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In this study, eighteen samples of\n                    <jats:italic>canga<\/jats:italic>\n                    were collected from the Serra da Piedade State Natural Monument, and their taxonomic diversity was investigated using 16S rRNA metabarcoding. Additionally, the influence of physicochemical variables on microbial diversity and community structure was estimated using statistical tools. Most of the 856,667 reads clustered into ASVs corresponded to Bacteria (99.7%), and the most abundant of the 184 identified genera were\n                    <jats:italic>Conexibacter<\/jats:italic>\n                    ,\n                    <jats:italic>Acidothermus<\/jats:italic>\n                    and\n                    <jats:italic>Bryobacter<\/jats:italic>\n                    . Microbial diversity was explained by a combination of physicochemical variables, such as organic matter (OM), iron (Fe), aluminum (Al) and pH, whereas microbial community structure was influenced by the concentrations of Fe, Al and the cation exchange capacity (CEC). Functional prediction analysis based on the main genera identified in the samples indicated that the denitrification pathway may play an important role in the ecosystem. The investigation of the genera and their metabolic pathways based on the literature revealed that they are unexplored and emphasized the biodiversity hotspot yet to be explored in ferruginous duricrust. Therefore, our results reinforce the importance of further studies in this environment, both for future biotechnological applications and for appropriate management and preservation actions.\n                  <\/jats:p>","DOI":"10.1007\/s10482-026-02315-9","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T04:49:55Z","timestamp":1777006195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bacterial diversity in ferruginous duricrust (canga) and the physicochemical variables affecting their prevalence, distribution and predicted metabolic pathways"],"prefix":"10.1007","volume":"119","author":[{"given":"Viviane Faria Morais","family":"Jotta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carla Alessandra","family":"Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glen Jasper Yupanqui","family":"Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea Rodrigues","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andria","family":"dos Santos Freitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arist\u00f3teles","family":"G\u00f3es-Neto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernanda","family":"Badotti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"2315_CR1","unstructured":"Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. 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