Facilitation as a ubiquitous driver of biodiversity
Bibliographic record
Abstract
Summary Models describing the biotic drivers that create and maintain biological diversity within trophic levels have focused primarily on negative interactions (i.e. competition), leaving marginal room for positive interactions (i.e. facilitation). We show facilitation to be a ubiquitous driver of biodiversity by first noting that all species use resources and thus change the local biotic or abiotic conditions, altering the available multidimensional niches. This can cause a shift in local species composition, which can cause an increase in beta, and sometimes alpha, diversity. We show that these increases are ubiquitous across ecosystems. These positive effects on diversity occur via a broad host of disparate direct and indirect mechanisms. We identify and unify several of these facilitative mechanisms and discuss why it has been easy to underappreciate the importance of facilitation. We show that net positive effects have a long history of being considered ecologically or evolutionarily unstable, and we present recent evidence of its potential stability. Facilitation goes well beyond the common case of stress amelioration and it probably gains importance as community complexity increases. While biodiversity is, in part, created by species exploiting many niches, many niches are available to exploit only because species create them. Contents Summary 403 I. Introduction 403 II. Facilitative mechanisms increasing diversity 405 III. Facilitation as an evolutionary driver in proximate interactions 410 IV. Why has facilitation been just recently added to ecological theory? 411 V. Facilitation and the plant functional trait programme 412 VI. Predictability and testability 412 VII. Conservation, restoration and management 413 VIII. Conclusions and next steps 413 Acknowledgements 413 References 414
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".