Soil microbes regulate ecosystem productivity and maintain species diversity
Bibliographic record
Abstract
One of the major goals in ecology is to determine the mechanisms that drive the asymptotic increase in ecosystem productivity with plant species diversity. Niche complementarity, the current paradigm for the asymptotic diversity-productivity pattern, posits that the addition of species to a community increases productivity because each species specializes on different resources and thus can more thoroughly utilize the available resources. At higher diversity the increase in productivity decreases because resources become limiting, resulting in the classic asymptotic diversity-productivity pattern. An alternative but less tested explanation is that density-dependent disease from species-specific soil microbes drive the diversity-productivity relationship by increasing disease and thus decreasing productivity at low diversity. At higher diversity, productivity asymptotes because disease decreases with increasing diversity until it reaches a uniformly low level. Using a series of field experiments, we found that the classic asymptotic diversity-productivity pattern existed only when soil microbes were present. Soil microbes created the well-known pattern by depressing plant growth at low productivity though negative density dependent disease. In contrast, niche complementarity played only a weak role in explaining the diversity-productivity relationship because productivity remained high at low abundance in the absence of soil microbes. Based on our findings, the ongoing loss of species in natural ecosystems will likely increase per capita plant disease and lower ecosystem productivity. Furthermore, recent evidence suggests that negative density dependent disease maintains plant species diversity, and thus this single mechanism appears to link diversity maintenance to the diversity-productivity curve--two important ecological processes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".