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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
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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
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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).
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