To Different Levels of Aridity Tesis Entregada a La Universidad De Chile

Adaptation of Liolaemus fuscus (Squamata: Liolaemidae) to different levels of aridity

Tesis Entregada A La Universidad De Chile En Cumplimiento Parcial De Los Requisitos Para Optar Al Grado De

Magíster en Ciencias Biológicas

Facultad De Ciencias Por

Raúl Ignacio Araya Donoso Agosto, 2019

Director de Tesis Dr. David Véliz Baeza Co Directora Dra. Madeleine Lamborot Chastía

FACULTAD DE CIENCIAS UNIVERSIDAD DE CHILE

INFORME DE APROBACION TESIS DE MAGÍSTER

Se informa a la Escuela de Postgrado de la Facultad de Ciencias que la Tesis de Magíster presentada por el candidato.

Raúl Ignacio Araya Donoso

Ha sido aprobada por la comisión de Evaluación de la tesis como requisito para optar al grado de Magíster en Ciencias Biológicas, en el examen de Defensa Privada de Tesis rendido el día 31 de Julio de 2019.

Director de Tesis: Dr. David Véliz Baeza …………...…...... Codirector de Tesis: Dra. Madeleine Lamborot Chastía …………...…......

Comisión de Evaluación de la Tesis

Dra. Caren Vega Retter …………...…......

Dr. Fernando Torres Pérez …………...…......

Más extraño que lo esperado por azar

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BIOGRAFÍA

AGRADECIMIENTOS

ÍNDICE DE CONTENIDOS

ÍNDICE DE CONTENIDOS ...... vi

ÍNDICE DE FIGURAS ...... vii

ÍNDICE DE TABLAS ...... viii

RESUMEN ...... 1

ABSTRACT ...... 2

INTRODUCTION ...... 3

HYPOTHESIS ...... 6

OBJECTIVES ...... 7

MATERIALS AND METHODS ...... 7

RESULTS ...... 13

DISCUSSION ...... 23

REFERENCES ...... 27

SUPPLEMENTARY MATERIAL ...... 38

ÍNDICE DE FIGURAS

Figure 1: Liolaemus fuscus sampling sites. Two localities (LH and LR) were in the desert shrub and the CH and EM were in the sclerophyllous-thorn forest...... 9

Figure 2: Homologous landmarks in the dorsal and lateral views of the head of

Liolaemus fuscus...... 13

Figure 3: DAPC for the neutral SNP data of Liolaemus fuscus ...... 14

Figure 4: Principal Component Analysis for the neutral SNP data of Liolaemus fuscus 15

Figure 5: Snout-vent length for desert (yellow) and forest (green) Liolaemus fuscus ..... 17

Figure 6: Principal Component Analysis for morphometric variables of Liolaemus fuscus from desert (yellow) and forest (green) populations ...... 18

Figure 7: Tail length for desert (yellow) and forest (green) Liolaemus fuscus ...... 19

Figure 8: Principal Component Analysis for the geometric morphometry of the dorsal view of the head from desert (yellow) and forest (green) Liolaemus fuscus. Head shape differences are two-fold magnified ...... 21

Figure 9: Principal Component Analysis for the geometric morphometry of the right lateral view of the head from desert (yellow) and forest (green) Liolaemus fuscus. Head shape differences are two-fold magnified ...... 22

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ÍNDICE DE TABLAS

Table 1: Environmental characteristics of the sampling sites ...... 8

Table 2: Pairwise FST between Liolaemus fuscus populations ...... 15

Table 3: ANOVA for SVL and TL, and MANOVA for morphometric variables of L. fuscus ...... 17

Table 4: Procrustes ANOVA (5,000 iterations) for the dorsal and lateral head shape of L. fuscus...... 20

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RESUMEN

La adaptación a ambientes desérticos en reptiles es un tema relevante en biología evolutiva. La aridez puede constituir una fuerte presión selectiva para los organismos que colonizan estos hábitats, pudiendo desarrollar características morfológicas, fisiológicas y conductuales que les confieran mayor supervivencia en condiciones extremas. En este estudio, se utilizó la lagartija Liolaemus fuscus para estudiar adaptaciones a la aridez.

Mediante una aproximación genómica (ddRAD sequencing) y morfológica/morfométrica, se buscaron loci y caracteres corporales candidatos a constituir adaptaciones al desierto, comparando poblaciones de L. fuscus que habitan el Desierto de Atacama con otras de los bosques Mediterráneos de Chile central. Los resultados mostraron diferencias genéticas y morfológicas claras entre las poblaciones desérticas y del bosque. Los análisis detectaron

81 genes candidatos a estar bajo selección divergente que mostraron sobre-representación de funciones relacionadas a la membrana celular, degradación de macromoléculas y transcripción. Las variables morfológicas, mostraron que las lagartijas del desierto presentan un menor tamaño corporal, con extremidades más largas, cabezas más grandes y colas más largas que los L. fuscus del bosque, además de globos oculares de mayor tamaño y cabezas más comprimidas dorsoventralmente. Las características genéticas y morfológicas sugieren la existencia de adaptaciones en procesos de osmorregulación, utilización de recursos y locomoción, que podrían ser ventajosas para enfrentar las condiciones desérticas. Este estudio provee conocimientos acerca de la adaptación a la aridez en reptiles en un nivel genético, especialmente en grupos diversos como Liolaemus.

ABSTRACT

Reptile adaptation to desert environments is a relevant topic in evolutionary biology.

