Diversity, Distribution and Abundance of Prokaryotic Communities within Selected Forests Ecosystem in Kenya

Abstract

Soil microbiomes in forest ecosystems play a crucial role in serving as either sources or sinks of nutrients by participating in activities such as decomposing organic matter, cycling nutrients, incorporating humic compounds into the soil, and facilitating the connection between plant and ecosystem functions. Prokaryotic communities colonize numerous habitats within forests ecosystem; comprising litter, deadwood, rhizosphere and bulk soil where populations are shaped by nutrient availability and biotic interactions. This study determined the composition, diversity and distribution of prokaryotes within selected forests ecosystem in Kenya. Thirty-one (31) soil samples were collected from selected forests ecosystem in Kenya. To identify the possible abiotic drivers for prokaryotic distribution, physiochemical characteristics for the soil samples were analyzed. This was followed by total DNA extraction, purity assessment, amplification and sequencing of the hypervariable region (V4 - V5) of the 16S rRNA gene using Illumina platform. Demultiplexing of high throughput sequence and statistical analysis was done using QIIME2 and R programming language. Linear discriminant analysis (LDA) effect size (LEfSe) was used to detect prokaryotic taxa that were differentially abundant within and between soil samples. Biodiversity metrics (alpha diversity) and community structure dissimilarity (beta diversity) were calculated using the vegan (version 2.5.7) and phyloseq (version 1.16.2) packages in RStudio. The environmental drivers of prokaryotic community structure were estimated using Redundancy analysis. The meta data file and their associated sequence datasets from selected forests around the globe were downloaded from publicly available databases and processed using the QIIME2 pipeline as described above. From this study, the key prokaryotic community drivers included sodium, silt, magnesium, calcium, potassium, pH and carbon whereas aluminium, phosphorus, iron, clay and sand negatively influenced diversity in both Principal Component Analysis 1 and Principal Component Analysis 2. A total of 1,944,316 high quality sequence reads were generated and clustered into 41,901 ASVs (Amplicon Sequence Variants) at 3% genetic distance. Taxonomic classification of the obtained ASVs were assigned to a prokaryotic Kingdom, 2 Phyla, 120 Classes, 280 Orders, 450 Families, 873 Genera and 2313 Species within selected forests ecosystem. Archaeal groups recovered from the obtained ASVs within the selected forests ecosystem were distributed among seven phyla with Crenarchaeota as the most abundant Archaeal phylum represented across all samples, with 91.6% mean relative abundance. Analysis of sample alpha-diversity showed that soils from Western and Taita Taveta regions had significantly different (P = 0.0124603) levels of Archaeal richness, Western and Aberdare regions soil displayed Archaeal Shannon diversity index (P=0.00399513) but there were no significant differences between bacterial communities displayed within various forests ecosystems. However, beta-diversity analysis of soil samples from Western, Aberdare and Taita Taveta regions revealed a significant difference (P = 0.0010998) on bacterial and archaeal community structure (Bacteria R2= 0.19; Archaea R2 = 0.22). Samples from the different ecoregions showed significant differences (p-value= 0.001998, R 2= 0.45) in soil physiochemical properties, specifically in soil pH, soil texture, macro- and micro-nutrient composition and Enhanced Vegetation Index-2. Taita Taveta forest soil were highly distinct from those obtained from the Nairobi, Aberdare and Western regions. Nairobi and Western region soils exhibited the least variability. The examination of beta-diversity scores across these datasets, utilizing the Bray-Curtis index, uncovered distinctions in the community structures of forest soil microbiomes, partly influenced by their country of origin (R2 = 0.63; p-value = 0.0098). The notable variations in composition between national datasets were further corroborated by Linear Discrimination Analysis, which highlighted 177 taxa distributed disparately among the datasets. The study demonstrates that Kenyan forest soils are unique and harbor potentially distinct soil microbiomes. However, more studies on forest microbiome should be done with focus on revealing vulnerability to possible future losses in forest soil microbial diversity and productivity due to climate change.