Inferring the role of habitat heterogeneity in SLOSS (single large or several small) for beetles, spiders, and birds in forest reserves
Bibliographic record
Abstract
Global conservation strategies aim to increase the area of nature reserves. To implement this goal, especially in fragmented landscapes like Central Europe, we must understand whether a Single Large (SL) reserve Or Several Small (SS) reserves has higher species richness (SLOSS), and why. To date, most studies find more species in SS than SL (SS>SL). The most commonly invoked explanation is higher habitat heterogeneity across SS than SL. We assessed SLOSS for beetles, spiders and birds in 44 forest reserves of three forest types in Central Europe, and tested several predictions based on this heterogeneity hypothesis. We assessed SLOSS in two ways: Quinn-Harrison-curves, and a new approach, the ‘SLOSS ratio’ of species richness standardized by sample coverage, in SL vs. SS. As habitat heterogeneity is challenging to measure directly, we indirectly tested the heterogeneity hypothesis through the following predictions: the SS>SL pattern should be stronger (1) for taxa with finer space use, (2) when there are more SS reserves, spread over more area, (3) when the reserves have been established for a longer time, allowing divergence among patches, (4) when the SS reserves include multiple forest types, and (5) for forest types with a lower frequency of stand-replacing disturbances. We found SS>SL for all taxa, and we found support for three of the predictions based on the heterogeneity hypothesis: (1), (2), and (4). We infer that a set of many small forest reserves is an appropriate objective for conservation planning and can make a strong contribution to global conservation goals.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".