1. Ecology
  2. Evolutionary Biology
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Inbreeding in a dioecious plant has sex- and population origin-specific effects on its interactions with pollinators

  1. Karin Schrieber  Is a corresponding author
  2. Sarah Catherine Paul
  3. Levke Valena Höche
  4. Andrea Cecilia Salas
  5. Rabi Didszun
  6. Jakob Mößnang
  7. Caroline Müller
  8. Alexandra Erfmeier
  9. Elisabeth Johanna Eilers
  1. Kiel University, Institute for Ecosystem Research, Geobotany, Germany
  2. Bielefeld University, Faculty of Biology, Department of Chemical Ecology, Germany
  3. German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Germany
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Cite this article as: eLife 2021;10:e65610 doi: 10.7554/eLife.65610

Abstract

We study the effects of inbreeding in a dioecious plant on its interaction with pollinating insects and test whether the magnitude of such effects is shaped by plant individual sex and the evolutionary histories of plant populations. We recorded spatial, scent, colour, and rewarding flower traits as well as pollinator visitation rates in experimentally inbred and outbred, male and female Silene latifolia plants from European and North American populations differing in their evolutionary histories. We found that inbreeding specifically impairs spatial flower traits and floral scent. Our results support that sex-specific selection and gene expression may have partially magnified these inbreeding costs for females, and that divergent evolutionary histories altered the genetic architecture underlying inbreeding effects across population origins. Moreover, the results indicate that inbreeding effects on floral scent may have a huge potential to disrupt interactions among plants and nocturnal moth pollinators, which are mediated by elaborate chemical communication.

Introduction

Plant-pollinator interactions are of central importance for the emergence as well as the maintenance of global biodiversity (Crepet and Niklas, 2009; Ollerton, 2017) and provide ecosystem services with tangible sociocultural and economic value (Gill et al., 2016; Porto et al., 2020). Global change continues to disrupt these interactions by altering the physiology, phenology, and particularly the spatial distribution of component species (Burkle et al., 2013; Vanbergen, 2013; Glenny et al., 2018). Habitat degradation and fragmentation reduce the size and connectivity of plant populations, which results in lowered pollinator visitation rates (Aguilar et al., 2006; Dauber et al., 2010). Plant population retraction and isolation may also affect interactions with pollinators at the plant individual level by increasing inbreeding rates (Carr et al., 2014). The mating among closely related plant individuals may compromise floral traits attracting pollinators and hence cause negative feedback on pollinator visitation. Mechanistic insight into the effects of inbreeding on plant-pollinator interactions and intrinsic factors shaping the magnitude of such effects is limited but urgently required for the conservation of component species.

Inbreeding increases homozygosity in the offspring generation. This may enhance the phenotypic expression of deleterious recessive mutations (i.e., dominance) and reduce heterozygote advantage (i.e., over-dominance), which can result in severe declines of Darwinian fitness in inbred relative to outcrossed offspring (i.e., inbreeding depression) (Charlesworth and Willis, 2009). Inbreeding may in addition disrupt plant-insect interactions. While it is well established that inbreeding can increase a plant’s susceptibility to herbivores by diminishing morphological and chemical defences (Campbell et al., 2013; Kariyat et al., 2012; Kalske et al., 2014), its effects on plant-pollinator interactions are less well understood. Inbreeding may reduce a plant’s attractiveness to pollinating insects by compromising the complex set of floral traits involved in interspecific communication. These traits comprise (i) the spatial arrangement of individual flowers (e.g., size, shape) and multiple flowers within an inflorescence (e.g., number, height above ground, degree of aggregation), hereinafter referred to as spatial flower traits (Dafni et al., 1997); (ii) the scent bouquet as determined by the composition of floral volatile organic compounds (VOC) such as terpenoids, benzenoids, and phenylpropanoids (Muhlemann et al., 2014; Borghi et al., 2017); (iii) flower colour as defined by the composition of pigments with wavelength-selective light absorption and the backscattering of light by petal surface structures (van der Kooi et al., 2016; Borghi et al., 2017); and (iv) the quality and quantity of rewards such as nectar, pollen, oviposition sites, or shelter (Simpson and Neff, 1981). These cues are particularly efficient in attracting pollinators across either long, medium, or short distances and act synergistically in determining visitation rates (Dafni et al., 1997; Muhlemann et al., 2014) . Although in a few cases inbreeding has been shown to alter single floral traits (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into more syndrome-wide effects is restricted to a single study. Kariyat et al., 2021 demonstrated that inbred Solanum carolinense L. display reduced flower size, pollen and scent production, and receive fewer visits from diurnal generalists. It is necessary to broaden such integrated methodological approaches to other plant-pollinator systems (e.g., nocturnal specialist pollinators) and further floral traits (e.g., flower colour).

The magnitude and slope of inbreeding effects in plants can vary across environments, since local conditions partly determine the selective value of recessive alleles unmasked by inbreeding (Fox and Reed, 2011). While the influence of environmental stress on the expression of inbreeding depression is well studied, the effects of plant sex, which considerably shapes an individual’s interaction with its environment, remain largely unexplored. Individuals of dioecious plant species invest into either male or female reproductive function. This partitioning goes along with different life histories, resource demands, stress susceptibilities, and consequently sex-specific selection regimes in identical habitats (Moore and Pannell, 2011; Barrett and Hough, 2013). Sex-specific selection may modify the magnitude of inbreeding depression in dioecious plants. Studies on animals reported higher inbreeding depression in females than males resulting from higher reproductive investment and prolonged life cycles in the former (Ebel and Phillips, 2016). In plants, such relations have rarely been investigated (Teixeira et al., 2009), and if so, not with a focus on floral traits. If inbreeding effects on floral traits are more pronounced in female than male plants, the relative frequency of pollinator visits may be biased towards the latter sex, with devastating consequences for the effective size and persistence of populations. Studies on sex-specific inbreeding effects on floral traits are thus needed to improve the risk assessment for the conservation of dioecious plant species.

Plant populations may escape progressive retractions under increased inbreeding rates by purging. Inbreeding unmasks deleterious recessive mutations, which facilitates their selective removal from the population gene pool and may result in a rebound of fitness when the demographic bottleneck is intermediate (Crnokrak and Barrett, 2002). Plant species that have successfully colonised distant geographic regions provide perfect models for studying the relevance of purging in natural plant populations. As colonisation events are associated with successive demographic bottlenecks, purging is expected to be one determinant for the successful establishment and proliferation of plant populations in novel habitats (Facon et al., 2011; Schrieber and Lachmuth, 2017). However, only few empirical studies verified the role of purging in plant colonisation success by revealing significantly lower inbreeding depression following experimental crossings in invasive than native plant populations (Rosche et al., 2016; Schrieber et al., 2019b). Again, the focus of these studies was on fitness components rather than floral traits. Yet, attractiveness to pollinators is a key for successful colonisation in species introduced into novel communities (Morales and Traveset, 2009), especially if plants are not capable of selfing (e.g., dioecious). Floral syndromes are likely under strong selection in such species, which should rapidly purge deleterious recessive mutations affecting spatial, scent, colour, and rewarding flower traits.

In the present study, we investigated the effects of inbreeding on plant-pollinator interactions and tested whether the magnitude of such effects depends on plant sex and population origin using the Silene latifolia Pior. (Caryophyllaceae) and its crepuscular moth pollinators. Natural S. latifolia populations partly suffer from biparental inbreeding due to limited seed and pollen dispersal (McCauley, 1997). As inbreeding reduces not only fitness (Teixeira et al., 2009) but also impairs interactions with herbivorous insects in S. latifolia (Schrieber et al., 2019a; Schrieber et al., 2019b), a disruption of plant-pollinator interactions can be expected for this species. Moreover, the dioecious reproductive system of S. latifolia provides the opportunity to quantify variation in the magnitude of inbreeding effects in these traits among females and males. Finally, the species expanded successfully from parts of its native distribution range in Europe to North America in the early 19th century (Keller et al., 2009; Keller et al., 2012), which may have given rise to purging events. We assessed spatial flower traits, headspace floral scent composition, flower colour and rewards, and quantified pollinator visitation in experimentally inbred and outbred male and female S. latifolia individuals from European and North American populations. We hypothesised that (i) inbreeding compromises floral traits, (ii) these inbreeding effects are more pronounced in female than male plants, (iii) inbreeding effects are more pronounced in European than North American populations, and (iv) the combined effects of inbreeding, sex, and population origin cause feedback on pollinator visitation rates.

Materials and methods

Study species

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S. latifolia shows a distinct moth pollination syndrome with large, white, and funnel-shaped flowers (Dafni et al., 1997). The flowers open from dusk till mid-morning to release a scent bouquet composed of more than 60 VOC, whereby emission peaks around dusk (Dötterl and Jürgens, 2005; Dötterl et al., 2009; Mamadalieva et al., 2014). During the daytime, no measurable floral scent is emitted (Dötterl et al., 2005). Nectar production peaks 3–4 days after flower opening and is just as floral scent emission reduced after pollination (Gehring et al., 2004; Dötterl and Jürgens, 2005; Muhlemann et al., 2006). S. latifolia exhibits various sexual dimorphisms with male plants producing more and smaller flowers that excrete lower volumes of nectar with higher sugar concentrations as compared to females (Gehring et al., 2004; Delph et al., 2010). The quality of floral scent exhibits no clear sex-specific patterns, while male plants have been shown to emit higher or equal total amounts of VOC as compared to females in different studies (Dötterl and Jürgens, 2005; Waelti et al., 2009).

Various diurnal generalist pollinators as well as crepuscular moths visit S. latifolia flowers. The latter, including the specialist Hadena bicruris Hufn. (Lepidoptera: Noctuidae), were shown to be the most efficient pollinators for S. latifolia (Young, 2002), which is the reason why we exclusively focus on nocturnal pollination in our study. All nocturnal pollinators are rewarded with nectar, while the specialist H. bicruris is additionally rewarded with oviposition sites. S. latifolia and H. bicruris form a well-studied nursery pollination system, in which female moths pollinate female plants while ovipositing on the flower ovaries to provide their larvae with developing seeds. Pollination services provided by male H. bicruris likely over-compensate the costs of seed predation by their offspring (Labouche and Bernasconi, 2010). A substantial fraction of floral VOC produced by S. latifolia triggers antennal and behavioural responses in male and female H. bicruris moths (Dötterl et al., 2006). The activity of H. bicruris peaks at dusk between May and July (Bopp and Gottsberger, 2004). H. bicruris is abundant in 90% of European S. latifolia populations but has not yet been introduced to North America. Other nocturnal moths including the specialist Hadena ectypa Morrison (Lepidoptera: Noctuidae) provide main pollination services to S. latifolia in the invaded range without imposing costs by seed predation (Young, 2002; Castillo et al., 2014).

Plant material

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We collected seed capsules from five female individuals (maternal families) in each of eight European and eight invasive North American S. latifolia populations (Figure 1; Figure 1—figure supplement 1). Seeds from all maternal families (consisting of full-sibs and/or half-sibs, hereinafter referred to as sibs) were germinated and plants were grown under controlled greenhouse conditions for experimental crossings within populations. Each female individual from the P-generation received pollen from a male derived from the same maternal family (inbreeding) and pollen from a male derived from a different maternal family within the same population (outcrossing) at separate flowers (Figure 1—figure supplement 2). During the crossings, plants were kept at randomised positions in the greenhouse. Female flower buds were covered with mesh bags prior to opening until fruit maturation and opened flowers were released from bags only for directed pollen transfer. The field sampling, rearing conditions, and experimental crossing are described in detail in Schrieber et al., 2019a, Schrieber et al., 2019b. Seeds were dried and stored at room temperature until further use.

Figure 1 with 8 supplements see all
Graphical sketch of the applied methods.

Each of the eight listed methodologies is illustrated in detail in a figure supplement.

