A macroevolutionary analysis of cryptic coloration in sexually dichromatic grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomophidae).

A macroevolutionary analysis of cryptic coloration in sexually dichromatic grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomophidae). | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A macroevolutionary analysis of cryptic coloration in sexually dichromatic grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomophidae). Victor Hugo Ramírez-Delgado, Martín Alejandro Serrano-Meneses, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3895114/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2025 Read the published version in Oecologia → Version 1 posted 4 You are reading this latest preprint version Abstract Background matching and disruptive coloration are defense mechanisms of animals against visual predators. Disruptive coloration tends to evolve in microhabitats that are visually heterogeneous, while background matching is favored in microhabitats that are chromatically homogeneous. Controlling for the phylogeny, we explored the evolution of the coloration and the marking patterns in the sexual dichromatic and widely distributed Neotropical grasshoppers of the genus Sphenarium . These grasshoppers represent an excellent model to investigate the evolution of cryptic coloration on insects due to the heterogeneity of the environments where they have evolved. We found a correlation between the grasshoppers' coloration and disruptive markings with the chromatic properties of their environments that was inferred by the levels of precipitation during the rainy season. The results suggest that colors and marks patterns could evolve due to predation pressures. color in both sexes could offer camouflage that is not perfectly background matched to a single habitat but instead offers a degree of resemblance to multiple backgrounds. Moreover, we found that males and females chromatic properties differ between them and precipitation levels where the species are found. This suggests that the sexes have diverged in their response to the environments, favoring the evolution of sexual dichromatism in these grasshoppers. Background matching Comparative analysis Crypsis Disruptive coloration Grasshoppers Sexual dichromatism Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction In many animal taxa, coloration can be adaptative in several ways, including social signaling, thermoregulation, protection from ultraviolet light, and antipredator defenses (Cott 1940 ; Cuthill et al. 2017 ). Cryptic coloration is adaptative because it reduces the signal produced by an organism; it thus becomes less visually conspicuous to potential predators (Bond 2007 ; Théry and Gomez 2010 ). Two of the most spread strategies to reach cryptic coloration are background matching (BM) and disruptive coloration (DC) (Stevens et al. 2006 a; Stevens and Merilaita 2009 ; Quicke 2017 ). BM is the resemblance between the colors and patterns of an organism with its surroundings (Hughes et al. 2019 ). DC is the concealment of an organism's outline due to contrasting markings that break or distract the attention of the predators from the organism's outline (Stevens and Cuthill 2006 , Stevens and Merilaita 2009 ). BM can be favoured in chromatically homogeneous habitats (Robledo-Ospina et al. 2017 ; Orton and McBrayer 2019 ); its success depends on the coloration of the environment and the probability that an individual stays on the backgrounds where it is cryptic (Merilaita et al. 1999 ; Kang et al. 2016 ). However, BM can be ineffective at reducing the risk of detection when animals are in motion in heterogeneous environments (Ioannou and Krause 2009 ). DC can co-occur with BM and enhances concealment from various predators (Cuthill et al. 2005 ; Schaefer and Stobbe 2006 ; Fraser et al. 2007 ; Adams et al. 2019 ). DC success could be maximized when some marks on the organism's surface match the background and other marks have high contrast with the rest of the body (Stevens and Merilaita 2009 ). DC also has evolved in visually heterogeneous microhabitats because it breaks the outlines of the organisms independently of the variable background patterns and can conceal an organism while it is in motion (Stevens et al. 2006 a, b). The evolution of BM colors represents a compromise between matching closely a single background or adopting a generalist strategy where the individuals resemble multiple backgrounds to some extent but not perfectly (Hughes et al. 2019 ). On the other hand, DC could be independent of the background coloration and depends on the organisms' markings and shape (Merilaita and Lind 2005 ). DC could be more adaptative than BM in individuals with high vagility, which have a low probability of staying in specific backgrounds or inhabiting heterogeneous color habitats (Théry 2007 ; Théry and Gomez 2010 ; Robledo-Ospina et al. 2017 ). Because BM and DC strategies decrease the probability of detection by predators, selection will act on organisms so that they match the geographical variation in substrate color (Endler 1990 ; Stuart-Fox and Ord 2004 ; Rosenblum 2006 ; Marshall et al. 2015 ; Hantak and Kuchta 2018 ;Adams 2019). However, if males and females occupy different microhabitats because of their different sexual roles, natural selection could favour a divergence between females and males (Slatkin 1984), thereby favouring sex-specific cryptic color patterns (Forsman and Appelqvist 1999 ; Medina et al. 2016 ; Ramírez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. 2021 ). Sexual dichromatism studies have focused mainly on vertebrates (Font et al. 2009 ), and its evolution has been explained by differences in the selective pressures imposed by visual predators in only a few cases (Orton and McBrayer 2019 ). In invertebrates, the study of the evolution of sexual dichromatism has focused on arthropods, mainly also in the context of sexual selection (see Quicke 2017 and references there), although it is still poorly documented in the context of cryptic coloration (Forsman and Appelqvist 1999 ; Ramírez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. 2021 ). For instance, in some arthropod species, BM and DC tactics have evolved closely with the environment. Jumping spiders exhibit BM in relatively flat chromatic backgrounds, whereas in dynamically changing backgrounds, they exhibit DC (Robledo-Ospina et al. 2017 ). Also, crabs living in homogeneous backgrounds evolved BM, whereas those living in heterogeneous backgrounds have evolved DC (Price et al. 2019 ). In many grasshopper species, cryptic coloration has evolved as an anti-predator strategy (Tsurui et al. 2010 , 2013 ; Edelaar et al. 2019 ; Camacho et al. 2020 ); even different morphs from a grasshopper species have been described as disruptive or showing resemblance to their environment (Tsurui et al. 2013 ; Forsman 2018 ; Ramírez-Delgado and Cueva del Castillo 2020). Grasshoppers of the genus Sphenarium are found in a wide variety of environments and show wide variation in their color and marking patterns (Sanabria-Urbán et al. 2017 ). Moreover, some species of this genus exhibit a notorious sexual dichromatism; BM is associated with females and DC to males (Ramírez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. 2021 ; Ramírez-Delgado and Cueva del Castillo 2023). In S. zapotecum , in visually heterogeneous areas, predators spent more time searching for striped male morphs with lower BM and higher disruptive properties. On the other hand, females with high BM improve survival and significantly increase predator searching time (Ramírez-Delgado and Cueva del Castillo 2023). Sphenarium grasshoppers are the prey of many visually oriented vertebrates, including birds, mammals, reptiles (Kevan 1977 ), and humans, which have extensively consumed species of the genus since pre-Columbian times (Ortiz de Montellano 1978; Ramos-Elorduy and Pino-Moreno 1989). The Sphenarium genus consists of 17 flightless univoltine generalist herbivorous species distributed from central Mexico to northern Guatemala in a high altitudinal margin (0 to > 2600 masl, Sanabria-Urbán et al. 2015 ). Because the differences in altitude and climate associated with their distribution are substantial, these species are found in rainy and dry deciduous tropical forests, temperate forests, shrublands, and grasslands. In low seasonal environments, the vegetation remains mainly greenish all year round, while in highly seasonal environments, there is a transition between brownish and greenish tonalities. They hatch in May and June during the rainy season and die during the coldest months (December to February) (Sanabria-Urbán et al. 2017 ). Females are larger than males (Sanabria-Urbán et al. 2017 ), and both sexes are polygamous (Cueva del Castillo and Núñez-Farfán 1999; Cueva del Castillo and Núñez-Farfán 2002 ). The males tend to search actively for females, whereas females are less mobile and can be found close to the ground when they are about to lay their eggs (Ramírez-Delgado and Cueva del Castillo 2020). The diversity of environments these grasshoppers inhabit allows us to analyse the potential adaptive chromatic cryptic divergences between them. Despite the implication of DC and BM on the evolution of the chromatic patterns of the species, very few comparative studies have tested their relative impact on the chromatic evolution of lineages (Caro and Koneru 2021 ). Moreover, given that many cryptic species are sexually dichromatic, focusing on these taxa may reveal profound implications regarding the evolution of intraspecific chromatic variation with respect to selective pressures imposed by visual predators on the members of each sex. In this study, we explored the relationship between the coloration patterns of males and females of the grasshoppers of the genus Sphenarium and their environment marking patterns whilst controlling for phylogenetic effects. Because matching coloration between habitats and individuals can be impacted by seasonal and geographic climatic variation (Caro et al. 2016 ), we investigated the relationship between the environment and the grasshoppers' chromatic patterns by using the precipitation patterns in the localities and months where the adult grasshoppers were located. Since environmental coloration is strongly related to water availability, i.e. different color and patterns are expected from different levels of green vegetation and projected shadows of the vegetation (Yom-Tov and Geffen 2006 ), and due to local adaptation and BM, we further expected to find a positive relationship between environmental coloration and the marking patterns of males and females. Also, we hypothesized that this relationship would be stronger in females than males because of their lower mobility (Ramírez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. 2021 ). We predicted a positive relationship between the coloration of the grasshoppers and their environment with precipitation, and a positive relationship between the marking patterns of the grasshoppers and their environment with precipitation, due to the high contrast between lights and shadows associated with dense vegetation. Furthermore, if the selective pressure on males' and females' chromatic patterns differs, we would expect a divergence in their chromatic coevolution. 2 Materials and Methods 2.1 Image acquisition Between October and September 2017, and 2018. We obtained photographs of male and female adults of the 17 Sphenarium taxa , as well as their backgrounds in 17 localities across in central and southern Mexico (Fig. 1 ). The places varied in elevation from 77 to 2,374 masl (table S1 ). Details of number of photographs taken per species, sex, and data obtained for color and pattern analyses are provided in supplementary material (table S2). During data collection, the geographic position and elevation of each locality was recorded using a GPS-map 60CSx (Garmin, Kansas City, USA). In each location, we walked at a slow and steady pace whilst looking for grasshoppers in areas of approximately 100 m²; we registered the exact point where grasshoppers were found, and we caught them by hand. Once collected, the grasshoppers were placed in a cooler for approximately five minutes; this allowed us to lower the temperature and activity of individuals. We then placed each grasshopper back in the site where it was collected, we took a photograph of it against the background where it was found. We took the photographs below a white diffuser umbrella on sunny days. In the photographs we included a color checker card (X-Rite Color Checker Passport 2, Munsell Color Laboratories), which also included a size and a grey color scale. The photographs were taken with a Canon EOS 70D digital camera, fitted with 18–55 mm, f/3.5–5.6 lens. The settings of the camera, lens, and illumination were constant in all photographs. The aperture of the sensor was set to f-stops f/5.6; the values of light sensitivity (ISO) were set to 400; the focal distance was set at 55 mm; the camera was held at approximately 40 to 50 cm apart from the objective. The only setting that was adjusted between photographs was shutter speed; in this way we prevented overexposure in photographs. After the photographs were taken, the grasshoppers were temporarily placed in a plastic bag in order to avoid their potential recapture, and they were released after all the caught individuals in a given locality were photographed. We stored the images as RAW format. The camera has no modifications to allow ultraviolet sensitivity, so the photographs only contain information of the visible light spectrum; note, however, that grasshoppers have very little ultraviolet light reflectance (Tsurui et al. 2010 ). To obtain the photographs and make them objectively measurable we follow the suggestions outlined for Stevens et al. ( 2007 ), and Troscianko and Stevens ( 2015 ). 2.2 Image analysis To analyze the images, we used the Multispectral Image Calibration and Analysis (MICA) plugin (Troscianko and Stevens 2015 ) available for ImageJ software (Schneider et al. 2012 ). MICA plugin uses linear data from the raw images, controls the light conditions variation with the gray scales from the color checker and creates a multispectral image made of a stack of the images corresponding to long wave (R Channel), medium wave (G channel), and short wave (B channel). The multispectral image made it possible take objective measurements from different channel reflectances, in order to later compare the color and pattern between our photographs. From the multispectral images, we measured the reflectance of the RGB channels of the grasshoppers’ dorsal surface, and in the same photo, a similar area adjacent to the grasshoppers was considered the background surface of the grasshoppers. We also performed a granularity analysis on both surfaces. 