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Almost as it appears in the final version published in Journal of Evolutionary Biology (doi: 10.1111/jeb.12595)

Visual modelling suggests a weak relationship between the of vision and colouration in

Olle Lind1,2 and Kaspar Delhey3

1Department of Philosophy, Lund University 2Department of optometry and Vision Science, The University of Auckland 3School of Biological Sciences, Monash University

Corresponding author: olle.lind[at]lucs.lu.se tel. +64 (0)21 08174657

Short title: Visual sensitivity and colours

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Abstract

Birds have sophisticated colour vision mediated by four cones types that cover a wide visual spectrum including ultraviolet (UV) wavelengths. Many birds have modest UV- sensitivity provided by violet-sensitive (VS) cones with sensitivity maxima between 400- 425 nm. However, some birds have evolved higher UV-sensitivity and a larger visual spectrum given by UV-sensitive (UVS) cones maximally sensitive at 360-370 nm. The reasons for VS-UVS transitions and their relationship to visual ecology remain unclear. It has been hypothesized that the evolution of UVS-cone vision is linked to plumage colours so that visual sensitivity and colouration are “matched”. This leads to the specific prediction that UVS-cone vision enhance the discrimination of plumage colours of UVS-birds while such an advantage is absent or less pronounced for VS-bird colouration. We test this hypothesis using knowledge of the complex distribution of UVS-cones among birds combined with mathematical modelling of colour discrimination during different viewing conditions. We find no support for the hypothesis, which, combined with previous studies suggests only a weak relationship between UVS-cone vision and plumage colour evolution. Instead we suggest that UVS- cone vision generally favours colour discrimination, which creates a non-specific selection pressure for the evolution of UVS-cones.

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Introduction Many birds have conspicuous plumage colours that strongly contrast with their surroundings. It is widely accepted that the function of many such colours is to attract mating partners or deter rivals (e.g. Andersson & Amundsen, 1997; Bennett et al., 1997; Hunt et al., 1997; Andersson et al., 1998; reviews in Bennett & Cuthill, 1994; Bennett & Théry, 2007). Matching the colourful , birds have well developed colour vision mediated by four types of single cones with different visual ; the UVS/VS (ultraviolet or violet sensitive), SWS (short-wavelength-sensitive), MWS (medium- wavelength-sensitive), and LWS-cones (long-wavelength-sensitive; Hart, 2001; fig. 1). Visual sensitivity is highly conserved across bird except for the UVS/VS pigments that exist in two variants; UVS-pigments that have sensitivity maxima between 360-370 nm, and VS-pigments that have sensitivity maxima between 400-425 nm (Bowmaker, 2008). According to this characteristic, birds fall into two major groups; birds with UVS-cones and higher UV-sensitivity, and birds with VS-cones and lower UV-sensitivity (fig. 1). Hereafter, we will refer to species within these classes simply as UVS-birds and VS-birds, and their corresponding visual systems as UVS-cone and VS- cone vision. The ancestral UVS/VS-cone in birds was of the VS-type while several independent VS-UVS transitions have led to a complex distribution of increased UV-sensitivity among species within the orders of , , Passeriformes, Psittaciformes, Pteroclidiformes, Trogoniformes, and possibly Struthioniformes (Ödeen & Håstad, 2013). It has been suggested that the evolution of UVS-cone vision is linked to UV-reflecting plumage feathers so that feather colouration and a higher UV-sensitivity in UVS-birds are “matched”. UV-reflecting feathers are potentially important for colour signalling in sexual communication, and studies have demonstrated that UV-reflectance in amounts relevant for vision, is ubiquitous in bird feathers (Eaton & Lanyon, 2003; Hausmann et al., 2003) while other studies indicate stronger UV-reflectance or UV- reflectance peaks at shorter wavelengths in UVS-birds (Mullen and Pohland, 2008; Ödeen et al, 2012; Bleiweiss, 2014). However, these studies rely on analyses of correlation between feather reflectance properties and visual pigment sensitivity, which represents a simplification of visual processes that has little relevance to perception and the evolution of colours signals. Instead, visual modelling provides an adequate estimate of and a powerful tool for addressing the complexity of visual ecology (Endler & Théry, 1996) and exploring the evolution of colour vision signals (e.g. Kelber et al., 2003; Delhey et al., 2013). Here, we use visual modelling to test the hypothesis of a general match between bird plumage colouration and high UV-sensitivity using extensive data on 1657 own and published reflectance spectra from 72 UVS species and 85 VS species.

Material and methods Spectral data Feather reflectance data were kindly provided by M.C. Stoddard (Stoddard & Prum, 2011; 76 species, 665 spectra), obtained from our own measurements (56 species, 887 spectra), and from the literature (25 species, 105 spectra). A list of species and a more detailed description of measured plumage regions is available in Supplementary methods and table S1. We obtained reflectance spectra from live birds and museum specimen using a back-scattering probe (FCR-7UV400-2-ME, Avantes, Eerbeek, The Netherlands) connected to a spectroradiometer (Avaspec 2048, Avantes) and a pulsed Xenon light source (XE Avalight) through a bifurcated light guide (diameter 400 µm). The probe was fitted with a plastic cylinder to standardize measuring distance and angle (perpendicular to feather surface), and to exclude ambient light. Reflectance was expressed relative to a

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UVSVS SWS MWS LWS 1 Figure 1. The spectral sensitivity of single cones in (Melopsittacus 0.8 undulatus, black) and (Gallus gallus, 0.6 grey). Cone sensitivity (normalized to 1) is the result of the transmittance of ocular 0.4 media, transmittance of oil droplets, and

Sensitivity (a.u.) Sensitivity 0.2 the absorbance of the visual pigments. The UVS/VS-cone comes in two variants UVS 0 350 400 450 500 550 600 650 700 and VS, each associated with two less Wavelength (nm) differentiated variants of the SWS cones. By contrast, there is little variation in MWS and LWS cone sensitivity across species (Hart, 2001; Hart & Vorobyev, 2005).

WS-2 white standard with the program Avaspec (Avantes). Reflectance data from the literature were collected by scanning plots with plot Digitizer (Huwaldt, 2010) and fit the spectral function with an 11-point running average. We used data for species with known UVS/VS-cone type that included measurements from at least two plumage patches and spectral range of at least 320 to 700 nm. For most species, the UVS/VS-cone type has been inferred based on DNA sequencing of the SWS1 . This approach agrees well with other more direct methods such as microspectrometry (Ödeen et al. 2009), with the possible exception of the (Ödeen & Håstad 2013). All data are from male individuals and for measurements of feather reflectance at multiple angles in the literature, we included the data with the most pronounced reflectance peaks.

Visual modelling The quantum catch of cone photoreceptors depends on their sensitivity given by a combination of the absorption of the visual pigment (r), the transmittance of pigmented (p), and the transmittance of the ocular media (o) (Hart, 2001):

�! � = �! � �! � � � , (1) where R is the sensitivity of cone i (i = UVS/VS, SWS, MWS, LWS). Visual pigment absorption was estimated using the Govardovskii template (Govardovskii et al., 2000), and oil droplets were assumed to function as cut-off filters as described by Hart and Vorobyev (2005). We ignored self-screening of the visual pigment that is negligible in the short photoreceptors of birds (Lind et al., 2013). The quantum catch of a cone, Q, for a visual stimulus, S, is given by:

!"" � = � � � � � � � ��, ! ! !"# ! (2) where k is a scaling coefficient for cone sensitivity given by receptor adaptation and I is the illuminating spectrum given by the ambient light condition. We assumed bright light conditions and von Kries adaptation (Wyszecki & Stiles, 2000) so that the adaptation coefficient is determined as:

! �! = !"" , (3) !"# !! ! !! ! ! ! !"

