DOI: 10.5061/dryad.mgqnk9901
title: The role of plant-pollinator interactions in structuring nectar microbial communities

description: 1. Floral nectar harbours a diverse microbiome of yeasts and bacteria that
 depend predominantly on animal visitors for their dispersal. Since
 pollinators visit specific sets of flowers and carry their own unique
 microbiota, we hypothesize that plant species visited by the same set of
 pollinators may support non-random nectar microbial communities linked
 together by the type of pollinator. 2. Here we explore the importance of
 plant-pollinator interactions in the assembly of nectar microbiome and
 study the role of plant geographic location as a determinant of microbial
 community composition. We intensively sampled the nectar of 282 flowers of
 48 plant species with beetles, birds, long-tongued and short-tongued
 insects as pollinators in wild populations in South Africa, one of the
 world’s biodiversity hotspots, and using molecular techniques we
 identified nectar yeast and bacteria taxa. The analyses provided new
 insights into the richness, geographic structure and phylogenetic
 characterization of nectar microbiome, and compared patterns of
 composition of bacteria and yeast communities in relation to plant and
 pollinator guild. 3. Our results showed that plant-pollinator interactions
 played a crucial role in shaping nectar microbial communities. Plants
 visited by different pollinator guilds supported significantly different
 yeast and bacterial communities. The pollinator guild also contributed to
 the maintenance of beta diversity and phylogenetic microbial segregation.
 The results revealed different patterns for yeast and bacteria; whereas
 plants visited by beetles supported the highest richness and phylogenetic
 diversity of yeasts, bacteria communities were significantly more diverse
 in plants visited by other insect groups. We found no clear microbial
 spatial segregation at different geographical scales for bacteria, and
 only the phylogenetic similarity of yeast composition was correlated
 significantly with geography. 4. Synthesis. Interactions of animal vector,
 plant host traits and microbe physiology contribute to microbial community
 assemblages in nectar. Our results suggest that plants visited by the same
 pollinator guild have a characteristic nectar microbiota signature that
 may transcends the geographic region they are in. Contrasted patterns for
 yeast and bacteria stress the need for future work aimed at better
 understanding the causes and consequences of the importance of plants and
 pollinators in shaping nectar microbial communities in nature.
description: The composition and diversity of microbial communities naturally
 occurring in the floral nectar of animal-pollinated plants were studied in
 the KwaZulu-Natal province and in the Cape Region of South Africa. Nectar
 samples were collected from 282 flowers (from 282 different individual
 plants) of 48 plant species belonging to 16 angiosperm families. The study
 was conducted in 10 ecologically diverse localities separated by 20 to
 1250 km.
description: DATASET DESCRIPTION. This
 excel file includes six sheets as follows <b>Dataset#Sheet1.</b>
 GenBank_yeast. GenBank accession number of the nectar yeast isolated from
 floral nectar in South Africa. Sequence_ID: code of the yeast isolate;
 Organism: scientific name of the yeast isolate; Isolation_source: medium
 from which yeasts were isolated; GenBank accession number. <b>Dataset# Sheet
 2.</b> GenBank_bacteria. GenBank accession number of the nectar
 bacterial isolated from floral nectar in South Africa. Sequence_ID: code
 of the bacterial isolate; Organism: scientific name of the bacterial
 isolate; Isolation_source: medium from which bacteria were isolated;
 GenBank accession number. <b>Dataset# Sheet
 3.</b> OTU matrix. Matrix with bacterial and yeast isolates for each
 nectar sample and plants species. Sample: code of the nectar sample; Plant
 species: scientific name of the plant species from which the microbial
 communities were isolated; Family: taxonomic family for each plant
 species; Floral visitor: main floral visitor associated to each plant
 species; Isolate1-Isolate5: identification of the yeast and/or bacterial
 isolates for each nectar sample. If 0, no isolate was recovered for the
 nectar sample. <b>Dataset# Sheet
 4.</b> Floral visitors and populations. Plant species studied,
 associated floral visitors, populations where the species were sampled and
 geographic coordinates. Plant species: each plant species for which
 microbial communities were studied; Population: localities were plants
 species were collected; Latitude and Longitude: coordinates of the
 populations in which plants species were surveyed. Acronyms of localities:
 BKHV (Bains Kloof), BNR (Blinkwater Nature Reserve), GC (Garden Castle),
 GG (Giba Gorge, Winston Park, KZN), HhP (Houw Hoek Pass), MG (Mount Gilboa
 in the Karkloof Range), NV (Nature's valley), SP (Sani Pass below the
 South African border post), VC (Vernon Crookes Nature Reserve), WF
 (Wahroonga farm). <b>Datset# Sheet
 5.</b> OTU_incidence counts_yeast. Incidence counts of yeast OTUs by
 floral visitor type (Birds (76 samples), beetles (103 samples), other
 insects (103 samples)). <b>Dataset# Sheet
 6.</b> OTU_incidence counts_bacteria. Incidence counts of bacterial
 OTUs by pollinator type (Birds (76 samples), beetles (103 samples), other
 insects (103 samples)).   TREE DESCRIPTION <b>Tree_OTUs_bacteria</b>. Phylogenetic tree of bacterial OTUs obtained using Bayesian inference. We used MrBayes v.3.2.7 on XSEDE via the CIPRES Science Gateway and performed two independent runs for 5 x 10<sup>6</sup> generations with four chains each and trees sampled every 1000 generations. Parameter values from each run and convergence of runs was assessed with Tracer v.1.6. We allowed MrBayes to sample across the general time reversible (GTR) nucleotide substitution model space using reversible jump Markov chain Monte Carlo (rjMCMC) with the function nst = mixed. The first 25% of trees of each run were discarded as burnin and a 50% majority-rule consensus tree was constructed. For this tree we select one representative of each bacterial OTU. <b>Tree_OTUs_yeast</b>. Phylogenetic tree of yeast OTUs obtained using Bayesian inference. We used MrBayes v.3.2.7 on XSEDE via the CIPRES Science Gateway and performed two independent runs for 5 x 10<sup>6</sup> generations with four chains each and trees sampled every 1000 generations. Parameter values from each run and convergence of runs was assessed with Tracer v.1.6. We allowed MrBayes to sample across the general time reversible (GTR) nucleotide substitution model space using reversible jump Markov chain Monte Carlo (rjMCMC) with the function nst = mixed. The first 25% of trees of each run were discarded as burnin and a 50% majority-rule consensus tree was constructed. For this tree we select one representative of each yeast OTU.