Geodiversity as a Driver of Soil Microbial Community Diversity and Adaptation in a Mediterranean Landscape

Samuel Pelacani (), Maria Teresa Ceccherini, Francesco Barbadori, Sandro Moretti and Simone Tommasini
Additional contact information
Samuel Pelacani: Department of Earth Science, University of Florence, Via G. La Pira 4, 50121 Florence, Italy
Maria Teresa Ceccherini: Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine, 28, 50144 Florence, Italy
Francesco Barbadori: Department of Earth Science, University of Florence, Via G. La Pira 4, 50121 Florence, Italy
Sandro Moretti: Department of Earth Science, University of Florence, Via G. La Pira 4, 50121 Florence, Italy
Simone Tommasini: Department of Earth Science, University of Florence, Via G. La Pira 4, 50121 Florence, Italy

Land, 2025, vol. 14, issue 3, 1-22

Abstract: Extreme meteorological events and anthropogenic influences determine important variations in microbial community composition. To know the extent of these variations, it is necessary to delve deeper into the geogenic factors to be considered as a baseline. The purpose of this study was to assess the effect of topographic characteristics and soil geochemistry on the spatial distribution of three Actinobacteria genera considered as molecular biomarkers of landforms belonging to Mediterranean environments. Given the important role that Actinobacteria play in the ecosystem, we performed a spatial distribution model of the genera Rubrobacter , Gaiella , and Microlunatus and investigated the fungi/bacteria ratio in a machine learning (ML)-based framework. Variable importance provided insight into the controlling factor of geomicrobial spatial distribution. The spatial distribution of the predicted Actinobacteria genera generally follows topographic constraints, mostly altitude. Rubrobacter was related to the slope aspect and lithium; Microlunatus was related to the topographic wetness index (TWI) and normalized difference water index (NDWI), as well as the fungi/bacteria ratio; and Gaiella was related to flow path and metals. Our results provide new information on the adaptation of Actinobacteria in Mediterranean areas and show the potential of using ML frameworks for the spatial prediction of OTUs distribution.

Keywords: geomorphometry; geomicrobiology; lanthanides; NDVI; machine learning; soil bacterial diversity; Actinobacteria; olive tree (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/3/583/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/3/583/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:3:p:583-:d:1609110

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().