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where Sb is the spectrum of the adaptive background. We assume that chromatic discrimination is independent of intensity differences and thus given by relative quantum catch;

!! �! = , (4) !!"#/!"!!!"!!!!"#!!!"#

Receptor contrast, c, between the cone responses for two visual stimuli, j and k, is given by the Euclidean distance between the projection of relative cone quantum catch in receptor space;

! ! �!" = !!! �!,! − �!,! , (5)

Finally, we determined contrast gain as:

!!,!,!"# �!" = 100 − 1 , (6) !!,!,!" where contrast gain is the percentage increase or decrease in receptor contrast resulting from using UVS instead of VS-cone based vision (including all corresponding changes in other visual pigments, oil droplet absorbance and ocular media transmittance, Hart, 2001; Lind et al., 2914) . To estimate the performance of “average” UVS and VS-cone visual systems, we used average receptor contrasts for three UVS birds (Blackbird, Zebra , and ), and three VS birds (Chicken, Pigeon, and Peafowl; model parameters are tabulated in Table S2). Calculations were carried out in Matlab (R2011a, MathWorks). Equations 1-5 represent an estimation of chromatic contrast using measured physiological data and few assumptions (Kelber et al., 2003). The conclusions are not changed by the use of a more elaborate visual model that incorporates additional assumptions about receptor noise and post-receptor processing (Vorobyev & Osorio, 1998; Supplementary material).

Viewing conditions We considered four different viewing conditions that the birds may encounter; (i) “daylight”, the observer is adapted to standard daylight (d65, Wyszecki & Stiles, 2000), which also illuminates the display bird; (ii) “field”, the observer is adapted to green vegetation while standard daylight illuminates the display bird; (iii) “sun patch”, the observer is adapted to a deciduous forest while standard daylight illuminate the stimuli; (iv) forest, the observer is adapted to the deciduous forest spectrum (Håstad et al., 2005b) that also illuminate the display bird (fig. 2). For each condition, we modelled the contrast between plumage colour and background (green vegetation in condition i and ii, and the deciduous forest in condition iii and iv, fig. 2), and the contrast between all possible pair of plumage colours. We refer to these comparisons as background contrast and intra-plumage contrast respectively (fig. S1).

Statistical methods The contrast gain data typically have non-normal distributions skewed towards lower values. Therefore, we use the median and the percentile as descriptions of our data. Comparative analyses were carried out in the R environment (R Core Team 2014) with the packages ape (Paradis et al. 2004) and caper (Orme et al. 2013). Median and maximum plumage contrast gain (against background and intra-plumage) were compared

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1

0.5 Quantum flux (a.u) flux Quantum

0 400 500 600 700 Wavelength (nm)

i iv

ii iii

Figure 2. Different viewing conditions included in the colour vision modelling. (i-ii) “daylight” and “field”, the observing bird is adapted to a standard daylight (d65, light grey line in insert) and green vegetation (medium grey line in insert) respectively. In both conditions, daylight illuminates the display bird and background contrast is given against green vegetation. (iii-iv) “sun patch” and “forest”, the observing bird is adapted to the spectrum of a sun lit deciduous forest (black line in insert; Håstad et al., 2005) while the display bird is illuminated by daylight and deciduous light respectively. In both conditions, background contrast is given against deciduous forest. between UVS and VS species using phylogenetic linear models with the function “pgls”. To control for phylogenetic effects, for each model the function estimated the value of lambda, which is used to compute the evolutionary covariance matrix. For all models we inspected residuals to assess departures from normality and heterogeneity of variance. In most cases, a log10 or a square-root-transformation was required to meet the requirement of parametric statistics (see tables S2-S5). Negative values were made positive prior transformation by adding the absolute value of the minimum + 1. Phylogenies for our study species were obtained from http://birdtree.org/ using the Hackett backbone (Jetz et al. 2012). All analyses were repeated with a set of 100 different phylogenies to test how phylogenetic uncertainty affected the results.

Results and discussion From the hypothesis of a specific match between plumage colouration and high UV- sensitivity in UVS birds, we predict that discrimination of UVS plumage colouration should be better for UVS compared to VS-cone vision, and importantly, that this benefit is higher for UVS than for VS-bird colours. To test this prediction, we measured contrast gain (Eq. 6), which we define as the change in receptor contrast resulting from using UVS instead of VS-cone vision (estimated from “average” visual systems of UVS and VS-birds, see methods). At the species level, we computed the median and maximal contrast gain for feather spectra of each species and compared these parameters between UVS and VS birds. In

Background contrast Intra-plumage contrast 7 300 300 Figure 3. Contrast gain for plumage B patches from UVS (filled boxes) and VS- 250 A 250 birds (open boxes). Contrast gain is 200 200 defined as the percentage contrast change 150 150 given by UVS compared to VS-cone vision 100 100 (see Methods). The boxes show the 25th, th th 50 50 Species median 50 , and 75 percentiles of the

0 0 distributions of species median (A-B) and species maximum contrast gain (C-D) for −50 −50 Daylight Field Sun patch Forest Daylight Field Sun patch Forest all UVS (n = 72) and VS birds (n = 85; See Table S1 for a species list). The whiskers 1000 C 1000 D span the full range of the distribution to a Contrast gain (%) maximum of ± 1.5 * interquartile range 800 800 (75th-25th), and data outside this interval 600 600 are counted as outliers (dots). We found

400 400 no significant difference between UVS and VS birds in any of the modelled scenarios. 200 200 Species maximum Horizontal grey lines indicate the level of

0 0 equal contrast for UVS and VS-cone Daylight Field Sun patch Forest Daylight Field Sun patch Forest vision (zero contrast gain). Visual condition

Background contrast Intra-plumage contrast Figure 4. Comparison of contrast B 150 A 150 gain in UVS and VS-birds in the orders of Passeriformes (A-B, nUVS = 100 100 48, nVS = 31) and Charadriiformes

50 50 (C-D, nUVS = 4, nVS = 5). Each box

Passeriformes represents the distribution of species 0 0 medians within each order and visual pigment- (UVS with filled boxes, −50 −50 Daylight Field Sun patch Forest Daylight Field Sun patch Forest VS with open boxes). *p<0.05 and **p<0.01. All notations are as in CD Contrast gain (%) 150 150 figure 3. ∗ 100 ∗∗ 100

50 ∗ ∗ 50 Charadriiformes 0 0

−50 −50 Daylight Field Sun patch Forest Daylight Field Sun patch Forest

Visual condition

addition, we determined maximum contrast gain for each species and any viewing condition (fig. S2). We found no significant difference between UVS and VS-birds at any of the examined conditions (fig. 3, tables S3-S6). In all cases R2 values for these models were very small (<0.011, tables S3-S6) further indicating that visual sensitivities explain very little variation in contrast gain. We obtained similar results and never higher contrast gain for UVS-bird when using alternative modelling assumptions (colour discrimination limited by receptor noise or mediated by an isolated UVS/VS-SWS-cone channel, fig. S2-S4), assuming other viewing conditions (brown vegetation and coniferous forest, fig. S5), or accounting for phylogenetic uncertainty (see methods). Under most viewing conditions, contrast gain is positive (fig. 3), which demonstrates a general advantage of UVS-cone vision for plumage colour discrimination. This is in agreement with earlier studies (Vorobyev et al., 1998; Delhey et al., 2013; Lind et al.,

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1 A 1 B

daylight Figure 5. Plumage reflectance spectra from UVS (black lines, A, C, E, G) and VS-birds 0.5 0.5 (grey lines, B, D, F, H) that yield highest background contrast gain and thus are 0 0 400 500 600 700 400 500 600 700 maximally favoured by high compared to low UV-sensitivity. For comparison, the 1 1 CD background spectra of green vegetation il forest field and deciduous forest (see insert in figure 0.5 0.5 2) are shown as dashed lines in (A) and (E) respectively. The data represent a selection 0 0 400 500 600 700 400 500 600 700 of the top 2% from all values of contrast 1 1 gain in each class comprising 16 UVS bird

EFsun patch Reflectance spectra and 19 VS bird spectra (all species 0.5 0.5 listed in table S7, supplementary material).

