DOI: 10.5061/dryad.cc2fqz6g3
title: Body temperature differences between green and brown grasshoppers do not result from thermal physiology or thermal preferences

description: Colour polymorphisms correspond to the co-occurrence of several distinct
 colour morphs that vary in hue and/or brightness, independently of sex,
 age or any other state-dependent modifiers. Colour morphs could represent
 different life-history strategies, maximising their fitness locally in the
 trait space through trade-offs between all their biological functions.
 This mechanism could play a role in the maintenance of the green-brown
 polymorphism in Orthoptera. Grasshoppers are characterized by a widespread
 green-brown polymorphism and continuous variability in brightness within
 colour morphs. It has previously been found that brown individuals are
 warmer in the field than green conspecifics, but it is unclear if these
 differences are related to thermal physiology and/or thermal preferences.
 Therefore, we experimentally tested the thermal physiology and thermal
 preferences of three green-brown polymorphic species of acridid
 grasshoppers. We found no differences between green and brown
 grasshoppers, either in heat-up and equilibrium temperature patterns, or
 in temperature preferences. Nor did we find support that the brightness
 variation is involved in the thermal physiology of these species. Instead,
 we show that body mass shapes the thermal physiology, with heavier
 individuals heating more slowly, and that males heated up faster and
 reached higher equilibrium temperatures than females. As females are
 heavier than males, the sex differences might be largely explained by size
 differences. Our results suggest that neither the thermal physiology nor
 the thermal preferences explain temperature differences in the field.
 However, green and brown individuals might still select different
 microhabitats in the field, which may indirectly lead to differences in
 body temperature. The persistence of the green-brown polymorphism may
 result from other mechanisms such as niche partitioning via microhabitat
 choice, mating preferences or frequency-dependent apostatic selection.
description: Sampling We studied the colour morphs of
 <em>Acrida ungarica</em>, <em>Pseudochorthippus
 parallelus</em>, and <em>Gomphocerus sibiricus
 </em>(hereafter simply referred as to <em>ungarica</em>,
 <em>sibiricus</em>, and <em>parallelus</em>,
 respectively). Individuals were collected in the field during the 2023
 summer season (in mid to late June in east-central Germany for
 <em>parallelus</em>; in mid-July and mid-august in northern
 Italy for <em>ungarica</em>; in mid-July in the French Alps
 for <em>sibiricus</em>). <em>Ungarica</em> were
 collected as nymphs, <em>parallelus</em> and
 <em>sibiricus</em> were collected mostly as nymphs. The first
 sampling of <em>ungarica </em>led to an over-representation of
 males because field sex ratios were highly unbalanced (17:2,
 males:females). We, therefore, oversampled females to compensate for
 missing individuals during the second sampling campaign.
 All three species are green-brown polymorphic (Figure 1).
 <em>Ungarica</em> features two colour morphs: green and brown.
 <em>Sibiricus</em> presents green and brown individuals. Brown
 <em>sibiricus</em> can be subdivided into a plain-brown morph
 and a pied morph. Though, the difference between brown and pied is rather
 subtle and both are characterized by the lack of green (Schielzeth and
 Dieker, 2020). Due to limited sample sizes of pied morphs and since field
 results – that motivated this study – pooled pied and plain-brown (Köhler
 and Schielzeth, 2020), we considered as the ‘brown’ morph both pied and
 plain-brown individuals. <em>Parallelus</em> features four
 colour morphs characterised by dorsal-lateral variation in the
 distribution of green. Individuals can be uniform green (laterally and
 dorsally green), lateral green (laterally green and dorsally brown),
 dorsal green (laterally brown and dorsally green), or uniform brown
 (laterally and dorsally brown). All species show continuous colour
 variation in brightness within their discrete colour morphs, resulting in
 significant inter-individual differences. Body colours may range from
 light-yellowish green (and brown) to rich dark green (and dark brown,
 sometimes almost blackish). Housing &
 weighing Field-caught grasshoppers were kept and
 raised to adulthood under controlled conditions in the lab (70% humidity,
 temperature around 20 °C at night and around 35 °C during the day).
