Positive effects of fragmentation per se on the most iconic metapopulation
Bibliographic record
Abstract
Abstract While habitat loss is a major threat to species, the effects of habitat fragmentation independent of habitat loss (fragmentation per se) are debated. Metapopulation studies often assert negative fragmentation effects, but they do not measure fragmentation per se. We evaluate the effects of fragmentation per se (patch density) across 20 years of patch occupancy patterns of the Åland Islands Glanville fritillary butterfly, Finland, a famous model system in metapopulation studies. Fragmentation per se had mainly positive effects on patch occupancy, the proportion of years occupied per patch, and patch colonization, and negative effects on patch extinction. These results suggest that fragmentation per se does not threaten persistence of the Åland Islands Glanville fritillary butterfly. Our results support the growing body of research challenging the paradigm that habitat fragmentation per se is mostly negative for species, highlighting the value of small patches for species conservation.
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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".