0 0 400 500 600 700 400 500 600 700 1 GH1

0.5 0.5

0 0 400 500 600 700 400 500 600 700 Wavelength (nm)

2013). However, high UV-sensitivity favours the discrimination of UVS and VS-bird plumage colours equally and at the species level, there is no support for a match between UVS-bird colours and UVS-cone vision (fig. 3). To test whether matches between visual sensitivities and plumage colours are possible within specific , we repeated the analyses separately for the orders of Passeriformes and Charadriiformes, for which we had spectral data from both UVS and VS-species (Table S1). Again, contrast gain is predominantly positive, but in conflict with earlier work (Bleiweiss, 2014), we found no support for a specific match between plumage colouration and high UV-sensitivity (UVS-cones) in Passeriformes (fig. 4A-B). In Charadriiformes, median background contrast gain is significantly lower in UVS birds under open viewing conditions (conditions i and ii, fig. 2) while significantly higher under forest viewing conditions (conditions iii and iv, fig. 2; fig. 4C-D). However,

sample size is small (nUVS = 4, nVS = 5, table 1) and these species (, ) are unlikely to encounter forest conditions on a regular basis. Significant differences disappear when species maxima are analysed instead of species medians (data not shown). We conclude that the potential matches between plumage colouration and UV-sensitivity we find among Charadriiform species are of dubious biological relevance. Our results do not exclude the possibility of small subsets of species with plumage colours exceptionally well adapted for their visual sensitivity (see Delhey et al., 2013). As a final analysis, we determined what types of plumage spectra are best matched to UVS compared to VS-cone vision and thus could be associated with an evolutionary VS-UVS transition. As plumage spectra better matched to UVS than to VS-cone vision produce higher contrast gain, we identified the best matches by pooling all values of contrast gain and selecting only the highest 2% values (at and above the 98th percentile), comprising 16 spectra in UVS birds and 19 spectra in VS birds (fig. 5, and see S7 for a species list). We find that highest contrast gain is given by double-peaked spectra under open conditions (fig 5A-D; conditions i and ii, fig. 2), and single peaked or cut-off spectra

9 under forest conditions (fig. 5E-H; conditions iii and iv, fig. 2). For both forest conditions (fig. 5E-H), we also found matches for spectra with consistently low reflectance (< 10%), however, such spectra would probably yield stimuli too dim for colour vision under most natural conditions. A common property of all the best “matched” spectra (Fig. 5) is that spectral contrast against the background is modest or low at longer wavelengths above 400 nm, while high within the UV-region (fig. 5A,E). Future research could investigate if there is a correlation between such “matched” colours (fig. 5, table S7) and how different species make use of variability in viewing conditions during courtship or signalling (e.g. Endler and Théry, 1996). To conclude, this is the first time the hypothesis of a general match between plumage colouration and visual sensitivity in UVS birds is tested taking complexity of visual processes and visual ecology into account. In contrast to earlier studies (Mullen & Pohland, 2008; Bleiweiss, 2014), our results demonstrate a similarity in plumage colouration relevant for UV-vision in UVS and VS birds (figs. 3-4). This strongly implies that UVS cone-vision has evolved for reasons unrelated to the particular properties of UVS bird plumage colours. Instead, we suggest that UVS-cone vision has emerged from a non-specific selection pressure given by the general advantage of high UV-sensitivity and a wide visual spectrum for colour discrimination under most viewing conditions (Vorobyev et al., 1998; Stoddard and Prum, 2012; Delhey et al., 2013; Lind et al., 2013). The reason for the patchy distribution of UVS-cones among birds is likely related to physical constraints of biological tissue. All tissue attenuates of UV-light, and the ocular media (, ) of larger may be too UV-opaque to make UVS-cones advantageous (Lind et al., 2014). Indeed, UVS-cones are almost exclusively confined to small birds with small eyes (Lind et al., 2014). Still, we note that the complex distribution of UVS cone even among small birds suggests that additional factors limit evolutionary flexibility. Moreover, our study includes only a fraction of the total bird diversity, and future studies should attempt an even more unbiased sampling of plumage colouration and visual sensitivities across all species within clades or geographic regions.

Acknowledgement We thank Cassie Stoddard for generously sharing her spectral data, Almut Kelber and Peter Olsson for constructive comments on an earlier version of the manuscript, Misha Vorobyev, Matthew Toomey for helpful discussions, and 3 anonymous referees for constructive comments that improved the manuscript. We are grateful for generous funding by the Swedish Research Council (Dnr. 637-2013-388), the Australian Research Council (to KD, DE12012323) and Monash University Research Accelerator Program (KD). Supporting data are deposited in Dryad (MS Dryad ID: 39380681JEB-2015- 00023). The authors declare that no competing interests exist.

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References Andersson, S. & Amundsen, T. 1997. Ultraviolet and ornamentation in bluethroats. Proc. R. Soc. Lond. B 264: 1587-1591. Andersson, S., Örnborg, J. & Andersson, M. 1998. Ultraviolet and assortative mating in blue tits. Proc. R. Soc. Lond. B 265: 445-450. Bennett, A.T.D. & Cuthill, I.C. 1994. Ultraviolet vision in birds: what is its function? Vision Res. 34: 1471-1478. Bennett, A.T.D., Cuthill, I.C., , J.C. & Lunau, K. 1997. Ultraviolet plumage colors predict mate preferences in . PNAS 94: 8618-8621. Bennett, A.T.D. & Théry, M. 2007. Avian color vision and coloration: multidisciplinary evolutionary biology. Am. Nat. 169: S1-6. Bleiweiss, R. 2014. Physical alignments between plumage spectra and cone sensitivities in ultraviolet-sensitive (UVS) birds (Passerida: Passeriformes). Evol. Biol. 41: 404-424. Bowmaker, J.K. 2008. Evolution of visual pigments. Vision Res. 48: 2022-2041. Delhey, K., Hall, M., Kingma, S.A., Peters, A. 2013. Increased conspicuousness can explain the match between visual sensitivities and blue plumage colours in fairy- wrens. Proc. R. Soc. B 280: 20121771. Eaton, M.D. & Lanyon, S.M. 2003. The ubiquity of avian ultraviolet plumage reflectance. Proc. R. Soc. Lond. B 270: 1721-1726. Endler, J.A. & Théry, M. 1996. Interacting effects of lek placement, display behavior, ambient light, and color patterns in three neotropical forest-dwelling birds. Am. Nat. 148: 421-452. Govardovskii, V.I., Fyhrquist, N., Reuter, T., Kuzmin, D.G. & Donner, K. 2000. In search of the visual pigment template. Vis. Neurosci. 17: 509-528. Hart, N.S. 2001. The visual ecology of avian photoreceptors. Prog. Retin. Res. 20: 675- 703. Hart, N.S. & Vorobyev, M. 2005. Modelling oil droplet absorption spectra and spectral sensitivities of bird cone photoreceptors. J. Comp. Physiol. A 191: 381-392. Håstad, O. Victorsson, J. & Ödeen, A. 2005. Difference in color vision make less conspicuous in the eyes of their predators. PNAS 102: 6391-6394. Hausmann, F., Arnold, K.E., Marshall, N.J. & Owens, I.P.F. 2003. Ultraviolet signals in birds are special. Proc. R. Soc. Lond. B 270: 61-67. Hunt, S., Cuthill, I.C., Swadle, J.P. & Bennett, A.T.D. 1997. Ultraviolet and band-colour preferences in female zebra , Taeniopygia guttata. Anim. Behav. 54: 1383- 1392. Huwaldt, J.A. 2010. Plot Digitizer, open source software available at http://plotdigitizer.sourceforge.net/ Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. 2012. The global diversity of birds in space and time. Nature 491: 444–448. Kelber, A., Vorobyev, M. & Osorio, D. 2003. colour vision – behavioural tests and physiological concepts. Biol. Rev. 78: 81-118. Lind, O., Mitkus, M., Olsson, P. & Kelber, A. 2013. Ultraviolet sensitivity and colour vision in raptor foraging. J. Exp. Biol. 216: 1819-1826. Lind, O., Mitkus, M., Olsson, P. & Kelber, A. 2014. Ultraviolet vision in birds: the importance of transparent eye media. Proc. R. Soc. B 281: 20132209. Mullen, P. & Pohland, G. 2008. Studies on UV reflection in feathers of some 1000 bird species: are UV peaks in feathers correlated with violet-sensitive and ultraviolet- sensitive cones? Ibis. 150: 59-68.

