Modelling the distribution of plant‐associated microbes with species distribution models
Bibliographic record
Abstract
Abstract Plants interact with diverse microorganisms that play a crucial role in plant growth and development. The diversity and distribution of plant microbiota are altered by anthropogenic environmental change, leading to subsequent impacts on ecosystems. Modeling the distribution of plant‐associated microbes is critical for predicting and managing future changes in microbial function, but challenges and open questions when developing these models still remain. We present a conceptual framework for process‐oriented predictive modeling of the distribution of plant‐associated microbiota. We first describe different approaches to incorporate host plants into modeling microbial distributions, namely by including them as static variables, nesting them within microbial distribution models or incorporating them simultaneously via joint species distribution models. Additionally, we discuss issues associated with collecting and analyzing sequencing‐based microbial data, emphasizing the importance of data normalization and careful interpretation of species distribution models. We further discuss how to incorporate evolutionary history into microbial distribution modeling. Finally, we present a case study demonstrating how incorporating host information can improve the prediction of microbial distributions. Synthesis: This study provides insights for predicting future distributions of plant‐associated microbes under climate change and plant species redistribution scenarios, which can be generalized to other host‐associated microbial systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".