Adult weight, wing size and shape of the four melanic morphs ofBactrocera dorsalis
After emergence, 30 males and 30 females of respective morphs were
killed, individually weighed, and their right-wing excised from the
thorax using a fine clamp. The insects were then preserved in in 70%
ethanol. The removed wings were singly slide-mounted before image
capture using a stereo microscope with LED and HD Camera Leica EZ4 HD
(Leica Microsystems, Switzerland) at 16× magnification, to avoid
deformation and enhance accuracy during photography and landmark
collections.
The digital photographs were opened in the ImageJ software and Cartesian
coordinates for 15 wing landmarks were generated (Fig. 1B). Using the
same software, wing size parameters including (i) wing length (distance
between the 5th and 15th landmark); (ii) wing width (distance between
the 3rd and 13th landmark) (Fig. 1B); and (iii) wing area were
generated. We used the PAST software V.3.09 (Hammer et al. 2001) to
compute the centroid size parameter considered as a multidimensional
measurement of wing size. It is calculated as the square root of the sum
of squared Euclidean distances between each landmark and the wing
centroid (Baleba et al. 2019). Also, based on the adult weight
parameter, we calculated wing loading using the following formula:\(wing\ loading\ (\frac{\text{kg}}{m^{2})}=\frac{\text{mass}}{\text{wing\ area}}\ \).
To assess whether these wing size parameters were affected by the sex of
the adults, the different level of melanin, and their interaction, we
ran a two-way analysis of variance (ANOVA). The only significant effect
that was conserved across the different wing size parameters was that
related to the melanin level. For this, we only considered this factor
in the 2 by 2 comparison, then performed an SNK posthoc test. All
statistical comparisons were considered significant whenP < 0.05. To identify correlations between centroid
size, wing length, wing width, wing area, adult weight and wing loading,
we performed separate principal component analysis (PCA) for four groups
using the R packages called “FactoMineR” (Le et al. 2008) and
“Factoextra” (Kassambara and Mundt 2020).
To determine the change of the wing shape across the different melanic
morphs, we imported the raw landmark Cartesian coordinates into MorphoJ
software (Klingenberg, 2011). We first performed a generalised
Procrustes analysis to extract shape information from the data and
eliminate differences in orientation, position and isometric size.
Afterwards, using sex and level of melanin as factors, we executed a
multivariate analysis of variance (MANOVA) to see whether these factors
could impact the wing shapes of B. dorsalis . To visualise the
wing shape deformations, we use the PAST software to generate the wing
deformation grid of the wing of each sex and morph using the thin-plate
spline analysis. Also, we used canonical variate analysis combined with
discriminant analysis to analyse the relative similarities (or
dissimilarities) of the different melanic morphs. To see the
significance of pairwise differences in mean shapes, we performed
permutation tests (10,000 rounds) with Procrustes distances.