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Ödeen, A., Hart, N.S., & Håstad, O. 2009. Assessing the use of genomic DNA as a predictor of the maximum absorbance wavelength of avian SWS1 opsin visual pigments. J Comp Physiol A 195:167-173. Ödeen, A. & Håstad, O. 2013. The phylogenetic distribution of ultraviolet sensitivity in birds. BMC Evol. Biol. 13: 36 Ödeen, A., Pruett-Jones, S., Driskell, A.C., Armenta, J.K. & Håstad, O. 2012. Multiple shifts between violet and ultraviolet vision in a family of birds with associated changes in plumage coloration. Proc. R. Soc. B. 279: 1269-1276. Orme, D., Freckleton, R., Thomas, G., Petzoldt, T., Fritz, S., Isaac, N., & Pearse, W. 2013. caper: Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.5.2. http://CRAN.R-project.org/package=caper Paradis E., Claude J. & Strimmer K. 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20: 289-290. Stoddard, M.C. & Prum, R.O. 2011. How colourful are birds? Evolution of the avian plumage color gamut. Behav. Ecol. 22: 1042-1052. Vorobyev, M., Osorio, D. 1998 Receptor noise as a determinant of colour thresholds. Proc. R. Soc. Lond. B 265, 351-358. Vorobyev, M., Osorio, D., Bennett, A.T.D., Marshall, N.J. & Cuthill, I.C. 1998. Tetrachromacy, oil droplets and bird plumage colours. J. Comp. Physiol. A 183: 621-633. Wyszecki G, Stiles WS. 2000 Color science: concepts and methods, quantitative data and formulae, 2nd edn. New York, NY: Wiley.

Supplementary methods for: Visual modelling suggests a weak relationship between the evolution of ultraviolet vision and plumage colours in birds Olle Lind and Kaspar Delhey

Measurements of feather reflectance Reflectance spectra from Stoddard and Prum (2011) were measured on museum specimens and correspond to 6 plumage patches (crown, back, rump, throat, breast and belly) together with additional measurements of any plumage patches appearing distinct to the (Stoddard and Prum, 2011). We measured reflectance spectra from 17 standardized plumage patches (front, crown, dorsal neck, upper back, lower back, rump, tail proximal, tail distal, cheek, wing covers, wing primaries, throat, ventral neck, upper breast, lower breast, belly, and vent). Poor plumage quality and naturally occurring bare patches limited measurements in some cases while we made measurements for any additional patches suggesting the presence of distinct colouration.

Modelling chromatic contrast To calculate chromatic contrast according to the receptor noise-limited model (figs. S2B, S4; Vorobyev and Osorio, 1998), we determine receptor quantum catch, Q, as in equations 1 and 2 (Eqs. 1-2, main text). These quantum catches are compared to determine the contrast between the stimuli, Δf, for each receptor type i:

!!,!"#$%&%! ! Δ�! = ln (Eq. S1) !!,!"#$%&%! !

The contrast values from equation 3 are calculated assuming logarithmic scaling of photoreceptor responses and thus independent of receptor adaptation (Schaefer et al., 2007). For the receptor noise-limited model, it is assumed that the colour discrimination threshold is set by receptor noise and that spatial summation can improve signal strength. The limiting Weber fraction in each receptor mechanisms is estimated as:

! � = ! (Eq. S2) !! where ω denotes Weber fraction, v the noise-to-signal ratio of an individual cone and η is the number of this cone type per receptive field. We used a Weber fraction of the LWS mechanism of 0.1 (Lind et al., 2014s) and a cone abundance ratio of 1:2:4:4, which resulted in Weber fractions of 0.2, 0.1414, 0.1, and 0.1 in the UVS/VS, SWS, MWS, and LWS cones respectively. Finally, the chromatic contrast, ΔS, between stimuli is calculated as:

! ! ! ! ! ∆� = ω!"ω! Δq! − Δq! + ω!"ω! Δq! − Δq! + ! ! ! ! ω!"ω! Δq! − Δq! + ω!ω! Δq! − Δq!" + ! ! ! ! ω!ω! Δq! − Δq!" + ω!ω! Δq! − Δq!" ! ! ! ! ω!"ω!ω! + ω!"ω!ω! + ω!"ω!ω! + ω!ω!ω!

(Eq. S3)

The unit of ΔS is JNDs (just noticeable differences), and the discrimination threshold is 1 JND. A detailed description of the receptor noise-limited model has been provided elsewhere (Vorobyev and Osorio, 1998; Lind et al., 2014). Finally, we used the chromatic contrast as the input in equation 6 (main text) to calculate contrast gain as shown in figure S2B and S4. To calculate contrast gain for a hypothetical isolated sensory channel combining only input from UVS/VS and SWS cones (figs. S2C, S3), we ignored MWS and LWS cones in the original calculations, thus using equations 1-6 (see main text) while setting i = UVS/VS, SWS, and modifying equation 4 to:

!! �! = (Eq. S4) !!"#/!"!!!"!

References Lind, O., Chavez, J. & Kelber, A. 2014s. The contribution of single and double cones to spectral sensitivity in budgerigars during changing light conditions. J. Comp. Physiol. A 200, 197-207.

Schaefer, H. M., Schaefer, V. and Vorobyev, M. (2007). Are fruit colors adapted to consumer vision and birds equally efficient in detecting colorful signals? Am. Nat. 169, S159-S169.

Supplementary tables for: Visual modelling suggests a weak relationship between the evolution of ultraviolet vision and plumage colours in birds Olle Lind and Kaspar Delhey

Table S1. List of species included in the analyses. Reference Reference Order Family Species Common name n Plumage Spectra UVS/VS-cone Species with UVS cones Charadriiformes Laridae Larus argentatus European 8 Stoddard & Ödeen & Håstad, 2003 herring Prum, 2011 Håstad et al., 2005 Larus dominicanus Kelp gull 13 this study Larus marinus Great black- 8 Stoddard & backed gull Prum, 2011 Pagophila eburnea 7 Stoddard & Prum, 2011 Coraciiformes Momotidae Momotus momota Blue-crowned 12 Stoddard & Ödeen & Håstad, 2013 motmot Prum, 2011 Passeriformes Emberizidae Emberiza citrinella Yellowhammer 19 this study Ödeen & Håstad, 2011 Estrildidae Erythrura gouldiae 8 Stoddard & Bowmaker et al., 1997 Prum, 2011 Hart et al., 2000a Neochmia modesta Plum-headed 15 this study Ödeen & Håstad, 2003 finch Taeniopygia guttata Zebra finch 5 Vorobyev et al., 1998 Uraeginthus ianthinogaster Purple grenadier 7 Stoddard & Prum, 2011 Hirundinidae Hirundo rustica Barn 8 Stoddard & Ödeen & Håstad, 2011 Prum, 2011 Icteridae Agelaius phoeniceus Red-winged 8 Stoddard & Aidala et al., 2012 blackbird Prum, 2011 Beason & Loew, 2008 Icterus galbula Baltimore oriole 2 Hofmann et al., Ödeen & Håstad, 2010 2008 Icterus gularis Altamira oriole 2 Hofmann et al., 2008 Icterus spurious Orchard oriole 3 Hofmann et al., 2007b Molothrus ater Brown-headed 2 McGraw et al., cowbird 2002 Sturnella militaris Red-breasted 7 Stoddard & blackbird Prum, 2011 Maluridae amabilis Lovely fairywren 16 this study Ödeen et al., 2012