Aridity could constitute a strong selective pressure on organisms that colonize these habitats which can develop morphological, physiological and behavioral features that confer better survival in this extreme environmental condition. In our study, we used the lizard Liolaemus fuscus to study adaptation to aridity. Using both a genomic (ddRAD sequencing) and a morphological/morphometric approach, we searched for candidate loci and corporal features related to adaptation to the desert by comparing populations of

Liolaemus fuscus that inhabit the Atacama Desert with others from the Mediterranean forests from central Chile. Results showed clear differences between desert and forest populations with both genetic and morphological data. Analyses detected 81 candidate genes under divergent selection that showed overrepresentation of functions related to cellular membrane, macromolecules degradation and gene transcription. In the case of morphological data, the analyses showed that desert lizards had smaller body size, longer limbs, bigger heads and longer tails than forest L. fuscus, and bigger eyeballs and more dorsoventrally compressed heads. The genetic and morphological features suggest adaptations related to processes of osmoregulation, resource utilization and locomotion, that could be advantageous for lizards to better confront desert conditions. This study provides insights for the research of genetic adaptation to aridity in reptiles, especially in diverse groups as Liolaemus.

INTRODUCTION

One of the most relevant topics to study in evolutionary biology is the organismal adaptation to different conditions, understood as any modification in structure or function that allows an organism to better confront environmental conditions (Simpson, 1944).

Environmental selective pressures could trigger adaptive changes and as consequence, it has direct relationship with phenotypic features (Brandon, 2014). In the last decades it has been increased interest in the genetic basis of adaptation, addressing questions such as how many genes are involved in adaptation, what kind of genetic variation is responsible for adaptation, or if adaptation utilizes pre-existing genetic variation or requires new mutations to appear (Stapley et al. 2010).

In the context of animal adaptation, is important to know how organisms adapt to live in extreme environments, in which resource availability is limiting the maintenance of biological processes, as deserts or arid environments (Lillywhite & Navas, 2006). Arid environments are characterized by low rainfall and high evapotranspiration rate; thus, water availability determines ecosystem processes (Whitford, 2002). Deserts have been described as hostile habitats that generally present low species richness, low diversification and/or recent colonization of lineages (Wiens et al. 2013). Animals that colonize deserts must confront water and osmotic stress, thermal stress, lower resource availability and a habitat structure characterized by less vegetation coverage. Furthermore, the relevance of the knowledge of adaptations to desert increases in a climate change scenario, since deserts are predicted to expand in many parts of the world (Reed &

Stringer, 2016) and reptile species must have to quickly adapt to environmental changes to increase the survival likelihood (Urban et al., 2014).

Reptiles have successfully colonized arid environments, exhibiting behavioral, morphological and physiological characteristics that improve biological processes of thermoregulation, water-osmotic balance and locomotion, which allows them to successfully live in dry habitats (Pie et al. 2017; Bradshaw 2018). These adaptations seem to be common in different reptile groups such as turtles, lizards and snakes. However, it remains unclear the genetic mechanisms allowing the evolution and development of those kind of adaptions.

Examples of reptile adaptations to aridity include modifications in their behavioral thermoregulation, activity patterns and changes in their thermal tolerance (Davis &

DeNardo, 2009). Furthermore, changes in water homeostasis and less evaporative water loss was also described in desert reptiles (Guillon et al. 2013; Cox & Cox 2015).

Morphological features that have been related to arid environments include larger bodies and shorter limbs, that could be associated to the change of habitat structure (Grizante et al. 2012); and to optimization of physiological traits such as heat and water exchange via skin, associated to the differences in the body length and the number and size of scales

(Oufiero et al. 2011). In a genetic-genomic level, studies have found candidate genes to be under natural selection relating biological processes of thermoregulation and limb morphology associated to climatic differences (Brown et al. 2016, Campbell-Staton et al.

2016, Rodriguez et al. 2017). At our knowledge, there are no studies on genomic adaptation to desertic environments.

The genus Liolaemus is an interesting lizard group that comprises over 250 species throughout southern South America (Esquerré et al. 2019), being a model to study evolutionary biology (Lamborot, 2008), since it has been described as a continental adaptive radiation suitable to study adaptation to different environments (Pincheira-

Donoso et al. 2015). Liolaemus species have colonized a wide range of environmental conditions, including the driest desert in the world, the Atacama Desert in Chile.

Liolaemus expansion to the Atacama Desert constituted an evolutionarily relevant event, that started after the origin of arid conditions in South America around 10 million years ago (Guerrero et al. 2013), highlighting the importance of the genetic and ecological mechanisms that allow the colonization of this new habitat. Studies performed with different Liolaemus species inhabiting the desert showed physiological and behavioral strategies related to their thermal regulation (Labra et al. 2001; Labra et al. 2009) and morphological characteristics associated to this environment (Aguilar-Puntriano et al.

2018; Tulli & Cruz 2018).

Few Liolaemus species inhabit both desertic and non-desertic environments. One of them is the dark lizard, Liolaemus fuscus, a small lizard endemic from Chile, that inhabits rock, shrub and forest microhabitats, from Huasco in the Atacama Desert (28°28’S, 71°11’W) to Machalí in the Mediterranean forests of the central Chile (34°12’S, 70°35’W), covering a wide latitudinal gradient of aridity of 600 km approximately (Ramírez-Álvarez et al.

