Effective population size dynamics reveal impacts of historic climatic events and recent anthropogenic pressure in African elephants
Bibliographic record
Abstract
Two hundred years of elephant hunting for ivory, peaking in 1970-1980s, caused local extirpations and massive population declines across Africa. The resulting genetic impacts on surviving populations have not been studied, despite the importance of understanding the evolutionary repercussions of such human-mediated events on this keystone species. Using Bayesian coalescent-based genetic methods to evaluate time-specific changes in effective population size, we analysed genetic variation in 20 highly polymorphic microsatellite loci from 400 elephants inhabiting the greater Samburu-Laikipia region of northern Kenya. This area experienced a decline of between 80% and 90% in the last few decades when ivory harvesting was rampant. The most significant change in effective population size, however, occurred approximately 2500 years ago during a mid-Holocene period of climatic drying in tropical Africa. Contrary to expectations, detailed analyses of four contemporary age-based cohorts showed that the peak poaching epidemic in the 1970s caused detectable temporary genetic impacts, with genetic diversity rebounding as juveniles surviving the poaching era became reproductively mature. This study demonstrates the importance of climatic history in shaping the distribution and genetic history of a keystone species and highlights the utility of coalescent-based demographic approaches in unravelling ancestral demographic events despite a lack of ancient samples. Unique insights into the genetic signature of mid-Holocene climatic change in Africa and effects of recent poaching pressure on elephants are discussed.
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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".