Malurus cyaneus 14 this study Malurus elegans Red-winged 17 this study fairywren Malurus lamberti Variegated 16 this study fairywren Malurus pulcherrimus Blue-breasted 17 this study fairywren Malurus splendens Splendid 9 Stoddard & fairywren Prum, 2011 Menuridae Menura alberti Albert’s lyrebird 14 this study Ödeen & Håstad, 2011

Menura novaehollandiae Superb lyrebird 15 this study Muscicapidae Erithacus rubecula 2 Delhey & Ödeen & Håstad, 2011 Peters, 2008 Luscinia calliope Siberian 7 Stoddard & rubythroat Prum, 2011 Orthonychidae Orthonyx temminckii Logrunner 16 this study Ödeen & Håstad, 2011 Pachycephalidae Mohoua albicilla Whitehead 2 Igic et al., 2010 Aidala et al., 2012 Paridae Cyanistes caeruleus Blue tit 11 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 Parus major 19 this study Parus monticolus Green-backed tit 8 Hofmann, et al. 2007a Parulidae Dendroica petechia American yellow 7 Stoddard & Ödeen & Håstad, 2011 warbler Prum, 2011 Aidala et al., 2012 Setophaga ruticilla American 10 Stoddard & redstart Prum, 2011 Petroicidae Eopsaltria australis Yellow robin 17 this study Ödeen & Håstad, 2011

Microeca fascinans Jacky winter 15 this study Petroica goodenovii Red-capped 14 this study robin Petroica rosea Rose robin 15 this study Sittidae Sitta europaea Eurasian 20 this study Ödeen & Håstad, 2011 nuthatch Sylviidae Acrocephalus australis Moustached 14 this study Ödeen & Håstad, 2011 warbler Acrocephalus scirpaceus Eurasian reed- 22 this study Ödeen & Håstad, 2011 warbler Sylvia atricapilla Blackcap 3 Delhey & Ödeen & Håstad, 2003, Peters, 2008 2011 Sturnidae Sturnus vulgaris 11 Stoddard & Hart et al., 1998 Prum, 2011 Thraupidae Piranga olivacea Scarlet tanager 8 Stoddard & Ödeen & Håstad, 2010 Prum, 2011 Tangara chilensis Paradise tanager 9 Stoddard & Prum, 2011 Tersina viridis Swallow tanager 9 Stoddard & Prum, 2011 Volatinia jacarina Blue-backed 2 Doucet, 2002 grassquit Timaliidae Leiothrix argentauris Silver-eared 10 Stoddard & Maier & Bowmaker, 1993 mesia Prum, 2011 Turdidae Myophonus caeruleus Blue whistling 14 Stoddard & Aidala et al., 2012 thrush Prum, 2011 Ödeen & Håstad, 2003, Sialia mexicana Western 5 Budden & 2011 Dickinson, 2009 Hart et al., 2000b Sialia sialis 6 Stoddard & Prum, 2011 Turdus merula Eurasian 4 Delhey & blackbird Peters, 2008 Vireonidae Cyclarhis gujanensis Rufous-browed 18 this study Ödeen & Håstad, 2011 peppershrike Psittaciformes Cacatuidae Cacatua galerita Sulphur-crested 14 this study Carvalho et al., 2011 Aidala et al., 2012 Cacatua sulphurea Yellow-crested 12 Stoddard & Ödeen & Håstad, 2013 cockatoo Prum, 2011 Calyptorhynchus latirostris Slender-billed 15 this study black cockatoo Nymphicus hollandicus Cockatiel 16 this study Psittacidae Amazona aestivae Blue-fronted 3 Santos et al., Bowmaker et al., 1997; Amazon 2006 Carvalho et al., 2011 Amazona albifrons White-fronted 13 Stoddard & Aidala et al., 2012 amazon Prum, 2011 Ödeen & Håstad, 2003, chloropterus Green-winged 10 Stoddard & 2013 macaw Prum, 2011 Barnardius zonarius Port Lincoln 17 this study parrot Chalcopsitta atra Black lory 8 Stoddard & Prum, 2011 Charmosyna papou Papuan lorikeet 13 Stoddard & Prum, 2011 Cyanoliseus patagonus Burrowing 3 Masello et al., parrot 2009 Forpus xanthopterygius Blue-winged 5 Barreira et al., parrotlet 2012 Melopsittacus undulatus Budgerigar 8 Pearn et al., 2003 Neophema chrysostoma Blue-winged 6 Vorobyev et al., parrot 1998 Pionus menstruus Blue-headed 9 Stoddard & parrot Prum, 2011 Platycercus elegans Crimson rosella 16 this study Platycercus eximius Eastern rosella 16 this study Trichoglossus haematodus 16 this study Trogoniformes Trogonidae Harpactes erythrocephalus Red-headed 11 Stoddard & Ödeen & Håstad, 2013 Prum, 2011 Species with VS-cones Aviceda subcristata Pacific baza 15 this study Ödeen & Håstad, 2013

Circus cyaneus Hen harrier 7 Stoddard & Prum, 2011 Pandionidae Pandion cristatus Australian 17 this study Ödeen & Håstad, 2003 Aix galericulata Mandarin 13 Stoddard & Jane & Bowmaker, 1988 Prum, 2011 Moore et al., 2012 Anas clypeata Northern 8 Stoddard & Ödeen & Håstad, 2003, shoveler Prum, 2011 2013 Cygnus olor Mute 6 Stoddard & Prum, 2011 Nettapus auritus African pygmy 11 Stoddard & Prum, 2011 Somateria mollissima 8 Stoddard & Prum, 2011 Trochilidae Chalcostigma herrani Rainbow- 8 Stoddard & Ödeen & Håstad, 2010 bearded thornbill Prum, 2011 Heliangelus clarisse Longuemare's 8 Stoddard & sunangel Prum, 2011 Sephanoides sephaniodes Green-backed 3 Herrera et al., firecrown 2008 Topaza pella Crimson 9 Stoddard & Prum, 2011 Nyctibiidae* Nyctibius griseus Common 7 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 Charadriiformes Alcidae Fratercula arctica Atlantic puffin 8 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 Sternidae Chlidonias hybrida Whiskered 17 this study Håstad et al., 2005

