DOI: 10.5061/dryad.6djh9w1gz
title: Data from: Is bee-avoidance by bird-pollinated flowers driven by nectar robbing in <em>Erica</em>?

description: Nectar robbing (consuming nectar from a perforated flower without
 pollinating) generally negatively affects plant fecundity, and plants
 exhibit multiple mechanisms in defence. The colour of bird-pollinated
 flowers that are prone to robbing by bees may consequently be explained by
 the bee-avoidance hypothesis, proposing that bird-pollinated flowers are
 less visible to bees than bee-pollinated flowers. This visual avoidance
 mechanism may co-occur with mechanical avoidance mechanisms; however,
 flower conspicuousness has not yet been related to nectar robbing rates
 across multiple species using a combination of comparative methods and
 field observations. To test the bee-avoidance hypothesis, we quantified
 conspicuousness and flower visitor rates in multiple congeneric
 ornithophilous and entomophilous Erica species from South Africa. Visual
 modelling and Phylogenetic Generalised Least Squares analyses of spectral
 reflectance of 33 ornithophilous and 31 entomophilous species revealed
 that bird-pollinated flowers were less conspicuous (relative to their leaf
 background) to honeybees than insect-pollinated flowers, but equally
 conspicuous to birds, supporting the bee-avoidance hypothesis. However,
 natural nectar robbing rates in 27 populations of bird-pollinated species
 were unrelated to conspicuousness to bees, and pollination rates were
 unrelated to conspicuousness to birds. Finally, the conspicuousness of
 bird-pollinated flowers to bees and birds was unrelated to mechanical
 traits associated with nectar robbing (variation in corolla length,
 corolla aperture diameter, sepal size, or corolla stickiness). These
 results suggest that nectar-robbing bees may have influenced the evolution
 of flower colour of sunbird-pollinated species, although the maintenance
 of this trait appears decoupled from current robbing pressure.
description: <strong>Study species</strong> To
 obtain spectral reflectance and morphological measurements, we collected
 flowers from 33 ornithophilous (i.e., with bird-pollination traits) and 31
 entomophilous (i.e., with insect-pollination traits)
 <em>Erica</em> species occurring in the Fynbos, South Africa,
 from both natural habitats and from Kirstenbosch National Botanical Garden
 (Cape Town) (Fig 1, Table S1). All species are shrubs with bisexual
 flowers that last several days. We sampled species randomly with respect
 to phylogeny and colour. For 18 species, we sampled multiple populations,
 including different colour morphotypes (from different sites, except for
 two sites that contained multiple morphs of the same species), for a total
 of 108 populations. This research was approved by South African National
 Parks (CRC/2016-2017/020—2016/V1) and CapeNature
 (AAA043-0008-0056). We classified species into
 pollination syndrome categories based on flower size and shape, but not
 colour (Rebelo & Siegfried, 1985). We measured morphological
 traits with digital callipers (to two decimal places) (Fig S1) on 3–28
 flowers per population (limited by the number of available flowers), and
 calculated means per population. We measured the outer diameter of the
 corolla aperture (where corolla tips separate); corolla length from the
 aperture to the receptacle; pistil length from the receptacle to the
 stigma; and stamen length from the receptacle to the anther tip. Sepal
 surface area was quantified as the product of sepal length and width (at
 the widest point). Flowers with short corollas, or long corollas with
 narrow corolla apertures (&lt; 2 mm), were classified as
 entomophilous, and flowers with long corollas and large apertures as
 ornithophilous (Fig 1). Sampled species showed bimodal distributions in
 corolla length, aperture diameter, pistil length, stamen length, and sepal
 surface area, all of which differed significantly between assigned
 syndromes, supporting these classifications (Fig S2). Another exertion
 length also differed significantly, ly but was not as distinctly different
 between the syndromes and therefore was not included in syndrome
 classification. Published pollinator observations were available for 57
 populations, and for the remaining populations, we predicted syndromes
 based on flower morphological traits. A Principal Component Analysis using
 corolla, pistil, and stamen length (n = 81 populations) revealed that the
 morphology of species with predicted syndromes overlapped completely with
 those species with confirmed syndromes of the same type (Fig S3). Since
 pollinator data are lacking for most <em>Erica</em> species
 (van der Niet, 2020), syndrome categories could not be refined further.
