Wing morphological responses to latitude and colonisation in a range expanding butterfly

Study species and sample collection

Pararge aegeria is a multivoltine species (capable of completing multiple generations in a year) which occurs throughout Europe to western Asia. Pararge aegeria can follow three different developmental pathways—pupal diapause, larval diapause or direct development—that may differentially affect morphology (Van Dyck & Wiklund, 2002). Within the UK, the range of P. aegeria has changed dynamically in the past 200 years. At the end of the 19th century there was a contraction of populations to south-west England, Wales and a refugial population in western Scotland, assumed to be in response to a change in climate (Emmet & Heath, 1990; Warren et al., 2001). Since the 1970s, the distribution of P. aegeria began to expand northwards from south-west England and Wales, argued to be mainly driven by increasing temperatures, but also other factors such as habitat preference. A secondary range expansion from the refugial population in western Scotland has recolonised much of central and northern Scotland. Increased habitat fragmentation of woodland habitats resulted in a greater than expected lag in the rate of range expansion compared to the climatic envelope (Hill, Thomas & Huntley, 1999). P. aegeria is now widespread across the UK.

A total of 774 male P. aegeria were hand netted across 54 sites (10–20 males/site) during the summers of 2016–2018 in the UK (Fig. 1; Table S1). In order to capture the pattern of range expansion, the site locations were chosen at a 10km grid scale aimed, firstly, at covering the whole of the geographic range and, secondly, to include the full range of number of years since P. aegeria was first recorded at 10km grid resolution. Specimens collected in 2018 were frozen on or the day after collection using a liquid nitrogen dry shipper and subsequently transferred to −80 ° C freezer for storage. Samples collected in 2016/17 were kept alive in a cool box for two days until transfer to a −80 ° C freezer. Permission for sampling at sites was obtained from landowners, including, but not limited to: Natural England, National Trust, Forestry Commission (England and Scotland), Woodland Trust, Yorkshire Wildlife Trust, Norfolk Wildlife Trust, local councils and site rangers through correspondence and verbal communication.

Photography

Wings were carefully removed from the body at their point of attachment using fine scissors. The photographic setup consisted of Nikon D80 with Micro-Nikkor 40 mm lens attached to a camera stand, and two stand-alone speedlight (YN560IV) flashes (ISO 160, aperture f/25, exposure time of 1/80 s and compensation level +5.0). Size and shape analyses were carried out on jpeg files, but for colour analysis, to retain more information, images were converted from RAW Nikon images (NEF) to portable network graphics (png) using the programme XnConvert v.1.82. Photographs were calibrated by the R package Patternize (Van Belleghem et al., 2018), using the ColorGauge Micro Target (Image Science Associates) to account for any changes in lighting (Fig. 2A). Wings were graded into four wear categories, both for physical damage and scale damage (1= no damage, 4= significant/total damage). The sum of these two factors, assumed to be loosely correlated with butterfly age, was used as a factor in the analyses, or as a filter to remove significantly damaged and old butterflies. The left wings were selected as a priority for analysis, but if significant damage was present the right side was used instead.

Environmental and demographic variables

We focused on four demographic or environmental variables: (1) number of years since first record or colonisation (years colonised); (2) latitude; (3) mean temperature during development; and (4) 10-year mean annual temperature.

To determine if developmental temperature accounts for any differences in morphology, latitudinal variation in emergence peaks had to be taken into account, due to P. aegeria being a multivoltine species. The specimens in this study were mainly collected late May—early August, which generally corresponds to the start of the second generation. However, more southerly populations emerge earlier in the summer and undergo more generations than those further north. Therefore, the emergence patterns of each 10km grid sampled, based on 10-year abundance data from the Butterflies for the New Millennium (BNM) recording scheme (2007–2017), was investigated using generalised additive models, in the R package mgcv (Wood, 2004). This allowed identification of the number of generations at each grid, and the month of the second-generation peak. To assess the effect of temperature during embryo, larva and pupa development on morphological traits, we used the average temperature of the local second-generation peak month and the preceding two months, in the year of collection (e.g., for a site with a second generation peak in June, the mean temperature during development was calculated as the mean of April- June in the collection year).