2.2.1 color analysis We estimated brightness, saturation and hue from the RGB reflectance data. The three parameters allowed us to separate the achromatic (brightness) and chromatic (saturation and hue) properties of the images. Brightness refers to intensity of light on the image. Hue is determined by the dominant wavelength of the visible spectrum. It is the attribute that permits colors to be classified as red, yellow, green, blue, or an intermediate color, and saturation pertains the amount of white light mixed with a hue. High-saturation colors contain little or no white light. Brightness was obtained using the means of the three channel values: (R + G + B) / 3. Saturation was calculated as the Euclidean distance between completely white, and the RGB values obtained in our photographs. By following this approach, large distance values represent high saturation. We obtained two hue values: Hue1 = R / G, and Hue2 = (R + G) / B. These were calculated this way following the principle of opponent channels, which is based in the way opponent color channels work to detect color (Osorio and Vorobyev 2005 ). We must emphasize that the analyzes are interpreted from a human visible spectrum perspective. Spectral sensitivity can be different in other possible predators such as birds or mice, and their prey detectability could involve elements that we did not consider in this study (Théry and Gomez 2010 ). Nonetheless, humans’ processing capabilities are similar to those of natural predators, especially birds (Dukas and Kamil 2001 ), and some studies have noticed that human "predation" can predict predation by other visual predators under natural conditions (Karpestam et al. 2013 ). Moreover, in central and southern Mexico, S. zapotecum and other species of the genus have been traditional elements of the human diet since pre-Columbian times (Ortiz de Montellano 1978; Ramos-Elorduy and Pino-Moreno 1989). 2.2.2 Pattern analysis We performed a granularity (energy) analysis to measure the patterns in our photographs. This method roughly resembles the way animals decompose the visual information in different spatial frequencies (Stevens 2011 ). The photographs had to meet the following minimum pixel scale for this analysis 1 mm: 15 pixels; if the photographs did not meet this requirement, they were not used for pattern analysis (Troscianko and Stevens 2015 ). We used the average pixel reflectance of red and green channels to calculate the energy spectrum of grasshoppers and their background across 15 filters ranging from 2 to 256 pixels, in increments of multiples of √2. We obtained three descriptive values from this process: (i) overall pattern contrast: the amount of energy across all scales; (ii) the dominant marking size: the scale of dominant marking contrast (the filter where the highest value of energy is reached); and (iii) pattern diversity: the proportion between dominant marking contrast and rest of the measured energy. 2.3 Climatic data acquisition Because the Sphenarium species complete their reproductive cycle during the wettest and the driest periods of the year (Sanabria-Urbán et al. 2015 ), we obtained values from high-resolution climate surfaces (Fick and Hijmans 2017 ) of the precipitation of the wettest (PTW) and the driest (PDT) trimesters of the year for each sampling location. We expected green tonalities to be positively related with the amount of humidity and the amount of primary productivity of the plants, and brownish tonalities stronger associated with dryer environments (Yom-Tov and Geffen 2006 ). 2.4 Comparative analyses To correct for the phylogenetic non-independence of taxa, we used the phylogenetic relationships of the Sphenarium genus (Sanabria-Urbán et al. 2017 ), and the phylogenetic generalized least squares (PGLS) method to test the association between (a) the color and patterns of the grasshoppers, with (b) the colors and patterns of their environment and the climatic factors of the localities where the grasshoppers were found. PGLS were performed using the caper package (Orme et al. 2018 ) as implemented in R (ver. 4.0.1) (R Core Team 2020 ). PGLS is a comparative method that incorporates the phylogenetic autocorrelation of the data in the structure of errors (variance-covariance matrix; (Martins and Hansen 1997 ; Freckleton et al. 2002 ). In this case, the PGLS method was used to test the maximum likelihood of the evolutionary regression coefficient between traits (Pagel 1999 ). We also estimated the weighting parameter λ to improve the data's fit to the model and correct for phylogenetic effects in all generated PGLS models (Pagel 1999 ). λ measures phylogenetic dependence of observed trait data (Pagel 1999 ; Freckleton et al. 2002 ): the unit value approaches one when related species resemble each other more than they resemble species drawn at random from a phylogenetic tree (Blomberg and Garland Jr 2002 ). 2.5 Testing the association between the coloration of grasshoppers, their background coloration, and climatic factors. We constructed four PGLS models for each sex to test for associations between the coloration parameters and climatic factors and background coloration. The natural log-transformed values of hue, saturation, and brightness of the grasshoppers were used as response variables, and the natural log-transformed values of hue, saturation, and brightness of the background, were used as respective explanatory variables. We further included the precipitation variables; ln(PWT and ln(PDT)) as explanatory variables in all models. 2.6 Sexual dimorphism in color patterns To test the divergence between the chromatic patterns of males and females, we performed major axis regression of the chromatic patterns of males on the chromatic patterns of the females. A slope steeper than 1 would mean that the divergence in the chromatic could be higher in males than females (> 1), whereas a slope lower than 1(< 1) would mean higher in females than males. We used the phylogenetic independent contrasts method (PIC method; Felsenstein 1985 ), as implemented by the R package caper (Orme et al. 2013) to control for the phylogenetic non-independence of species (Harvey et al. 1991). We examined the studentized residuals looking for outliers >|±3|, which were discarded from the analysis. Ultimately, we regressed the independent contrasts of the log-transformed values of hue 1 and 2, saturation, brightness, overall patterns’ contrast, dominant marking size, and pattern diversity of the male grasshoppers on their counterparts on females by fitting major axis regression using the R package smatr (Warton et al. 2012 ). 3 Results 3.1 Association between grasshoppers’ coloration and background coloration + climatic factors The PGLS models in which we tested the association between coloration parameters show a general strong, positive association between the color parameters of male and female grasshoppers and their microhabitats (Table 1 A-F; Fig. 2 ). The only exception was hue2 (Table 1 G, H). Moreover, the analyses show a significant positive association between the brightness and saturation of the females with the driest trimester of the year (Table 1 A, B; Fig. 3 A, B), and a significant positive association between male brightness with the wettest trimester of the year (Table 1 E; Fig. 3 B). The values of λ are overall very low (zero or close to zero) (Table 1 A-H), which suggests a lack of phylogenetic signal and a rapid evolution of these coloration traits. 3.2 Association between climatic factors with grasshoppers and background patterns. In females, there is a significant, positive relationship between pattern diversity and habitat (Table 1 K; Fig. 2 E), whereas the PGLS models of males indicated that only the relationship between dominant marking contrast and the driest trimester of the year was significant and positive (Table 1 L; Fig. 2 F). The λ values were high for both, female pattern diversity and males dominant marking contrast (Table 1 K, L), which suggests high phylogenetic signal and slow evolution of traits. 3.3 Sexual dimorphism in color patterns The independent contrasts of the major axis regressions of males on females for hue 1 and 2, saturation, and brightness, overall patterns’ contrast and dominant marking size were positive and highly correlated, whereas the patterns diversity evolves independently in males and females (Table 2 ). The rate of divergence of brightness, saturation and hue2 was similar in males and females, while the divergence rate of hue1, overall patterns’ contrast and dominant marking size was higher on males than females (slope stepper than 1; Fig. 4 C, E, F). 4 Discussion The findings from our study contribute significant insights into the intricate evolution of coloration among Sphenarium grasshopper species. The observed disparities in color patterns among these species are attributed to localized adaptations to distinct environmental conditions. It is evident that natural selection has favoured the development of cryptic coloration in both male and female grasshoppers. However, a nuanced gender-specific divergence is discernible in their responses to environmental cues. Notably, the brightness of males exhibits a positive correlation with the wettest trimester of the year, while the brightness and saturation of females are positively associated with the driest trimester. Intriguingly, despite the absence of direct correlations with climatic variables, female mark patterns demonstrate a positive connection with their backgrounds. Conversely, male patterns show no such relationship with their surroundings, although a significant link between male marks (overall pattern contrast) and the driest trimester is evident. An interesting aspect that emerges is the relatively rapid evolution of chromatic patterns compared to the evolution of marking patterns. The disparity in the pace of evolutionary divergence between background resemblance and disruptive markings can be rationalized by the varied payoffs associated with each strategy. In scenarios where a species colonizes a new, vibrant, and uniform environment, natural selection tends to hasten the convergence of coloration with the new environment (referred to as BM). Conversely, the adaptability inherent in disruptive markings allows for a reduced divergence among species that face novel environments or alterations in their distribution range. In this context, the chromatic patterns exhibited by Sphenarium grasshoppers may embody a generalized strategy that moderately matches various backgrounds without precisely mimicking anyone. It's worth noting that the benefits derived from cryptic strategies differ between males and females. The divergence rate of hue1, overall pattern contrast, and dominant marking size are notably higher in males than in females. Conversely, the evolution of pattern diversity has proceeded independently within both genders, indicating a distinct niche specialization concerning light and shadow contrast for males and females. Nonetheless, the parallel evolution rates of brightness, saturation, and hue2 in males and females can be attributed to similar selective pressures faced by different species within their respective habitats. The divergence in chromatic patterns within Sphenarium grasshoppers finds its basis in the diverse environments where the genus has diversified (as evidenced by Sanabria-Urbán et al. 2017 ) and the behavioural disparities between males and females. The synchronization of Sphenarium life cycles with rainy seasons, as discussed by Sanabria-Urbán et al. ( 2015 ), likely underlies the strong associations between chromatic patterns and environmental conditions exhibited across all species. This linkage is attributable to the profound influence of precipitation on primary productivity (Yom-Tov and Geffen 2006 ), resulting in alterations in vegetation color and shadow patterns. Furthermore, the variations in male and female populations due to precipitation fluctuations on different timescales are notable. For instance, the protandrous nature of males in certain species aligns their maturation with the onset of the rainy season, maximizing mating opportunities. During this phase, male brightness synchronizes with light-green shades, and their marks may serve them well during the subsequent driest trimester, characterized by deeper contrasts between light and shadow. In contrast, females, strategically maturing later than males, exhibit color and mark patterns that correlate with vegetation plots and backgrounds, which they frequent during their reproductive season. Males' active pursuit of females, often atop plants, juxtaposes the stationary female behaviour, placing them closer to the ground where they lay eggs. The pattern of marks on the dorsal surface of these grasshoppers serves dual purposes, providing BM and potentially contributing to DC, as postulated by Robledo-Ospina et al. ( 2017 ). Such highly contrasting markings might disrupt predators' visual perception or divert attention from the organism's contours, an adaptive advantage, as Merilaita ( 1998 ) and Hughes et al. ( 2019 ) suggested. However, the potential implications of these marking patterns on processes such as sexual selection warrant consideration (with reference to Cueva del Castillo and Cano-Santana 2001). Notably, the divergence rate in male marking patterns surpasses that of females, indicating heightened natural selection pressure on these male-specific traits. Remarkably, in the case of S. zapotecum , predators allocate more time to locate striped male morphs with reduced BM and increased disruptive characteristics and females with heightened background resemblance. This behaviour augments prey survival (Ramírez-Delgado and Cueva del Castillo 2023). It is conceivable that these grasshoppers adopt a form of camouflage that offers partial resemblance to multiple backgrounds rather than a perfect match to a singular habitat. Alternatively, their camouflage may operate somewhat autonomously from BM strategies (as posited by Hughes et al. 2019 ). Nevertheless, the apparent convergence between Sphenarium chromatic patterns and their environments stems from a blend of differential predation dynamics and proactive habitat selection. This phenomenon parallels other grasshopper species (as demonstrated by Edelaar et al. 2017 , 2019 ; Heinze et al. 2022 ). The survival of these grasshoppers hinges on their ability to navigate diverse predator threats, including avian, mammalian, reptilian, and arthropod predators (Kevan 1977 ), as well as human predation (highlighted by Sanabria-Urbán and Cueva del Castillo 2020 ). Their chromatic and achromatic visual cues have evolved in response to the distinct searching strategies of these predators. Remarkably, the resemblance of marking patterns to the environment extends to chromatic and achromatic variables. However, while chromatic cues excel in close-range searches, achromatic information proves more effective in long-distance prey detection (Schaefer and Stobbe 2006 ; Cazetta et al. 2009 ). Notably, human behaviour as a predator mirror that of natural predators, particularly birds, a similarity exploited in some studies to predict predation by other visual predators in natural settings (Karpestam et al. 2013 ). This intriguing correspondence has likely influenced the evolution of Sphenarium grasshoppers' chromatic patterns. Considering the relative scarcity of phylogenetic comparative studies concerning cryptic coloration evolution, our research undoubtedly contributes to a foundational comprehension of color evolution within the context of selection pressures imposed by visual predators. Nonetheless, it is imperative to acknowledge that future investigations into habitat preference and the exploration of other potential functions of grasshopper coloration, such as thermoregulation or UV radiation protection, hold the potential to unveil additional factors shaping the evolution of coloration within this cohort of neotropical grasshoppers. Declarations Acknowledgments We are grateful to Judith X. Ponce-Wainer for her valuable suggestions to improve the manuscript's quality. We are also grateful to Hojun Song for his thoughtful review of the manuscript. VHR-D is grateful for receiving a CONAHCyT scholarship (385795) for graduate studies. Funding: This research was supported by a PAPIIT, UNAM grant (IN211617) to RCC. Conflicts of interest: None declared. Data