2017; Mella, 2017). Fuentes & Jaksic (1979) and Medina et al. (2012) found differences in thermoregulation when compared individuals inhabiting the coast and mountain environments. Thus, it would be interesting to analyze populations from different latitudes

5 in order to detect adaptations to the aridity gradient. Compared to other Liolaemus species that distribute in both arid and mesic habitats, this species is relatively abundant, and it is a suitable model to study the adaptation to arid systems.

The aim of this study was to compare populations of Liolaemus fuscus from desert and forest environments in order to detect genomic and morphological features associated to the contrasting habitats that could correspond to adaptations to aridity. The main objective was to elucidate how the genetic variation is related to morphological and ecological characteristics and how it could contribute to species diversification and colonization of novel environments in the highly radiated genus Liolaemus.

HYPOTHESIS

Considering that L. fuscus inhabits localities with contrasting climatic characteristics, and there are previous evidences of morphological and genetic changes associated to aridity in reptiles, populations of L. fuscus inhabiting contrasting aridity conditions will show adaptations associated to their habitat. It is proposed that populations of L. fuscus from arid areas will have a bigger body size, shorter limbs, and selection of alleles of genes associated to physiological mechanisms of water balance and/or thermoregulation, when compared to individuals inhabiting the Mediterranean forests.

OBJECTIVES

General Objective: Evaluate the existence of adaptations to systems with different levels of aridity, in a genetic and morphological level, in the wide distributed species in Chile

Liolaemus fuscus.

Specific Objectives:

• To compare the morphology of lizards from 4 populations of L. fuscus,

corresponding to 2 populations from desert shrub in the arid zone, with 2

populations from forest formations in the Mediterranean zone.

• To detect candidate loci to be under natural selection, when comparing populations

pairs and determine their possible physiological and ontological function.

• To relate the candidate loci under natural selection and the morphological features

with the environmental characteristics from each population.

MATERIALS AND METHODS

Study sites

Populations of Liolaemus fuscus from two contrasting environments respect to their aridity condition were compared, covering a geographic range of 430 km, approximately at 80 km from the northern and the southern limits of the species distribution respectively

(Ramírez-Álvarez, 2017; Mella, 2017). Two localities (La Higuera (LH) and Las Rojas

(LR), 70 km from each other) were sampled in desert shrubs in the southern limit of the

Atacama Desert, and two localities (Chicauma (CH) and El Manzano (EM), also separated by 70 km) in the sclerophyllous-thorn forests from the Mediterranean region of Chile

(Fig. 1) (Luebert & Pliscoff, 2006). Table I summarizes the environmental characteristics of the sampling sites. Climatic characterization was obtained from Di Castri & Hajek

(1976). Annual mean temperature and annual precipitation were obtained from the bioclimatic models developed by Pliscoff et al. (2014) (Table S1 includes detailed description of all 19 bioclimatic variables). Annual mean temperature, despite being a broad measurement, has been described as a good predictor of the thermoregulatory physiology in Liolaemus (Vidal et al. 2008). In each site, vegetation was sampled by means of three transects of 30 m each and vegetation coverage was estimated as the proportion of total vegetation adding data of the three transects performed per site. Within vegetation, we estimate the coverage of tree, bush and cacti. In each site, lizards were sampled by noosing (LH = 18, LR = 18, CH = 24, EM= 24) (Collection Permit SAG: Res

6696/2017). After measurements and tissue collection lizards were released in the place they were captured.

Table I: Environmental characteristics of the sampling sites.

Forest Desert Chicauma El Manzano Las Rojas La Higuera Mean Temperature 14.7 13.1 15 15.3 (°C) Annual Rainfall (mm) 354 492 72 45 Climate Semirarid Semiarid Arid Arid Vegetation cover (%) 89.67 94 55.67 75.44 Tree cover (%) 54.34 50.53 0 0 Bush cover (%) 45.06 48.73 89.12 54.39 Cacti cover (%) 0 0.74 10.88 45.61

Figure 1: Liolaemus fuscus sampling sites. Two localities (LH and LR) were in the

desert shrub and the CH and EM were in the sclerophyllous-thorn forest.

Genetic analyses

From each lizard a tail tip was obtained and preserved in ethanol 95%. Genomic DNA was extracted with the salt extraction method (Aljanabi & Martínez, 1997). 1000 ng of

9 pure DNA per sample was sent to the service Floragenex (Oregon) to perform the ddRAD sequencing (Peterson et al. 2012). Briefly, genomic DNA was digested with the restriction enzymes PstI and MseI. Resulting fragments had an average size of 100bp. Single nucleotide polymorphisms (SNPs) were genotyped from the dataset utilizing the STACKS

(Catchen et al. 2013) software, following the “de novo” pipeline for SNP detection

(Rochette & Catchen 2017).

SNPs with a minor allele frequency (MAF) lower than 0.05 between forest and desert were filtered (Ahrens et al. 2018) and pooled apart, since fixed differences between populations could be caused by natural selection or genetic drift (Hey 1991). Populations were compared between pairs, we extracted only those loci that were present in all desert- forest comparisons, excluding loci differentiated within desert or forest populations. The remaining SNPs (i.e. those with MAF > 0.05) were used to detect outlier loci with two approaches: i) The “fsthet” package (Flanagan & Jones, 2017), implemented in the R 3.6.0 software (R Core Team, 2019) was used to detect candidate outlier loci based on smoothed quantiles from the FST-Heterozygosity distribution, outliers were selected with a 95% confidence level threshold. ii) The “pcadapt” package (Luu et al. 2017) also implemented in the R 3.6.0 software was used to identify outlier loci respect to the population structure, determined by a principal component analysis with the SNP dataset. To avoid type I error, the candidate markers were identified using a false discovery rate (FDR) of 5% (Luu et al. 2017). Both methodologies are described to detect less false positives than other methods that use the FST-Heterozygosity relationship (Flanagan & Jones 2017; Luu et al.

