Sky-islands diversification: The case of the alpine garter snakes (Natricidae: Thamnophis), their evolutionary and biogeographic history in the Mexican highlands

Sky-islands diversification: The case of the alpine garter snakes (Natricidae: Thamnophis), their evolutionary and biogeographic history in the Mexican highlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sky-islands diversification: The case of the alpine garter snakes (Natricidae: Thamnophis), their evolutionary and biogeographic history in the Mexican highlands Luis Fernando Hidalgo Licona, Antonio Yolocalli Cisneros-Bernal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9023509/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The Mexican highlands, a biodiversity hotspot characterized by complex topography and dynamic paleoenvironments, function as "Sky Islands," isolating temperate-adapted species and promoting in situ diversification. This pattern is exemplified by the Mexican Highland Clade (MHLC) of Thamnophis, a genus exhibiting high ecological and phylogenetic diversity in North America. We hypothesize that MHLC diversification is the result of ecological specialization and divergence driven by the environmental heterogeneity of the Mexican highlands. Through an integrative approach, combining phylogenetics, morphological analyses, and ecological niche modelling, we reconstruct the clade’s evolutionary and biogeographic history to identify key drivers of diversification. Our results indicate that MHLC diversification was driven by the geologic and climatic heterogeneity of the highlands, with initial divergence in the Late Miocene (~5.62 Ma) consistent with Trans-Mexican Volcanic Belt activity. These patterns align with other highland-endemic taxa, suggesting shared biogeographic processes. Niche specialization and correlated shifts in head morphology suggest possible adaptive responses to habitat variation, potentially reflecting ecomorphological convergence independent of phylogenetic constraints. These findings highlight the Mexican highlands as a crucial area for evolutionary processes in North America, containing significant undocumented biodiversity and confirming their importance as a global endemism hotspot. endemism ecological niche modelling geometric morphometry trans-mexican volcanic belt ecomorphology mountains divergence phylogenetic signal phylomorphospace Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION Mountainous regions comprise approximately 20% of the Earth's terrestrial surface, which host a substantial proportion of global biodiversity, particularly in tropical regions (i.e., between the Tropic of Cancer at 23° 26' 17'' N and the Tropic of Capricorn at 23° 26' 17'' S) (Körner, 2019). These regions, characterized by climatic diversity resulting from their complex topography, function as independent biogeographic units analogous to oceanic islands (McCormack et al., 2009 ; Körner et al., 2011 ). Because of this similarity, they are often called “Sky Islands”, as they are isolated by lowland areas unsuitable for species adapted to alpine environments (McCormack et al., 2009 ). Mountainous regions comprise approximately 20% of the Earth's terrestrial surface, which host a substantial proportion of global biodiversity, particularly in tropical regions ( i.e. , between the Tropic of Cancer at 23° 26' 17'' N and the Tropic of Capricorn at 23° 26' 17'' S) (Körner 2019). These regions, characterized by climatic diversity resulting from their complex topography, function as independent biogeographic units analogous to oceanic islands (McCormack et al., 2009 ; Körner et al., 2011 ). Because of this similarity, they are often called “Sky Islands”, as they are isolated by lowland areas unsuitable for species adapted to alpine environments (McCormack et al., 2009 ). In Mexico, mountain systems dominate 85% of the territory. This topography, combined with the country's paleoenvironmental history and its transitional position between biogeographical regions, has fostered in situ lineage evolution at multiple scales (Arriaga et al., 1997 ; Espinosa et al., 2008 ; Mastretta-Yanes et al., 2015 ; Morrone, 2020 ). Consequently, Mexico is not only recognized as a global biodiversity hotspot where biota of both Nearctic and Neotropical affinities converge, but its highlands are also considered diversification centers for various reptile taxa, such as Crotalus (Blair et al., 2019 ), Barisia (Bryson & Riddle, 2012 ), and Plestiodon (Bryson et al., 2017 ). For this reason, the Mexican highlands have been a key area for studying the biogeographical patterns and evolutionary processes of North American taxa (Mastretta-Yanes et al., 2015 ). Since diversification is a dynamic and complex process, it needs to be studied using an integrative and multidisciplinary approach. Wiens ( 2017 ) and Li and Wiens ( 2022 ) suggested that the simultaneous analysis of multiple traits ( e.g. , biogeographical, ecological, morphological and genetic) is essential to understand the patterns and mechanisms underlying species diversification. This has been evidenced in recent research that has explored these processes from ecological and molecular perspectives (Cisneros-Bernal et al., 2022 ; Hallas et al., 2022 ; León-Tapia et al., 2023 ). While these studies offer valuable insights into the historical factors shaping diversification patterns in the Mexican highlands, the mechanisms driving divergence among ecologically and phylogenetically related lineages remain poorly understood. Thamnophis Fitzinger, 1843 is one of the most ecologically and phylogenetically diverse snake´s genera in North America, with approximately 38 recognized species (Uetz et al., 2025 ). These species exhibit remarkable variation in their degree of trophic, climatic, and habitat specialization (Rossman et al., 1996). Phylogenetic hypotheses based on mitochondrial DNA (mtDNA) (de Queiroz et al., 2002 ; Grünwald et al., 2024 ) and genomic data (Hallas et al., 2022 ; Nuñez et al., 2023) have revealed two geographically distinct clades: (1) a northern clade, consisting of ~ 14 species distributed across southern Canada, the United States, and northern Mexico; and (2) a more diverse southern clade, consisting of ~ 24 species distributed in central and southern Mexico and Central America. Within the southern clade, the Mexican Highland Clade (MHLC) includes ten temperate-adapted species (detailed below), which are primarily restricted to the major mountain ranges of Mexico (Rossman et al., 1996; Flores-Villela & García-Vázquez, 2014 ; Hallas et al., 2022 ). Previous studies suggest that the diversification center of Thamnophis was located in west-central Mexico during the Middle Miocene ( ca. 15 Mya; de Queiroz et al., 2002 ; Hallas et al., 2022 ; Nuñez et al., 2023). During this period, a rapid radiation likely occurred, leading to the evolution of diverse trophic and climatic niches, possibly driven by the heterogeneous Mexican landscape and the prevailing paleoclimatic conditions of the Neogene-Quaternary (N-Q) (Mastretta-Yanes et al., 2015 ; Hallas et al., 2022 ; Nuñez et al., 2023). This process likely gave rise to the current pattern of species and ecological diversity that characterizes the genus (McVay et al., 2015 ). However, some authors have documented a decline in diversification rates within Natricidae, a group that includes Thamnophis , potentially as a consequence of their rapid Miocene radiation and the subsequent saturation of available habitats (McVay et al., 2015 ). This phenomenon may be linked to morphological stasis observed in both extant and extinct lineages of the genus (Holman, 2000 ; Eldredge et al., 2005 ). This contrasts with patterns documented in other montane taxa like the Sceloporus torquatus group (Campillo-García et al., 2021 ) and Pituophis deppei (Bryson et al., 2011 a; Hidalgo-Licona et al., 2022 ), which have been shown to exhibit substantial intraspecific diversity across molecular, morphological, and ecological scales. Despite the findings discussed above, research exploring the ecological and biogeographic dynamics shaping the evolutionary history of Thamnophis remains limited (de Queiroz et al., 2002 ; Wood et al., 2011 ; Hallas et al., 2022 ). In particular, a knowledge gap persists regarding the northern clade species distributed in Mexico, and even more so for the southern clade, despite the latter comprising approximately 80% of the total diversity of the genus (Rossman et al., 1996; Heimes, 2016 ; Uetz et al., 2025 ). Consequently, the MHLC is not only an ideal model for investigating the ecological, geographical and evolutionary processes driving Thamnophis diversity, but also a key indicator of the characteristic biota of the Mexican highlands. Our study aims to reconstruct the evolutionary and biogeographic history of the MHLC by testing the overarching hypothesis that its diversification is the result of ecological specialization and divergence driven by the environmental heterogeneity of the Mexican highlands. If this hypothesis holds, each species should display distinctive ecomorphological traits independent of their phylogenetic relationships. To test this, we employ a multidisciplinary approach integrating and analyzing molecular, ecological, and morphological data. This approach addresses four specific questions: 1) How have geological, climatic, and geographic factors influenced diversification patterns within the MHLC? 2) What is the likely center of diversification of the MHLC, and how have the Mexican highlands influenced the clade’s evolutionary history? 3) What role has climatic niche conservatism and/or divergence played in its evolutionary history? and 4) Is there a phylogenetic signal in the MHLC ecomorphological traits, or does their variation reflect independent evolution associated with specific ecological conditions? 2 MATERIALS AND METHODS 2.1 Study site The Mexican highlands extend from the southern Rocky Mountains in the United States to northern Central America (Mastretta-Yanes et al., 2015 ). Within Mexico, this region comprises seven mountain ranges that vary in orientation, age, and origin, with elevations ranging from 1,000 to 5,000 meters (Espinosa et al., 2008 ; Halffter et al., 2008 ; Morrone, 2019 ). The most relevant mountain ranges for this study are: 1) Sierra Madre Occidental (SMOc), the largest mountain system in Mexico, extending approximately 1,200 km in a north-south direction. It features a temperate sub-humid climate and diverse vegetation, including pine forests, oak forests, and grasslands (Arriaga et al., 1997 ; Espinosa et al., 2008 ; Morrone, 2019 ; Escalante et al., 2021 ); 2) Sierra Madre Oriental (SMOr), a discontinuous north to south mountain range, with elevations between 2,000 and 4,000 m. Its predominant vegetation consists of pine and oak forests, with a lesser presence of cloud forests (Arriaga et al., 1997 ; Morrone, 2019 ); 3) Trans-Mexican Volcanic Belt (TMVB), the highest mountain range in Mexico, with elevations ranging from 2,350 to 5,610 m. Located in the central Mexico and oriented east-west, it consists of volcanoes of various ages, with a geomorphological origin dating back to the Early Miocene ( ca. 19 Mya) and continues to the present. The climate is predominantly temperate sub-humid, with pine and oak forests as the dominant vegetation (Arriaga et al., 1997 ; Espinosa et al., 2008 ; Halffter et al., 2008 ; Morrone, 2020 ); 4) Sierra Madre del Sur (SMS), a discontinuous mountain range running parallel to the Pacific coast and the Gulf of Mexico, with elevation ranging from 1,800 to 3,750 m. Its slopes are primarily covered by pine and oak forests, with a lesser presence of cloud forests (Arriaga et al., 1997 ; Espinosa et al., 2008 ; Morrone, 2019 ) (Fig. S1 ). 2.2 Model system The Mexican Highland Clade (MHLC) consists of ten small to medium-sized species, as defined by their Snout-Vent Length (SVL). These taxa are distinguished by their limited distributions and endemism to the mountain ranges of central-southern Mexico (Rossman et al., 1996; Heimes, 2016 ; Hallas et al., 2022 ). Although Thamnophis lineri and the recently described T. ahumadai (Grünwald et al., 2024 ), are recognized as members of the MHLC, they were excluded from this study due to the lack of available specimens for geometric morphometric analyses and associated ecological data. Additionally, while Grünwald et al. ( 2024 ) suggest that T. conanti and T. lineri should be considered a junior synonym of T. bogerti , the primary focus of this work is not to evaluate the taxonomy of the group. Consequently, we have chosen to follow the phylogenetic hypothesis proposed by Hallas et al. ( 2022 ). The species included in this study are as follows: 1) Thamnophis bogerti , a medium-sized (SVL: 600 mm) semi-aquatic species found in mesohabitats within pine and pine-oak forests of the SMS, at elevation ranging from 1300 to 2900 m (Heimes, 2016 ); 2) Thamnophis conanti , a small-sized (SVL: 450 mm) semi-aquatic species inhabiting mesohabitats in pine and pine-oak forests in the southeastern TMVB and northern SMS, at elevations from 2100 to 2900 m (Heimes, 2016 ); 3) Thamnophis errans , a medium-sized (SVL: 700 mm) terrestrial species restricted to pine and pine-oak forests along the SMOc, at elevations from 1860 to 2545 m (Rossman et al., 1996; Heimes, 2016 ); 4) Thamnophis exsul , a small-sized (SVL: 400 mm) terrestrial species limited to pine and pine-oak forests, as well as grasslands, in the northern SMOr, at elevations between 2650 and 3237 m (Rossman et al., 1996; Heimes, 2016 ); 5) Thamnophis godmani , a medium-sized species (SVL: 700 mm) semi-aquatic restricted to mesohabitats associated with pine and pine-oak forests in the western SMS, at elevations from 1700 to 2600 m (Rossman et al., 1996; Heimes, 2016 ); 6) Thamnophis scalaris , a medium-sized (SVL: 700 mm) terrestrial species confined to mesohabitats in pine forests, pine-oak forests, and subalpine grasslands along the TMVB, at altitudes ranging from 2100 to 4273 m (Rossman et al., 1996; Rossman & Gongora, 1997 ; Heimes, 2016 ); 7) Thamnophis scaliger , a medium-sized (SVL: 570 mm) terrestrial species limited to mesohabitats in pine forests, pine-oak forests, shrublands, and grasslands in the central TMVB and isolated regions of the central-southern Chihuahuan Province, at elevations from 2240 to 2720 m (Rossman et al., 1996; Rossman & Gongora, 1997 ; Heimes, 2016 ); and 8) Thamnophis sumichrasti , a medium-sized species (SVL: 756 mm) semi-aquatic species restricted to mesohabitats in pine, pine-oak, and cloud forests along the SMOr, at elevations between 1365 and 2400 m (Rossman et al., 1996; Heimes, 2016 ). 