 Grasshoppers were fed ad libitum with freshly cut grass blades, provided
 with a water tube for humidity and exposed to visible and UV lights
 (visible lights on from 6 am and 10 pm, and UV lights on from 8 am to 8
 pm). Only adults with fixed colour morph were used for the experiments
 and, therefore, the colour morph of <em>ungarica</em>
 individuals remained unchanged throughout the experiments. Prior to the
 experiments, all individuals were weighed with a microbalance (XS105
 Mettler Toledo, ± 0.1 mg). Each individual was tested in the same day in
 both experimental set-ups presented below (thermal preferences tested
 between 10 am and 1 pm, thermal physiology tested between 1 pm and 7
 pm). Thermal preferences We tested
 thermal preferences using thermal gradients. Each gradient setup was made
 of an 88 x 41.5 x 0.8 cm Aluminium plate mounted on 22 cm high Aluminium
 feet. The preferred body temperature of different acridid species is known
 to range from 22 to 41 °C (Harris et al., 2013; Springate and Thomas,
 2005; Blanford and Thomas, 2000). The thermal amplitude of the gradients
 was setup to contain this range by bathing one foot in ice and the other
 in water heated to 60 °C (using an immersion heater [Rommelsbacher TS
 2003, 2000W] and a thermostat [Schego thermostat TR2]). This yielded the
 runways to range from 10.0 ± 1.1 °C to 51.0 ± 2.3 °C (mean ± SD).
 Plexiglass dividers were mounted vertically on top to create 4 identical
 runways of 80 x 8 cm. The lower part of the Plexiglas dividers was coated
 with Fluon (PTFE) up to 12 cm height to prevent grasshoppers from climbing
 the walls. The gradients were covered with a thin plexiglass lid allowing
 the direct observation of the grasshoppers’ positions without disturbance.
 The setup was lit with fluorescent tubes. Using 4 identical setups, we
 were able to test 16 individuals in one session. All recording sessions
 took place between 10 am and 1 pm. Before starting the
 experiment, the temperature of each runway was measured at 5
 equally-spaced positions (distance from cold edge: 0, 20, 40, 60, and 80
 cm) using a handheld infrared thermometer (Farnell, dual focus infrared
 thermometer, ± 0.1 °C). We interpolated temperatures at intermediate
 positions by fitting a degree 3 polynomial with the
 <em>nls</em> function from R. Since the drift in temperatures
 for the duration of the experiment was relatively low (0.9 ± 0.2 °C, mean
 ± SD), we measured the temperature of the gradient only once at the
 beginning of the session. Individuals were then released in the middle of
 the runway (27.1 ± 1.3 °C, mean ± SD) and were free to move for an hour.
 Their positions were recorded every 10 min leading to 6 measures per
 individual. Their positions were converted to temperatures for further
 analyses using the <em>nls</em> fits described above. When an
 individual was seen perching on the wall or the lid, it was gently
 directed towards the middle of the runway and missing data were recorded
 for that reading (1.6% of all sightings). On each
 gradient setup, we ensured that morphs and as much as possible sexes were
 balanced to minimise confounding effects (see <em>Thermal
 physiology</em> since we used the same groups of animals in both
 experiments). Thermal physiology
 Adaptive variation in animal colouration, particularly in visible
 reflectance, represents a trade-off between various competing functions,
 including camouflage and thermoregulation. Alternatively, adaptive
 variation in infrared reflectance is likely driven predominantly by
 thermoregulatory needs, as most animal visual systems are largely
 insensitive to near-infrareds wavelengths (Stuart-Fox et al., 2017).