 Our sampled ornithophilous species were dominated by pink and red flowers,
 and entomophilous flowers by white and pink, but our sample also included
 purple, cream, green, yellow,w and orange (according to human vision; Fig
 1I-J, Table S2, Fig S4). Blue flowers are absent in the genus (Rebelo
 & Siegfried, 1985). <strong>Reflectance
 measurements</strong> We measured the spectral
 reflectance of the corollas of at least three fresh flowers and two leaves
 per population. To quantify the most obvious signal, we measured the
 colour covering the largest area of the perianth in bi-coloured flowers
 and the sepals when the sepals covered two-thirds of the corolla. We took
 measurements indoors using a calibrated spectrophotometer (Ocean Optics
 Jaz models), with a QR200-7-UV-VIS reflectance probe and PX-2 Pulsed Xenon
 light source (Ocean Optics, Dunedin, FL) and a Spectralon 99% white
 standard (Labsphere, Congleton, UK), with the probe placed at a 45° angle
 and 5 mm distance from samples. We captured reflectance data with
 Spectrasuite software using an integration time of 30 milliseconds,
 average scans of 5, and a boxcar width of 5. Reflectance spectra of
 300–700 nm were interpolated to 1 nm bins, smoothed (span = 0.2; Fig S5),
 and negative values were removed by adding the lowest reflectance value to
 all wavelengths (thus preserving the spectrum shape). All processing and
 modelling of reflectance data was conducted with the
 <em>pavo</em> 2.7.1 package (Maia et al., 2019) in the R
 environment (R Core Team, 2022). <strong>Visual
 modelling</strong> To illustrate how flower
 visitors likely view colour distances, we modelled the reflectance data as
 colour loci points in the tetrahedral visual space for birds (Goldsmith,
 1990) and hehexagonalisual space for bees (hyperbolic-transformed quantum
 catches) (Chittka et al., 1992) using the visual models described below
 (Fig.). 1K- L. Two complementary approaches
 (conspicuousness and visual discrimination abilities) were used to assess
 how bees and birds perceive flower colours. Conspicuousness quantifies how
 easily an object can be distinguished from its background by comparing an
 object’s reflectance with its background reflectance. This is modelled for
 a taxon using the known maximum absorbance values of its photoreceptors.
 Visual discrimination determines if a colour is adapted to a visual system
 by comparing its reflectance spectrum to a taxon’s discrimination optima
 (Shrestha, Dyer, & Burd, 2013). We compared the conspicuousness
 and discriminability of ornithophilous and entomophilous flowers by
 calculating both metrics at the population level.
 <strong>Conspicuousness</strong>
 We quantified flower conspicuousness as chromatic and achromatic
 distance (in Just Noticeable Differences, JNDs (Vorobyev & Osorio,
 1998)) using the Receptor Noise-Limited Model. Chromatic contrast
 quantifies the colour difference, and achromatic contrast quantifies the
 brightness difference between a flower and its background. We modelled the
 visual systems of ultraviolet-sensitive birds (maximum photoreceptor
 absorbance at 372, 456, 544, and 609 nm (Endler & Mielke,
 2005))and honeybees (<em>Apis mellifera</em>; maximum
 absorbance at 344, 43,  and 544 nm). Since visual systems within
 Hymenoptera are relatively conserved (Peitsch et al., 1992), the
 <em>Apis mellifera</em> visual system is appropriate for
 testing our predictions. Using the
 <em>vismodel</em> function, we applied the visual models with
 the logarithm of the quantum catches, the standard daylight function (D65)
 as illuminant, scaled by 10,000 for bright illumination, and a typical
 “green” background (Stoddard & Prum, 2008). For chromatic
 contrasts (noise-weighted Euclidean distances, using the
 <em>coldist</em> function), we applied the more conservative
 Weber fraction of 0.1 (Maier & Bowmaker, 1993) and the neural
 noise model, which is appropriate for bright daylight conditions. In bird
 vision, we specified the photoreceptor ratio of (1, 2, 2, 4) and for
 achromatic contrasts we applied the Weber fraction 0.34 (Olsson et al.,
 2018) and double cone spectral sensitivity function of Blue Tit (Hart et
 al., 2000), and in bee vision, we applied a photoreceptor ratio of (1:1:1)
 and achromatic contrast as the difference between flower and background
 stimuli in the green photoreceptor with a Weber fraction 0.16. We
 calculated chromatic contrast for each <em>Erica</em>
 population (n = 101) by comparing each flower’s corolla reflectance to its
 own leaf reflectance and then averaging across flowers (3-28 flowers)
 within each population. For 13 populations without leaf reflectance data,
 we used average reflectance from leaves of 37 species (representing both
 pollination syndromes), giving 100 populations for this
 analysis. <strong>Colour discrimination abilities
 of pollinators</strong> Although using visual
 systems is a powerful way of analysing reflectance spectra, this method
