For colonization success, should hosts and microbes travel alone, together, or swap partners along the way?
Bibliographic record
Abstract
Microbiomes that enhance the performance of host plants are likely to be co-introduced with their host during colonization because of their intimate association. Yet, it is unclear how co-introduced microbes will impact host colonization, as both the microbiome and its effects could vary upon introduction into a new habitat. Using the duckweed Lemna japonica - a cosmopolitan, freshwater angiosperm - and its microbiome, we tracked the colonization of both plants and microbes during an experimental co-introduction in the wild. We tested how plant performance varied during colonization when plants were co-introduced with microbes from their home habitat or with microbes local to the introduced habitat. We found that plant performance was substantially reduced when plants were co-introduced with microbes from their home habitat (i.e. with microbes that are non-local to the introduced habitat), relative to hosts with a local microbiome. Moreover, negative impacts from the initial, non-local microbiome persisted for multiple host generations despite a rapid turnover in microbiome composition. Our results suggest that the initial microbiome plants are co-introduced and can leave lasting impacts on plant performance during colonization. Considering the identity of the co-introduced microbiome will therefore be critical to predicting plant colonization dynamics in an era of global change.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".