitat, and parasite variables impacted stress metabolite concentrations and nutritional restriction of radio-collared moose calves, both of which related to winter survival probability. This effort supports novel means of monitoring wildlife populations using data that can be acquired non-invasively and illustrates the compounding suite of challenges facing moose in eastern North America.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".