 Hence, we tested heat-up patterns and equilibrium temperatures under a
 radiant infrared regime. To monitor internal body temperature, we inserted
 the tip of a K-type thermocouple connected to a datalogger into the
 ventral side of the thorax of living grasshoppers piercing the
 intersegmental membrane between the thorax and the abdomen. The thorax was
 then glued (KIMTEC sekundenkleber speed 40, ethyl-2-cyanacrylat) onto a
 small pole of 7 mm in diameter. The pole was mounted onto a platform that
 held the thermocouple so that the setup stayed immobile throughout the
 experiment. Once glued, grasshoppers were placed in a freezer for 10 min
 to chill them to a temperature of 5.8 ± 3.0 °C (mean ± standard deviation,
 SD). The grasshoppers were then placed under an infrared lamp emitting
 near- and mid-infrared radiation (Elstein HTS/1, 230 V, 400 W, wavelength
 range: 2-10 µm) placed 41 cm above the grasshoppers. The K-type
 thermocouples recorded the internal temperature of the thorax every 5
 seconds. At the same time, a thermal camera (HIKMICRO B20, ± 2 °C)
 positioned in front of the experimental setup at an angle of about 60°
 took pictures every 20 seconds. Using the spot function of the HIKMICRO
 Analyzer software (v.1.3.1.5), we measured the external temperature of the
 thorax. One experimental session comprised 4
 grasshoppers. We ensured that morphs were balanced (2 greens and 2 browns
 for <em>ungarica</em> and <em>sibiricus</em>, and
 1 individual of each 4 morphs for <em>parallelus</em>) within
 sessions and that sexes were also balanced as much as possible (due to
 unbalanced sex ratios in <em>ungarica</em>,<em>
 </em>4 sessions were sex-balanced while 15 were male-only, and 13
 were female-only). The experiment lasted for 13.2 ± 0.7 min (mean ± SD),
 which allowed to reach equilibrium for most experimental sessions. For the
 analyses, we truncated the 20 first seconds, because internal temperatures
 kept going down during this period in 24% of the individuals, likely due
 to temperature homogenisation via circulating haemolymph and were not
 representative of the heat-up effect of the radiant lamp.
 Brightness measurements Individuals were
 placed in the refrigerator over the night following the experiments. The
 next day, body colour was measured using a handheld spectrophotometer
 (Avantes, AvaSpec-ULS2048x16; optic fibre: FCR-7UVIR200-2-1.5X100, 1.5 mm
 diameter) coupled with a deuterium-halogen light source (Avantes,
 AvaLight-DH-S). The device was calibrated with a commercial white standard
 (Avantes WS-2) on each day. The AvaSoft 7 software (Avantes, v.7.8)
 captured the reflectance spectra configured with an integration time of
 100 ms and an automatic averaging of 5 readings. Reflectance between 300
 and 1000 nm was measured in a dark room, holding the probe at a 45° angle
 while gently touching the cuticle surface. Our device always displays a
 narrow peak in reflectance between 654 and 659 nm. This peak apparently
 represents an artefact of the device and was replaced by the average
 across reflectance at 650-654 nm and 659-664 nm.
 Reflectance spectra were captured for the dorsal side and the
 right lateral lobe of the pronotum. Grasshopper body parts display complex
 colouration patterns (Figure 1). To account for this variability, we took
 5 independent measurements of both body parts. For each individual, we
 averaged reflectance across these 10 measurements using the
 <em>aggspec</em> function from the <em>pavo</em>
 package (version 2.9.0, on R) (Maia <em>et al.</em>, 2019).
 This led to a total of 432 reflectance spectra. We then derived the
 brightness value from each spectrum using the
 <em>summary.rspec</em> function from <em>pavo</em>
 by extracting the mean brightness. Mean brightness, simply referred to as
 brightness hereafter, corresponds to the mean relative reflectance over
 the entire spectral range, <em>i.e.</em> 300 – 1000 nm here.
 We deem this measure of brightness to be physiologically relevant, but
 note that it is different from how animals perceive brightness, since
 perception depends on the specificities of the visual system.
 Replication statement For a description of
 the replication design we used for each of our three predictors (colour
 morph, brightness, and weight), see Table 1.
