Evolutionary acceleration in the most endangered mammal of Canada: speciation and divergence in the Vancouver Island marmot (Rodentia, Sciuridae)
Bibliographic record
Abstract
The Vancouver Island marmot is the most endangered mammal of Canada. Factors which have brought this population to the verge of extinction have not yet been fully elucidated, but the effects of deforestation and habitat fragmentation on survival rates, as well as those of variation in rainfall, temperature, snowpack depth and snowmelt strongly suggest that marmots on the island are struggling to keep pace with environmental changes. Genetic analyses, however, seem to indicate that the Vancouver Island marmot may merely represent a melanistic population of its parental species on the mainland. Were it not for its black pelage colour, it is unlikely that it would have attracted much attention as a conservation priority. Our study uses three-dimensional coordinates of cranial landmarks to further assess phenotypic differentiation of the Vancouver Island marmot. A pattern of strong interspecific divergence and low intraspecific variation was found which is consistent with aspects of drift-driven models of speciation. However, the magnitude of shape differences relative to the putatively neutral substitutions in synonymous sites of cytochrome b is too large for being compatible with a simple neutral model. A combination of bottlenecks and selective pressures due to natural and human-induced changes in the environment may offer a parsimonious explanation for the large phenotypic differentiation observed in the species. Our study exemplifies the usefulness of a multidisciplinary approach to the study of biological diversity for a better understanding of evolutionary models and to discover aspects of diversity that may be undetected by using only a few genetic markers to characterize population divergence and uniqueness.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".