will be available under reasonable request Authors' contributions: VHRD and RCCM originally formulated the idea, VHR developed methodology, VHRD conducted fieldwork, VHRD conducted imaging analysis, VHRD, RCCM and MASM performed statistical analyses, VHRD, RCCM and MASM wrote the manuscript. References Adams WJ, Graf EW, Anderson M (2019) Disruptive coloration and binocular disparity: breaking camouflage. Proc. R. Soc. B-Biol. Sci. 286: 20182045. doi.org/10.1098/rspb.2018.2045 Blomberg SP, Garland T Jr (2002) Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J Evol Biol 15:899–910. doi.org/10.1046/j.1420-9101.2002.00472.x Bond AB (2007) The Evolution of Color Polymorphism: Crypticity, Searching Images, and Apostatic Selection. Ann Rev Ecol Evol Syst 38:489–514. doi.org/10.1146/annurev.ecolsys.38.091206.095728 Camacho C, Sanabria-Fernández A, Baños-Villalba A, Edelaar P (2020) Experimental evidence that matching habitat choice drives local adaptation in a wild population. Proc. R. Soc. B-Biol. Sci 287: 20200721. doi.org/10.1098/rspb.2020.0721 Caro T, Koneru M (2021) Towards an ecology of protective coloration. Biol Rev 96:611–641. doi.org/10.1111/brv.12670 Caro T, Sherratt TN, Stevens M (2016) The ecology of multiple colour defences. Evol Ecol 30:797–809. doi.org/10.1007/s10682-016-9854-3 Cazetta E, Schaefer HM, Galetti M (2009) Why are fruits colorful? the relative importance of achromatic and chromatic contrasts for detection by birds. Evol Ecol 23:233–244. doi.org/10.1007/s10682-007-9217-1 Cott HB (1940) Adaptive Coloration in Animals. Oxford University Press, London del Cueva R, Cano-Santana Z (2001) Variación de la coloración corporal de Sphenarium Purpurascens (Orthoptera: Pyrgomorphidae) en función del sexo y su relación con la formación de parejas en un ambiente heterogéneo. Fol Entomol Mex 40:297–309 Cueva del Castillo R, González-Zertuche M, Ramírez-Delgado VH (2021) Interpopulation variation in sexual dichromatism in the Neotropical grasshopper Sphenarium purpurascens (Orthoptera: Pyrgomorphidae). Biol J Linnean Soc132(4):900–911. doi.org/10.1093/biolinnean/blaa227 Cueva del Castillo R, Núñez-Farfán J (2002) Female mating success and risk of pre-reproductive death in a protandrous grasshopper. Oikos 96:217–224. doi.org/10.1034/j.1600-0706.2002.960203.x Cueva del Castillo R, Núñez-Farfán J, Cano-Santana Z (1999) The role of body size in mating success of Sphenarium purpurascens in Central Mexico. Ecol Entomol 24:146–155. doi.org/10.1046/j.1365-2311.1999.00188.x Cuthill IC, Stevens M, Sheppard J, Maddocks T, Párraga CA, Troscianko TS (2005) Disruptive coloration and background pattern matching. Nature 434:72–74. doi.org/10.1038/nature03312 Cuthill IC, Allen WL, Arbuckle K, Caspers B, Chaplin G, Hauber ME et al (2017) The biology of color. Science 357(6350):eaan0221. 10.1126/science.aan0221 Dukas R, Kamil AC (2001) Limited attention: the constraint underlying search image. Behav Ecol 12:192–199. doi.org/10.1093/beheco/12.2.192 Edelaar P, Baños-Villalba A, Escudero G, Rodríguez-Bernal C (2017) Background colour matching increases with risk of predation in a colour-changing grasshopper. Behav Ecol 28:698–705. doi.org/10.1093/beheco/arx016 Edelaar P, Baños-Villalba A, Quevedo DP, Escudero G, Bolnick DI, Jordán-Andrade A (2019) Biased movement drives local cryptic coloration on distinct urban pavements. Proc R Soc B-Biol Sci 286:20191343. doi.org/10.1098/rspb.2019.1343 Endler JA (1990) On the measurement and classification of colour in studies of animal colour patterns. Biol J Linnean Soc 41:315–352. doi.org/10.1111/j.1095-8312.1990.tb00839.x Felsenstein J (1985) Phylogenies and the Comparative Method. Am Nat 125:1–15. doi.org/10.1086/286013 Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. doi.org/10.1002/joc.5086 Font E, Pérez i de Lanuza G, Sampedro C (2009) Ultraviolet reflectance and cryptic sexual dichromatism in the ocellated lizard, Lacerta (Timon) lepida (Squamata: Lacertidae). Biol J Linnean Soc 97: 766–780. doi.org/10.1111/j.1095-8312.2009.01251.x Forsman A (2018) On the role of sex differences for evolution in heterogeneous and changing fitness landscapes: insights from pygmy grasshoppers. Philos Trans R Soc B-Biol Sci 373:20170429. https://doi.org/10.1098/rstb.2017.0429 Forsman A, Appelqvist S (1999) Experimental manipulation reveals differential effects of colour pattern on survival in male and female pygmy grasshoppers. J Evol Biol 12:391–401. doi.org/10.1046/j.1420-9101.1999.00041.x Fraser S, Callahan A, Klassen D, Sherratt TN (2007) Empirical tests of the role of disruptive coloration in reducing detectability. Proc R Soc B-Biol Sci 274:1325–1331. doi.org/10.1098/rspb.2007.0153 Freckleton RP, Harvey PH, Pagel M (2002) Phylogenetic Analysis and Comparative Data: A Test and Review of Evidence. Am Nat 160:712–726. doi.org/10.1086/343873 Hantak MM, Kuchta SR (2018) Predator perception across space and time: relative camouflage in a colour polymorphic salamander. Biol J Linnean Soc 123:21–33. doi.org/10.1093/biolinnean/blx132 Harvey PH, Pagel MD (1991) The comparative method in evolutionary biology. Oxford University Press Oxford Heinze P, Dieker P, Rowland HM, Schielzeth H (2022) Evidence for morph-specific substrate choice in a green-brown polymorphic grasshopper. Behav Ecol 33:17–26. doi.org/10.1093/beheco/arab133 Hughes A, Liggins E, Stevens M (2019) Imperfect camouflage: How to hide in a variable world? Proc R Soc B-Biol Sci 286:20190646. doi.org/10.1098/rspb.2019.0646 Ioannou CC, Krause J (2009) Interactions between background matching and motion during visual detection can explain why cryptic animals keep still. Biol Lett 5:191–193. doi.org/10.1098/rsbl.2008.0758 Kang C, Kim YE, Jang Y (2016) Colour and pattern change against visually heterogeneous backgrounds in the tree frog Hyla japonica . Sci Rep 6:22601. doi.org/10.1038/srep22601 Karpestam E, Merilaita S, Forsman A (2013) Detection experiments with humans implicate visual predation as a driver of colour polymorphism dynamics in pygmy grasshoppers. BMC Ecol 13:17. doi.org/10.1186/1472-6785-13-17 Kevan DK (1977) The American Pyrgomorphidae (Orthoptera). Rev Soc Entomol Argentina 36:3–28 Marshall KLA, Philpot KE, Damas-Moreira I, Stevens M (2015) Intraspecific Colour Variation among Lizards in Distinct Island Environments Enhances Local Camouflage. PLoS ONE 10(9):e0135241. doi.org/10.1371/journal.pone.0135241 Martins EP, Hansen TF (1997) Phylogenies and the Comparative Method: A General Approach to Incorporating Phylogenetic Information into the Analysis of Interspecific Data. Am Nat 149:646–667. doi.org/10.1086/286013 Medina I, Losos JB, Mahler DL (2016) Evolution of dorsal pattern variation in Greater Antillean Anolis lizards. Biol J Linnean Soc 120:427–435. doi.org/10.1111/bij.12881 Merilaita S (1998) Crypsis through disruptive coloration in an isopod. Proc R Soc B-Biol Sci 265:1059–1064. doi.org/10.1098/rspb.1998.0399 Merilaita S, Lind J (2005) Background-matching and disruptive coloration, and the evolution of cryptic coloration. Proc R Soc B-Biol Sci 272:665–670. doi.org/10.1098/rspb.2004.3000 Merilaita S, Tuomi J, Jormalainen V (1999) Optimization of cryptic coloration in heterogeneous habitats. Biol J Linnean Soc 67:151–161. doi.org/10.1111/j.1095-8312.1999.tb01858.x Orme D et al (2018) The caper package: comparative analysis of phylogenetics and evolution in R, R package version 2.10 de Ortiz BR (1978) Aztec Cannibalism: An Ecological Necessity? Science 200:611–617. 10.1126/science.200.4342.611 Orton RW, McBrayer LD (2019) Resolving tradeoffs among crypsis, escape behavior, and microhabitat use in sexually dichromatic species. Oecologia 189:91–104. doi.org/10.1007/s00442-018-4301-5 Osorio D, Vorobyev M (2005) Photoreceptor sectral sensitivities in terrestrial animals: adaptations for luminance and colour vision. Proc R Soc B-Biol Sci 272:1745–1752. doi.org/10.1098/rspb.2005.3156 Pagel M (1999) Inferring the historical patterns of biological evolution. Nature 401:877–884. doi.org/10.1038/44766 Price N, Green S, Troscianko J, Tregenza T, Stevens M (2019) Background matching and disruptive coloration as habitat-specific strategies for camouflage. Sci Rep 9:1–10. doi.org/10.1038/s41598-019-44349-2 Quicke DL (2017) Mimicry, crypsis, masquerade and other adaptive resemblances. John Wiley & Sons, Oxford R Core Team (2020) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ Ramírez-Delgado VH, del Cueva R (2023) Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment. Entomol Exp Appl 171:258–267. doi.org/10.1111/eea.13278 Ramírez-Delgado VH, del Cueva R (2020) Background matching, disruptive coloration, and differential use of microhabitats in two neotropical grasshoppers with sexual dichromatism. Ecol Evol 10:1401–1412. doi.org/10.1002/ece3.5995 Ramos-Elorduy J, Moreno JMP (1989) Los Insectos comestibles en el México antiguo: estudio etnoentomológico. AGT Robledo-Ospina LE, Escobar-Sarria F, Troscianko J, Rao D (2017) Two ways to hide: Predator and prey perspectives of disruptive coloration and background matching in jumping spiders. Biol J Linnean Soc 122:752–764. doi.org/10.1093/biolinnean/blx108 Rosenblum EB (2006) Convergent Evolution and Divergent Selection: Lizards at the White Sands Ecotone. Am Nat 167:1–15. doi.org/10.1086/498397 Sanabria-Urbán S, Song H, Oyama K, Gonzalez-Rodriguez A, Serrano-Meneses MA, Cueva del Castillo R (2015) Body Size Adaptations to Altitudinal Climatic Variation in Neotropical Grasshoppers of the Genus Sphenarium (Orthoptera: Pyrgomorphidae). PLoS ONE 10(12):e0145248. https://doi.org/10.1371/journal.pone.0145248 Sanabria-Urbán S, Song H, Oyama K, Gonzalez-Rodríguez A, Cueva del Castillo R (2017) Integrative taxonomy reveals cryptic diversity in neotropical grasshoppers: Taxonomy, phylogenetics, and evolution of the genus Sphenarium Charpentier, 1842 (Orthoptera: Pyrgomorphidae). Zootaxa 4274:1–86. doi.org/10.11646/zootaxa.4274.1 Sanabria-Urbán S, Cueva del Castillo R (2020) The evolution and diversification of neotropical generalist herbivores: the evolutionary history of the grasshopper genus Sphenarium Charpentier, 1842. In: Núñez-Farfán J, Valverde PL (eds) Evolutionary Ecology of Plant-Herbivore Interaction. Springer, Belin, pp 277–292 Schaefer HM, Stobbe N (2006) Disruptive coloration provides camouflage independent of background matching. Proc R Soc B-Biol Sci 273:2427–2432. doi.org/10.1098/rspb.2006.3615 Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675. doi.org/10.1038/nmeth.2089 Stevens M, Cuthill IC, Windsor AMM, Walker HJ (2006) Disruptive contrast in animal camouflage. Proc R Soc B-Biol Sci 273:2433–2438. 10.1098/rspb.2006.3614 Stevens M, Cuthill IC, Párraga AC, Troscianko T (2006) The effectiveness of disruptive coloration as a concealment strategy. Prog Brain Res 155:49–64. 10.1016/S0079-6123(06)55004-6 Stevens M, Párraga CA, Cuthill IC, Partridge JC, Troscianko TS (2007) Using digital photography to study animal coloration. Biol J Linnean Soc 90:211–237. doi.org/10.1111/j.1095-8312.2007.00725.x Stevens M (2011) Avian vision and egg colouration: Concepts and measurements. Avian Biol Res 4:168–184. doi.org/10.3184/175815511X1320779017795 Stevens M, Cuthill IC (2006) Disruptive coloration, crypsis and edge detection in early visual processing. Proc R Soc B-Biol Sci 273:2141–2147. doi.org/10.1098/rspb.2006.3556 Stevens M, Merilaita S (2009) Animal camouflage: Current issues and new perspectives. Philos Trans R Soc B-Biol Sci 364:423–427. doi.org/10.1098/rstb.2008.0217 Stuart-Fox DM, Ord TJ (2004) Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc R Soc B-Biol Sci Sciences 271:2249–2255. /doi.org/10.1098/rspb.2004.2802 Théry M (2007) Colours of background reflected light and of the preys eye affect adaptive coloration in female crab spiders. Anim Behav 73:797–804. doi.org/10.1016/j.anbehav.2006.06.015 Théry M, Gomez D (2010) Insect Colours and Visual Appearance in the Eyes of Their Predators. Adv Insect Physiol 10:267–353. doi.org/10.1016/S0065-2806(10)38001-5 Troscianko J, Stevens M (2015) Image calibration and analysis toolbox - a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol Evol 6:1320–1331. doi.org/10.1111/2041-210X.12439 Tsurui K, Honma A, Nishida T (2010) Camouflage effects of various colour-marking morphs against different microhabitat backgrounds in a polymorphic pygmy grasshopper Tetrix japonica . PLoS ONE 5(7):e11446. https://doi.org/10.1371/journal.pone.0011446 Tsurui K, Honma A, Nishida T (2013) Size-dependent predation risk partly explains the sex-related marking polymorphism in the sexually size-dimorphic pygmy grasshopper Tetrix japonica . Entomol Sci 16:136–144. doi.org/10.1111/j.1479-8298.2012.00543.x Warton DI, Duursma RA, Falster DS, Taskinen S (2012) smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol Evol 3:257–259. doi.org/10.1111/j.2041-210X.2011.00153.x Yom-Tov Y, Geffen E (2006) Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148:213–218. 10.1007/s00442-006-0364-9 Tables Table 1. PGLS models of Sphenarium grasshoppers on coloration and pattern parameters for both sexes. Response variable Explanatory variable D.f. ¶ T F Multiple R² P λ †† A) ♀ Brightness ♀ Bg-Brightness † 6.871 13.637 <0.0001 PDT ‡ 3,13 -3.131 2.831 0.861 0.008 0 PWT § 0.929 0.249 0.369 B) ♀ Saturation ♀ Bg-Saturation 7.253 15.200 <0.0001 PDT 3,13 3.119 2.811 0.867 0.008 0 PWT -1.320 0.503 0.209 C) ♀ Hue 1 ♀ Bg-Hue1 6.653 12.787 <0.0001 PDT 3,13 -0.849 0.208 0.770 0.411 0 PWT 1.110 0.356 0.286 D) ♀ Hue 2 ♀ Bg-Hue2 1.866 1.005 0.084 PDT 3,13 1.497 0.648 0.335 0.158 0 PWT -0.853 0.210 0.158 E) ♂ Brightness ♂ Bg-Brightness 6.013 10.445 <0.0001 PDT 3,13 -1.489 0.641 0.824 0.160 0.267 PWT 2.437 1.716 0.030 F) ♂ Saturation ♂ Bg-Saturation 6.558 12.423 <0.0001 PDT 3,13 1.142 0.377 0.833 0.274 0.214 PWT -2.117 1.295 0.054 G) ♂ Hue 1 ♂ Bg-Hue1 3.658 3.866 0.006 PDT 3,13 -0.081 0.002 0.514 0.936 0 PWT 0.936 0.253 0.419 H) ♂ Hue 2 ♂ Bg-Hue2 1.866 1.005 0.084 PDT 3,13 1.497 0.648 0.335 0.158 0 PWT -0.853 0.210 0.409 I) ♀Overal patterns' contrast ♀ Bg-overall patterns’ contrast 1.483 0.635 0.161 PDT 3,13 -1.930 1.076 0.2702 0.076 0 PWT 0.610 0.107 0.552 J) ♀ dominant marking size ♀ Bg-dominant marking size 0.552 0.088 0.872 PDT 3,13 0.167 0.008 0.1073 0.870 0 PWT -1.036 0.310 0.319 K) ♀ Pattern diversity ♀ Bg-pattern diversity 2.901 2.431 0.012 PDT 3,13 -1.022 0.301 0.2630 0.325 0.603 PWT 1.629 0.767 0.127 L) ♂Overall patterns' contrast ♂ Bg-overall patterns’ contrast 0.846 0.207 0.412 PDT 3,13 -2.444 1.725 0.315 0.029 0.588 PWT 1.052 0.320 0.312 M) ♂ dominant marking size ♂ Bg-dominant marking size 0.439 0.056 0.667 PDT 3,13 0.6179 0.110 0.335 0.547 0 PWT -1.364 0.538 0.196 N) ♂ Pattern diversity ♂ Bg-dominant marking contrast 0.329 0.031 0.748 PDT 3,13 -1.147 0.380 0.335 0.272 0 PWT 0.556 0.089 0.588 † Bg: background; ‡ PDT: precipitation of the driest trimester; § PWT precipitation of the wettest trimester, ¶ D.f.: degrees of freedom, †† λ: Pagel’s Lambda. The data was natural log-transformed prior to analyses. Table 2. Major axis regressions values of the chromatic patterns of males on the chromatic patterns of the females. Coloration /patterns traits β † UCL ‡ LCL § r 2 r w ¶ D.f. P Brightness 1.05 1.29 0.85 0.86 0.13 13 0.6298 Saturation 1.10 1.34 0.9 0.87 0.26 13 0.3449 Hue1 1.66 2.37 1.16 0.59 0.63 13 0.0096 Hue2 1.30 2.02 0.84 0.37 0.32 13 0.2373 Overall patterns’ contrast 1.73 2.21 1.35 0.81 0.80 13 0.0002 Dominant marking Size 2.61 4.33 1.57 0.21 0.78 12 0.0007 Patterns’ diversity -20.4 - - 0.002 - - - † β: Coefficients of regressions , ‡ UCL and § LCL: upper and lower 95% confidence intervals, ¶ r w : Wald statistic values. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3895114","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270839658,"identity":"70597da5-bed1-484b-9463-93fa88d230b5","order_by":0,"name":"Victor Hugo Ramírez-Delgado","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México Facultad de Estudios Superiores Iztacala: Universidad Nacional Autonoma de Mexico Facultad de Estudios Superiores Iztacala","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"Hugo","lastName":"Ramírez-Delgado","suffix":""},{"id":270839659,"identity":"7b19dcfb-ac30-43dd-9e57-f5e7eb19ca1d","order_by":1,"name":"Martín Alejandro Serrano-Meneses","email":"","orcid":"","institution":"Universidad de las Américas Puebla: Universidad de las Americas Puebla","correspondingAuthor":false,"prefix":"","firstName":"Martín","middleName":"Alejandro","lastName":"Serrano-Meneses","suffix":""},{"id":270839660,"identity":"415e548c-3a1e-444a-9bda-31e47b7e5cde","order_by":2,"name":"Raúl Cueva del Castillo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYPACGyBmbmBgMCBeSxoQM5Km5TBUCzHAvP2M4WOemvNy/O2NjQ+/FNgxGNxufrqBMecwTi0yZ3KMjXmO3TaWOHOw2VjGIJnB4M4xsxuM29JwapGQ4DGTnMF2O3GDRGKbtIQBM4PBjQSQFht8Wsx/zvh3rh6qpR6oJf0bUIsEXlsYPrYdSDAAapH8YHAYqCWHgC08acUSH/uSDWeA/MJgcJxH8kZO2Y1EfH5hP7zxQ8I3O3n+9uaDD3/8qZbju5G+7cbHbbhDDAUw8zAw8IBZCcRpAEbmD2JVjoJRMApGwYgCAFkmUkqMQHeaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6385-0729","institution":"Universidad Nacional Autonoma de Mexico Facultad de Estudios Superiores Iztacala","correspondingAuthor":true,"prefix":"","firstName":"Raúl","middleName":"Cueva del","lastName":"Castillo","suffix":""}],"badges":[],"createdAt":"2024-01-24 20:03:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3895114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3895114/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00442-024-05643-7","type":"published","date":"2025-01-10T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50722574,"identity":"92ffc812-db1f-48a9-8df2-86a03f8296b9","added_by":"auto","created_at":"2024-02-06 10:16:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1580733,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sites where the photographs of \u003cem\u003eSphenarium\u003c/em\u003egrasshoppers were taken: \u003cem\u003eS. purpurascens\u003c/em\u003e (19.31N, 99.19W), \u003cem\u003eS. borrei\u003c/em\u003e(19.92N, 101.74W), \u003cem\u003eS. occidentalis\u003c/em\u003e (18.66N, 101.65W), \u003cem\u003eS. cypticum\u003c/em\u003e(18.18N, 100.65W), \u003cem\u003eS.rugosum\u003c/em\u003e (18.68N, 99.53W), \u003cem\u003eS. planum\u003c/em\u003e (18.48N, 97.45W), \u003cem\u003eS. histrio \u003c/em\u003e(16.93N, 96.42N), \u003cem\u003eS. variabile \u003c/em\u003e(16.93N, 96.42N), \u003cem\u003eS. miztecum\u003c/em\u003e (16.37N, 98.47W), \u003cem\u003eS. adelinae\u003c/em\u003e (17.43N, 99.47W), \u003cem\u003eS. macrophallicum \u003c/em\u003e(19.38N, 100.39W),\u003cem\u003e S. tarascum \u003c/em\u003e(19.20N, 101.73W), \u003cem\u003eS. infernalis\u003c/em\u003e (18.94N, 101.81W), \u003cem\u003eS. zapotecum\u003c/em\u003e (17.07N, 97.83N), \u003cem\u003eS. mexicanum\u003c/em\u003e (17.75N, 96.31W), \u003cem\u003eS. minimum \u003c/em\u003e(19.09N, 97.03W), and \u003cem\u003eS. totonacum\u003c/em\u003e (19.72N, 96.68W).\u003c/p\u003e","description":"","filename":"Figure154.png","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/42d5d3ef0be4b997e398b3c2.png"},{"id":50722576,"identity":"484dc7b8-1bdb-47cc-a15a-e6101e9a71a8","added_by":"auto","created_at":"2024-02-06 10:16:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":536186,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between chromatic variables of \u003cem\u003eSphenarium \u003c/em\u003egrasshoppers with the chromatic variables of their background, as tested using PGLS; (A) Brightness, (B) Saturation, (C) Hue1. Ordinary least squares regressions fitted are shown for illustrative purposes.\u003c/p\u003e","description":"","filename":"Figure248.png","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/2e2c676c92cffa8a192f72e5.png"},{"id":50722578,"identity":"1018a1fc-82d7-4dbc-8ea2-592d3dcdab7a","added_by":"auto","created_at":"2024-02-06 10:16:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":752447,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between chromatic variables of \u003cem\u003eSphenarium \u003c/em\u003egrasshoppers: (A) Female brightness and PDT, (B) male brightness and PWT, (C) female saturation and PDT, (D) male saturation and PWT (E) male overall patterns contrast and PDT, and (F) Female pattern diversity and background pattern diversity. Note these graphs are shown only for illustrative purposes and were created with ordinary least squares linear models.\u003c/p\u003e","description":"","filename":"Figure328.png","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/16089a3c81c4230a9fb8c2e6.png"},{"id":50722577,"identity":"279c447d-aea3-4c02-bab1-2e40f602cecb","added_by":"auto","created_at":"2024-02-06 10:16:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":974463,"visible":true,"origin":"","legend":"\u003cp\u003eMajor axis regressions (Solid lines) of the chromatic components of males on the chromatic components of the females of \u003cem\u003eSphenarium \u003c/em\u003egrasshoppers: (A) PIC brightness, (B) PIC saturation, (C) PIC hue 1, (D) PIC hue 2, (E) PIC Overall contrast patterns, (C) PIC Dominant marking size and (G) PIC Pattern diversity. Dotted lines: Slope = 1.\u003c/p\u003e","description":"","filename":"Figure425.png","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/db162b4e4e0d0dc45de888a3.png"},{"id":73693788,"identity":"c09811e7-6d54-4b1a-9381-a92d31440af5","added_by":"auto","created_at":"2025-01-13 16:06:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4205322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/b27b1b99-f8c8-42b8-8c96-c06b8fc96214.pdf"},{"id":50722575,"identity":"73f936da-5109-4595-a4c7-de3919f5dbe6","added_by":"auto","created_at":"2024-02-06 10:16:16","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":16693,"visible":true,"origin":"","legend":"","description":"","filename":"renamedcb904.docx","url":"https://assets-eu.researchsquare.com/files/rs-3895114/v1/476f4257748b67c4288b8788.docx"}],"financialInterests":"","formattedTitle":"A macroevolutionary analysis of cryptic coloration in sexually dichromatic grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomophidae).","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn many animal taxa, coloration can be adaptative in several ways, including social signaling, thermoregulation, protection from ultraviolet light, and antipredator defenses (Cott \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1940\u003c/span\u003e; Cuthill et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cryptic coloration is adaptative because it reduces the signal produced by an organism; it thus becomes less visually conspicuous to potential predators (Bond \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Th\u0026eacute;ry and Gomez \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Two of the most spread strategies to reach cryptic coloration are background matching (BM) and disruptive coloration (DC) (Stevens et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003ea; Stevens and Merilaita \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Quicke \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). BM is the resemblance between the colors and patterns of an organism with its surroundings (Hughes et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). DC is the concealment of an organism's outline due to contrasting markings that break or distract the attention of the predators from the organism's outline (Stevens and Cuthill \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Stevens and Merilaita \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). BM can be favoured in chromatically homogeneous habitats (Robledo-Ospina et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Orton and McBrayer \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); its success depends on the coloration of the environment and the probability that an individual stays on the backgrounds where it is cryptic (Merilaita et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Kang et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, BM can be ineffective at reducing the risk of detection when animals are in motion in heterogeneous environments (Ioannou and Krause \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). DC can co-occur with BM and enhances concealment from various predators (Cuthill et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Schaefer and Stobbe \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fraser et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). DC success could be maximized when some marks on the organism's surface match the background and other marks have high contrast with the rest of the body (Stevens and Merilaita \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). DC also has evolved in visually heterogeneous microhabitats because it breaks the outlines of the organisms independently of the variable background patterns and can conceal an organism while it is in motion (Stevens et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2006\u003c/span\u003ea, b). The evolution of BM colors represents a compromise between matching closely a single background or adopting a generalist strategy where the individuals resemble multiple backgrounds to some extent but not perfectly (Hughes et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, DC could be independent of the background coloration and depends on the organisms' markings and shape (Merilaita and Lind \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). DC could be more adaptative than BM in individuals with high vagility, which have a low probability of staying in specific backgrounds or inhabiting heterogeneous color habitats (Th\u0026eacute;ry \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Th\u0026eacute;ry and Gomez \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Robledo-Ospina et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause BM and DC strategies decrease the probability of detection by predators, selection will act on organisms so that they match the geographical variation in substrate color (Endler \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Stuart-Fox and Ord \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Rosenblum \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Marshall et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hantak and Kuchta \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Adams 2019). However, if males and females occupy different microhabitats because of their different sexual roles, natural selection could favour a divergence between females and males (Slatkin 1984), thereby favouring sex-specific cryptic color patterns (Forsman and Appelqvist \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Medina et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sexual dichromatism studies have focused mainly on vertebrates (Font et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and its evolution has been explained by differences in the selective pressures imposed by visual predators in only a few cases (Orton and McBrayer \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In invertebrates, the study of the evolution of sexual dichromatism has focused on arthropods, mainly also in the context of sexual selection (see Quicke \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e and references there), although it is still poorly documented in the context of cryptic coloration (Forsman and Appelqvist \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, in some arthropod species, BM and DC tactics have evolved closely with the environment. Jumping spiders exhibit BM in relatively flat chromatic backgrounds, whereas in dynamically changing backgrounds, they exhibit DC (Robledo-Ospina et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, crabs living in homogeneous backgrounds evolved BM, whereas those living in heterogeneous backgrounds have evolved DC (Price et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn many grasshopper species, cryptic coloration has evolved as an anti-predator strategy (Tsurui et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Edelaar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Camacho et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); even different morphs from a grasshopper species have been described as disruptive or showing resemblance to their environment (Tsurui et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Forsman \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020). Grasshoppers of the genus \u003cem\u003eSphenarium\u003c/em\u003e are found in a wide variety of environments and show wide variation in their color and marking patterns (Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, some species of this genus exhibit a notorious sexual dichromatism; BM is associated with females and DC to males (Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2023). In \u003cem\u003eS. zapotecum\u003c/em\u003e, in visually heterogeneous areas, predators spent more time searching for striped male morphs with lower BM and higher disruptive properties. On the other hand, females with high BM improve survival and significantly increase predator searching time (Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2023). \u003cem\u003eSphenarium\u003c/em\u003e grasshoppers are the prey of many visually oriented vertebrates, including birds, mammals, reptiles (Kevan \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), and humans, which have extensively consumed species of the genus since pre-Columbian times (Ortiz de Montellano 1978; Ramos-Elorduy and Pino-Moreno 1989).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eSphenarium\u003c/em\u003e genus consists of 17 flightless univoltine generalist herbivorous species distributed from central Mexico to northern Guatemala in a high altitudinal margin (0 to \u0026gt;\u0026thinsp;2600 masl, Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Because the differences in altitude and climate associated with their distribution are substantial, these species are found in rainy and dry deciduous tropical forests, temperate forests, shrublands, and grasslands. In low seasonal environments, the vegetation remains mainly greenish all year round, while in highly seasonal environments, there is a transition between brownish and greenish tonalities. They hatch in May and June during the rainy season and die during the coldest months (December to February) (Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Females are larger than males (Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and both sexes are polygamous (Cueva del Castillo and N\u0026uacute;\u0026ntilde;ez-Farf\u0026aacute;n 1999; Cueva del Castillo and N\u0026uacute;\u0026ntilde;ez-Farf\u0026aacute;n \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The males tend to search actively for females, whereas females are less mobile and can be found close to the ground when they are about to lay their eggs (Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020).\u003c/p\u003e \u003cp\u003eThe diversity of environments these grasshoppers inhabit allows us to analyse the potential adaptive chromatic cryptic divergences between them. Despite the implication of DC and BM on the evolution of the chromatic patterns of the species, very few comparative studies have tested their relative impact on the chromatic evolution of lineages (Caro and Koneru \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, given that many cryptic species are sexually dichromatic, focusing on these taxa may reveal profound implications regarding the evolution of intraspecific chromatic variation with respect to selective pressures imposed by visual predators on the members of each sex.