 Statistical analyses All descriptive
 statistics given in the results are presented as the mean ± standard
 deviation. All statistical analyses were performed on R version 4.2.2 (R
 Core Team, 2023). We analysed the three species and two
 sexes separately by subsetting species and sex (six subsets in total).
 Within each subset we tested for morph differences as well as for effects
 of brightness and body weight as predictors. Confidence intervals (CI)
 were estimated by non-parametric bootstrapping with 1000 iterations.
 Bootstrap resamples were made out of complete time series of individuals
 that were resampled with replacement, while constraining each resample to
 have the same sample size for each colour morph as in the original subset.
 For each bootstrap iteration, we calculated the statistic of interest –
 differences in means between morphs or Spearman’s correlation coefficients
 between brightness (or weight) and the response variable (internal
 temperature, external temperature or substrate temperature). We derived
 the 95% CI from the distribution of the statistic of interest using the
 percentile method. Differences in means or correlation coefficients were
 treated as significant if the bootstrapped 95% CI did not contain zero.
 When testing the colour morph effect (<em>e.g.</em>
 mean<sub>green</sub> - mean<sub>brown</sub>), a CI
 above zero indicates that green individuals were hotter (when internal or
 external temperatures are tested) or that they were standing on hotter
 spots (when the substrate temperature is tested) than brown individuals.
 When testing the brightness (or weight) effect, a positive effect size
 means that brighter (or heavier) individuals were hotter or that they were
 standing on hotter spots than darker (or lighter) individuals.
 We derived three summary statistics from the time series. From
 the thermal physiology time series, we derived the <em>heat-up
 speed</em>, which represents the rate of temperature change between
 the 20<sup>th</sup> and the 240<sup>th</sup>
 second of the experiment, and the <em>equilibrium
 temperature</em>, which averages the temperature reached by an
 individual between the 660<sup>th</sup> and
 760<sup>th</sup> second of the experiment. Both of those
 statistics have been derived for the internal and external temperatures.
 From the thermal preferences time series, we derived the
 <em>preferred temperature</em>, which corresponds to the
 average temperature of the substrate where the individual was standing
 between the 30<sup>th</sup> and the
 60<sup>th</sup> minutes. Normality was
 checked from quantile-quantile plots. Homoscedasticity was checked using
 Fisher tests (two levels) or Bartlett tests (more than two levels). When
 heteroscedasticity was found, data were log-transformed to meet
 homoscedasticity when possible (it only concerned the heat-up speed for
 <em>parallelus</em> males tested against morph and for
 <em>sibiricus</em> tested against sex). The morph effect was
 tested using t tests (when normality and homoscedasticity were met), Welch
 tests (when normality only was met), and Mann-Whitney tests (when
 normality was not met) for<em> ungarica</em> and<em>
 sibiricus</em>, and using one-way ANOVAs for
 <em>parallelus</em>. For the Kruskal-Wallis tests featuring a
 <em>p</em> &lt; 0.05, means were
 separated using pairwise Mann-Whitney tests. The brightness and weight
 effects were tested using Pearson’s correlation tests when bivariate
 normality was met or Spearman’s correlation tests otherwise. Confidence
 intervals (CI) were derived from correlation coefficients of 1000
 bootstrap resamples (sampling with replacement while constraining the
 resample to have the same sample size as its original dataset).
 Correlation coefficients were treated as significant if the bootstrapped
 95% CI did not contain zero. The sex effect on the summary statistics was
 tested using Mann-Whitney tests when normality was not met, and using t
 tests otherwise. Tests on equilibrium temperatures of
 <em>ungarica</em> females were not performed because no
 equilibrium was reached (Figure 3, Figure S1).
description: # Body temperature differences between green and brown grasshoppers do not
 result from thermal physiology or thermal preferences Short summary:
 Colour polymorphisms correspond to the co-occurrence of several distinct
 colour morphs that vary in hue and/or brightness, independently of sex,
 age or any other state-dependent modifiers. Colour morphs could represent
 different life-history strategies, maximising their fitness locally in the
 trait space through trade-offs between all their biological functions.
