Linking metapopulation structure to elk population management in Idaho: a genetic approach
Bibliographic record
Abstract
Wildlife managers are challenged to manage spatially structured populations efficiently and effectively, therefore dispersal and gene flow are vital to understand and manage, particularly for a harvested species.We used a genetic approach to describe the metapopulation structure of Rocky Mountain elk (Cervus elaphus) in Idaho to assess past patterns of population distribution and influences of harvest.We used elk tissue and DNA samples (n ¼ 216) to examine genetic dissimilarity between 7 regions and 9 elk management zones throughout Idaho using microsatellite loci (n ¼ 11).Using 5 approaches, including pairwise F ST -values, assignment tests, and a Bayesian model-based clustering of genotypes, we examined the distribution of genetic variation.The distribution of genetic variation between elk populations indicated low levels of genetic differentiation among regions (expected heterozygosity [H E ] ¼ 0.55-0.61,overall F ST ¼ 0.011) and elk management zones (H E ¼ 0.54-0.60,overall F ST ¼ 0.017).Assignment tests and migration rates indicated directional gene flow between elk populations.A patchy metapopulation best describes the distribution of genetic variation among Idaho elk populations because likely enough individual interchange occurs between geographically separated populations.The elk populations we sampled could be part of a geographically larger patchy metapopulation potentially stretching from Yellowstone National Park through Idaho into western Canada.Because of historical translocations of elk from Yellowstone National Park, insufficient time may have passed to detect differences in genetic variation.Subtle differences in the distribution of genetic variation were observed in 2 of the 9 elk management zones within 2 different regions of the state.Our findings indicate management of Idaho elk populations and dispersal are maintaining sufficient gene flow.Metapopulation structure of a harvested species based on the distribution of genetic variation is an indicator of potential genetic consequences of harvesting and sustainable harvest levels.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".