Alternative stable states and the sustainability of forests, grasslands, and agriculture
Bibliographic record
Abstract
Endangered forest-grassland mosaics interspersed with expanding agriculture and silviculture occur across many parts of the world, including the southern Brazilian highlands. This natural mosaic ecosystem is thought to reflect alternative stable states driven by threshold responses of recruitment to fire and moisture regimes. The role of adaptive human behavior in such systems remains understudied, despite its pervasiveness and the fact that such ecosystems can exhibit complex dynamics. We develop a nonlinear mathematical model of coupled human-environment dynamics in mosaic systems and social processes regarding conservation and economic land valuation. Our objective is to better understand how the coupled dynamics respond to changes in ecological and social conditions. The model is parameterized with southern Brazilian data on mosaic ecology, land-use profits, and questionnaire results concerning landowner preferences and conservation values. We find that the mosaic presently resides at a crucial juncture where relatively small changes in social conditions can generate a wide variety of possible outcomes, including complete loss of mosaics; large-amplitude, long-term oscillations between land states that preclude ecosystem stability; and conservation of the mosaic even to the exclusion of agriculture/silviculture. In general, increasing the time horizon used for conservation decision making is more likely to maintain mosaic stability. In contrast, increasing the inherent conservation value of either forests or grasslands is more likely to induce large oscillations-especially for forests-due to feedback from rarity-based conservation decisions. Given the potential for complex dynamics, empirically grounded nonlinear dynamical models should play a larger role in policy formulation for human-environment mosaic ecosystems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".