Resolving the SLOSS dilemma for biodiversity conservation: a research agenda
Bibliographic record
Abstract
In biodiversity conservation, the “SL > SS principle” that a single (or few) large habitat patches (SL) conserve more species than several small patches (SS) is used to prioritize protection of large patches while down-weighting small ones. However, empirical support for this principle is lacking; most studies find SS > SL. We propose a research agenda to resolve this dilemma by asking, “are there consistent, empirically-demonstrated conditions leading to SL > SS?” We develop a hypothesis to answer this question, the “SLOSS cube hypothesis,” which predicts SL > SS only when all three of the following are true: between-patch movement is low, population dynamics are not influenced by spreading-of-risk, and large-scale across-habitat heterogeneity is low. We then propose methods to test this prediction. Many tests are needed, comparing gamma diversity across multiple landscapes varying in number and sizes of patches. If the prediction is not generally supported across tests, then either the mechanisms leading to SL > SS are extremely rare in nature, or they are outweighed by countervailing mechanisms leading to SS > SL (e.g. lower competition or higher immigration in SS), or both. In that case, the SL > SS principle should be abandoned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.011 | 0.045 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".