\u003c/p\u003e \u003cp\u003eIn this study, we explored the relationship between the coloration patterns of males and females of the grasshoppers of the genus \u003cem\u003eSphenarium\u003c/em\u003e and their environment marking patterns whilst controlling for phylogenetic effects. Because matching coloration between habitats and individuals can be impacted by seasonal and geographic climatic variation (Caro et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we investigated the relationship between the environment and the grasshoppers' chromatic patterns by using the precipitation patterns in the localities and months where the adult grasshoppers were located. Since environmental coloration is strongly related to water availability, i.e. different color and patterns are expected from different levels of green vegetation and projected shadows of the vegetation (Yom-Tov and Geffen \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and due to local adaptation and BM, we further expected to find a positive relationship between environmental coloration and the marking patterns of males and females. Also, we hypothesized that this relationship would be stronger in females than males because of their lower mobility (Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2020; Cueva del Castillo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We predicted a positive relationship between the coloration of the grasshoppers and their environment with precipitation, and a positive relationship between the marking patterns of the grasshoppers and their environment with precipitation, due to the high contrast between lights and shadows associated with dense vegetation. Furthermore, if the selective pressure on males' and females' chromatic patterns differs, we would expect a divergence in their chromatic coevolution.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Image acquisition\u003c/h2\u003e \u003cp\u003eBetween October and September 2017, and 2018. We obtained photographs of male and female adults of the 17 \u003cem\u003eSphenarium taxa\u003c/em\u003e, as well as their backgrounds in 17 localities across in central and southern Mexico (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The places varied in elevation from 77 to 2,374 masl (table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Details of number of photographs taken per species, sex, and data obtained for color and pattern analyses are provided in supplementary material (table S2). During data collection, the geographic position and elevation of each locality was recorded using a GPS-map 60CSx (Garmin, Kansas City, USA). In each location, we walked at a slow and steady pace whilst looking for grasshoppers in areas of approximately 100 m\u0026sup2;; we registered the exact point where grasshoppers were found, and we caught them by hand. Once collected, the grasshoppers were placed in a cooler for approximately five minutes; this allowed us to lower the temperature and activity of individuals. We then placed each grasshopper back in the site where it was collected, we took a photograph of it against the background where it was found. We took the photographs below a white diffuser umbrella on sunny days. In the photographs we included a color checker card (X-Rite Color Checker Passport 2, Munsell Color Laboratories), which also included a size and a grey color scale. The photographs were taken with a Canon EOS 70D digital camera, fitted with 18\u0026ndash;55 mm, f/3.5\u0026ndash;5.6 lens. The settings of the camera, lens, and illumination were constant in all photographs. The aperture of the sensor was set to f-stops f/5.6; the values of light sensitivity (ISO) were set to 400; the focal distance was set at 55 mm; the camera was held at approximately 40 to 50 cm apart from the objective. The only setting that was adjusted between photographs was shutter speed; in this way we prevented overexposure in photographs. After the photographs were taken, the grasshoppers were temporarily placed in a plastic bag in order to avoid their potential recapture, and they were released after all the caught individuals in a given locality were photographed. We stored the images as RAW format. The camera has no modifications to allow ultraviolet sensitivity, so the photographs only contain information of the visible light spectrum; note, however, that grasshoppers have very little ultraviolet light reflectance (Tsurui et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To obtain the photographs and make them objectively measurable we follow the suggestions outlined for Stevens et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and Troscianko and Stevens (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Image analysis\u003c/h2\u003e \u003cp\u003eTo analyze the images, we used the Multispectral Image Calibration and Analysis (MICA) plugin (Troscianko and Stevens \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) available for ImageJ software (Schneider et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). MICA plugin uses linear data from the raw images, controls the light conditions variation with the gray scales from the color checker and creates a multispectral image made of a stack of the images corresponding to long wave (R Channel), medium wave (G channel), and short wave (B channel). The multispectral image made it possible take objective measurements from different channel reflectances, in order to later compare the color and pattern between our photographs. From the multispectral images, we measured the reflectance of the RGB channels of the grasshoppers\u0026rsquo; dorsal surface, and in the same photo, a similar area adjacent to the grasshoppers was considered the background surface of the grasshoppers. We also performed a granularity analysis on both surfaces.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 color analysis\u003c/h2\u003e \u003cp\u003eWe estimated brightness, saturation and hue from the RGB reflectance data. The three parameters allowed us to separate the achromatic (brightness) and chromatic (saturation and hue) properties of the images. Brightness refers to intensity of light on the image. Hue is determined by the dominant wavelength of the visible spectrum. It is the attribute that permits colors to be classified as red, yellow, green, blue, or an intermediate color, and saturation pertains the amount of white light mixed with a hue. High-saturation colors contain little or no white light.\u003c/p\u003e \u003cp\u003eBrightness was obtained using the means of the three channel values: (R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B) / 3. Saturation was calculated as the Euclidean distance between completely white, and the RGB values obtained in our photographs. By following this approach, large distance values represent high saturation. We obtained two hue values: Hue1\u0026thinsp;=\u0026thinsp;R / G, and Hue2 = (R\u0026thinsp;+\u0026thinsp;G) / B. These were calculated this way following the principle of opponent channels, which is based in the way opponent color channels work to detect color (Osorio and Vorobyev \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe must emphasize that the analyzes are interpreted from a human visible spectrum perspective. Spectral sensitivity can be different in other possible predators such as birds or mice, and their prey detectability could involve elements that we did not consider in this study (Th\u0026eacute;ry and Gomez \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nonetheless, humans\u0026rsquo; processing capabilities are similar to those of natural predators, especially birds (Dukas and Kamil \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and some studies have noticed that human \"predation\" can predict predation by other visual predators under natural conditions (Karpestam et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, in central and southern Mexico, \u003cem\u003eS. zapotecum\u003c/em\u003e and other species of the genus have been traditional elements of the human diet since pre-Columbian times (Ortiz de Montellano 1978; Ramos-Elorduy and Pino-Moreno 1989).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Pattern analysis\u003c/h2\u003e \u003cp\u003eWe performed a granularity (energy) analysis to measure the patterns in our photographs. This method roughly resembles the way animals decompose the visual information in different spatial frequencies (Stevens \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The photographs had to meet the following minimum pixel scale for this analysis 1 mm: 15 pixels; if the photographs did not meet this requirement, they were not used for pattern analysis (Troscianko and Stevens \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We used the average pixel reflectance of red and green channels to calculate the energy spectrum of grasshoppers and their background across 15 filters ranging from 2 to 256 pixels, in increments of multiples of \u0026radic;2. We obtained three descriptive values from this process: (i) overall pattern contrast: the amount of energy across all scales; (ii) the dominant marking size: the scale of dominant marking contrast (the filter where the highest value of energy is reached); and (iii) pattern diversity: the proportion between dominant marking contrast and rest of the measured energy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Climatic data acquisition\u003c/h2\u003e \u003cp\u003eBecause the \u003cem\u003eSphenarium\u003c/em\u003e species complete their reproductive cycle during the wettest and the driest periods of the year (Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), we obtained values from high-resolution climate surfaces (Fick and Hijmans \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) of the precipitation of the wettest (PTW) and the driest (PDT) trimesters of the year for each sampling location. We expected green tonalities to be positively related with the amount of humidity and the amount of primary productivity of the plants, and brownish tonalities stronger associated with dryer environments (Yom-Tov and Geffen \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Comparative analyses\u003c/h2\u003e \u003cp\u003eTo correct for the phylogenetic non-independence of taxa, we used the phylogenetic relationships of the \u003cem\u003eSphenarium\u003c/em\u003e genus (Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the phylogenetic generalized least squares (PGLS) method to test the association between (a) the color and patterns of the grasshoppers, with (b) the colors and patterns of their environment and the climatic factors of the localities where the grasshoppers were found. PGLS were performed using the \u003cem\u003ecaper\u003c/em\u003e package (Orme et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) as implemented in R (ver. 4.0.1) (R Core Team \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PGLS is a comparative method that incorporates the phylogenetic autocorrelation of the data in the structure of errors (variance-covariance matrix; (Martins and Hansen \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Freckleton et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this case, the PGLS method was used to test the maximum likelihood of the evolutionary regression coefficient between traits (Pagel \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). We also estimated the weighting parameter \u003cem\u003eλ\u003c/em\u003e to improve the data's fit to the model and correct for phylogenetic effects in all generated PGLS models (Pagel \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). \u003cem\u003eλ\u003c/em\u003e measures phylogenetic dependence of observed trait data (Pagel \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Freckleton et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e): the unit value approaches one when related species resemble each other more than they resemble species drawn at random from a phylogenetic tree (Blomberg and Garland Jr \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Testing the association between the coloration of grasshoppers, their background coloration, and climatic factors.\u003c/h2\u003e \u003cp\u003eWe constructed four PGLS models for each sex to test for associations between the coloration parameters and climatic factors and background coloration. The natural log-transformed values of hue, saturation, and brightness of the grasshoppers were used as response variables, and the natural log-transformed values of hue, saturation, and brightness of the background, were used as respective explanatory variables. We further included the precipitation variables; ln(PWT and ln(PDT)) as explanatory variables in all models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sexual dimorphism in color patterns\u003c/h2\u003e \u003cp\u003eTo test the divergence between the chromatic patterns of males and females, we performed major axis regression of the chromatic patterns of males on the chromatic patterns of the females. A slope steeper than 1 would mean that the divergence in the chromatic could be higher in males than females (\u0026gt;\u0026thinsp;1), whereas a slope lower than 1(\u0026lt;\u0026thinsp;1) would mean higher in females than males. We used the phylogenetic independent contrasts method (PIC method; Felsenstein \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), as implemented by the R package \u003cem\u003ecaper\u003c/em\u003e (Orme et al. 2013) to control for the phylogenetic non-independence of species (Harvey et al. 1991). We examined the studentized residuals looking for outliers \u0026gt;|\u0026plusmn;3|, which were discarded from the analysis. Ultimately, we regressed the independent contrasts of the log-transformed values of hue 1 and 2, saturation, brightness, overall patterns\u0026rsquo; contrast, dominant marking size, and pattern diversity of the male grasshoppers on their counterparts on females by fitting major axis regression using the R package \u003cem\u003esmatr\u003c/em\u003e (Warton et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Association between grasshoppers\u0026rsquo; coloration and background coloration\u0026thinsp;+\u0026thinsp;climatic factors\u003c/h2\u003e \u003cp\u003eThe PGLS models in which we tested the association between coloration parameters show a general strong, positive association between the color parameters of male and female grasshoppers and their microhabitats (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-F; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The only exception was hue2 (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H). Moreover, the analyses show a significant positive association between the brightness and saturation of the females with the driest trimester of the year (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B), and a significant positive association between male brightness with the wettest trimester of the year (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The values of λ are overall very low (zero or close to zero) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-H), which suggests a lack of phylogenetic signal and a rapid evolution of these coloration traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between climatic factors with grasshoppers and background patterns.