Population structure of caribou in an ice‐bound archipelago
Bibliographic record
Abstract
Abstract Aim Archipelagos provide ideal natural systems for testing the effects of isolation and fragmentation of habitats on the genetic makeup of populations—an important consideration, given that many insular species are of conservation concern. Two theories predominate: Island Biogeography Theory ( IBT ) posits that proximity to the mainland drives the potential for migrants and gene flow. The Central Marginal Hypothesis ( CMH ) predicts that island populations at the periphery of a species range may experience low gene flow, small population size and high rates of genetic drift. We investigated population genetic structure, genetic diversity and key drivers of diversity for Arctic island‐dwelling caribou ( Rangifer tarandus ). Our aim was to inform intraspecific units for conservation and decipher how IBT and CMH could act in an archipelago where isolation is highly variable due to sea ice and open water. Location Canadian Arctic Archipelago, Canada (Latitude, 55–82°N; Longitude, 61–123°W). Methods We genotyped 447 caribou at 16 microsatellite loci; these caribou represented two subspecies ( R. t. groenlandicus , R. t. pearyi ) and three designatable units. We used hierarchical Bayesian clustering and ordination to determine genetic groups. We evaluated the influence of ecological and geographic variables on genetic diversity using linear mixed‐effects models and compared diversity among mainland and island herds. Results Bayesian clustering revealed nine genetic clusters with differentiation among and within caribou subspecies. Genetic differentiation was explained predominantly by isolation‐by‐distance across all caribou, even at the scale of subspecies. Island caribou were less genetically diverse than mainland herds; individual heterozygosity was negatively correlated with distance‐to‐mainland and the extent of autumn ice‐free coastline and positively correlated with unglaciated island size. Main conclusions Our findings underscore the importance of hierarchical analysis when investigating genetic population structure. Genetic diversity and its key drivers lend support to both IBT and CMH and highlight the pending threat of climate change for Arctic island caribou.
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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 | 0.000 | 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 | 0.000 | 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".