For the experiment, we grew plants from the F1 generation under greenhouse conditions (16/8 hr light/dark at 20/10°C±6°C). After the onset of flowering, we randomly chose one female and one male individual per breeding treatment (inbred, outbred) × maternal family (1−5)×population (1−8) × origin (Europe, North America) combination, resulting in 320 plant individuals for the experiment (Figure 1—figure supplement 3). Using these individuals, we assessed the combined effects of breeding treatment, plant sex, and population origin on different flower traits and pollinator visitation rates over the summers 2019 and 2020. Plants were grown in 3 L (2019) and 6 L (2020) pots filled with a 3:1 mixture of potting soil (TKS2 Instant Plus, Floragrad, Oldenburg, Germany) and pine bark (Pine Bark 1–7 mm, Neede, Oosterbeek Humus Producten, The Netherlands). They were kept in pots with randomised positions either in the greenhouse, a common garden in Kiel, Germany (Europe) with sealed ground (54.346794°N, 10.107990°E, 19 m elevation) or a field site in Kiel, Germany (Europe), covered by an extensively used meadow (54.347742°N, 10.107661°E, 19 m elevation) for different parts of data acquisition. For an overview of the time schedule, locations, and exact sample sizes for data acquisition, see Table 1. Plants received water and fertilisation (UniversolGelb 12-30-12, Everris-Headquarters,Geldermalsen, The Netherlands) when necessary for the entire experimental period and were prophylactically treated with biological pest control agents under greenhouse conditions to prevent thrips (agents Amblyseius barkeri and Amblyseius cucumeris) and aphid (agent Chrysoperla carnea) infestation (Katz Biotech GmbH, Baruth, Germany).

Table 1
Overview of locations, times, and sample sizes for data acquisition.
Trait categoryLocationAcquisition time
(year, month, duration)
Nesting and intended
total sample size
Realised total sample sizeReplicates per group
(breeding treatment × sex ×
origin combination)
Reason for sample size reduction
Spatial flower traits (synflorescence height, flower number)Greenhouse and common garden2019
Jun., Jul., Aug.
5 days, respectively
Two breeding treatments × 2 sexes × 5 maternal families × 8 populations × 2 origins=32031636–40Four individuals died
Flower scentGreenhouse2019
Jul.
8 hr
Two breeding treatments × 2 sexes × 3 maternal families × 8 populations × 2 origins=19219223–35-
Flower colourCommon garden2019
Aug.
2 weeks
Two breeding treatments × 2 sexes × 5 maternal families × 8 populations × 2 origins=32028623–25Four individuals died, no flowers available for remaining plants
Spatial flower traits (petal limb area and expansion)Common garden2019
Aug.
2 weeks
Two breeding treatments × 2 sexes × 5 maternal families × 8 populations × 2 origins=32028623–35Four individuals died, no flowers available for remaining plants
Pollinator visitation ratesField site2020
May–Jul.
8 weeks
Two breeding treatments × 2 sexes × 5 maternal families × 8 populations × 2 origins=32031636–40Four individuals died
Floral rewardsCommon garden2020
Aug.
4 weeks
Two breeding treatments × 2 sexes × 5 maternal families × 8 populations × 2 origins=32028030–40Four individuals died, no flowers available for remaining plants

Floral traits

Spatial flower traits

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We determined the maximum height of synflorescences above ground level and the number of fully opened flowers per individual (Figure 1—figure supplement 4a). These traits were acquired thrice, in June, July, and August 2019 to account for phenological variation. For statistical analyses, these data were averaged over the three time points at the individual level. The size of S. latifolia flowers was not assessed via the length of their petal limbs as in previous studies, since this estimate does not account for the severe variation in their overall shape (Figure 1—figure supplement 4b). Instead, we assessed the exact area covered by all petal limbs and the expansion of the corolla (i.e., the area covered by the smallest possible circle drawn around all five petal limbs) (Figure 1—figure supplement 4a). Both traits were derived from digital images taken from one well-developed and fully opened flower per plant (see Flower colour section for further details) using the software ImageJ 1.47 t (Rueden et al., 2017).

Flower scent

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For characterisation of flower scent, we trapped the headspace VOC of S. latifolia flowers on absorbent polydimethylsiloxane (PDMS) tubing following the method of Kallenbach et al., 2014, Kallenbach et al., 2015. We placed the plants in a spatial distance of 50 cm to one another in the greenhouse and maintained high air ventilation 1 week prior to and during VOC collection. We selected one well-developed flower per individual and enclosed it in a VOC collection unit (Figure 1—figure supplement 5). The collection units consisted of polypropylene cups with lids (50 mL, Premium Line, Offenburg, Tedeco-Gizeh, Germany), both having holes (diameter 15 mm) to prevent heat and waterlogging. They were fixed via wooden sticks at the exterior of the plant pot. In addition, 14 control collection units were fixed on empty plant pots and positioned throughout the greenhouse. Prior use, the absorbent PDMS tubes (length 5 mm, external diameter 1.8 mm, internal diameter 1 mm; Carl Roth, Karlsruhe, Germany) were cleaned with solvents and heat as described in Kallenbach et al., 2014. Two PDMS tubes were added to each collection unit and remained in the floral headspace between 9 p.m. and 5 a.m., which is the time of peak scent emission in S. latifolia (Dötterl and Jürgens, 2005). Afterwards, the PDMS tubes were removed and stored at −20°C in sealed glass vials until analysis via thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS, TD 30 – GC 2010plus – MS QP2020, Shimadzu, Kyoto, Japan).

All samples were measured in a single trial in a fully randomised order. Trapped VOC were desorbed from PDMS tubes for 8 min at 230°C under a helium flow of 60 mL min−1 and adsorbed on a Tenax cryo-trap with a temperature of –20°C. From the trap, compounds were desorbed at 250°C for 3 min, injected to the GC in a 3:10 split mode, and migrated with a helium flow of 1.6 mL min−1 on a VF5-MS column (30 m × 0.25 mm + 10 m guard column, Agilent Technologies, Santa Clara, CA). The GC temperature program started at 40°C for 5 min and increased to 125°C at a rate of 10°C min−1 with a hold time of 5 min and to 280°C at a rate of 30 °C min−1 with a hold time of 1 min. Line spectra (30–400 m/z) of separated compounds were acquired in quadrupole MS mode. An alkane standard mix (C8-C20, Sigma-Aldrich,Darmstadt, Germany) was analysed under the same conditions in order to calculate Kovats retention indices (KI) for targeted compounds (Kováts, 1958).

Compounds were identified by comparing the KI and mass spectra with those of synthetic reference compoundsor with library entries of the National Institute of Standards and Technology (NIST) (Smith et al., 2004), Pherobase (El-Sayed, 2011), the PubChem database (Kim et al., 2016), and Adams, 2007. Control samples (collection units without flowers), and blanks (cleaned PDMS tubes) were used to identify and exclude contaminations, leaving a total number of 70 VOC (Supplementary file 1). Compounds were not quantified but the intensity of the total ion chromatogram of peaks was compared among treatment groups (hereinafter referred to as intensity). A linear relationship among peak areas and compound concentrations has been validated for the passive sorption method in Kallenbach et al., 2014. The intensities of VOC were not corrected for flower size because we wanted to capture all variation in scent emission that is relevant for the receiver, that is, the pollinator. For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al., 2006. We analysed the Shannon diversity per plant (calculated with R-package: vegan v.2.5–5, Oksanen et al., 2019) for 20 floral VOC in our data set that were shown to elicit electrophysiological responses in the antennae of H. bicruris (Supplementary file 1). Moreover, we analysed the intensities of three lilac aldehyde isomers, which trigger oriented flight and landing behaviour in both male and female H. bicruris most efficiently when compared to other VOC in the floral scent of S. latifolia. Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al., 2006).

Flower colour

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Flower colour was quantified using a digital image transformation approach that accounts for the visual system of the pollinator as well as natural light conditions (Troscianko and Stevens, 2015). Images were acquired in the common garden after plants had acclimated to ambient light conditions for 3 weeks. All images were taken during 1 hr of dusk time on rain-free days in order to fit the natural light conditions perceived by H. bicruris (Bopp and Gottsberger, 2004). We picked one well-developed, fully opened flower per plant and inserted it into a black ethylene vinyl acetate platform equipped with two reflectance standards (PTFB 10%; Spectralon 99% Labsphere, Congleton, UK) and a size standard (Figure 1—figure supplement 6). The platform had a fixed location in the field and was oriented towards the setting sun. Raw images were taken with a digital camera (Samsung NX1000,Suwon, South Korea, ) converted to full spectrum sensitivity (300–1000 nm) via removal of the sensor’s filter and fitted with an ultraviolet (UV) sensitive lens (Nikon EL 80‐mm, Japan). We took images in the visible and in the UV part of the light spectrum by fitting an UV and infrared (IR) blocking filter (UV/IR Cut, transmittance 400–700 nm, Baader Planetarium,Reutlingen, Germany) and an UV pass plus IR block filter (U-filter, transmittance 300–400 nm, Baader Planetraium,Reutlingen, Germany) to the lens, respectively. All images were taken as RAWs with an aperture of 5.6, an iso of 800, and a shutter speed varying according to light conditions.

Images were processed using the Multispectral Image Calibration and Analysis (MICA)-Toolbox plugin (Troscianko and Stevens, 2015) in ImageJ 1.47 t (Rueden et al., 2017). They were linearised to correct for the non-linear response of the camera to light intensity and equalised with respect to the two light standards in order to account for variation in natural light perceived among images (Stevens et al., 2007). All petals were selected for analysis, and the reproductive organs and para-corolla were omitted. Linearised images were then mapped to the visual system of a nocturnal moth. As the visual system of H. bicruris is unexplored, we used the tri-chromatic visual system of Deilephila elpenor L. (Lepidoptera: Sphingidae), which includes three rhodopsins with absorption maxima of 350 nm (UV), 440 nm (blue), and 525 nm (green) (Johnsen et al., 2006). We considered this system to be comparable to that of H. bicruris, given the similar activity behaviour of adults, morphological similarity of the preferred plant species (Lonicera periclymenum L. [Caprifoliaceae] with white-creamy funnel-shaped flowers) and overlapping distribution ranges. We fitted the images to a cone catch model incorporating (i) the spectral sensitivity of our Samsung NX1000-Nikkor EL 80 mm 300–700 nm camera (data derived from Troscianko and Stevens, 2015); (ii) the spectral sensitivities of the three photoreceptors in the D. elpenor compound eye (data derived from Johnsen et al., 2006); and (iii) the spectral composition of sun light during dusk (data derived from Johnsen et al., 2006).

Floral rewards

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As moths forage on liquids only, we measured nectar as floral reward (Figure 1—figure supplement 7). We selected one well-developed, closed flower bud per plant in the common garden and enclosed it in a transparent mesh bag (Organza mesh bags, Saketos, Sieniawka, Poland) until harvest to avoid pollination and nectar removal. All flowers were harvested at noon of the fourth day after opening and were stored immediately at 4°C until processing to prevent further nectar secretion. Nectar was extracted into 1–2 µL microcapillary tubes (Minicaps NA-HEP, Hirschmann Laborgeräte, Eberstadt, Germany). The length of the nectar column was measured with a calliper to determine the exact volume. Nectar sugar content was analysed with a refractometer adjusted for small sample sizes (Eclipse Low Volume 0–50°brix, Bellingham and Stanley, UK). Since nectar volume trades off against nectar quality in pollinator attraction (Cnaani et al., 2006), we addressed floral rewards in S. latifolia via the total amount of sugar excreted per flower as calculated based on the following equation: gsugar=volume[L](brix(1+4.25brix1000)10).