2.3 Phylogenetic framework, molecular dating, and biogeographic reconstruction To reconstruct the evolutionary history of the MHLC and identify the spatiotemporal context and geographic drivers of its diversification (research questions 1 and 2), we collected a total of 67 tissue samples ( i.e. , liver, shed skins, and tail tips) from six of the eight MHLC species, sourced from both scientific collections and fieldwork to maximize representation across their distribution range. The sampled species included T. bogerti ( n = 3), T. conanti ( n = 4), T. godmani ( n = 2), T. scalaris ( n = 41), T. scaliger ( n = 12), and T. sumichrasti ( n = 4). To complement the sampling, 25 additional sequences of the MHLC species were obtained from GenBank, along with sequences from other Natricidae species as outgroups (see Supplementary Data Table S1 ). Genomic DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen). DNA quality and concentration were assessed using an Epoch microplate spectrophotometer (BioTek, Winooski, VT, USA). Subsequently, fragments ranging from 728–1051 base pairs (bp) of two mitochondrial genes, NADH dehydrogenase 4 ( ND4 ) and Cytochrome B ( Cytb ), and one nuclear gene, Dynein Axonemal Heavy Chain 3 ( DNAH3 ), were sequenced using primers and PCR protocols described in Supplementary Data Appendix S1. The sequencing was performed at MacroGen. Forward and reverse sequences of the resulting electropherograms were assembled and aligned in Geneious Prime v11.0.4 (Kearse et al., 2012 ). The final dataset comprised 83 individuals from the MHLC species and representing 17 species in total (see Supplementary Data Table S1 ). Sequences were aligned using MAFFT v.7 (Katoh et al., 2019 ) under the E-INS-i strategy and manually edited in PhyDE v.0.9971(Muller 2005 ). Each gene was partitioned by codon position, and the best-fit partitions were estimated based on the Bayesian Information Criterion (BIC). The optimal model of sequence evolution for the Maximum Likelihood (ML) analysis was selected using PartitionFinder v.2.1.1 (Lanfear et al., 2017 ). ML analyses were conducted in IQ-TREE v 3.0.1 (Trifinopoulos et al., 2016 ), the best tree and nodal support values were simultaneously estimated using the embedded ultraFast bootstrap approach (UFB) with 5,000 replicates. The resulting tree was visualized and edited in FIGTREE v.1.4.4 (Rambaut, 2009 ). 2.3.1 Molecular dating Divergence times among MHLC lineages were estimated using BEAST v2.7.7 (Bouckaert et al., 2014 ) with a concatenated dataset ( Cytb + ND4 + DNAH3 ) and an uncorrelated relaxed molecular clock for all loci. Nucleotide substitution models were based on PartitionFinder results, and a birth-death speciation process was applied, incorporating four fossil calibration points: C1, the most recent common ancestor (MRCA) of the North American Natricidae, is represented by the oldest fossil record of the genus Nerodia (lognormal mean = 14 mya; standard deviation = 0.6); C2, the MRCA of the genus Thamnophis , represented by the oldest fossil record of this genus (lognormal mean = 14 mya; standard deviation = 0.6); C3, the MRCA of the northern clade within Thamnophis , from a fossil record associated with Thamnophis cyrtopsis (lognormal mean = 0.122 mya; standard deviation = 0.2); and C4, the MRCA of the MHLC, is represented by a fossil record linked to Thamnophis scalaris (lognormal mean = 0.014 mya; standard deviation = 0.1 (Álvares & Huerta, 1975; Van Devender et al., 1985 ; Holman, 2000 ). The analysis was run for 100 million generations, sampling every 1,000 generations. Convergence was assessed in Tracer v1.5 (Rambaut & Drummond, 2009 ). A maximum credibility consensus tree was generated using TreeAnnotator (Bouckaert et al., 2014 ) after discarding 25% as burn-in. 2.3.2 Ancestral area reconstruction To infer the biogeographic history of the MHLC, we reconstructed ancestral areas using the six biogeographic models implemented in BioGeoBEARS (Matzke 2014 ) via the RASP 4.2 interface (Yu et al., 2020 ). The analysis was based on the time-calibrated phylogenetic tree and the biogeographic provinces proposed by Escalante et al., ( 2021 ), which comprises five areas: Sierra Madre del Sur (I), Sierra Madre Occidental (II), Sierra Madre Oriental (III), Trans-Mexican Volcanic Belt (IV), and Chihuahuan Desert (V). To avoid overparameterization, the maximum number of areas per node was constrained to two (Yu et al., 2015 ; Yu et al., 2020 ). The models tested included Dispersal-Extinction-Cladogenesis ( DEC ) this model assumes speciation is strictly allopatric, occurring either through the division of an ancestral range or via founder-event dispersal to an already connected area; Dispersal-Vicariance Analysis-like ( DIVALIKE ) this model assumes sympatric speciation, where a lineage can diverge within its ancestral range without the need for geographic isolation; and Bayesian Analysis of Biogeography-like ( BAYAREALIKE ) this model assumes speciation is always sympatric and all geographical range differentiation, resulting from dispersal and local extinction events, is presumed to occur after the lineage splitting event (Ree et al., 2005 ; Ree & Smith, 2008 ). Each of these models was also run with the addition of the founder-event ( + J ) parameter. This parameter models the probability of a jump-dispersal event to a non-connected area being directly associated with a speciation event (Matzke, 2014 ). Model selection was performed using the corrected Akaike Information Criterion ( AICc ). 2.4 Climatic niche characterization and evolution To characterize the climatic niche of each MHLC species and assess its role in driving biogeographic patterns and diversification processes (research question 3) we first obtained occurrence records for MHLC species from GBIF using the rgbif package (Chamberlain et al., 2022 ) and complemented with data from the literature, field collections, and specimens deposited in scientific collections mentioned later. The preliminary dataset included 2,918 records distributed as follows: T. bogerti ( n = 37), T. conanti ( n = 56), T. errans ( n = 167), T. exsul ( n = 54), T. godmani ( n = 480), T. scalaris ( n = 1,280), T. scaliger ( n = 533), and T. sumichrasti ( n = 301). Duplicate, incomplete, and highly spatially correlated records ( i.e. , records with a separation < 1 km) were removed. Records falling outside the known distribution of each species (Rossman et al., 1996) or exhibiting temporal mismatches with the bioclimatic variables ( i.e ., 1979–2013; (Karger et al., 2017 ) were excluded. Additionally, records with atypical environmental values were discarded, based on the biological knowledge of MHLC species ( i.e ., records outside the previously reported altitudinal range; Rossman et al.,1996). For training the MHLC climatic niche models, a species-specific " M ", defined as the region available to a species without dispersal barriers (Soberón & Nakamura, 2009 ), was bounded using the sp (Pebesma et al., 2012 ), raster (Hijmans et al., 2015 ), and rgdal (Bivand et al., 2015) packages. This delimitation was based on biological and geographic evidence to restrict the set of climatic conditions used in each model, a crucial step for model development and subsequent analyses (Barve et al., 2011 ; Peterson, 2011 ; Luna et al., 2024 ). The delimitation of “ M ” was guided by three criteria: 1) known distribution range of each species, 2) biogeographic provinces of Mexico (Escalante et al., 2021 ) and their associated terrestrial ecosystems proposed by Olson et al. ( 2001 ), and 3) a 5 km² buffer around each occurrence record to account for potential individual dispersal, based on available information on other Thamnophis species (Gregory & Stewart, 1975 ; Shonfield et al., 2019 ). For further details, see Hidalgo-Licona et al. ( 2023 ). To quantify the climatic niches of MHLC species, bioclimatic data from CHELSA v1.2b (Karger et al., 2017 ) were used. This data set includes 19 variables related to precipitation and temperature, collected between 1979 and 2013 at a spatial resolution of 30 arc-seconds (~ 1 km² per pixel). These variables were clipped according to the species-specific " M " configuration. Subsequently, three variable sets were constructed: Set1, designed to capture extreme climatic conditions using the same variables for all species: Bio1, Bio5, Bio6, Bio12, Bio13, and Bio14; Set2, selected using the Variance Inflation Factor ( VIF ) using the usdm package (Naimi 2015 ), removing variables with high collinearity ( i.e., VIF > 10) (Montgomery & Peck, 1992 ; Naimi et al., 2014 ); and Set3, based on Pearson correlation values, excluding variables with r² > 0.7 (Soberón & Nakamura, 2009 ). Using the kuenm package (Cobos et al., 2019 ), 357 models were generated for each species. Regularization multiplier values were tested (ranging from 0.1 to 1 in increments of 0.1, followed by 2, 3, 4, 5, 6, 8, and 10) in combination with all possible linear, quadratic, and product feature classes ( FC ) in Maxent 3.4.1 (Phillips et al., 2004). The objective was to identify the optimal configuration that minimized model over-parameterization. Models were built using 75% of occurrence records for calibration and 25% for evaluation, with 10 bootstrap replicates, following the methodology of Phillips et al. (2004) and Cobos et al. ( 2019 ). The final model selection was based on three criteria: 1) statistical significance of the lowest partial Receiver Operating Characteristic ( pROC ) values; 2) predictive power, indicated by low omission rates ( OR < 5%) (Cobos et al. ,2019); and 3) lowest AICc values (Lobo et al., 2008 ; Peterson et al., 2008 ; Cobos et al., 2019 ). Finally, the geographical projection of models was binarized (0 = unsuitable, 1 = suitable) using the 10th percentile for minimum training presence, excluding the lowest 10% of values as they might represent erroneous records in the final dataset (Pearson et al., 2007 ). 2.4.1 Niche breadth, overlap, and similarity. To quantify the range of climatic conditions each species of the MHLC can tolerate, climatic niche breadth was measured using Levins I Index (Levins, 1968 ) in the enmtools package (Warren et al., 2021 ). This index produces values between 0 and 1, where values near 0 indicate low breadth ( i.e. , specialist species), and values near 1 indicate high breadth ( i.e. , generalist species) (Carscadden et al., 2020 ). Climatic niche overlap between species was evaluated using Schoener’s D index (Schoener, 1968) with the PCA-env approach (Broennimann et al., 2012 ) in the ecospat package (Di Cola et al., 2017 ). Interpretation followed the metric proposed by Rödder and Engler ( 2011 ) null or minimal overlap (0–0.2), low (0.2–0.4), moderate (0.4–0.6), high (0.6–0.8), and very high (0.8–1.0). To evaluate the presence of phylogenetic climatic niche conservatism (PCNC) the tendency to retain ancestral ecological traits (Pyron et al., 2015 ) or climatic niche divergence (CND), which involves the acquisition of novel ecological traits distinct from the ancestral condition (Pyron & Burbrink, 2009 ), a niche similarity test was performed using the ecospat package (Di Cola et al., 2017 ). This test aimed to verify the presence of PCNC by determining whether niche overlap between two species was greater than expected by chance. The hypothesis was accepted if observed overlap ( Schoener’s D ) was significantly different ( p < 0.05) from niche overlap values obtained through pseudoreplicates. The test was repeated 1,000 times for each comparison to ensure the null hypothesis was rejected with high confidence (Warren et al., 2008 ; Broennimann et al., 2012 ). Given the unidirectional nature of the similarity test, two tests were conducted for each comparison ( i.e. , Sp1 vs. Sp2 and Sp2 vs. Sp1). 2.4.2 Climatic niche evolution Climatic niche evolution among MHLC lineages was analyzed in relation to their phylogenetic relationships using the phytools R package (Revell, 2012 ). The weighted mean of the PC-env values for each species was calculated, and the phylogenetic tree obtained in this study was pruned to include only the eight MHLC species, using the ape package (Paradis et al., 2004 ). The mode of climatic niche evolution was assessed by testing five evolutionary models using the Geiger package (Harmon et al., 2015 ): 1) Brownian Motion ( BM ): random evolution of traits (Felsenstein, 1985 ); 2) White Noise ( WN ): evolution independent of phylogenetic relationships (Butler & King, 2004 ); 3) Single Peak ( SP ): evolution constrained to a single adaptive peak (Butler & King, 2004 ); 4) Early Burst ( EB ): evolutionary rates that decline exponentially over time (Harmon et al., 2010 ); and 5) Kappa: trait divergence linked to speciation events (Harmon et al., 2015 ). The optimal model was selected by comparing AICc values . 