Microbial Ecology

, Volume 78, Issue 3, pp 575–588 | Cite as

The Water Hyacinth Microbiome: Link Between Carbon Turnover and Nutrient Cycling

  • Marcelo P. Ávila
  • Ernandes S. Oliveira-Junior
  • Mariana P. Reis
  • Eric R. Hester
  • Cristiane Diamantino
  • Annelies J. Veraart
  • Leon P. M. Lamers
  • Sarian Kosten
  • Andréa M. A. NascimentoEmail author
Environmental Microbiology


Water hyacinth (WH), a large floating plant, plays an important role in the biogeochemistry and ecology of many freshwaters globally. Its biogeochemical impact on wetland functioning is strongly mediated by the microbiome associated with its roots. However, little is known about the structure and function of this WH rhizobiome and its relation to wetland ecosystem functioning. Here, we unveil the core and transient rhizobiomes of WH and their key biogeochemical functions in two of the world’s largest wetlands: the Amazon and the Pantanal. WH hosts a highly diverse microbial community shaped by spatiotemporal changes. Proteobacteria lineages were most common, followed by Actinobacteria and Planctomycetes. Deltaproteobacteria and Sphingobacteriia predominated in the core microbiome, potentially associated with polysaccharide degradation and fermentation of plant-derived carbon. Conversely, a plethora of lineages were transient, including highly abundant Acinetobacter, Acidobacteria subgroup 6, and methanotrophs, thus assuring diverse taxonomic signatures in the two different wetlands. Our findings point out that methanogenesis is a key driver of, and proxy for, community structure, especially during seasonal plant decline. We provide ecologically relevant insights into the WH microbiome, which is a key element linking plant-associated carbon turnover with other biogeochemical fluxes in tropical wetlands.


Eichhornia crassipes Root Rhizosphere 16S rRNA Tropical wetland Methane and nitrogen cycle 



Our acknowledgments to the people involved in the field campaign (Tamara van Bergen, Andrea Budiša, Janne Nauta, Claumir Muniz, and Jan Roelofs) and in the laboratory analysis (Beatriz Buhler, Roy Peters, Germa Verheggen, Sebastian Krosse, and Paul van der Ven).

Funding Information

This work was supported by the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Koninklijke Nederlandse Akademie van Wetenschappen (KNAW-Ecology Fund), Fundação de Amparo à Pesquisa de Mato Grosso (FAPEMAT), and the Erasmus plus program (ERASMUS+). Ernandes Sobreira Oliveira Júnior was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – CAPES by the Science without Borders program - Process BEX 13607/13-8. Sarian Kosten was supported by NWO-VENI grant 8631(2012).

Supplementary material

248_2019_1331_MOESM1_ESM.pdf (686 kb)
Fig. S1 Community structure ordination using principal coordinate analysis, implemented with different distance indexes (BC - Bray-Curtis; CAN – Canberra; GOW – Gowler; MOR - Morisita-Horn; WU - weighted Unifrac; UWU - unweighted Unifrac) and normalization procedures (m - metagenomeseq’s CSS; p - phyloseq’s rarefaction, both with raw data (raw) and squared-root transformed OTU counts (sqrt root). Each line represents the percentage of variation explained by each axis (PCoA1, PCoA2 and PCoA3) with increasing sample size (number of OTUs) under specific distance/normalization procedure. The sum of variance shown in [PCoA1 + PCoA2 + PCoA3] was used to select the distance/normalization procedure. (PDF 685 kb)
248_2019_1331_MOESM2_ESM.pdf (475 kb)
Fig. S2 Single linear regressions estimated between mcrA and archaea (A), pmoA (B), bacteria (C), as well as between archaea and pmoA (D). All qPCR counts were log 10 transformed. (PDF 475 kb)
248_2019_1331_MOESM3_ESM.pdf (1.4 mb)
Fig. S3 Mean relative frequency and standard deviation of the 85 most abundant lineages according to each wetland/sampling time, classified by low, medial and highly frequent taxa. The relative frequencies of all OTUs belonging to a given genus were summed, for family and class level the mean relative frequency denote individual OTUs, which are distinguished by a superscripted number. Bacterial phyla are shown in italic. (PDF 1466 kb)
248_2019_1331_MOESM4_ESM.pdf (353 kb)
Fig. S4 Box-plots exhibiting alpha-diversity measurements according to wetland (A) and sampling time (B). Richness corresponds to number of OTUs per sample and evenness was calculated as (1/D)/S, both indices were evaluated after sub-sampling at a depth of 4653 reads. (PDF 353 kb)
248_2019_1331_MOESM5_ESM.pdf (916 kb)
Fig. S5 OTU’s rank-abundance curves of each sample. Ranked OTUs are shown in the X axis and log 10-transformed relative frequency in the Y axis. The red line represents the threshold accounting for 90% of the total reads in each sample. (PDF 915 kb)
248_2019_1331_MOESM6_ESM.pdf (537 kb)
Fig. S6 Three-dimensional biplot showing community dissimilarity according to results of PCoA community ordination (Morisita-Horn distances). The relative contribution of each axis’ eigenvalues is shown between parentheses. The numbers correspond to the site sampled in different wetlands (Amazon and Pantanal) and sampling times (ST1 and ST2). (PDF 537 kb)
248_2019_1331_MOESM7_ESM.pdf (1.1 mb)
Fig. S7 Scatter plots displaying single linear regressions trendline (dotted line), as also shown in Table S6, which were determined between different subset of variables: candidate community structure drivers (axes extracted from environmental data PCA, qPCR counts), functional pathways (gene relative counts), alpha-diversity (Simpson (1/D)/S and Shannon diversity) and key microbial taxa relative frequency. (PDF 1116 kb)
248_2019_1331_MOESM8_ESM.pdf (917 kb)
Fig. S8 Three-dimensional biplots showing the relationship between community dissimilarity and the predicted genes associated with PCoA2 (A and B) and PCoA3 (C and D). (A) nitrogen fixation (nifH), (B) iron oxi-reductase (petC), (C) nitrite transporter (nirK) and (D) nitrate reduction (narH). (PDF 916 kb)
248_2019_1331_MOESM9_ESM.pdf (370 kb)
Fig. S9 Dendrogram showing the similarity in the occurrence pattern of the most abundant lineages. UPGMA was used as agglomeration method and euclidean distances were calculated after all microbial lineages had their relative frequencies standardized. (PDF 370 kb)
248_2019_1331_MOESM10_ESM.pdf (893 kb)
ESM 1 (PDF 893 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Marcelo P. Ávila
    • 1
  • Ernandes S. Oliveira-Junior
    • 2
  • Mariana P. Reis
    • 1
  • Eric R. Hester
    • 3
  • Cristiane Diamantino
    • 1
  • Annelies J. Veraart
    • 2
  • Leon P. M. Lamers
    • 2
  • Sarian Kosten
    • 2
  • Andréa M. A. Nascimento
    • 1
    Email author
  1. 1.Departamento de Biologia Geral, Instituto de Ciências BiológicasUniversidade Federal de Minas GeraisBelo HorizonteBrazil
  2. 2.Department of Aquatic Ecology and Environmental Biology, Institute for Water and Wetland ResearchRadboud UniversityNijmegenThe Netherlands
  3. 3.Department of Microbiology, Institute for Water and Wetland ResearchRadboud UniversityNijmegenThe Netherlands

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