 makes assumptions regarding perception. We therefore also used inflection
 point analyses, which do not rely on physiological data (Shrestha, Dyer,
 & Burd, 2013). This approach assesses the overlap between the
 discrimination abilities of a specific visual system and the inflection
 points (rapid changes of slope) in a reflectance spectrum. Flower colours
 are thought to be adapted to a pollinator’s discrimination ability when
 their inflection points are closely matched to the hue discrimination
 maxima. The hue discrimination maxima of birds and bees differ: 416, 489
 a, nd 557 nm for ultraviolet-sensitive (UVS) birds and 400 and 500 nm for
 hymenopteran insects. We first averaged reflectance spectra within
 populations and smoothed them (span = 0.2), before identifying wavelengths
 at which major inflection points occurred (&gt; 10% change in
 reflectance within 50 nm) using the Spectra-MP program (Dorin et al.,
 2020). We then calculated the absolute distance between each inflection
 point and its closest visual optimum. For each spectrum, we calculated
 mean absolute deviation (MAD) as the mean distance for all inflection
 points in the spectrum. We also calculated minimum absolute deviation
 (minAD) for each discrimination optimum: the distance to its closest
 inflection point (Fig S6). Of 104 populations, 33 had no major inflection
 points in their reflectance spectra and were therefore excluded. These
 included diverse colours, and 27 (ca. 82%) were ornithophilous. This left
 71 populations (37 ornithophilous, 34 entomophilous) for this
 analysis. <strong>Comparing the visibility of
 flowers</strong> We compared the conspicuousness
 (chromatic and achromatic contrasts) of ornithophilous and entomophilous
 flowers in bee and bird vision separately. Similarly, we compared the
 discrimination ability (one model for each metric) between pollination
 syndromes. We used Phylogenetic Generalised Least Squares (PGLS) analyses
 to account for non-independence due to phylogenetic relatedness
 (<em>pgls</em> in the <em>caper</em> package (Orme
 et al., 2013) with Maximum Likelihood estimate of lambda). We constructed
 the phylogenetic hypothesis (Fig S7) by excluding taxa from a
 rate-smoothed chronogram of <em>Erica</em> (Pirie et al.,
 2024) inferred under maximum likelihood from a dataset extended from
 (Pirie et al., 2016). Five species could not be represented in the
 phylogeny due to sequence paralogy (Pirie et al., 2024), giving a final
 sample of 59 species. Not all of the pertinent subspecific variation was
 represented directly in the original phylogeny. Where there was evidence
 for species monophyly (i.e., from multiple samples), we included
 populations manually as additional taxa, arbitrarily resolving internal
 nodes with minimal length branches and adding terminal branch lengths
 consistent with those within other species in the phylogeny.
 <strong>Nectar robbing and pollination
 rates</strong> We sampled robbing and pollination
 rates in 11 bird-pollinated <em>Erica</em> communities
 occurring in undisturbed natural fynbos habitat during 2016–2017 (see
 Coetzee et al., 2021), for locations, seasons, and other details. All
 study communities were at least 5 km apart, and each included two or more
 co-flowering ornithophilous <em>Erica</em> species. At each
 site, we surveyed 3–28 flowers per plant and scored each flower as robbed,
 pollinated,d or unvisited. We identified robbed flowers as those with a
 slit or hole at the base of the corolla, and pollinated flowers by a
 disturbed anther ring (Geerts & Pauw, 2011). From these data, we
 calculated cumulative visit rates as the proportion of visited flowers
 (robbed/pollinated) out of the total flowers surveyed per plant (Davies et
 al., 2025). We then averaged this proportion across all plants in a
 population (8–40 plants per population). We tested
 whether floral visitor rates were related to flower visibility at the
 population level using Linear Mixed Effect Models (LMM, using
 <em>the me4</em> package) with site identity as a random
 intercept effect. For the nectar robbing rate, we included conspicuousness
 to bees (chromatic contrast), corolla length, and stickiness as predictor
 variables. We repeated this analysis, replacing conspicuousness with
 discriminability (MAD in bee vision, the metric that summarises
 information of the whole visual system). For pollination rate, we used the
 same approach, but with conspicuousness to birds, corolla length, and
 stickiness as predictors, and repeated the analysis with discriminability
 (MAD for bird vision) instead of conspicuousness. The final dataset
 comprised 27 populations, from 12 different species. Insufficient data
 were available for corolla aperture and sepal size, so these could not be
 included in the analysis. Power analyses showed these tests only had
 sufficient power to detect large effect sizes. Statistical model
 assumptions were checked with Q-Q plots and Residual vs Fitted plots. We
 report adjusted R<sup>2</sup> for each analysis.
