Confidently identifying the correct <i>K</i> value using the Δ<i>K</i> method: When does <i>K</i> = 2?
Bibliographic record
Abstract
Populations delineated based on genetic data are commonly used for wildlife conservation and management. Many studies use the program structure combined with the ΔK method to identify the most probable number of populations (K). We recently found K = 2 was identified more often when studies used ΔK compared to studies that did not. We suggested two reasons for this: hierarchical population structure leads to underestimation, or the ΔK method does not evaluate K = 1 causing an overestimation. The present contribution aims to develop a better understanding of the limits of the method using one, two and three population simulations across migration scenarios. From these simulations we identified the "best K" using model likelihood and ΔK. Our findings show that mean probability plots and ΔK are unable to resolve the correct number of populations once migration rate exceeds 0.005. We also found a strong bias towards selecting K = 2 using the ΔK method. We used these data to identify the range of values where the ΔK statistic identifies a value of K that is not well supported. Finally, using the simulations and a review of empirical data, we found that the magnitude of ΔK corresponds to the level of divergence between populations. Based on our findings, we suggest researchers should use the ΔK method cautiously; they need to report all relevant data, including the magnitude of ΔK, and an estimate of connectivity for the research community to assess whether meaningful genetic structure exists within the context of management and conservation.
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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 | 0.094 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".