2017). To be conservative, we retained as candidate markers only those loci shared by

10 both methodologies when comparing population pairs, keeping outlier loci present in desert-forest comparisons and excluding outliers within desert or forest populations; the remaining loci were considered as neutral.

To test for population structure, a Discriminant Analysis for Principal Components

(DAPC; Jombart et al. 2010) was performed with the neutral dataset. The “find.clusters” function was used to detect the best number of principal components to retain for the posterior discriminant analysis. Furthermore, pairwise FST were calculated between populations with Arlequin 3.5 software (Excoffier & Lischer, 2010). To visualize the genetic variation between populations a Principal Component Analysis was performed with the neutral loci. Both DAPC and PCA were performed with the “adegenet” package

(Jombart, 2008) implemented in the R software.

To identify genes and gene function in both outlier and fixed loci datasets, we compared them against the BLASTn database. Genes that presented unambiguous matches were analyzed with the PANTHER overrepresentation tool (Gene Ontology Consortium, 2015).

This software was used to detect which gene ontology (GO) terms were statistically overrepresented in the outlier and fixed loci respect to the reference genome of Anolis carolinensis. In other words, we searched for biological processes, molecular functions and cellular components that showed allelic differences between lizards located in the desert and the forest.

Morphometric analyses

Lizards were measured with a 0.01 mm precision caliper for the following measurements according to Araya-Donoso et al. (2017): snout-vent length (SVL), axilla-groin distance

(AGD), arm length (AL) foot length (FL), maximum head width (MHW), head length

(HL) and tail length (TL). SVL was compared between habitat (Desert or Forest) and sexes with an ANOVA. The remaining variables were standardized dividing by SVL.

AGD, AL, FL, MHW and HL were compared with a MANOVA considering sex and habitat as factors, and a Principal Component Analysis was performed to visualize the morphological variation. Since some of the individuals had an autotomized tail, TL was compared separately between sexes and habitat with an ANOVA, only for those lizards with an intact tail. All analyses of this section were performed with the R 3.6.0 software.

Geometric morphometrics analyses (Rohlf & Slice, 1990) were performed. Each individual was photographed from dorsal and right lateral view of the head with a Fujifilm

FinePix S4600 camera. Ten homologous landmarks were marked by replicates in the intersection of cranial scales for the dorsal view, and nine for the lateral view, according to Araya-Donoso et al. (2017) (Figure 2). Then, the coordinates of the landmarks were aligned and superimposed to leave only variation due to shape (removing variation due to size, rotation and translation) using the least squares method based on the generalized

Procrustes analysis. A Principal Component Analysis was performed to visualize shape variations and deformation grids were produced by regression on the Principal Component scores. To assess differences between desert and forest localities in head configuration, a

Procrustes ANOVA with 5,000 iterations (Goodall, 1991) was performed with the

Procrustes distances among samples of Liolaemus fuscus. Statistical analyses were

12 performed with the “geomorph” package (Adams and Otarola-Castillo, 2013) implemented in the R 3.6.0 software.

Figure 2: Homologous landmarks in the dorsal and lateral views of the head of

Liolaemus fuscus.

RESULTS

Genetic variation

Sequences from 82 samples were analyzed, a total of 260,022,858 reads were retained after filtering by quality. The mean number of reads per sample was 2,685,184 and the mean read coverage per sample was 46.4x. A total of 20,454 SNP loci were retained after filtering with the STACKS de novo pipeline. Further, 1,842 loci showed a MAF < 0.05, these loci where stored in a data set called “fixed loci” that were used together with loci detected under selection in further analyses. After filtering, 18,612 loci were retained.

Fsthet detected 1206 loci as outlier, and pcadapt detected 787 outlier loci with a most likely number of clusters of k = 4. Only 457 loci were shared between both methodologies.

The DAPC identified four clusters agreeing with the studied populations. The first and second discriminant function accounted for 59.48% and 30% of the variance respectively, segregating desert and forest populations (Figure 3). Pairwise FST between populations was significant in all comparisons; differences between forest and desert were larger than comparisons between sites within each habitat (Table II). Furthermore, The Principal

Component Analysis segregated the four studied populations (Figure 4). The first component (28.91% of variance) accounted for the differences associated to desert and forest populations. The second component explained 5.49% of the variance and corresponded to differentiation between forest localities. The third component accounted for 4.4% of variance and segregated the populations located in the desert.

Figure 3: DAPC for the neutral SNP data of Liolaemus fuscus.

Table II: Pairwise FST between Liolaemus fuscus populations

Las Rojas La Higuera El Manzano Chicauma Las Rojas - 0.11504* 0.51986* 0.47738* La Higuera - 0.53221* 0.49307* El Manzano - 0.20845* Chicauma - *: p < 0.00001

Figure 4: Principal Component Analysis for the neutral SNP data of Liolaemus fuscus.

