Ecological Function and Diversity of the Endosphere Microbiome in Leaves and Fruits of <i>Coffea arabica</i> L. Across Elevation and Shade Gradients
Bibliographic record
Abstract
ABSTRACT Coffee is a globally important commodity that supports millions of livelihoods, from smallholder farmers to international traders. However, the sustainability of the coffee value chain is increasingly threatened by environmental changes. In this context, the phytomicrobiome, shaped by both environmental factors and plant genotypes, plays a vital role in plant health, productivity, and the quality of coffee beans. In this study, we explored the diversity and ecological functions of the endosphere microbiome in the leaves and fruits of Coffea arabica L. cultivated under an agroforestry system on Mount Gorongosa, within Gorongosa National Park, Mozambique. Using next‐generation sequencing, we characterized microbial communities along gradients of elevation and shade to assess how environmental variables shape microbiome composition and function. Our findings revealed a rich diversity of microbial communities, with elevation emerging as the primary driver of community structure. Taxonomic analyses showed that both elevation and shade significantly influenced the composition of bacterial and fungal communities. Microbial families such as Debaryomycetaceae, Enterobacteriaceae, Eremotheciaceae, Nocardiaceae, and Pseudonocardiaceae exhibited distinct adaptations to environmental conditions. Notably, we detected the presence of pathogenic genera (e.g., Colletotrichum , Erwinia , Fusarium , and Phaeosphaeria ) without visible disease symptoms, indicating possible plant tolerance to biotic stressors. Predicted functional pathways, including heme biosynthesis and phospholipid metabolism, alongside ecological guilds such as saprotrophs and fungal parasites, suggested microbial adaptations essential to maintaining plant health and coffee quality. Key microbial biomarkers, including Debaryomyces , Eremothecium , and Rhodococcus , emerged as indicators of functional adaptations across environmental gradients, highlighting their potential for informing optimized, environmentally responsive coffee management strategies. Altogether, the results highlight the integral role of coffee‐associated endophytes, in concert with plant genotypes, in shaping innovative, biodiversity‐driven strategies for sustainable coffee production.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".