Back

Multilayered human activities shape the microbial communities of groundwater-dependent ecosystems in an arid oceanic island

Di Nezio, F.; Di Cesare, A.; Garcia- Cobo, M.; Brankovits, D.; Sabatino, R.; Borgomaneiro, G.; Fresno-Lopez, Z.; Kurtz, M. N.; Boulamail, S.; Cozzoli, F.; Fumarola, L.; Gonzalez, B. C.; Roldan, A.; Camacho, C.; Garcia-Herrera, A.; Moro, L.; Valdivia, C.; Mateo, E.; Garcia-Gomez, G.; Fontaneto, D.; Corno, G.; Eckert, E. M.; Martinez, A.

2025-12-02 microbiology
10.64898/2025.12.01.691164 bioRxiv
Show abstract

Island coastal aquifers, though physically small compared to continental groundwater systems, are of huge ecological and societal importance, sustaining functions that connect to locally crucial provision, maintenance and culture ecosystem services. Those functions are largely dependent on the presence of highly adapted biological communities, for which, their microbial communities remain understudied. Our goal is to describe the bacterial communities across the groundwater-dependent ecosystems on Lanzarote, spanning a gradient of anthropogenic pollution using 16SrRNA amplicon sequencing. We sampled coastal caves and pools, wells and water galleries, springs, saltworks and marine bays affected by submarine groundwater discharge. Ecological analyses highlight that richness and composition of bacterial communities strongly depend on the type of habitats. Pathogens and human-derived species were ubiquitous in our samples, but, strikingly, caves and wells were strongly enriched with them compared to other habitats. We propose that our results highlight the susceptibility of groundwater environments to pollution and indicate that aquifers act as reservoirs of biological contamination in addition to natural diversity--regardless of their salinity. Since this enrichment might compromise some of the functions and services that groundwater-dependent ecosystems provide in oceanic islands, we call for integrative conservation strategies that include hydrological and biological perspectives into the decision making.

Published in Microbial Ecology (predicted rank #18) · training set

Matching journals

The top 6 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.