Functional traits of plant roots and Collembola determine their tri‐trophic interactions with soil microbes
Bibliographic record
Abstract
Abstract Traditionally, leaf litter has been recognized as the main driver of the soil food web, but more recently roots have been shown to play an important role in fuelling soil organisms. Root functional traits were shown to have direct effects on microbes and Nematoda, but many knowledge gaps remain such as the effects of root traits on Collembola. Here, in a microcosm experiment, we studied the tri‐trophic interactions between roots, microbes and Collembola in relation to 10 plant species individually. Eleven root traits were measured to test whether they have an influence on Collembola and microbe community structure and Collembola functional structure based on six traits. The interactions between microbes and Collembola were also tested. Our results show that plant species identity significantly influences the structure of Collembola communities, and this variation is primarily explained by root traits and microbial communities. Collembola feeding traits based on mandibular morphology were useful to identify top‐down control on microbial communities. Our study also suggests that root traits such as fine root length and root diameter modify Collembola–microbe interactions, hypothetically by modifying soil porosity. Overall, we obtained better results by looking at the whole system, rather than looking at bi‐trophic interactions. This illustrates the importance of a holistic approach when studying biotic interactions in soil ecosystems. Read the free Plain Language Summary for this article on the Journal blog.
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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".