David Rozen-Rechels
email: david.rozen-rechels@normalesup.org

Alexis Rutschmann

Andréaz Dupoué

Pauline Blaimont

Victor Chauveau

Donald B Miles

Michael Guillon

Murielle Richard

Arnaud Badiane

Sandrine Meylan

Rozen-Rechels David

Rutschmann Alexis

Dupoué Andréaz

Blaimont Pauline

Chauveau Victor

Guillon Michael

Richard Murielle

Badiane Arnaud

Meylan Sandrine

Clobert Jean

Le Galliard

Interaction of hydric and thermal conditions drive geographic variation in thermoregulation in a widespread lizard

Keywords: Body temperature, elevational gradient, moisture gradient, landscape heterogeneity, non-avian reptiles

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

3 influenced by the thermal quality of the habitat. Our study highlights complex patterns of variation in thermoregulation strategies, which are mostly explained by the interaction between temperature and water availability, independent of the elevation gradient or thermal heterogeneity. Although changes in landscape structure were expected to be the main driver of extinction rate of temperate zone ectotherms with ongoing global change, we conclude that changes in water availability coupled with rising temperatures might have a drastic impact on the population dynamics of some ectotherm species.

Introduction

In a climate changing world, as average temperatures and frequency of heatwaves increase (IPCC 2014), organisms are exposed to repeated challenges in their capacity to regulate their body temperature in order to optimize their physiological performance and fitness [START_REF] Angilletta | The evolution of thermal physiology in ectotherms[END_REF].

Behavioral thermoregulation (i.e., regulation of body temperature through behavioral means; [START_REF] Angilletta | Thermal adaptation: a theoretical and empirical synthesis[END_REF]) is essential for ectotherms to rapidly adjust performance level to temperature fluctuations [START_REF] Kearney | The potential for behavioral thermoregulation to buffer "cold-blooded" animals against climate warming[END_REF][START_REF] Huey | Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation[END_REF][START_REF] Gunderson | Plasticity in thermal tolerance has limited potential to buffer ectotherms from global warming[END_REF][START_REF] Buckley | Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change[END_REF]. These behaviors involve flexible changes in activity patterns (e.g., [START_REF] Porter | Behavioral implications of mechanistic ecology[END_REF][START_REF] Grant | Trade-offs in activity time and physiological performance for thermoregulating desert lizards, Sceloporus merriami[END_REF]), micro-habitat selection (e.g., [START_REF] Kleckova | Thermoregulation and microhabitat use in mountain butterflies of the genus Erebia: Importance of fine-scale habitat heterogeneity[END_REF], movement decisions (e.g., [START_REF] Vickers | A random walk in the park: An individual-based null model for behavioral thermoregulation[END_REF] or adjustments in body posture (e.g., [START_REF] Barton | Behavioural thermoregulation and the relative roles of convection and radiation in a basking butterfly[END_REF]. The suitability of a habitat for ectotherms depends on the spatial variation in local temperatures and the tradeoffs associated with in the costs and benefits of thermoregulatory behaviors [START_REF] Sunday | Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation[END_REF]. For example, differences in body temperature along elevational gradients can be small or even non-existent in some ectotherms despite substantial changes in ambient temperatures [START_REF] Van Damme | Altitudinal variation of the thermal biology and running performance in the lizard Podarcis tiliguerta[END_REF][START_REF] Zamora-Camacho | Patterns of seasonal activity in a Mediterranean lizard along a 2200m altitudinal gradient[END_REF], because behavioral compensation facilitates the ability of animals to attain physiological optimal body temperatures even in cold environments [START_REF] Adolph | Influence of behavioral thermoregulation on microhabitat use by two Sceloporus lizards[END_REF][START_REF] Gvoždík | To heat or to save time? Thermoregulation in the lizard Zootoca vivipara (Squamata: Lacertidae) in different thermal environments along an altitudinal gradient[END_REF]. However, the predictors of these costs and benefits from thermoregulation are diverse, and we lack multivariate studies of geographic variation in thermoregulatory behaviors [START_REF] Angilletta | Thermal adaptation: a theoretical and empirical synthesis[END_REF].

The classical cost and benefit model of thermoregulation predicts that behavioral strategies of ectotherms should range from active thermoregulation (i.e., maintaining a constant body temperature despite environmental variation) to thermoconformity (i.e., body temperature matches environmental conditions) depending on the costs of thermoregulation imposed by local thermal conditions [START_REF] Huey | Cost and benefits of lizard thermoregulation[END_REF]. When local thermal conditions deviate from an organism's preferred body temperature, the model predicts that ectotherms should lower their thermoregulation [START_REF] Huey | Cost and benefits of lizard thermoregulation[END_REF][START_REF] Herczeg | Experimental support for the cost-benefit model of lizard thermoregulation[END_REF], 2008). However, when benefits of active thermoregulation increase in poor quality habitats and there is a risk of overheating, increased thermoregulation effort and accuracy can be beneficial for ectotherms despite costs due to missed opportunities for foraging or other behaviors [START_REF] Blouin-Demers | Habitat-specific behavioural thermoregulation by black rat snakes (Elaphe obsoleta obsoleta)[END_REF][START_REF] Blouin-Demers | The cost-benefit model of thermoregulation does not predict lizard thermoregulatory behavior[END_REF][START_REF] Vickers | Extending the cost-benefit model of thermoregulation: high-temperature environments[END_REF]. Thus, terrestrial ectotherms often increase thermoregulation effort in poor thermal environments to realize the benefits of attaining preferred temperatures or avoiding lethal overheating [START_REF] Sunday | Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation[END_REF][START_REF] Buckley | Thermoregulatory behaviour limits local adaptation of thermal niches and confers sensitivity to climate change[END_REF]. Given that mean thermal conditions change with elevation, latitude, or time of the year, an implicit prediction of the cost and benefit model of thermoregulation is that thermoregulatory behaviors should change along an elevational or latitudinal gradient (Hypothesis 1, Table 1, [START_REF] Adolph | Influence of behavioral thermoregulation on microhabitat use by two Sceloporus lizards[END_REF][START_REF] Caldwell | Plasticity of thermoregulatory behaviour in response to the thermal environment by widespread and alpine reptile species[END_REF] or in response to short-term changes in ambient temperatures (Hypothesis 2, Table 1, [START_REF] Blouin-Demers | Habitat-specific behavioural thermoregulation by black rat snakes (Elaphe obsoleta obsoleta)[END_REF]. The relative importance of short-term changes in thermoregulatory behaviors versus stable differences across a geographic gradient is not yet clear and can only be elucidated with multiyear studies of several populations [START_REF] Samietz | Altitudinal variation in behavioural thermoregulation: local adaptation vs. plasticity in California grasshoppers[END_REF][START_REF] Huey | Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation[END_REF][START_REF] Caldwell | Plasticity of thermoregulatory behaviour in response to the thermal environment by widespread and alpine reptile species[END_REF].

Recent models and experiments have suggested that the spatial heterogeneity of thermal conditions within a habitat might be a better predictor of the costs of thermoregulation than the mean thermal environment [START_REF] Caillon | Warming decreases thermal heterogeneity of leaf surfaces: implications for behavioural thermoregulation by arthropods[END_REF][START_REF] Sears | Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs[END_REF][START_REF] Sears | Configuration of the thermal landscape determines thermoregulatory performance of ectotherms[END_REF][START_REF] Basson | Lizards paid a greater opportunity cost to thermoregulate in a less heterogeneous environment[END_REF].

Heterogeneity of thermal conditions at a small spatial scale can be caused by differences in vegetation height or topography (i.e. shade availability) within the home ranges of animals. One prediction from these recent models is that spatial heterogeneity reduces the costs of thermoregulation by allowing energy-free investment in basking through shuttling of an individual between different thermal microhabitats (Sears andAngilletta 2015, Sears et al. 2016). Thus, behavioral thermoregulation should be more efficient in heterogeneous habitats than in homogeneous habitats even when the mean thermal quality of the habitat is poor (e.g., Goller et [START_REF] Sears | Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs[END_REF], a low thermoregulation efficiency should, for example, evolve in more homogeneous habitats (Hypothesis 3, Table 1). In addition, the importance of landscape heterogeneity should be more critical under strong thermal constraints, whether cold or hot, because landscape heterogeneity favors a greater reduction in the costs of thermoregulation when mean conditions deviate from the preferred temperature range (Hypothesis 4, Table 1, [START_REF] Herczeg | To thermoconform or thermoregulate? An assessment of thermoregulation opportunities for the lizard Zootoca vivipara in the subarctic[END_REF][START_REF] Sears | Configuration of the thermal landscape determines thermoregulatory performance of ectotherms[END_REF]. Unfortunately, support for both predictions are rare, since most comparative studies of behavioral thermoregulation are based on the mean thermal quality at the population level without consideration of the spatial distribution of micro-habitats within a landscape [START_REF] Gvoždík | To heat or to save time? Thermoregulation in the lizard Zootoca vivipara (Squamata: Lacertidae) in different thermal environments along an altitudinal gradient[END_REF][START_REF] Herczeg | To thermoconform or thermoregulate? An assessment of thermoregulation opportunities for the lizard Zootoca vivipara in the subarctic[END_REF][START_REF] Blouin-Demers | The cost-benefit model of thermoregulation does not predict lizard thermoregulatory behavior[END_REF]. As suggested by [START_REF] Sears | Configuration of the thermal landscape determines thermoregulatory performance of ectotherms[END_REF], we need an explicit map of operative environmental temperatures (T e , i.e., steady-state temperature of the animal without behavioral or physiological regulation, [START_REF] Bakken | Operative and standard operative temperature: tools for thermal energetics studies[END_REF] from each habitat to reach a full understanding of the costs of thermoregulation across an environmental gradient.