\u003c/h2\u003e \u003cp\u003eIn females, there is a significant, positive relationship between pattern diversity and habitat (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), whereas the PGLS models of males indicated that only the relationship between dominant marking contrast and the driest trimester of the year was significant and positive (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eL; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The λ values were high for both, female pattern diversity and males dominant marking contrast (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK, L), which suggests high phylogenetic signal and slow evolution of traits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sexual dimorphism in color patterns\u003c/h2\u003e \u003cp\u003eThe independent contrasts of the major axis regressions of males on females for hue 1 and 2, saturation, and brightness, overall patterns\u0026rsquo; contrast and dominant marking size were positive and highly correlated, whereas the patterns diversity evolves independently in males and females (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The rate of divergence of brightness, saturation and hue2 was similar in males and females, while the divergence rate of hue1, overall patterns\u0026rsquo; contrast and dominant marking size was higher on males than females (slope stepper than 1; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, E, F).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe findings from our study contribute significant insights into the intricate evolution of coloration among \u003cem\u003eSphenarium\u003c/em\u003e grasshopper species. The observed disparities in color patterns among these species are attributed to localized adaptations to distinct environmental conditions. It is evident that natural selection has favoured the development of cryptic coloration in both male and female grasshoppers. However, a nuanced gender-specific divergence is discernible in their responses to environmental cues. Notably, the brightness of males exhibits a positive correlation with the wettest trimester of the year, while the brightness and saturation of females are positively associated with the driest trimester. Intriguingly, despite the absence of direct correlations with climatic variables, female mark patterns demonstrate a positive connection with their backgrounds. Conversely, male patterns show no such relationship with their surroundings, although a significant link between male marks (overall pattern contrast) and the driest trimester is evident. An interesting aspect that emerges is the relatively rapid evolution of chromatic patterns compared to the evolution of marking patterns.\u003c/p\u003e \u003cp\u003eThe disparity in the pace of evolutionary divergence between background resemblance and disruptive markings can be rationalized by the varied payoffs associated with each strategy. In scenarios where a species colonizes a new, vibrant, and uniform environment, natural selection tends to hasten the convergence of coloration with the new environment (referred to as BM). Conversely, the adaptability inherent in disruptive markings allows for a reduced divergence among species that face novel environments or alterations in their distribution range. In this context, the chromatic patterns exhibited by \u003cem\u003eSphenarium\u003c/em\u003e grasshoppers may embody a generalized strategy that moderately matches various backgrounds without precisely mimicking anyone. It's worth noting that the benefits derived from cryptic strategies differ between males and females. The divergence rate of hue1, overall pattern contrast, and dominant marking size are notably higher in males than in females. Conversely, the evolution of pattern diversity has proceeded independently within both genders, indicating a distinct niche specialization concerning light and shadow contrast for males and females. Nonetheless, the parallel evolution rates of brightness, saturation, and hue2 in males and females can be attributed to similar selective pressures faced by different species within their respective habitats.\u003c/p\u003e \u003cp\u003eThe divergence in chromatic patterns within \u003cem\u003eSphenarium\u003c/em\u003e grasshoppers finds its basis in the diverse environments where the genus has diversified (as evidenced by Sanabria-Urb\u0026aacute;n et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the behavioural disparities between males and females. The synchronization of \u003cem\u003eSphenarium\u003c/em\u003e life cycles with rainy seasons, as discussed by Sanabria-Urb\u0026aacute;n et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), likely underlies the strong associations between chromatic patterns and environmental conditions exhibited across all species. This linkage is attributable to the profound influence of precipitation on primary productivity (Yom-Tov and Geffen \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), resulting in alterations in vegetation color and shadow patterns. Furthermore, the variations in male and female populations due to precipitation fluctuations on different timescales are notable. For instance, the protandrous nature of males in certain species aligns their maturation with the onset of the rainy season, maximizing mating opportunities. During this phase, male brightness synchronizes with light-green shades, and their marks may serve them well during the subsequent driest trimester, characterized by deeper contrasts between light and shadow. In contrast, females, strategically maturing later than males, exhibit color and mark patterns that correlate with vegetation plots and backgrounds, which they frequent during their reproductive season.\u003c/p\u003e \u003cp\u003eMales' active pursuit of females, often atop plants, juxtaposes the stationary female behaviour, placing them closer to the ground where they lay eggs. The pattern of marks on the dorsal surface of these grasshoppers serves dual purposes, providing BM and potentially contributing to DC, as postulated by Robledo-Ospina et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Such highly contrasting markings might disrupt predators' visual perception or divert attention from the organism's contours, an adaptive advantage, as Merilaita (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Hughes et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggested. However, the potential implications of these marking patterns on processes such as sexual selection warrant consideration (with reference to Cueva del Castillo and Cano-Santana 2001). Notably, the divergence rate in male marking patterns surpasses that of females, indicating heightened natural selection pressure on these male-specific traits. Remarkably, in the case of \u003cem\u003eS. zapotecum\u003c/em\u003e, predators allocate more time to locate striped male morphs with reduced BM and increased disruptive characteristics and females with heightened background resemblance. This behaviour augments prey survival (Ram\u0026iacute;rez-Delgado and Cueva del Castillo 2023).\u003c/p\u003e \u003cp\u003eIt is conceivable that these grasshoppers adopt a form of camouflage that offers partial resemblance to multiple backgrounds rather than a perfect match to a singular habitat. Alternatively, their camouflage may operate somewhat autonomously from BM strategies (as posited by Hughes et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nevertheless, the apparent convergence between \u003cem\u003eSphenarium\u003c/em\u003e chromatic patterns and their environments stems from a blend of differential predation dynamics and proactive habitat selection. This phenomenon parallels other grasshopper species (as demonstrated by Edelaar et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Heinze et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe survival of these grasshoppers hinges on their ability to navigate diverse predator threats, including avian, mammalian, reptilian, and arthropod predators (Kevan \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), as well as human predation (highlighted by Sanabria-Urb\u0026aacute;n and Cueva del Castillo \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their chromatic and achromatic visual cues have evolved in response to the distinct searching strategies of these predators. Remarkably, the resemblance of marking patterns to the environment extends to chromatic and achromatic variables. However, while chromatic cues excel in close-range searches, achromatic information proves more effective in long-distance prey detection (Schaefer and Stobbe \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Cazetta et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Notably, human behaviour as a predator mirror that of natural predators, particularly birds, a similarity exploited in some studies to predict predation by other visual predators in natural settings (Karpestam et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This intriguing correspondence has likely influenced the evolution of \u003cem\u003eSphenarium\u003c/em\u003e grasshoppers' chromatic patterns.\u003c/p\u003e \u003cp\u003eConsidering the relative scarcity of phylogenetic comparative studies concerning cryptic coloration evolution, our research undoubtedly contributes to a foundational comprehension of color evolution within the context of selection pressures imposed by visual predators. Nonetheless, it is imperative to acknowledge that future investigations into habitat preference and the exploration of other potential functions of grasshopper coloration, such as thermoregulation or UV radiation protection, hold the potential to unveil additional factors shaping the evolution of coloration within this cohort of neotropical grasshoppers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Judith X. Ponce-Wainer for her valuable suggestions to improve the manuscript\u0026apos;s quality. We are also grateful to Hojun Song for his thoughtful review of the manuscript. VHR-D is grateful for receiving a CONAHCyT scholarship (385795) for graduate studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by a PAPIIT, UNAM grant (IN211617) to RCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e None declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData will be available under reasonable request\u003c/strong\u003e\u003c/p\u003e\u003ch2\u003eAuthors' contributions:\u003c/h2\u003e \u003cp\u003eVHRD and RCCM originally formulated the idea, VHR developed methodology, VHRD conducted fieldwork, VHRD conducted imaging analysis, VHRD, RCCM and MASM performed statistical analyses, VHRD, RCCM and MASM wrote the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams WJ, Graf EW, Anderson M (2019) Disruptive coloration and binocular disparity: breaking camouflage. \u003cem\u003eProc. R. Soc. B-Biol. Sci.\u003c/em\u003e286: 20182045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2018.2045\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2018.2045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlomberg SP, Garland T Jr (2002) Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. J Evol Biol 15:899\u0026ndash;910. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1046/j.1420-9101.2002.00472.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1420-9101.2002.00472.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBond AB (2007) The Evolution of Color Polymorphism: Crypticity, Searching Images, and Apostatic Selection. Ann Rev Ecol Evol Syst 38:489\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1146/annurev.ecolsys.38.091206.095728\u003c/span\u003e\u003cspan address=\"10.1146/annurev.ecolsys.38.091206.095728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho C, Sanabria-Fern\u0026aacute;ndez A, Ba\u0026ntilde;os-Villalba A, Edelaar P (2020) Experimental evidence that matching habitat choice drives local adaptation in a wild population. \u003cem\u003eProc. R. Soc. B-Biol. Sci\u003c/em\u003e 287: 20200721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2020.0721\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2020.0721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaro T, Koneru M (2021) Towards an ecology of protective coloration. Biol Rev 96:611\u0026ndash;641. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/brv.12670\u003c/span\u003e\u003cspan address=\"10.1111/brv.12670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaro T, Sherratt TN, Stevens M (2016) The ecology of multiple colour defences. Evol Ecol 30:797\u0026ndash;809. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1007/s10682-016-9854-3\u003c/span\u003e\u003cspan address=\"10.1007/s10682-016-9854-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCazetta E, Schaefer HM, Galetti M (2009) Why are fruits colorful? the relative importance of achromatic and chromatic contrasts for detection by birds. Evol Ecol 23:233\u0026ndash;244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1007/s10682-007-9217-1\u003c/span\u003e\u003cspan address=\"10.1007/s10682-007-9217-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCott HB (1940) Adaptive Coloration in Animals. Oxford University Press, London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edel Cueva R, Cano-Santana Z (2001) Variaci\u0026oacute;n de la coloraci\u0026oacute;n corporal de \u003cem\u003eSphenarium Purpurascens\u003c/em\u003e (Orthoptera: Pyrgomorphidae) en funci\u0026oacute;n del sexo y su relaci\u0026oacute;n con la formaci\u0026oacute;n de parejas en un ambiente heterog\u0026eacute;neo. Fol Entomol Mex 40:297\u0026ndash;309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCueva del Castillo R, Gonz\u0026aacute;lez-Zertuche M, Ram\u0026iacute;rez-Delgado VH (2021) Interpopulation variation in sexual dichromatism in the Neotropical grasshopper \u003cem\u003eSphenarium purpurascens\u003c/em\u003e (Orthoptera: Pyrgomorphidae). Biol J Linnean Soc132(4):900\u0026ndash;911. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/biolinnean/blaa227\u003c/span\u003e\u003cspan address=\"10.1093/biolinnean/blaa227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCueva del Castillo R, N\u0026uacute;\u0026ntilde;ez-Farf\u0026aacute;n J (2002) Female mating success and risk of pre-reproductive death in a protandrous grasshopper. Oikos 96:217\u0026ndash;224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1034/j.1600-0706.2002.960203.x\u003c/span\u003e\u003cspan address=\"10.1034/j.1600-0706.2002.960203.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCueva del Castillo R, N\u0026uacute;\u0026ntilde;ez-Farf\u0026aacute;n J, Cano-Santana Z (1999) The role of body size in mating success of \u003cem\u003eSphenarium purpurascens\u003c/em\u003e in Central Mexico. Ecol Entomol 24:146\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1046/j.1365-2311.1999.00188.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2311.1999.00188.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuthill IC, Stevens M, Sheppard J, Maddocks T, P\u0026aacute;rraga CA, Troscianko TS (2005) Disruptive coloration and background pattern matching. Nature 434:72\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/nature03312\u003c/span\u003e\u003cspan address=\"10.1038/nature03312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuthill IC, Allen WL, Arbuckle K, Caspers B, Chaplin G, Hauber ME et al (2017) The biology of color. Science 357(6350):eaan0221. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aan0221\u003c/span\u003e\u003cspan address=\"10.1126/science.aan0221\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDukas R, Kamil AC (2001) Limited attention: the constraint underlying search image. Behav Ecol 12:192\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/beheco/12.2.192\u003c/span\u003e\u003cspan address=\"10.1093/beheco/12.2.192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdelaar P, Ba\u0026ntilde;os-Villalba A, Escudero G, Rodr\u0026iacute;guez-Bernal C (2017) Background colour matching increases with risk of predation in a colour-changing grasshopper. Behav Ecol 28:698\u0026ndash;705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/beheco/arx016\u003c/span\u003e\u003cspan address=\"10.1093/beheco/arx016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdelaar P, Ba\u0026ntilde;os-Villalba A, Quevedo DP, Escudero G, Bolnick DI, Jord\u0026aacute;n-Andrade A (2019) Biased movement drives local cryptic coloration on distinct urban pavements. Proc R Soc B-Biol Sci 286:20191343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2019.1343\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2019.1343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndler JA (1990) On the measurement and classification of colour in studies of animal colour patterns. Biol J Linnean Soc 41:315\u0026ndash;352. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1095-8312.1990.tb00839.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8312.1990.tb00839.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelsenstein J (1985) Phylogenies and the Comparative Method. Am Nat 125:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1086/286013\u003c/span\u003e\u003cspan address=\"10.1086/286013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302\u0026ndash;4315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1002/joc.5086\u003c/span\u003e\u003cspan address=\"10.1002/joc.5086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFont E, P\u0026eacute;rez i de Lanuza G, Sampedro C (2009) Ultraviolet reflectance and cryptic sexual dichromatism in the ocellated lizard, \u003cem\u003eLacerta (Timon) lepida\u003c/em\u003e (Squamata: Lacertidae). Biol J Linnean Soc 97: 766\u0026ndash;780. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1095-8312.2009.01251.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8312.2009.01251.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForsman A (2018) On the role of sex differences for evolution in heterogeneous and changing fitness landscapes: insights from pygmy grasshoppers. Philos Trans R Soc B-Biol Sci 373:20170429. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1098/rstb.2017.0429\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2017.0429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForsman A, Appelqvist S (1999) Experimental manipulation reveals differential effects of colour pattern on survival in male and female pygmy grasshoppers. J Evol Biol 12:391\u0026ndash;401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1046/j.1420-9101.1999.00041.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1420-9101.1999.00041.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraser S, Callahan A, Klassen D, Sherratt TN (2007) Empirical tests of the role of disruptive coloration in reducing detectability. Proc R Soc B-Biol Sci 274:1325\u0026ndash;1331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2007.0153\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2007.0153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreckleton RP, Harvey PH, Pagel M (2002) Phylogenetic Analysis and Comparative Data: A Test and Review of Evidence. Am Nat 160:712\u0026ndash;726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1086/343873\u003c/span\u003e\u003cspan address=\"10.1086/343873\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHantak MM, Kuchta SR (2018) Predator perception across space and time: relative camouflage in a colour polymorphic salamander. Biol J Linnean Soc 123:21\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/biolinnean/blx132\u003c/span\u003e\u003cspan address=\"10.1093/biolinnean/blx132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarvey PH, Pagel MD (1991) The comparative method in evolutionary biology. Oxford University Press Oxford\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeinze P, Dieker P, Rowland HM, Schielzeth H (2022) Evidence for morph-specific substrate choice in a green-brown polymorphic grasshopper. Behav Ecol 33:17\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/beheco/arab133\u003c/span\u003e\u003cspan address=\"10.1093/beheco/arab133\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughes A, Liggins E, Stevens M (2019) Imperfect camouflage: How to hide in a variable world? Proc R Soc B-Biol Sci 286:20190646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2019.0646\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2019.0646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIoannou CC, Krause J (2009) Interactions between background matching and motion during visual detection can explain why cryptic animals keep still. Biol Lett 5:191\u0026ndash;193. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rsbl.2008.0758\u003c/span\u003e\u003cspan address=\"10.1098/rsbl.2008.0758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang C, Kim YE, Jang Y (2016) Colour and pattern change against visually heterogeneous backgrounds in the tree frog \u003cem\u003eHyla japonica\u003c/em\u003e. Sci Rep 6:22601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/srep22601\u003c/span\u003e\u003cspan address=\"10.1038/srep22601\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarpestam E, Merilaita S, Forsman A (2013) Detection experiments with humans implicate visual predation as a driver of colour polymorphism dynamics in pygmy grasshoppers. BMC Ecol 13:17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1186/1472-6785-13-17\u003c/span\u003e\u003cspan address=\"10.1186/1472-6785-13-17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKevan DK (1977) The American Pyrgomorphidae (Orthoptera). Rev Soc Entomol Argentina 36:3\u0026ndash;28\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarshall KLA, Philpot KE, Damas-Moreira I, Stevens M (2015) Intraspecific Colour Variation among Lizards in Distinct Island Environments Enhances Local Camouflage. PLoS ONE 10(9):e0135241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1371/journal.pone.0135241\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0135241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartins EP, Hansen TF (1997) Phylogenies and the Comparative Method: A General Approach to Incorporating Phylogenetic Information into the Analysis of Interspecific Data. Am Nat 149:646\u0026ndash;667. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1086/286013\u003c/span\u003e\u003cspan address=\"10.1086/286013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedina I, Losos JB, Mahler DL (2016) Evolution of dorsal pattern variation in Greater Antillean \u003cem\u003eAnolis\u003c/em\u003e lizards. Biol J Linnean Soc 120:427\u0026ndash;435. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/bij.12881\u003c/span\u003e\u003cspan address=\"10.1111/bij.12881\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerilaita S (1998) Crypsis through disruptive coloration in an isopod. Proc R Soc B-Biol Sci 265:1059\u0026ndash;1064. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.1998.0399\u003c/span\u003e\u003cspan address=\"10.1098/rspb.1998.0399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerilaita S, Lind J (2005) Background-matching and disruptive coloration, and the evolution of cryptic coloration. Proc R Soc B-Biol Sci 272:665\u0026ndash;670. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2004.3000\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2004.3000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerilaita S, Tuomi J, Jormalainen V (1999) Optimization of cryptic coloration in heterogeneous habitats. Biol J Linnean Soc 67:151\u0026ndash;161. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1095-8312.1999.tb01858.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8312.1999.tb01858.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrme D et al (2018) The caper package: comparative analysis of phylogenetics and evolution in R, \u003cem\u003eR package version 2.10\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Ortiz BR (1978) Aztec Cannibalism: An Ecological Necessity? Science 200:611\u0026ndash;617. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.200.4342.611\u003c/span\u003e\u003cspan address=\"10.1126/science.200.4342.611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrton RW, McBrayer LD (2019) Resolving tradeoffs among crypsis, escape behavior, and microhabitat use in sexually dichromatic species. Oecologia 189:91\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1007/s00442-018-4301-5\u003c/span\u003e\u003cspan address=\"10.1007/s00442-018-4301-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsorio D, Vorobyev M (2005) Photoreceptor sectral sensitivities in terrestrial animals: adaptations for luminance and colour vision. Proc R Soc B-Biol Sci 272:1745\u0026ndash;1752. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2005.3156\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2005.3156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagel M (1999) Inferring the historical patterns of biological evolution. Nature 401:877\u0026ndash;884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/44766\u003c/span\u003e\u003cspan address=\"10.1038/44766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice N, Green S, Troscianko J, Tregenza T, Stevens M (2019) Background matching and disruptive coloration as habitat-specific strategies for camouflage. Sci Rep 9:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/s41598-019-44349-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-44349-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuicke DL (2017) Mimicry, crypsis, masquerade and other adaptive resemblances. John Wiley \u0026amp; Sons, Oxford\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2020) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam\u0026iacute;rez-Delgado VH, del Cueva R (2023) Testing background matching and disruptive colouration in a sexually dichromatic grasshopper: a computer detection experiment. Entomol Exp Appl 171:258\u0026ndash;267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/eea.13278\u003c/span\u003e\u003cspan address=\"10.1111/eea.13278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRam\u0026iacute;rez-Delgado VH, del Cueva R (2020) Background matching, disruptive coloration, and differential use of microhabitats in two neotropical grasshoppers with sexual dichromatism. Ecol Evol 10:1401\u0026ndash;1412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1002/ece3.5995\u003c/span\u003e\u003cspan address=\"10.1002/ece3.5995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos-Elorduy J, Moreno JMP (1989) Los Insectos comestibles en el M\u0026eacute;xico antiguo: estudio etnoentomol\u0026oacute;gico. AGT\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobledo-Ospina LE, Escobar-Sarria F, Troscianko J, Rao D (2017) Two ways to hide: Predator and prey perspectives of disruptive coloration and background matching in jumping spiders. Biol J Linnean Soc 122:752\u0026ndash;764. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1093/biolinnean/blx108\u003c/span\u003e\u003cspan address=\"10.1093/biolinnean/blx108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenblum EB (2006) Convergent Evolution and Divergent Selection: Lizards at the White Sands Ecotone. Am Nat 167:1\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1086/498397\u003c/span\u003e\u003cspan address=\"10.1086/498397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanabria-Urb\u0026aacute;n S, Song H, Oyama K, Gonzalez-Rodriguez A, Serrano-Meneses MA, Cueva del Castillo R (2015) Body Size Adaptations to Altitudinal Climatic Variation in Neotropical Grasshoppers of the Genus \u003cem\u003eSphenarium\u003c/em\u003e (Orthoptera: Pyrgomorphidae). PLoS ONE 10(12):e0145248. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0145248\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0145248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanabria-Urb\u0026aacute;n S, Song H, Oyama K, Gonzalez-Rodr\u0026iacute;guez A, Cueva del Castillo R (2017) Integrative taxonomy reveals cryptic diversity in neotropical grasshoppers: Taxonomy, phylogenetics, and evolution of the genus \u003cem\u003eSphenarium\u003c/em\u003e Charpentier, 1842 (Orthoptera: Pyrgomorphidae). Zootaxa 4274:1\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.11646/zootaxa.4274.1\u003c/span\u003e\u003cspan address=\"10.11646/zootaxa.4274.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanabria-Urb\u0026aacute;n S, Cueva del Castillo R (2020) The evolution and diversification of neotropical generalist herbivores: the evolutionary history of the grasshopper genus \u003cem\u003eSphenarium\u003c/em\u003e Charpentier, 1842. In: N\u0026uacute;\u0026ntilde;ez-Farf\u0026aacute;n J, Valverde PL (eds) Evolutionary Ecology of Plant-Herbivore Interaction. Springer, Belin, pp 277\u0026ndash;292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaefer HM, Stobbe N (2006) Disruptive coloration provides camouflage independent of background matching. Proc R Soc B-Biol Sci 273:2427\u0026ndash;2432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2006.3615\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2006.3615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671\u0026ndash;675. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1038/nmeth.2089\u003c/span\u003e\u003cspan address=\"10.1038/nmeth.2089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, Cuthill IC, Windsor AMM, Walker HJ (2006) Disruptive contrast in animal camouflage. Proc R Soc B-Biol Sci 273:2433\u0026ndash;2438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rspb.2006.3614\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2006.3614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, Cuthill IC, P\u0026aacute;rraga AC, Troscianko T (2006) The effectiveness of disruptive coloration as a concealment strategy. Prog Brain Res 155:49\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0079-6123(06)55004-6\u003c/span\u003e\u003cspan address=\"10.1016/S0079-6123(06)55004-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, P\u0026aacute;rraga CA, Cuthill IC, Partridge JC, Troscianko TS (2007) Using digital photography to study animal coloration. Biol J Linnean Soc 90:211\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1095-8312.2007.00725.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1095-8312.2007.00725.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M (2011) Avian vision and egg colouration: Concepts and measurements. Avian Biol Res 4:168\u0026ndash;184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.3184/175815511X1320779017795\u003c/span\u003e\u003cspan address=\"10.3184/175815511X1320779017795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, Cuthill IC (2006) Disruptive coloration, crypsis and edge detection in early visual processing. Proc R Soc B-Biol Sci 273:2141\u0026ndash;2147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rspb.2006.3556\u003c/span\u003e\u003cspan address=\"10.1098/rspb.2006.3556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens M, Merilaita S (2009) Animal camouflage: Current issues and new perspectives. Philos Trans R Soc B-Biol Sci 364:423\u0026ndash;427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1098/rstb.2008.0217\u003c/span\u003e\u003cspan address=\"10.1098/rstb.2008.0217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStuart-Fox DM, Ord TJ (2004) Sexual selection, natural selection and the evolution of dimorphic coloration and ornamentation in agamid lizards. Proc R Soc B-Biol Sci Sciences 271:2249\u0026ndash;2255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/doi.org/10.1098/rspb.2004.2802\u003c/span\u003e\u003cspan address=\"/10.1098/rspb.2004.2802\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026eacute;ry M (2007) Colours of background reflected light and of the preys eye affect adaptive coloration in female crab spiders. Anim Behav 73:797\u0026ndash;804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/j.anbehav.2006.06.015\u003c/span\u003e\u003cspan address=\"10.1016/j.anbehav.2006.06.