Hydroprognes caspia Caspian tern 16 this study Sterna hirundo 6 Stoddard & Prum, 2011 Sternula albifrons Little tern 17 this study Columbiformes Caloenas nicobarica 9 Stoddard & Bowmaker et al., 1997; Prum, 2011 Ödeen & Håstad, 2003, Hemiphaga novaeseelandiae New Zealand 8 Cousins, 2010 2013 pigeon Claravis pretiosa Blue ground 8 Stoddard & dove Prum, 2011 Columbina picui Picui dove 12 Mahler & Kempenaers, 2002 Ptilinopus jambu Jambu 7 Stoddard & Prum, 2011 Ptilinopus magnificus Wompoo pigeon 9 Stoddard & Prum, 2011 Ptilinopus marchei Flame-breasted 11 Stoddard & fruit dove Prum, 2011 Treron apicauda Pin-tailed green 9 Stoddard & pigeon Prum, 2011 Coraciiformes Alcedinidae Ceyx lepidus Variable 6 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 Ceyx pictus African pygmy 8 Stoddard & kingfisher Prum, 2011 Halcyon senegalensis Woodland 9 Stoddard & kingfisher Prum, 2011 Coracias caudatus Lilac-breasted 13 Stoddard & Ödeen & Håstad, 2003 roller Prum, 2011 Coracias garrulus European roller 3 Silva et al., 2008 Meropidae Nyctyornis amictus Red-bearded 10 Stoddard & Ödeen & Håstad, 2013 eater Prum, 2011 Falco peregrinus 14 this study Ödeen & Håstad, 2003 Ödeen & Håstad, 2011 * Crax rubra Great currasow 6 Stoddard & Bowmaker et al., 1997 Prum, 2011 Hart et al., 1999, 2002 Håstad et al., 2005 Ödeen & Håstad, 2003, 2013 Meleagrididae Meleagris gallopavo Wild 2 Hill et al., 2005 Hart et al., 1999 Ödeen & Håstad, 2003 Numididae* Acryllium vulturinum Vulturine 10 Stoddard & Bowmaker et al., 1997 Prum, 2011 Hart, 2002 Hart et al., 1999 Håstad et al., 2005 Ödeen & Håstad, 2003, 2013 Pavo cristatus India peafowl 4 Burkhardt, 1989 Bowmaker et al., 1997 Hart, 2002 Lophophorus impejanus Himalayan 11 Stoddard & Ödeen & Håstad, 2003 monal Prum, 2011 Rollulus rouloul Crested partridge 8 Stoddard & Prum, 2011 Tetraonidae Lagopus lagopus Willow 6 Stoddard & Håstad et al., 2005 ptarmigan Prum, 2011 Ödeen & Håstad, 2013 Otididae* Eupodotis senegalensis White-bellied 9 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 Psophiidae* Psophia leucoptera Pale-winged 8 Stoddard & Ödeen & Håstad, 2003 trumpeter Prum, 2011 Rallidae Fulica atra Eurasian coot 14 this study Ödeen & Håstad, 2003 Porphyrio martinica American purple 9 Stoddard & gallinule Prum, 2011 Passeriformes Campephagidae Coracina novaehollandiae Black-faced 16 this study Ödeen & Håstad, 2011 -shrike Corvus brachyrhynchos Common crow 6 Stoddard & Ödeen & Håstad, 2003, Prum, 2011 2009, 2011 Cyanocitta cristata Blue jay 11 Stoddard & Prum, 2011 Dicruridae Dicrurus bracteatus Ribbon-tailed 14 this study Ödeen & Håstad, 2011 drongo Fringillidae Carduelis tristis American 9 Stoddard & Baumhardt et al., 2014 goldfinch Prum, 2011 Maluridae Amytornis barbatus Short-tailed 16 this study Ödeen et al., 2012 Amytornis striatus Striated 15 this study grasswren Malurus coronatus Purple-crowned 16 this study fairywren Malurus cyanocephalus Emperor fairy 7 Stoddard & wren Prum, 2011 Malurus leucopterus White-winged 17 this study fairywren Malurus melanocephalus Red-backed 14 this study fairywren Stipiturus mallee Mallee emuwren 13 this study Meliphagidae Acanthorhynchus tenuirostris Eastern spinebill 16 this study Ödeen & Håstad, 2010 Ödeen et al., 2012 Conophila rufogularis Rufous-throated 14 this study honeyeater Lichenostomus flavescens Yellow-tinted 16 this study honeyeater Philemon argenticeps Silver-crowned 16 this study friarbird Phylidonyris novaehollandiae New Holland 16 this study honeyeater Oriolidae Oriolus xanthornus Black hooded 8 Stoddard & Ödeen & Håstad, 2011 oriole Prum, 2011 Paradisaeidae Astrapia splendidissima Splendid astrapia 11 Stoddard & Ödeen & Håstad, 2011 Prum, 2011 Astrapia stephaniae Princess 4 Vorobyev et al., Stephanie's 1998 astrapia Lophorina superba Superb bird-of- 7 Stoddard & paradise Prum, 2011 Ptiloris magnificus Magnificent 15 this study riflebird Pipridae Pipra erythrocephala Golded-headed 8 Stoddard & Aidala et al., 2012 Prum, 2011 Ödeen & Håstad, 2003, 2013 Pomatostomidae Pomatostomus ruficeps Chestnut- 16 this study Ödeen & Håstad, 2011 crowned babbler Pomatostomus temporalis Grey-crowned 16 this study babbler Ptilonorhynchidae Ailuroedus crassirostris White-eared 17 this study Coyle et al., 2012 catbird Ödeen et al., 2012 Chlamydera nuchalis Great bowerbird 9 Stoddard & van Hazel et al., 2013 Prum, 2011 Ptilonorhynchus violacues Satin bowerbird 4 Doucet & Montgomerie, 2003 Sericulus bakeri Fire-maned 9 Stoddard & bowerbird Prum, 2011 Sericulus chrysocephalus Regent 14 this study bowerbird Rhipiduridae Rhipidura leucophrys 14 this study Ödeen & Håstad, 2011 Threskiornithidae Eudocimus ruber 6 Stoddard & Ödeen & Håstad, 2013 Prum, 2011 Plegadis falcinellus Glossy ibis 16 this study Capitonidae Megalaima chrysopogon Golden- 9 Stoddard & Ödeen & Håstad, 2013 whiskered barbet Prum, 2011 Galbulidae* Galbula cyanescens Bluish-fronted 6 Stoddard & Ödeen & Håstad, 2003, Prum, 2011 2013 Picidae Dendrocopos major Great spotted 19 this study Ödeen & Håstad, 2003 Ödeen & Håstad, 2013 Dryocopus pileatus Pileated 9 Stoddard & woodpecker Prum, 2011 Picus viridis European green 10 Stoddard & woodpecker Prum, 2011 Podicipediformes Podicipedidae cristatus Great crested 15 this study Ödeen & Håstad, 2013 Fregatidae Fregata magnificens Magnificent 8 Stoddard & Wright and Dearborn, frigatebird Prum, 2011 2009 Phalacrocoracidae Phalacrocorax aristotelis Common shag 7 Stoddard & Ödeen & Håstad, 2003 Prum, 2011 The number of spectra included in the analyses is given by n. References for the classification of the UVS/VS-cone type are given family-wise, except for Fringillidae where the reference apply for the of Carduelis, and Trogonidae and Maluridae where references apply for species. Asterisk indicates family with VS-cone type inferred from phylogeny. The follows Dunning, 2008.

Table S2. The parameters used to estimate bird cone sensitivities using equation 1 (see main text), the Govardovskii pigment template (Govardovskii et al., 2000), and the oil droplet model suggested by Hart and Vorobyev (2005). Blackbird Budgerigar Zebra finch Chicken Peafowl Pigeon Turdus Melopsittacus Taeniopygia Gallus Pavo Columba merula undulatus guttata gallus cristatus livia Oil droplets (λcut/λmid) C 414/429 411/431 414/432 443/460 449/462 448/470 Y 515/532 497/517 510/537 505/523 511/525 514/542 R 570/593 568/591 571/597 561/586 569/592 586/613 Ocular media 343 320 321 351 365 337 (t50) Visual pigments (λmax) sws1 373 371 359 418 424 404 sws2 454 440 427 453 458 452 rh2 504 499 505 507 505 506 m/lws 557 566 566 571 567 566 Bowmaker et al., Hart et al., Bowmaker et Bowmaker Hart, Bowmaker Ref. oil droplet 1997; Knott et 2000b al., 1997 et al., 1997 2002 et al., 1997 transmittance al., (2012) Ref. ocular Hart et al., Lind et al., Lind et al., Hart, Lind et al., media Lind et al., 2014 2000b 2014 2014 2002 2014 transmittance Ref. visual Hart et al., Bowmaker et al., Bowmaker et Bowmaker Hart, Bowmaker pigment 2000b 1997; al., 1997 et al., 1997 2002 et al., 1997 absorbance All values are in nm. Definitions of parameters and modelling details can be found in (Hart & Vorobyev, 2005).

