10.5061/DRYAD.KPRR4XH6T
Rabideau Childers, Richard
0000-0002-7137-3192
Harvard University
Data for: A hypothesis to explain polarization color vision in
butterflies, with an example from the Australian Imperial Blue, Jalmenus
evagoras
Dryad
dataset
2022
butterfly vision
eyeshine
edge detection
polarization detection
Lycaenidae
polarization vision
FOS: Biological sciences
National Science Foundation
http://dx.doi.org/10.13039/100000001
National Science Foundation
http://dx.doi.org/10.13039/100000001
PHY-1411445
National Science Foundation
http://dx.doi.org/10.13039/100000001
CMMI-2005747
National Science Foundation
http://dx.doi.org/10.13039/100000001
DEB-1541560
Air Force Office of Scientific Research
http://dx.doi.org/10.13039/100000181
FA9550-16-1-0322
Swedish Research Council
http://dx.doi.org/10.13039/501100004359
VR 2020-0517
en
10435432 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
The Australian lycaenid butterfly, Jalmenus evagoras, has iridescent wings
that are sexually dimorphic in both spectral reflection and degree of
polarization, suggesting that these wing properties are likely to be
important in mate recognition. We first describe the results of a field
experiment showing that free-flying individuals of J. evagoras can
discriminate between visual stimuli that vary in polarization content in a
wavelength-dependent manner. We then present detailed reflectance
spectrophotometry measurements of the polarization content of male and
female wings, showing that female wings exhibit blue-shifted reflectance,
with a lower degree of polarization relative to male wings. Finally, we
describe a possible mechanism by which these butterflies could
discriminate such differences in polarization, employing a novel method
for characterizing orientations of rhabdomeric microvilli: By measuring
variation of depolarized eyeshine intensity from individual ommatidia as a
function of eye rotation, we show that individual ommatidia contain
mutually perpendicular microvilli, and that they are rotated with respect
to one another such that their ommatidial principal axes differ by as much
as 45º. By mapping the distribution of the ommatidial principal axes in
eye regions of J. evagoras, we show that males and females exhibit
differences in the extent to which ommatidial axes are aligned, and that
the ratio of combinations of ommatidia suitable for polarization-detection
or edge-detection varies with respect to both sex and eye patch elevation.
Thus, Jalmenus evagoras exhibits finely-tuned production and perception of
polarized light, likely to match sex-specific life history differences in
the utility of polarized light cues.
POLARIZATION-DISCRIMINATION BEHAVIORAL EXPERIMENT: Responses of
free-flying, patrolling individuals of Jalmenus evagoras to our mock
flapper treatments were recorded in 20-minute trials for each of the 4
color/ polarization treatments, and measured in between 7-8 separate
geographic field sites each, with each site consisting of patches of
acacias occupied by larvae and pupae of J. evagoras. Specifically, ‘3M Red
G280’ was tested in twelve 20 min trials carried out in 8 sites, ‘3M
Yellow R312’ was tested in 7 trials at 7 different sites, ‘3M+blue R374’
was tested in 11 trials at 8 different sites, and ‘HNPB’ was tested 10
times in 8 different sites. Sites were spaced no closer than 300 m apart,
with the farthest sites separated by over 40 km, to reduce the likelihood
that individuals encountered at one site would also be present in
subsequent trials at nearby sites. To further avoid the possibility of
counting responses by the same individual butterflies more than once, we
also analyzed these results using site as the unit of replication, summing
the total number of responses to polarized and depolarized models at each
site for each treatment, and calculating the student’s t-tests of the
average difference between polarized and depolarized responses for each
site. WING REFLECTANCE: Spectroscopic reflectance measurements were
conducted for the dorsal forewings and hindwings of 12 female and 13
male J. evagoras pinned museum specimens. The mean and standard error of
the mean reflectance values were calculated and plotted, from which the
Full Width at Half-Maximum (FWHM) values were derived. These
many-dimensional reflectance data (consisting of values from 360 nm-810 nm
for each specimen), were reduced via Principal Components Analysis (PCA)
to 3 principal components (PCs), together explaining over 99% of the
variation in the original dataset. The scores of each sample in PCs 1, 2
and 3 were plotted as points, with the loadings of the original
