Genotypic Identification of Trees Using DNA Barcodes and Microbiome Analysis of Rhizosphere Microbial Communities

Abstract

DNA barcodes can provide accurate identification of plants. We used previously reported DNA primers targeting the internal transcribed spacer (ITS1) region of the nuclear ribosomal cistron, internal transcribed spacer (ITS2), and chloroplast trnL (UAA) intron to identify four trees at Bergen Community College. Two of the four trees were identified as Acer rubrum and Fagus sylvatica. However, Quercus was only identified at the genus level, and the fourth tree did not show similar identification between barcodes. Next-generation sequencing of 16S rRNA genes showed that the predominant bacterial communities in the rhizosphere mainly consisted of the Pseudomonadota, Actinomycetota, Bacteroidota, and Acidobacteriota. A. rubrum showed the most diverse bacterial community while F. sylvatica was less diverse. The genus Rhodoplanes showed the highest relative bacterial abundance in all trees. Fungal ITS sequence analysis demonstrated that the communities predominantly consisted of the Ascomycota and Basidiomycota. Quercus showed the highest fungi diversity while F. sylvatica showed the lowest. Russula showed the highest abundance of fungi genera. Average similarity values in the rhizosphere for fungi communities at the phylum level were higher than for bacteria. However, at the genus level, bacterial communities showed higher similarities than fungi. Similarity values decreased at lower taxonomical levels for both bacteria and fungi, indicating each tree has selected for specific bacterial and fungal communities. This study confirmed the distinctiveness of the microbial communities in the rhizosphere of each tree and their importance in sustaining and supporting viability and growth but also demonstrating the limitations of DNA barcoding with the primers used in this study to identify genus and species for some of the trees. The optimization of DNA barcoding will require additional DNA sequences to enhance the resolution and identification of trees at the study site.