Ongoing climate change is not only modifying the thermal quality of the environment, but also affects the water availability, through changes in precipitation rates and frequency and magnitude of drought [START_REF] Field | Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change[END_REF]. Water availability, in the form of moisture or free-standing water in the environment, is another predictor of the costs of thermoregulatory behavior. Water restriction can compromise the regulation of water balance and the accuracy of thermoregulation at high body temperatures (e.g., [START_REF] Dupoué | Intergenerational trade-off for water may induce a mother-offspring conflict in favour of embryos in a viviparous snake[END_REF][START_REF] Lourdais | Hydric "costs" of reproduction: pregnancy increases evaporative water loss in the snake Vipera aspis[END_REF][START_REF] Pirtle | Hydroregulation. A neglected behavioral response of lizards to climate change? Pages[END_REF]. Water restriction in the environment is therefore expected to decrease behavioral activity [START_REF] Lorenzon | Effect of water constraint on growth rate, activity and body temperature of yearling common lizard (Lacerta vivipara)[END_REF][START_REF] Kearney | Field tests of a general ectotherm niche model show how water can limit lizard activity and distribution[END_REF][START_REF] Pirtle | Hydroregulation. A neglected behavioral response of lizards to climate change? Pages[END_REF]) and to change micro-habitat selection such that ectotherms avoid the risks of dehydration [START_REF] Pintor | Hydroregulation in a tropical dry-skinned ectotherm[END_REF][START_REF] Pirtle | Hydroregulation. A neglected behavioral response of lizards to climate change? Pages[END_REF]). In addition, thermal performance curves are often maximized at lower body temperatures in dehydrated compared to fully-hydrated animals, which may select for lower preferred body temperature and thermal

F o r R e v i e w O n l y
7 depression in more seric environments (e.g., [START_REF] Ladyman | The influence of dehydration on the thermal preferences of the Western tiger snake, Notechis scutatus[END_REF][START_REF] Muir | Osmotic and metabolic responses to dehydration and urea-loading in a dormant, terrestrially hibernating frog[END_REF][START_REF] Anderson | Trading heat and hops for water: Dehydration effects on locomotor performance, thermal limits, and thermoregulatory behavior of a terrestrial toad[END_REF]. The number of studies focusing on the effect of water availability and water balance on behavioral thermoregulation in ectotherms is growing [START_REF] Pintor | Hydroregulation in a tropical dry-skinned ectotherm[END_REF][START_REF] Rozen-Rechels | Water restriction induces behavioral fight but impairs thermoregulation in a dry-skinned ectotherm[END_REF]), but few have quantified the joint effects of water availability and the thermal environment on thermoregulation strategies to date. Variation in water availability in the environment may be described by persistent differences in the presence or absence of free water (Dupoué et al. 2017b(Dupoué et al. , 2018) ) or by moisture gradients [START_REF] Tieleman | Adaptation of metabolism and evaporative water loss along an aridity gradient[END_REF]. When population sites have consistent differences in the availability of permanent water, adaptive shifts towards lower optimal body temperatures can be expected to limit water loss in dry environments where the water debt cannot be recovered by drinking (Hypothesis 5 in Table 1, [START_REF] Davis | Water supplementation affects the behavioral and physiological ecology of Gila monsters (Heloderma suspectum) in the Sonoran Desert[END_REF]DeNardo 2009, Lillywhite et al. 2012). Moreover the magnitude of change in thermoregulation strategies should be greater in xeric than in mesic habitats as the environment gets warmer (Hypothesis 7, Table 1) and in homogeneous landscapes (Hypothesis 8 in Table 1) as proposed by the concept of thermohydroregulation [START_REF] Rozen-Rechels | When water interacts with temperature: Ecological and evolutionary implications of thermo-hydroregulation in terrestrial ectotherms[END_REF]. Another possibility is that variation in water availability in the environment is caused by short-term changes in air moisture or rainfall due to weather fluctuations. Rainfall variability has for example been shown to drive changes in behavioral activity of ectotherms or to change their micro-habitat selection [START_REF] Davis | Water supplementation affects the behavioral and physiological ecology of Gila monsters (Heloderma suspectum) in the Sonoran Desert[END_REF][START_REF] Ryan | Too dry for lizards: short-term rainfall influence on lizard microhabitat use in an experimental rainfall manipulation within a piñon-juniper[END_REF][START_REF] Kearney | Field tests of a general ectotherm niche model show how water can limit lizard activity and distribution[END_REF]. This variability can be described by year to year differences in the risk of dehydration for which atmospheric water vapor pressure is an accurate predictor [START_REF] Spotila | Role of temperature and water in the ecology of lungless salamanders[END_REF][START_REF] Lourdais | Hydric "costs" of reproduction: pregnancy increases evaporative water loss in the snake Vipera aspis[END_REF]. In this context, one can therefore predict that dry local conditions, favoring dehydration and characterized by low water vapour pressure in the air, should alter thermoregulation strategies just as surface water would (Rozen-Rechels et al. 2020, Hypothesis 6 in Table 1).

All things considered, quantifying the relative importance of thermal conditions, hydric conditions and habitat heterogeneity thus requires comparative, multi-year studies to understand the key

F o r R e v i e w O n l y
8 drivers of geographic variation in thermoregulatory behavior. Here, we surveyed geographical variation in the thermoregulation strategies of a cold-adapted vertebrate species, the common lizard (Zootoca vivipara, Lacertidae, Lichtenstein, 1823) over three years. The sampled sites included in our study are located at the southern margin of the distribution of the species. Previous work has demonstrated that climate warming induces changes in life-history strategies in these populations (e.g., [START_REF] Chamaillé-Jammes | Global warming and positive fitness response in mountain populations of common lizards Lacerta vivipara[END_REF]. Annual variability in the phenology of reproduction as well as reproductive effort correlates with changes in ambient temperature (Le [START_REF] Galliard | Cohort variation, climate effects and population dynamics in a short-lived lizard[END_REF], Rutschmann et al. 2016b, 2016a). Moreover, geographic variation in water availability influences the species' thermal ecology and water balance [START_REF] Lorenzon | Effect of water constraint on growth rate, activity and body temperature of yearling common lizard (Lacerta vivipara)[END_REF], 2001, Dupoué et al. 2017a, 2018) as well as their physiological sensitivity to extreme thermal conditions (Dupoué et al. 2018).

Thermal and water conditions further interact in predicting dispersal and population extinctions [START_REF] Massot | Condition-dependent dispersal and ontogeny of the dispersal behaviour: an experimental approach[END_REF], 2008, Dupoué et al. 2017b). The common lizard has been described as an active, efficient thermoregulator relying on flexible changes in microhabitat selection and basking behavior to regulate its body temperature [START_REF] Gvoždík | To heat or to save time? Thermoregulation in the lizard Zootoca vivipara (Squamata: Lacertidae) in different thermal environments along an altitudinal gradient[END_REF][START_REF] Herczeg | To thermoconform or thermoregulate? An assessment of thermoregulation opportunities for the lizard Zootoca vivipara in the subarctic[END_REF]. Although body temperatures vary among populations due to differences in local temperature, previous comparative studies have suggested that thermal preferences and indices of thermoregulation do not differ between low and high elevation populations with contrasted mean thermal conditions [START_REF] Van Damme | Responses of the lizard Lacerta vivipara to predator chemical cues: the effects of temperature[END_REF][START_REF] Gvoždík | To heat or to save time? Thermoregulation in the lizard Zootoca vivipara (Squamata: Lacertidae) in different thermal environments along an altitudinal gradient[END_REF]. Here, we moved a step forward to sample 21 populations and ca. 2000 lizards along an elevational gradient at a regional scale (Massif Central, France) over three summers to gather data across 31 population-year combinations. We used an information-theoretic approach to test the eight hypotheses summarized in Table 1 and evaluate how the thermal and hydric quality of the environment may explain variation in behavioral thermoregulation of the common lizard.

Material and methods

Study species and sampling

F o r R e v i e w O n l y
The common lizard (Zootoca vivipara) is a widespread Eurasian ectotherm species distributed from the southern European mountains up to Finland and from Western Europe to the Pacific Coast of Russia. This species inhabits wetlands (e.g., peatbogs and marshes), mesic meadows, and heathland where persistence of local populations depends on cool climates and high soil moisture conditions [START_REF] Lorenzon | The contribution of phenotypic plasticity to adaptation in Lacerta vivipara[END_REF][START_REF] Massot | Condition-dependent dispersal and ontogeny of the dispersal behaviour: an experimental approach[END_REF], Dupoué et al. 2018). Most of the natural populations outside of the southern Europe refugia are characterized by ovoviviparous reproduction, including the focal populations of in this study. In our study area, males emerge from hibernation in April.

Yearlings and adult females emerge later from end of April to early May. Mating occurs soon after the emergence of adult females and gestation lasts approximately two months. Parturition occurs between early July and early August depending on weather conditions [START_REF] Bleu | Reproductive allocation strategies: a long-term study on proximate factors and temporal adjustments in a viviparous lizard[END_REF], Rutschmann et al. 2016b).

Our focal populations are located in the mountain ranges of Massif Central in south-central France on a 500 meters elevation gradient clustering most of the species population in the area (see Table S1). The studiy sites are located in a variety of habitat types and include: wet meadows, heath lands, and glades within dense forest patches. The habitat may also include open forest with dispersed trees or shrubby vegetation (pine trees, Pinus spp., juniper trees, Juniperus spp., scotch broom, Cytisus scoparius and gorse, Ulex spp.) or low growth shrubs (heather, Caluna vulgaris, and blueberry, Vaccinium spp.) that offer contrasting thermal micro-habitats and shelters. We sampled 21 populations between 2016 and 2018. Most populations were sampled at least twice in differing years, but some were only sampled once (Table S1). Sites ranged in size from 0.6 to 12 ha and occurred in a diversity of habitats ranging from open meadows without permanent access to water to forest clearing with free standing water (Figure 1 and Table S1). The two closest sites were more than 700 meters away from each other and the landscape in the region is fragmented by roads, closed forest, pasture and othe unsuitable habitats for the species. The probability that two sites are connected by a permanent flow of migrants is thus low. In each population, we captured yearlings (less than 2 years old), adult gravid females and adult males (2 years old or more). We attempted to capture 20-24 adult females, 10 adult males and 20 yearlings (sex ratio 1:1) per population (see Table S2). We measured snout-vent length (average SVL ± SD; yearlings: 43 ± 6 mm; adult females: 62 ± 5 mm; adult males: 55 ± 4 mm) and body mass (BM: yearlings: 1.50 ± 0.57 g; adult females: 4.47 ± 1.32 g; adult males: 3.35 ± 0.67 g).

Adult gravid females and adult males were then brought to the laboratory and housed in individual terraria (females: 25 × 15 × 16 cm, males: 18 × 11 × 12 cm) with peat soil and a shelter. A temperature gradient between 20 to 35°C, which is representative of the gradient of temperatures found in natural population sites, was created in the terraria for 6 hours in the day (from 09:00 to 12:00 and from 14:00 to 17:00) with 25W incandescent bulbs placed above one side of each of the terrarium to allow thermoregulation. Water was supplied to individuals by misting the terraria three times per day at 09:00, 12:00 and 17:00. Individuals were not fed for two days after capture to ensure a post-absorptive state during the thermal preference tests (see below). Lizards were fed once every two days with 2 domestic crickets (Acheta domestica) until they were released (after parturition for females or after measurements for males; Rutschmann et al. 2016b, Dupoué et al. 2018). In one population subject to a long-term mark-recapture study (ROB; Table S2), we also brought yearlings back to the laboratory to assess age-related differences in thermal preferences.

Description of climate and habitat characteristics

We described the focal sites using well-defined meteorological, microhabitat and landscape variables to test our hypotheses (summarized in Table S1, with accurate locations and area of the sites). First, elevation ELE and the Emberger pluviometric quotient Q [START_REF] Daget | Le bioclimat Mediterraneen: Analyse des formes climatiques par le systeme d[END_REF], see below, for potential trends of thermoregulatory strategies variations from the lowest altitude populations in this geographic area up to mountain tops. The elevation gradient in our study area ranged from 1099 to 1527 meters. In addition, the calculation of the pluviometric quotient Q allowed us to test for a consistent trend along a historic, climatic gradient of aridity (lower precipitations and higher temperatures). To calculate the pluviometric quotient, meteorological variables were extracted from the AURELHY database of temperature and precipitation records over France from 1971 to 2000 extrapolated at a 1 km 2 scale (provided by Meteo France, see [START_REF] Bénichou | AURELHY: une méthode d'analyse utilisant le relief pour les besoins de l'hydrométéorologie[END_REF] for a description of the dataset). This database has the finest resolution grid and accounts for relief differences in interpolating the data, which is important for extracting correct statistics for each population in this geographic area. From these data, the Emberger pluviometric quotient for each site was given by:

Q= 100 × P T ARLmax 2 -T ARLmin 2
where P is the average annual precipitation in cm and T ARLmin (respectively T ARLmax ) the average of daily minimum temperature of the coldest month (respectively average of daily maximum temperature of the warmest month). The pluviometric quotient among the focal sites ranged from 228.1 to 563.3. None of the sites could thus be characterized as arid.