To investigate potential morphological responses to multi-generational selection related to geographic variation in temperature, we used 10-year mean annual temperature (T₁₀), at 1km resolution, from the year prior to collection (e.g., for a butterfly sample collected in 2018, T₁₀ was calculated from 2007–2017). Ten years was chosen as a suitable timescale for phenotypic response to selection, even for more recently colonised populations, whilst reducing the effect of any large fluctuations in annual temperature. Temperature (monthly mean at a 1km resolution) data for 2007–2017 were obtained from the Met Office HadUK-Grid UKCP18 dataset, available through CEDA Archives. 2018 data were provided directly by the UK Met Office.

The pattern of range expansion was described by the number of years since the first record of P. aegeria at each 10km grid (referred to as years colonised), using distribution data from the BNM from 1965 onwards. The reliability of this type of data as an accurate reflection of changes in species distribution relies on recorder effort and geographic coverage of records across the UK. Previous studies that have accounted for recorder effort reveal that the expansion of P. aegeria is a true occurrence and not due to changes in recorder effort (Parmesan et al., 1999). To assess the reliability of the assumed pattern of expansion we applied an approach used in Macgregor et al. (2019). For each grid, the percentage of regional species richness recorded was calculated (using data for 58 butterfly species provided by BNM, where regional species richness was the total number of species recorded in the 100 nearest neighbouring grids) in each year between 1965-2014. Grids were considered well-recorded in a given year if 10% or more of regional species richness was recorded. We then used this recording level to determine the level of confidence for the year each 10km grid was colonised by P. aegeria. Grids in which P. aegeria was recorded prior to 1975 were considered to be part of the core range of the species. From the remaining grids, we considered that we had high confidence of the colonisation year for grids which had been well-recorded in at least three years prior to the first record of P. aegeria (i.e., probable absence, followed by presence). We considered that we had low confidence of the colonisation year for grids in which the first record of P. aegeria coincided with or preceded the onset of good recording (i.e. P. aegeria first recorded in or before the third well-recorded year), since it was unclear whether such records represented true colonisation or simply the discovery of pre-existing populations. The reported pattern of range expansion is evident even when core and high confidence grids are considered in isolation (Fig. 1), showing that it is not an artifact of increased or changing patterns of recorder effort. Furthermore, the majority of sites sampled are from 10km grids that are categorised as core or high confidence sites, supporting our use of ‘years colonised’ as an accurate metric for the sites studied.

Wing landmarking and morphometrics

To allow for comparison of geometric morphometry and pattern across individuals, 14 and 11 landmarks were digitised on the forewing and hindwing, respectively, using tpsUtil version 1.78 (Rohlf, 2019) and tpsDig2 version 2.31 (Rohlf, 2017). All landmarks were placed on vein-vein or vein-wing margin intersections and provided adequate coverage of the overall shape and internal structure of the wings (Fig. 2). Landmarks were superimposed using generalised Procrustes analysis performed using the R package geomorph (Adams & Otárola-Castillo, 2013) and within MorphoJ version 1.07a (Klingenberg, 2011). This method standardises specimens to a common coordinate system through controlling size, orientation and position to align corresponding landmarks as closely as possible (Rohlf & Slice, 1990). Centroid size (CS) was also calculated from the landmarks (square root of the sum of squared distances between each landmark and the wing centroid), and was used as a measure of wing size and to account for allometry in the analysis of wing shape.

Wing colour, pattern and contrast

Pararge aegeria wings are brown with a principally cream spot pattern. Four complementary measures of colour and pattern were investigated: (1) average degree of lightness across the basal 3rd and entire wing surfaces (dorsal and ventral surfaces on forewing and hindwing); (2) average lightness within brown and cream areas (forewing dorsal only); (3) the relative area of brown and cream (forewing dorsal only); and (4) the contrast between brown to cream areas (forewing dorsal only).

Degree of lightness of the forewing and hindwing dorsal and ventral surfaces was investigated using the mean grey value for the whole wing and the basal third of the wing, closest to the body (basal colour). It has been suggested that wing basal colour is the most important for thermal regulation (Wasserthal, 1975). An ImageJ (FiJi distribution) macro was created to select the individual wings from the background, rotate them to the same orientation, separate wings into thirds (perpendicular to the longest axis), convert RGB images to 8-bit grey, and calculate the mean grey value for the whole wing and each wing section. The full 8-bit grey scale ranges from 0 (complete black) to 255 (complete white). Wings with scale damage of 4 were removed from the analysis, leaving 709 forewings and 642 hindwings. The effect of the environmental variables on mean grey values was analysed in a LMM, fitted by restricted maximum likelihood and nmkbw optimisation (lme4 v.1.1-21 package; Bates et al., 2015). Site nested within regional expansion (i.e., south-west England or western Scotland), and the standardised collection date metric were included as random effects. Significance testing followed the method detailed for size analysis.

