EFFECTIVE POPULATION SIZES AND TEMPORAL STABILITY OF GENETIC STRUCTURE IN RANA PIPIENS, THE NORTHERN LEOPARD FROG
Bibliographic record
Abstract
Although studies of population genetic structure are very common, whether genetic structure is stable over time has been assessed for very few taxa. The question of stability over time is particularly interesting for frogs because it is not clear to what extent frogs exist in dynamic metapopulations with frequent extinction and recolonization, or in stable patches at equilibrium between drift and gene flow. In this study we collected tissue samples from the same five populations of leopard frogs, Rana pipiens, over a 22-30 year time interval (11-15 generations). Genetic structure among the populations was very stable, suggesting that these populations were not undergoing frequent extinction and colonization. We also estimated the effective size of each population from the change in allele frequencies over time. There exist few estimates of effective size for frog populations, but the data available suggest that ranid frogs may have much larger ratios of effective size (Ne) to census size (Nc) than toads (bufonidae). Our results indicate that R. pipiens populations have effective sizes on the order of hundreds to at most a few thousand frogs, and Ne/Nc ratios in the range of 0.1-1.0. These estimates of Ne/Nc are consistent with those estimated for other Rana species. Finally, we compared the results of three temporal methods for estimating Ne. Moment and pseudolikelihood methods that assume a closed population gave the most similar point estimates, although the moment estimates were consistently two to four times larger. Wang and Whitlock's new method that jointly estimates Ne and the rate of immigration into a population (m) gave much smaller estimates of Ne and implausibly large estimates of m. This method requires knowing allele frequencies in the source of immigrants, but was thought to be insensitive to inexact estimates. In our case the method may have failed because we did not know the true source of immigrants for each population. The method may be more sensitive to choice of source frequencies than was previously appreciated, and so should be used with caution if the most likely source of immigrants cannot be identified clearly.
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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".