Second, we obtained local weather conditions for each year at each site including air temperature and water vapor pressure. These variables allowed us to measure both geographic and temporal changes in the microclimate conditions, and therefore quantify the specific meteorological conditions during the study period instead of long-term historical trends. We measured local weather conditions at each site and each year with 1 to 3 temperature and humidity dataloggers placed on the ground in the shade of the representative vegetation (Hygrochron iButtons, Maxim Integrated Products, Sunnyvale, CA, USA, ± 0.5 °C and 5% relative humidity-RH). At one site (ROB), we placed an additional18 temperature loggers in different types of vegetation to calculate spatial variation in thermal conditions. For each sample site, we calculated the average 10:00 to

F o r R e v i e w O n l y
12 18:00 minimal and maximal air temperatures (hereafter called T min and T max ) and the average 10:00 to 18:00 maximal water vapor pressure (hereafter called P max ; see Supplementary Information 1 for further information on measurements, calculations and choice of variable as well as Rutschmann et al. 2016A andDupoué et al. 2018). We restricted these calculations from 10:00 to 18:00 in order to compare local microclimatic conditions during the diurnal activity period of the lizards, because this interval is the most relevant to thermal quality of the environment during thermoregulation.

Third, we characterized habitat attributes including the presence of free water (permanent access or temporary access, hereafter called FW, see Dupoué et al. 2017b), an index of forest cover, the evenness in thermal micro-habitat diversity and the homogeneity of the thermal landscape. Sixteen sites had permanent sources of water, either streams or ponds, whereas 5 sites had access to temporary pools or streams. To characterize the landscape, we took aerial georeferenced digital photographs of all sites with a Phantom 4 Pro drone (DJI, Shenzen, China). In most cases except for small sites (BARN, MON, LAJO), we took a georeferenced grid of pictures centered on each site with Pix4Dcapture (Pix4D SA, Prilly, Switzerland) and then reconstructed a georeferenced orthophoto with the Maps Made Easy application (https://www.mapsmadeeasy.com/). For small sites, the map was created from one picture and we aligned georeferencing with control points accurately geolocated on Google Earth maps (version 7.3.2, Google Inc. 2019). From each georeferenced orthophoto, we then generated an 8-level vegetation map by creating a vector layer where each polygon corresponds to a patch of one of 8 pre-defined vegetation types (grass, heather, bilberry, tree, bush, rock, naked soil, and dead tree). Vectors layers were constructed in QGIS (version 2.18; QGIS Development Team, 2018, QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org) and downscaled to a 1-meter accuracy map (Figure S2).

We assessed the thermal quality of each vegetation type (i.e, each thermal microhabitat) by recording operative temperatures in all vegetation types and sites. To do so, we placed between 6

F o r R e v i e w O n l y
13 and 24 physical copper tube models fitted with HOBO® Pro v2 U23-003 (ONSET Cape Cod, Massachusetts) temperature loggers in representative vegetation types at each site. Sampled vegetation types were chosen in order to characterize as much as possible the vegetation diversity observed at each site. Each copper tube was 6 cm long and painted to mimic the dorsal coloration of a common lizard to match the biophysical, heat exchange properties of the species and record operative environmental temperatures (T e ) in a diversity of vegetation types [START_REF] Dzialowski | Use of operative temperature and standard operative temperature models in thermal biology[END_REF].

Indeed, operative environmental temperatures provide the best estimate for microhabitat thermal quality [START_REF] Bakken | Operative and standard operative temperature: tools for thermal energetics studies[END_REF][START_REF] Angilletta | Thermal adaptation: a theoretical and empirical synthesis[END_REF][START_REF] Sears | Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs[END_REF]. The loggers recorded temperatures every minute during each capture episode. We calibrated the temperatures made with operative temperatures models by comparing the temperatures with dead individuals (Supplementary Information 2). In subsequent calculations, we averaged these measurements in 15minute bins in order to compensate for short time changes in microclimatic conditions (e.g., due to wind or cloud cover). This time scale was also chosen because it captures heat exchange properties, i.e., thermal inertia, of small lizards such as the common lizard [START_REF] Fraser | Control of thermal conductance is insignificant to thermoregulation in small reptiles[END_REF]. The average operative environmental temperature score of each site per year (hereafter called T e ) over 15-minute bins was calculated from the average T e of all loggers in the site weighted by the abundance of the vegetation types in the site.

We also calculated an average T e score, independent from site and year, for each of the 8 vegetation types, in order to obtain an objective estimate of thermal differences across all microhabitats (see Supplementary Information 2 and Figure S3 for details). The chosen vegetation types represented a diversity of thermal microhabitats with short vegetation (grass), short bush (heather or bilberry), bush, shade under a tree, full sun (rock, bare soil) and dead tree. We then constructed a "thermal map" of operative temperatures at each site by linking each pixel of our rasterized map to the average T e score of the corresponding vegetation type. From the vegetation map and the thermal map, we then calculated three different indices of landscape heterogeneity. First, we calculated a

F o r R e v i e w O n l y
14 forest cover index defined as the proportion of trees within the site (hereafter called FCI). This index is a proxy of the proportion of shade in the habitat and has been often used to quantify habitat constraints on thermoregulation in heliothermic lizards [START_REF] Huey | Cost and benefits of lizard thermoregulation[END_REF]. It ranges from 0 (open landscape) to 1 (closed forest). In the focal sites, FCI ranges from 0 (open prairies, mainly grass) to 0.75 (sites in forest environments). Second, we calculated an index of thermal evenness (hereafter called TE). This index measures the diversity of average operative temperatures available in the landscape, corrected by the abundance of each microhabitat. TE ranges from 0 (low diversity and/or large dominance of a close range of operative temperatures) to 1 (diversified and equally abundant operative temperatures in the landscape). This index ranges from 0.06 to 0.47 indicating a low diversity of operative temperatures in the landscape. The lowest scored sites are those mainly covered in grass and open with few bushes. Third, we calculated an index of homogeneity based on the vegetation dissimilarity between adjacent pixels corrected by the difference of average operative temperatures between these pixels (hereafter called H). This index ranges from 0 (heterogeneous thermal landscape) to 1 (homogeneous thermal landscape). The focal sites were relatively homogeneous, with an index ranging from 0.78 to 0.96. All methods and calculations are described in detail in Supplementary Information 2.

Thermal preferences and thermoregulation traits

Immediately after capture, we measured cloacal body temperature T b using a K-type thermocouple connected to a digital thermometer (Ecoscan PT100, Eutech Instruments Pte Ltd) inserted ca. 1 cm within the cloaca. We also obtained T b data on additional individuals in the field that were not included in the laboratory studies. Two days after capture, we measured the thermal preferences (T pref ) of all lizards returned back to the laboratory. Thermal preference represents the selected body temperature of an individual in an environment free of costs for thermoregulation and is assumed to match the optimal body temperatures for physiological performance [START_REF] Hertz | Evaluating temperature regulation by fieldactive ectotherms: the fallacy of the inappropriate question[END_REF][START_REF] Angilletta | The evolution of thermal physiology in ectotherms[END_REF]. Here, thermal preferences were measured in 20 separate thermal gradients (120 × 25 × 20

F o r R e v i e w O n l y 15 cm
) with a substrate of dried peat soil. One side of each gradient was heated with a 60W incandescent bulb in order to approach the species critical thermal maximum CT max (43.9 ± 4.8 °C, [START_REF] Gvoždík | A comparative study of preferred body temperatures and critical thermal tolerance limits among populations of Zootoca vivipara (Squamata: Lacertidae) along an altitudinal gradient[END_REF] and the other end of the gradient was maintained at ambient temperature (24.1 ± 2.0 °C). The range of temperatures provided in the thermal gradient therefore allowed the lizards to select a body temperature with low thermoregulatory costs [START_REF] Angilletta | Thermal adaptation: a theoretical and empirical synthesis[END_REF].

A plastic shelter was placed under the heat bulb and water was supplied continuously during the experiment with a permanently filled small petri dish on the cold side in order to limit the stress of individuals in the terrarium. We note that this can confound measurements of thermal preferences with shelter or water preferences, but the design was the same for all populations. Two UVB 30W neon tubes provided natural white light above each gradient. Heat bulbs were turned on at approximately 08:00 in the morning. At 9:00 in the morning or at 14:00 in the afternoon, two individuals of the same age, sex and SVL (±3 mm maximum in the same gradient) were placed in each gradient to acclimate for 30 minutes. Both individuals were identified with a drop of nail polish placed on the dorsum side between the pelvis and tail. We did not observe any aggressive behavior between individuals during any run. In addition, it was not unusual to observe the lizards basking at the same spot, indicating that avoidance behaviors may not strongly influence our measurements. After acclimation, the temperature of the dorsal surface T pref of each lizard was measured with an infrared thermometer (Raytek, Raynger MX2) every 15 minutes for 3h15. We calculated the corresponding core body temperature from a calibration curve performed with additional data on the same species [START_REF] Artacho | Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard[END_REF]; R² = 0.96; T core = -4.50 (± 0.5 SE) + 1.17 (± 0.03 SE) × T pref ). We excluded from analyses the extremes of T core (lower than 25°C and higher than 41°C) with consideration that they corresponded to failed measurements aimed at the substratum instead of the lizard back (results were similar qualitatively with all data included). T pref referred to T core in the following parts of the manuscript.

F o r R e v i e w O n l y 16
Variation of T pref with the time of the day, date or individual status was investigated (see the Statistical Analyses section, Supplementary Information 3 and 4). We further tested for differences between thermal preferences of yearlings and adults at the same time of the year in one population (ROB). These data showed that thermal preferences of yearlings (regardless of sex) and adult males were not different (Figure 4A, Table S5, all details in Supplementary Information 3). Thus, we assume that thermal preferences of males in each population were a good proxy of the thermal preferences of yearlings. For each population and year, we calculated the average thermal preferences of adult females and adult males/yearlings hereafter called (average of all T pref T pref measurements for each year, population and age-sex category of individuals). To quantify how field body temperatures of lizards deviate from their thermal preferences, we calculated the thermoregulation inaccuracy, defined as the absolute difference between body temperature during activity in the field and thermal preferences , for each T b measurement (Hertz et al.

D b = | T pref -T b |
1993, Blouin-Demers and Nadeau 2005). For each individual record, we used the average T pref from the same category of individuals (age and sex class) in the same population of the same year.

We further calculated thermal quality of the habitat, defined as the absolute difference between operative temperatures and thermal preferences , for each age-sex category of

D e = | T pref -T e |
lizards in each population each year and each 15 minutes' time bin (Hertz et al. 1993, Blouin-Demers and[START_REF] Blouin-Demers | The cost-benefit model of thermoregulation does not predict lizard thermoregulatory behavior[END_REF]. The ability of an individual to reach their thermal preference given the thermal quality of the habitat is called thermoregulation efficiency or effectiveness of thermoregulation. For each capture event, we calculated the index of thermoregulation efficiency, E the D e for the same population during the 15 minutes' time bin of the capture time. When E = 0, the body temperature of the lizard is the same as that of a physical model that is not thermoregulating and there is therefore no evidence that individuals are active thermoregulators. If E > 0, individuals are thermoregulating as they reach their preferred temperature even though the thermal environment

F o r R e v i e w O n l y
is not of sufficient quality for a thermoconformer. If E < 0, individuals are avoiding thermally suitable microhabitats.