2.5. Geometric morphometry To evaluate whether morphological variation is shaped more by phylogeny or ecological factors (research question 4), we analyzed head shape in relation to habitat use and diet. A total of 113 specimens from the eight MHLC species were photographed, obtained from scientific collections and fieldwork to ensure adequate representation of their distributional range (see Supplementary Table S2 ). Each specimen was photographed in dorsal, lateral, and ventral views, considering only adult individuals (SVL > 280 mm) to minimize ontogenetic variation. Specimens in poor condition were also excluded to prevent artificial distortions in head shape. The number of photographed specimens per species was as follows: T. bogerti ( n = 4), T. conanti ( n = 7), T. errans ( n = 5), T. exsul ( n = 4), T. godmani ( n = 10), T. scalaris ( n = 52), T. scaliger ( n = 24), and T. sumichrasti ( n = 7). Images were captured using a Nikon Z50 digital camera equipped with an AF-S DX Micro-NIKKOR 40 mm f/2.8G lens. The camera was mounted on a fixed stand at a height of 15 cm. All photographs were taken from a top-down perspective under standardized lighting conditions, with a remote shutter release to prevent vibration. A millimeter grid paper served as the background for each shot to provide a scale for landmark digitization. Head shape variation was quantified using a bidimensional Cartesian coordinate system that included 22 landmarks and 24 semilandmarks for the dorsal view (Fig. S2 a), 17 landmarks and 30 semilandmarks for the lateral view (Fig. S2 b), and 14 landmarks for the ventral view (Fig. S2 c) (for a detailed description see Supplementary Data Table S3 ). This landmark configuration was selected based on its representativeness of shape, ease of identification, and homologous, unambiguous localization across MHLC species. Semilandmarks were defined as sliders using define.sliders function in geomorph package (Adams & Otárola-Castillo, 2013 ). Images for each view were assembled using the TpsUtil software (Rohlf, 2015 ), and landmarks were digitized with tpsDig2 software (Rohlf, 2006 ). Each landmark configuration underwent a generalized Procrustes analysis (GPA) to remove differences in scale, position, and orientation, preserving only variables related to shape (Rohlf & Slice, 1990) using the geomorph package (Adams & Otárola-Castillo, 2013 ). The three subsets ( i.e. , dorsal, lateral, and ventral) were then combined to capture total head shape variation. A second GPA was performed on this new configuration to scale all views to a single centroid size, following the procedure suggested by (Collyer et al., 2020 ). Before conducting any comparative analyses, a linear regression analysis was performed to assess whether head size serves as a significant predictor of head shape. For this purpose, head shape in each of its views ( i.e. , dorsal, lateral, and ventral) was used as the dependent variable, while the log-transformed size of their respective centroids was employed as the independent variable. Bootstrap resampling with 5000 permutations was used to estimate the significance of the regression parameters. If head size explains only a small proportion of the variation in head shape, this suggests that this factor has a limited influence on the morphological diversity observed in the MHLC species. A principal component analysis (PCA) was conducted using the mean Procrustes shape coordinates to visualize head morphology variation among MHLC species. Shape variation, as described by PC1 and PC2, was represented using deformation grids, while the main directions of shape change for each view along the PCA axes were visualized with vectors. Additionally, Procrustes mean coordinates and centroid size were calculated for each species, generating phylomorphospace plots that provide a detailed visualization of the relationship between morphology and phylogeny. Phylogenetic signal in head shape variation ( i.e ., Procrustes coordinates from combined datasets) was assessed using the geomorph package (Adams & Otárola-Castillo, 2013 ), based on the multivariate K statistic calculated from the previously generated phylogeny (Adams, 2014 ). A K value 1 indicates a stronger-than-expected phylogenetic signal. To test for the presence of phylogenetic signal ( i.e., K > 0), the species order in the phylogenetic tree was randomly permuted 10,000 times, and the K value was recalculated for each permutation. The observed K value was then compared to the distribution of K values generated under the null model. The hypothesis of phylogenetic signal was accepted if the observed K value was significantly different ( p < 0.05) from the null distribution. 2.6 Ecological traits data Habitat use data were obtained from the literature (Rossman et al., 1996; Heimes, 2016 ) and categorized into two groups: terrestrial ( i.e. , species that move and feed exclusively on land) and semiaquatic ( i.e. , species that move and feed in riverbeds or lentic/lotic water bodies). Additionally, the classification proposed by Heptinstall et al. ( 2024 ) was used to group MHLC species according to their trophic niche ( i.e. , specialist or generalist) and diet type ( i.e. , vertebrate or invertebrate). A phylogenetic generalized least squares (PGLS) analysis with 10000 permutations was performed using the caper package (Orme et al., 2013 ) to evaluate whether head size variation, based on centroid size, and head shape covaried with ecological factors such as habitat use, trophic niche, and diet. Independent models were developed for each explanatory variable to assess their predictive power on head shape and facilitate result interpretation. 2.6 Ancestral state reconstruction of ecological traits To reconstruct the evolutionary history of key ecological traits ( i.e. , trophic niche, habitat use, and diet) specifically within the MHLC, we pruned the time-calibrated phylogeny to include the MHLC species and five selected outgroup taxa ( Nerodia sipedon , Thamnophis rufipunctatus , T. cyrtopsis , T. marcianus , and T. hammondii ) for which ecological trait data were available in the literature (Rossman et al., 1996; Heimes, 2016 ; Heptinstall et al., 2024 ). Ancestral state reconstructions were performed on this pruned tree using the phytools R package (Revell, 2012 ). Three models of discrete character evolution, based on Markov chains, were evaluated: Equal Rates ( ER) , assumes the same probability of trait gain and loss. Symmetric ( SYM) , assumes equal forward and reverse transition rates, and All Rates Different ( ARD ), allows different rates for trait gain and loss. The best-fitting model was selected based on the AIC values. All statistical and comparative analyses were performed in the R environment v4.3.3 (R CoreTeam, 2024). The time-calibrated phylogeny generated in section 2.2 was used as the basis for niche evolution, PGLS, and ancestral state reconstruction analyses. 3 RESULTS 3.1 Phylogenetic framework, molecular dating, and biogeographic reconstruction The final matrix consisted of 2,553 bp, including 1,051 bp for Cytb , 773 bp for ND4, and 727 bp for DNAH3 . Maximum Likelihood (ML) analyses were conducted using the best-fitting substitution models for each partition: Cytb (HKY + F+G4), ND4 (TN + F+G4), and DNAH3 (TNe + G). The MHLC was recovered as a monophyletic group with high support, most phylogenetic nodes showed high robustness, with bootstrap support values greater than 80%. (Fig. 1 ). 3.1.1 Molecular dating Molecular clock analyses delineate two distinct temporal phases in the diversification of the MHLC (Fig. 2 ). The initial phase marks the clade's origin during the Late Miocene, dated at approximately 5.62 Mya (95% HPD: 3.23–8.31 Mya). This timeframe aligns with a principal period of intense volcanic activity and significant changes in landscape structure and elevation in the central region of the TMVB. The second and most intensive phase of speciation transpired considerably later, spanning the Middle to Late Pleistocene. This latter phase coincides with the final episode of TMVB formation, characterized by the emergence of large stratovolcanoes (> 3500 m) over the last 1.5 million years, some of which remain active. Divergence times for all extant species fall within this Pleistocene interval: T. errans at 1.29 Mya (95% HPD: 0.49–2.23), T. scaliger at 1.31 Mya (0.66–2.01), T. scalaris at 0.93 Mya (0.70–1.18), T. sumichrasti at 0.60 Mya (0.21–1.21), T. exsul at 0.53 Mya (0.04–1.28), T. bogerti at 0.43 Mya (0.13–0.75), T. conanti at 0.21 Mya (0.04–0.39), and T. godmani at 0.07 Mya (0.004–0.16). Consequently, while the MHLC originated during late Neogene orogeny, its contemporary species richness is temporal and spatially congruent with Pleistocene climatic fluctuations and the pronounced geological dynamism of the TMVB as detailed below. 3.1.2 Ancestral area reconstruction The ancestral area reconstruction, based on the best-fit model ( DIVALIKE + J ), indicates that the diversification of the MHLC appears to have been driven primarily by the formation of geographic barriers, leading to allopatric isolation, alongside colonization events that that promoted the formation of new lineages. This pattern is further supported by inferred vicariance events, predominantly associated with lowland valleys and basins that fragmented once-continuous distributions. A key event separated T. errans (SMOc) from T. exsul (SMOr), likely mediated by the lowlands of the Chihuahuan Desert province. Additional vicariance includes the separation between the two lineages of T. scalaris across the Mexico Valley Basin lowlands; the divergence between T. scaliger populations in the Chihuahuan and TMVB provinces; and the split within T. sumichrasti between populations north and south of the SMOr (Fig. 2 ). The analysis identifies the TMVB as the most probable ancestral area and the primary center of diversification for the MHLC (Fig. 2 ). From this central region, our results infer a pattern of predominantly unidirectional dispersal events: northwards into the SMOc and SMOr by the ancestor of T. errans and T. exsul ; eastward along the TMVB axis and the southern SMOr by the ancestors of T. scalaris and T. sumichrasti ; and southwards into the SMS by the ancestor of the T. conanti , T. bogerti , and T. godmani subclade (Fig. 2 ). 3.2 Climatic niche characterization and evolution After cleaning the original dataset following the previously mentioned criteria, 268 presence records were obtained for the eight species within the MHLC: T. bogerti ( n = 11), T. conanti ( n = 11), T. errans ( n = 26), T. exsul ( n = 17), T. godmani ( n = 16), T. scalaris ( n = 107), T. scaliger ( n = 63), and T. sumichrasti ( n = 18). These records were used in subsequent analyses. Of the 357 models generated for each species, the optimal models were selected based on the previously established statistical criteria: T. bogerti (Set1, RM = 0.4, FC = q, pROC = p < 0.01 , OR = 0.03, AICc = 244.181), T. conanti (Set1, RM = 0.4, FC = p, pROC = p < 0.01, OR = 0.0001, AICc = 247.897), T. errans (Set1, RM = 0.1, FC = lq, pROC = p < 0.01, OR = 0.0001, AICc = 609.052), T. exsul (Set3, RM = 0.3, FC = lq, pROC = p < 0.01, OR = 0.0001, AICc = 190.211), T. godmani (Set1, RM = 0.9, FC = p, pROC = p < 0.01, OR = 0.0001, AICc = 334.279), T. scalaris (Set1, RM = 0.7, FC = lqp, pROC = p < 0.01, OR = 0.037, AICc = 1877.40), T. scaliger (Set1, RM = 0.1, FC = lp, pROC = p < 0.01, OR = 0.0001, AICc = 1447.151), and T. sumichrasti (Set1, RM = 0.2, FC = lq, pROC = p < 0.01, OR = 0.0001, AICc = 380.567). All models yielded statistically significant values, indicating that the predictions generated for each species were robust enough for further analyses. The percentage contribution of climatic variables for each MHLC species is detailed in Supplementary Table S4 . Temperature was the primary factor influencing species distribution, contributing between 51.2% and 98.9% to the models, except for T. sumichrasti , where precipitation-related variables had the greatest influence (71.2%) (Supplementary Data Table S4 ). The current potential distribution of MHLC species exhibits a disjunct pattern, restricted to higher elevations (Fig. 3 ). This distribution is likely shaped by climatically unsuitable conditions in the surrounding lowlands, where warm, dry climates dominated by xeric vegetation limit habitat availability (Fig. 3 ). 3.2.1 Niche breadth, overlap, and similarity. Species within the MHLC are predominantly climatic specialists, exhibiting narrow niche breadths: T. bogerti ( I = 0.33), T. conanti ( I = 0.27), T. errans ( I = 0.41), T. exsul ( I = 0.18), T. godmani ( I = 0.40), T. scalaris ( I = 0.36), T. scaliger ( I = 0.24), and T. sumichrasti ( I = 0.20). Consequently, pairwise climatic niche overlap is remarkably low, with 92.2% of comparisons (26 of 28) showing null to minimal overlap ( Schoener's D < 0.2; Table 1 , Supplementary Data Table S5 ). Niche similarity tests indicate that this prevalent pattern is driven by climatic niche divergence (CND). In 92.2% of species pairs, niche similarity did not exceed random expectations ( p > 0.05), supporting the hypothesis that lineages have diverged into distinct climatic spaces. Table 1 Schoener D index ecological niche overlap values for MHLC species. Letter D indicates evidence of divergence in the climate niche in at least one of the paired comparisons, while letter C indicates evidence of climate niche conservatism Overlap Schoener´s D T. bogerti T. conanti T. errans T. exsul T. godmani T. scalaris T. scaliger T. sumichrasti T. bogerti 0.3274(D) 0.1001 (D) 0.0001 (D) 0.0593 (D) 0.0796 (D) 0.1042 (D) 0.1723 (D) T. conanti 0.1490 (D) 0.0001 (D) 0.1479 (D) 0.1227 (D) 0.1407 (D) 0.14851 (D) T. errans 0.1954 (C) 0.0425 (D) 0.0883 (D) 0.2759 (D) 0.0237 (D) T. exsul 0.0001 (D) 0.0597 (D) 0.0001 (D) 0.0049 (D) T. godmani 0.1706 (D) 0.1181 (D) 0.0670 (D) T. scalaris 0.0782 (D) 0.0508 (D) T. scaliger 0.0193 (D) T. sumichrasti In the two remaining pairwise comparisons (7.14%), which involve the sister species T. errans and T. exsul, niche overlap was low ( D = 0.19). Critically, niche similarity tests for this pair revealed evidence of phylogenetic climatic niche conservatism (PCNC) in at least one direction ( p < 0.05), indicating they have retained similar ancestral climatic preferences (Table 1 ). This finding contrasts with the general pattern within the clade and is notable given the species' current allopatric distributions in the Sierra Madre Occidental and Sierra Madre Oriental, respectively (Fig. 3 ). 3.2.2 Climatic niche evolution The best-fit evolutionary model for the data was the “ WN” model, indicating that climatic niche variation within the MHLC is independent of phylogenetic relationships. This result is consistent with the previously observed patterns of PCNC and CND. Notably, T. errans and T. exsul , which exhibited evidence of CND, showed a similar pattern across both principal components (Fig. 4 ). 