DOI: 10.5061/dryad.rbnzs7hjh
title: Effects of herbivory and pathogen infection on plant-pollinator interactions

description: Plant enemies can indirectly affect pollinators by modifying plant traits,
 but simultaneous tests of herbivore and pathogen effects are lacking, and
 the role of floral volatiles has seldom been mechanistically assessed. In
 this study, we tested for indirect effects of insect herbivores and
 pathogens on pollinator attraction via altered floral volatile emissions,
 and its consequences for plant fitness in Brassica rapa. Plants in the
 field were exposed to either no damage or damage by caterpillars (Mamestra
 brassicae), aphids (Brevicoryne brassicae), a leaf fungus (Sclerotinia
 sclerotiorum), or a bacterium (Xanthomonas campestris pv. campestris). We
 recorded pollinator visits, and measured floral traits (flower number,
 volatiles) and plant fitness-correlates. We additionally performed a
 greenhouse experiment with artificial emitters to test for effects of
 target floral volatiles on pollinator attraction. In the field
 experiments, plants subjected to herbivory by the aphid B. brassicae (but
 not the other enemies) exhibited a marked reduction in the emission of two
 VOCs (nonanal and 2-butyl-1-octanol), experienced lower pollinator visits,
 and produced seeds of lower quality in terms of seed biomass and
 germination rate, while flower output itself was not affected. Artificial
 emitters with reduced amounts of these compounds were less attractive to
 pollinators under greenhouse conditions. Synthesis: These results provide
 strong evidence for volatile-mediated indirect interactions between plant
 enemies and pollinators ultimately impacting plant fitness, and further
 point at enemy and compound specificity underlying such effects.
description: # Herbivore and pathogen effects on pollination
 In this study, we tested
 for indirect effects of insect herbivores and pathogens on pollinator
 attraction via altered floral volatile emissions, and its consequences for
 plant fitness in *Brassica rapa*. Plants in the field were exposed to
 either no damage or damage by caterpillars (*Mamestra brassicae*)*,*
 aphids (*Brevicoryne brassicae*), a leaf fungus (*Sclerotinia
 sclerotiorum*), or a bacterium (*Xanthomonas campestris* pv.
 *campestris*). We recorded pollinator visits, and measured floral traits
 (flower number, volatiles) and plant fitness-correlates. We additionally
 performed a greenhouse experiment with artificial emitters to test for
 effects of target floral volatiles on pollinator attraction. ###
 Description of the data and file structure | variable | location | units |
 | :--------------------- | :---------------- |
 :---------------------------------------------------------- | | height |
 Table1/Fig1 | cm | | fruit\_set | Table1/Fig1 | proportion | |
 seeds\_number | Table1/Fig1 | mean number of seeds per silique | |
 seed\_weight | Table1/Fig1 | mean seed weight in g | | seed\_germination |
 Table1/Fig1 | proportion | | height | Table2 and 3/Fig2 | cm | | VOCs |
 Table2 and 3/Fig2 | nanograms per hour | | height | Table 4/Fig 3 | cm | |
 flowers | Table 4/Fig 3 | total number of flowers | | pollinator\_visits |
 Table 4/Fig 3 | total number of bumblebee visits  | | frui\_set | Table
 4/Fig 3 | proportion | | seed\_number | Table 4/Fig 3 | mean number of
 seeds per silique | | seed\_weight | Table 4/Fig 3 | mean seed weight in g
 | | seed\_germination | Table 4/Fig 3 | proportion | |
 pollinator\_attraction | Fig 4 | "0" for not landing vs.
 "1" for landing on an inflorescence | | height | Fig 5 | cm | |
 fruit\_set | Fig 5 | proportion | Note: N/A means no values ##
 Code/Software Table 1 and Figure 1. We analyzed the effects of leaf damage
 treatment, plant accession, and their interaction (all fixed factors) on
 flower number, fruit-set, mean number of seeds per silique, mean seed
 weight, and seed germination (i.e., proportion of germinated seeds) using
 linear mixed effect models with PROC MIXED in SAS 9.4. We included the
 cages as a random factor and plant height as a covariate to account for
 differences in plant size affecting reproductive output and success. We
 log-transformed proportions of germinated seeds to achieve normality of
 residuals. Tables 2 and 3 and Figure 2. We analyzed the effects of leaf
 damage treatment and plant accession (all fixed factors) on the emission
 of individual floral VOCs and on total VOC emission using linear models
 with PROC GLM in SAS 9.4. We also ran a permutational multivariate
 analysis of variance (PERMANOVA) to test for an effect of leaf damage
 treatment on floral VOC composition (using abundances of each compound).
 This analysis was based on 10,000 permutations and was performed with the
 ‘vegan’ package in R ver. 4.0.2 software. To visualize these results, we
 conducted a Principal Coordinates Analysis based on Bray-Curtis pairwise
 dissimilarities, and graphed the centroids of each leaf damage treatment
 effect. We also identified floral VOCs that correlated strongly
 (R<sup>2</sup> &gt; 0.60) with the first two ordination axes
 (using ‘envfit’ in vegan), and displayed these relationships using biplot
 arrows with length scaled to R<sup>2</sup> values Table 4 and
 Figure 3. We analyzed the effects of leaf damage treatment on total number
 of pollinator visits, fruit-set, mean number of seeds per silique, mean
 seed weight, and seed germination (i.e., proportion of germinated seeds)
 using linear mixed effect models with PROC MIXED in SAS 9.4. For all these
 models, we included the effect of plant accession as a random factor and
 plant height as a covariate. Moreover, to test for leaf damage effects on
 pollinator visits we tested for the leaf damage treatment as well as plant
 accession as a random effect and also included the total number of
 flowering stalks (across the two surveys) as a covariate. We
 log-transformed number of pollinator visits and fruit-set to achieve
 normality of residuals. Figure 4. For each dual-choice assay type, we
 analyzed the effect of VOC blend type (two levels: control vs. *B.
 brassicae* blend, fixed factor) on the odds of bumble bees’ first choice
 using a generalized linear mixed model with a binomial distribution and
 logit-link function (PROC GLIMMIX in SAS 9.4). Odds ratio values are the
 ratio between successful and unsuccessful events (i.e., bees landing vs.
 not landing on an inflorescence of a given type, respectively) for each
 treatment level, i.e., a likelihood of a bee being attracted to the
 control or *B. brassicae* blend treatments. In both cases, we included the
 effect of the choice testing device or set-up (i.e., cylinder or flight
 cage) as a random factor to control for non-independence of each pair of
 inflorescences per replicate. Figure 5. We assessed pollen limitation
 (i.e., difference in fruit-set for hand-pollinated vs. open-pollinated
 inflorescences) and whether it varied in enclosures vs. open field
 conditions by testing for the effect of stalk treatment (open vs.
 hand-pollinated), environment type (enclosure vs. open field), and their
 interaction on fruit-set using a linear mixed model with PROC MIXED in SAS
 9.4. We additionally ran the same model on total number of pollinator
 visits per plant to assess differences in pollinator activity between
 environments potentially related to effects on fruit-set. For both models,
 we included the total number of flowering stalks per plant as a covariate
 to account for an effect of overall flowering intensity in testing for the
 pollination treatment on the focal stalks. In addition, both models
 included the effects of plant accession and individual plant (to control
 for non-independence of paired stalks per plant) as random factors, as
 well as plant height as a covariate to account for residual variation in
 plant size affecting reproductive output and success. We log-transformed
 number of pollinator visits to achieve normality of residuals.