To investigate variation in the brown and cream areas separately (dorsal forewing only as it has the most discrete pattern), a macro script for ImageJ (FiJi distribution) was written to calculate the mean grey value and area (number of pixels) for each (filtered for scale damage of 4). Linear regression was used to assess the relationship of brown (or cream) area with latitude, and an ANCOVA to determine if these relationships differed significantly from each other. The relationship of brown to cream colours in the forewing was quantified by linear regression on the residuals of each colour to latitude (to focus on the underlying relationship). Finally, the difference between the cream and brown mean grey value was calculated to produce a simple measure of average contrast between the dark and light areas of the wing. The effect of the environmental variables on contrast between brown and cream was analysed in a LMM, fitted by restricted maximum likelihood and nmkbw optimisation (lme4 v.1.1-21 package; Bates et al., 2015). The random effects included were the same as for the overall lightness analysis.

Wing morphometrics

Size

Forewing and hindwing size increase significantly with latitude (t-values > 1.96) and in more recently colonised populations (shown by negative relationship of size to an increase in number of years colonised), consistent with Bergmann’s rule and spatial selection. Forewing size is also weakly associated with temperature during development (t = 2.64) but not in hindwings (t = 1.65). In general, each of the environmental factors (latitude, years colonised, 10-year temperature average, and temperature during development) show a consistent effect (both in the strength and direction) on forewing and hindwing size, although latitude seems to have a stronger effect on hindwings compared to forewings (Table 1; Fig. 3). T₁₀ (10-year mean annual temperature) produced the lowest t-values (0.57, 0.58) across the environmental variables for both wings, indicating no effect of recent past temperature on wing size. Correlation between explanatory variables was considered acceptable for LMMs (Fig. S1). The Pearson correlation coefficient ranged between 0.14 (years colonised with temperature during development) to −0.74 (latitude with mean annual temperature). Although latitude is often considered a proxy for annual temperature, it also incorporates other environmental gradients that follow latitude, for example day length and amount of sunlight etc. Therefore, it was decided to retain both T₁₀ and latitude in the model.

Table 1:

Linear mixed model results for forewing and hindwing size (centroid size).

Predictors	Forewing centroid size				Hindwing centroid size
	Est.	SE	df	t	Est.	SE	df	t
(Intercept)	0.31	0.56	20.44	0.55	−0.69	0.54	1.62	−1.28
Latitude	0.04	0.01	13.31	3.85	0.05	0.01	0.48	6.43
Years colonised	−0.002	0.001	15.09	−2.83	−0.002	0.00	0.77	−2.53
Mean 10yr annual temp (T10)	0.01	0.02	40.83	0.57	0.012	0.02	28.63	0.58
Temp. during development	0.02	0.01	31.30	2.64	0.02	0.01	33.83	1.65
Random Effects
2	0.007				0.007
τ00	0.001 Julian day difference				0.003 Julian day difference
	0.002 Grid.10km:Expansion				0.002 Grid.10km:Expansion
	0.003 Expansion				0.000 Expansion
ICC	0.47				0.42
N	40 Grid.10km:Expansion				40 Grid.10km:Expansion
	2 Expansion				2 Expansion
	51 Julian day difference				51 Julian day difference
Observations	686				614
Marginal R2 / Conditional R2	0.33 / 0.64				0.48 / 0.70

DOI: 10.7717/peerj.10352/table-1

Notes:

Significant t values with a magnitude greater than 1.96 are indicated in bold.

σ²

Residual variance

T₀₀

Random effect variance

ICC

Interclass correlation coefficient

The populations varied between two and three generations per year across the 10km grid sampled, and size of forewings (mean CS of 2.60 (2 generations) and 2.55 (3 generations)) and hindwings (mean CS of 2.39 (2 generations) and 2.33 (3 generations)) are significantly smaller (1.9% and 2.5% smaller in forewings and hindwings, respectively) in populations with three generations (t-test: t = 4.96, df = 661.36, p < 0.001 for forewing; t = 4.45, df = 598.26, p < 0.001 for hindwing).