3.3 Geometric morphometry The regression analysis of the lateral view did not reveal a significant dependence between size and shape ( F = 2.021, DF = 110, Z = 1.418, p = 0.062, r ²= 0.017). In contrast, the dorsal ( F = 19.876, DF = 110, Z = 3.766, p = 0.003, r² = 0.151) and ventral ( F = 4.453, DF = 110, Z = 2.825, p = 0.001, r² = 0.038) views showed a significant influence of size on shape. However, in both cases, size explains only approximately 15.19% and 3.86% of the variability in head shape, respectively. This suggests that size has a relatively limited effect on shape and that other factors not considered in this analysis, such as diet or microhabitat use (see below), might be better predictors of variation in these structures. The first two axes of the Principal Component Analysis (PCA) explained 89% of the total variation in mean head shape among MHLC species (PC1 = 67%, PC2 = 22%). The major axis of variation (PC1) described a morphological continuum associated with anteroposterior and dorsoventral elongation of the head and widening of the posterior maxilla (Fig. 5 ). Notably, this continuum separated species according to their habitat use (see below). PC2 reflected secondary shape changes related to the widening of the occipital region and anterior maxilla (Fig. 5 ). Analysis of the phylomorphospace encompassing total head shape variation revealed common patterns. Species occupying the same biogeographic province tended to cluster together, exhibiting similarities in head shape that appear independent of their phylogenetic relationships (Fig. 5 ). Additionally, semi-aquatic species formed a distinct cluster, separating from terrestrial species along PC1 (Fig. 5 ). The multivariate K analysis indicated a low phylogenetic signal in mean head shape variation among MHLC species ( Kmult = 0.318, p = 0.691), suggesting that head shape variation is primarily influenced by factors independent of phylogenetic relationships. Instead, other factors, such as habitat use, may play a more significant role in shaping head morphology in the MHLC (Fig. 5 ). 3.3.1 Ecological traits The phylomorphospace clustering pattern was consistent with the PGLS results (Fig. 5 ). The PGLS analyses revealed that habitat use had a significant positive covariation with head size ( F = 8.053, DF = 6, p = 0.0074, r² = 0.67). In contrast, neither trophic niche ( F = 0.3111, DF = 6, p = 0.5972, r² = 0.1092) nor diet ( F = 3.304, DF = 6, p = 0.119, r² = 0.2476) were significantly associated with this morphological variable. Regarding head shape, the analyses detected no significant associations with any of the ecological variables tested: habitat use ( F = 0.4893, DF = 6, p = 0.5423, r² = 0.07), trophic niche ( F = 0.1193, DF = 6, p = 0.8864, r² = 0.01), or diet ( F = 0.3204, DF = 6, p = 0.6638, r² = 0.05). Consistent with these results, species with semi-aquatic habits ( T. bogerti , T. conanti , T. sumichrasti , and T. godmani ) exhibited negative values along PC1, indicating a tendency toward larger heads characterized by a broader posterior region, greater dorsoventral depth, an expanded posterior maxilla, and larger parietal scales. Conversely, terrestrial species ( T. errans , T. exsul , T. scalaris , and T. scaliger ) occupied positive values along PC1, displaying relatively smaller and dorsoventrally narrower heads with a more anteroposteriorly elongated maxilla and smaller parietal scales (Fig. 5 ). 3.4 Ancestral state reconstruction of ecological traits The best-fitting evolutionary model for microhabitat use was the " ER " model, this suggests that colonizing a semi-aquatic niche was not inherently more difficult than abandoning it and returning to a fully terrestrial life within the MHCL. For trophic niche and diet, the " ARD " model was best-fitting, this implies that the likelihood of becoming a specialist or having an invertebrate-based diet is different and lower than the probability of becoming a generalist and feeding on vertebrates. The ancestral states reconstruction indicates a terrestrial habitat use as the most probable condition for the common ancestor of the MHLC and for the genus Thamnophis in general (Fig. 6 a). Within the MHLC, semi-aquatic habits evolved independently on two occasions: once in the clade comprising T. bogerti , T. godmani , and T. conanti , and a second time in T. sumichrasti . Regarding a vertebrate-based diet was reconstructed as the ancestral condition for the MHLC (Fig. 6 b), aligning with the predominant feeding habit reported for the genus, which primarily targets small vertebrates such as fish, anurans, and lizards. In contrast, a less common invertebrate-based diet evolved independently at least twice within the group, specifically in T. exsul and T. scaliger . Concurrently, a generalist trophic niche was inferred as the most likely ancestral state for the MHLC (Fig. 6 c), representing a distinct strategy from the more specialized piscivorous or anuran-based diets typical of many Thamnophis species. From this vertebrate generalist ancestor, a derived trophic specialization towards more typical Thamnophis diets appears to have evolved independently in T. scaliger , T. exsul which feed almost exclusively on annelids, and in T. sumichrasti , whose diet consists almost exclusively of anurans. 4 DISCUSSION Our integrative analysis supports the hypothesis that MHLC diversification resulted from ecological specialization and environmental heterogeneity of the Mexican highlands. Results suggest that the MHLC began to diversify in south-central Mexico during the Late Miocene, approximately 5.62 million years ago. These estimates align with previous Natricidae studies (Guo et al., 2012 ; McVay et al., 2015 ; Hallas et al., 2022 ). Although these studies did not focus specifically on the MHLC or include all eight species analyzed here, their consistent findings support the hypothesis that the orogenic and climatic changes of the Late Miocene in this region may have been a key factor in lineage diversification across our study group and multiple co-distributed taxa, as we detail below. By the Late Miocene, the main mountain ranges of Mexico ( i.e. , SMOc, SMOr, and SMS) had largely reached their present configuration (Ferrari et al., 2005 ; Ferrari et al., 2012 ). However, intense volcanic activity in the central-eastern region of the current TMVB was beginning to shape the first low-elevation volcanoes, restructuring local ecosystems through climatic and topographic changes (Mastretta-Yanes et al., 2015 ; Arce et al., 2019 ). Fossil evidence suggests that these environmental shifts were associated with the replacement of tropical affinity plant communities ( e.g., Cedrela and Terminalia ), with temperate affinity communities ( e.g., Pinus and Quercus ) (Castañeda-Posadas et al., 2009 ). The synchrony of these geological and climatic processes makes it difficult to estimate their individual effects, but their combined influence likely created a dynamic landscape of isolated high-elevation habitats and shifting connectivity, providing a favourable scenario for the MHLC radiation (Mastretta-Yanes et al., 2015 ). The Sky-Island Dynamic (SI-D) model provides a plausible conceptual framework to explain the Pleistocene diversification event observed in the MHLC. This model posits that glacial-interglacial cycles drove repeated expansions and contractions of temperate habitats (McCormack et al., 2009 ; Mastretta-Yanes et al., 2015 ; Körner, 2019). During cooler glacial periods, populations could disperse across now-inhospitable lowland corridors, connecting adjacent mountain ranges. Subsequent interglacial warming forced range contractions to higher elevations, isolating populations on mountain peaks and fostering allopatric divergence. An exemplary case of this process is provided by the pygmy rattlesnake Crotalus ravus , endemic to the TMVB highlands. Its phylogeographic history, reconstructed from genetic, genomic and paleoclimatic data, aligns with population structure patterns predicted under the SI-D (Cisneros-Bernal et al., 2022 ). Beyond this integrative case, the same biogeographic signature is recurrent among other Mexican highland endemic taxa from different lines of evidence, documented in reptiles like Phrynosoma orbiculare (Bryson et al., 2012 ), Barisia imbricata (Bryson & Riddle, 2012 ) and the Crotalus triseriatus complex (Bryson et al., 2011 c), and mammals such as Romerolagus diazi (Osuna et al., 2020 ) and Peromyscus hylocetes (León-Tapia et al., 2021 ). The recurrence of this pattern across independent lineages underscores that the SI-D model reflects a generalizable biogeographic process that has profoundly shaped the evolutionary history of the region's temperate biota, including the MHLC. Phylogenetic climatic niche conservatism (PCNC) appears to have promoted diversification primarily through vicariance supporting the previous approach. Pyron et al. ( 2015 ) suggest that PCNC can drive speciation when environmental changes isolate populations into refugia, reducing gene flow and enabling divergence through genetic drift. The sister species T. errans and T. exsul , which inhabit the SMOc and SMOr respectively, show significant niche similarity (Table 1 ) despite their current allopatry (Fig. 2 ). As Kozak and Wiens ( 2006 ) argue, disjunct montane distributions in sister lineages often reflect historical connectivity, with ancestors likely occupying lower elevations under more favorable climatic conditions. This scenario of vicariance driven by PCNC is analogous to that proposed for other taxa with disjunct distributions across these mountain ranges, such as Crotalus pricei (Bryson et al., 2011 b), where paleoclimatic models suggest that during glacial cycles, Quercus forests might have formed a dispersal corridor between the SMOc and SMOr. In contrast, climatic niche divergence (CND) was the predominant pattern across the MHLC and could be associated with inferred dispersal events from the TMVB to other mountain ranges (Fig. 2 , Table 1 ). The evolution of distinct climatic preferences in lineages such as T. scalaris , T. scaliger , T. sumichrasti , T. godmani , T. conanti and T. bogerti likely facilitated their colonization of novel environments in the TMVB, SMOr and SMS respectively. This link between CND and dispersal is consistent with the hypothesis that CND can enable lineages to exploit new ecological opportunities presented by heterogeneous landscapes (Pyron & Burbrink, 2009 ; Pyron et al., 2015 ). The results of Hidalgo-Licona et al. ( 2023 ) support this hypothesis, as their characterizing of the climatic niches of T. scalaris and T. scaliger suggest that climatic segregation and specialization in these species may have been shaped by climatic heterogeneity of the TMVB and climatic fluctuations of the Neogene-Quaternary. Both processes, PCNC and CND, may have contributed to in situ evolution, also potentially shaping the divergent patterns observed in the MHLC at the morphological level (Fig. 5 ). Simpson ( 1953 ) originally conceptualized this phenomenon under the ecological opportunity hypothesis, which posits that environmental shifts facilitate increased morphological variation and species diversification (Burbrink & Pyron, 2010 ; Deepak et al., 2023 ). The decoupling of morphology variation from phylogeny into de MHLC combined with its significant covariation with microhabitat use provides further evidence for diversification driven by ecological opportunity. Critically, the ancestral state reconstruction supports this interpretation by revealing that semiaquatic habits evolved independently at least twice within the MHLC (Fig. 6 a), concurrently with the evolution of the distinctive larger, dorsoventrally deeper head morphology observed in these lineages (Fig. 5 ). This pattern of convergence, also documented in other Natricidae snakes, particularly among semiaquatic and terrestrial species by Deepak et al. ( 2023 ), arguing that repeated evolution of functional adaptations for locomotion and prey capture across different environments results from adaptive responses to ecological pressures, rather than shared ancestry. Furthermore, the inference of a generalist vertebrate diet as the ancestral condition for the MHLC (Fig. 6 b, c), from which derived specializations ( e.g. , annelid-based diets in T. scaliger and T. exsul ) evolved independently, underscores a dynamic history of trophic niche partitioning that likely complemented divergence in habitat use. The evolutionary and biogeographic history of the MHLC, marked by SI-D, dispersal/ vicariance, niche conservatism/divergence, and ecomorphological variation, exemplifies a broader macroevolutionary pattern in the Mexican highlands. Similar signatures, have been documented across multiple lines of evidence ( e.g. , morphological, ecological, and molecular) in other Mexican montane taxa, include reptiles such as Pituophis (Bryson et al., 2011 a; Hidalgo-Licona et al., 2022 ), Crotalus (Cisneros-Bernal et al., 2022 ; Caballero-Viñas et al., 2025), Sceloporus (Leaché et al., 2013 ); mammals like Nelsonia (León-Tapia, 2021 ); and birds such as Arremon (Moreno-Contreras et al., 2020 ), and Aphelocoma (McCormack et al., 2010 ). The recurrence of this pattern across disparate vertebrate groups strongly suggests that the complex topography and environmental heterogeneity of the Mexican Highlands have been a key driver of biological diversification across multiple scales. However, it is important to recognize some cautions of this study. First, while the SI-D model provides a plausible explanation for the observed patterns of structuring and divergence, its validation requires more integrative phylogeographic approaches. Future studies incorporating genetic, genomic, morphological, and ecological data would enable more robust biogeographic and demographic reconstructions, clarifying how the documented patterns relate to the SI-D model, TMVB volcanism, and Neogene-Quaternary climatic oscillations Second, our ecomorphological inferences are based on correlations between morphology and microhabitat use, which, though suggestive, require direct biomechanical testing ( e.g. , locomotor performance, predatory efficiency) to confirm adaptive causality. Additionally, ancestral state reconstructions were limited by incomplete taxon sampling ( i.e. , the exclusion of T. lineri and T. ahumadai ) which may affect the inferred history of trait evolution. Moreover, while our discrete ecological categorizations ( i.e. , semi-aquatic vs. terrestrial) are based on available ecological information (Rossman et al., 1996; Holman, 2000 ), they may oversimplify continuous ecological variation and obscure gradual shifts in habitat use (Wiens et al., 2010 ). Third, it should be noted that some species were represented by few individuals after applying the selection criteria for the geometric morphometric analyses. This sampling limitation likely stems from the rarity of these taxa, which have restricted distributions and, consequently, a scarce representation in scientific collections. This scarcity also accounts for the low availability of molecular data for these species. Therefore, the results of the morphometric and molecular analyses for these taxa should be interpreted with caution, as they could be refined in future studies that incorporate a larger number of specimens. Finally, although our data alling with a pattern of rapid diversification through predominantly Pleistocene speciation in line with the estimates of McVay et al. ( 2015 ), several critical aspects remain unresolved. Specifically, our study lacks direct estimates of speciation and extinction rates and does not explicitly test whether the observed phenotypic variation reflects true adaptive radiation driven by ecological niche divergence. To rigorously test these evolutionary hypotheses, future studies should combine phylogenomic-scale datasets with high-resolution 3D geometric morphometrics and ecological niche modeling within a formal macroevolutionary framework. While Our results establish an important foundation for such work by revealing cryptic diversity patterns and their potential associations with Mexican highlands complex geography and environmental heterogeneity, a large-scale integrative approach would facilitate the quantification of morphological disparity, tests of evolutionary rate heterogeneity across traits, and explicit assessment of niche divergence patterns, ultimately providing deeper insights into the mechanisms underlying the MHLC exceptional cryptic diversity. 