The effects of mycorrhizal plant-soil feedbacks on plant evenness in US forests
Bibliographic record
Abstract
Plant-soil feedbacks (PSFs) mediated by mycorrhizal fungi are thought to influence plant community and structure in a variety of ways. Specifically, it has been hypothesized that EcM fungi protect their hosts from conspecific negative density dependence (CNDD)[1]. CNDD is thought to prevent dominance by any one species through the accumulation of antagonists when a species is abundant.[2] As a result, the dominant species may suffer reduced fitness, which would prevent competitive exclusion of less-dominant species by the more-dominant species. Therefore, CNDD is a mechanism that may help maintain richness and evenness in natural ecosystems. If EcM fungi do provide their hosts with protection against CNDD, then we would expect that EcM host-dominated communities should have lower evenness compared to AM host-dominated communities (the “EcM dominance hypothesis”)*. Contrary to these expectations, a recent study of forests in the US did not support their hypothesis that AM host-dominated forests would have higher plant diversity than EcM host-dominated forests. [3] Instead, they found that forest plots with roughly 50% of each mycorrhizal host type showed the greatest diversity, even after accounting for the fact that plots with both host types would also have the greatest pool of potential species. The authors proposed a “mycorrhizal dominance hypothesis” to explain this pattern. However, in most temperate forests, the majority of plant diversity resides in the herbaceous and shrub plants of the ground layer, and studies have shown a potential for tree and ground plants to interact through common mycorrhizal networks [4]. Our goal therefore is to investigate the influence of dominance of host mycorrhizal type on plant species evenness in both the tree and ground layers in US forests. *While other studies have also considered richness in response to the proportion of EcM hosts, it is possible that increased cover by one host mycorrhizal type could increase the richness of the associated fungal community, which could facilitate the recruitment/establishment of a greater diversity of hosts, thereby increasing plant species richness. These hypotheses yield similar predictions and are thus not easy to disentangle, therefore we are choosing to focus solely on evenness in our analyses. However, summary statistics of richness and diversity indices will be included in an appendix.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".