Long-term impacts of conservation pasture management in manuresheds on system-level microbiome and antibiotic resistance genes

Abstract

Animal manure improves soil fertility and organic carbon, but long-term deposition may contribute to antibiotic resistance genes (ARGs) entering the soil-water environment. Additionally, long-term impacts of applying animal manure to soil on the soil-water microbiome, a crucial factor in soil health and fertility, are not well understood. The aim of this study is to assess: (1) impacts of long-term conservation practices on the distribution of ARGs and microbial dynamics in soil, and runoff; and (2) associations between bacterial taxa, heavy metals, soil health indicators, and ARGs in manures, soils, and surface runoff in a study following 15 years of continuous management. This management strategy consists of two conventional and three conservation systems, all receiving annual poultry litter. High throughput sequencing of the 16S ribosomal RNA was carried out on samples of cattle manure, poultry litter, soil, and runoff collected from each manureshed. In addition, four representative ARGs (intl1, sul1, ermB, and bla_ctx-m-32) were quantified from manures, soil, and runoff using quantitative PCR. Results revealed that conventional practice increased soil ARGs, and microbial diversity compared to conservation systems. Further, ARGs were strongly correlated with each other in cattle manure and soil, but not in runoff. After 15-years of conservation practices, relationships existed between heavy metals and ARGs. In the soil, Cu, Fe and Mn were positively linked to intl1, sul1, and ermB, but trends varied in runoff. These findings were further supported by network analyses that indicated complex co-occurrence patterns between bacteria taxa, ARGs, and physicochemical parameters. Overall, this study provides system-level linkages of microbial communities, ARGs, and physicochemical conditions based on long-term conservation practices at the soil-water-animal nexus.

Keywords: antibiotic resistance genes; cattle manure; heavy metals; microbial communities; poultry litter; runoff.

Figure 1

Levels of antibiotic resistance genes in the manures (A), soil (B), and surface runoff water (C) samples. RB – rotationally grazed with riparian buffer; R – rotationally grazed; RBR – rotationally grazed with fenced riparian buffer; H – hayed; CG – continuously grazed.

Figure 2

Alpha diversity measures of bacterial communities in different treatments. Evenness and Shannon indices were evaluated in manures (A,B), soil (C,D) and surface runoff water (E,F).

Figure 3

Beta diversity of the manures (A), surface runoff (B), and soil (C) microbial communities. Principal coordinates analysis (PCoA) plot based on the Bray-Curtis distance matrix. On the ordination, data points that are closer together are more similar communities.

Figure 4

Correlations between heavy metals, levels of ARGs and physicochemical parameters for the cattle manure (A), poultry litter (B), soil (C) and runoff (D). Negative relationships are displayed in blue color while positive relationships are displayed in red. The size of the circles and color intensity are proportional to the correlation coefficients. Cross (X) indicates non-significant difference (p > 0.05).

Figure 5

Co-occurrence network associations among ARGs, heavy metals and bacterial taxa at the phylum level in poultry litter (A), cattle manure (B), soil (C), and surface runoff water (D). Purple nodes represent ARGs and intl1, light green nodes represent bacterial taxa and orange represent heavy metals and soil properties. The node size is proportional to the connection numbers. The thickness of edge indicates Spearman’s correlation coefficient. Red lines represent positive and significant relationships (p < 0.05), and blue lines represent negative and significant relationships between the ARGs, metals and bacterial communities (p < 0.05).