Bibliographic record
Abstract
Abstract Background The principle that a single large habitat patch should hold more species than several small patches totalling the same area (SL > SS) is used by conservation agencies to favour protection of large, contiguous areas. Previous reviews of empirical studies have found the opposite, SS > SL, creating the single large or several small (SLOSS) debate. Aims Review the empirical and theoretical SLOSS literature; identify potential mechanisms underlying the SS > SL pattern; evaluate these where possible. Location Global. Time period 1976–2018. Major taxa Plants, invertebrates, vertebrates. Methods Literature review. Results Like previous reviews, I found that SS > SL dominates empirical findings. This pattern remained, although it was somewhat weakened, in studies where sampling intensity was proportional to patch size. I found six classes of theory, and conducted five preliminary evaluations of theory. None of the predictions was supported. The SS > SL pattern held for specialist species groups, suggesting it does not result from incursion by generalists into small patches. I found no evidence for the prediction that the reverse pattern (SL > SS) becomes more common over time since patch creation, through gradual species losses from SS. I found no difference between results for natural and anthropogenic patches. There was also no evidence for predictions that SL > SS is more common when the matrix is more hostile, or for stable than ephemeral patches. Main conclusions Most empirical comparisons find SS > SL. While there are several potential causes, more empirical work is needed to identify those at play. Meanwhile, conservation practitioners should understand that there is no ecological evidence supporting a general principle to preserve large, contiguous habitat areas rather than multiple small areas of the same total size.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".