Statistical analyses

All analyses were performed with R version 3.4.4 "Someone to Lean On" (R Core Team 2018). We analysed variation of T b , T pref , D b , D e , and E using linear mixed models with the function lme from the package nlme [START_REF] Pinheiro | Mixed-effects models in S and S-PLUS[END_REF] after assessment of homoscedasticity and normality of residuals. As the distribution of D b was not Gaussian, we used a square root transformation. For each variable, we followed the same logic for model selection procedure. We first fit a full model including all individual and time covariates as fixed effects and then selected a first minimum adequate model using backward model selection based on likelihood ratio tests. Time covariates included year (categorical, all response variables), hour of the day (continuous, for T b , D b , and E), and time of the day (morning or afternoon session, T pref ). Individual covariates were age and/or sex (categorical, for T b , T pref and D b ) or category (categorical, adult females versus adult males and both sexes yearlings, D e , E, see Supplementary Information 3 and Figure 4A for motivation). Full models included additive effects of all covariates and two-way interaction terms between all individual covariates. For T pref data, we analysed all temperatures recorded for each individual rather than a composite variable (e.g. mean or variance), unpublished data from the team showed that thermal preferences sometimes change depending on the hour of the day but also with the time spent in the gradient (unpublished data). We therefore also fit a two-way interaction between the time of the day the experiment was run (morning vs. afternoon) and the time spent since the beginning of the test (i.e., end of habituation). Population identity was included as a random effect. Individual identity nested in the population identity was also included as a random effect to account for repeated measurements on the same individual (T pref data).

When a minimum adequate model was selected, we determined whether the traits differed across populations. We tested the significance of the population identity random effect with log-likelihood

F o r R e v i e w O n l y
18 ratio test comparison of models fitted by REML [START_REF] Pinheiro | Mixed-effects models in S and S-PLUS[END_REF]Bates 2006, Bell et al. 2019). We subsequently compared the relative importance (sum of AICc over all models in which the variable appears) of environmental variables related to elevation, Emberger pluviometric quotient, microclimatic conditions and habitat features (see Table S6) using the Akaike information-based criterion (AICc) comparison procedure [START_REF] Burnham | Multimodel inference understanding AIC and BIC in model selection[END_REF]) and a model averaging procedure from maximum likelihood estimates of model parameters [START_REF] Burnham | Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach[END_REF]. Models were constructed from the best model selected during the first step described above (hereafter, called our null model) in which we added the environmental variables alone as fixed effects, or in two-way interactions. The main effects and interaction terms were selected based on our working hypotheses (Table 1). In order to test for sex or age-dependent effects, when sex, age, or the age-sex category of individuals was retained in the null model, we also added to our list of models the two-way interactions as well as the three-way interactions of environmental characteristic with the sex and/or the age variables. Correlations among the environmental variables tended to be low (see Table S4), but we excluded models with significant collinear variables (r > 0.5). We also removed the few models that showed no convergence (listed in Table S6). For the purpose of model averaging calculations, all continuous covariates were mean centered and scaled by the standard deviation and categorical covariates (FW, sex and age) had their contrasts summed to zero. We selected the main effects and interaction terms with significant contributions to the variability of the traits based on the AICc difference between models, the relative weight of the models and the importance as well as conditional averages from model averaging procedure.

Results

Measured and calculated thermoregulation statistics for each population are presented in Table S2.

Detailed results of the model selection procedures are presented in Table S6. Conditional averages of the most important environmental variables from model averaging are presented in Table 2. and T max (RI = 0.95) and the two-way interaction Q × T max (RI = 0.91, Figure 3A andB, Table S6).

Variation in body temperature

According to the best supported model, mean body temperatures were higher in populations characterized by a mesic environments (higher Q coefficient) with high T max (Table 2, Figure 3A).

In drier environments (low Q), body temperatures were almost consistant with minimal air temperatures T max , whereas T b increased with T max in mesic environments (high Q).

Variation in thermal preference

We identified significant differences in records from thermal gradient T pref measurements among populations, however the variance explained among populations was quite low (variance explained = 2%; LRT = 48.2, df = 1, p < 0.0001) compared to the variation among individuals (proportional variance explained = 24%). Variation in T pref was also significantly explained by an interaction of sex × year, and between time of day (morning versus afternoon) when the T pref was measured with the time since beginning of the test. Despite little quantitative variation among populations, our model comparison procedure uncovered that the forest cover index FCI as well as the two-way interaction between FCI and sex were significant variables explaining geographic variation in T pref (RI of 0.85 and 0.84 respectively, Table 2). T pref of females significantly increased with FCI (Figure 4B). t 1959 = -0.9, p = 0.37). However, in 2017, males displayed mean √D b values 0.2 ± 0.1 °C higher than females (t 1959 = 2.0, p = 0.05) and yearlings of both sexes displayed mean √D b values 0.4 ± 0.1 °C higher than females (yearlings: t 1959 = 3.7, p = 0.0003, males × yearlings: t 1959 = -2.4, p = 0.02).

They were thus less precise in their accuracy during that year. Geographic variation in D b was mostly explained by T max (RI=1), pluviometric quotient Q (RI=0.1) and their two-way interaction (RI=0.1, Table S6). Thermal inaccuracy (D b values) was lowest in drier (low Q) and cooler habitats (low T max ) and higher in more mesic (high Q) and low T max (Table 2). The inaccuracy of thermoregulation (D b ) increased with pluviometric quotient at low minimal air temperature T max , and decreased with pluviometric quotient at high T max (Figure 3B).

Variation in thermal quality of the habitat

Thermal quality of the habitat D e differed among populations (proportional variance explained = 36%; LRT = 678.3, df = 1, p < 0.0001). Values for D e varied following a quadratic function of the hour of the day. Geographic variation in D e was mostly explained by P max (RI=1), T min (RI=1) and their two-way interactions (RI=1, Table S6). In populations with average T min , D e increased with an increase of P max . This effect vanished and was even inverted with an increase in local minimal air temperature (Table 2). In most cases, T e were below thermal preferences, i.e. thermal quality of the habitat was in average too cold (Figures S3 andS4).

Variation in thermoregulation efficiency

F o r R e v i e w O n l y 21
The D b values increased significantly with D e (Figure 5, slope: 0.2 ± 0.02, intercept: 6.4 ± 0.4; F 1,1836 = 144.5, p < 0.0001). Variation in thermoregulation efficiency E followed a quadratic function of the hour of the day (decreased with the hour of the day after which it increased again).

In addition, E differed among populations (proportional variance explained = 46%; LRT = 781.7, df = 1, p < 0.0001). Geographic variation in E was significantly explained by T min (RI=1), P max (RI=1), and the two-way interaction P max × T min (RI=1, Table S6, Figure 5). In other words, thermoregulation efficiency E increased with minimal air temperature T min in populations with higher water vapour pressure P max , whereas it decreased with T min in populations with high P max (Table 2, Figure 6).

Discussion

In this study, we tested whether geographic differences in elevation or climate dryness, year-and site-specific differences in air temperature and moisture, habitat differences in water availability and habitat differences in spatial landscape features of the vegetation influenced thermoregulation strategies across multiple populations of a widespread lizard species. Our results highlight that thermoregulation behaviors are mainly shaped by interactions between local site-and year-specific air temperatures, historical differences in the Emberger pluviometric quotient and the local annual air moisture conditions, supporting the thermo-hydroregulation hypothesis (Hypothesis 7; results supporting each hypothesis are summarized in Table 3). Body temperature, and as a consequence thermoregulation inaccuracy, was influenced by an interaction between local air temperatures and the historical differences in climate dryness of the site. Preferred temperature of lizards was significantly lower for adult females and in populations with permanent access to water. Finally, thermoregulation efficiency and the quality of the thermal habitat, were highly variable among populations and were influenced by the interaction between air temperature and air moisture, which were calculated for each site and each year. In contrast, persistent, historical differences in weather conditions and habitat features had little effects on these thermoregulation statistics. 3).

Thermo-hydroregulation as a driver of the geographic trends in thermoregulatory behaviors

Geographic variation in body temperature and in thermoregulation accuracy was best explained by the interaction between the local ambient thermal conditions quantified each year at each population and the pluviometric quotient (Q score, an index of the dryness of the climate) calculated from historical weather data for each population. As expected, body temperatures of lizards increased with local air temperatures at the study site, but this correlation was evident in only the most mesic climates (high Q score). In addition, the thermoregulation inaccuracy decreased with local air temperatures. However, the pattern was stronger in more mesic climates, as indicated by a high value of Q. We note that, on average, field active body temperatures were below the thermal preference sets and field thermoregulation behavior was "sub-optimal". However, according to our best supported statistical models, we predicted that thermal preferences of lizards should be almost attained in the warmer local climate conditions and in the sites occurring in the most historically mesic climates.

According to the thermo-hydroregulation hypothesis (hypothesis 7 in Table 1), the costs of thermoregulation depend on the risks of desiccation and over-heating, which are generally more important in drier climates (e.g., Dupoué et al. 2017bDupoué et al. , 2018) ) and should increase in warmer climatic conditions (e.g., [START_REF] Lourdais | Hydric "costs" of reproduction: pregnancy increases evaporative water loss in the snake Vipera aspis[END_REF][START_REF] Rozen-Rechels | When water interacts with temperature: Ecological and evolutionary implications of thermo-hydroregulation in terrestrial ectotherms[END_REF]. We thus expect thermoregulation to be more accurate in sites with lower costs of desiccation when local climate conditions are warmer (Rozen-Rechels et al. 2019). Our results are concordant with these expectations, which emphasizes the importance of non-energetic costs of thermoregulation due to a higher risk of dehydration in populations with long-term patterns of a dry climate accompanied by high ambient temperature. One explanation is that selection favoring behavioral thermoregulatory strategies to limit dehydration led to either plastic or genetic adaptive responses in lizard populations occupying

F o r R e v i e w O n l y
23 historically drier climate conditions. To our knowledge, this is the first demonstration that a climate dryness index, calculated from rainfall and temperature data, accurately predicts geographic variation in thermoregulation strategies in an ectotherm (see Tieleman et al. 2003 in endotherms). This complements recent data by [START_REF] Kearney | Field tests of a general ectotherm niche model show how water can limit lizard activity and distribution[END_REF], who observed that activity patterns in another species of lizard were also sensitive to both local temperature and rainfall patterns.