5 CONCLUSIONS The evolutionary history of the MHLC clade has been profoundly influenced by the interplay of climate, geography, and paleoenvironmental dynamics in the Mexican highlands. Our results indicate that the group’s diversification in south-central Mexico during the Late Miocene (~ 5.62 Ma) was spatiotemporally coupled with regional orographic evolution and volcanic activity, which drove the transition from tropical to temperate plant communities (Castañeda-Posadas et al., 2009 ; Mastretta-Yanes et al., 2015 ; Arce et al., 2019 ). These environmental shifts likely promoted the observed divergence patterns across multiple scales here analysed ( i.e. , morphological, ecological, and molecular). The MHLC, niche specialization and correlated variation in head morphology among species, corresponding to distinct habitat preferences, suggest that each lineage has evolved unique ecomorphological traits independently of their phylogenetic relationships. Such patterns mirror those documented in other regional endemics, underscoring the role of montane landscapes as key drivers of diversification in Mexican biota. Pleistocene structure and divergence events further align with the synergistic effects of volcanism and Neogene-Quaternary climate fluctuations, consistent with predictions of the Sky-Island Dynamic model. This framework proposes diversification through glacial-interglacial range expansions and contractions, a pattern corroborated by phylogeographic structure in other co-distributed montane taxa. The observed congruence suggests that the Mexican highlands may harbour greater undocumented diversity than currently recognized. Declarations Author Contribution LFHL and OFV initiated and designed the project. LFHL collected original data, performed analyses, and wrote the manuscript with help from OFV, AYCB and UOGV. OFV, AYCB and UOGV advised the theory and methodology. Acknowledgement We extend our sincere gratitude to the curators, technicians, and institutions that generously supported this work through the donation of tissue samples, access to specimens, and use of databases. Special thanks to Guadalupe Gutiérrez Mayen, Héctor Eliosa León, and Carlos Hernández Jiménez from the Facultad de Ciencias Biológicas at the Benemérita Universidad Autónoma de Puebla; Omar Hernández Ordóñez and Víctor Hugo Reynoso Rosales from the Colección Nacional de Anfibios y Reptiles (UNAM); Gustavo Campillo García from the Museo de Zoología of the Facultad de Ciencias (UNAM); Alejandro Carbajal Saucedo and David Lazcano Villareal from the Facultad de Ciencias Biológicas at the Universidad Autónoma de Nuevo León; Aníbal Helios Díaz de la Vega Pérez from the Centro Tlaxcala de Biología de la Conducta at the Universidad Autónoma de Tlaxcala; Irene Goyenechea Mayer Goyenechea and Norma Leticia Manríquez Moran from the Instituto de Ciencias Básicas e Ingeniería at the Universidad Autónoma del Estado de Hidalgo. We are also deeply grateful to María Eugenia Muñiz Díaz de León from the Departamento de Biología Comparada of the Facultad de Ciencias (UNAM) for providing the equipment used to quantify the quality and concentration of the DNA samples employed in this study. We thank Brett O. Butler for the translation and suggestions provided for this manuscript. We are deeply grateful to the following individuals who assisted us during fieldwork: Ricardo Palacios Aguilar, Gonzalo Medina Rangel, Mauricio Tepos-Ramírez, María Concepción Puga y Colmenares León, Irving Yahan Rojas-Velasco, Liliana Tovar, Carolina Jaramillo Alba, José Luis Jaramillo Alba, Vianey Heredia-Domínguez, Stephani Rendis Ceja, Cristóbal Torres-Velasco, Manuel García-Rosas, Aldo Dávalos, Florencia Edith Juárez Robles, and Leonardo Martínez. We also thank Luis Canseco Márquez, Leonardo Fernández Badillo, and Ricardo Palacios-Aguilar for providing the photographs included in Fig. 3 (T. conanti, T. sumichrasti, and T. godmani, respectively), as well as Carla Mariana Mendoza Licona for creating the illustrations used in Fig. 6 and 7. Luis F. Hidalgo-Licona (LFHL) and Antonio Y. Cisneros-Bernal (AYCB) would like to thank to the Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI) for their support through the scholarship (CVU: #850195 and # 669772). Additionally, we appreciate the funding provided by DGAPA-PAPIIT (grants IN227720 and IN200624).This article is part of the requirements for LFHL to obtain the Doctoral in Sciences degree at the Posgrado en Ciencias Biológicas, UNAM. Data Availability Genetic data are available in GenBank. The ID numbers of each sequence in GenBank are referred to in Table S1. References Adams, D. C. (2014). 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T., von Haeseler, A., & Minh, B. Q. (2016). W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic acids research , 44 (W1), W232–W235. https://doi.org/10.1093/nar/gkw256 Uetz, P., Freed, P., Aguilar, R., & Hošek, J. (2025). The Reptile Database. http://www.reptile-database.org (accessed 8/10/2025). Van Devender, T. R., Rea, A. M., & Smith, M. L. (1985). The Sangamon interglacial vertebrate fauna from Rancho la Brisca, Sonora, Mexico . San Diego Society of Natural History. Warren, D. L., Glor, R. E., & Turelli, M. (2008). Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution , 62 (11), 2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x Warren, D. L., Matzke, N. J., Cardillo, M., Baumgartner, J. B., Beaumont, L. J., Turelli, M., & Dinnage, R. (2021). ENMTools 1.0: an R package for comparative ecological biogeography. Ecography , 44 (4), 504–511. https://doi.org/10.1111/ecog.05485 Wiens, J. J. (2017). What explains patterns of biodiversity across the Tree of Life? New research is revealing the causes of the dramatic variation in species numbers across branches of the Tree of Life. Bioessays , 39 (3), 1600128. https://doi.org/10.1002/bies.201600128 Wiens, J. J., Ackerly, D. D., Allen, A. P., Anacker, B. L., Buckley, L. B., Cornell, H. V., & Stephens, P. R. (2010). Niche conservatism as an emerging principle in ecology and conservation biology. Ecology Letters , 13 (10), 1310–1324. https://doi.org/10.1111/j.1461-0248.2010.01515.x Wood, D. A., Vandergast, A. G., Espinal, L., Fisher, J. A., R. N., & Holycross, A. T. (2011). Refugial isolation and divergence in the Narrowheaded Gartersnake species complex ( Thamnophis rufipunctatus ) as revealed by multilocus DNA sequence data. Molecular Ecology , 20 (18), 3856–3878. https://doi.org/10.1111/j.1365-294X.2011.05211.x Yu, Y., Blair, C., & He, X. (2020). RASP 4: ancestral state reconstruction tool for multiple genes and characters. Molecular Biology and Evolution , 37 (2), 604–606. https://doi.org/10.1093/molbev/msz257 Yu, Y., Harris, A. J., Blair, C., & He, X. (2015). RASP (Reconstruct Ancestral State in Phylogenies): a tool for historical biogeography. Molecular Phylogenetics and Evolution , 87 , 46–49. https://doi.org/10.1016/j.ympev.2015.03.008 Additional Declarations No competing interests reported. Supplementary Files ApendixS1.docx TableS1.docx TableS2.docx TableS3.docx TableS4.docx FigureS2.docx FigureS1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 19 Mar, 2026 Editor assigned by journal 08 Mar, 2026 Submission checks completed at journal 08 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9023509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610602066,"identity":"7cabf5c9-3aec-433a-ae08-d94d62c3779d","order_by":0,"name":"Luis Fernando Hidalgo Licona","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBAC9gYwxcxgwM4IZsoBMeMBBoYEnFp4DsC0MEO0GIM4xGqBCCQ2ENTC3vvswc8d1nLmzMyNHz7usEvvl8g/cOBDRRoDf3s3Vn08PMfNDXvPpBtbNjM2S848k5w7c0Yyw8EZZ3IYJM6c3YBNi71EGpsEb9vhxA2HGduYeduYczfcSGY4zNtWwWAgkYtVC4/8MzbJvwgt9ekGBLVIsLFJI9lyOAGqJQe3Fp40NmnZtnRjg8Mgv7QdN5zZ89gA6Jc0Hlx+4WE/xib5ts1azuB4+8MPH9uq5fnZEx8++FCRLMff3otVC27AQ5ryUTAKRsEoGAXIAABICV+52s/+7QAAAABJRU5ErkJggg==","orcid":"","institution":"Posgrado en Ciencias Biológicas, Unidad de Posgrado, Edificio D, 1° Piso, Circuito de Posgrados, Ciudad Universitaria, Coyoacán, C.P. 04510, Ciudad de México, México.","correspondingAuthor":true,"prefix":"","firstName":"Luis","middleName":"Fernando Hidalgo","lastName":"Licona","suffix":""},{"id":610602069,"identity":"0208e4be-92d5-4d54-815f-42d9a5cdd914","order_by":1,"name":"Antonio Yolocalli Cisneros-Bernal","email":"","orcid":"","institution":"Posgrado en Ciencias Biológicas, Unidad de Posgrado, Edificio D, 1° Piso, Circuito de Posgrados, Ciudad Universitaria, Coyoacán, C.P. 04510, Ciudad de México, México.","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"Yolocalli","lastName":"Cisneros-Bernal","suffix":""},{"id":610602070,"identity":"0bd0854b-8737-4b56-816e-20bfb69406f3","order_by":2,"name":"Uri Omar García-Vázquez","email":"","orcid":"","institution":"UMIEZ, Facultad de Estudios Superiores Zaragoza, Universidad Nacional Autónoma de México, Batalla 5 de Mayo s/n, Ejército de Oriente, C.P. 09230, Ciudad de México, México.","correspondingAuthor":false,"prefix":"","firstName":"Uri","middleName":"Omar","lastName":"García-Vázquez","suffix":""},{"id":610602071,"identity":"f687c871-8828-46ab-8a93-705783a9ab72","order_by":3,"name":"Oscar Alberto Flores-Villela","email":"","orcid":"","institution":"Museo de Zoología “Alfonso L. Herrera”, Departamento de Biología Evolutiva, Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510, Ciudad de México, México.","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"Alberto","lastName":"Flores-Villela","suffix":""}],"badges":[],"createdAt":"2026-03-03 20:09:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9023509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9023509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566297,"identity":"637127b9-3b80-4435-976e-5c6941dedfa3","added_by":"auto","created_at":"2026-03-27 12:56:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4728964,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum Likelihood (ML) phylogenetic tree of the Mexican Highland Clade, inferred from concatenated sequences of two mitochondrial genes (\u003cem\u003eCytB \u003c/em\u003eand \u003cem\u003eND4\u003c/em\u003e) and one nuclear gene (\u003cem\u003eDNAH3\u003c/em\u003e). Bootstrap support values are indicated at the nodes.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/c35d2dcf3b9d8a80bb214766.jpeg"},{"id":105728160,"identity":"5088409d-e1df-4527-b5d4-aa967680fd55","added_by":"auto","created_at":"2026-03-30 11:10:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4792264,"visible":true,"origin":"","legend":"\u003cp\u003eAncestral range reconstruction analysis and divergence time estimation of the Mexican Highland Clade. Each branch is labeled with the most probable ancestral range, corresponding to the biogeographic provinces included in this study: the Transmexican Volcanic Belt (green), Sierra Madre Occidental (yellow), Sierra Madre Oriental (blue), and Sierra Madre del Sur (red). Dispersal events are indicated by orange triangles, and vicariance events are represented by purple circles. The estimated divergence times for each node are indicated in millions of years (Mya).\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/c945be4d8755cd7ace79be0c.jpeg"},{"id":105431606,"identity":"eac957a3-0d6e-4cb2-8123-b3eeacbef96e","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13482342,"visible":true,"origin":"","legend":"\u003cp\u003ePotential distribution maps for each species of the Mexican Highland Clade: \u003cem\u003eThamnophis bogerti\u003c/em\u003e (a), \u003cem\u003eThamnophis conanti\u003c/em\u003e (b), \u003cem\u003eThamnophis errans\u003c/em\u003e (c), \u003cem\u003eThamnophis exsul\u003c/em\u003e (d), \u003cem\u003eThamnophis godmani\u003c/em\u003e (e), \u003cem\u003eThamnophis scalaris\u003c/em\u003e (f), \u003cem\u003eThamnophis scaliger\u003c/em\u003e (g), and \u003cem\u003eThamnophis sumichrasti\u003c/em\u003e (h). Yellow squares represent occurrence records used to generate climatic niche models, blue circles denote specimens included in geometric morphometric analyses, and magenta triangles indicate individuals utilized in molecular analyses.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/9e32d523e49fe61832cc8d59.jpeg"},{"id":105431610,"identity":"f2a5c9e1-87a5-412f-8d41-89c6b460421b","added_by":"auto","created_at":"2026-03-26 02:34:05","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10110332,"visible":true,"origin":"","legend":"\u003cp\u003eClimatic niche evolution of the Mexican Highland Clade species in environmental space, analyzed using \u003cem\u003ePC-env\u003c/em\u003e values. Ancestral character estimates were projected onto the phylogeny under a White-Noise (\u003cem\u003eWN\u003c/em\u003e) model.