Table S3. Results for phylogenetic linear models analysing variation in median and maximum background contrast gain for all species. All analyses were carried out on log10 transformed data. Median contrast gain parameter effect SE t p daylight intercept 1.350 0.027 50.034 0.000 R2=0.011; lambda=0 (VS) 0.048 0.037 1.301 0.195 field intercept 1.821 0.030 60.326 0.000 R2=0.004; lambda=0 visual system (VS) 0.031 0.041 0.751 0.454 sun patch intercept 1.098 0.085 12.984 0.000 R2=0.009; lambda=0.142 visual system (VS) -0.083 0.070 -1.179 0.240 forest intercept 1.460 0.104 14.095 0.000 R2=0.001; lambda=0.431 visual system (VS) -0.020 0.067 -0.298 0.766

Maximum contrast gain parameter effect SE t p daylight intercept 1.661 0.084 19.734 0.000 R2=0.002; lambda=0.361 visual system (VS) -0.032 0.057 -0.572 0.568 field intercept 2.090 0.062 33.493 0.000 R2=0.002; lambda=0.210 visual system (VS) -0.025 0.048 -0.530 0.597 sun patch intercept 1.553 0.179 8.656 0.000 R2=0.004; lambda=0.672 visual system (VS) -0.078 0.098 -0.793 0.429 forest intercept 1.799 0.099 18.154 0.000 R2=0.003; lambda=0.201 visual system (VS) 0.050 0.077 0.652 0.515

Overall maximum intercept 2.211 0.064 34.755 0.000 2 R =0.001; lambda=0.257 visual system (VS) -0.034 0.046 -0.744 0.458

Table S4. Results for phylogenetic linear models analysing variation in median and maximum intra-plumage contrast gain for all species. All analyses were carried out on log10 transformed data except for the overall maximum contrast gain which was square –root transformed. Median contrast gain parameter effect SE t p daylight intercept 1.553 0.179 8.656 0.000 R2=0.004; lambda=0.672 visual system (VS) -0.078 0.098 -0.793 0.429 field intercept 1.724 0.070 24.566 0.000 R2=0.004; lambda=0.239 visual system (VS) 0.042 0.052 0.802 0.424 sun patch intercept 1.396 0.090 15.500 0.000 R2=0.001; lambda=0.311 visual system (VS) 0.019 0.063 0.298 0.766 forest intercept 1.324 0.084 15.778 0.000 R2=0.002; lambda=0.311 visual system (VS) 0.030 0.059 0.507 0.613

Maximum contrast gain parameter effect SE t p daylight intercept 2.047 0.114 17.877 0.000 R2=0; lambda=0.239 visual system (VS) 0.011 0.085 0.128 0.898 field intercept 2.342 0.088 26.562 0.000 R2=0; lambda=0.151 visual system (VS) 0.020 0.073 0.273 0.785 sun patch intercept 2.113 0.113 18.751 0.000 R2=0; lambda=0.220 visual system (VS) 0.012 0.086 0.135 0.893 forest intercept 2.024 0.114 17.725 0.000 R2=0; lambda=0.235 visual system (VS) 0.021 0.086 0.245 0.807

Overall maximum intercept 15.109 1.753 8.621 0.000 2 R =0.003; lambda=0.279 visual system (VS) -0.447 1.286 -0.347 0.729

Table S5. Results for phylogenetic linear models analysing variation in median contrast gain for Charadriiformes. The variables did not require transformation. Background contrast gain parameter effect SE t p daylight intercept 14.959 1.509 9.912 0.000 R2=0.486; lambda=0 visual system (VS) 5.210 2.025 2.573 0.037 field intercept 45.534 1.589 28.649 0.000 R2=0.766; lambda=0 visual system (VS) 10.210 2.132 4.788 0.002 sun patch intercept 7.976 3.168 2.517 0.040 R2=0.605; lambda=0 visual system (VS) -11.151 4.251 -2.623 0.034 forest intercept 86.228 7.006 12.308 0.000 R2=0.536; lambda=0 visual system (VS) -26.724 9.399 -2.843 0.025

Intra-plumage contrast gain parameter effect SE t p daylight intercept 26.159 13.888 1.884 0.102 R2=0.418 ; lambda=0 visual system (VS) 41.803 18.632 2.244 0.060 field intercept 45.034 20.488 2.198 0.064 R2=0.35 ; lambda=0 visual system (VS) 53.321 27.487 1.940 0.094 sun patch intercept 31.264 16.477 1.897 0.100 R2=0.434 ; lambda=0 visual system (VS) 51.177 22.106 2.315 0.054 forest intercept 24.958 13.309 1.875 0.103 R2=0.42 ; lambda=0 visual system (VS) 40.183 17.855 2.250 0.059

Table S6. Results for phylogenetic linear models analysing variation in median contrast gain for Passeriformes. All analyses were carried out on log10 transformed data except background contrast gain for daylight condition which did not need a transformation (see supplementary data for results on maximum contrast gain). Background contrast gain parameter effect SE t p daylight intercept 19.825 1.393 14.235 0.000 R2=0.01; lambda=0 visual system (VS) 1.967 2.223 0.885 0.379 field intercept 1.753 0.040 44.092 0.000 R2=0.016; lambda=0 visual system (VS) 0.072 0.063 1.135 0.260 sun patch intercept 1.243 0.261 4.757 0.000 R2=0.008; lambda=0.976 visual system (VS) -0.112 0.145 -0.771 0.443 forest intercept 1.378 0.077 17.801 0.000 R2=0.001; lambda=0.135 visual system (VS) 0.018 0.084 0.212 0.833

Intra-plumage contrast gain parameter effect SE t p daylight intercept 1.334 0.061 22.009 0.000 R2=0; lambda=0.091 visual system (VS) 0.008 0.071 0.107 0.915 field intercept 1.675 0.041 40.982 0.000 R2=0.008; lambda=0 visual system (VS) 0.050 0.065 0.767 0.445 sun patch intercept 1.377 0.063 21.898 0.000 R2=0; lambda=0.079 visual system (VS) 0.001 0.075 0.010 0.992 forest intercept 1.310 0.067 19.637 0.000 R2=0; lambda=0.126 visual system (VS) 0.008 0.073 0.112 0.911

Table S7. Plumage spectra particularly well matched for UVS compared to VS-cone vision (fig. 7)

UVS species

Condition (i), ‘daylight’ Condition (ii), ‘field’ Condition (iii), ‘sun patch’ Condition (iv), ‘forest’

Feather Species Feather region Species Feather region Species Feather region Species region Amazona albifrons throat, tail, breast, Amazona albifrons throat, tail, breast Chalopsitta atra back, rump Chalopsitta atra breast, White-fronted amazon rump, belly White-fronted amazon rump, belly Black lory Black lory rump. back Charmosyna papou green tail, yellow Charmosyna papou tail Charmosyna papou belly Charmosyna papou belly, Papuan lorikeet tail Papuan lorikeet Papuan lorikeet Papuan lorikeet rump Erythrura gouldiae back Erythrura gouldiae Back Cyanistes caeruleus tail Cyclarhis gujanensis throat, Gouldian finch Gouldian finch Blue tit Rufous-browed peppershrike Forpus xanthopterygius chest Forpus xanthopterygius chest head Hirundo rustica breast Harpactes erythrocephalus undertail Blue-winged parrotlet Blue-winged parrotlet Red-headed trogon Melopsittacus undulatus chest Melopsittacus undulatus chest Malurus cyaneus rump Larus argentatus back Budgerigar Budgerigar Superb fairywren European herring gull Trichoglossus haematodus tail, wing primaries, Trichoglossus haematodus tail, wing scapulars, Myophonus caeruleus crown, throat, Malurus cyaneus Belly Rainbow lorikeet wing scapulars, Rainbow lorikeet lower back, upper Blue whistling thrush breast, back Superb fairywren lower back, upper back, vent, rump back, vent Parus major crown, neck, front Malurus elegans neck, Great tit Red-winged fairywren rump, Parus monticolus tail, crown Myophonus caeruleus back, Green-backed tit Blue whistling thrush Sturnus vulgaris crown Parus major cheek Starling Great tit