Pollinator visitation rates

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We quantified visits by crepuscular pollinators belonging to the order of Lepidoptera at the field site. For this purpose, plants were arranged in plots (1.5 m × 1.5 m, distance among plants = 0.5 m) that consisted of eight individuals representing all populations from one breeding treatment × sex × origin combination. Each of the possible combinations (N = 8) was replicated five times at the level of maternal families, resulting in a total number of 40 plots (N = 320 plants in total). Plots were spaced from each other at a distance of 6 m in order to provide pollinators with the choice of visiting specific breeding treatment × sex × origin combinations (Glenny et al., 2018). The position of plots and plants within plots was fully randomised (Figure 1—figure supplement 8). We performed 14 observation trials between May and July to cover the annual peak activity of H. bicruris (Bopp and Gottsberger, 2004). Each trial comprised 5 min observation time for each of the plots (total observation time: 2800 min, observation time per plot: 70 min) and was completed within 1 hr in the dawn time by four observers. The exact daytime of observation was acquired at the plot level for each of the trials. Plant and flower visits were determined at the plant individual level. If a moth had first contact with a flower, this was counted as a plant visit. The number of approached flowers per plant during a visit was counted until a moth either left or switched to another plant. The number of plant and flower visits per trial was averaged at the plot level for further analyses. The number of visiting moth individuals and moth species was not determined. The vast majority of visits were performed by H. bicruris (personal observation).

Please note that North American S. latifolia populations were tested in their ‘away’ habitat only and that the observed plant performance and pollinator visitation rates can thus provide no direct implications for their ‘home’ habitat. However, we neither aimed at elaborating on the invasion success of S. latifolia nor on adaptive differentiation among European and North American populations, but at investigating inbreeding effects on plant-pollinator interactions in multiple plant populations in a common environment. Given the close taxonomic relationship of H. bicruris (main pollinator in Europe) and H. ectypa (main pollinator in North America) (Young, 2002; Castillo et al., 2014), the behavioural responses of the former species to variation in the quality of its host plant were considered to overlap sufficiently with responses of the latter species.

Statistical analyses

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All statistical analyses were performed in R v4.0.3 (R Development Core Team, 2020) with (generalised) linear mixed effects models (LMMs: R-package lme4 v1.1–23, Bates et al., 2014, GLMMs: R-package glmmTMB v1.0.2.1, Brooks et al., 2017). Models for responses reflecting spatial flower traits, floral scent, colour, and rewards included the predictors breeding treatment, sex, and origin, as well as all possible interactions among these factors. The latitudinal coordinate of the population origin was included as covariate in all models, whereas the exact age of the plant individuals (accounts for difference of 12 days in planting date) was included only in models for flower scent, which was acquired in early phases of the experiment. Both covariates were centred and scaled (i.e., subtraction of mean and division by standard deviation). The random effects for floral trait models were population, affiliation of paternal plant in P-generation to field collected family nested within population, and affiliation of maternal plant in P-generation to field collected family nested within population. Models for pollinator visitation rates included the predictors breeding treatment, sex, and origin, as well as all possible interactions among them, the covariate daytime (centred and scaled), and the random effects of plot and trail (latitude of population origin, population, maternal and paternal affiliation not included, since data were averaged on plot level, see Pollinator visitation rates section). Several of the described models included count data responses with an access of zeroes (intensities of lilac aldehydes and pollinator visitation rates). These models were additionally fitted with zero inflation formulas. The fit of lilac aldehydes models was best when including only an intercept model for zero inflation, whereas the fit of pollinator visitation rate models was best when including the same predictors and random effects in the conditional and zero inflation part of the model.

All of the described models (Table 1) were validated based on checking plots (quantile-quantile, residual versus fitted) and tests provided in the R-package DHARMa v0.3.3.0 (Hartig, 2020). Sum-to-zero contrasts were set on all factors for the calculation of type III ANOVA tables based on Wald χ² tests (R-package: car v3.0–10, Fox and Weisberg, 2018). If origin, breeding treatment and/or sex were involved in significant interactions, we calculated post hoc contrasts on the estimated marginal means of their levels within levels of other factors involved in the respective interaction (R-package: emmeans v1.5.1, Lenth, 2020). Variance components were extracted from all models using the R-package insight (Lüdecke et al., 2019) and are summarised in Figure 2—figure supplement 1. Multivariate statistical analyses of the full VOC dataset are summarised in Figure 2—figure supplement 2.

Results

Floral traits

Spatial flower traits of S. latifolia varied pronouncedly between plants of different breeding treatments, sexes, and population origins (Table 2). Synflorescences of inbreds had lower maximal height above ground than those of outbreds (p<0.001, χ²(1DF)=37.31, Figure 2a). Flower number (Figure 2b) was higher in plants from North America than Europe (p=0.005, χ²(1DF)=8.01) and additionally depended on the interaction breeding treatment × sex (p=0.003, χ²(1DF)=8.99). Inbred plants generally produced fewer flowers than outbreds, and this effect was more severe in females (35% reduced by inbreeding, ppost <0.001) than males (12% reduced by inbreeding, ppost = 0.011). The number of flowers produced was lower in male than female plants in both inbreds (78% reduced in females, ppost <0.001) and outbreds (71% reduced in females, ppost <0.001). The area of petal limbs (Figure 2c) was smaller in female than male plants (p<0.001, χ²(1DF)=51.35) and reduced by inbreeding (p=0.002, χ²(1DF)=9.25). The expansion of the corolla depended on the interaction breeding treatment × sex (p=0.004, χ²(1DF)=8.17). Inbreeding reduced corolla expansion in females by 17% (ppost <0.001) but had no effect in male plants, and differences between sexes in corolla expansion were consequently apparent in inbreds (23% lower in females than males, ppost <0.001) but not in outbreds (Figure 2d). Corolla expansion additionally depended on the interaction sex × origin (p=0.009, χ²(1DF)=6.86). It was lower in female than male plants in populations originating from North America only (23% lower in females, ppost <0.001).

Figure 2 with 2 supplements see all
Effects of breeding treatment, sex, and origin on spatial flower traits (a–d), floral scent traits (e–f), flower colour as perceived by crepuscular moths (g–h), and floral rewards in Silene latifolia.

Graphs show estimated marginal means and standard errors for outbred (Ou, filled bars) and inbred (In, open bars), female (Fe, red bars) and male (Ma, blue bars) plants from Europe (Eu, dark coloured bars) and the North America (Na, bright coloured bars). Estimates were extracted from (generalised) linear mixed effects models for significant interaction effects and main effects of factors not involved in an interaction (significance levels based on Wald χ² tests denoted at top of plot). Interaction effect plots additionally indicate significant differences among breeding treatments, sexes, or origins within levels of other factors involved in the respective interaction (estimated based on post hoc comparisons, denoted within plots). Exact sample sizes for all traits are listed in Table 1. Significance levels: ***p<0.001, **p<0.01, *p<0.05, •p<0.06.

Table 2
Overview and results of statistical analyses with (generalised) linear mixed effects models.

The table summarises the model types and error distributions used for each of the responses (printed in subscript), the parameter estimates on the link function scale with significance levels assessed based on Wald χ² tests for all fixed effects (***p<0.001, **p<0.01, and *p<0.05 printed in bold), and random effect variances (printed in italic). For zero inflated responses, estimates from the conditional model parts appear in the first line and estimates from zero inflation model parts in the second line. All listed fixed effects consume 1 degree of freedom.

InterceptBtmt
[outbred –inbred]
Sex
[female –male]
Origin
[Europe –US]
Btmt × sexBtmt × originSex × originBtmt × sex × originLatitudePlant ageObs. timePopPop: motherPop : fatherPlotObs. trial
Spatial flower traits
Synflorescence heightLMM(G)81.03 3.56***−0.40NS1.840.670.111.07Ns−0.151.00Nt.Nt. 5.42 16.76 0.00Nt.Nt.
No. flowersGLMM(NBQ)2.61 0.14***−0.70***−0.25**0.08**−0.040.010.010.12Nt.Nt. 0.03 0.02 0.02Nt.Nt.
Petal limb areaLMM(G)2.76 0.16**−0.38***0.070.060.040.100.030.19Nt.Nt. 0.15 0.08 0.00Nt.Nt.
Corolla expansionLMM(G)5.21 0.21**−0.44***0.33 0.23**0.02 0.21**0.110.06Nt.Nt. 0.290.30 0.00Nt.Nt.
Flower scent traits
Shannon index VOCLMM(G)1.860.03−0.13*−0.060.010.040.020.010.03−0.02*Nt.0.010.000.00Nt.Nt.
Lilac aldehyde AZI-GLMM(NBQ)15.110.02−0.060.020.01−0.08−0.04−0.16*0.01−0.08Nt.0.010.000.00Nt.Nt.
−1.93***Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.
Lilac aldehyde B/CZI-GLMM(NBL)15.78−0.08−0.090.120.06−0.10−0.01−0.03−0.09−0.03Nt.0.010.000.03Nt.Nt.
−1.93***Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.
Lilac aldehyde DZI-GLMM(NBQ)13.70−0.050.14−0.07−0.09−0.16−0.06−0.080.08−0.17Nt.0.002.750.47Nt.Nt.
0.02NsNt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.Nt.
Flower colour
Reflectance UVLMM(G)2.04−0.05−0.27***0.050.040.08Ns−0.040.020.01Nt.Nt.0.010.030.01Nt.Nt.
Reflectance blueLMM(G)26.72−0.02−0.95***0.100.240.050.210.14Ns0.47Nt.Nt.0.470.850.00Nt.Nt.
Reflectance greenLMM(G)39.87−0.10−0.190.400.260.170.120.31Ns0.05Nt.Nt.1.120.370.07Nt.Nt.
Floral rewards
Excreted sugarLMM(G)5.700.03−0.18***−0.080.04−0.02−0.020.040.04Nt.Nt.0.070.000.00Nt.Nt.
Pollinator visitation
No. plant visitsZI-GLMM(P)0.280.09 −0.67***0.090.22−0.20−0.16−0.29*Nt.Nt.−0.11*Nt.Nt.Nt.0.270.15
−0.48Ns0.050.29−0.21−0.11−0.110.10−0.46*Nt.Nt.0.10Nt.Nt.Nt.0.140.44
No. flowers visitedZI-GLMM(P)0.790.04−0.56***−0.03 0.34*−0.23−0.22−0.46**Nt.Nt. 0.19***Nt.Nt.Nt.0.680.23
−0.09Ns−0.04 0.47**−0.23−0.19−0.020.17−0.31*Nt.Nt. 0.47**Nt.Nt.Nt.0.160.47
  1. Abbreviations. Btmt: breeding treatment, LMM: linear mixed effects model, (NBQ): negative binomial distribution with quadratic parametrisation and log-link, (NBL): negative binomial distribution with linear parametrisation and log-link, No.: number, Nt.: not tested, GLMM: generalised linear mixed effects model, Obs: observation, (P): Poisson distribution with log-link, Pop: population, ZI-GLMM: zero inflation generalised mixed effects model, (G): Gaussian distribution with identity link.

Breeding treatment, sex, and population origin affected floral VOC in S. latifolia interactively (Table 2). The Shannon diversity of those VOC known to elicit antennal responses in H. bicruris depended on the interaction breeding treatment × origin (p=0.016, χ²(1DF)=5.83, Figure 2e). Inbreeding reduced the Shannon diversity of these VOC by 7% in European plants (ppost = 0.013) but had no significant effect on the Shannon diversity of the VOC in plants from North America. The intensity of lilac aldehyde A depended on the interaction breeding treatment × sex × origin in the conditional model (p=0.025, χ²(1DF)=5.03, Figure 2f). Post hoc comparisons yielded a marginally significant lower intensity of this compound in inbred than outbred females in plants from North America (41% reduced by inbreeding, ppost = 0.056) but no further differences occurred among other groups. Similar non-significant trends were observed for the other lilac aldehyde isomers (Supplementary file 1). Multivariate statistical analyses of 20 H. bicruris active VOC and all 70 VOC detected in S. latifolia revealed no clear separation of floral headspace VOC patterns for any of the treatments (Figure 2—figure supplement 2). In summary, the combined effects of breeding treatment, sex, and range on floral scent were rather weak.

The proportion of flower colour detectable for crepuscular moths and the sugar excreted as reward with nectar were independent of breeding treatment and population origin but exhibited differences between plants of different sex (Table 2). Male flowers reflected more light in the spectrum detectable by the UV receptor (350 nm) (p<0.001, χ²(1DF)=41.92, Figure 2g) and the blue receptor (440 nm) (p<0.001, χ²(1DF)=39.59, Figure 2h) of moths than flowers of females. Likewise, the amount of sugar excreted with nectar was higher in male than female plants (p<0.001, χ²(1DF)=14.16, Figure 2i).

