A single large restored patch has lower tree diversity than several smaller ones
Bibliographic record
Abstract
Abstract 1. Restoration initiatives often target restoring the largest possible amount of habitat to provide the greatest benefits for biodiversity. However, the optimal configuration (e.g., the size and number of restored patches) of habitat, given a fixed total area, remains an unresolved question. 2. Here, we ask whether restoring a single large habitat patch or a mixture of smaller patches of the same total area supports higher plant diversity. To address this question, we measured taxonomic, phylogenetic, and functional diversity of all naturally recruiting woody species in 52 restored vegetation patches in Jambi Province, Sumatra, Indonesia. Thirteen restored patches of each of four sizes (25, 100, 400, and 1,600 m²) were established within conventional oil palm plantations six years before vegetation sampling. From these 52 patches, we generated 750 random comparisons between a single large patch vs. several small patches, ensuring equal total area (100, 400, or 1,600 m²). We evaluated taxonomic, phylogenetic, and functional diversity separately for all species, for native species, and for native forest species, using three diversity measures: species richness, the exponential of Shannon entropy, and the inverse of Simpson concentration. 3. Our findings indicate that restoring several smaller patches results in greater taxonomic, phylogenetic, and functional diversity of recruiting woody species than restoring a single large patch of the same total area. This result holds across the three total habitat areas (100, 400, and 1,600 m²), all species groupings, and all diversity metrics. As expected, species diversity also increased with total area in all cases. 4. Our findings challenge restoration strategies that focus exclusively on enlarging patches. Instead, biodiversity will be enhanced by increasing the total restored area across many patches of different sizes, including very small ones (e.g., 25 m²).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".