Parus monticolus cheek, Green-backed tit

Sialia sialis belly Eastern bluebird

Tersina viridis crown, Swallow tanager

VS species Condition (i), ‘daylight’ Condition (ii), ‘field’ Condition (iii), ‘sun patch’ Condition (iv), ‘forest’

Feather Species Feather region Species Feather region Species Feather region Species region Ailuroedus crassirostris lower back, Ailuroedus crassirostris wing scapulars, Anas clypeata wing Acryllium vulturinum back, White-eared catbird upper back, White-eared catbird breast, rump, Nothern shoveler Vulturine guineafowl breast wing scapulars back

Anas clypeata wing, Carduelis tristis back Caloenas nicobarica Breast, crown Coracias caudatus tail Nothern shoveler Nicobar pigeon Lilac-breasted roller Caloenas nicobarica back, Galbula cyanescens throat, Carduelis tristis crown Coracina novaehollandiae rump Nicobar pigeon Blueish-fronted jacamar American goldfinch Black-faced cuckoo-shrike Galbula cyanescens throat Heliangelus clarisse upper belly, Columbina picui undertail, Claravis pretiosa breast Blueish-fronted jacamar Longuemare’s sunangel lower belly Picui dove primaries Blue ground dove Heliangelus clarisse belly Lophophorus impejanus throat, crown Cyanocitta cristata back, crown, Cygnus olor belly, Longuemare’s sunangel Blue jay rump Mute swan breast, rump Lophophorus impejanus throat, crown, Megalaima chrysopogon rump Dendrocopus major back, crown Dendrocopus major wing, Himalayan monal Golden-wheskered barbet Great spotted woodpecker Great spotted woodpecker Megalaima chrysopogon rump Nyctyornis amictus belly, rump, tail, Pipra erythrocephala breast, back, Eupodotis senegalensis crown Golden-wheskered barbet Red-bearded beeeater back Golden-headed manakin rump, throat White-bellied bustard Nettapus auritus crown Ptilinopus magnificus back, rump Ptilinopus marchei back, Porphyrio martinica rump African pygmy goose Wompoo pigeon Flame-breasted fruit dove American purple gallinule Nyctyornis amictus upper belly, Treron apicauda back, wing Ptilonorhynchus violaceus breast, wing Ptilinopus marchei belly Red-bearded beeeater lower belly, tail Pin-tailed Satin bowerbird coverts Flame-breasted fruit dove

Ptilinopus jambu back, Rollulus rouloul rump Sericulus chrysocephalus breast, Jambu fruit dove Crested partridge Regent bowerbird neck, back, cheek, throat Ptilinopus magnificus back, rump Somateria mollissima back, Wompoo pigeon Common eider throat Treron apicauda wing, back Pin-tailed green pigeon

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Supplementary figures for: Visual modelling suggests a weak relationship between the evolution of ultraviolet vision and plumage colours in birds Olle Lind and Kaspar Delhey

background contrast: r - bg, g - bg 0.6 r intra-plumage contrast: 0.4 g - r g

Reflectance 0.2 bg 0 400 500 600 700 Wavelength (nm)

bg r g

Figure S1. Different types of visual contrasts included in the modelling. The observing bird can make comparisons between plumage colours and the background, or between the different plumage colours. The example includes a green background (see fig. 2) and the red side and green tail of a white-fronted amazon (Stoddard & Prum, 2011). Intra- plumage is calculate between all possible colour pairs so that the example of two colour patches yields 2 background contrasts but only one intra-plumage contrast, while intra- plumage contrasts for several plumage colours greatly outnumber background contrasts (e.g. 5 plumage colours produces 5 background contrasts and 10 intra-plumage contrasts).

Species maximum for any condition

5000 1000 A 1000 B C

4000 800 800

600 600 3000

400 400 2000 Contrast gain (%)

200 200 1000

0 0 0 Background Intra-plumage Background Intra-plumage Background Intra-plumage contrast contrast contrast contrast contrast contrast

Figure S2. Maximum contrast gain for plumage colours of UVS-birds (filled boxes) and VS-birds (open boxes) for any viewing condition, with (A) contrast calculated simply as receptor contrast (see Methods), (B) with a receptor noise-limited model of colour discrimination as suggested by Vorobyev and Osorio (1998), and (C), calculated as receptor contrast but for an isolated UVS/VS and SWS channel while ignoring contrast in MWS and LWS cones (see supplementary methods for details). All notations as in figure 3.

Background contrast Intra-plumage contrast A B 1000 1000

800 800

600 600

400 400

200 200 Species median

0 0

Daylight Field Sun patch Forest Daylight Field Sun patch Forest

C D 5000 5000 Contrast gain (%) 4000 4000

3000 3000

2000 2000

1000 1000 Species maximum

0 0 Daylight Field Sun patch Forest Daylight Field Sun patch Forest

Visual condition

Figure S3. Contrast gain for plumage colours of UVS-birds (filled boxes) and VS-birds (open boxes) during different viewing conditions (fig. 2). Contrast gain is calculated for an isolated UVS/VS and SWS channel while ignoring contrast in MWS and LWS cones (see supplementary methods for details). The boxes show the 25th, 50th, and 75th percentiles of the distributions of species median (A-B) and species maximum contrast gain (C-D). The whiskers span the full range of the distribution to a maximum of ± 1.5 * interquartile range (75th-25th), and data outside this interval are counted as outliers (dots).

Background contrast Intra-plumage contrast A B 200 200

150 150

100 100

50 50 Species median 0 0

Daylight Sun patch Forest Daylight Forest

C D 1000 1000 Contrast gain (%) 800 800

600 600

400 400

200 200 Species maximum 0 0 Daylight Sun patch Forest Daylight Forest

Visual condition

Figure S4. Contrast gain for plumage colours of UVS-birds (filled boxes) and VS-birds (open boxes) calculated with the receptor noise-limited model of colour discrimination (for details, see supplementary methods and Vorobyev and Osorio, 1998). In this model, receptor adaptation is ignored and the results from viewing conditions only differing in receptor adaptation are identical and thus omitted (results for “daylight” and “field” are identical for background contrast and results for “daylight”, “field”, and “sun patch” are identical for intra-plumage contrast). All notations as in figure 3 and S3.

Background contrast Intra-plumage contrast

300 1 300

250 A QF 250 B

0 200 400 500 600 700 200 W 150 150 100 100

50 50 Species median 0 0 −50 −50 Daylight Field Sun patch Forest Daylight Field Sun patch Forest

1200 1200 C D

Contrast gain (%) 1000 1000

800 800

600 600

400 400

200 200 Species maximum

0 0 Daylight Field Sun patch Forest Daylight Field Sun patch Forest

Visual condition

Figure S5. Contrast gain for plumage colours of UVS-birds (filled boxes) and VS-birds (open boxes) during various conditions; “daylight” and “field”, the observer is adapted to standard daylight (d65) and a “brown” spectrum (expected from old vegetation) respectively while the display bird is illuminated by standard daylight and background contrast is for a brown background; “sun patch” and “forest”, the observer adapted to a coniferous background (Håstad et al., 2005) while standard daylight (“sun patch”) or a coniferous light spectrum (“forest”) illuminate the display bird and background contrast is given against the coniferous light. The insert shows the “brown” (light grey) and the coniferous (black line) light spectra in the units of arbitrary quantum flux (QF) over wavelength in nm (W). All notations as in figure 3.