Pollinator visitation rates

The number of pollinator visits per plant by moths was shaped by the interaction breeding treatment × sex × origin in the conditional model (p=0.016, χ²(1DF)=5.84, Table 2, Figure 3a). Post hoc comparisons yielded that plant visits were reduced by 79% following inbreeding in female plants from North America (ppost = 0.007), but unaffected by inbreeding in European females and males from both origins. Moreover, plant visits were fewer in female than male plants in European outbreds (83% fewer in females, ppost < 0.001), European inbreds (78% fewer in females, ppost = 0.001), and North American inbreds (87% fewer in females, ppost < 0.001) as well as 77% lower in plants from Europe than North America in outbred females (ppost = 0.014). The number of flowers approached per plant visit was likewise shaped by the interaction breeding treatment × sex × origin in the conditional model (p=0.001, χ²(1DF)=10.61, Figure 3b, Table 2). Post hoc comparisons yielded that flower visits were 88% lower for inbred than outbred females (ppost = 0.001) but 64% higher for inbred than outbred males (ppost = 0.031) in plant populations from North America, whereas flower visits were unaffected by inbreeding in European male and female plants. Moreover, flower visits were reduced in females relative to males for European outbred plants (83% reduction in females, ppost = 0.003), European inbreds (73% reduction in females, ppost = 0.027), and North American inbreds (90% reduced in females, ppost = 0.002) but 59% higher in females than males in outbreds from North America (ppost < 0.001). Finally, flower visits were higher in North American than European outbred female plants (ppost = 0.001) but lower in European than North American outbred males (ppost = 0.001). Both the number of plant and flower visits depended on the interaction of breeding treatment × sex × origin in the zero inflation part of the model as well (Table 2, Figure 3—figure supplement 1). The direction and magnitude of these effects did not contrast with the conditional models.

Figure 3 with 1 supplement see all
Effects of breeding treatment, sex, and origin on pollinator visitation rates in Silene latifolia.

Graphs show estimated marginal means and standard errors for outbred (Ou, filled bars) and inbred (In, open bars), female (Fe, red bars) and male (Ma, blue bars) plants from Europe (Eu, dark coloured bars) and North America (Us, bright coloured bars). Estimates were extracted for significant interaction effects from the conditional part of generalised linear mixed effects models (significance levels based on Wald χ² -tests denoted at top of plot). Plots additionally indicate significant differences between breeding treatments, sexes, or origins within levels of other factors involved in the respective interaction (estimated based on post -hoc comparisons, denoted within plots). Exact sample sizes for all traits are listed in Table 1. Significance levels: ***p<0.001, **p<0.01, and *p<0.05.

Discussion

Using an integrated methodological approach, we observed that (i) inbreeding compromises several flower traits in S. latifolia. The magnitude of these effects depended partially on (ii) plant sex, which demonstrates that the intrinsic biological differences between males and females shape the consequences of inbreeding in dioecious plant species as they are filtered through the selective environment. Inbreeding effects also depended on (iii) origin in a way indicating that divergent evolutionary histories have shaped the underlying genetic architecture. Finally, our study showed that (iv) the effects of inbreeding, sex, and origin on pollinator visitation rates specifically mirrored variation in floral scent, which yields interesting insight into the relative importance of different floral traits in shaping the behaviour of crepuscular moths.

Inbreeding compromises floral traits

In partial accordance with our first hypothesis, inbreeding compromised several, but not all floral traits in S. latifolia. Spatial flower traits suffered most strongly from inbreeding in males and females from both origins (Figure 2a–c). These results are in line with previous studies on hermaphroditic, self-compatible species (Ivey and Carr, 2005; Glaettli and Goudet, 2006) and support that the complex genetic architecture underlying such traits (Feng et al., 2019) gives rise to dominance and over-dominance effects at multiple loci. The chemodiversity and abundance of floral VOC involved in communication with H. bicruris moths was reduced in a sex- and origin-specific manner in inbred relative to outbred S. latifolia (Figure 2e–g), while the full floral scent profile exhibited no differences among inbreds and outbreds (Figure 2—figure supplement 2). So far, lower emissions of floral VOC in inbreds have been reported for only few plant species pollinated by diurnal generalists (Ferrari et al., 2006; Haber et al., 2019; Kariyat et al., 2021). Our study revealed such effects for plants pollinated by specialist moths that use scent as a major cue for plant location (Riffell and Alarcón, 2013). Dominance and over-dominance may either have directly interfered with genes involved in VOC synthesis and their regulation in S. latifolia or unfolded their effects by disrupting physiological homoeostasis and thereby inducing intrinsic stress that came at the cost of scent production (Kristensen et al., 2010; Fox and Reed, 2011). A recent study indicates that the effects of inbreeding on the diversity of floral VOC in our study may even have been underestimated. Kergunteuil et al., 2021 demonstrated that porous polymers may differ in their affinity with specific VOC and hence in their sensitivity in recording variation in VOC diversity entailing blind spots. They recommend a shift in practice from the use of single to multiple porous polymers (e.g., a combination of PDMS and Poropak Q) for VOC collection in future plant ecological studies, which may uncover the full impact of plant inbreeding on the composition of floral volatiles.

In contrast to spatial flower traits and scent, flower colour and the total amount of sugar excreted with nectar exhibited no differences among inbreds and outbreds in S. latifolia (Table 2). Flower colour is a trait that has, to our knowledge, not yet been studied in the context of inbreeding, despite its crucial role in flower identification and localisation (Garcia et al., 2019). Our data suggest that the flower colour perceived by moths is not altered by inbreeding and generally seems to be a conserved trait in S. latifolia. Other species with high intraspecific variation in flower colour may be ideal models to further examine the relationship with inbreeding in the future by combining visual modelling with choice experiments (Kelber et al., 2003). The independence of sugar excretion from inbreeding in S. latifolia indicates that the strong reduction of flower number in inbreds allows compensating the quality of floral rewards via a resource allocation trade-off.

Overall, the observed inbreeding effects on floral traits were partially small and variable in their magnitude as compared to previous investigations. However, our findings highlight that even weak degrees of biparental inbreeding (i.e., one generation sib-mating) can result in an impairment of multiple flower traits that is detectable against the background of natural variation among multiple plant populations from a broad geographic region. This observation indirectly supports that the selfing syndrome (i.e., smaller, less scented flowers observed in selfing relative to outcrossing populations of hermaphroditic plant species) may not merely be a result of natural selection against resource investment into floral traits, but also a direct negative consequence of inbreeding (Andersson, 2012). Most importantly, we observed that variation in inbreeding effects was consistent in its dependency on plant sex, which gives insight into the role of intrinsic biological differences between males and females in the expression of inbreeding depression.

The cost of inbreeding for floral traits is higher in females than males

Males outperformed females in all floral traits, except scent production (Figure 2). As such, our study confirmed previously observed sexual dimorphisms in S. latifolia (nectar: Gehring et al., 2004; flower number: Delph et al., 2010) but also yielded contradicting results. As opposed to Delph et al., 2010, we observed larger instead of smaller flowers in males. This may base on the use of a size estimate that accounts for variation in flower shape or the comparably large geographic range and higher number of populations covered by our study. Moreover, we discovered a novel sexually dimorphic trait in the colour appearance of S. latifolia to crepuscular moths in the UV and blue light spectrum (Figure 2g–h). Given that moths use blue light as a major cue to start feeding on nectar (Cutler et al., 1995), the lower light reflectance observed for female flowers is another trait rendering them less attractive than males.

The evolution of lower female attractiveness to pollinators is driven by sex-specific resource allocation, that is, high costs for production of ovaries and seeds may restrict allocation to floral traits (Moore and Pannell, 2011; Barrett and Hough, 2013). This process may also explain the larger magnitude of inbreeding effects in female plants of S. latifolia, which we observed in accordance with our second hypothesis (Figure 2b,d). High reproductive expenditure in females may increase the frequency and intensity of resource depletion stress (e.g., drought) under field conditions (Obeso, 2002; Li et al., 2004; Zhang et al., 2010). Consequently, females may suffer disproportionally from inbreeding when dominance and over-dominance affect loci mediating resistance to such stress (Fox and Reed, 2011). The assumption that sex-specific viability selection plays a role in the expression of inbreeding depression seems likely for S. latifolia. Previous research elaborated that females of this species experience resource depletion stress more often than males in the late growing season during fruit maturation (Gehring and Monson, 1994) and that inbreeding reduces resistance to environmental stress (Schrieber et al., 2019a; Schrieber et al., 2019b). Another non-exclusive explanation for the different inbreeding effects on female versus male S. latifolia may be found in sexual selection. Compared to females, the reproductive success of males is more limited by the availability of mates than by the availability of resources, which results in selection for increased attractiveness to pollinators (Moore and Pannell, 2011; Barrett and Hough, 2013). Competition for increased siring success among male plant individuals may create strong selection pressures that could rapidly purge deleterious recessive mutations in genes directly linked to attractiveness of male flowers to pollinators. Finally, a proportion of sex-specific inbreeding effects may be attributed to differential gene expression. S. latifolia harbours numerous genes with alleles that affect male and female fitness in opposite directions. These sexually antagonistic genes are partly subsumed in non-recombining regions of gonosomes (Scotti and Delph, 2006). Those located at the X-chromosome are always effectively dominant in males (XY) but may be recessive and therefore contribute to inbreeding depression in females (XX). The remaining fraction of sexually antagonistic genes is located at autosomal regions but exhibits sex-specific expression as controlled by the gonosomes (Scotti and Delph, 2006). These genes may exhibit systematic differences in the abundance and effect magnitudes of deleterious recessive alleles between males and females, thus contributing to sex-specific inbreeding effects.

Not only floral traits but also plant viability may exhibit sex-specific inbreeding depression in dioecious species. This could result in deviations from optimal sex ratio and, consequently, reductions of effective population sizes that accelerate local extinctions under global change (Hultine et al., 2016; Rosche et al., 2018). Future studies should aim at disentangling the relative contribution of sex-specific selection and gene expression to differences in the magnitude of inbreeding depression between males and females and at assessing their feedback on sex ratios to predict and manage these specific threats.

Evolutionary history shapes the genetic architecture underlying inbreeding effects

Plants exhibited a general difference among geographic origins in merely one floral trait (Figure 2b). Indeed, we had not expected broad differences in floral traits among European and North American S. latifolia plants (i.e., significant main effects of origin). A sufficient overlap in the composition of pollinator communities (H. ectypa replaces H. bicruris in the invaded range, Castillo et al., 2014) and appropriate pre-adaptations in floral traits were probably essential for S. latifolia as an obligate outcrossing plant species to successfully colonise North America. As discussed in detail in previous studies, higher flower numbers in North American S. latifolia (Figure 2b) may result from changes in the selective regimes for numerous abiotic factors (Keller et al., 2009) or from the release of seed predation. As opposed to H. bicruris, H. ectypa pollinates North American S. latifolia without incurring costs for seed predation, which may result in the evolution of higher flower numbers, specifically in female plants (Elzinga and Bernasconi, 2009).

While adaptive differentiation among S. latifolia populations from different origins was not in the focus of this study, we hypothesised that North American populations purged genetic load linked to floral traits during the colonisation process (i.e., interaction breeding treatment × origin). In contrast to hypothesis iii, the magnitude of inbreeding effects was not consistently higher in European than North American populations. Instead, it was independent of origin for most floral traits, except flower scent, and either higher or lower in European plants for different scent traits (Figure 2f–g). These findings provide no support for recent purging events in North American populations. They rather add to evidence that the magnitude of inbreeding effects is highly specific for the traits as well as the populations or population groups under investigation (e.g., Escobar et al., 2008; Angeloni et al., 2011). This specifity roots in the composition of gene loci affected by dominance and over-dominance and is determined by the complex interplay of demographic population histories (i.e., size retractions and expansions, genetic drift, isolation, gene flow) and the selective environment (Charlesworth and Willis, 2009). As such, the precise mechanisms underlying variation in inbreeding effects on different scent traits across population origins of S. latifolia can only be explored based on comprehensive genomic resources, which are currently not available. Future studies should also incorporate field data on the abundance of specialist pollinators and extend the focus from variation in the magnitude of inbreeding effects among geographic origins to variation among populations within geographic origins and individuals within populations. This would allow a detailed quantification of geographic variation in inbreeding effects and elaborating on the causes and ecological consequences of such variation (Thompson, 2005; Schrieber and Lachmuth, 2017; Thompson et al., 2017).