Shape

Shape is significantly associated with size (log CS) both for forewing and hindwing, reflecting shape allometry (p < 0.0001; Figs. 4A and 4B). Allometry is less pronounced in the forewing, where the regression explained 1.25% of shape variance compared to 4.14% in the hindwing (ANCOVA of regressions, F = 21.53, df = 1, p < 0.001; slope of 1.31 ± 0.14 and 2.25 ± 0.14 respectively; Figs. 4A and 4B). Shape changes for forewings and hindwings relative to size and the consensus shape are shown in Figs. 4C–4F, respectively. Overall, larger wings have increased width and roundness compared to smaller individuals. The shape difference between small and large forewings is most noticeable for landmark 7, which moves further away from the consensus shape with increasing size, and for landmark 1, which is shifted inwards, producing relatively broader and shorter wings. In the hindwings, landmarks 5 and 11 are more separated from each other in large hindwings, resulting in relatively wider wings compared to smaller hindwings.

The 2B-PLS analysis focused on covariation between the size-adjusted shape (block 1) and environmental variables (block 2). Overall, the permutation test showed a significant covariation between the two blocks both for the forewing (p = 0.006) and hindwing (p = 0.0001), supporting non-independence of the two blocks. The overall strength of association between the two blocks (as explained by the RV coefficient) is weak, at 0.012 and 0.025, respectively. The first PLS axis (PLS1) explained 73% and 91% for forewing and hindwing, respectively, and was principally loaded by years colonised and latitude (for both wings), but showed a weak correlation (0.20 and 0.22 respectively; Table 2; Figs. 5A and 5B). This suggests that, out of the environmental factors studied, the range expansion process has the largest effect on shape. T₁₀ was always loaded on the fourth PLS axis, explaining the least variation across both wings, implying a minimal effect of recent past temperature on wing shape.

Table 2:

Statistics for two-block partial least squares (2B-PLS) anlaysis for size-adjusted shape variables to environmental variables for forewing and hindwings.

The environmental variable most strongly associated with each partial least square (PLS) axis is indicated with an asterisk. Significant correlations (p-values) using 10,000 permutations are indicated in bold.

	FOREWING				HINDWING
	PLS1	PLS2	PLS3	PLS4	PLS1	PLS2	PLS3	PLS4
Loadings against:
Latitude	−0.18	−0.77*	0.55	0.28	−0.16	−0.88*	0.20	−0.40
Years colonised	0.98*	−0.14	0.12	0.00	0.98*	−0.18	0.05	0.02
Dev. temp.	−0.02	0.58	0.81*	0.00	−0.02	0.17	0.98*	0.12
Mean 10yr temp (T10)	0.05	0.22	−0.17	0.96*	0.09	0.41	0.04	−0.91*
Covariation explained	72.96	23.60	2.73	0.71	90.92	7.41	1.5	0.14
Correlation between blocks (r)	0.20	0.25	0.18	0.19	0.22	0.26	0.18	0.15
Correlation coeff. P-value (perm.)	0.001	<.0001	0.001	0.0001	0.0002	<.0001	0.0012	0.02

DOI: 10.7717/peerj.10352/table-2

Shape changes associated with PLS1 (associated most strongly with years colonised; Figs. 5C–5F) and the second PLS axis (PLS2; mainly latitude; not shown) indicate a general tendency towards longer, narrower forewings (i.e., with increasing numbers of years colonised). Within PLS1 this increase mainly occurs between the base or shoulder of the wing (landmark 1) to the apex (landmarks 12-14), while the distance between landmark 1 and 7 decreases (Figs. 5C and 5D). In comparison, the hindwing PLS1 (and PLS2) becomes more rounded. For PLS1, the increased roundness seems to be due to the majority of landmarks at the tail edge of the wing being more separated from one another (Figs. 5E and 5F).

Colour, pattern and contrast

The average lightness (average grey value) of whole wing surfaces, whether dorsal or ventral, has a complex relationship with latitude, with periodic fluctuations of lightness that are consistent in the forewing and hindwing (Fig. 6). The relationship of average lightness with the demographic and environmental variables are similar across the basal third of the wing (Table 3) and whole wing (Table S2). The lightness of ventral surfaces, both of forewings and hindwings, becomes significantly darker with increasing temperature during development, but significantly lighter with increasing latitude (Table 3; greyness value of 0 is black and 255 is white). The direction of effects is consistent among all four wing surfaces, although the trend is not significant for dorsal surfaces. There is no detectable effect of T₁₀ and years colonised on any wing surfaces. These effects account for variation due to population (England or Scotland) and sampling date.

