Species‐level view of population structure and gene flow for a critically endangered primate (<i><scp>V</scp>arecia variegata</i>)
Bibliographic record
Abstract
Lemurs are among the world's most threatened mammals. The critically endangered black-and-white ruffed lemur (Varecia variegata), in particular, has recently experienced rapid population declines due to habitat loss, ecological sensitivities to habitat degradation, and extensive human hunting pressure. Despite this, a recent study indicates that ruffed lemurs retain among the highest levels of genetic diversity for primates. Identifying how this diversity is apportioned and whether gene flow is maintained among remnant populations will help to diagnose and target conservation priorities. We sampled 209 individuals from 19 sites throughout the remaining V. variegata range. We used 10 polymorphic microsatellite loci and ∼550 bp of mtDNA sequence data to evaluate genetic structure and population dynamics, including dispersal patterns and recent population declines. Bayesian cluster analyses identified two distinct genetic clusters, which optimally partitioned data into populations occurring on either side of the Mangoro River. Localities north of the Mangoro were characterized by greater genetic diversity, greater gene flow (lower genetic differentiation) and higher mtDNA haplotype and nucleotide diversity than those in the south. Despite this, genetic differentiation across all sites was high, as indicated by high average F ST (0.247) and ΦST (0.544), and followed a pattern of isolation-by-distance. We use these results to suggest future conservation strategies that include an effort to maintain genetic diversity in the north and restore connectivity in the south. We also note the discordance between patterns of genetic differentiation and current subspecies taxonomy, and encourage a re-evaluation of conservation management units moving forward.
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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 | 0.001 |
| 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".