 <strong>Bee-avoidance traits</strong>
 We identified four mechanical bee avoidance traits based on the
 literature and on the observation that they differed significantly between
 ornithophilous and entomophilous <em>Erica</em> flowers:
 corolla length, aperture diameter, sepal size (Fig S2), and stickiness. We
 classified corollas as sticky (any degree of viscosity detectable by
 touch) or non-sticky. Stickiness was almost exclusively found in
 ornithophilous species: only one entomophilous species was sticky, whereas
 30 ornithophilous populations were sticky and 36 non-sticky. Although
 corolla hairiness and flower bracts could also function as mechanical
 defence traits, we did not include them because most populations had
 glabrous corolla surfaces, and because the bracts of
 <em>Erica</em> flowers are mostly smaller than the
 sepals. Focusing on 26 ornithophilous species, we
 tested for associations between flower conspicuousness (for birds and bees
 separately) and four mechanical bee-avoidance traits separately: corolla
 length (n = 56 populations), corolla aperture diameter (n = 32), sepal
 surface area (n = 35), and corolla stickiness (n = 59). Sample sizes
 varied because not all morphological measurements were available for all
 populations. <em>Erica halicacaba</em> L. was removed as an
 outlier for analyses with corolla aperture, since its closed flowers
 require mechanical force by birds to open the corolla (Turner et al.,
 2012). We also tested for associations between achromatic contrast and
 mechanical traits. We conducted one PGLS analysis for each mechanical
 avoidance trait, with chromatic contrast asthe response variable and the
 mechanical avoidance trait as the predictor variable. All p-values were
 adjusted for false discovery rates following Benjamini & Hochberg
 (Benjamini & Hochberg, 1995). Statistical model assumptions were
 checked with Q-Q plots and Residual vs Fitted plots. We report adjusted
 R<sup>2</sup> for each analysis.
description: # Data from: Is bee-avoidance by bird-pollinated flowers driven by nectar
 robbing in *Erica*? Dataset DOI:
 [10.5061/dryad.6djh9w1gz](https://doi.org/10.5061/dryad.6djh9w1gz) ##
 Description of the data and file structure To obtain spectral reflectance
 and morphological measurements, we collected flowers from 33
 ornithophilous (i.e., with bird-pollination traits) and 31 entomophilous
 (i.e, with insect-pollination traits) *Erica* species occurring in the
 Fynbos, South Africa, from both natural habitats and from Kirstenbosch
 National Botanical Garden (Cape Town) (Fig 1, Table S1). We measured the
 spectral reflectance of the corollas of at least three fresh flowers and
 two leaves per population. To quantify the most obvious signal, we
 measured the colour covering the largest area of the perianth in
 bi-coloured flowers and the sepals when the sepals covered two-thirds of
 the corolla. We took measurements indoors using a calibrated
 spectrophotometer (Ocean Optics Jaz models), with a QR200-7-UV-VIS
 reflectance probe and PX-2 Pulsed Xenon light source (Ocean Optics,
 Dunedin, FL) and a Spectralon 99% white standard (Labsphere, Congleton,
 UK), with the probe placed at a 45° angle and 5 mm distance from samples.
 We captured reflectance data with Spectrasuite software using an
 integration time of 30 milliseconds, average scans of 5, and a boxcar
 width of 5. We measured morphological traits with digital callipers (to
 two decimal places) (Fig S1) on 3–28 flowers per population (limited by
 the number of available flowers), and calculated means per population. ###
 Files and variables #### File:
 Data_Is_bee-avoidance_driven_by_nectar_robbing__Data_Dryad_20Jan2026.xlsx
 **Description:** multiple data sheets. One with the metadata, one with
 flower colour metrics, one with flower size measurements, one with flower
 visitor rates, and la ist of species. Missing values are indicated with
 "NA". ##### Variables * A full list of variables is provided in
 the meta spreadsheet.  #### File: Erica.population.spectra.1.csv
 **Description:** The reflectance spectra of all study populations mwere
 easured with a spectrophotometer. Average spectra (of multiple flower
 measurements) for each population are provided, thus one column for each
 population. The column headings provide the species name (with the genus
 name in full or abbreviated) and the collection site. See
 "species" spreadsheet in the file "Data Is bee-avoidance by
 bird-pollinated flowers driven by nectar robbing in Erica?".