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/2c61cb2446319c0f618e94bc.jpeg"},{"id":105431612,"identity":"634223f2-477c-4dde-8639-68f7f390106f","added_by":"auto","created_at":"2026-03-26 02:34:05","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4624895,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Phylomorphospace of the Mexican Highland Clade. The biogeographic province of each species is represented by circles: Transmexican Volcanic Belt (green), Sierra Madre Occidental (yellow), Sierra Madre Oriental (blue), and Sierra Madre del Sur (red). Microhabitat use is indicated by illustrations: a water body for semi-aquatic species and rocks for terrestrial species. Diet composition is symbolized by illustrations of a salamander for vertebrate-dominated diets and an earthworm for invertebrate-dominated diets. Deformation grids along each PC axis illustrate shape changes in dorsal, lateral, and ventral views, with vectors indicating the direction and magnitude of these changes. (b) Three-dimensional phylomorphospace incorporating PC1, PC2, and Time as the third axis\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/25089c4dbd272bdfac65ff82.jpeg"},{"id":105431608,"identity":"6307a330-2864-4d0b-8c43-6717a4a82cda","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":7379734,"visible":true,"origin":"","legend":"\u003cp\u003eAncestral state reconstruction of the Mexican Highland Clade for (a) microhabitat use, inferred under an Equal Rates (\u003cem\u003eER\u003c/em\u003e) model, and for (b) diet and (c) trophic niche, inferred under an All Rates Different (\u003cem\u003eARD\u003c/em\u003e) model\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/bbec90ddea1517dfaee5d25d.jpeg"},{"id":105731444,"identity":"686c969c-0803-41b3-beee-f2877ee0ffef","added_by":"auto","created_at":"2026-03-30 11:30:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":46544170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/3da26d0d-376c-46a8-9dc0-3ec692604a05.pdf"},{"id":105431601,"identity":"fbf0c99a-2200-42d0-b688-e0ea8dbf9a9a","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16153,"visible":true,"origin":"","legend":"","description":"","filename":"ApendixS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/7999d4108eacff7f741d48a4.docx"},{"id":105431602,"identity":"6a9e6a67-4f78-43f9-9285-64f0ec8dfb60","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38002,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/81d1229a9c450723b3f62407.docx"},{"id":105431603,"identity":"6a318a70-ee49-4611-8b99-155aeb6612ba","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33604,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/d177afc887f36d282cd98001.docx"},{"id":105431604,"identity":"e8860983-d43f-4069-8bfb-08e4e5d61c9d","added_by":"auto","created_at":"2026-03-26 02:34:04","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":30476,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/1cd129e194b6546316e49b8a.docx"},{"id":105565963,"identity":"ee952699-2c74-427c-98cd-c387d6d87c61","added_by":"auto","created_at":"2026-03-27 12:54:54","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20445,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/fa57e46d8a06023bf82a9e70.docx"},{"id":105565981,"identity":"95ab640e-073f-40c6-aba0-8343608561f4","added_by":"auto","created_at":"2026-03-27 12:54:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1967487,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/f9317370435c823c4d8fdcd1.docx"},{"id":105431611,"identity":"b7b7cfee-010a-4b53-9661-49dd9f8a4702","added_by":"auto","created_at":"2026-03-26 02:34:05","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6670810,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9023509/v1/06ddb906c348936747dfc7a5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSky-islands diversification: The case of the alpine garter snakes (Natricidae: Thamnophis), their evolutionary and biogeographic history in the Mexican highlands\u003c/p\u003e","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eMountainous regions comprise approximately 20% of the Earth's terrestrial surface, which host a substantial proportion of global biodiversity, particularly in tropical regions (i.e., between the Tropic of Cancer at 23\u0026deg; 26' 17'' N and the Tropic of Capricorn at 23\u0026deg; 26' 17'' S) (K\u0026ouml;rner, 2019). These regions, characterized by climatic diversity resulting from their complex topography, function as independent biogeographic units analogous to oceanic islands (McCormack et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K\u0026ouml;rner et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Because of this similarity, they are often called \u0026ldquo;Sky Islands\u0026rdquo;, as they are isolated by lowland areas unsuitable for species adapted to alpine environments (McCormack et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMountainous regions comprise approximately 20% of the Earth's terrestrial surface, which host a substantial proportion of global biodiversity, particularly in tropical regions (\u003cem\u003ei.e.\u003c/em\u003e, between the Tropic of Cancer at 23\u0026deg; 26' 17'' N and the Tropic of Capricorn at 23\u0026deg; 26' 17'' S) (K\u0026ouml;rner 2019). These regions, characterized by climatic diversity resulting from their complex topography, function as independent biogeographic units analogous to oceanic islands (McCormack et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; K\u0026ouml;rner et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Because of this similarity, they are often called \u0026ldquo;Sky Islands\u0026rdquo;, as they are isolated by lowland areas unsuitable for species adapted to alpine environments (McCormack et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Mexico, mountain systems dominate 85% of the territory. This topography, combined with the country's paleoenvironmental history and its transitional position between biogeographical regions, has fostered in situ lineage evolution at multiple scales (Arriaga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, Mexico is not only recognized as a global biodiversity hotspot where biota of both Nearctic and Neotropical affinities converge, but its highlands are also considered diversification centers for various reptile taxa, such as \u003cem\u003eCrotalus\u003c/em\u003e (Blair et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eBarisia\u003c/em\u003e (Bryson \u0026amp; Riddle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and \u003cem\u003ePlestiodon\u003c/em\u003e (Bryson et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For this reason, the Mexican highlands have been a key area for studying the biogeographical patterns and evolutionary processes of North American taxa (Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince diversification is a dynamic and complex process, it needs to be studied using an integrative and multidisciplinary approach. Wiens (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Li and Wiens (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) suggested that the simultaneous analysis of multiple traits (\u003cem\u003ee.g.\u003c/em\u003e, biogeographical, ecological, morphological and genetic) is essential to understand the patterns and mechanisms underlying species diversification. This has been evidenced in recent research that has explored these processes from ecological and molecular perspectives (Cisneros-Bernal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Le\u0026oacute;n-Tapia et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these studies offer valuable insights into the historical factors shaping diversification patterns in the Mexican highlands, the mechanisms driving divergence among ecologically and phylogenetically related lineages remain poorly understood.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThamnophis\u003c/em\u003e Fitzinger, 1843 is one of the most ecologically and phylogenetically diverse snake\u0026acute;s genera in North America, with approximately 38 recognized species (Uetz et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These species exhibit remarkable variation in their degree of trophic, climatic, and habitat specialization (Rossman et al., 1996). Phylogenetic hypotheses based on mitochondrial DNA (mtDNA) (de Queiroz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Gr\u0026uuml;nwald et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and genomic data (Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nu\u0026ntilde;ez et al., 2023) have revealed two geographically distinct clades: (1) a northern clade, consisting of ~\u0026thinsp;14 species distributed across southern Canada, the United States, and northern Mexico; and (2) a more diverse southern clade, consisting of ~\u0026thinsp;24 species distributed in central and southern Mexico and Central America. Within the southern clade, the Mexican Highland Clade (MHLC) includes ten temperate-adapted species (detailed below), which are primarily restricted to the major mountain ranges of Mexico (Rossman et al., 1996; Flores-Villela \u0026amp; Garc\u0026iacute;a-V\u0026aacute;zquez, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies suggest that the diversification center of \u003cem\u003eThamnophis\u003c/em\u003e was located in west-central Mexico during the Middle Miocene (\u003cem\u003eca.\u003c/em\u003e 15 Mya; de Queiroz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nu\u0026ntilde;ez et al., 2023). During this period, a rapid radiation likely occurred, leading to the evolution of diverse trophic and climatic niches, possibly driven by the heterogeneous Mexican landscape and the prevailing paleoclimatic conditions of the Neogene-Quaternary (N-Q) (Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nu\u0026ntilde;ez et al., 2023). This process likely gave rise to the current pattern of species and ecological diversity that characterizes the genus (McVay et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, some authors have documented a decline in diversification rates within Natricidae, a group that includes \u003cem\u003eThamnophis\u003c/em\u003e, potentially as a consequence of their rapid Miocene radiation and the subsequent saturation of available habitats (McVay et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This phenomenon may be linked to morphological stasis observed in both extant and extinct lineages of the genus (Holman, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Eldredge et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This contrasts with patterns documented in other montane taxa like the \u003cem\u003eSceloporus torquatus\u003c/em\u003e group (Campillo-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and \u003cem\u003ePituophis deppei\u003c/em\u003e (Bryson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003ea; Hidalgo-Licona et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which have been shown to exhibit substantial intraspecific diversity across molecular, morphological, and ecological scales.\u003c/p\u003e \u003cp\u003eDespite the findings discussed above, research exploring the ecological and biogeographic dynamics shaping the evolutionary history of \u003cem\u003eThamnophis\u003c/em\u003e remains limited (de Queiroz et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wood et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, a knowledge gap persists regarding the northern clade species distributed in Mexico, and even more so for the southern clade, despite the latter comprising approximately 80% of the total diversity of the genus (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Uetz et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, the MHLC is not only an ideal model for investigating the ecological, geographical and evolutionary processes driving \u003cem\u003eThamnophis\u003c/em\u003e diversity, but also a key indicator of the characteristic biota of the Mexican highlands.\u003c/p\u003e \u003cp\u003eOur study aims to reconstruct the evolutionary and biogeographic history of the MHLC by testing the overarching hypothesis that its diversification is the result of ecological specialization and divergence driven by the environmental heterogeneity of the Mexican highlands. If this hypothesis holds, each species should display distinctive ecomorphological traits independent of their phylogenetic relationships.\u003c/p\u003e \u003cp\u003eTo test this, we employ a multidisciplinary approach integrating and analyzing molecular, ecological, and morphological data. This approach addresses four specific questions: 1) How have geological, climatic, and geographic factors influenced diversification patterns within the MHLC? 2) What is the likely center of diversification of the MHLC, and how have the Mexican highlands influenced the clade\u0026rsquo;s evolutionary history? 3) What role has climatic niche conservatism and/or divergence played in its evolutionary history? and 4) Is there a phylogenetic signal in the MHLC ecomorphological traits, or does their variation reflect independent evolution associated with specific ecological conditions?\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study site\u003c/h2\u003e \u003cp\u003eThe Mexican highlands extend from the southern Rocky Mountains in the United States to northern Central America (Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Within Mexico, this region comprises seven mountain ranges that vary in orientation, age, and origin, with elevations ranging from 1,000 to 5,000 meters (Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Halffter et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most relevant mountain ranges for this study are: 1) Sierra Madre Occidental (SMOc), the largest mountain system in Mexico, extending approximately 1,200 km in a north-south direction. It features a temperate sub-humid climate and diverse vegetation, including pine forests, oak forests, and grasslands (Arriaga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Escalante et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); 2) Sierra Madre Oriental (SMOr), a discontinuous north to south mountain range, with elevations between 2,000 and 4,000 m. Its predominant vegetation consists of pine and oak forests, with a lesser presence of cloud forests (Arriaga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); 3) Trans-Mexican Volcanic Belt (TMVB), the highest mountain range in Mexico, with elevations ranging from 2,350 to 5,610 m. Located in the central Mexico and oriented east-west, it consists of volcanoes of various ages, with a geomorphological origin dating back to the Early Miocene (\u003cem\u003eca.