Vegetation clump size and number as indicators for alternative stable states in semi-arid ecosystems
Bibliographic record
Abstract
Abstract Dryland ecosystems are vulnerable to desertification, a pressing issue in the face of global climate change. In these ecosystems, vegetation often grows in spatially periodic patterns that differ as aridity increases (gaps, labyrinths and clumps), which has been widely studied theoretically, aiming to assess the proximity of the system to desertification. While some theoretical models predict smooth desertification transitions, most typically predict an abrupt transition linked to the possibility of two alternative stable states (desert and vegetated states), predictions that are yet to be confirmed empirically. If this bistability of alternative stable states occurs, however, environmental fluctuations and the history of the ecosystem determine which state ultimately materializes. This uncertainty makes it harder to predict desertification, compounding the challenges posed by it. Here, we combine empirical data and theoretical methods to investigate the links between bistability and vegetation spatial organization, which can help identify the presence of alternative stable states. We found that, although the presence of vegetation clumps is not indicative of bistability, changes in clump morphology can provide reliable indicators of bistability and an impending desertification transition. Thus, our methodology indirectly identifies whether desertification will occur abruptly, and whether restoration efforts should consider a potential ecosystem history-dependence.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".