Testing theoretical metapopulation conditions with genotypic data from Boreal Chorus Frogs (<i>Pseudacris maculata</i>)
Bibliographic record
Abstract
The metapopulation concept has far-reaching implications in ecology and conservation biology. Hanski’s criteria operationally define metapopulations, yet testing them is hindered by logistical and financial constraints inherent to the collection of long-term demographic data. Hence, ecologists and conservationists often assume metapopulation existence for dispersal-limited species that occupy patchy habitats. To advance understanding of metapopulation theory and improve conservation of metapopulations, we used population and landscape genetic tools to develop a methodological framework for evaluating Hanski’s criteria. We used genotypic data (11 microsatellite loci) from a purported metapopulation of Boreal Chorus Frogs (Pseudacris maculata (Agassiz, 1850)) in Colorado, U.S.A., to test Hanski’s four criteria. We found support for each criterion: (1) significant genetic differentiation between wetlands, suggesting distinct breeding populations; (2) wetlands had small effective population sizes and recent bottlenecks, suggesting populations do not experience long-term persistence; (3) population graphs provided evidence of gene flow between patches, indicating potential for recolonization; and (4) multiscale bottleneck analyses suggest asynchrony, indicating that simultaneous extinction of all populations was unlikely. Our methodological framework provides a logistically and financially feasible alternative to long-term demographic data for identifying amphibian metapopulations.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".