Nectar sugars and amino acids in day- and night-flowering Nicotiana species are more strongly shaped by pollinators' preferences than organic acids and inorganic ions

Abstract

Floral nectar contains mainly sugars but also amino acids, organic acids, inorganic ions and secondary compounds to attract pollinators. The genus Nicotiana exhibits great diversity among species in floral morphology, flowering time, nectar compositions, and predominant pollinators. We studied nectar samples of 20 Nicotiana species, composed equally of day- and night-flowering plants and attracting different groups of pollinators (e.g. hummingbirds, moths or bats) to investigate whether sugars, amino acids, organic acids and inorganic ions are influenced by pollinator preferences. Glucose, fructose and sucrose were the only sugars found in the nectar of all examined species. Sugar concentration of the nectar of day-flowering species was 20% higher and amino acid concentration was 2-3-fold higher compared to the nectar of night-flowering species. The sucrose-to-hexose ratio was significantly higher in night-flowering species and the relative share of sucrose based on the total sugar correlated with the flower tube length in the nocturnal species. Flowers of different tobacco species contained varying volumes of nectar which led to about 150-fold higher amounts of total sugar per flower in bat- or sunbird-pollinated species than in bee-pollinated or autogamous species. This difference was even higher for total amino acids per flower (up to 1000-fold). As a consequence, some Nicotiana species invest large amounts of organic nitrogen for certain pollinators. Higher concentrations of inorganic ions, predominantly anions, were found in nectar of night-flowering species. Therefore, higher anion concentrations were also associated with pollinator types active at night. Malate, the main organic acid, was present in all nectar samples but the concentration was not correlated with pollinator type. In conclusion, statistical analyses revealed that pollinator types have a stronger effect on nectar composition than phylogenetic relations. In this context, nectar sugars and amino acids are more strongly correlated with the preferences of predominant pollinators than organic acids and inorganic ions.

Fig 1. Flowers of the examined Nicotiana species.

a) N. paniculata b) N. nudicaulis c) N. rustica d) N. palmeri e) N. langsdorffii f) N. knightiana g) N. attenuata h) N. benthamiana i) N. africana j) N. otophora k) N. glauca l) N. plumbaginifolia m) N. tabacum n) N. suaveolens o) N. nesophila p) N. acuminata q) N. stocktonii r) N. sylvestris s) N. alata t) N. longiflora

Fig 2. Boxplots of different nectar traits grouped into day- (left, white background) and night-flowering (right, grey background) species.

The data are arranged according to their main pollinators Trochilidae, Nectariniidae, Apidae, Sphingidae, Glossophaginae and self-pollinating species. (A) Boxplot diagram illustrating concentration of total sugars [mM]. (B) Sucrose-hexose-ratio is calculated by dividing sucrose concentration [g L^-1] by the sum of glucose and fructose [g L^-1]. (C) Boxplots illustrating the concentration of total amino acids [mM]. (D) Ratio sum of sugars-to-sum of amino acids. (E) Concentration of the amides glutamine and asparagine [mM]. (F) Total malate concentrations [mM]. (G) Concentration of the sum of inorganic anions (chloride, nitrate, phosphate, sulphate) [mM]. (H) Concentration of the sum of inorganic cations (potassium, sodium, ammonium, magnesium, calcium) [mM]. Different letters designate significantly different groups determined via ANOVA, post hoc Tukey’s HSD test and Kruskal-Wallis test for non-parametrical data (p ≤ 0.05).

Fig 3

Boxplots of sugar (A) and amino acid (B) amount in μmol per flower. Data were calculated by multiplication of the sugar or amino acid concentrations (Table 2 and Table 3) with the approximate nectar volume of each species. Results are grouped by their main pollinators Trochilidae, Nectariniidae, Apidae, Sphingidae, Glossophaginae and self-pollinating species. Different letters designate significantly different groups determined via ANOVA, post hoc Tukey’s HSD test and Kruskal-Wallis test for non-parametrical data (p ≤ 0.05).

Fig 4. Loadings and scatterplot of PCA scores in rotated space.

(A) Loadings of the original variables are shown as vectors in PCA space (B) The first principal component (PC 1) accounts for 24.4% and the second principal component (PC 2) accounts for 10.3% of the variation in the dataset. Eigenvalues are 2.837 for PC 1 and 1.846 for PC 2. Data (n = 99) are grouped both by main pollinators (colours) and sections (markings).