## 3 Results and Discussion
### High-Throughput Sequencing Data Analysis
Furthermore, genera were significantly different among samples, and L1 and L3 presented the greatest number of specific genera (Figure 5). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Sample ID**} & \multirow{2}{*}{**Reads**} & \multirow{2}{*}{\
\begin{tabular}{c} **Good’s** \\ **Coverage** \\ \end{tabular} } & \multirow{2}{*}{**Observed**} & \multicolumn{3}{c}{**Richness Estimator**} & \multicolumn{3}{c}{**Diversity Index**} & \multirow{2}{*}{
\begin{tabular}{c} **Dominance** \\ **(D)** \\ \end{tabular} } \\  & & & **Overage** & & **OtU** & & **Chao 1** & & **ACE** \\ \hline L1 & 93,237 & 99.96\% & 612 & 621 & 633 & 4.40 & 0.95 & 0.05 \\ L2 & 99,703 & 99.86\% & 662 & 764 & 775 & 3.10 & 0.89 & 0.11 \\ L3 & 130,788 & 99.93\% & 833 & 858 & 892 & 3.38 & 0.85 & 0.15 \\ L4 & 10,075 & 99.80\% & 270 & 281 & 278 & 3.87 & 0.90 & 0.10 \\ \hline \end{tabular}
\end{table} 
Table 1: Richness and diversity statistical data generated from the metabarcoding analysis. Figure 4: Relative abundance of the metabarcoding fungal diversity at (**a**) phylum, (**b**) family and (**c**) genus level per sample. The most dominant genera (relative abundance \(\geq\) 1.5%) were _Cladosporium_ (22.9%), Capnodiales_unclassified (6.9%) and _Aspergillus_ (4.9%), _Candida_ (4.8%), _Botrytis_ (3.2%), Didymelaceae_unclassified (3.1%), _Toxicocalasosporium_ (2.5%), _Penicillium_ (2.3%), _Xylodon_ (2.2%), _Corticum_ (1.7%) and _Saitozyma_ (1.5%) in L1; _Cladosporium_ (24.7%), _Needevriseia_ (18.2%), Sordariomycetes_unclassified (10.3%), _Cyphellophora_ (8.3%), Hypocrales_fam _Incertae sedis_unclassified (5%), Capnodiales_unclassified (4.6%), Didymelaceae_unclassified (4%), _Lcanicillium_ (3.4%), _Penicillium_ (1.7%), _Talarmyces_ (1.6%) and _Toxicocalasporum_ (1.5%) in L2; _Needevriseia_ (39.9%), Capnodiales_unclassified (17.4%), _Cladosporium_ (7.2%), _Lcanicillium_ (4.5%), _Abergyllus_ (4%), _Copriellus_ (1.7%) and _Botrytis_ (1.6%) in L3; and Capnodiales_unclassified (34.5%), _Parengyodinium_ (5.6%), _Morierella_ (5.3%), _Eurotionycetes_unclassified (4.4%), _Cladosporium_ (3.3%), _Fusarium_ (2.5%), _Gibberellla_ (2.3%), _Malassezia_ (1.9%) and _Alternaria_ (1.5%) in L4. Fungal communities differed among the studied samples, both in structure and taxonomic composition which supported our first hypothesis. These findings are consistent with the literature. Some of the 20 most abundant fungal genera detected in the present study, including _Alternaria, Aspergillus_, _Cladosporium, Corpinellus, Neophysalospora, Penicillium, Peniophora, Psathyrella_ and _Udenomyces_ had been previously reported in Anca limestone through metabarcoding analysis [16]. Additionally, these results follow the same study, where it is stated that the Mycobiota associated with different biodeterioration types were taxonomically distinct, elucidating that different populations occur in distinct niches across similar lithology, denoting the importance and influence of microconditions in shaping these communities. The distribution of the different communities (the 20 most abundant genera in each sample) can be observed in Figure 6. Specific taxa are clearly associated with the different samples (although not exclusively), suggesting a relationship between the present genera and the observed biodeterioration phenomena. The outlines with higher number of specific genera were SE (L4) and DGB (L1), for which the most relevant were: _Alternaria, Cladorathinum, Fusarium, Malassezia, Mortierella, Mucor, Parengyodinium, Rhodotorula, Trichoderma_ and _Udeniomyces_ in L4; _Acidomelania, Candida, Corticum, Neophysalospora, Phlebia, Saitozyma, Vishniacozyma_ and _Xylodon_ in L1. In the other two settings, _Exophila, Cyphellophora_ and _Talarmyces_ were prevalent in GB (L2) while _Knufia, Neodevriseia_ and _Psathyrella_ were more related to BD (L3). Figure 5: Venn diagram analysis of shared genera between samples. A \(\beta\)-diversity analysis was also performed.