Inbreeding effects on floral traits cause limited feedback on pollinator visitation rates

Pollinator visitation rates partially mirrored the above-discussed variation in flower traits. They depended on the breeding treatment in a highly sex- and origin-specific manner: In North American populations, inbred females received significantly fewer plant and flower visits than outbreds, whereas flower visits were higher in inbred than outbred males (Figure 3). We conclude that the severe inbreeding effects on spatial flower traits alone do not necessarily reduce moth visitation rates because these effects were observed for both plant sexes and origins (Figure 2a,b,c). A floral trait that was negatively affected by inbreeding only in North American female plants, just like pollinator visitation rates, was the abundance of lilac aldehyde A (Figure 2f). The other lilac aldehyde isomers exhibited similar but non-significant trends (Supplementary file 1). Although these findings provide limited support for our fourth hypothesis, they yield interesting insight into the relative importance of floral traits for the behaviour of a lepidopteran specialist pollinator.

The seemingly low importance of inbreeding effects on spatial flower traits for pollinator visitation rates may be explained with the limited visual system of nocturnal insects (van der Kooi et al., 2021Sondhi et al., 2021). Moths can likely perceive differences in the spatial arrangement of flowers on inbred versus outbred plants only when they have already approached close to them (Barnett et al., 2018). Our setup for pollinator observations had relatively large distances among replicate inbred and outbred plots (Figure 1—figure supplement 8) and hence may have not enabled a choice based on spatial flower traits by the moths. In contrast, differences in scent cues should be perceived across large distances. The antennae of H. bicruris can detect slight differences in lilac aldehyde concentrations at very low dosages and the compounds elicit oriented flight and landing responses in the moth more than any other VOC in the scent of S. latifolia flowers (Dötterl et al., 2006). Consequently, a low lilac aldehyde abundance may have resulted in a low attraction of moths to North American inbred female plants from the distance in our experiment. The non-significant trend for higher abundances of lilac aldehydes in inbred than outbred males from North America (Supplementary file 1) could also explain the corresponding variation observed in flower visitation rates. However, bioassays under more controlled conditions are needed to further evaluate a mechanistic relationship among pollinator visitation and intensities of lilac aldehydes.

In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2019a), it affects plant interactions with pollinators by altering flower chemistry. Our observations are in line with studies on other plant species (Ivey and Carr, 2005; Kariyat et al., 2012; Kariyat et al., 2021) and highlight that inbreeding has the potential to reset the equilibrium of species interactions by altering functional traits that have developed in a long history of co-evolution. These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change. As such, our study adds to a growing body of literature supporting the need to maintain or restore sufficient genetic diversity in plant populations during conservation programs.

Data availability

All Data supporting this article have been deposited in Dryad. The code for all statistical analyses presented in this manuscript is deposited in Zenedo.

The following data sets were generated
    1. Schrieber K
    2. Paul SC
    3. Höche LV
    4. Salas AC
    5. Didszun R
    6. Mößnang J
    7. Müller C
    8. Erfmeier A
    9. Eilers EJ
    (2021) Dryad Digital Repository
    Data: Inbreeding in a dioecious plant has sex- and population origin-specific effects on its interactions with pollinators.
    https://doi.org/10.5061/dryad.612jm643d
    1. Schrieber K
    2. Paul SC
    3. Höche LV
    4. Salas AC
    5. Didszun R
    6. Mößnang J
    7. Müller C
    8. Erfmeier A
    9. Eilers EJ
    (2021) Zenodo
    Data: Inbreeding in a dioecious plant has sex- and population origin-specific effects on its interactions with pollinators.
    https://doi.org/10.5281/zenodo.4746164

References

  1. Book
    1. Adams RP
    (2007)
    Identification of Essential Oil Components by Gas Chromatography-Mass Spectrometry (4th Ed)
    Illinois, United States: Allured Publishing Corporation.
    1. Dafni A
    2. Lehrer M
    3. Kevan PG
    (1997) Spatial flower parameters and insect spatial vision
    Biological Reviews of the Cambridge Philosophical Society 72:239–282.
    https://doi.org/10.1017/S0006323196005002
  2. Report
    1. El-Sayed AM
    (2011)
    The Pherobase: Database of Insect Pheromones and Semiochemicals
    HortResearch Linc.
  3. Book
    1. Fox J
    2. Weisberg S
    (2018)
    An R Companion to Applied Regression
    California, United States: SAGE Publications.
    1. Gehring JL
    2. Parsons M
    3. Delph LF
    (2004)
    Whole-plant investment in nectar is greater for males than pollinated females in the dioecious plant Silene latifolia
    Evolutionary Ecology Research 6:1237–1252.
  4. Book
    1. Gill RJ
    2. Baldock KCR
    3. Brown MJF
    4. Cresswell JE
    5. Dicks L
    6. Fountain MT
    (2016) Chapter Four - Protecting an Ecosystem Service: Approaches to Understanding and Mitigating Threats to Wild Insect Pollinators
    In: Woodward G, Bohan D. A, editors. Advances in Ecological Research Ecosystem Services: From Biodiversity to Society. Academic Press. pp. 135–206.
    https://doi.org/10.1016/bs.aecr.2015.10.007
  5. Software
    1. Hartig F
    (2020) DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models
    DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models.
  6. Software
    1. Lenth R
    (2020) Emmeans: Estimated Marginal Means, Aka Least-Squares Means
    Emmeans: Estimated Marginal Means, Aka Least-Squares Means.
  7. Software
    1. R Development Core Team
    (2020) R: A language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.
  8. Book
    1. Thompson JN
    (2005)
    The Geographic Mosaic of Coevolution
    Illinois, United States: University of Chicago Press.

Decision letter

  1. Meredith C Aldrich
    Senior Editor; The University of Zurich, Switzerland
  2. Youngsung Joo
    Reviewing Editor; Chungbuk National University, Republic of Korea

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below.

Thank you for submitting your article "Sex and origin-specific inbreeding effects on flower attractiveness to specialised pollinators" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Meredith Schuman as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. A recent study has addressed some of the questions detailed in the manuscript. So, the introduction needs to be tweaked to reflect this.

2. The authors found stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin. However, those plants were only tested in non-native habitat. As the main pollinator is also a seed predator, the Na populations could be released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. This issue should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

3. Please revise the title according to the comments from the reviewers.

Reviewer #1 (Recommendations for the authors):

1. Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is lacking.

This statement is no longer correct. Please refer https://bsapubs.onlinelibrary.wiley.com/doi/abs/10.1002/ajb2.1594 where the authors show flower size, pollen content, floral volatiles and floral visits are affected by inbreeding. The introduction needs to be reframed adding this and any additional notes on the novelty if needed.

2. Diurnal and nocturnal visitors and their rewards, are VOC's different during the day? There seems to be no explanation for this.

3. Line 111: This is a bit unclear; are the female floral volatiles oviposition attractants? rather than a typical floral scent?

Again, in line 189 authors discuss EAG data, but not clear whether it is from male or female moths, especially since female moths may have been using these compounds as oviposition cues.

I understand that this is not directly relevant to the current study, but it is indeed an important part of this system and should be discussed.

4. 143: What are these biological pest control agents, and for what reason they were used. There is a need to explain this since floral and foliar chemistry can be affected (differentially due to inbreeding) due to any foliar or floral herbivory. I have to say that I am surprised that such a treatment has been carried out before chemistry experiments.

5. 154: Floral volatiles are collected on a per flower basis, however, there is a huge variation for size and possibly flower mass among the treatments. The authors should explain how that would not have affected their results?

6. While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example., there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. While this is irrelevant to some of the traits studied in this manuscript, it does matter for floral scent. While the floral scent is usually semi-distinct from foliar volatiles (Baldwin group, Raguso group), the volatiles also shares structural and functional similarities. The authors I think, should address how these effects might be either additive or antagonistic in nature.

7. 265: If each flower for scent extraction was of the same age (4th day) irrespective of the age of the plant (12-day variation), is it necessary to add that into the model for scent analyses in statistics?

Reviewer #2 (Recommendations for the authors):

Below, please find my detailed comments in the order of appearance:

Title (and throughout the manuscript): I am not sure about the term floral attractiveness. Why not "floral traits" or "pollination-linked traits" or something along these lines? You did measure floral attractiveness to pollinators, and for most traits the reduction of the floral attractive traits did not really matter for the pollinator (and Hadena seed predator?) visitation rates.

Line 24. It is unclear here both what you mean about plant attractiveness and how you mean that it may be affected by inbreeding. I think you need to establish why you think that inbreeding may play a role already here, and that you expect that traits that are involved in pollinator attraction may be negatively affected by inbreeding by citing previous work (e.g. Andersson 2012 Am J Bot).

Line 33: This sentence is a bit unclear, because you connect (Darwinian) fitness, which is a relative measure varying among individuals within a population, and inbreeding depression, which most often is used as a population measure. I wonder if instead of fitness you are focusing on e.g. population viability?

Line 43: In many cases it is unclear at what distance the floral scent is actually detected and utilized by the pollinators (cf. examples of e.g. scent nectar guides, scented nectar etc.).

Line 57-58: I think that it would be better to introduce here the plant examples where inbreeding may affect plant traits, and then make a case why it is important to study the impact of inbreeding on floral traits rather than just justifying the study by saying that it has not been done before.

Line 114: I would add a "may" between "H. biuris" and "overcompensate", because the outcome of the Silene-Hadena interaction may very likely depend on the presence (or absence) of alternative, less costly, pollinator species.

Line 135 and 269-273: Why was the factor "year" not included in these models? Would it impact the results?

Line 147. The height and number of was scored three times. Once, then again after two weeks and then again after six weeks from the first scoring? Or once, then again after two weeks and then again after six weeks after the second scoring? Please clarify.

Line 155-190. I am a bit puzzled by the floral scent collections and analyses. Usually, static collection with PMDS absorbents is used qualitatively rather than quantitatively, and in the methods it is mentioned that the compounds were not quantified but that the relative intensity of the different VOC peaks was compared. Hence, you focused on floral scent composition. This is fine, but then in the analyses of the lilac aldehydes (e.g. F), I understand it as you measure the absolute number of counts from the chromatograms.

Hence, I wonder (i) were these data stilled analysed quantitatively, and, if so, how did you validate that the number of counts was comparable across samples? The traditional method is to use dynamic headspace sampling, and then add a standard volume of a known substance to account for e.g. variable GC-MS sensitivity. Alternatively, (ii) this measure is relative (as indicated by the text and the y-axis label in the figure), but then I would like a better description of how this variable was calculated. For example, if using proportions, two samples could emit the exact same amount of the particular compound, but the proportional contribution of the same compound each overall scent bouquet could be very different, if one of the two samples include large amounts also of other scent compounds. Under such a scenario, it would be misleading to use the relative contribution of one single floral scent compound to try to predict pollinator behaviour. I apologize if I have missed something obvious here, but if so, it still makes sense to clarify how these data were obtained and what may be their limitation.

Line 335-344: Again, I was confused by the term floral attractiveness, since most traits were not that important for pollinator visitation rates. This was confusing throughout the discussion.

Line 347: Also "spatial attractiveness" is a bit misleading. Why not talk about the actual traits?

