Although these forest fragments shared similar vegetation, they varied in terms of their conservation statuses. The evaluation of the forest fragment areas (1) revealed direct negative impacts caused by forest loss and the expansion of anthropogenic land cover, such as agricultural and urban areas, on the habitats of wild black capuchin monkeys. Despite Santa Catarina State having the largest area of the Atlantic Forest (437,6511 m2), only 15 core areas were identified, while Parque Ecológico Maracajá (PEM) had 113,5003 m2 of forest fragments but only one core area (Supplementary Material 2). Surprisingly, the Normalized Difference Vegetation Index (NDVI) (2) indicated that PEM, despite being an ecological park, had the lowest value (0.06) compared to the other sites (0.08) (Figs. 2a-d; Supplementary Material 2). In terms of land use and cover (3), PEM and Morro dos Macacos (MMA) had the largest areas allocated for agriculture (11 km2 and 4.24 km2, respectively), while urban areas were predominantly concentrated in Santa Cruz do Sul (SCS) (15.41 km2) and São Sebastião do Caí (SSC) (4.71 km2).
Based on the previously analyzed characteristics, the environmental quality of the forest fragments inhabited by wild black capuchin monkeys was assessed using the CMEM index. The CMEM indices ranged from 8.33 to 6.00, with higher values indicating better quality (Figs. 3a-d). Interestingly, SSC and SCS being considered fragmented and branched environments, the areas inhabited by primates exhibited high CMEM values (Figs. 3a-b). Conversely, the forest fragment in MMA, despite being an ecological reserve, had the lowest CMEM index (Fig. 3c). In PEM, the fragment had the highest CMEM value in the region but lacked proximity to other fragments with good environmental quality (Fig. 3d).
Comparing Gut Bacterial Diversity Between Wild and Captive Black Capuchin Monkeys
A total of 1,966,559 sequence reads were obtained from the samples, with an average of 57,839 reads per sample (34856.56 ± SD). After quality filtering, 1,901,995 high-quality reads remained, with an average of 55,941 reads per sample (33323.80 ± SD). The paired-end sequences were merged, resulting in 1,820,498 reads, with an average of 53,544 reads per sample (33104.122 ± SD). More detailed information can be found in Supplementary Material 5. A total of 22 phyla, 37 classes, 84 orders, 158 families, 301 genera, and 80 species were identified in the gut microbiota of black capuchin monkeys (Supplementary Material 3).
The alpha diversity metrics, including species richness (Shannon and Inverse Simpson & Fisher), showed significant changes (p < 0.05) among the bacterial communities in response to different habitats (Fig. 4). The analysis of the collection sites revealed that captive monkeys (ZSS and ZCS) exhibited higher species richness compared to the wild individuals (Fig. 4a). This difference was statistically significant in terms of the number of observed species based on the Shannon (p = 0.006) and Simpson (p = 0.007) indexes (Fig. 4b). The wild individuals displayed lower values for both alpha diversity metrics.
The beta diversity analysis demonstrated significant differences in bacterial community structure in relation to habitat, as indicated by the PERMANOVA of the Bray-Curtis dissimilarity matrices (Situation - R2 = 0.19322, P(Fr) = 0.001; Region Code - R2 = 0.24883, P(Fr) = 0.001). This change in community structure was also visualized in the principal coordinates analysis (PCoA) plot based on the Bray-Curtis dissimilarity (Fig. 5). Similar habitats exhibited similar species compositions, and there was lower beta diversity between wild and captive monkeys from ZCS compared to captive monkeys from ZSS.
The analysis of gut bacterial communities showed significant differences between wild and captive black capuchin monkeys at the phylum and genus levels (Fig. 6). Wild monkeys had high proportions of Proteobacteria (83%), Firmicutes (9%), and Bacteroidota (8%), while captive monkeys had higher abundance of Bacteroidota (48%), Proteobacteria (33%), and Firmicutes (16%) (Fig. 6a). The distribution of phyla varied across status and regions, with Proteobacteria dominant in SSC, SCS, PEM, MMA, and ZCS, and Bacteroidota dominant in captive monkeys from ZSS (Fig. 6b). The CcpnA graph confirmed the contribution of Proteobacteria and Bacteroidota to the distinction between wild and captive monkeys (Supplementary Figure S1). Enterobacterales was prevalent in wild and ZCS captive monkeys, while Bacteroidales was more abundant in ZSS captive monkeys (Fig. 6c).