In addition, we found that the thermoregulation inaccuracy had a positive correlation with the thermal quality of the habitat with a slope < 1 (Figure 5), suggesting that in our study system, the common lizard is intermediate between a perfect thermoregulator and a perfect thermoconformer [START_REF] Hertz | Evaluating temperature regulation by fieldactive ectotherms: the fallacy of the inappropriate question[END_REF]). Variation in thermoregulation efficiency, calculated from the difference between the thermal inaccuracy of the lizard and the thermal quality of the habitat, was best explained by an interactive effect between ambient thermal conditions and local air moisture, which provides additional support to the thermo-hydroregulation hypothesis (hypothesis 7 in Table 1). Air moisture, another measure of the hydric quality of the habitat, explained geographic variation in the thermal quality of the habitat (D e ) and in thermoregulation efficiency (E), whereas the historical aridity index (Q score) best explained the thermoregulation inaccuracy. In low air moisture conditions, thermoregulation efficiency exhibited a slight increase with minimal air temperature. In contrast, in high air moisture conditions, thermoregulation efficiency was maximal at low air temperatures and displayed a striking decrease as air temperatures increased. These results suggest that the relation between air temperature and thermoregulation efficiency is constrained at low air moisture conditions, i.e. when local microclimatic conditions at the site are more desiccating. They further indicate that variation in thermoregulation efficiency are mainly explained by local and yearly moisture-driven changes in the thermal quality of the habitat rather than historical differences in climatic conditions characterizing each population (as described by the pluviometric quotient Q). However geographic variation of thermoregulation accuracy was best explained by long-term and historical weather conditions rather than local annual weather conditions. We note

F o r R e v i e w O n l y
24 that this was not due to some model inconsistency due to a collinearity between variables because the pluviometric quotient and the moisture conditions of a site were uncorrelated (F 1,29 = 0.32, p = 0.57, R=-0.13). Furthermore, differences in air moisture are mainly associated with potential differences in total rates of water loss from lizards in each site and each year [START_REF] Spotila | Role of temperature and water in the ecology of lungless salamanders[END_REF][START_REF] Mautz | Patterns of evaporative water loss[END_REF]), whereas differences in historical pluviometry may further influence soil moisture, availability of free-standing water throughout the year and ecosystem productivity. One potential interpretation of our findings is therefore that the risk of dehydration, which is typically enhanced in drier air conditions (Rozen-Rechels et al. 2019), decreased thermoregulation efficiency, which indicates that some non-energetic costs linked to water loss act as a constraint to thermoregulatory behaviors. Altogether, these results provide strong support for the thermo-hydroregulation hypothesis (Rozen-Rechels et al. 2019, Hypothesis 7 in Table 1), which posits that the thermoregulatory behavior of common lizards is shaped by a trade-off between body temperature regulation and water balance regulation.

Low geographical variability of thermal preferences

Thermal preferences exhibited the lowest geographic variation among all thermoregulation statistics investigated in this study. A striking pattern was that intra-population variation in thermal preferences (24%) was unexpectedly higher than inter-population variation (2%), as seen in previous geographic comparisons of thermal preferences in this species [START_REF] Trochet | Variation of preferred body temperatures along an altitudinal gradient: A multispecies study[END_REF] also consistent with the observation that thermal preferences vary less than field body temperatures across elevational ranges or along climate gradients in many lizard species [START_REF] Van Damme | Responses of the lizard Lacerta vivipara to predator chemical cues: the effects of temperature[END_REF][START_REF] Angilletta | The evolution of thermal physiology in ectotherms[END_REF], but see Gilbert and Miles 2019 for a counter-example in an arid zone lizard). Interestingly, average thermal preferences reported here were surprisingly high, especially for gravid females, compared to previous values reported for the same species in other study sites (gravid females: 34.0 ± 3.2 °C, males: 35.8 ± 3.4 °C; see Gvoždík 2002 who used a different method at higher latitude, in the Czech Republic, Le [START_REF] Galliard | Timing of locomotor impairment and shift in thermal preferences during gravidity in a viviparous lizard[END_REF][START_REF] Artacho | Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard[END_REF][START_REF] Rozen-Rechels | Water restriction induces behavioral fight but impairs thermoregulation in a dry-skinned ectotherm[END_REF] with similar methods in an experimental set-up, Trochet et al. 2018 using oviparous populations located in the Pyrénées, in the South-Western European range).

Methodological differences, in part, between these studies may contribute to this contrast. However, an alternative is that the differences may be real and the high values reported in our study might suggest directional selection for higher thermal preference along the warmer, southern margin of the species distribution, which is under warming pressure (Chamaillé-Jammes et al. 2006, Gilbert andMiles 2017 for another example in an other species). Overall, our results suggest higher variation in thermal preferences at a greater spatial scale than the one investigated here. Further latitudinal comparisons of populations spanning a broader geographic range than this study is needed to confirm this pattern.

The only noticeable geographic trend in thermal preference that we detected was a significant shift towards higher thermal preference in females from populations with a high forest cover. We suggest the shift is an indirect response associated with differences in the stage of gestation of females among populations rather than a direct effect of forest cover per se. Indeed, thermal preference has a higher correlation with the stage of gestation, which overwhelmed the effect of the forest cover index (see Supplementary Information 4). The geographic difference between populations with low versus high forest cover was mostly driven by one site (ROB, the population with the highest forest cover), where females were more intensively sampled early in pregnancy (Figure S6). In addition, a

F o r R e v i e w O n l y
delayed timing of parturition in populations with higher forest cover had also been demonstrated in a smaller sample of our study sites (Rutschmann et al. 2016a). In the common lizard, advancement of gestation is associated with a shift toward lower thermal preferences, which is more obvious at the end of pregnancy (ca. 29-30°C at the end versus 33-34°C before gestation; see Le [START_REF] Galliard | Timing of locomotor impairment and shift in thermal preferences during gravidity in a viviparous lizard[END_REF]). As we sampled a larger number of females early in gestation in sites with high forest cover, differences in the timing of sampling and timing of parturition provided the best explanation for why females had lower thermal preference in more open habitats.

Absence of thermal landscape effects

An unexpected result of our geographic comparison was that indices of the spatial heterogeneity of the thermal landscape made no significant contribution to population variation in body temperature and thermoregulation accuracy contra our hypotheses 3 and 4 (see Tables 1 and3). According to recent individual-based models of thermoregulatory behavior [START_REF] Sears | Costs and benefits of thermoregulation revisited: both the heterogeneity and spatial structure of temperature drive energetic costs[END_REF], which were confirmed by detailed experiments of heterogeneous thermal landscapes with desert lizards [START_REF] Sears | Configuration of the thermal landscape determines thermoregulatory performance of ectotherms[END_REF], we expected a higher thermoregulation accuracy for lizards in more heterogeneous landscapes, because more heterogeneous thermal environments entail lower costs of behavioral thermoregulation. For example, optimality models of thermoregulation predict that opportunities for behavioral thermoregulation are weaker in more homogeneous habitats and the energetic costs of thermoregulation are higher in homogeneous habitats when contrasting habitats of different thermal quality are highly segregated in space.

A first potential explanation for the pattern of thermoregulation behavior of common lizards to be similar in homogeneous and heterogeneous thermal landscapes, is that the costs of thermoregulation in homogeneous environments are not as high as hypothesized in optimality models of thermoregulation. For example, [START_REF] Basson | Lizards paid a greater opportunity cost to thermoregulate in a less heterogeneous environment[END_REF] showed that the energetic cost of locomotion between microhabitats are negligible for Oelofsen's Girdled lizards Cordylus oelofseni moving inside their typical home ranges. Similar results may apply to common lizards because these are

F o r R e v i e w O n l y
27 actively foraging, ground dwelling lizards with a relatively good endurance capacity [START_REF] Garland | Ecological morphology: integrative organismal biology[END_REF]Losos 1994, Vitt and[START_REF] Vitt | Lizard ecology: historical and experimental perspectives[END_REF]

, Miles unpublished data).

A second potential explanation is thatthe ambient temperatures during capture were below the thermal preference set of common lizards as opposed to the desert lizard species in previous theoretical and empirical studies, where there was a significant risk of over-heating [START_REF] Sears | Configuration of the thermal landscape determines thermoregulatory performance of ectotherms[END_REF]). In the cool temperature range of our geographic area and study sites and at the time of sampling (Figure S4), the risks of overheating were in general negligible with the exception of a few populations over short time periods of the day. In cool climate conditions, the benefits of thermoregulation imply faster and more prolonged access to the thermal preference set by selection of warmer microhabitats. Given the asymmetric shape of the thermal performance curves of reptiles and the generalist (wide thermal breadth) thermal physiology of the common lizard [START_REF] Van Damme | The thermal dependence of feeding behaviour, food consumption and gut-passage time in the lizard Lacerta vivipara Jacquin[END_REF][START_REF] Artacho | Interindividual variation in thermal sensitivity of maximal sprint speed, thermal behavior, and resting metabolic rate in a lizard[END_REF], the benefits of thermoregulation by microhabitat selection of warm sites are smaller in cool climates on average in this species than the benefits of thermoregulation by microhabitat avoidance of warm sites in warm climates. This is because individual physiological performances, such as maximal locomotor capacities, are weakly ascending and reach a plateau over the body temperature range of 32-36°C including most of the observed variation in temperature conditions in the vegetation. Since, a heterogeneous landscape would be beneficial when it allows for behavioral shifts among contrasted cold and hot microhabitats in order to heat faster in cool weather conditions and to avoid overheating in warm weather conditions; heterogeneity would thus be critical only when the habitat overlaps the critical thermal limits of the species which is not the case in our study focal period in the focal sites.

A third potential explanation is that our methods failed to characterize the complete range of variation in the thermal landscape despite substantial differences in vegetation characteristics and heterogeneity [START_REF] Sears | The world is not flat: defining relevant thermal landscapes in the context of climate change[END_REF][START_REF] Caillon | Warming decreases thermal heterogeneity of leaf surfaces: implications for behavioural thermoregulation by arthropods[END_REF]. Our methods showed that the thermal landscapes had limited variation. Our estimates of the homogeneity index (H) varied between 0.75

F o r R e v i e w O n l y
28 to 1. Most of the study sites were covered in large expanses of grass and other vegetation of short stature. We could not characterize and quantify the fine-scale, spatial variability of thermal conditions within vegetation patches inside each pixel of our vegetation maps. It is obvious from field observations that active lizards can take advantage of the varying shade levels and vertical thermal structure within a vegetation patch, and this fine scale heterogeneity to which lizards are familiar might be more critical determinants of their thermoregulation behavior than vegetation homogeneity at the scale of meters or tens of meters. Quantifying the very fine details of the vegetation thermal landscape however represents a strong methodological challenge that is beyond the scope of this study.

Implications for ecological responses to climate change

Future risks of extinction for temperate lizard species as a result of global warming are expected to show a dramatic increase due to a reduction in the availability of shade and an increase in the risks of overheating. However, there is an ongoing debate about the critical determinants of population loss from global warming for these species [START_REF] Sinervo | Erosion of lizard diversity by climate change and altered thermal niches[END_REF][START_REF] Kearney | Activity restriction and the mechanistic basis for extinctions under climate warming[END_REF]). In the common lizard, climate warming in our study area has led to simultaneous changes in stress physiology, ageing, life history, reproduction phenology and trajectories of population extinction in the warmest sites [START_REF] Chamaillé-Jammes | Global warming and positive fitness response in mountain populations of common lizards Lacerta vivipara[END_REF], Rutschmann et al. 2016b, 2016a, Dupoué et al. 2017a,2017b, 2018). In parallel, we found no evidence of a reduction of shade availability in the our study sites, contrary to predictions of global mechanistic models [START_REF] Kearney | Activity restriction and the mechanistic basis for extinctions under climate warming[END_REF]. Stasis in forest cover (shade) is a result of little or no change in vegetation among some study sites or an expansion of forest cover in other sites during the past 15 years due to changes in land use (Clobert et al. pers. obs.).