81 out of 2299 sequences containing SNPs from the fixed and the outlier loci dataset together aligned successfully to a gene (Table S2, Table S3). 67% of the genes aligned to

Anolis carolinensis genes, the remaining sequences aligned to genes from Pogona vitticeps, Podarcis muralis, Gekko japonicus, Chelonia mydas, Python bivittatus,

Pseudonaja textilis and Protobothrops mucrosquamatus. Most of the genes that aligned to different reptile species had an ortholog in the Anolis carolinensis genome, except for

ATP6V1B1, FBN3, TRPV2 and WDR34 from Pogona vitticeps, that were not included in our analyses. The overrepresentation analyses showed significant overrepresentation of molecular functions related to transporter (GO:0005215) and channel (GO:0022838,

GO:0022803, GO:0005261) activities and macromolecules degradation (GO:0070628,

GO:0008252, GO:0015926) (Table S4). Furthermore, overrepresented biological processes were related to transcription (GO:0006390, GO:0000290, GO:0000184), cellular organization (GO:0007018, GO:0071709, GO:0048870) and synaptic transmission (GO:0060291, GO:0007268) (Table S4). Finally, overrepresented cellular components corresponded to sarcoplasmic reticulum (GO:0016529) and cellular membrane (GO:0045211, GO:0034703, GO:0005887) (Table S4).

Morphological traits

Significant differences were found between sexes and habitat in SVL (Table III), lizards from the desert were smaller than those from the forests (Figure 5). MANOVA detected significant differences in the interaction between type of habitat and sex (Table III). The

Principal Component Analysis described the morphological variability of L. fuscus: PC1

(43.72% of variance) was related positively to limb size (AL, FL) and head size (MHW,

HL) variables, individuals from the desert had higher values of PC1 (i.e. bigger heads and longer limbs). On the other hand, the PC2 (20.96% of variance) was positively related to

AGD and explained differences between sexes, females had higher values of AGD (Figure

6). Finally, lizards from the desert populations presented significantly bigger tails than those from the forests (Table III, Figure 7).

Table III: ANOVA for SVL and TL, and MANOVA for morphometric variables of L.

fuscus.

Source of SVL MANOVA TL variation Wilk’s F P P F P Lambda Sex 29.41 <0.001 0.67 <0.001 0.93 0.344 Habitat 59.68 <0.001 0.63 <0.001 7.56 0.01 Sex:Habitat 1.25 0.261 0.85 0.035 3.17 0.086

Figure 5: Snout-vent length for desert (yellow) and forest (green) Liolaemus fuscus.

Figure 6: Principal Component Analysis for morphometric variables of Liolaemus fuscus

from desert (yellow) and forest (green) populations.

Figure 7: Tail length for desert (yellow) and forest (green) Liolaemus fuscus.

Geometric morphometrics analyses showed differences between forest and desert lizards.

Significant differences were found because of sex and type of habitat (Table IV) for the dorsal geometric morphometry; the two first Principal Components (PC1 29.95% and PC2

11.83% of Procrustes variance) described a change in the dorsal head shape corresponding to bigger eyeballs with a most posterior position to lower values; desert lizards showed lower values of PC1 and PC2 (Figure 8). In the lateral shape of the lizard’s head, significant differences were detected because of the interaction between sex and habitat

(Table IV), the first (23.37% of Procrustes variance) and the second (12.33% of Procrustes variance) Principal Components described a dorsoventral elongation of the head to

19 positive values; desert lizards presented lower values corresponding to more compressed heads (Figure 9).

Table IV: Procrustes ANOVA (5,000 iterations) for the dorsal and lateral head shape of

L. fuscus.

Source of Dorsal Lateral variation F P F P Sex 3.73 <0.001 1.88 0.025 Habitat 4.81 <0.001 2.65 0.009 Sex:Habitat 1.51 0.066 2.23 0.009 Error -1.21 0.88 -2.29 0.99

Figure 8: Principal Component Analysis for the geometric morphometry of the dorsal view of the head from desert (yellow) and forest (green) Liolaemus fuscus. Head shape

differences are two-fold magnified.

Figure 9: Principal Component Analysis for the geometric morphometry of the right lateral view of the head from desert (yellow) and forest (green) Liolaemus fuscus. Head

shape differences are two-fold magnified.

DISCUSSION

Adaptation to desert is a relevant topic in evolutionary biology since aridity could act as a selective pressure promoting evolutionary changes (Whitford, 2002) especially in highly radiated groups. At our knowledge, the present study is one of the first studies showing candidate loci and candidate morphological features related with desert adaptation. Deep genetic divergence was found between desert and forest populations; candidate genes associated to macromolecule degradation, transcription and cellular membrane components, processes and functions showed the most important differences. In the case of the morphology, main differences showed that lizards from the desert presented a smaller body size, larger limbs, bigger heads and longer tails than forest L. fuscus.

Furthermore, the dorsal shape of the head presented a bigger eyeball, and the lateral shape of the head was more dorsoventrally compressed in desert lizards, when compared to forest L. fuscus.

Genetic divergence between populations of L. fuscus

Main neutral genetic differences among populations were explained by habitat differences and the long geographic distance between populations (Figure 4, Table II), suggesting reduced gene flow throughout L. fuscus distribution and an old divergence time among the studied populations. This could also explain the large number of loci with a MAF <

0.05 between desert and forest populations, since long divergence times could accumulate fixed differences by genetic drift and/or natural selection (Hey, 1991). Other ddRAD studies with an amphibian (Rödin-Mörch et al. 2019) and a lizard (Yang et al. 2018) species, have not detected such high divergences among similar geographic distances.

This result suggests that gene flow could be also restricted by geographic barriers such as rivers or climatic zones, as described for other reptile species in Chile (Lamorot et al.