\u003c/em\u003e 19 Mya) and continues to the present. The climate is predominantly temperate sub-humid, with pine and oak forests as the dominant vegetation (Arriaga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Halffter et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); 4) Sierra Madre del Sur (SMS), a discontinuous mountain range running parallel to the Pacific coast and the Gulf of Mexico, with elevation ranging from 1,800 to 3,750 m. Its slopes are primarily covered by pine and oak forests, with a lesser presence of cloud forests (Arriaga et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Espinosa et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Morrone, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Model system\u003c/h2\u003e \u003cp\u003eThe Mexican Highland Clade (MHLC) consists of ten small to medium-sized species, as defined by their Snout-Vent Length (SVL). These taxa are distinguished by their limited distributions and endemism to the mountain ranges of central-southern Mexico (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although \u003cem\u003eThamnophis lineri\u003c/em\u003e and the recently described \u003cem\u003eT. ahumadai\u003c/em\u003e (Gr\u0026uuml;nwald et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), are recognized as members of the MHLC, they were excluded from this study due to the lack of available specimens for geometric morphometric analyses and associated ecological data. Additionally, while Gr\u0026uuml;nwald et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggest that \u003cem\u003eT. conanti\u003c/em\u003e and \u003cem\u003eT. lineri\u003c/em\u003e should be considered a junior synonym of \u003cem\u003eT. bogerti\u003c/em\u003e, the primary focus of this work is not to evaluate the taxonomy of the group. Consequently, we have chosen to follow the phylogenetic hypothesis proposed by Hallas et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe species included in this study are as follows: 1) \u003cem\u003eThamnophis bogerti\u003c/em\u003e, a medium-sized (SVL: 600 mm) semi-aquatic species found in mesohabitats within pine and pine-oak forests of the SMS, at elevation ranging from 1300 to 2900 m (Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 2) \u003cem\u003eThamnophis conanti\u003c/em\u003e, a small-sized (SVL: 450 mm) semi-aquatic species inhabiting mesohabitats in pine and pine-oak forests in the southeastern TMVB and northern SMS, at elevations from 2100 to 2900 m (Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 3) \u003cem\u003eThamnophis errans\u003c/em\u003e, a medium-sized (SVL: 700 mm) terrestrial species restricted to pine and pine-oak forests along the SMOc, at elevations from 1860 to 2545 m (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 4) \u003cem\u003eThamnophis exsul\u003c/em\u003e, a small-sized (SVL: 400 mm) terrestrial species limited to pine and pine-oak forests, as well as grasslands, in the northern SMOr, at elevations between 2650 and 3237 m (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 5) \u003cem\u003eThamnophis godmani\u003c/em\u003e, a medium-sized species (SVL: 700 mm) semi-aquatic restricted to mesohabitats associated with pine and pine-oak forests in the western SMS, at elevations from 1700 to 2600 m (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 6) \u003cem\u003eThamnophis scalaris\u003c/em\u003e, a medium-sized (SVL: 700 mm) terrestrial species confined to mesohabitats in pine forests, pine-oak forests, and subalpine grasslands along the TMVB, at altitudes ranging from 2100 to 4273 m (Rossman et al., 1996; Rossman \u0026amp; Gongora, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); 7) \u003cem\u003eThamnophis scaliger\u003c/em\u003e, a medium-sized (SVL: 570 mm) terrestrial species limited to mesohabitats in pine forests, pine-oak forests, shrublands, and grasslands in the central TMVB and isolated regions of the central-southern Chihuahuan Province, at elevations from 2240 to 2720 m (Rossman et al., 1996; Rossman \u0026amp; Gongora, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and 8) \u003cem\u003eThamnophis sumichrasti\u003c/em\u003e, a medium-sized species (SVL: 756 mm) semi-aquatic species restricted to mesohabitats in pine, pine-oak, and cloud forests along the SMOr, at elevations between 1365 and 2400 m (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Phylogenetic framework, molecular dating, and biogeographic reconstruction\u003c/h2\u003e \u003cp\u003eTo reconstruct the evolutionary history of the MHLC and identify the spatiotemporal context and geographic drivers of its diversification (research questions 1 and 2), we collected a total of 67 tissue samples (\u003cem\u003ei.e.\u003c/em\u003e, liver, shed skins, and tail tips) from six of the eight MHLC species, sourced from both scientific collections and fieldwork to maximize representation across their distribution range. The sampled species included \u003cem\u003eT. bogerti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eT. conanti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eT. godmani\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), \u003cem\u003eT. scalaris\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;41), \u003cem\u003eT. scaliger\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12), and \u003cem\u003eT. sumichrasti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4). To complement the sampling, 25 additional sequences of the MHLC species were obtained from GenBank, along with sequences from other Natricidae species as outgroups (see Supplementary Data Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenomic DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen). DNA quality and concentration were assessed using an Epoch microplate spectrophotometer (BioTek, Winooski, VT, USA). Subsequently, fragments ranging from 728\u0026ndash;1051 base pairs (bp) of two mitochondrial genes, NADH dehydrogenase 4 (\u003cem\u003eND4\u003c/em\u003e) and Cytochrome B (\u003cem\u003eCytb\u003c/em\u003e), and one nuclear gene, Dynein Axonemal Heavy Chain 3 (\u003cem\u003eDNAH3\u003c/em\u003e), were sequenced using primers and PCR protocols described in Supplementary Data Appendix S1. The sequencing was performed at MacroGen.\u003c/p\u003e \u003cp\u003eForward and reverse sequences of the resulting electropherograms were assembled and aligned in Geneious Prime v11.0.4 (Kearse et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The final dataset comprised 83 individuals from the MHLC species and representing 17 species in total (see Supplementary Data Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sequences were aligned using MAFFT v.7 (Katoh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) under the E-INS-i strategy and manually edited in PhyDE v.0.9971(Muller \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEach gene was partitioned by codon position, and the best-fit partitions were estimated based on the Bayesian Information Criterion (BIC). The optimal model of sequence evolution for the Maximum Likelihood (ML) analysis was selected using PartitionFinder v.2.1.1 (Lanfear et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). ML analyses were conducted in IQ-TREE v 3.0.1 (Trifinopoulos et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the best tree and nodal support values were simultaneously estimated using the embedded ultraFast bootstrap approach (UFB) with 5,000 replicates. The resulting tree was visualized and edited in FIGTREE v.1.4.4 (Rambaut, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Molecular dating\u003c/h2\u003e \u003cp\u003eDivergence times among MHLC lineages were estimated using BEAST v2.7.7 (Bouckaert et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) with a concatenated dataset (\u003cem\u003eCytb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eND4\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eDNAH3\u003c/em\u003e) and an uncorrelated relaxed molecular clock for all loci. Nucleotide substitution models were based on PartitionFinder results, and a birth-death speciation process was applied, incorporating four fossil calibration points: C1, the most recent common ancestor (MRCA) of the North American Natricidae, is represented by the oldest fossil record of the genus \u003cem\u003eNerodia\u003c/em\u003e (lognormal mean\u0026thinsp;=\u0026thinsp;14 mya; standard deviation\u0026thinsp;=\u0026thinsp;0.6); C2, the MRCA of the genus \u003cem\u003eThamnophis\u003c/em\u003e, represented by the oldest fossil record of this genus (lognormal mean\u0026thinsp;=\u0026thinsp;14 mya; standard deviation\u0026thinsp;=\u0026thinsp;0.6); C3, the MRCA of the northern clade within \u003cem\u003eThamnophis\u003c/em\u003e, from a fossil record associated with \u003cem\u003eThamnophis cyrtopsis\u003c/em\u003e (lognormal mean\u0026thinsp;=\u0026thinsp;0.122 mya; standard deviation\u0026thinsp;=\u0026thinsp;0.2); and C4, the MRCA of the MHLC, is represented by a fossil record linked to \u003cem\u003eThamnophis scalaris\u003c/em\u003e (lognormal mean\u0026thinsp;=\u0026thinsp;0.014 mya; standard deviation\u0026thinsp;=\u0026thinsp;0.1 (\u0026Aacute;lvares \u0026amp; Huerta, 1975; Van Devender et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Holman, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The analysis was run for 100\u0026nbsp;million generations, sampling every 1,000 generations. Convergence was assessed in Tracer v1.5 (Rambaut \u0026amp; Drummond, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A maximum credibility consensus tree was generated using TreeAnnotator (Bouckaert et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) after discarding 25% as burn-in.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Ancestral area reconstruction\u003c/h2\u003e \u003cp\u003eTo infer the biogeographic history of the MHLC, we reconstructed ancestral areas using the six biogeographic models implemented in BioGeoBEARS (Matzke \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) via the RASP 4.2 interface (Yu et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The analysis was based on the time-calibrated phylogenetic tree and the biogeographic provinces proposed by Escalante et al., (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which comprises five areas: Sierra Madre del Sur (I), Sierra Madre Occidental (II), Sierra Madre Oriental (III), Trans-Mexican Volcanic Belt (IV), and Chihuahuan Desert (V). To avoid overparameterization, the maximum number of areas per node was constrained to two (Yu et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe models tested included Dispersal-Extinction-Cladogenesis (\u003cem\u003eDEC\u003c/em\u003e) this model assumes speciation is strictly allopatric, occurring either through the division of an ancestral range or via founder-event dispersal to an already connected area; Dispersal-Vicariance Analysis-like (\u003cem\u003eDIVALIKE\u003c/em\u003e) this model assumes sympatric speciation, where a lineage can diverge within its ancestral range without the need for geographic isolation; and Bayesian Analysis of Biogeography-like (\u003cem\u003eBAYAREALIKE\u003c/em\u003e) this model assumes speciation is always sympatric and all geographical range differentiation, resulting from dispersal and local extinction events, is presumed to occur after the lineage splitting event (Ree et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ree \u0026amp; Smith, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Each of these models was also run with the addition of the founder-event (\u003cem\u003e+\u0026thinsp;J\u003c/em\u003e) parameter. This parameter models the probability of a jump-dispersal event to a non-connected area being directly associated with a speciation event (Matzke, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Model selection was performed using the corrected Akaike Information Criterion (\u003cem\u003eAICc\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Climatic niche characterization and evolution\u003c/h2\u003e \u003cp\u003eTo characterize the climatic niche of each MHLC species and assess its role in driving biogeographic patterns and diversification processes (research question 3) we first obtained occurrence records for MHLC species from GBIF using the \u003cem\u003ergbif\u003c/em\u003e package (Chamberlain et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and complemented with data from the literature, field collections, and specimens deposited in scientific collections mentioned later. The preliminary dataset included 2,918 records distributed as follows: \u003cem\u003eT. bogerti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37), \u003cem\u003eT. conanti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;56), \u003cem\u003eT. errans\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;167), \u003cem\u003eT. exsul\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54), \u003cem\u003eT. godmani\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;480), \u003cem\u003eT. scalaris\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,280), \u003cem\u003eT. scaliger\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;533), and \u003cem\u003eT. sumichrasti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;301).\u003c/p\u003e \u003cp\u003eDuplicate, incomplete, and highly spatially correlated records (\u003cem\u003ei.e.\u003c/em\u003e, records with a separation\u0026thinsp;\u0026lt;\u0026thinsp;1 km) were removed. Records falling outside the known distribution of each species (Rossman et al., 1996) or exhibiting temporal mismatches with the bioclimatic variables (\u003cem\u003ei.e\u003c/em\u003e., 1979\u0026ndash;2013; (Karger et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were excluded. Additionally, records with atypical environmental values were discarded, based on the biological knowledge of MHLC species (\u003cem\u003ei.e\u003c/em\u003e., records outside the previously reported altitudinal range; Rossman et al.,1996).