Genetic Evidence for Restricted Dispersal along Continuous Altitudinal Gradients in a Climate Change-Sensitive Mammal: The American Pika
Bibliographic record
Abstract
When faced with rapidly changing environments, wildlife species are left to adapt, disperse or disappear. Consequently, there is value in investigating the connectivity of populations of species inhabiting different environments in order to evaluate dispersal as a potential strategy for persistence in the face of climate change. Here, we begin to investigate the processes that shape genetic variation within American pika populations from the northern periphery of their range, the central Coast Mountains of British Columbia, Canada. At these latitudes, pikas inhabit sharp elevation gradients ranging from sea level to 1500 m, providing an excellent system for studying the effects of local environmental conditions on pika population genetic structure and gene flow. We found low levels of neutral genetic variation compared to previous studies from more southerly latitudes, consistent with the relatively recent post-glacial colonization of the study location. Moreover, significant levels of inbreeding and marked genetic structure were detected within and among sites. Although low levels of recent gene flow were revealed among elevations within a transect, potentially admixed individuals and first generation migrants were identified using discriminant analysis of principal components between populations separated by less than five kilometers at the same elevations. There was no evidence for historical population decline, yet there was signal for recent demographic contractions, possibly resulting from environmental stochasticity. Correlative analyses revealed an association between patterns of genetic variation and annual heat-to-moisture ratio, mean annual precipitation, precipitation as snow and mean maximum summer temperature. Changes in climatic regimes forecasted for the region may thus potentially increase the rate of population extirpation by further reducing dispersal between sites. Consequently, American pika may have to rely on local adaptations or phenotypic plasticity in order to survive predicted climate changes, although additional studies are required to investigate the evolutionary potential of this climate change sensitive species.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".