 This mechanism could play a role in the maintenance of the green-brown
 polymorphism in Orthoptera. Grasshoppers are characterized by a widespread
 green-brown polymorphism and continuous variability in brightness within
 colour morphs. It has previously been found that brown individuals are
 warmer in the field than green conspecifics, but it is unclear if these
 differences are related to thermal physiology and/or thermal preferences.
 Therefore, we experimentally tested the thermal physiology and thermal
 preferences of three green-brown polymorphic species of acridid
 grasshoppers. We found no differences between green and brown
 grasshoppers, either in heat-up and equilibrium temperature patterns, or
 in temperature preferences. Nor did we find support that the brightness
 variation is involved in the thermal physiology of these species. Instead,
 we show that body mass shapes the thermal physiology, with heavier
 individuals heating more slowly, and that males heated up faster and
 reached higher equilibrium temperatures than females. As females are
 heavier than males, the sex differences might be largely explained by size
 differences. Our results suggest that neither the thermal physiology nor
 the thermal preferences explain temperature differences in the field.
 However, green and brown individuals might still select different
 microhabitats in the field, which may indirectly lead to differences in
 body temperature. The persistence of the green-brown polymorphism may
 result from other mechanisms such as niche partitioning via microhabitat
 choice, mating preferences or frequency-dependent apostatic selection.
 Code version: R version 4.2.2
 \---------------------------------------------------------------------------------------------------------------------------------------------------------------------- To back-up the bootstrap outputs that the script is generating, we describe below the folder architecture that is to be used in order to process further into the script: Create a folder "1000BOOTSTRAPS" that will contain all backup files of the bootstrap resamples performed (saved as *.RData). The path to each subfolder will be readily accessed from the R script just by running the script while both the script and the 1000BOOTSTRAPS folder are in the same folder. Structure of the (sub)subfolders: 1000BOOTSTRAPS ``` &gt;&gt; 1) 1000BOOT Tin                 &gt;&gt; a) 1000BOOT Tin ~ Morphs                 &gt;&gt; b) 1000BOOT Tin ~ Brightness                 &gt;&gt; c) 1000BOOT Tin ~ Weight         &gt;&gt; 2) 1000BOOT Tout                 &gt;&gt; a) 1000BOOT Tout ~ Morphs                 &gt;&gt; b) 1000BOOT Tout ~ Brightness                 &gt;&gt; c) 1000BOOT Tout ~ Weight         &gt;&gt; 3) 1000BOOT StandTemp                 &gt;&gt; a) 1000BOOT StandTemp ~ Morphs                 &gt;&gt; b) 1000BOOT StandTemp ~ Brightness                 &gt;&gt; c) 1000BOOT StandTemp ~ Weight ``` Each sub-subfolder have to contain all bootstrap resamples for a specific dependent variable (Tin: internal temperature, Tout: external temperature, StandTemp: standing/substrate temperature) and a specific explanatory variable (Morphs, Brightness, Weight). In each sub-subfolder, you are asked to paste 6 files (each corresponding to the species*sex subdatasets: 2 sexes * 3 species). Each of these files is named as follow: XxxXxx_Yyyy_Zzz, where: ```         ¤ XxxXxx corresponds to the statistics that has been calculated from the explanatory variable:                 - MorphDiff: pairwise differences between morphs                 - BrightCorr: correlation coefficient between brightness and the dependent variable                 - WeightCorr: correlation coefficient between weight and the dependent variable         ¤ Yyyy corresponds to the dependent variable:                 - Tin: internal temperature                 - Tout: external temperature                 - StandTemp: standing/substrate temperature         ¤ Zzz corresponds to the species x sex dataset:                 - Auf: Acrida ungarica females                 - Aum: Acrida ungarica males                 - Gsf: Gomphocerus sibiricus females                 - Gsm: Gomphocerus sibiricus males                 - Ppf: Pseudochorthippus parallelus females                 - Ppm: Pseudochorthippus parallelus males ``` For example, the