The increase of availability of shade in some populations could benefit the common lizard in the context of global warming because tall and dense vegetation ameliorates the risk of overheating [START_REF] Grimm-Seyfarth | Too hot to die? The effects of vegetation shading on past, present, and future activity budgets of two diurnal skinks from arid Australia[END_REF]). Our study suggests, however, that thermoregulation strategies in DRR and J-FLG led the writing of the manuscript to which all authors significantly contributed.

F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w O n l y F o r R e v i e w
O n l y 40 Table 1 Our sampling protocol aims to quantify the relative importance of climate and habitat factors in explaining the geographical variation in thermoregulation statistics of the common lizard (Zootoca vivipara). This table summarises our working hypotheses and defines how these working hypotheses were tested with statistical models. The effect of thermal landscape on thermoregulation strategies depends on persistent differences in temperature (thermal cline) or yearly differences in temperature, such that for example spatial heterogeneity is critical only in hot or cold conditions. The use of diverse thermal microhabitats could also be dictated by differences in water loss rates. A wet and hot homogeneous habitat could be of lower cost than a dry one if water loss is the main thermoregulation costs.

Hypotheses

F o r R e v i e w O n l y
Table 2 Conditional average of selected variables from the model averaging procedure (Table S6).

Continuous variables are scaled and centered. Categorical variables had their contrasts summed to zero, i.e., the intercept is the average trait for average continuous variable and for a theoretical average level of the categorical variables. Effects are obtained from the deviation from this average. In more xeric conditions, an increase of temperature is riskier in terms of water loss, the benefits of thermoregulation are more constrained. In more mesic conditions, these constraints are relaxed. These constraints may have shaped the evolution of thermoregulation behaviors in these populations. Thermal quality of the habitat is modulate by moisture conditions. When moist, cold air conditions make the environment too cold. When warmer, the refreshing effect of moisture might be attenuated.

Yes 8 Thermo-None.

In the absence of a cost of overheating, landscape features might not affect thermoregulation behaviors. Sites No

  Based on the individual-based model of thermoregulatory behavior in a ground dwelling ectotherm of

Figure 2 )

 2 Figure2) were scored in order to test for consistent differences among populations along the

  ( ; Blouin-Demers and Nadeau 2005) based on the D b calculated with T b at capture and E= D e -D b

  Field body temperature of lizards varied significantly among populations (proportional variance explained = 41%; LRT = 487.4, df = 1, p < 0.0001). The average body temperature also changed with the hour of the day following a quadratic function and was significantly explained by the interactions between age × sex and sex × year. Average adult female T b was 27.8 ± 0.5 °C, whereas female yearlings T b were 1.5 ± 0.1 °C warmer than adult females (t 1963 = 10.0, p < 0.0001). Adult males T b were 1.2 ± 0.3 °C warmer than adult females (t 1963 = 4.2, p < 0.0001). Male yearlings had approximately the same body temperatures as female yearlings and adult males (yearlings × males: t 1945 = -7.5, p < 0.0001). The most important environmental variables explaining geographic variation of body temperature included the pluviometric quotient Q (relative importance RI: 0.95)

  The inaccuracy of thermoregulation D b differed among populations (proportional variance explained = 39%; LRT = 519.1, df = 1, p < 0.0001). Variation in D b followed a quadratic function of the hour of the day and was also significantly explained by a three-way interaction of sex × age × year. Values of mean D b among males and yearlings did not differ from adult females in 2016 (males: t 1959 = -0.3, p = 0.79, yearlings: t 1959 = -1.1, p = 0.27, males × yearlings: t 1959 = 0.9, p = 0.34) and in 2018 (males: t 1959 = -1.7, p = 0.09, yearlings: t 1959 = -0.3, p = 0.76, males × yearlings:

  not influenced by elevation or by the thermal landscape of the sites, rejecting hypotheses 1, 3, 4 and 8 (Table

  but also across lizard species[START_REF] Clusella-Trullas | Lizard thermal trait variation at multiple scales: a review[END_REF]. The low geographic variability of thermal preference is consistent with previous findings in the same species comparing populations at low and high elevations[START_REF] Van Damme | Responses of the lizard Lacerta vivipara to predator chemical cues: the effects of temperature[END_REF][START_REF] Gvoždík | To heat or to save time? Thermoregulation in the lizard Zootoca vivipara (Squamata: Lacertidae) in different thermal environments along an altitudinal gradient[END_REF], but see[START_REF] Trochet | Variation of preferred body temperatures along an altitudinal gradient: A multispecies study[END_REF] for oviparous populations of Z. vivipara). The low inter-population variability of thermal preference also supports earlier findings that thermoregulatory preferences exhibit limited variation compared to field active body temperature (generally a standard deviation of 1.7 °C compared to 2.9°C for the body temperature range among Lacertidae species based on Clusella-Trullas and Chown 2014). It is

  more strongly constrained by changes in hydric conditions and temperature than by landscape features, and are best explained by an hypothesis that assume a trade-off between behavioral thermoregulation and hydroregulation. The role of water balance is underappreciated in current studies of thermoregulation. Our understanding of ectotherm responses to global change would benefit from further studies that evaluate the role of water balance regulation in modulating thermoregulatory behaviors[START_REF] Rozen-Rechels | When water interacts with temperature: Ecological and evolutionary implications of thermo-hydroregulation in terrestrial ectotherms[END_REF]).

  and generated Figure2. DRR analyzed the data with help of J-FLG.

  shaped by thermal clines and differences in duration of seasonal activity correlated with elevation. Elevation (ELE) of the study site Adolph 1990, Caldwell et al. 2017 2 Annual thermal conditions Thermoregulation strategies respond flexibly to annual changes in thermal conditions (hot years, cold years). Average daily minimal (respectively maximal) air temperatures T min (respectively T max ) of the year an study site Blouin-Demers and Weatherhead 2002 3 Thermal landscape Different thermal landscapes occur among sites because of differences in forest cover and the spatial distribution of vegetation. The spatial distribution of shade and full-sun patch dictates thermoregulation strategies.Evenness in operative temperature microhabitats (TE), forest cover index (FCI), homogeneity in the thermal landscape (

ELE

  a limiting factor, persistent differences in water availability or habitat moisture caused by climate differences in rainfall and temperatures should be the main predictor of thermoregulation statistics. Dry climate and habitat should select for water conservation strategies in dry environments.Emberger pluviometric quotient (Q, an aridity index) or the presence/absence index of free water (FW) of the study site[START_REF] Davis | Water supplementation affects the behavioral and physiological ecology of Gila monsters (Heloderma suspectum) in the Sonoran Desert[END_REF][START_REF] Lillywhite | Dehydration and drinking responses in a pelagic sea snake[END_REF][START_REF] Rozen-Rechels | Water restriction induces behavioral fight but impairs thermoregulation in a dry-skinned ectotherm[END_REF] 6Annual moisture conditionsThe instantaneous risk of desiccation is correlated with dry environmental conditions. Site specific, annual, variation in air moisture select for plastic changes in thermoregulation strategies and explain most of their variation.Average daily maximum in air moisture (P max ) of the year (Rozen-Rechels et al. 2020) 7Thermo-hydroregulationIf thermoregulation at high body temperatures compromises water balance, thermoregulation strategies should respond differently to short-term changes in temperature or thermal clines depending on water availability of the site or moisture conditions of the year.

  T b : cloacal body temperature in field active lizards, T pref : mean body temperature measured in thermal preferences tests in the laboratory, D b : thermoregulation inaccuracy, D e : thermal quality of the habitat, E: thermoregulation efficiency.

F

Figure 1

 1 Figure 1 Aerial views of four contrasting sites. JON and PUY are two open habitats. The former is

Figure 2

 2 Figure 2 Geographic location of studied populations in the Massif Central mountain range from

Figure 3

 3 Figure 3 Variation of cloacal body temperatures at capture T b (A) and variation of thermoregulation

Figure 4

 4 Figure 4 Variation of preferred body temperatures measured in the laboratory T pref as a function of

Figure 5

 5 Figure 5 Variation of thermoregulation inaccuracy D b as a function of the thermal quality of the habitat D e (A). Points are for and per population per year per age-sex group. Colors Db De

Figure 6

 6 Figure 6 Variation of thermoregulation efficiency E as a function of the mean daily maximum air

Figure S4

 S4 Figure S4 Differences of operative environmental temperatures T e among populations

|

  Figure S3

  Figure S5

F

Figure S6 Number

 S6 Figure S6

  Capture dates in focal populations varied toJune 20 th to June 29 th , 2016, from June 12 th to June 26 th , 2017 and from June 9 th to June 28 th , 2018.

	accommodate current weather conditions within each sample year. Our capture dates occurred from
	F
	o r
	R
	e v i e w
	O
	n l
	y
	10

Table 3

 3 Our sampling protocol aims to quantify the relative importance of climate and habitat factors in explaining the geographical variation in thermoregulation statistics of the common lizard (Zootoca vivipara). This table summarizes our working hypotheses and defines how these working hypotheses were tested with statistical models.

					Adjusted
	Trait	Variable	Estimate	SE		z value	p value
					SE
		Intercept	30.2	0.38	0.38	85.2	< 0.0001
		Q	-0.9	0.4	0.4	2.1	0.03
	T b
		T max	1.2	0.2	0.2	4.7	< 0.0001
		Q × T max	0.7	0.2	0.2	3.5	0.0005
		Intercept	35.3	0.1	0.1	350.9	< 0.0001
		FCI	0.3	0.1	0.1	2.7	0.006
	T pref
		Female	-0.9	0.1	0.1	13.9	< 0.0001
		FCI × Female	0.2	0.1	0.1	3.8	0.0002
		Intercept	2.1	0.1	0.1	27.4	< 0.0001
		Q	0.2	0.1	0.1	1.8	0.07
	√D b
		T max	-0.4	0.1	0.1	8.1	< 0.0001
		Q × T max	-0.3	0.05	0.05	6.0	< 0.0001
		Intercept	4.9	0.9	0.9	5.9	< 0.0001
	D e	T min	-4.0	0.3	0.3	12.0	< 0.0001
		P max	3.3	0.3	0.3	12.6	< 0.0001

Table S1

 S1 Environmental characteristics of sites for each year of study. FW: 1 = permanent presence of free water / temporary presence of free water, 1037 Q: Emberger pluviometric quotient, FCI: forest cover index, TE: index of thermal evenness, H: homogeneity index, T min : minimal day air temperature, 1038 T max : maximal day air temperature, P max : maximal day air moisture.

