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Appendix S2 The role of trait combination in the conspicuousness of fruit display among birddispersed plants Mariano Ordano, Pedro G. Blendinger, Silvia B. Lomáscolo, Natacha P. Chacoff, Mariano S. Sánchez, M. Gabriela Núñez Montellano, Julieta Jiménez, Román A. Ruggera, Mariana Valoy Methods for collection and analysis for data on fruit reflectance Fruit chromatic contrast was quantified by first quantifying reflectance using a JAZ EL-200 spectrometer equipped with a DT-Mini light source and a USB2000 spectrometer with a PX-2 pulsed xenon light source (Ocean Optics, Dunedin, Florida, USA). We used a sensor with five optical fibres for fruit scanning and a sixth fibre that returned the reflected light to the spectrometer. Fruits were scanned using a black metal stand with a hole positioned at a fixed angle of 45°, which allowed to keep constant the distance to the fruit for all measurements, and to block external light. We also fastened a non-UV-filtering microscope slide to the hole opening of the metal stand to ensure that fruits that were small enough to enter the hole did not come closer to the end of the fiber. Whenever possible, we scanned up to five fruits and five leaves from each of four individuals from each plant species (Appendix S1). Collected fruits were put in sealed plastic bags and stored in the fridge until they were measured in the laboratory one-three days later. Specifically, we scanned the fleshy reward of the diaspora and the leaves collected close to the fruits. We calculated reflectance as the proportion of a spectral on white reflectance standard (Labsphere or WS-1 Ocean Optics); therefore the reflectance measure is unit-less. As it is desirable to analyse fruit colour according to the visual system 1 of fruit consumers (Endler & Mielke 2005), we characterized the fruit colour based on the bird visual system, in which the spectral sensitivity between 300 and 700 nm is classified as either the violet-sensitive (VS) or the ultraviolet-sensitive (UVS) classes, according to the sensitivity peak of the pigment type with the lowest absorption range (Vorobyev & Osorio 1998). We calculated the corresponding photon quantum catch values (Qi) for each avian photoreceptor class (Vorobyev et al. 1998; Kelber, Vorobyev & Osorio 2003). We used the tetrahedral colour space model (TCS) for avian visual systems (Endler & Mielke 2005; Stoddard & Prum 2008). In this model, the tetrahedron depicts the avian colour vision space in which each point represents each measured colour as much as it is the excitation of each photoreceptor type (Stoddard & Prum 2008). The central point is the achromatic origin, and each vertex depicts the maximal stimulation of the corresponding photoreceptor. We calculated the chromatic contrast or chromatic distance (ΔS) between fruits and leaves (in both cases, these measurements represented the reflectance mean values of all the samples taken from a given plant species). Because the leaves of a same plant are the structures more closely to fruits they represent an acceptable estimation of the fruit background (Camargo et al. 2014). The chromatic distance was expressed in units of just noticeable differences (JNDs), which 4 is considered as discrimination threshold. Those values < 4 JND indicate that animals cannot distinguish between the two colours compared; values > 4 JND indicate that animals are capable of discriminating the compared spectra (Osorio & Vorobyev 1996; Schaefer, Schaefer & Vorobyev 2007; Stournaras, Prum & Schaefer 2015). The chromatic distance was also calculated for the tetrachromatic visual system following equation 5 of Vorobyev & Osorio (1998), where it is a function of Weber fractions (ω) and the contrast for each receptor type (Vorobyev & Osorio 1998; Vorobyev 2 et al. 2001; Schaefer, Schaefer & Vorobyev 2007). We applied the von Kries transformation (ki) using the average reflectance spectra of samples of leaves (N = 1641 leaves from 350 plants). We assumed conditions of bright light, where discrimination is determined by the noise of photoreceptors. According to previous behavioural studies (Vorobyev & Osorio 1998; Schaefer, Schaefer & Vorobyev 2007), the photoreceptor densities were 1:2:2:4 (u, s, m, l), and the noise-to-signal ratio was 0.1. To analyse reflectance data, the vismodel function in the package pavo (Maia et al. 2013) allows estimation of quantum catch values according to avian visual model specifications (see previous section). The tcs function summarizes values for the tetrahedral space according to the TCS model (Endler & Mielke 2005; Stoddard & Prum 2008); the tcsvol function estimates the volume occupied by a set of colour points in the tetrahedral space (see Maia et al. 2013 for details). We used the average UVS avian visual system from the available data of bird species provided by the package pavo, ideal illuminant, background specification and von Kries transformation and relative quantum catches in the case of TCS models. The function coldist estimates the chromatic distance between two patches of colour in terms of JND (Maia et al. 2013). References Camargo, M.G.G., Cazetta, E., Morellato, L.P.C. & Schaefer, H.M. (2014) Characterizing background heterogeneity in visual communication. Basic and Applied Ecology, 15, 326-335. Endler J.A. & Mielke P.W. (2005) Comparing entire colour patterns as birds see them. Biological Journal of the Linnean Society, 86, 405-431. 3 Kelber, A., Vorobyev, M. & Osorio, D. (2003) Animal colour vision - behavioural tests and physiological concepts. Biological Reviews, 78, 81-118. Maia, R., Eliason, C.M., Bitton, P.P., Doucet, S.M., & Shawkey, M.D. (2013) pavo: an R package for the analysis, visualization and organization of spectral data. Methods in Ecology and Evolution, 4, 906-913. Osorio, D., & Vorobyev, M. (1996) Colour vision as an adaptation to frugivory in primates. Proceedings of the Royal Society of London. Series B: Biological Sciences, 263, 593599. Schaefer, H.M., Schaefer, V. & Vorobyev, M. (2007) Are fruit colors adapted to consumer vision and birds equally efficient in detecting colourful signals? American Naturalist, 169, S159-S169. Stournaras, K.E., Prum, R.O & Schaefer, H.M. (2015) Fruit advertisement strategies in two Neotropical plant–seed disperser markets. Evolutionary Ecology, 29, 1-21. Vorobyev, M. & Osorio, D. (1998) Receptor noise as a determinant of colour thresholds. Proceedings of the Royal Society of London. Series B: Biological Sciences, 265, 351358. Vorobyev, M., Brandt, R., Peitsch, D., Laughlin, S.B. & Menzel, R. (2001) Colour thresholds and receptor noise: behaviour and physiology compared. Vision Research, 41, 639-653. Vorobyev, M., Osorio, D., Bennett, A.T.D., Marshall, N.J. & Cuthill, I.C. (1998) Tetrachromacy, oil droplets and bird plumage colours. Journal of Comparative Physiology A, 183, 621-633. 4 Stoddard, M. C., & Prum, R. O. 2008. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of new world buntings. American Naturalist 171: 755-776. 5