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.

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.

DATASET DESCRIPTION. This excel file includes six sheets as follows

Dataset#Sheet1. 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.

Dataset# Sheet 2. 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.

Dataset# Sheet 3. 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.

Dataset# Sheet 4. 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).

Datset# Sheet 5. OTU_incidence counts_yeast. Incidence counts of yeast OTUs by floral visitor type (Birds (76 samples), beetles (103 samples), other insects (103 samples)).

Dataset# Sheet 6. OTU_incidence counts_bacteria. Incidence counts of bacterial OTUs by pollinator type (Birds (76 samples), beetles (103 samples), other insects (103 samples)).

TREE DESCRIPTION

Tree_OTUs_bacteria. 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 10⁶ 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.

Tree_OTUs_yeast. 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 10⁶ 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.