The relationship between latitude and mean grey value of the dorsal forewing depends significantly on the colour considered (ANCOVA, F = 27.69, df = 1, p < 0.001). Both brown and cream become significantly lighter with latitude (p < 0.001), but the cream area becomes lighter faster (i.e., further north) than the brown area (slopes of 3.38 ± 0.25 vs 1.49 ± 0.26 greyscale units per degree of latitude, respectively). These slopes reflect a strong positive correlation between the lightness of brown and cream areas, which is independent of the latitudinal trend (R² = 0.68; p < 0.001; Fig. 7). However, the relative proportion of the dorsal forewing surface that is brown increases significantly with latitude (R² = 0.09, df = 702, p < 0.001). The analysis of contrast (difference between cream and brown mean grey values) shows that the level of contrast increases significantly with latitude (est. = 1.26 ± 0.50, df = 46.37, t = 2.54; Fig. 8), even when accounting for population (England or Scotland) variation and collection date relative to the site-specific emergence peak.

Size and shape variation

Forewing and hindwing size in P. aegeria increases with colonisation and latitude. Larger individuals were found in more recently colonised populations, following the expectation under spatial selection if larger wings are associated with dispersal, for which there is some evidence (Sekar, 2012; Flockhart et al., 2017). Our findings support the conclusion of Hughes, Hill & Dytham (2003), which were based on thorax size across a much more limited number of sites and geographic range of P. aegeria. On the reasonable assumption that wing size is directly correlated to body size in this species, as in other Lepidoptera (Chai & Srygley, 1990), increased wing size with latitude follows Bergmann’s rule, in agreement with previous studies (Dennis & Shreeve, 1989; Sibly, Winokur & Smith, 1997). Temperature during development shows a positive relationship with forewing size, which runs counter to the prediction of the temperature size rule (Atkinson & Sibly, 1997), but is consistent with experimental results (C Macgregor, 2020, unpublished data). Within this underlying temperature-size relationship, there is also an additional effect of season length and development time, as reflected by the observed reduction in size with number of generations. A limitation of using ambient temperature as a measure of temperature during development is that the micro-climate that individuals experience can be significantly modified by behaviour and other environmental factors, such as humidity and sunlight. Under fast demographic change, as in the range expansion of P. aegeria, the process of spatial selection may override selection pressures from environmental gradients. For example, in the Scottish expansion, wing size has responded more strongly to selection for dispersal than to the environmental gradients (all recently colonised site are noticeably larger than the core populations, irrespective of latitude and temperature).

The shape of forewings and hindwings, independent of size, were found to change during the range expansion process and with increasing latitude. Forewings are more rounded and hindwings are narrower in more recently colonised populations. Spatial selection during a range expansion often results in increased dispersal ability towards the leading edge (Phillips, Brown & Shine, 2010). The finding that the number of years colonised has the strongest loading to shape in both the forewings and hindwings suggests these shapes are more dispersive in this species. However, interpreting the functional consequences of fine-scale wing shape variation for different dimensions of flight performance (e.g., dispersal efficiency, acceleration, manoeuvrability) and tying shape changes to adaptive evolution is a complex and currently an unresolved problem (Le Roy, Debat & Llaurens, 2019). In fact, large, long and more pointed forewings are often considered the most dispersive form as this is associated with (less metabolically costly) gliding flight and migratory species, e.g., the Monarch butterfly (Danaus plexippus; Altizer & Davis, 2010; Flockhart et al., 2017). However, this assumption may not hold true for all species. For example, in the Glanville Fritillary (Melitaea cinxia), dispersive females have more rounded wings (Breuker, Brakefield & Gibbs, 2007), whilst another study found little or no evidence linking wing morphology to dispersal ability (Hanski et al., 2002).

