Agricultural practices and pollinators modulate the anthosphere microbiome

Abstract

The flower microbiome is pivotal in plant health, influencing reproductive success, fruit quality, and pathogen vulnerability. However, the impact of intensified agricultural practices on these microbial communities remains to be understood. This study examines how specific agricultural practices influence the bacterial composition of the strawberry anthosphere, focusing on cultivation intensification. Intensified systems were defined by practices such as indoor glasshouse substrate-based cultivation, increased use of plant protection products, larger cultivation areas, and reliance on managed pollinators. Using citizen science and V4 16S rRNA gene sequencing, we found that flowers in these more intensively managed systems had lower bacterial diversity, more variable microbiomes, and loss of core taxa such as Sphingomonas and Pseudomonas. To determine if pollinators could help mitigate these effects, we conducted exclusion experiments. In a tunnel system, we observed that foraging pollinators facilitated the dispersal of specific bacteria, such as Staphylococcus and Pseudomonas, and increased flower bacterial richness. However, in an open field, foraging pollinators had no significant impact. Our findings highlight the significant impact of cultivation intensification on the anthosphere microbiome and suggest that pollinators may play a role in restoring microbiome diversity. This research fills a critical gap in understanding how agricultural practices shape plant microbiomes and underscores the potential for microbe-based strategies to improve plant health in intensively managed systems.

Keywords: anthosphere; cultivation intensification; flower; microbiome; pollinator; strawberry.

Graphical Abstract

Figure 1

Graphical abstract: Effect of cultivation type, pollinators, plant protection products, substrate type and other factors on the strawberry anthosphere bacterial community.

Figure 2

(A) Cultivation types sampled during the Sabofleur citizen science project (B) bacterial richness and inverse Simpson index in strawberry flowers per cultivation type with post-hoc Dunn p-value significance levels with Bonferroni correction for the inverse Simpson index. (C) MDA analysis via the Maaslin2 algorithm [42]: Greenhouse vs open-air environment. (D) PCOA plot (E) Beta diversity calculated among all samples within each cultivation type. P-value significance levels are the result of pairwise Betadisper analyses followed by ANOVA. All other pairwise comparisons were insignificant. (F–I) abundance-occupancy curves: The filled dots represent the ASVs that are part of the core community following the method by Shade et al (2019) [39] (Supplementary Fig. 10). Additionally, ASVs identified in (B) as significantly preferring open-field flowers over greenhouse flowers are labelled.

Figure 3

(A) Overview of both pollinator exclusion experiments. (B) Transfer index calculated as the proportion of unique ASVs in a flower (with a minimum absolute abundance >3 reads) also occurring in the forager samples in the same environment. (C) + (D) Relative abundance of bacterial genera on flowers and all pollinators in the open field and tunnel pollinator exclusion experiment. (E) + (F) Foraging pollinator taxa in covered and uncovered flowers on an ASV level in the tunnel and the open field. (G) + (H) Occupancy in netted vs unnetted strawberry flowers in an open field (G) and a commercially maintained strawberry tunnel (H). Genera that were significantly more prevalent in either netted or unnetted flowers were marked darker according to Fischer’s test.