2003; Victoriano et al. 2008; Salaberry-Pincheira et al. 2011). Aditionally, this result highlights the use of methodologies that incorporate the genetic structure for outlier detection or that are less sensitive to the experimental design and population structure (as pcadapt or fsthet); especially when populations are highly divergent (Lottheros &

Withlock, 2014; Lottheros & Withlock, 2015; Ahrens et al., 2018).

Candidate genes to adaptation to desert and forest environments

The overrepresented GO terms in the fixed and outlier dataset included biological processes, molecular functions and cellular components related to the cellular membrane

(Table S4). The cellular membrane plays an important role in osmoregulation and water balance, since it determines the cellular permeability to water and other solutes by, for example, the disposition and organization of channels and transporters (Bradley, 2009;

Larsen et al., 2011). Arid environments are characterized by a scarce water availability, thus organisms that live in the desert may have mechanisms to optimize water and osmotic balance (Withford, 2002). The existence of differences in the cellular membrane suggests that L. fuscus inhabiting the desert could have optimized their water regulation processes, such as evaporative water loss or renal water reabsorption, as the case of the other reptiles

(Cox & Cox, 2015; Lillywhite, 2017; Dupoué et al. 2017).

Furthermore, GO terms related to degradation of cellular components and gene transcription were overrepresented. Differences in the mechanisms of cellular component degradation could be advantageous for organisms that live in lower quality environments

(such as the deserts), since macromolecule degradation and recycling are described as important processes to confront harsh conditions (Yin et al. 2016; Dikic, 2017). On the other hand, differences related to transcription regulatory mechanisms are important since gene expression is directly related to the regulation of functional traits that could be adaptative (Romero et al. 2012). To elucidate this hypothesis, it is necessary to experimentally demonstrate if desert L. fuscus present physiological differences with the forest populations, comparing for example, their metabolic rate, evaporative water loss and urine concentration.

Morphological features

In this study, L. fuscus lizards from the desert presented smaller body size than the individuals inhabiting the forests, contrary to our hypothesis. Ectotherms are described to have an inverted Bergmann rule (i.e. bigger body size associated to higher temperatures), since a smaller body implies faster heating rates in ectotherms (Pincheira-Donoso et al.

2008; Oufiero 2011). However, we detected low temperature differences between habitats for L. fuscus (Table I), thus thermal characteristics may not explain the body size differences in this species. Lizard body size is influenced by many other ecological variables like microhabitat use, reproduction, parity mode, resource availability, diet and predation risk (Meiri, 2008; Pincheira-Donoso & Meiri, 2013). Alternative explanations could be that desert environments are poor in resources; therefore, lizards could not have enough available energy to grow (Dunham, 1978); or there could be more predation risk in desert habitats since vegetation cover is lower, and bigger lizards are more susceptible

25 to be detected by predators (e.g. Pérez-Tris et al. 2004). Here again, these hypotheses need to be tested formally.

Furthermore, differences between individuals located in the desert and the forest were detected on the limb, head and tail size. Morphological studies in lizards, associate limb and tail morphology to the habitat structure; longer limbs and tails have been described in lizards with climbing behavior or that inhabit in areas with more vegetation (Losos 2011;

Grizante et al. 2012; Tollis et al. 2018). Studies in Liolaemus have found no relationship between morphology and habitat structure, but a relationship was found with climbing or running behavior (Jaksic & Nuñez, 1979; Jaksic et al., 1980; Schulte et al., 2003). L. fuscus from the Mediterranean zone in central Chile are found mostly in rocks, ground or trees

(Jaksic & Nuñez, 1979). However, individuals of L. fuscus inhabiting the desert are currently found climbing the cacti and shrubs according to our field observations, thus this could explain the bigger limbs and tails. Further study is needed to formally analyze the relationship between morphology and microhabitat use in this species.

L. fuscus from the Atacama Desert presented heads with bigger eyeballs and in a more posterior position, and heads more dorsoventrally compressed. The lizard head shape characteristics could be related to diet and bite performance (Vidal et al. 2006;

Kaliontzopoulou et al. 2012; Aguilar-Puntriano et al. 2018). In Liolaemus from the montanus group, a head with a posterior elongation has been described as a desert adaptation (Aguilar-Puntriano et al. 2018), but this was not the case for L. fuscus.

However, a bigger eyeball has been reported in lizards that inhabit in more saxicolous or terrestrial environments and could be related to habitat use (Kaliontzopoulou et al. 2010;

Urošević et al. 2013). Further research is needed to determine if the morphological conformations are related to lizard adaptation to desert.

Finally, our results suggest that there are morphological and genetic differences between populations of Liolaemus fuscus inhabiting contrasting environments. The detected candidate genes are mostly related to physiological processes that could imply adaptations to arid environments, such as water and osmotic balance. Furthermore, morphological traits could be related to desert habitat structure and resource availability. These results highlight the importance of understanding the interaction between genetic and environmental mechanisms that allow adaptation. In this case, it is relevant to assess arid environments and their selective pressure for organisms to adapt, specially how this relates to the evolution of highly radiated genus such as Liolaemus.