In P. aegeria, the female is thought to be the more dispersive sex (Shreeve, 1986) and, in general, is larger with more rounded wings compared to males (Pellegroms, Van Dongen & Van Dyck, 2009). Mate location strategy in male P. aegeria varies between two behaviours, perching or patrolling, requiring different flight dynamics (Shreeve, 1984). Perchers require high acceleration to defend a territorial sunlit patch and intercept passing females, whereas patrollers require attributes for more sustained flight as they move from one spot to another in search of a female. The differing flight requirements of these behaviours is reflected in their thorax size, wing morphology and colour (Van Dyck, Matthysen & Dhondt, 1997; Van Dyck & Matthysen, 1998; Berwaerts, Van Dyck & Aerts, 2002). An increase in forewing roundness was also found in P. aegeria males across a large latitudinal (700km) gradient from France to Netherlands, where a decrease in aspect ratio (AR; calculated as 4 x forewing length/forewing area), was found further north (Van dewoestijne & Van Dyck, 2011). The AR is widely used as a predictor of flight performance but does not account for allometry, as in the geometric morphometric approach used in this study. Therefore, comparisons between studies based on AR and this one should be considered carefully. Nonetheless, Vandewoestijne and Van Dyck’s finding supports the pattern seen here, suggesting a common trend in wing shape with increasing latitude in this species.

The majority of studies focus solely on forewing shape. Although butterflies are mostly antermotoric (require, and predominantly use, forewings for flight), hindwings increase linear and turning acceleration, so are particularly important for aerial agility and predator evasion (Jantzen & Eisner, 2008). The distinct roles of wings in flight, coupled with our finding that shape changes are different between forewings and hindwings, suggest that evolutionary factors may differentially affect forewing and hindwing shape. This study highlights the need to include both wings in future studies.

Colour and pattern variation

Pararge aegeria has been previously described as becoming darker further north in Britain (Dennis & Shreeve, 1989). However, using quantitative measurements and a more spatially extensive sample, our results do not follow the expectation from the thermal melansim hypothesis (i.e., decreasing lightness with latitude). Lightness fluctuates with latitude producing a wave-like pattern that is consistent across both wings. The basal area becomes lighter with latitude, whereas average lightness decreases with temperature during development (darker with increasing temperature). Increased lightness at cooler temperatures during development could support an energetic trade-off between overall growth and melanin production, which is costly to synthesise (Talloen, Van Dyck & Lens, 2004). The production of melanin may be subject to selection pressures unrelated to thermoregulation (True, 2003). The level of melanism has implications for disease resistance (Wilson et al., 2001; Dubovskiy et al., 2013) UV protection (Bishop et al., 2016; Katoh, Tatsuta & Tsuji, 2018), predation (Bond & Kamil, 2002) and sexual selection (Jiggins et al., 2001; Kemp, 2007). Our samples are of male individuals and so changes in colour due to selection for thermoregulatory properties (increased melanism) are likely to be constrained by sexual selection (Tuomaala, Kaitala & Rutowski, 2012). Behavioural traits such as posture during basking, which was not assessed in this study, also effect thermal regulation (Kingsolver, 1985; Berwaerts et al., 2001). In P. aegeria, wing colour is associated with alternative mate-location strategies, with perchers being lighter in colour than patrollers (Van Dyck & Matthysen, 1998). The sample used here is likely biased towards perchers due to the increased likelihood of spotting and netting perching individuals. Furthermore, previous studies that find decreased lightness with latitude also include thorax colour which was not possible in this study (Zeuss et al., 2014).

The perceived darkening of P. aegeria at higher latitudes (as reported by recorders and the authors of this study) is probably due to the relative changes of brown and cream areas on the forewing. The area of brown colour increases significantly with latitude, which may also have thermal regulatory consequences. The strong correlation between the lightness of brown and cream areas is indicative of an underlying genetic and developmental mechanism controlling the ‘background’ production of melanin across the whole wing surface that is also sensitive to environmental cues during the larval or pupal stages. Nevertheless, the cream colour increases in lightness significantly faster than the brown colour, resulting in an overall increase in contrast between the brown and cream patches. Few studies have looked at the effect of pattern on thermoregulation, but the wing band pattern in Banded Peacock (Anartia fatima) has been shown to slow the rate of heating but not the overall thoracic temperature equilibrium (Brashears, Aiello & Seymoure, 2016). The consequences of the specific traits detailed here on thermal properties of P. aegeria wings have not been studied to date, and so conclusions relating these findings to the thermal regulation should be made with care.