When Theory Meets Practice: Balancing Genetic Diversity and Behaviour When Choosing Founders for a Recently Reintroduced Bison (Bison bison) Herd in Banff National Park, Canada
Bibliographic record
Abstract
The tragic decline of plains bison (Bison bison bison) in North America is well known. By the turn of the 20th century, plains bison, once numbering in the tens of millions, were extirpated from the wild in Canada. Banff National Park, AB, Canada, is within the original range of bison in North America, and still contains suitable habitat for the species. In 2017, we used measures of genetic diversity, as well as age and reproductive status, to select 16 plains bison for reintroduction to an unoccupied corner of their former historic range in Banff National Park. However, five of the preferred founders from this genetically diverse group needed to be substituted for less ideal candidates when aggressive behavior was observed, which could have compromised animal welfare during translocation. The level of genetic diversity in the founding population was therefore lower than if all proposed founders had been used, although the difference was not substantial. As expected, adults in this small, reintroduced herd had lower observed heterozygosity and number of alleles than the larger source herd, but expected heterozygosity was higher, likely a result of preferentially choosing individuals with rare alleles. Another substantial contribution to genetic diversity was in unborn fetuses: sired by males that were not translocated, the calves born a few months later provided a boost to the genetic diversity of this small founder herd. Where opportunities allow, the inclusion of pregnant females can significantly increase the genetic diversity of small founder populations. When compared to other founded herds of bison, the techniques described here have ensured that the newly established Banff NP herd is one of the most diverse bison herds in Canada, despite its relatively small number of founders.
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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 | 0.032 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".