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SUPPLEMENTARY MATERIAL

Table S1: Bioclimatic variables for each of the studied sites (Pliscoff et al. 2014)

Forest Desert Chicauma El Manzano Las Rojas La Higuera Annual Mean 14.7 13.1 15 15.3 Temperature (°C) Mean Diurnal Range 14.5 14.9 11.1 7.6 (°C) Isothermality 5.7 5.7 6 5.2 Temperature 398.3 447.3 263.3 232.1 Seasonality (°C) Max Temperature of 28.3 28.2 25.1 22 Warmest Month (°C) Min Temperature of 3 2.3 6.6 7.4 Coldest Month (°C) Temperature Annual 25.3 25.9 18.5 14.6 Range (°C) Mean Temperature of 9.5 8 11.7 12.9 Wettest Quarter (°C) Mean Temperature of 19.8 18.9 16.1 14.6 Driest Quarter (°C) Mean Temperature of 19.8 18.9 18.4 18.3 Warmest Quarter (°C) Mean Temperature of 9.5 7.4 11.7 12.5 Coldest Quarter (°C) Annual Precipitation 354 492 72 45 (mm) Precipitation of Wettest 89 115 30 20 Month (mm) Precipitation of Driest 0 1 0 0 Month (mm) Precipitation 107 95 136 158 Seasonality Precipitation of Wettest 235 299 62 39 Quarter (mm) Precipitation of Driest 5 9 0 0 Quarter (mm) Precipitation of 5 9 0 0 Warmest Quarter (mm) Precipitation of Coldest 235 292 62 39 Quarter (mm)

Table S2: Fixed genes (MAF < 0.05) between forest and desert populations.

Gene Gene Description Organism ADGB Androglobin Anolis carolinensis AQP4 Aquaporin 4 Anolis carolinensis ATP6V1B1 ATPase H+ Transporting V1 Subunit B1 Pogona vitticeps BAP1 BRCA1 associated protein 1 Podarcis muralis BIRC6 Baculoviral IAP repeat containing 6 Podarcis muralis C11ORF58 Chromosome 11 open reading frame 58 Anolis carolinensis CAPN8 Calpain 8 Anolis carolinensis CFAP57 Cilia and flagella associated protein 57 Anolis carolinensis CUX2 Homeobox protein cut-like Podarcis muralis DCHS2 Dachsous Cadherin-Related 2 Anolis carolinensis EXT2 Exostosin glycosyltransferase 2 Anolis carolinensis FERM, ARH/RhoGEF and pleckstrin Protobothrops FARP1 domain protein 1 mucrosquamatus FBLN2 Fibulin 2 Anolis carolinensis FBN3 Fibrillin 3 Pogona vitticeps Gamma-aminobutyric acid type A receptor GABRA5 Anolis carolinensis alpha5 subunit N-acetylglucosamine-1-phosphate GNPTAB Anolis carolinensis transferase alpha and beta subunits GOLGA4 Golgin A4 Anolis carolinensis GPN2 GPN-loop GTPase 2 Pseudonaja textilis Glutamate ionotropic receptor NMDA type GRIN2A Anolis carolinensis subunit 2A GSG1 Germ cell associated 1 Podarcis muralis HCFC1 Host cell factor C1 Anolis carolinensis Interferon-related developmental regulator IFRD2 Anolis carolinensis 2 IL10 Interleukin 10 Anolis carolinensis ITGB2 Integrin beta Anolis carolinensis ITPR3 Inositol 1,4,5-trisphosphate receptor type 3 Anolis carolinensis KIAA1614 KIAA1614 Anolis carolinensis KIF1A Kinesin family member 1A Pogona vitticeps KL Klotho Anolis carolinensis KLF1 Kruppel-like factor 1 Chelonia mydas LEKR1 Leucine, glutamate and lysine rich 1 Anolis carolinensis LEO1 homolog, Paf1/RNA polymerase II LEO1 Gekko japonicus complex component LOC100557814 Uncharacterized oxidoreductase ZK1290.5 Anolis carolinensis LOC100558604 Cystine/glutamate transporter Anolis carolinensis

LOC100560499 Unconventional myosin-X Anolis carolinensis LOC100561409 Cytosolic phospholipase A2 epsilon Anolis carolinensis LOC103277788 Uncharacterized Anolis carolinensis LOC107983756 Uncharacterized Anolis carolinensis Mitochondrial transcription termination MTERF4 Anolis carolinensis factor 4 NT5C2 5'-nucleotidase, cytosolic II Pogona vitticeps OTOP1 Otopetrin 1 Anolis carolinensis P3H3 Prolyl 3-hydroxylase 3 Anolis carolinensis PAPOLG Poly (A) polymerase gamma Anolis carolinensis PAT1 homolog 1, processing body mRNA PATL1 Podarcis muralis decay factor PAX5 Paired box 5 Pogona vitticeps PHACTR3 Phosphatase and actin regulator Anolis carolinensis PHF14 PHD finger protein 14 Anolis carolinensis PPFIA3 PTPRF interacting protein alpha 3 Anolis carolinensis PRDM2 PR/SET domain 2 Podarcis muralis PUM1 Pumilio RNA binding family member 1 Pogona vitticeps RAD23 homolog B, nucleotide excision RAD23B Anolis carolinensis repair protein SCN1A Sodium channel protein Anolis carolinensis SEMA3C Semaphorin 3C Pogona vitticeps SLC2A2 Solute carrier family 2 member 2 Anolis carolinensis SLC25A16 Solute carrier family 25 member 16 Pogona vitticeps SMG1 Serine/threonine-protein kinase SMG1 Anolis carolinensis STARD13 STAR-related lipid transfer protein 13 Anolis carolinensis Sushi, von Willebrand factor type A, EGF SVEP1 Anolis carolinensis and pentraxin domain containing 1 TEX10 Testis expressed 10 Anolis carolinensis TOR1AIP2 Torsin 1A interacting protein 2 Anolis carolinensis TTN Titin Python bivittatus WDR34 WD repeat domain 34 Pogona vitticeps ZNF653 Zinc finger protein 653 Anolis carolinensis

Table S3: Outlier genes detected with fsthet and pcadapt between forest and desert populations.

