Relative Effects of Habitat Amount and Fragmentation <i>Per Se</i> on the Genetic Diversity of The Glanville Fritillary Butterfly
Bibliographic record
Abstract
Abstract Habitat loss and fragmentation are considered the key drivers of biodiversity loss. Understanding their relative roles is difficult as habitat loss and fragmentation tend to co-occur. It has been proposed that the total habitat amount available in the local landscape mainly drives species richness while fragmentation per se – the breaking apart of habitat independent of habitat amount - has negligible or even a positive effect on biodiversity. Several studies support this at the species richness level. Yet, the potential effects of fragmentation per se on genetic diversity at the landscape scale are understudied. Using the Glanville fritillary butterfly metapopulation in the Åland islands, we tested the effects of fragmentation per se on genetic diversity using a landscape-based approach and 2,610 individuals genotyped at 40 neutral SNP markers. We assessed the independent effect of habitat amount and fragmentation (i.e. number of patches) within the local landscape on the focal patch genetic diversity. The amount of habitat in the local landscape had a positive effect on genetic diversity, while fragmentation per se had a more negligible impact on the genetic diversity. Our results thus highlight that all fragments, even the small ones, likely contribute to the maintenance of genetic diversity of the focal population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".