Line 445-477: I like the discussion about how the inbreeding effects on most traits, with the potential exception of floral scent, did not result in a reduced pollinator visitation rate. I think, however, that there is room for a deeper discussion here. These other traits, are they not important for pollinator attraction at all? Or were they not reduced to a large enough extent to affect pollinator visitation? Also, considering the impressive amount of data produced and the numerous predictor variables, how well can we trust that the floral scent reduction is indeed not just a spurious correlation? This is particularly relevant in the light of the comment above about whether or not we can be sure that you have detected variation in "aldehyde concentrations" (line 460). I was pondering whether there was an additional way to analyze these data by comparing effect sizes (trait variation between inbred and outcrossed plants of the same category) and ask how well these explained the variation in effect size in pollinator visitation rate between inbred and outcrossed plants of the same categories. I could not come up with a straightforward way to do so, so I realize the problem of drawing too strong conclusions based on a single data point (a slight reduction in aldehyde concentration in inbred females from North America and a corresponding reduced visitation rate on inbred North American females). I think that one important take-home message here is the lack of trait-based explanations for pollinator visitation rates (which further emphasizes the need to avoid using "floral attractiveness").

Figure S8: I wonder if there is a mistake here, because the two plots in the left panel are identical.

Finally, I wanted to reiterate the potential to study variation also at smaller scale. How consistent were the trait changes among populations from the same continent. Is it reasonable to lump these into the same category and to compare among continents. It would be very interesting to see some variance partitioning.

Reviewer #3 (Recommendations for the authors):

Line 2: The first sentence of the abstract is difficult to follow, consider rephrasing. In addition, while I would definitely agree that looking at effects of inbreeding on flowers in dioecious plants is most interesting and important because of the obligate outcrossing, this study does not explore whether these effects are "particularly fatal in dioecious" as there is no comparison with a hermaphroditic or monoecious plant.

I found the term spatial attractiveness (first encounter on line 8) quite confusing (later on also spatial floral traits). Wouldn't it simply be visual attractiveness? Of course this would not be an appropriate umbrella term considering that you also include number of flowers and inflorescence height.

Considering flower number only as a component of floral attractiveness is not taking full advantage of the measure. This could also be considered an important predictor of fitness and thus quite separate from the other floral morphological traits. Same could be, to some degree, said for inflorescence height, which I assume simultaneously means plant height?

In any case, I would like the authors to reconsider calling this group of floral traits "spatial".

Line 31: Change raises to increases.

Line 91: Be more specific about what you mean by spatial traits or rethink the term altogether.

Line 114: bicruris.

Line 124: Are the inbred crosses between full-sibs or half-sibs? Are seeds of one capsule always sired by the same father, and if so, were the plants from each capsule kept separate? What would be the resulting F of the inbred progeny? also correct this on line 372 if they are half-sibs.

Line 167: Check the time, does not make sense with the 8h reported in the table.

Line 285: or S8?

Line 289: Height of inflorescence was lower? This sounds like you mean the length of the corolla.

Line 293: Perhaps it would be better to report effect sizes or percent changes here. Both differences are statistically significant, and a difference in two <0.05 p-values is not really an appropriate way of reporting a difference in the effect.

Line 303: It is somewhat misleading to call this composition of floral VOCs when in reality you tested diversity and three individual compounds our of 70.

Line 404: Why would purging only affect male flowers?

Line 449: "visits were higher in inbred males." Compared to inbred females or outcrossed males?

Table 2. Ns's could be removed for clarity, a simple absence of asterisks already indicates a non-significant effect. Add degreed of freedom.

https://doi.org/10.7554/eLife.65610.sa1

Author response

Essential revisions:

1. A recent study has addressed some of the questions detailed in the manuscript. So, the introduction needs to be tweaked to reflect this.

We adjusted the writing in the introduction and the discussion accordingly. Introduction pp 4-5, ll 48-54:

“Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is restricted to a single study. [...] It is necessary to broaden such integrated methodological approaches to other plant-pollinator systems (e.g., nocturnal specialist pollinators) and further floral traits (i.e., flower colour).”

Discussion p 19, ll 535-542:

“In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. […] These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change.”

2. The authors found stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin. However, those plants were only tested in non-native habitat. As the main pollinator is also a seed predator, the Na populations could be released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. This issue should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

Indeed, North American populations are tested in their “away”-habitat only and the observed plant performance and pollinator visitation rates can thus provide no direct implications for their “home”-habitat. We state this now more clearly at pp 11-12, ll 283-285. However, our design is appropriate for investigating inbreeding effects on plant-pollinator interactions in multiple plant populations in a common environment. Given the close taxonomic relationship of H. bicruris (main pollinator in Europe) and H. ectypa (main pollinator in North America), the behavioural responses of the former species to variation in the quality of its host plant was considered to overlap sufficiently with responses of the latter species as outlined at pp 11-12, ll 285-291.

The hypothesis that North American (NA) S. latifolia evolved higher attractiveness to female Hadena moths because H. ectypa is not able to oviposit on female plants in contrast to H. bicruris is indeed a highly interesting one. However, as you have outlined correctly, our study is not designed to elaborate on questions related to adaptive evolutionary differentiation among North American and European plants. Instead of addressing this hypothesis based on our data, we thus take reference to previous studies in the discussion p 17, ll 482-487:

“As discussed in detail in previous studies, higher flower numbers in North American S. latifolia plants (Figure 1b) may result from changes in the selective regimes for numerous abiotic factors (Keller et al., 2009) or from the release of seed predation. As opposed to H. bicruris, H. ectypa pollinates North American S. latifolia without incurring costs for seed predation, which may result in the evolution of higher flower numbers, specifically in female plants (Elzinga and Bernasconi, 2009).”

3. Please revise the title according to the comments from the reviewers.

The title was changed to: “Inbreeding in a dioecious plant has sex- and population origin-specific effects on its interactions with pollinators”.

Reviewer #1 (Recommendations for the authors):

1. Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is lacking.

This statement is no longer correct. Please refer https://bsapubs.onlinelibrary.wiley.com/doi/abs/10.1002/ajb2.1594 where the authors show flower size, pollen content, floral volatiles and floral visits are affected by inbreeding. The introduction needs to be reframed adding this and any additional notes on the novelty if needed.

Thank you very much for bringing this excellent article to our attention! We adjusted the writing in the introduction and the discussion accordingly. Please consider that this article was first published at the 15th of January 21, while our manuscript was submitted at the 9th of January. Hence, we were not able to account for this study in the first submission. Introduction pp 4-5, ll 48-54:

“Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is restricted to a single study. [...] These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change.”

2. Diurnal and nocturnal visitors and their rewards, are VOC's different during the day? There seems to be no explanation for this.

Hadena bicruris and other nocturnal moths are the most efficient pollinators of S latifolia, and the pollination syndrome of the S. latifolia points clearly to nocturnal pollination (p 6, ll 106-109). We therefore focussed on nocturnal pollinators and their specific rewards, which is now stated more clearly in the methods section at p 7, ll 117-120:

“Various diurnal generalist pollinators as well as crepuscular moths visit S. latifolia flowers. The latter, including the specialist Hadena bicruris Hufn. (Lepidoptera: Noctuidae), were shown to be the most efficient pollinators for S. latifolia (Young, 2002), which is the reason why we exclusively focus on nocturnal pollination in our study.”

To keep the focus on what has been done, we do not describe the rewards for diurnal generalists. However, we now give more detail about the rewards for nocturnal pollinators already in the study species section at p 7, ll 120-121:

“All nocturnal pollinators are rewarded with nectar, while the specialist H. bicruris is additionally rewarded with oviposition sites.”

Moreover, we added more information on changes in scent emission during the day at p 6, ll 107-109:

“The flowers open from dusk till mid-morning to release a scent bouquet composed of more than 60 VOC, whereby emission peaks around dusk (Dötterl et al., 2005, 2009; Mamadalieva et al., 2014). During the daytime, no measurable floral scent is emitted (Dötterl et al. 2005).”

3. Line 111: This is a bit unclear; are the female floral volatiles oviposition attractants? rather than a typical floral scent?

Again, in line 189 authors discuss EAG data, but not clear whether it is from male or female moths, especially since female moths may have been using these compounds as oviposition cues.

I understand that this is not directly relevant to the current study, but it is indeed an important part of this system and should be discussed.

We clarified this issue at different occasions in the methods section. Previous studies (and our study) on S. latifolia have shown no clear differences in the quality of floral scent between sexes. However, one study found higher total emission of VOC in males, while others found no differences. Hence, females produce no specific VOC that are used as oviposition cues but may be differentiated from males by the total amount of emitted VOC and pronounced differences in spatial flower traits. We highlight this at p 6, ll 111-116:

Silene latifolia exhibits various sexual dimorphisms with male plants producing more and smaller flowers that excrete lower volumes of nectar with higher sugar concentrations as compared to females (Gehring et al., 2004; Delph et al., 2010). The quality of floral scent exhibits no clear sex-specific patterns, while male plants have been shown to emit higher or equal total amounts of VOC as compared to females in different studies (Dötterl and Jürgens 2005, Waelti et al. 2009)”.

Both male and female moths show pronounced behavioural responses to lilac aldehyde isomers and other VOC in the floral scent of S. latifolia (Dötterl et al., 2006). We therefore treated these VOC as typical floral scent compounds. We clarified this at p 7, ll 125-126:

“A substantial fraction of floral VOC produced by S. latifolia triggers antennal and behavioural responses in male and female H. bicruris moths (Dötterl et al., 2006).”

and p 9, ll 2010-218:

”For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al. (2006). […] Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al. 2006).”

4. 143: What are these biological pest control agents, and for what reason they were used. There is a need to explain this since floral and foliar chemistry can be affected (differentially due to inbreeding) due to any foliar or floral herbivory. I have to say that I am surprised that such a treatment has been carried out before chemistry experiments.

Thank you very much for highlighting this point! We used biological pest control agents in a preventive manner because S. latifolia is often infested by thrips and aphids under greenhouse conditions. The writing in the previous manuscript version was not clear with this regard and we changed the text at p 8, ll 157-161:

”Plants received water and fertilisation (UniversolGelb 12-30-12, Everris-Headquarters, NL) when necessary for the entire experimental period and were prophylactically treated with biological pest control agents under greenhouse conditions to prevent thrips (agent Amblyseius barkeri and Amblyseius cucumeris) and aphid (agent Chrysoperla carnea) infestation (Katz Biotech GmbH, GE).”

5. 154: Floral volatiles are collected on a per flower basis, however, there is a huge variation for size and possibly flower mass among the treatments. The authors should explain how that would not have affected their results?

Indeed, flower size and scent emission can be correlated. Although the question whether differences in scent emission were based on a difference in flower size is an interesting one, it seemed less relevant to us because it is unlikely that our pollinators correct their perception of a scent for the size of a flower (see also p 19, 520-526). We were rather interested in whether scent emission differs between the plant treatments and thus pollinators may chemically perceive such differences.

Moreover, we found it problematic to correct our models for flower size by including it as a covariate, which is the reason why we have not assessed this trait during scent collection. In this case, we would have corrected our scent responses for the effects of inbreeding, sex and population origin (i.e., the predictors we are interested in) because all of them determine the size of a flower (Figure 2 c,d). Hence, the inbreeding, sex and origin effects on flower scent would likely vanish. However, it is highly unlikely that the set of genes contributing to sex-, breeding treatment- and origin-based variation in flower size is exactly the same one that determines variation in scent emission per flower, which is basically the assumption underlying the model that includes flower size as a covariate. We critically mentioned the trade-off relationships and our reasoning to not correct for flower size at 9p ll 208-210:

“The intensities of VOC were not corrected for flower size because we wanted to capture all variation in scent emission that is relevant for the receiver i.e., the pollinator.”

6. While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example., there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. While this is irrelevant to some of the traits studied in this manuscript, it does matter for floral scent. While the floral scent is usually semi-distinct from foliar volatiles (Baldwin group, Raguso group), the volatiles also shares structural and functional similarities. The authors I think, should address how these effects might be either additive or antagonistic in nature.

We agree that this aspect is important and therefore addressed it in further detail in the introduction at p 4 ll 34-38:

“While it is well established that inbreeding can increase a plant’s susceptibility to herbivores by diminishing morphological and chemical defences (Campbell et al., 2013; Kariyat et al., 2012; Kalske et al., 2014), its effects on plant-pollinator interactions are less well understood. Inbreeding may reduce a plant’s attractiveness to pollinating insects by compromising the complex set of floral traits involved in interspecific communication.”

