Back

Mangrove Ecosystem Degradation Restructures Bacterial Community Composition and Metabolic Function: A Metagenomic Analysis

Gandhi, R. R.; Khandeparker, R.; R, N. P.; Paingankar, M.

2025-12-17 bioinformatics
10.1101/2025.09.19.677314 bioRxiv
Show abstract

Coastal pollution poses threats to mangrove ecosystems worldwide, but the mechanistic patterns of relationships among geochemical perturbation, microbial community reorganisation, and loss of functional genes have been insignificantly studied. To understand these relationships, full-length 16S rRNA sequencing was employed, along with physicochemical and predictive gene analysis. The results indicated dead mangrove sites contained evidence of nitrogen contamination (nitrate +14-fold, ammonia +5.6-fold), acidification ({Delta}pH = -1.3), and a collapse in bacterial diversity (p = 0.013). Community Stabilisation and restructuring proceeded according to deterministic patterns (R2 = 0.215, p = 0.018): metabolically versatile Alphaproteobacteria decreased by 75%, and acid-tolerant Bacillota increased, which is in accordance with the selection force of fermentative Metabolism in the face of compromised nitrogen cycling. Prediction of functional genes showed the loss of biosynthetic pathways (carbon fixation genes -12.6x, nitrogen fixation genes -2.4x, cellulose degradation genes -3.1x), detoxification systems (-1.7x to -2.2x), and maintenance machinery of genomes (DNA repair genes -3.3x to -6.4x), and substitution by stress response genes and fermentation genes enrichment ( +2.1x Environmental indicators (Hypha microbiaceae, Alicyclobacillaceae) classified the state of the ecosystem across sites. Such results suggest that microbial community measurements can serve as early-warning diagnostics of mangrove degradation and provide a framework for monitoring the health of coastal ecosystems through the use of sediment microbiomes. Planned functional degradation suggests that passive restoration may not be effective unless microbial recovery interventions are implemented.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.