Landscape‐scale habitat fragmentation is positively related to biodiversity, despite patch‐scale ecosystem decay
Bibliographic record
Abstract
Positive effects of habitat patch size on biodiversity are often extrapolated to infer negative effects of habitat fragmentation on biodiversity at landscape scales. However, such cross-scale extrapolations typically fail. A recent, landmark, patch-scale analysis (Chase et al., 2020, Nature 584, 238-243) demonstrates positive patch size effects on biodiversity, that is, 'ecosystem decay' in small patches. Other authors have already extrapolated this result to infer negative fragmentation effects, that is, higher biodiversity in a few large than many small patches of the same cumulative habitat area. We test whether this extrapolation is valid. We find that landscape-scale patterns are opposite to their analogous patch-scale patterns: for sets of patches with equal total habitat area, species richness and evenness decrease with increasing mean size of the patches comprising that area, even when considering only species of conservation concern. Preserving small habitat patches will, therefore, be key to sustain biodiversity amidst ongoing environmental crises.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".