Since other referees suggested to rather tone down than increase the discussion based on floral scent results, we stick to the general feedback relationship among of herbivory and pollination, rather than relating it specifically to volatiles in the discussion at p 19, ll 535-544:

“In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. […] As such, our study adds to a growing body of literature supporting the need to maintain or restore sufficient genetic diversity in plant populations during conservation programs.”

7. 265: If each flower for scent extraction was of the same age (4th day) irrespective of the age of the plant (12-day variation), is it necessary to add that into the model for scent analyses in statistics?

Yes, this potentially confounding effect explained sufficient variation in one of our scent responses (Table 2, Shannon index) and substantially improved the model fit for lilac aldehydes (see r-script ll 562-818).

Reviewer #2 (Recommendations for the authors):

Below, please find my detailed comments in the order of appearance:

Title (and throughout the manuscript): I am not sure about the term floral attractiveness. Why not "floral traits" or "pollination-linked traits" or something along these lines? You did measure floral attractiveness to pollinators, and for most traits the reduction of the floral attractive traits did not really matter for the pollinator (and Hadena seed predator?) visitation rates.

We now avoid the term floral attractiveness throughout the manuscript and instead refer to “floral traits”.

Line 24. It is unclear here both what you mean about plant attractiveness and how you mean that it may be affected by inbreeding. I think you need to establish why you think that inbreeding may play a role already here, and that you expect that traits that are involved in pollinator attraction may be negatively affected by inbreeding by citing previous work (e.g. Andersson 2012 Am J Bot).

We rephrased the text at p 4, ll 24-29:

“Plant population retraction and isolation may also affect interactions with pollinators at the plant individual level by increasing inbreeding rates (Carr et al., 2014). […] Mechanistic insight into the effects of inbreeding on plant-pollinator interactions and intrinsic factors shaping the magnitude of such effects is limited but urgently required for the conservation of component species.”

Line 33: This sentence is a bit unclear, because you connect (Darwinian) fitness, which is a relative measure varying among individuals within a population, and inbreeding depression, which most often is used as a population measure. I wonder if instead of fitness you are focusing on e.g. population viability?

Indeed, we forgot to set outbred individuals as a reference, when writing about fitness declines in inbreds. We changed the sentence at p 4, ll 30-33 accordingly:

“This may enhance the phenotypic expression of deleterious recessive mutations (i.e., dominance) and reduce heterozygote advantage (i.e., over-dominance), which can result in severe declines of Darwinian fitness in inbred relative to outcrossed offspring (i.e., inbreeding depression) (Charlesworth and Willis, 2009).”

Inbreeding depression is defined and quantified as a reduction of fitness in inbred relative to outbred offspring and can be assessed on the family, population or meta-population level (Charlesworth and Willis, 2009).

Line 43: In many cases it is unclear at what distance the floral scent is actually detected and utilized by the pollinators (cf. examples of e.g. scent nectar guides, scented nectar etc.).

We removed the information in the parentheses at p 4, ll 46-48:

“These cues are particularly efficient in attracting pollinators across either long, medium or short distances (Dafni et al., 1997; Muhlemann et al., 2014) and act synergistically in determining visitation rates.”

Line 57-58: I think that it would be better to introduce here the plant examples where inbreeding may affect plant traits, and then make a case why it is important to study the impact of inbreeding on floral traits rather than just justifying the study by saying that it has not been done before.

Thank you very much for highlighting this passage! We revised the text to improve the reading flow at p 5, ll 66-70:

“If inbreeding effects on floral traits are more pronounced in female than male plants, the relative frequency of pollinator visits may be biased towards the latter sex, with devastating consequences for the effective size and persistence of populations. Studies on sex-specific inbreeding effects on floral traits are thus needed to improve the risk assessment for the conservation of dioecious plant species.”

Line 114: I would add a "may" between "H. biuris" and "overcompensate", because the outcome of the Silene-Hadena interaction may very likely depend on the presence (or absence) of alternative, less costly, pollinator species.

Done p 7, l 124.

Line 135 and 269-273: Why was the factor "year" not included in these models? Would it impact the results?

Due to the extensive number and extent of measurements, we needed 2 years to acquire the full set of considered traits. However, none of the traits was assessed in both 2019 and 2020. We clarified this at p 8, ll 149-151:

“Using these individuals, we assessed the combined effects of breeding treatment, plant sex and population origin on different flower traits and pollinator visitation rates over the summers 2019 and 2020”.

See also Table 1, p 31. Instead, some data (flower number, synflorescence height) were assessed thrice in 2019 to reduce confounding effects of differences in flowering phenology. Indeed, one possible way to analyse such repeated measure data is to include time point as a fixed factor and plant individual as a random factor. An alternative solution is averaging the data over time at the individual level and with this approach it is neither necessary nor possible to account for time point in the model. As stated at p 8, ll 164-167, we used averaged data:

“We determined the maximum height of synflorescences above ground level and the number of fully opened flowers per individual (Figure 1—figure supplement 4a). […] For statistical analyses, these data were averaged over the three time points at the individual level.”

Line 147. The height and number of was scored three times. Once, then again after two weeks and then again after six weeks from the first scoring? Or once, then again after two weeks and then again after six weeks after the second scoring? Please clarify.

It was scored once, then again after two weeks and then again after six weeks from the first scoring. We clarified the writing at p 8, ll 164-166:

“We determined the inflorescence height and the number of fully opened flowers per individual (Figure 1—figure supplement 4a). These traits were acquired thrice, in June, July, and August 2019 to account for phenological variation.”

Line 155-190. I am a bit puzzled by the floral scent collections and analyses. Usually, static collection with PMDS absorbents is used qualitatively rather than quantitatively, and in the methods it is mentioned that the compounds were not quantified but that the relative intensity of the different VOC peaks was compared. Hence, you focused on floral scent composition. This is fine, but then in the analyses of the lilac aldehydes (e.g. F), I understand it as you measure the absolute number of counts from the chromatograms.

Hence, I wonder (i) were these data stilled analysed quantitatively, and, if so, how did you validate that the number of counts was comparable across samples? The traditional method is to use dynamic headspace sampling, and then add a standard volume of a known substance to account for e.g. variable GC-MS sensitivity. Alternatively, (ii) this measure is relative (as indicated by the text and the y-axis label in the figure), but then I would like a better description of how this variable was calculated. For example, if using proportions, two samples could emit the exact same amount of the particular compound, but the proportional contribution of the same compound each overall scent bouquet could be very different, if one of the two samples include large amounts also of other scent compounds. Under such a scenario, it would be misleading to use the relative contribution of one single floral scent compound to try to predict pollinator behaviour. I apologize if I have missed something obvious here, but if so, it still makes sense to clarify how these data were obtained and what may be their limitation.

We apologize because the writing definitely required clarification. Performing analyses based on compound proportions was indeed not an option for us, since we expected differences in the amount of emitted VOC among treatments that cannot be detected with these type of data. We used the described technique for a qualitative comparison (scent composition) and in addition, the total ion counts from the chromatograms were compared between samples, which we now define as “intensities”. The definition of this term has been clarified in the text p 9, ll 205-206:

“Compounds were not quantified but the intensity of the total ion chromatogram of peaks was compared among treatment groups (hereinafter referred to as intensity).”

A previous study has validated this method (Kallenbach et al. 2014, http://doi.wiley.com/10.1111/tpj.12523). Using representative plant VOC (i.e., the same chemical classes as found in this study) in a physiologically meaningful concentration range, the study has shown that the linear relationship among detected peak areas and compound concentration is comparable between VOC that have been passively trapped on PDMS (Figure S4) and the same compounds and concentrations analysed by direct liquid injection. Hence, we assume that the abundance of individual VOC in the floral headspace is correlated with the detected peak area based on passive sampling on PDMS and this justifies our comparison of intensities. We included this information into the methods section p 9, ll 207-208:

“A linear relationship among peak areas and compound concentrations has been validated for the passive sorption method in Kallenbach et al. (2014).”

It is correct that we have not added an internal standard to our individual samples. While the lack of an internal standard may cause unexplained variation in the intensity of individual VOC due to variable GC-MS sensitivity, it is highly unlikely that variable technical sensitivity caused the treatment effects illustrated in Figure 2f, as all samples were analysed in one trial in a fully randomised order, which is now stated at p 9, ll 190:

“All samples were measured in a single trial in a fully randomised order.“

Line 335-344: Again, I was confused by the term floral attractiveness, since most traits were not that important for pollinator visitation rates. This was confusing throughout the discussion.

We now avoid the term floral attractiveness throughout the manuscript and instead refer to “floral traits”.

Line 347: Also "spatial attractiveness" is a bit misleading. Why not talk about the actual traits?

We need an umbrella term for these traits for headings, tables, graphs and the discussion. “Spatial flower trait” is an umbrella term for traits describing variation in the spatial arrangement of individual flowers (e.g., size, shape, orientation, symmetry) or the spatial arrangement of multiple flowers within the inflorescence (flower number, degree of aggregation, orientation, height relative to surrounding vegetation). This term and the necessity to distinguish between spatial flower traits and flower colour traits (that were previously summarized as “visual traits”) was established in Dafny et al. 1997 “Spatial flower traits and insect spatial vision”. We clarified this definition upon first mention of the term in the introduction at p 4, ll 39-41:

“…the spatial arrangement of individual flowers (e.g., size, shape) and multiple flowers within an inflorescence (e.g., number, height above ground, degree of aggregation), i.e., spatial flower traits (Dafni et al., 1997)… ”.

Moreover, we now stick to “spatial flower traits” throughout the entire manuscript and avoided the term “spatial attractiveness”

Line 445-477: I like the discussion about how the inbreeding effects on most traits, with the potential exception of floral scent, did not result in a reduced pollinator visitation rate. I think, however, that there is room for a deeper discussion here. These other traits, are they not important for pollinator attraction at all? Or were they not reduced to a large enough extent to affect pollinator visitation?

We agree that this aspect required deeper discussion and revised the section at p 19, ll 520-526 accordingly. We believe that the limited spatial vision of H. bicruris in combination with our experimental setup for pollinator observations increased the relative importance of floral scent for pollinator visitation rates (suggested by referee #3).

Also, considering the impressive amount of data produced and the numerous predictor variables, how well can we trust that the floral scent reduction is indeed not just a spurious correlation? This is particularly relevant in the light of the comment above about whether or not we can be sure that you have detected variation in "aldehyde concentrations" (line 460).

As outlined in our ninth response to reviewer #2, the analyses of PDMS tubes via GC-MS were fully randomised across breeding treatments, sexes, origins and populations (so was the complete rearing of plants and the acquisition of all other data). In that way we did our best to avoid the generation of spurious correlations with treatments and random factors.

Predictors [breeding*sex*range+latitude(+age) = k = 8(9)] were tested in one model for each of the 14 responses. For sure, type 1 errors cannot be excluded here but this is rather not the range where FDR-correction is urgently required. High sample sizes rather reduce than increase the probability for type 1 errors as generated by randomly increased/decreased response values.

I was pondering whether there was an additional way to analyze these data by comparing effect sizes (trait variation between inbred and outcrossed plants of the same category) and ask how well these explained the variation in effect size in pollinator visitation rate between inbred and outcrossed plants of the same categories. I could not come up with a straightforward way to do so, so I realize the problem of drawing too strong conclusions based on a single data point (a slight reduction in aldehyde concentration in inbred females from North America and a corresponding reduced visitation rate on inbred North American females). I think that one important take-home message here is the lack of trait-based explanations for pollinator visitation rates (which further emphasizes the need to avoid using "floral attractiveness").

