Effects of plant community history, soil legacy and plant diversity on soil microbial communities
Bibliographic record
Abstract
Abstract Plant and soil microbial diversities are linked through a range of interactions, including the exchange of carbon and nutrients but also herbivory and pathogenic effects. Over time, associations between plant communities and their soil microbiota may strengthen and become more specific, resulting in stronger associations between plant and soil microbial diversity. We tested this hypothesis at the end of a 4‐year field experiment in 48 plots with different plant species compositions established 13 years earlier in a biodiversity experiment in Jena, Germany. We factorially crossed plant community history (old vs. new plant communities) and soil legacy (old vs. new soil) with plant diversity (species richness levels 1, 2, 4 and 8, each with 12 different species compositions). We use the term ‘plant community history’ to refer to the co‐occurrence history of plants in different species compositions in the Jena Experiment. We determined soil bacterial and fungal community composition in terms of operational taxonomic units (OTUs) using 16S rRNA gene and ITS DNA sequencing. Plant community history (old plants) did not affect overall soil community composition but differentially affected bacterial richness and abundances of specific bacterial taxa in association with specific plant species compositions. Soil legacy (old soil) markedly increased soil bacterial richness and evenness and decreased fungal evenness. Soil fungal richness increased with plant species richness, regardless of plant community history or soil legacy, with the strongest difference between plant monocultures and mixtures. Specific plant species compositions and functional groups were associated with specific bacterial and fungal community compositions. Grasses increased fungal richness and evenness and legumes decreased fungal evenness, but bacterial diversity was not affected. Synthesis . Our findings indicate that as experimental ecosystems varying in plant diversity develop over time (2002–2010), plant species associate with specific soil microbial taxa. This can have long‐lasting effects on below‐ground community composition in re‐assembled plant communities, as reflected in strong soil legacy signals still visible after 4 years (2011–2015). Effects of plant community history on soil communities are subtle and may take longer to fully develop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".