Population Structure And Hybridization Of Alaskan Caribou And Reindeer: Integrating Genetics And Local Knowledge
Bibliographic record
Abstract
Alaskan caribou (Rangifer tarandus grand) are a valued game species and a key grazer in Alaska's terrestrial ecosystem.Caribou herds, defined by female fidelity to calving grounds, are management units.However, the extent to which herds constitute genetic populations is unknown.Historical fluctuations in herd size, range, and distribution suggest periods of contact and isolation between herds.Likewise, historical contact between caribou and introduced domestic reindeer (R.t.tarandus) created opportunities for hybridization, but its extent is not known.I conducted an interdisciplinary study to understand how historical processes influence genetic identity and population structure of caribou and reindeer.Interviews with herders and hunters in Barrow, Alaska, revealed that many reindeer migrated away with caribou in the 1940s despite herder efforts to prevent mixing.Local observations of reindeer-like animals in caribou herds today suggest feral reindeer may survive and interbreed.Using genetic analysis of North Slope caribou and Seward Peninsula reindeer (w = 312) at 19 microsatellite loci, I detected individuals with hybrid ancestry in all four caribou herds and in reindeer.Selective hunting of reindeer-like animals, along with herd size and natural selection, may remove reindeer from caribou herds over time.I used genetics as well to describe caribou population structure and determine how it is influenced by geography, historical demography, and ecotypes.I found that Alaskan caribou from 20 herds (n = 655) are subdivided into two genetic clusters: the Alaska Peninsula and the mainland.Alaska Peninsula herds are genetically distinct, while many mainland herds are not.I hypothesize that Alaska Peninsula herds have diverged due to post-glacial founder effects and recent bottlenecks driven by constraints to population size from marginal habitat and reduced gene flow across a habitat barrier at the nexus of the peninsula.I hypothesize that mainland herds have maintained genetic connectivity and large effective population size via range expansions and shifts over time.However, I find evidence that herds of different ecotypes (migratory, sedentary) can remain differentiated despite range overlap.Genetic evidence provides information for herd-based management, while also demonstrating the importance of spatial connectivity of herds and their habitats over the long-term.I thank the many people who helped me throughout my research.To the herders and hunters who participated in this study -thank you for all that you taught me (for full list of names, see Appendix 2A).I am especially grateful to the Barrow elders who shared their stories, many of whom have since passed away but whose memories are not forgotten.Several collaborators played a role in my research process.Sverre Pederson was a great mentor for me as I began my interview research in Barrow and throughout my project.Kevin Colson was an important collaborator throughout all stages of my genetic research.Margaret Ahngasuk's work as a translator and interest in helping me to interview elder reindeer herders and hunters was very valuable.Thanks as well to the many other people who contributed to my research by providing samples, assisting with interviews, and discussing my results:
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".