Non-linear changes in modelled terrestrial ecosystems subjected to perturbations
Bibliographic record
Abstract
When perturbed ecosystems undergo rapid and non-linear changes, this can result in ‘regime shifts’ to an entirely different ecological state. The need to understand the extent, nature, magnitude and reversibility of these changes is urgent given the profound effects that humans are having on the natural world. It remains very challenging to empirically document non-linear changes and regime shifts within complex, real ecological communities, or even to demonstrate such shifts in simplified experimental systems. General ecosystem models, which simulate the dynamics of entire ecological communities based on a mechanistic representation of ecological processes, provide an alternative and novel way to project ecosystem changes across all scales and trophic levels and to forecast impact thresholds beyond which dramatic or irreversible changes may occur. We model non-linear changes in four terrestrial biomes subjected to human removal of plant biomass, such as occurs through agricultural land-use change. We find that irreversible and non-linear responses are predicted to be common where removal of vegetation exceeds 80% (a level that occurs across nearly 10% of the terrestrial surface), especially for organisms at higher trophic levels and in less productive ecosystems such as drylands. Very large, irreversible changes to the entire ecosystem structure are expected at levels of vegetation removal akin to those in the most intensively used real-world ecosystems. Our results suggest that the projected 21 st century rapid increases in agricultural land conversion to feed an expanding human population, may lead to widespread trophic cascades and in some cases irreversible changes to the structure of ecological communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".