 Reflectance spectra of 300–700 nm were interpolated to 1 nm bins, smoothed
 (span = 0.2; Fig S5), and negative values were removed by adding the
 lowest reflectance value to all wavelengths (thus preserving the spectrum
 shape). ##### Variables * wl * Erica.mammosa.CapePointc.1a *
 Ecoccinea.CapePointC1a * Eabietina.CapePoint.a * Ecoccinea.CapePoint.a *
 Erica.plukenetii.CapePoint.a.1a * Erica.plukenetii.Suikerbossie.1a *
 Ecerinthoides.Suikerbossie * E.abietina.abietina.Tafelberg *
 Erica.plukenetii.Tafelbergroad.1a * Egrandiflora.Paarl *
 Erica.plukenetii.Paarl * Ecerinthoides.Paarl * Egrandiflora.DuToitskloof *
 Erica.plukenetii.DuToitskloof * Ecurviflora.LourensfordL1a *
 Erica.plukenetii.Lourensford * Erica.plukenetii.BrodieLink *
 Ecoccinea.BrodieLink * Erica.sessiliflora.Perdeberg.1a *
 Erica.fascicularis.Perdeberg.1a * Erica.viscaria.Perdeberg.1a *
 Ecoccinea.Fernkloof * Erica.sessiliflora.Fernkloof *
 Erica.plukenetii.Kogelberg.green.corolla1 * Epinea.DuToitskloof.orange1 *
 Epinea.DuToitskloof.pink.corolla3 * E.melastoma.Vogelgat.jaz1 *
 Egrandiflora.perfoliosa.Jonkershoek * Erica.versicolor.JSMaraisPark *
 Eperspicua.white1 * Eperspicua.pink1 * Eglandulosa.pink1 *
 Eglandulosa.yellow.g1 * Erica.vestita.AH * Eplukenetii.MR1a *
 Erica.bauera.Kirstenbosch.1a * E.brachialis.Kirstenbosch.1a *
 Erica.chloroloma.Kirstenbosch.1a * Erica.coccinea.Kirstenbosch.1a *
 Erica.croceovirens.Kirstenbosch.1a * Erica.cruenta.Kirstenbosch.1a *
 Erica.curviflora.Kirstenbosch.1a * Erica.deflexa.Kirstenbosch.1a *
 Erica.diaphana.Kirstenbosch.1a * Erica.discolor.Kirstenbosch.1a *
 Erica.halicacaba.Kirstenbosch.1a * E.mammosa.Kirstenbosch.light.1a *
 E.mammosa.Kirstenbosch.dark.1a * Erica.nana.Kirstenbosch.1a *
 Erica.regia.Kirstenbosch.1a * Erica.versicolor.Kirstenbosch.1a *
 Erica.viridiflora.Kirstenbosch.1a * Erica.cerinthoides.Cairnbrogie *
 E.densifolia.Camferskloof * Erica.nabea.Knysna * E.curviflora.Knysna *
 Erica.viscaria.longifolia.pink.Napier * Erica.plukenetii.Napier *
 Erica.plukenetii.Bainskloof.2a * Erica.plukenetii.hemel.aarde.l1 *
 Erica.propinqua.Agulhas1 * Equadrangularis.Kirstenbosch.1a *
 Ecristiflora.MontRochelle.1a * Erica.caffra.Kirstenbosch.1a *
 Erica.fontana.Kirstenbosch.1a * Erica.formosa.Kirstenbosch.1a *
 Erica.glomiflora.Kirstenbosch.1a * Erica.haematocodon.Kirstenbosch.1a *
 Erica.heloiphila.Kirstenbosch.1a * Erica.mauritanica.Kirstenbosch.1a *
 Erica.peziza.JSMarais.1a * Erica.scabriuscula.Kirstenbosch.1a * Erica ##
 Code/software The files can be viewed with Microsoft Excel or similar
 software. It can also be viewed with R software, any version. No code or
 script is provided.  ## Access information Other publicly accessible
 locations of the data: * None Data was derived from the following sources:
 * Collected by the authors.