Land use is a determinant of plant pathogen alpha‐ but not beta‐diversity

• Published in: Molecular ecology 2019-08, Vol.28 (16), p.3786-3798
• Main Authors: Makiola, Andreas, Dickie, Ian A., Holdaway, Robert J., Wood, Jamie R., Orwin, Kate H., Glare, Travis R.
• Format: Article
• Language: English
• Subjects: Agriculture; Bacteria - classification; Bacteria - pathogenicity; Biodiversity; Composition; Deoxyribonucleic acid; DNA; DNA Barcoding, Taxonomic; environmental DNA; Environmental Sciences; Forests; Fungi; Fungi - classification; Fungi - pathogenicity; high‐throughput sequencing; illumina; Land use; Landscape; Leaves; Life Sciences; metabarcoding; New Zealand; Oomycetes - classification; Oomycetes - pathogenicity; Pathogens; Plant communities; Plant Diseases - microbiology; Plant diversity; Plant Leaves - microbiology; plant pathogen communities; Plant Roots - microbiology; Roots; Soil Microbiology; Soils; Substrates; Vegetal Biology
• ISSN: 0962-1083; 1365-294X
• DOI: 10.1111/mec.15177

Little is known about the diversity patterns of plant pathogens and how they change with land use at a broad scale. We employed DNA metabarcoding to describe the diversity and composition of putative plant pathogen communities in three substrates (soil, roots, and leaves) across five major land uses at a national scale. Almost all plant pathogen communities (fungi, oomycetes, and bacteria) showed strong responses to land use and substrate type. Land use category could explain up to 24% of the variance in composition between communities. Alpha‐diversity (richness) of plant pathogens was consistently lower in natural forests than in agricultural systems. In planted forests, there was also generally low pathogen alpha‐diversity in soil and roots, but alpha‐diversity in leaves was high compared with most other land uses. In contrast to alpha‐diversity, differences in within‐land use beta‐diversity of plant pathogens (the predictability of plant pathogen communities within land use) were subtle. Our results show that large‐scale patterns and distributions of putative plant pathogens can be determined using metabarcoding, allowing some of the first landscape level insights into these critically important communities.