Why is the world green? The interactions of top–down and bottom–up processes in terrestrial vegetation ecology
Bibliographic record
Abstract
A classic question in plant ecology is “why is the world green?” That is, if plants are food for animals why do not animals eat all the available food – changing a ‘green world’ into a ‘brown world’. We first reviewed this question in 2009 and now revisit our arguments in the light of new data and new thinking. Here we argue that (1) the top–down bottom–up dichotomy is probably too simple for understanding a complex system – such as vegetation – rich in feedback processes. (2) Nevertheless it appears that bottom–up processes are generally more important for maintaining the presence of some sort of vegetation while top–down control process are generally more important in determining the type of vegetation at a site. (3) Although this review mainly takes a qualitative and experimental approach to the question, we also argue that simple well-known mathematical models from population ecology can be very informative in thinking about the types of explanations for the green world phenomenon, and demonstrating that it is rarely a simple choice between one form of control or another.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".