A question of data quality-Testing pollination syndromes in Balsaminaceae

Abstract

Pollination syndromes and their predictive power regarding actual plant-animal interactions have been controversially discussed in the past. We investigate pollination syndromes in Balsaminaceae, utilizing quantitative respectively categorical data sets of flower morphometry, signal and reward traits for 86 species to test for the effect of different types of data on the test patterns retrieved. Cluster Analyses of the floral traits are used in combination with independent pollinator observations. Based on quantitative data we retrieve seven clusters, six of them corresponding to plausible pollination syndromes and one additional, well-supported cluster comprising highly divergent floral architectures. This latter cluster represents a non-syndrome of flowers not segregated by the specific data set here used. Conversely, using categorical data we obtained only a rudimentary resolution of pollination syndromes, in line with several earlier studies. The results underscore that the use of functional, exactly quanitified trait data has the power to retrieve pollination syndromes circumscribed by the specific data used. Data quality can, however, not be replaced by sheer data volume. With this caveat, it is possible to identify pollination syndromes from large datasets and to reliably extrapolate them for taxa for which direct observations are unavailable.

Fig 1. Morphometrical measurements conducted in the different flower types in Balsaminaceae: Lateral view: DP: dorsal petal, SL: Spur length, SpL: Spur carrying sepal length.

Frontal view: O: Width of flower opening, W: Flower width, FL: Flower length. Drawings: S. Lozada-Gobilard.

Fig 2

Cluster analyses generated from a PCA based on quantitative flower morphometry, signal and nectar traits for those 21 Balsaminaceae species, for which independent pollinator observations exist (S2 Table): A: generalist fly; B: butterfly; C: bird 1; D: large bee; E: bird 2; F: non-syndrome (containing several syndromes).

Fig 3. Cluster analyses generated from distance matrices (I.) based on quantitative and (II.) based on categorical flower morphometry, signal and nectar traits.

Pollination syndromes are assigned to the clusters of I. based on syndrome assignments by Grey-Wilson [37] (^g) and Erpenbach [45] (^e) as well as pollinator observations for individual species (*) from the literature: A: hawkmoth; B: butterfly; C: generalist fly; D: bird 1; E: large bee (Apidae); F1 and F2: non-syndrome (containing several syndromes); G: bird 2; H: large bee (Apidae; tribes Anthophorini, Apini, Bombini). Coloration of the species in II. is based on the coloration of the clusters in I. In contrast to Grey-Wilson [37] we assigned the hawkmoth syndrome to Impatiens sodenii, based on the 12.8 cm long spur and the largest frontal display size of all analysed species. In contrast to Erpenbach [45] we assigned the generalist fly syndrome to I. inaperta and I. aff. inaperta since we analysed only open, non-cleistogamous flowers.

Fig 4

Kruskal-Wallis tests for multiple comparisons of A: spur carrying sepal length, B: total display size, C: nectar volume and D: sugar concentration between pollination syndromes. Note that Large bee 1 (= Impatiens glandulifera) and Moth (= I. sodenii) was not included into the analyses due to too small sampling sizes.

Fig 5

Impatiens species belonging to the non-syndrome cluster but having well defined, functionally homogeneous pollinator guilds; A: I. hochstetteri, B: I. parviflora, C: I. pinganoensis, D: I. burtonii; photos of I. hochstetteri by JMK, wikipedia.org, and I. parviflora by ArtMechanic wikipedia.org.