MINERÍA la mejor puerta de acceso al sector minero MINERÍA / SEPTIEMBRE 2023 / EDICIÓN 552 122 Ezeokoli, O. T., Bezuidenhout, C. C., Maboeta, M. S., Khasa, D. P., & Adeleke, R. A. 2020. Structural and functional differentiation of bacterial communities in post-coal mining reclamation soils of South Africa: bioindicators of soil ecosystem restoration. Scientific Reports, 10(1), 1–14. https://doi.org/10.1038/s41598020-58576-5 Garris, H. W., Baldwin, S. A., Van Hamme, J. D., Gardner, W. C., & Fraser, L. H. 2016. Genomics to assist mine reclamation: A review. Restoration Ecology, 24(2), 165– 173. https://doi.org/10.1111/rec.12322 Gotelli, N. J., & Colwell, R. K. 2001. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4(4), 379–391. https://doi.org/10.1046/j.14610248.2001.00230.x Gwimbi, P., & Nhamo, G. 2016. Benchmarking the effectiveness of mitigation measures to the quality of environmental impact statements: lessons and insights from mines along the Great Dyke of Zimbabwe. Environment, Development and Sustainability, 18(2), 527–546. https://doi.org/10.1007/s10668-015-9663-9 Haferburg, G., Krichler, T., & Hedrich, S. 2022. Prokaryotic communities in the historic silver mine Reiche Zeche. Extremophiles, 26(1), 2. https://doi.org/10.1007/ s00792-021-01249-6 Helix Bioinformatics Solutions S.L. 2021. Base de datos ambiental para estudios de metagenómica de bacterias (16S) y Hongos (ITS). IDENTIFICADOR 2105187862705. Hugerth, L. W., & Andersson, A. F. 2017. Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing. Frontiers in Microbiology, 8. https://doi.org/10.3389/ fmicb.2017.01561 Kadnikov, V. V., Ivasenko, D. A., Beletsky, A. V., Mardanov, A. V., Danilova, E. V., Pimenov, N. V., Karnachuk, O. V., & Ravin, N. V. 2016. Effect of metal concentration on the microbial community in acid mine drainage of a polysulfide ore deposit. Microbiology, 85(6), 745–751. https:// doi.org/10.1134/S0026261716060126 Lin, H., & Peddada, S. Das. 2020. Analysis of microbial compositions: a review of normalization and differential abundance analysis. Npj Biofilms and Microbiomes, 6(1), 60. https://doi.org/10.1038/s41522-020-00160-w Love, M. I., Huber, W., & Anders, S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. https://doi. org/10.1186/s13059-014-0550-8 McMurdie, P. J., & Holmes, S. 2013. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217. https://doi.org/10.1371/journal.pone.0061217 Mendez-Garci¬a, C., Pelaez, A. I., Mesa, V., Sanchez, J., Golyshina, O. V., & Ferrer, M. 2015. Microbial diversity and metabolic networks in acid mine drainage habitats. Figura 12. Abundancia relativa (%) de los géneros significativos en ambientes contaminados y seleccionados por su relación con ambientes mineros, así como los más ubicuos. El tamaño de cada punto representa la abundancia relativa media por cada grupo para los cuatro tipos de muestras: agua contaminada (AC), agua no contaminada (ANC), suelo contaminado (SC) y suelo no contaminado (SNC). Entre corchetes, el filo al que pertenece dicho género. Figure 12. Relative abundance (%) of significant genera in contaminated environments and selected for their relationship with mining environments as well as the most ubiquitous. The size of each point represents the mean relative abundance for each group for the four types of samples: contaminated water (AC), uncontaminated water (ANC), contaminated soil (SC) and uncontaminated soil (SNC). In brackets, the phylum to which the genus belongs. on the microbial community in acid mine drainage of a polysulfide ore deposit. Microbiology, 85(6), 745– 751. https://doi.org/10.1134/S0026261716060126 Lin, H., & Peddada, S. Das. 2020. Analysis of microbial compositions: a review of normalization and differential abundance analysis. Npj Biofilms and Microbiomes, 6(1), 60. https://doi.org/10.1038/ s41522-020-00160-w Love, M. I., Huber, W., & Anders, S. 2014. Moderated estimation of fold change and dispersion for RNAseq data with DESeq2. Genome Biology, 15(12), 550. https://doi.org/10.1186/s13059-014-0550-8 McMurdie, P. J., & Holmes, S. 2013. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE, 8(4), e61217. https://doi.org/10.1371/journal. pone.0061217 Mendez-Garci¬a, C., Pelaez, A. I., Mesa, V., Sanchez, J., Golyshina, O. V., & Ferrer, M. 2015. Microbial diversity and metabolic networks in acid mine drainage habitats. Frontiers in Microbiology, 6. https://doi.org/10.3389/fmicb.2015.00475
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