Assembly and co-occurrence networks of nitrogen-fixing bacteria associated with epiphyllous liverworts in fragmented tropical forests
Bibliographic record
Abstract
Abstract Understanding the spatial dynamics of plant-associated microbial communities is increasingly urgent in the context of habitat loss and the biodiversity crisis. However, the influence of reduced habitat size and connectivity on the assembly mechanisms underlying microbial associations is fundamental to advancing microbial ecology and conservation. In the Brazilian Amazon, we investigated nitrogen-fixing (diazotrophic) bacterial communities associated with two epiphyllous liverworts, Cololejeunea surinamensis and Radula flaccida, across 11 forest sites within the Biological Dynamics of Forest Fragments Project landscape. Using amplicon sequencing targeting the nitrogenase gene (nifH), we characterized diazotroph community diversity, inferred assembly mechanisms through null models, and analyzed co-occurrence network structure. Host-specific associations were evident: C. surinamensis predominantly hosted Hassallia, while R. flaccida was primarily associated with Fischerella. Despite habitat fragmentation, diazotrophic richness and composition remained similar across habitats of different sizes, consistent with strong homogenizing dispersal. Network analyses revealed that smaller fragments harbored more modular communities with fewer module hubs, pronounced shifts in key species relative abundance, and reduced network robustness. Our findings underscore the influence of habitat size on the stability of liverwort-associated diazotrophs, with smaller fragments exhibiting lower key species specificity and disruption of microbe-microbe interactions. Our results emphasize the importance of conserving large, connected forest habitats to maintain the functional integrity of phyllosphere N-fixing microbiota.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".