Gene Gene Description Organism Ankyrin repeat and fibronectin type III ANKFN1 Python bivittatus domain containing 1 BTBD7 BTB domain containing 7 Anolis carolinensis CPB1 Carboxypeptidase B1 Anolis carolinensis DBNL Drebrin like Anolis carolinensis GOLIM4 Golgi integral membrane protein 4 Podarcis muralis LOC100557133 Solute carrier family 22 member 12 Pogona vitticeps LOC100565325 Vomeronasal type-2 receptor 26 Python bivittatus LOC103279150 Collagen Alpha-6 chain Anolis carolinensis LOC107982480 Monoacylglycerol lipase ABHD6-like Anolis carolinensis LOC107983756 Uncharacterized Anolis carolinensis LOC110079156 All-trans-retinol 13,14-reductase-like Anolis carolinensis Minichromosome maintenance complex MCMBP Anolis carolinensis binding protein NLGN1 Neurolignin 1 Pogona vitticeps SLC15A5 Solute carrier family 15 member 5 Anolis carolinensis THPO Thrombopoietin Anolis carolinensis TMEM131 Transmembrane protein 131 Anolis carolinensis Transient receptor potential cation channel TRPV2 Pogona vitticeps subfamily V member 2

Table S4: Significant overrepresented GO terms for the outlier and fixed loci dataset

GO Fold Raw p-value Enrichment GO Molecular Function Oligopeptide transmembrane GO:0035673 57.9 0.021 transporter activity Transmembrane transporter activity GO:0022857 2.68 0.016 Transporter activity GO:0005215 2.25 0.036 Proteasome binding GO:0070628 48.25 0.024 5’-nucleotidase activity GO:0008253 28.95 0.037 nucleotidase activity GO:0008252 26.32 0.041 Adenylyltransferase activity GO:0070566 28.95 0.037 RNA polymerase II core binding GO:0000993 22.27 0.047 Glucosidase activity GO:0015926 22.27 0.047 Extracellular ligand-gated ion channel GO:0005230 7.42 0.031 activity Ligand-gated ion channel activity GO:0015276 7.18 0.009 Gated channel activity GO:0022836 5.79 0.016 Channel activity GO:0015267 6.98 0.003 Passive transmembrane GO:0022803 6.98 0.003 transporter activity Ligand-gated channel activity GO:0022834 7.18 0.009 Substrate-specific channel activity GO:0022838 32.17 0.034 Neurotransmitter receptor activity GO:0030594 6.89 0.035 Cation channel activity GO:0005261 6.73 0.037 Neurotransmitter binding GO:0042165 6.58 0.039 GO Biological Process Mitochondrial transcription GO:0006390 57.9 0.021 RNA metabolic process GO:0016070 3.74 0.047 Nucleic acid metabolic process GO:0090304 3.64 0.025 Peptidyl-proline hydroxylation GO:0019511 48.25 0.024 Deadenylation-dependent decapping of GO:0000290 48.25 0.024 nuclear-transcribed mRNA Nuclear transcribed mRNA catabolic GO:0000956 14.85 0.009 process mRNA catabolic process GO:0006402 10.34 0.017 RNA catabolic process GO:0006401 7.93 0.028 Nuclear-transcribed mRNA catabolic GO:0000184 36.19 0.031 process, nonsense-mediated decay Cytoplasmic mRNA processing body GO:0033962 36.19 0.031 assembly Membrane assembly GO:0071709 32.17 0.034

Positive regulation of transcription GO:0032968 elongation from RNA polymerase II 26.32 0.041 promoter Positive regulation of transcription, GO:0045893 10.34 0.017 DNA-templated Positive regulation of nucleic acid- GO:1903508 10.34 0.017 templated transcription Positive regulation of RNA GO:1902680 10.34 0.017 biosynthetic process Cell adhesion mediated by integrin GO:0033627 26.32 0.041 Long-term synaptic potentiation GO:0060291 26.32 0.041 Modulation of chemical synaptic GO:0050804 7.82 0.028 transmission Regulation of trans-synaptic GO:0099177 7.82 0.028 signaling Regulation of signaling GO:0023051 7.15 0.033 Integrin-mediated signaling pathway GO:0007229 22.27 0.047 Cell surface receptor signaling pathway GO:1905114 12.87 0.011 involved in cell-cell signaling Regulation of membrane potential GO:0042391 6.79 0.010 Microtubule-based movement GO:0007018 6.43 0.040 Cell motility GO:0048870 5.79 0.016 Localization of cell GO:0051674 5.79 0.016 Chemical synaptic transmission GO:0007268 3.78 0.022 Anterograde trans-synaptic signaling GO:0098916 3.78 0.022 Trans-synaptic signaling GO:0099537 3.77 0.022 Synaptic signaling GO:0099536 3.77 0.022 GO Cellular component Sarcoplasmic reticulum GO:0016529 22.27 0.047 Postsynaptic membrane GO:0045211 9.81 0.019 Postsynapse GO:0098794 7.93 0.028 Membrane GO:0016020 2.37 0.009 Membrane part GO:0044425 2.17 0.036 Cation channel complex GO:0034703 5.73 0.049 Integral component of membrane GO:0016021 2.72 0.006 Intrinsic component of membrane GO:0031224 2.69 0.006 Integral component of plasma GO:0005887 2.6 0.018 membrane