													11.8	39.2	31.3
	ROB	Mont-Lozère	44.38418	3.877859	1.2	0	1411	563.3	0.75	0.42	0.87
													12.3	42.3	32.4
													16.2	39.7	26.4
	TIO	Mont d'Aubrac	44.587934	3.106771	5.8	1	1321	407.9	0.01	0.42	0.88
													16.4	38.3	37.3
	USA	Mont d'Aubrac	44.64629	3.125162	12	1	1274	306.9	0.07	0.13	0.96		16.0	24.6	19.5
	VIA	Mont-Lozère	44.33807	3.768619	0.6	1	1191	318.5	0.18	0.31	0.83		13.0	33.3	30.5
	Site	Massif	Latitude	Longitude	Surface (ha)	FW	Elevation (m)	Q	FCI	TE	H (°C -2 )	Year	T min (°C) T max (°C) P max (g.m -3 )
	BARN	Mont-Lozère	44.427913	3.763798	0.6	1	1527	459.8	0	0.18	0.96	2017	14.1	30.1	25.1
												2016	13.5	32.4	31.9
	BEL BES BIG BOB BON BOU CARM COM COP JOC JON	Mont du Velay Margeride Mont-Lozère Mont du Viverais Mont d'Aubrac Margeride Mont du Cantal Margeride Mont du Velay Mont du Viverais Mont du Viverais	44.672268 44.587835 44.4449 44.820279 44.560054 44.754214 45.1575 44.66701 44.655493 44.835167 44.841699	4.024641 3.508084 3.749366 4.226584 3.127512 3.515408 2.83787 3.53321 4.03064 4.211043 4.214639 F o r R e v i e w 1.8 0 1464 485.1 1.5 1 1277 234.0 3.4 0 1463 398.8 1.3 1 1450 473.3 2.0 1 1397 406.6 1.3 1 1461 277.9 1.9 1 1267 272.5 2.4 1 1454.2 289.5 1.9 1 1421 514.0 1.5 1 1282 460.2 0.4 0 1385 432.7 O n l y 0.62 0.46 0.39 0.43 0.46 0.28 0.19 0.43 0 0.29 0.15 0.44 0 0.06 0.21 0.42 0.04 0.36 0.11 0.47 0.23 0.23 F o r R e v i e w O n l y	0.85 0.86 0.81 0.82 0.92 0.86 0.96 0.82 0.91 0.82 0.90	2018 2016 2018 2017 2016 2018 2017 2016 2018 2018 2017 2018 2017 2017 2017	12.5 15.1 15.5 12.4 13.8 17.1 15.1 12.7 13.5 17.9 13.2 14.8 14.1 15.8 15.7	35.5 37.4 39.1 26.1 32.8 28.8 28.1 33.4 34.8 35.2 21.2 28.6 29.6 23.6 22.8	31.8 36.3 39.0 23.0 26.7 27.2 21.8 23.1 26.6 28.1 19.9 27.1 28.0 19.7 17.9
												2016	13.4	27.3	21.8
	LAJO	Margeride	44.84475	3.43069	0.6	0	1383	228.1	0.52	0.26	0.78
												2018	13.8	39.7	31.1
												2017	16.4	32.6	32.4
	MON	Mont du Velay	44.510983	4.008337	0.9	1	1099	240.7	0.13	0.24	0.86
												2018	17.7	33.2	28.4
												2016	14.2	36.9	40.3
	PAR	Margeride	44.599056	3.556981	3.6	1	1454.5	277.0	0.29	0.45	0.80
												2018	13.9	46.9	38.5
	PEJ	Mont du Cantal	45.164138	2.844013	1.1	1	1254	262.9	0.02	0.02	0.98	2017	16.2	31.6	29.6
	PUY	Mont du Cantal	45.107208	2.684955	2.8	1	1471	561.5	0.01	0.25	0.91	2017	13.9	26.7	28.7
							58 59

Table S2

 S2 Thermoregulation statistics and sample size for each population for all years combined. In addition to thermoregulation statistics used in our study, we provided alternative statistics also used in the literature[START_REF] Hertz | Evaluating temperature regulation by fieldactive ectotherms: the fallacy of the inappropriate question[END_REF]. T set is the range defined by the interquartile range (25% and 75% quartiles) of T pref . D' b (respectively D' e ) is the absolute difference of T b (respectively T e ) to the limits of this range and equals 0 if in the range. measured in thermal preference tests in the laboratory, D b : thermoregulation inaccuracy, D e : thermal quality of the habitat, E: thermoregulation efficiency. N 15min : number of 15 bouts with exploitable T e for all years combined. N pref : number of individuals with exploitable T pref for all years combined. N capture : number of individuals with exploitable T body for all years combined. Variations of temperature T and water vapor density P for the three post-sampling periods during which T min , T max , T mean , P min , P max and P mean were calculated (temperature and vapor density were averaged over the loggers present at the same time in the site for graphical purposes). explanation of the calculations of T min and T max . For each activity period (10:00 to1 8:00, gray areas) we measured the minimal and maximal temperatures. Each line corresponds to a logger on the site. We averaged these values over the whole measurement period to obtain T min and T max . A similar procedure was used for T mean , P min , P mean , and P max . Raster vegetation map of the 21 populations. Blank pixels stand for water pans or

	Thermoregulation efficiency E' is then calculated as Population Category of individual N 15min Adult females BARN Adult males + yearlings 9 Adult females BEL Adult males + yearlings F o r R e v i e w . T b : cloacal body temperature in field active lizards, T pref : mean body temperature 1-D ' b D ' e T e D e D ' e N pref T pref T set N capture T body D b D ' b 4.9 ± 4.4 3.2 ± 4.4 22 33.3 ± 2.8 [31.5 -35.2] 21 27.9 ± 3.8 5.3 ± 3.8 3.8 ± 3.6 31.4 ± 6.5 5.6 ± 6.5 4.4 ± 6.3 10 37.0 ± 2.1 [35.5 -38.4] 24 31.8 ± 2.6 5.1 ± 2.6 3.6 ± 2.6 -2.0 ± 4.8 E 0.6 ± 2.4 8.1 ± 2.8 6.5 ± 2.8 45 34.5 ± 3.2 [32.5 -36.9] 46 30.5 ± 2.6 4.2 ± 2.6 2.7 ± 2.5 4.5 ± 2.2 O n l y Adult males + yearlings 8.6 ± 6.8 7.6 ± 6.9 27 36.0 ± 3.5 [34.1 -38.7] 29.5 ± 2.2 6.5 ± 2.6 4.8 ± 3.1 -1.9 ± 5.1 Adult females 4.8 ± 2.4 3.3 ± 2.3 15 32.0 ± 3.5 [29.8 -33.5] 29.2 ± 1.0 2.8 ± 1.0 0.7 ± 0.7 2.2 ± 2.5 BON Adult males + yearlings F o r R e v i e w o r R e v i e w o r R e v i e w o r R e v i e w o r R e v i e w o r R e v i e w o r R e v i e w o r R e v i e w TE -0.63 o r R e v i e w o r R w e i v e O n l y Adult males + yearlings 5.6 ± 5.1 4.5 ± 5.2 22 35.9 ± 3.0 [34.0 -38.3] 45 31.4 ± 2.0 4.1 ± 2.5 2.6 ± 2.6 -0.6 ± 2.5 Adult females 3.1 ± 2.7 2.0 ± 2.2 17 34.7 ± 2.4 [33.1 -36.5] 25 31.8 ± 1.6 2.9 ± 1.6 1.5 ± 1.4 1.0 ± 2.5 PEJ Adult males + yearlings F F F F Figure S1A F Figure S1B Graphical F 1115 Table S3 Pearson's correlation coefficients R between each micro-climatic variable. T min T mean T max P min P mean P max T min 0.22 -0.11 0.77 0.25 -0.02 T mean 0.84 0.16 0.73 0.77 T max -0.06 0.60 0.80 P min 0.53 0.18 P mean 0.88 F 1117 Table S4 Pearson's correlation coefficients R between each selected environmental variable. T min T max Elevation P max Q FCI TE H T min -0.11 -0.60 -0.02 -0.30 -0.62 -0.40 0.37 T max -0.07 0.80 -0.11 0.34 0.28 -0.16 Elevation -0.12 0.45 0.25 0.40 -0.16 P max -0.13 0.28 0.28 -0.19 Q 0.17 0.35 0.18 FCI 0.40 -0.49 F 1119 Figure S2 F 1120 streams in the population. The scale stands for 50 meters.	E ' -0.2 0.2 0.6 0.4 0.8 0.4 0.3
	O O O O O O O O O
	n l n l n l n l n l n l n l n l n l y y y y y y y y y	60 61
	65 66 67 68 69 70

Table S5 .

 S5 Summary of estimated effects in the best model explaining variation in T pref of ROB in comparison of the reference: adult female in 2017 during the afternoon. SE: standard error, DF: degree of freedom.

	Effect		Value	SE	DF	t	p
	Intercept		35.1	0.3	3624 102.8 < 0.0001
	Yearling		1.5		0.4	325	3.8	0.0002
	Male		1.5		0.5	325	3.3	0.001
	2018		-1.0		0.4	325	-2.8	0.006
	F F
	o r o r Morning o r	0.2		0.2	325	0.9	0.35
	R R Time since habituation R	-0.8		0.1	3624	-10.4	< 0.0001
	Yearling : Male Yearling : 2018 Male : 2018	e v i e w e v i e w -0.9 -1.8 -1.5 e v i e w		0.4 0.4 0.5	325 325 325	-2.0 -3.9 -3.2	0.045 0.0001 0.002
	Time since habituation : Morning	O O 0.3 O	0.1	3624	3.1	0.002
	Time since habituation : Male	0.3	n l n l 0.1 n l	3624	3.0	0.003
				y y y
		80
		81

Table S6 .

 S6 Classification of the 10 best models in the model averaging classification procedure based on AICc. Model weights indicate the relative evidence in favor of one model over all other models included in the set and models are unlikely to represent the "truth" when weight is smaller than ca. 5-10%. The best models based on this rule and the ΔAICc < 2 rule are in boldface. T b : cloacal body temperature at capture, T pref : body temperature measured in the thermal preferences set-up, D b : thermoregulation inaccuracy, D e : thermal quality of the habitat, E: thermoregulation effiency. Models that showed wrong convergence were removed (FW, FW × TE and Q × ELE in T pref analyses).

