Using noninvasive genetic sampling methods to estimate demographic and genetic parameters for large carnivore populations in the Rocky Mountains
Bibliographic record
Abstract
Healthy carnivore populations are important to maintaining ecosystem balance, but many species are declining globally at disturbing rates due to anthropogenic causes. To effectively manage and conserve carnivores, wildlife managers must be able to obtain reliable estimates of population parameters. Noninvasive genetic sampling (NGS) methods such as hair or scat collection offer new and exciting alternatives to traditional carnivore research methods involving capture, drugging, and handling of animals; however, the potential of NGS methods to answer applied ecological questions has not been fully realized. The main objective of my doctoral research was to develop and apply NGS methods to estimate demographic and genetic parameters for large carnivore populations in the Rocky Mountains. First, I evaluated two NGS methods, hair snares and snow tracking, for cougars (Puma concolor) in Yellowstone National Park. I developed a method to collect hair while following cougar tracks in snow to bed sites and natural hair snags (e.g. thorn bushes, branch tips) from which I demonstrated that samples collected using NGS can provide reliable information on cougar population abundance. Next, I compared the ability of two NGS methods, hair traps and bear rub surveys, to estimate population abundance and trend of grizzly (Ursus arctos) and black bears (U. americanus) in Banff National Park. I found that bear rubs performed better than hair traps for estimating grizzly bear abundance and population growth rates, whereas hair traps worked better than bear rubs for black bears. I then used NGS to examine demographic and genetic connectivity at wildlife crossing structures along the Trans-Canada Highway that bisects Banff National Park. I compared genetic data collected from the bear populations surrounding the highway to data collected at the crossing structures using a novel hair sampling system. This comparison allowed me to show that wildlife crossing structures provided demographic connectivity for bear populations and maintained sufficient gene flow across the highway to prevent genetic isolation. In short, I have demonstrated the power of using an array of NGS methods, alone or in combination, to estimate abundance, gene flow, genetic structure, migration, and population growth rates for large carnivores in the Rocky Mountains.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".