file "MorphDiff_Tin_Auf.RData" has to be pasted in ./1000BOOTSTRAPS/1) 100BOOT Tin/a) 1000BOOT Tin ~ Morphs \----------------------------------------------------------------------------------------------------------------------------------------------------------------------- Overview of files: \=&gt; ThermalEcology_ColourPolymorphicGrasshoppers - 2024-12-09.Rmd = The R Markdown script of our analyses. \=&gt; ThermalEcology_ColourPolymorphicGrasshoppers - 2024-12-09.html = the html version of the R Markdown script (obtained via the "Render" function readily available from R) of our analyses. \=&gt; Heat_Up_Data.txt = data relative to the thermal physiology experiment, testing for the heat-up capacity of each grasshopper. ``` Column names and description:         md_row = an identifier for the row, in case returning to the initial order is needed         set_ID = identifier of the set (i.e. the group of 4 individuals that were assayed simultaneously) in which the individual has been assayed, formatting as yymmdd_X (year/month/day, and a single identifier ranging from 1 to 8)         GH_ID = single individual identifier. Individiuals are labelled as YXxAAA, where: ¤ Y is fixed (it identifies the field generation from season 2023) ¤ Xx stands for the species: ```                                                         - Au: Acrida ungarica                                                         - Gs: Gomphocerus sibiricus                                                         - Pp: Pseudochorthippus parallelus ``` ¤ AAA is a single 3-digit identifier (it ranges from 001 to 240)         time = time in seconds         T_int = internal temperature as measured by the thermocouples         T_ext = external temperature as measured by the thermal camera ``` \=&gt; Phenotypic_Traits_Data.txt = data relative to the phenotype of each individual. ``` Column names and description:         md_row = an identifier for the row, in case returning to the initial order is needed         GH_ID = single individual identifier (for label details, see above "Heat_Up_Data.txt")         species = species of the individual (Au: Acrida ungarica, Gs: Gomphocerus sibiricus, Pp: Pseudochorthippus parallelus)         sex = sex of the individual (f: female, m: male)         morph = colour morph of the individual (G: uniform green, D: dorsal green, L: lateral green, B: uniform brown)         weight = weight of the individual as measured right before going through the experiments, in grams ``` \=&gt; Runway_Gradient_Data.txt = data relative to the thermal preference experiment, tested using runway thermal gradients. ``` Column names and description:         md_row = an identifier for the row, in case returning to the initial order is needed         GH_ID = single individual identifier (for label details, see above "Heat_Up_Data.txt")         runway_row = identifier of the row in which the grasshopper has been tested (ranging from 1 to 16)         grad_pos = position on the runway where the temperature has been measured before the experiment (0, 20, 40, 60, 80 cm)         grad_temp = the temperature of the runway at each "grad_pos" position, in °C         time = time in seconds         stand_pos = the position on the runway where the grasshopper was standing during each sighting         comment = various comments. When NA in stand_temp, the comment column tells if the grasshopper was standing on the wall (wall), if it jumped at the moment it was sighted (jumpy), when it died (dead), or when they lost a jumping leg (-1JL) ``` \=&gt; spectro_data_all_ind.txt = all 10 spectra measured for each grasshopper (5 independent measures from the dorsal side of the pronotum, and 5 other from the lateral side of the pronotum) ``` Column names and description:         wl = wavelengths (nm)         YXxAAA.Zb = "YXxAAA" correspond to the individual that has been measured, and "Zb" to the side of the pronotum and its replicate: ¤ Y is fixed (it identifies the field generation from season 2023) ¤ Xx stands for the species: - Au: Acrida ungarica - Gs: Gomphocerus sibiricus                 - Pp: Pseudochorthippus parallelus ``` ``` ¤ AAA is a single 3-digit identifier (it ranges from 001 to 240) ¤ Z stands from the pronotum side (D: dorsal side, L: lateral side) ¤ b corresponds to the replicate onto the pronotum side (ranging from 1 to 5) ```