			FW × FCI FW × T max		16 13	-35177.60 -4812.36	70388.24 9650.91	14.48 67.55
			FW × TE FE × T max		16 13	-35177.68 -4813.72	70387.40 9653.64	14.63 70.29
			FW × H FCI × T max		16 13	-35177.97 -4818.76	70387.98 9663.71	15.21 80.36
		D b	Q × T max T max × H		19 13	-1551.76 -4819.91	3141.90 9666.01	0 82.65
			Q × T min T max		19 11	-1559.97 -4822.98	3158.33 9668.11	16.43 84.76
			TE × T max AgeSex Category × T max	19 12	-1563.38 -4822.33	3165.14 9668.84	23.24 85.48
			H × T max H × T min		19 13	-1565.32 -4821.85	3169.02 9669.90	27.12 86.55
			ELE × P max FCI × P max		19 13	-1566.34 -4836.97	3171.07 9700.14	29.17 116.79
		Response	Model F T max F Q × T min F F F		DF 17 13	Log-likelihood -1572.67 -4837.96	AICc 3179.66 9702.1	ΔAICc 37.75 118.76	Weight
	1223	T b	Q × T max o r T max × Sex × Age o r o r o r o r	15 20	-4536.30 -1569.82	9102.84 3180.06	0 38.16	0.91
	1224	D e	Q × T min H × T max T max × Sex ELE × P max T max × Age FCI × Sex × Age TE × T max FW × Sex × Age FCI × Age Age × T max T max × FCI P max × T min TE × T max FW × T max FCI × P max	15 15 R 18 R R R R 15 14 16 15 16 14 e v i e -4539.46 -4540.24 -1572.33 -4541.33 -4542.62 -4540.72 -4541.76 -4540.91 -4543.55 w O n l 9109.16 9110.72 3181.00 9112.90 9113.46 9113.71 9113.76 9114.10 9115.31 y 18 -1572.52 3181.40 19 -1572.03 3182.45 11 -4046.11 8114.39 11 -4070.09 8162.35 e v i e w e v i e w e v i e w e v i e w 11 -4076.64 8175.45 O O O O 11 -4081.76 8185.69 n l n l n l n l y y y y	6.33 7.89 39.09 10.07 10.63 10.88 10.93 11.26 12.48 39.49 40.55 0 47.96 61.06 71.30	0.04 0.02 0.01 0 0 0 0 0
			FCI × T max		11	-4092.67	8207.51	93.12
		T pref	FCI × Sex Q × T max		15 11	-35171.37 -4096.94	70372.77 8216.04	0 101.65	0.84
			FW × Sex H × P max		15 11	-35173.10 -4097.24	10376.24 8216.64	3.47 102.26	0.15
			FCI T max		14 9	-35178.52 -4099.50	70385.07 8217.10	12.30 102.72	0
			ELE × FW H × T max		16 11	-35176.68 -4098.14	70385.41 9218.44	12.64 104.05	0
			FCI × T max AgeSex Category × T max	16 10	-35176.87 -4099.23	70385.78 8218.59	13.01 104.20	0
			FCI × ELE Q × T min		16 11	-35177.14 -4098.77	70386.65 8219.71	13.55 105.32	0
		E	T max × FW P max × T min		16 13	-35177.52 -4778.58	70387.08 9583.35	14.30 0	0
						82 83 86

Acknowledgments

We thank Dr. Brian Todd, Subject Matter Editor for Ecological Monographs, and the two anonymous reviewers who contributed significantly to improve our manuscript. We thank B. Sinervo who initiated this population monitoring program with J. Clobert and D.B. Miles. We also thank Clotilde Biard who provided us with iButtons during field work and our field collaborators: Marine Ammeter, Jean Belliard, Éline Bertrand, Maxime Cartier-Millon, Julia Degez, Axelle Delaunay, Aurore Fayard, Alix Hallé, Anaïs Hillion, Mélusine Hureau, Maria Nikolaeva, François-Louis Pélissier, Syrine Saoud, Mary Varoux, and Qiang Wu. This study was carried out in accordance with institutional guidelines, under license from the French Ministry of the Environment and the veterinary and environmental services of every region where we sampled lizards (permits 81-2013-05; 2013274-0002, 2013/DREAL/259). We thank the Parc National des Cévennes for permits for sampling lizards. Drone flying was made according to French legislation and with authorisation from Parc National des Cévennes (#20170186 and #20170263). This study was funded by the Centre National de la Recherche Scientifique (CNRS) and the Agence Nationale de la Recherche (ANR-13-JSV7-0011-01 to SM and ANR-17-CE02-0013 to JFLG). DBM received funding from NSF (EF1241848).

Authors contributions

DRR, J-FLG, AR, SM and JC designed the study. DRR, AR, AD, PB, JC, DBM, MR, AB, and J-FLG participated to data collection and their formatting. VC produced the vegetation maps. MG

Appendix

Interaction of hydric and thermal conditions drive geographic variation in thermoregulation in a widespread lizard

Rozen-Rechels David 1,* , Rutschmann Alexis 2 , Dupoué Andréaz 3 , Blaimont Pauline 4 , Chauveau Victor 1 , Miles Donald B. 5 , Guillon Michael 6 , Richard Murielle 7 , Badiane Arnaud 1 , Meylan Sandrine 1,8 , Clobert Jean 7 , Le Galliard Jean-François 1,9 Supplementary Information 1: Description of the microhabitat of each site Supplementary Information 2: Difference in operative environmental temperature among vegetation substrates and description of the thermal landscape Supplementary Information 3: Age and sex related differences in thermal preferences in individuals from the population ROB Supplementary Information 4: Effect of the presence of water on parturition date Table S1 Environmental characteristics of sites for each year of study.

Table S2 Thermoregulation statistics measured and sample size in all populations of the study

Table S3 Correlations between micro-climate variables

Table S4 Correlations between environmental variables

Supplementary Information 1

We measured micro-habitat temperature and moisture conditions at each site by recording local temperature and relative humidity with temperature-humidity loggers (Hygrochron iButtons, Maxim Integrated Products, Sunnyvale, CA, USA, ± 0.5 °C and 5% relative humidity-RH) at 1 hour interval during approximately one month every year. A total of 1 to 3 loggers were set under cover at each site in diverse substrates, away from full sun in order to avoid high temperatures.

Each logger was placed in a net in a T-shaped PVC plumbing connection, in which air could circulate. This set-up enabled us to measure air temperature and relative humidity at approximately 2 cm from the soil. As part of another study, we deployed 18 temperature and temperature-humidity loggers on one of our focal site (ROB) and used these data for calculating relative micro-habitat characteristics in this study. We calculated the water vapor density at each sample point using temperature and relative humidity data, following the equation provided by [START_REF] Tieleman | Physiological adjustments to arid and mesic environments in larks (Alaudidae)[END_REF].

This calculation was made in three steps:

1. Calculation of the dew point DP: DP = (RH/100) 1/8 ×(112+0.9×T)+0.1×T-112 with T the temperature (°C) and RH the relative humidity (%). For each focal site and each sample year, we calculated the average 10:00 to 18:00 minimal, mean, and maximal temperatures (hereafter called T min , T mean , T max respectively) and the average 10:00 to 18:00 minimal, mean and maximal water density (i.e., water vapor pressure, hereafter called P min , P mean , P max respectively) during periods of approximately one month each year (Figure S1A andB).

To do this we extracted each variable for each day in the period for all loggers in the same site, the same year (maximal and minimal were thus the maximal and minimal values recorded from all loggers combined) and averaged it over the entire period to obtain a yearly mean allowing relative comparison of populations. Cross-correlation coefficients of these microclimatic variables were calculated (Table S3). We selected the microclimatic variables of interest based on these correlations. T min is only highly correlated to P min . As Dupoué et al. (2018) previously showed the importance of T min for lizard biology, we retained it and excluded P min from our analyses. We also kept T max over T mean as previous studies showed that maximum daily temperatures can significantly impact the physiology and phenology of the lizards in these populations (Rutschmann et al. 2016b, Dupoué et al. 2017b). We also kept P max , because we considered to be the best proxy of air moisture.

Supplementary Information 2

During field census, we put between 4 and 12 HOBO® Pro v2 U23-003 (ONSET Cape Cod, Massachusetts) temperature probes inside a painted 6cm long copper tube to measure a lizard operative temperatures in a diversity of substratum [START_REF] Dzialowski | Use of operative temperature and standard operative temperature models in thermal biology[END_REF]. The loggers recorded operative temperatures every minute in each substratum. In further calculations, we averaged these measures over 15-minute time bins (hereafter called T e ) in order to compensate for short-time changes in weather conditions such as wind, clouds, etc. The operative temperatures data were calibrated in September 2016 in CEREEP-Ecotron ÎleDeFrance, France facilities (48° 17' N, 2° 41' E) by temperature records inside the painted copper tube with those of a dead adult individual at the exact same location side by side without contact. We calibrated the operative temperatures under two different conditions (under sun radiation or on a shady patch) and used the calibration curve to transform records from the copper tube model into those representative of core body temperatures of a common lizard.

Under sun radiation: T e = -0.933331 + 1.035632 × T copper tube Under shade: T e = 2.589795 + 0.878049 × T copper tube

We then analyzed variation in operative temperatures across the hour of the day for each substrate in each site (see Figure S3). We fitted the non-linear variation in operative temperature to the hour of the day using a Generalized Additive Model (gam from the package mgcv, Wood 2017). We took into account the local site conditions (which could either be specific to the site or to the weather during capture) by adding a site-day fixed effect, which had as many levels as different capture days for each population. To assess average differences between substratum corrected by hour of the day and site-day, we also fitted a substratum fixed effect. We then extracted the estimated operative temperature score per substrate from the model and created temperature raster maps for each site by attributing the estimated operative temperature to each pixel based on the main substratum presence. In order to describe the thermal structure of the landscape we calculated two indices of

Supplementary Information 3

We fitted the variation of T pref calculated for all age and sex classes in the ROB population to the addition of the two-way interaction of the age and sex of individuals and the two-way interactions of sex and age with the year with a linear mixed-model using the function lme from the package nlme [START_REF] Pinheiro | Mixed-effects models in S and S-PLUS[END_REF]. We also took into account the time of the day by adding the twoway interaction of the time since habituation with the moment of day (morning vs. afternoon). To assess intrapopulation and intraindividual variances, we nested an individual identity random effect in a populational identity random effect. We then proceeded to perform a backward model selection based on log-likelihood ratio tests. T pref variation was significantly explained by the two-way interaction of the sex and the age (F 1,325 = 4.04, p = 0.045) as well as the two-way interactions between the sex and the year (F 1,325 = 10.2, p = 0.002) and between the age and the year (F 1,325 = 15.3, p = 0.0001). Adult male T pref as well as both sexes yearlings T pref were higher than females.

Yearlings and adult males T pref are not strongly different on average (Table S5, Figure 3A). In our study, we thus supposed that yearling is equivalent to adult males from the same population. T pref [START_REF] Pinheiro | Mixed-effects models in S and S-PLUS[END_REF]. Mixed-effects models in S and S-PLUS. Springer Science & Business Media.

Supplementary Information 4

We estimated stage of gestation for adult females when we measured T pref by calculating the number of days between the measure date and parturition date. As breeding conditions were the same for all females after capture, differences in the stage of gestation are likely consequences of differences in individual state variables among females and environmental conditions among natural populations as indicated in previous studies (Le [START_REF] Galliard | Cohort variation, climate effects and population dynamics in a short-lived lizard[END_REF], Rutschmann et al. 2016b). We analyzed whether the T pref of a gravid females differed according to the stage of gestation and FCI with a linear mixed model (lme from package "nlme"). We fitted inter-population and inter-individual deviance by adding the individual identity nested in the population identity as a random effect. As weather conditions in spring were different across years, we also fitted an additive year effect to account for yearly differences in average stage of gestation. We selected the best model with a backward model selection procedure based on loglikelihood.

T pref significantly decreased with the progress of pregnancy (F 1,619 = 75.4, p < 0.0001) and average body temperature did not change according to the forest cover index (F 1,19 = 1.5, p = 0.23).

Populations with high forest cover were on average sampled at earlier stage of pregnancy especially because we sampled the population ROB first in 2018, in which we sampled more individuals and gestation is usually delayed compared to other populations.

As a supplement, we also tested if adult male thermal preferences changed with the date. We calculated the difference in days between the date of capture and the earliest date at which a male had been captured for all year combined, which we will call the "date". We used the same starting mixed effect model as described in the Methods, removing the sex effect and adding the year × date. The male T pref did not change according to the date, whatever the year (year×date: F 2,334 = 0.01, p = 0.99; date: F 1,334 = 0.2, p = 0.64).

Le Galliard, J.-F., J. François, O. Marquis, and M. Massot. 2010. Cohort variation, climate effects and population dynamics in a short-lived lizard. Journal of Animal Ecology 79:1296-1307.