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026eacute;ry M, Gomez D (2010) Insect Colours and Visual Appearance in the Eyes of Their Predators. Adv Insect Physiol 10:267\u0026ndash;353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1016/S0065-2806(10)38001-5\u003c/span\u003e\u003cspan address=\"10.1016/S0065-2806(10)38001-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTroscianko J, Stevens M (2015) Image calibration and analysis toolbox - a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol Evol 6:1320\u0026ndash;1331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/2041-210X.12439\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.12439\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsurui K, Honma A, Nishida T (2010) Camouflage effects of various colour-marking morphs against different microhabitat backgrounds in a polymorphic pygmy grasshopper \u003cem\u003eTetrix japonica\u003c/em\u003e. PLoS ONE 5(7):e11446. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0011446\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0011446\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsurui K, Honma A, Nishida T (2013) Size-dependent predation risk partly explains the sex-related marking polymorphism in the sexually size-dimorphic pygmy grasshopper \u003cem\u003eTetrix japonica\u003c/em\u003e. Entomol Sci 16:136\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.1479-8298.2012.00543.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1479-8298.2012.00543.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarton DI, Duursma RA, Falster DS, Taskinen S (2012) smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol Evol 3:257\u0026ndash;259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1111/j.2041-210X.2011.00153.x\u003c/span\u003e\u003cspan address=\"10.1111/j.2041-210X.2011.00153.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYom-Tov Y, Geffen E (2006) Geographic variation in body size: The effects of ambient temperature and precipitation. Oecologia 148:213\u0026ndash;218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00442-006-0364-9\u003c/span\u003e\u003cspan address=\"10.1007/s00442-006-0364-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. PGLS models of \u003cem\u003eSphenarium\u003c/em\u003e grasshoppers on coloration and pattern parameters for both sexes.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"714\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" valign=\"top\"\u003e\n \u003cp\u003eResponse variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003eExplanatory variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003eD.f.\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lambda;\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u0026dagger;\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eA) ♀ Brightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-Brightness\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e6.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e13.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-3.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e2.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eB) ♀ Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e7.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e15.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e3.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e2.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eC) ♀ Hue 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-Hue1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e6.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e12.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eD) ♀ Hue 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-Hue2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e1.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eE) ♂ Brightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-Brightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e6.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e10.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e2.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e1.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eF) ♂ Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-Saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e6.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e12.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-2.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e1.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eG) ♂ Hue 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-Hue1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e3.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e3.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eH) ♂ Hue 2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-Hue2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e1.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-0.853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eI) ♀Overal patterns\u0026apos; contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-overall patterns\u0026rsquo; contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.2702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eJ) ♀ dominant marking size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-dominant marking size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.1073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eK) ♀ Pattern diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♀\u0026nbsp;Bg-pattern diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e2.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e2.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.2630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0.603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eL) ♂Overall patterns\u0026apos; contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-overall patterns\u0026rsquo; contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-2.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e1.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eM) ♂ dominant marking size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-dominant marking size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.6179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.769985974754558%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eN) ♂ Pattern diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.635343618513325%\" valign=\"top\"\u003e\n \u003cp\u003e♂\u0026nbsp;Bg-dominant marking contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.994389901823282%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.23842917251052%\" valign=\"top\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.274894810659186%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.957924263674615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.415147265077138%\" valign=\"top\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.713884992987377%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e3,13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e-1.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.184466019417474%\" valign=\"top\"\u003e\n \u003cp\u003ePWT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.067961165048544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.174757281553399%\" valign=\"top\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.45631067961165%\" valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.78640776699029%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.650485436893204%\" valign=\"top\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.679611650485437%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eBg: background; \u003csup\u003e\u0026Dagger;\u003c/sup\u003ePDT: precipitation of the driest trimester; \u003csup\u003e\u0026sect;\u003c/sup\u003ePWT precipitation of the wettest trimester, \u003csup\u003e\u0026para;\u003c/sup\u003eD.f.: degrees of freedom,\u0026nbsp;\u003cstrong\u003e\u003csup\u003e\u0026dagger;\u0026dagger;\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u0026lambda;: Pagel\u0026rsquo;s Lambda. The data was natural log-transformed prior to analyses.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"645\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\" valign=\"bottom\"\u003e\n \u003cp\u003eTable 2. Major axis regressions values of the chromatic patterns of males on the chromatic patterns of the females.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eColoration /patterns traits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026beta;\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003eUCL\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003eLCL\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003er\u003csub\u003ew\u003c/sub\u003e\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;D.f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eBrightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.6298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eSaturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eHue1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0096\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eHue2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eOverall patterns\u0026rsquo; contrast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.347826086956523%\" valign=\"bottom\"\u003e\n \u003cp\u003eDominant marking Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.161490683229813%\" valign=\"bottom\"\u003e\n \u003cp\u003e4.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.074534161490684%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.093167701863354%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.024844720496894%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.937888198757763%\" valign=\"bottom\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.751552795031056%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.302325581395348%\" valign=\"bottom\"\u003e\n \u003cp\u003ePatterns\u0026rsquo; diversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.596899224806202%\" valign=\"bottom\"\u003e\n \u003cp\u003e-20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.147286821705427%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.062015503875969%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.077519379844961%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.852713178294573%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.232558139534884%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\" valign=\"bottom\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.302325581395348%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.596899224806202%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.147286821705427%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.062015503875969%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.077519379844961%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.852713178294573%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0.31007751937984496%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.922480620155039%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.728682170542635%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u0026beta;: Coefficients of regressions , \u003csup\u003e\u0026Dagger;\u003c/sup\u003eUCL and \u003csup\u003e\u0026sect;\u003c/sup\u003eLCL: upper and lower 95% confidence intervals, \u003csup\u003e\u0026para;\u003c/sup\u003er\u003csub\u003ew\u003c/sub\u003e: Wald statistic values.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Background matching, Comparative analysis, Crypsis, Disruptive coloration, Grasshoppers, Sexual dichromatism","lastPublishedDoi":"10.21203/rs.3.rs-3895114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3895114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground matching and disruptive coloration are defense mechanisms of animals against visual predators. Disruptive coloration tends to evolve in microhabitats that are visually heterogeneous, while background matching is favored in microhabitats that are chromatically homogeneous. Controlling for the phylogeny, we explored the evolution of the coloration and the marking patterns in the sexual dichromatic and widely distributed Neotropical grasshoppers of the genus \u003cem\u003eSphenarium\u003c/em\u003e. These grasshoppers represent an excellent model to investigate the evolution of cryptic coloration on insects due to the heterogeneity of the environments where they have evolved. We found a correlation between the grasshoppers' coloration and disruptive markings with the chromatic properties of their environments that was inferred by the levels of precipitation during the rainy season. The results suggest that colors and marks patterns could evolve due to predation pressures. color in both sexes could offer camouflage that is not perfectly background matched to a single habitat but instead offers a degree of resemblance to multiple backgrounds. Moreover, we found that males and females chromatic properties differ between them and precipitation levels where the species are found. This suggests that the sexes have diverged in their response to the environments, favoring the evolution of sexual dichromatism in these grasshoppers.\u003c/p\u003e","manuscriptTitle":"A macroevolutionary analysis of cryptic coloration in sexually dichromatic grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomophidae).","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 10:16:11","doi":"10.21203/rs.3.rs-3895114/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-18T20:46:05+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-02T14:14:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-27T06:17:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oecologia","date":"2024-01-25T10:38:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oecologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"oeco","sideBox":"Learn more about [Oecologia](https://www.springer.com/journal/442)","snPcode":"442","submissionUrl":"https://submission.nature.com/new-submission/442/3","title":"Oecologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2fe2518a-2d75-44b1-8dff-9dd7843d1dd5","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T15:59:06+00:00","versionOfRecord":{"articleIdentity":"rs-3895114","link":"https://doi.org/10.1007/s00442-024-05643-7","journal":{"identity":"oecologia","isVorOnly":false,"title":"Oecologia"},"publishedOn":"2025-01-10 15:57:00","publishedOnDateReadable":"January 10th, 2025"},"versionCreatedAt":"2024-02-06 10:16:11","video":"","vorDoi":"10.1007/s00442-024-05643-7","vorDoiUrl":"https://doi.org/10.1007/s00442-024-05643-7","workflowStages":[]},"version":"v1","identity":"rs-3895114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3895114","identity":"rs-3895114","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}