Indeed, this question is highly interesting and we have tried to approach this issue before submitting the manuscript to eLive initially. The straightforward solution to this question is confirmatory path analysis or structural equation modelling. With this approach one could ask which floral trait was most strongly affected by which fixed effect (sex*breeding treatment*origin) and which floral trait affected pollinator visitation rates most strongly. With both approaches one basically runs a model over a set of component models (i.e., the ones we presented in Table 2). However, for these approaches one needs exactly the same sample size and random effect structures for all floral trait and pollinator visitation models. As our sample sizes vary among floral traits (Table 1) and pollinator visitation models need to be investigated with other random effect structures (trail, plot, Table 2), we cannot use these approaches. Moreover, these analyses still cannot handle complex zero-inflation formulas, which are, however, necessary to fit appropriate component models that do not seriously violate the underlying assumptions. As such we now highlight that the relationships among pollinator visitation rates and intensities of lilac aldehydes require further investigation p 19, ll 533-534:

“However, bioassays under more controlled conditions are needed to further evaluate a mechanistic relationship among pollinator visitation and intensities of lilac aldehydes”.

Furthermore, we use the term “floral traits” instead of “floral attractiveness” throughout the revised manuscript.

Figure S8: I wonder if there is a mistake here, because the two plots in the left panel are identical.

We are sorry for this fatal mistake. We corrected the graph.

Finally, I wanted to reiterate the potential to study variation also at smaller scale. How consistent were the trait changes among populations from the same continent. Is it reasonable to lump these into the same category and to compare among continents. It would be very interesting to see some variance partitioning.

Yes, it is necessary to put populations from the same continent into one category, since native and invasive plant populations differ significantly in their evolutionary history (p 5, ll 74-81, http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2012.05751.x). Origin explained sufficient amounts of variation in several traits including flower number, corolla expansion, VOC diversity, lilac aldehyde A intensity, and pollinator visitation rates (see Figures 2-3; and Table 2) and some variation in in the magnitude of inbreeding effects (Figure 2e, f; Figure 3). Even if we would not be interested in differences among native and invasive populations, we would have to include origin as a fixed effect in our models because: (i) populations within a distribution range are no independent samples, (ii) origin explains sufficient variation in many responses, (iii) origin cannot be fitted as a random factor, since it has only two levels (the minimum number of levels for random effect is 4).

We agree that it would be very interesting to specifically assess differences in the magnitude of breeding and sex effects among populations within origins. We now discuss this as important future research direction at p 18, ll 500-507:

“As such, the precise mechanisms underlying variation in inbreeding effects on different scent traits across population origins of S. latifolia can only be explored based on comprehensive genomic resources, which are currently not available. […] This would allow a detailed quantification of geographic variation in inbreeding effects and elaborating on the causes and ecological consequences of such variation (Thompson, 2005; Schrieber and Lachmuth, 2017; Thompson et al., 2017)”.

To empirically address within-origin variation of inbreeding effects with our data, we would have to (i) fit correlated random intercepts and slopes for the interaction breeding*sex on the population random factor (models consume min. 22 DF); or (ii) include population as a fixed effect in our models (models consume min. 67 DF). We have tried both of these approaches when preparing the revision, but unfortunately it turned out that our study is not designed to address this question. The models for both variants only partially converge (see R-script ll. 1568-1580), and even if they do this does not imply that one can draw solid inference from them. Approach i often results in multiple singular convergence warning messages implying that no variance is explained by population-specific reaction norms to the fixed effects specified in the random effects structure. Approach ii results in odd rank-deficient models (I was seriously worried about type I errors here, see our thirteenth response to reviewer #2). We simply have too few replicates (5) per population*breeding treatment*sex combination for both approaches. For solid inference we would need 10approach i-40approach ii replicates = 640-2600 individuals.

However, our experimental design is sufficient to address the hypothesis we have raised in the introduction as well as general differences in response variables among populations. We now provide information on variance partitioning for all models that include population as a random effect in S9. As you will see, population explains lower amounts of variation in our responses as the fixed effects in 9 out of 12 models. The random effects maternal and paternal genotype (mother&father) explain more variation than the random effect population in 6 of 12 cases. Thus, these data do not make a strong case for an extensive discussion of population-based differences in floral traits and this was also not a question or hypotheses we wanted to address with our study.

Reviewer #3 (Recommendations for the authors):

Line 2: The first sentence of the abstract is difficult to follow, consider rephrasing. In addition, while I would definitely agree that looking at effects of inbreeding on flowers in dioecious plants is most interesting and important because of the obligate outcrossing, this study does not explore whether these effects are "particularly fatal in dioecious" as there is no comparison with a hermaphroditic or monoecious plant.

We agree that the first sentence is misleading with regard to what has been investigated and rewrote it accordingly at p 3, ll 2-4:

“We study the effects of inbreeding in a dioecious plant on its interaction with pollinating insects and test whether the magnitude of such effects is shaped by plant sex and the evolutionary histories of plant populations.”

I found the term spatial attractiveness (first encounter on line 8) quite confusing (later on also spatial floral traits). Wouldn't it simply be visual attractiveness? Of course this would not be an appropriate umbrella term considering that you also include number of flowers and inflorescence height.

We agree that the term is confusing, as we did not define it in sufficient detail in the text. “Spatial flower trait” is an umbrella term for traits describing variation in the spatial arrangement of individual flowers (e.g., size, shape, orientation, symmetry) or the spatial arrangement of multiple flowers within the inflorescence (flower number, degree of aggregation, orientation, height relative to surrounding vegetation). This term and the necessity to distinguish between spatial flower traits and flower color traits (that were previously summarized as “visual traits”) was established in Dafny et al. 1997 “Spatial flower traits and insect spatial vision”. We could think of no more appropriate term for this set of traits. We clarified this definition upon first mention of the term in the introduction at p 4, ll 39-41. Moreover, we now stick to “spatial flower traits” throughout the entire manuscript and avoided the term “spatial attractiveness”.

Considering flower number only as a component of floral attractiveness is not taking full advantage of the measure. This could also be considered an important predictor of fitness and thus quite separate from the other floral morphological traits. Same could be, to some degree, said for inflorescence height, which I assume simultaneously means plant height?

In any case, I would like the authors to reconsider calling this group of floral traits "spatial".

Flower number and maybe even plant height may be employed as predictors for individual fitness (the better fitness measures would be seed output and seedling viability). However, as our article focusses on inbreeding effects on plant-pollinator interactions and not on plant fitness, we call them floral traits, which has also been done in numerous other studies on plant pollinator interactions (e.g., https://doi.org/10.1111/nph.14479).

Line 124: Are the inbred crosses between full-sibs or half-sibs? Are seeds of one capsule always sired by the same father, and if so, were the plants from each capsule kept separate? What would be the resulting F of the inbred progeny? also correct this on line 372 if they are half-sibs.

It is most likely that our experimental plants were full-sibs. However, there can be multiple paternity in S. latifolia, which is the reason why we cannot exclude the possibility that some family members are half-sibs and an F-value cannot be estimated. We still call it sib-mating but clearly define that this refers to either full or half-sibs at p 7, ll 134-137:

“Seeds from all maternal families (consisting of full-sibs and/or half-sibs, hereinafter referred to as sibs) were germinated and plants were grown under controlled greenhouse conditions for experimental crossings within populations”.

The plants were not kept separate in the greenhouse at the family level (in this case the family- identity effect could no longer be differentiated from the kept-at-the-same-place-in-the-greenhouse effect). The plant positions were fully randomised. We avoided uncontrolled pollination by bagging female flowers with mesh bags prior to opening. This is now described in the methods section at p 7, ll 140-142:

“During the crossings, plants were kept at randomised positions in the greenhouse. Female flower buds were covered with mesh bags prior to opening until fruit maturation and opened flowers were released from bags only for directed pollen transfer.”

Line 289: Height of inflorescence was lower? This sounds like you mean the length of the corolla.

We clarified what has been measured exactly at p 8, l 164-165:

“We determined the maximum height of synflorescences above ground level”;

p 13, ll 325-326:

“Synflorescences of inbreds had lower maximum height above ground…”; Figure 2a; and Table 1 and 2, pp 31-32.

Line 293: Perhaps it would be better to report effect sizes or percent changes here. Both differences are statistically significant, and a difference in two <0.05 p-values is not really an appropriate way of reporting a difference in the effect.

We now report percent changes calculated based on marginal estimated means.

Line 303: It is somewhat misleading to call this composition of floral VOCs when in reality you tested diversity and three individual compounds our of 70.

We agree that this formulation is misleading and changed the sentence at p 13, ll 341-342 accordingly:

“Breeding treatment, sex and population origin affected floral VOC in S. latifolia interactively (Table 2).”

Line 404: Why would purging only affect male flowers?

We added further details to improve clarity at pp 16-17, ll 453-459:

“Compared to females, the reproductive success of males is more limited by the availability of mates than by the availability of resources, which results in selection for increased attractiveness to pollinators (Moore and Pannell, 2011; Barrett and Hough, 2013). Competition for increased siring success among male plant individuals may create strong selection pressures that could rapidly purge deleterious recessive mutations in genes directly linked to attractiveness of male flowers to pollinators.”

Line 449: "visits were higher in inbred males." Compared to inbred females or outcrossed males?

We clarified the writing at p 18, ll 510-512.

“In North American populations, inbred females received significantly fewer plant and flower visits than outbreds, whereas flower visits were higher in inbred than outbred males (Figure 3)”.

Table 2. Ns's could be removed for clarity, a simple absence of asterisks already indicates a non-significant effect.

Done, p 32.

Add degreed of freedom.

The table caption at p 32 now states that:

“All listed fixed effects consume 1 degree of freedom.”

Moreover, DF are listed in detail in the written part of the Results section.

https://doi.org/10.7554/eLife.65610.sa2

Article and author information

Author details

  1. Karin Schrieber

    Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    kschrieber@ecology.uni-kiel.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7181-2741
  2. Sarah Catherine Paul

    Bielefeld University, Faculty of Biology, Department of Chemical Ecology, Bielefeld, Germany
    Contribution
    Resources, Supervision, Validation, Methodology, Writing - review and editing, Investigation
  3. Levke Valena Höche

    Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  4. Andrea Cecilia Salas

    Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  5. Rabi Didszun

    Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  6. Jakob Mößnang

    Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  7. Caroline Müller

    Bielefeld University, Faculty of Biology, Department of Chemical Ecology, Bielefeld, Germany
    Contribution
    Resources, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Alexandra Erfmeier

    1. Kiel University, Institute for Ecosystem Research, Geobotany, Kiel, Germany
    2. German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Leipzig, Germany
    Contribution
    Resources, Funding acquisition, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1002-9216
  9. Elisabeth Johanna Eilers

    Bielefeld University, Faculty of Biology, Department of Chemical Ecology, Bielefeld, Germany
    Contribution
    Data curation; Formal analysis; Validation; Investigation; Methodology; Writing - review and editing

Funding

Kiel University, Faculty of Mathematics and Natural Sciences, program for promotion of young female scientists

  • Karin Schrieber

Kiel University, Faculty of Mathematics and Natural Sciences, program for promotion of young female scientists

  • Alexandra Erfmeier

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was funded by the program for the promotion of young female scientists of the Faculty of Mathematics and Natural Sciences of Kiel University. We warmly thank Ms. Koopmann, who kindly provided her private property as field site for pollinator observations. Tim Diekötter provided germination chambers and greenhouse space and Wolfgang Bilger supported us with light standards for digital image acquisition. Susanne Petersen, Stephan Doose, David Eder, Carolin Böttcher, Jorun Jess, Sarai Guadalupe Quezada-Jimenez, Verena Zajonc, Ann-Cathrin Voss, and Pia Music provided technical assistance. CM and EE acknowledge support by the research unit FOR3000, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG).

Senior Editor

  1. Meredith C Aldrich, The University of Zurich, Switzerland

Reviewing Editor

  1. Youngsung Joo, Chungbuk National University, Republic of Korea

Publication history

  1. Received: December 9, 2021
  2. Accepted: May 9, 2021
  3. Accepted Manuscript published: May 14, 2021 (version 1)
  4. Version of Record published: May 27, 2021 (version 2)
  5. Version of Record updated: June 3, 2021 (version 3)

Copyright

© 2021, Schrieber et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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