Mid‐Holocene decline in African buffalos inferred from Bayesian coalescent‐based analyses of microsatellites and mitochondrial DNA
Bibliographic record
Abstract
Genetic studies concerned with the demographic history of wildlife species can help elucidate the role of climate change and other forces such as human activity in shaping patterns of divergence and distribution. The African buffalo (Syncerus caffer) declined dramatically during the rinderpest pandemic in the late 1800s, but little is known about the earlier demographic history of the species. We analysed genetic variation at 17 microsatellite loci and a 302-bp fragment of the mitochondrial DNA control region to infer past demographic changes in buffalo populations from East Africa. Two Bayesian coalescent-based methods as well as traditional bottleneck tests were applied to infer detailed dynamics in buffalo demographic history. No clear genetic signature of population declines related to the rinderpest pandemic could be detected. However, Bayesian coalescent modelling detected a strong signal of African buffalo population declines in the order of 75-98%, starting in the mid-Holocene (approximately 3-7000 years ago). The signature of decline was remarkably consistent using two different coalescent-based methods and two types of molecular markers. Exploratory analyses involving various prior assumptions did not seriously affect the magnitude or timing of the inferred population decline. Climate data show that tropical Africa experienced a pronounced transition to a drier climate approximately 4500 years ago, concurrent with the buffalo decline. We therefore propose that the mid-Holocene aridification of East Africa caused a major decline in the effective population size of the buffalo, a species reliant on moist savannah habitat for its existence.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".