Differences in microbial composition were observed at the genus level between wild and captive populations. The gut microbiome of wild monkeys had high proportions of Proteus (20%), Escherichia-Shigella (19%), and Hafnia-Obesumbacterium (9%). Captive monkeys from ZSS had dominant genera like Bacteroides (19%), Dysgonomonas (13%), and Escherichia-Shigella (8%). Escherichia-Shigella was also predominant in monkeys from ZCS (51%) and SSC (29%). Other dominant genera varied among regions and status, including Bacteroides in ZSS (34%), Raoultella in MMA (33%), and Proteus in PEM (31%) and SCS (42%). Wild monkeys had higher prevalence of Morganella morganii (4%) compared to captive monkeys (0.5%) (Fig. 6d; Supplementary Figure S2).
Using Venn diagrams (Fig. 7), prevalent genera unique to each status or shared across status were identified. Captive status had 30 unique genera, including Bacteroides, Comamonas, Aeromonas, Dysgonomonas, Macellibacteroides, Clostridium sensu stricto 1, Sphingobacterium, Streptococcus, Incertae Sedis, Cellulosilyticum, Anaerosporobacter, Stenotrophomonas, Trichococcus, Limnohabitans, Acinetobacter, Brevundimonas, Leuconostoc, Cellvibrio, Luteimonas, Devosia, Paracoccus, Dyadobacter, Flavobacterium, Sphingomonas, and Taibaiella. Wild monkeys had only four, Hafnia-Obesumbacterium, Morganella, Lactococcus, and Salmonella. Nine genera were found in both wild and captive status: Escherichia-Shigela, Proteus, Citrobacter, Raoultella, Serratia, Enterococcus, Klebsiella, Pseudomonas, and Yersinia.
These findings emphasize the significant differences in gut microbial communities between wild and captive black capuchin monkeys, suggesting that captivity may influence the composition and abundance of specific bacterial taxa. Further research is needed to investigate the functional implications of these microbial differences and their potential impact on the health and well-being of black capuchin monkeys in different environments.
Functional prediction and impact of environmental disturbance on the microbiota in black capuchin monkeys
Functional prediction of the gut microbiome of black capuchin monkeys was conducted using PICRUSt2, generating 139 functional pathways based on KEGG pathway metadata (Fig. 8). These pathways encompassed various metabolic processes, including carbohydrate metabolism, lipid metabolism, energy metabolism, nucleotide metabolism, amino acid metabolism, biosynthesis of secondary metabolites, xenobiotic biodegradation and metabolism, metabolism of other amino acids, glycan biosynthesis and metabolism, metabolism of cofactors and vitamins, and metabolism of terpenoids and polyketides (Fig. 8a; Supplementary Material 4).
To assess the impact of environmental disturbance on the metabolic functional pathways of the gut microbiome, we focused on the microbial degradation of xenobiotic compounds (Fig. 8b; Supplementary Material 2). The functional prediction revealed differential enrichment of microbial pathways related to xenobiotic metabolism between the captive and wild groups. Specifically, wild monkeys living near urban areas (SSC and SCS) showed an increase in pathways associated with dioxin biodegradation. Conversely, wild monkeys inhabiting forest fragments within intensive farming environments (PEM and MMA) (Figs. 3a-d) exhibited pathways involved in the degradation of xenobiotic components such as toluene (Fig. 8; Supplementary Material 4).
Collectively, these findings suggest that the gut microbiome of black capuchin monkeys can adapt and display distinct functional capacities in response to different environmental conditions, particularly in the metabolism of xenobiotic compounds.