reflectance values at each wavelength for each PC plotted as linear
vectors (Fig. 3C-D). To determine whether males were significantly
spectrally different from females, we used the RRPP package in R (Collyer
& Adams 2018) to analyze the effect of sex on the spectral
reflectance data as a whole, using multivariate response linear modeling
of the effect of sex on either the raw reflectance value dataset or
decomposed reflectance PC score dataset as multivariate response
variables. DEGREE OF POLARIZATION: We also measured the polarization
content of reflected light at both λ=360 nm and 450 nm. We computed the
Degree of Polarization (DOP) of male and female specimens as well as of
the mock wings, defined as |ITE-ITM|/(ITE+ITM),
with ITE and ITM calculated by taking the total intensity of a
polarization image, at a range of angles of incident light θ and detection
angles φ. We analyzed DOP as a function of the combined angle
between θ and φ (which we termed “angular contrast”). The relationship
between angular contrast and DOP in blue and UV appeared to be non-linear,
so we tested various linear and polynomial mixed-effects models that
analyzed the relationship between these two variables and Sex. The
relationship between DOP in UV and angular contrast was best described by
a 2nd order polynomial (Table S1), whereas for DOP in blue, a 3rd order
polynomial provided the best fit (Table S1). The significance of the
orthogonal polynomial terms of angular contrast, Sex, and their
interactions were evaluated using parametric bootstrapping and model
comparison: First, a maximal mixed effects linear model was fitted that
considered Sex, the orthogonal polynomial terms of Angular contrast, and
their 1st order interactions as fixed effect terms, accounting for
repeated measurement of individuals by fitting random intercepts for
individuals. Then, to calculate the significance of each of these fixed
effect terms, 'smaller' models were constructed that lacked each
of these terms and were compared via parametric bootstrapping against
'larger' models that included these terms (but no higher order
terms). Effect subtraction proceeded hierarchically from interaction to
individual fixed effects. Larger and smaller models compared in this way
thus differ only by the term of interest. The maximal model,
containing all of the fixed effects and their interactions, described
above, was the best fit, most parsimonious model, and therefore was
selected to generate model fits and confidence bands using the ‘effects’
package in R (Fox 2003).(Halekoh & Højsgaard 2014). Confidence
bands, where included, were generated using the Kenward-Roger
coefficient-covariance matrix to compute effect standard errors, as
implemented in the ‘effects’ package. Parametric bootstrapping was done
using the 'Pbmodcomp' function of the 'pbkrtest'
package in R All mixed effects models were fit using maximum likelihood
with the 'lmer' function of package 'lme4' (Bates et
al. 2015) in R (Version 3.4.1, R Core Team 2017). R-squared values for
testing model fits were made using the r.squaredGLMM function of the
‘MuMIn’ package (Bartoń 2020) and the R2 function in the semEff
package (Murphy 2020). ANALYSIS OF SEX AND ELEVATION ON EDGE AND
POLARIZATION DETECTION: The effects of Sex and Elevation within the eye on
the percent of putative edge detectors and polarization detectors (ED and
PD respectively, as well as the more specific “ED-only” and “PD-only”
metrics, see Results for metric definitions) were investigated using mixed
effects linear models followed by parametric bootstrapping and model
comparison in a similar fashion to the polynomial mixed-effects model
workflow above. The maximal model, that considered Sex, Elevation, and
their 1st order interaction as fixed effect terms, accounting for repeated
measurement of individuals by fitting random intercepts for individuals
within the effect of Elevation, was the best fit, most parsimonious model,
and therefore was selected to generate model fits and confidence bands
using the ‘effects’ package in R (Fox 2003). Confidence bands, where
included, were generated using the Kenward-Roger coefficient-covariance
matrix to compute effect standard errors, as implemented in the ‘effects’
package. Parametric bootstrapping was done using the 'Pbmodcomp'
function of the 'pbkrtest' package in R (Halekoh &
Højsgaard 2014). All mixed effects models were fit using maximum
likelihood with the 'lmer' function of package 'lme4'
(Bates et al. 2015) in R (Version 3.4.1, R Core Team 2017). Plots are
made using the ggplot2 package in R (Wickham et al. 2016), and manually
edited in Inkscape (version 0.92.4).