\u003c/p\u003e \u003cp\u003eFor training the MHLC climatic niche models, a species-specific \"\u003cem\u003eM\u003c/em\u003e\", defined as the region available to a species without dispersal barriers (Sober\u0026oacute;n \u0026amp; Nakamura, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), was bounded using the \u003cem\u003esp\u003c/em\u003e (Pebesma et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), \u003cem\u003eraster\u003c/em\u003e (Hijmans et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and \u003cem\u003ergdal\u003c/em\u003e (Bivand et al., 2015) packages. This delimitation was based on biological and geographic evidence to restrict the set of climatic conditions used in each model, a crucial step for model development and subsequent analyses (Barve et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Peterson, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Luna et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The delimitation of \u0026ldquo;\u003cem\u003eM\u003c/em\u003e\u0026rdquo; was guided by three criteria: 1) known distribution range of each species, 2) biogeographic provinces of Mexico (Escalante et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and their associated terrestrial ecosystems proposed by Olson et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and 3) a 5 km\u0026sup2; buffer around each occurrence record to account for potential individual dispersal, based on available information on other \u003cem\u003eThamnophis\u003c/em\u003e species (Gregory \u0026amp; Stewart, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Shonfield et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For further details, see Hidalgo-Licona et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo quantify the climatic niches of MHLC species, bioclimatic data from CHELSA v1.2b (Karger et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used. This data set includes 19 variables related to precipitation and temperature, collected between 1979 and 2013 at a spatial resolution of 30 arc-seconds (~\u0026thinsp;1 km\u0026sup2; per pixel). These variables were clipped according to the species-specific \"\u003cem\u003eM\u003c/em\u003e\" configuration.\u003c/p\u003e \u003cp\u003eSubsequently, three variable sets were constructed: Set1, designed to capture extreme climatic conditions using the same variables for all species: Bio1, Bio5, Bio6, Bio12, Bio13, and Bio14; Set2, selected using the Variance Inflation Factor (\u003cem\u003eVIF\u003c/em\u003e) using the \u003cem\u003eusdm\u003c/em\u003e package (Naimi \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), removing variables with high collinearity (\u003cem\u003ei.e., VIF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10) (Montgomery \u0026amp; Peck, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Naimi et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); and Set3, based on Pearson correlation values, excluding variables with \u003cem\u003er\u0026sup2;\u003c/em\u003e \u0026gt; 0.7 (Sober\u0026oacute;n \u0026amp; Nakamura, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing the \u003cem\u003ekuenm\u003c/em\u003e package (Cobos et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), 357 models were generated for each species. Regularization multiplier values were tested (ranging from 0.1 to 1 in increments of 0.1, followed by 2, 3, 4, 5, 6, 8, and 10) in combination with all possible linear, quadratic, and product feature classes (\u003cem\u003eFC\u003c/em\u003e) in Maxent 3.4.1 (Phillips et al., 2004). The objective was to identify the optimal configuration that minimized model over-parameterization.\u003c/p\u003e \u003cp\u003eModels were built using 75% of occurrence records for calibration and 25% for evaluation, with 10 bootstrap replicates, following the methodology of Phillips et al. (2004) and Cobos et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The final model selection was based on three criteria: 1) statistical significance of the lowest partial Receiver Operating Characteristic (\u003cem\u003epROC\u003c/em\u003e) values; 2) predictive power, indicated by low omission rates (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5%) (Cobos \u003cem\u003eet al.\u003c/em\u003e,2019); and 3) lowest \u003cem\u003eAICc\u003c/em\u003e values (Lobo et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Peterson et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cobos et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the geographical projection of models was binarized (0\u0026thinsp;=\u0026thinsp;unsuitable, 1\u0026thinsp;=\u0026thinsp;suitable) using the 10th percentile for minimum training presence, excluding the lowest 10% of values as they might represent erroneous records in the final dataset (Pearson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Niche breadth, overlap, and similarity.\u003c/h2\u003e \u003cp\u003eTo quantify the range of climatic conditions each species of the MHLC can tolerate, climatic niche breadth was measured using Levins \u003cem\u003eI\u003c/em\u003e Index (Levins, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1968\u003c/span\u003e) in the \u003cem\u003eenmtools\u003c/em\u003e package (Warren et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This index produces values between 0 and 1, where values near 0 indicate low breadth (\u003cem\u003ei.e.\u003c/em\u003e, specialist species), and values near 1 indicate high breadth (\u003cem\u003ei.e.\u003c/em\u003e, generalist species) (Carscadden et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimatic niche overlap between species was evaluated using \u003cem\u003eSchoener\u0026rsquo;s D\u003c/em\u003e index (Schoener, 1968) with the \u003cem\u003ePCA-env\u003c/em\u003e approach (Broennimann et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in the \u003cem\u003eecospat\u003c/em\u003e package (Di Cola et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Interpretation followed the metric proposed by R\u0026ouml;dder and Engler (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) null or minimal overlap (0\u0026ndash;0.2), low (0.2\u0026ndash;0.4), moderate (0.4\u0026ndash;0.6), high (0.6\u0026ndash;0.8), and very high (0.8\u0026ndash;1.0).\u003c/p\u003e \u003cp\u003eTo evaluate the presence of phylogenetic climatic niche conservatism (PCNC) the tendency to retain ancestral ecological traits (Pyron et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or climatic niche divergence (CND), which involves the acquisition of novel ecological traits distinct from the ancestral condition (Pyron \u0026amp; Burbrink, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), a niche similarity test was performed using the \u003cem\u003eecospat\u003c/em\u003e package (Di Cola et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This test aimed to verify the presence of PCNC by determining whether niche overlap between two species was greater than expected by chance. The hypothesis was accepted if observed overlap (\u003cem\u003eSchoener\u0026rsquo;s D\u003c/em\u003e) was significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from niche overlap values obtained through pseudoreplicates. The test was repeated 1,000 times for each comparison to ensure the null hypothesis was rejected with high confidence (Warren et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Broennimann et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given the unidirectional nature of the similarity test, two tests were conducted for each comparison (\u003cem\u003ei.e.\u003c/em\u003e, Sp1 vs. Sp2 and Sp2 vs. Sp1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Climatic niche evolution\u003c/h2\u003e \u003cp\u003eClimatic niche evolution among MHLC lineages was analyzed in relation to their phylogenetic relationships using the \u003cem\u003ephytools\u003c/em\u003e R package (Revell, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The weighted mean of the \u003cem\u003ePC-env\u003c/em\u003e values for each species was calculated, and the phylogenetic tree obtained in this study was pruned to include only the eight MHLC species, using the \u003cem\u003eape\u003c/em\u003e package (Paradis et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The mode of climatic niche evolution was assessed by testing five evolutionary models using the \u003cem\u003eGeiger\u003c/em\u003e package (Harmon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e): 1) Brownian Motion (\u003cem\u003eBM\u003c/em\u003e): random evolution of traits (Felsenstein, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1985\u003c/span\u003e); 2) White Noise (\u003cem\u003eWN\u003c/em\u003e): evolution independent of phylogenetic relationships (Butler \u0026amp; King, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); 3) Single Peak (\u003cem\u003eSP\u003c/em\u003e): evolution constrained to a single adaptive peak (Butler \u0026amp; King, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); 4) Early Burst (\u003cem\u003eEB\u003c/em\u003e): evolutionary rates that decline exponentially over time (Harmon et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); and 5) Kappa: trait divergence linked to speciation events (Harmon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The optimal model was selected by \u003cem\u003ecomparing AICc values\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Geometric morphometry\u003c/h2\u003e \u003cp\u003eTo evaluate whether morphological variation is shaped more by phylogeny or ecological factors (research question 4), we analyzed head shape in relation to habitat use and diet.\u003c/p\u003e \u003cp\u003eA total of 113 specimens from the eight MHLC species were photographed, obtained from scientific collections and fieldwork to ensure adequate representation of their distributional range (see Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Each specimen was photographed in dorsal, lateral, and ventral views, considering only adult individuals (SVL\u0026thinsp;\u0026gt;\u0026thinsp;280 mm) to minimize ontogenetic variation. Specimens in poor condition were also excluded to prevent artificial distortions in head shape. The number of photographed specimens per species was as follows: \u003cem\u003eT. bogerti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eT. conanti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), \u003cem\u003eT. errans\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), \u003cem\u003eT. exsul\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), \u003cem\u003eT. godmani\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), \u003cem\u003eT. scalaris\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52), \u003cem\u003eT. scaliger\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;24), and T. \u003cem\u003esumichrasti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e \u003cp\u003eImages were captured using a Nikon Z50 digital camera equipped with an AF-S DX Micro-NIKKOR 40 mm f/2.8G lens. The camera was mounted on a fixed stand at a height of 15 cm. All photographs were taken from a top-down perspective under standardized lighting conditions, with a remote shutter release to prevent vibration. A millimeter grid paper served as the background for each shot to provide a scale for landmark digitization.\u003c/p\u003e \u003cp\u003eHead shape variation was quantified using a bidimensional Cartesian coordinate system that included 22 landmarks and 24 semilandmarks for the dorsal view (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ea), 17 landmarks and 30 semilandmarks for the lateral view (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eb), and 14 landmarks for the ventral view (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003ec) (for a detailed description see Supplementary Data Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). This landmark configuration was selected based on its representativeness of shape, ease of identification, and homologous, unambiguous localization across MHLC species. Semilandmarks were defined as sliders using \u003cem\u003edefine.sliders\u003c/em\u003e function in \u003cem\u003egeomorph\u003c/em\u003e package (Adams \u0026amp; Ot\u0026aacute;rola-Castillo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImages for each view were assembled using the \u003cem\u003eTpsUtil\u003c/em\u003e software (Rohlf, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and landmarks were digitized with \u003cem\u003etpsDig2\u003c/em\u003e software (Rohlf, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Each landmark configuration underwent a generalized Procrustes analysis (GPA) to remove differences in scale, position, and orientation, preserving only variables related to shape (Rohlf \u0026amp; Slice, 1990) using the \u003cem\u003egeomorph\u003c/em\u003e package (Adams \u0026amp; Ot\u0026aacute;rola-Castillo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The three subsets (\u003cem\u003ei.e.\u003c/em\u003e, dorsal, lateral, and ventral) were then combined to capture total head shape variation. A second GPA was performed on this new configuration to scale all views to a single centroid size, following the procedure suggested by (Collyer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore conducting any comparative analyses, a linear regression analysis was performed to assess whether head size serves as a significant predictor of head shape. For this purpose, head shape in each of its views (\u003cem\u003ei.e.\u003c/em\u003e, dorsal, lateral, and ventral) was used as the dependent variable, while the log-transformed size of their respective centroids was employed as the independent variable. Bootstrap resampling with 5000 permutations was used to estimate the significance of the regression parameters. If head size explains only a small proportion of the variation in head shape, this suggests that this factor has a limited influence on the morphological diversity observed in the MHLC species.\u003c/p\u003e \u003cp\u003eA principal component analysis (PCA) was conducted using the mean Procrustes shape coordinates to visualize head morphology variation among MHLC species. Shape variation, as described by PC1 and PC2, was represented using deformation grids, while the main directions of shape change for each view along the PCA axes were visualized with vectors. Additionally, Procrustes mean coordinates and centroid size were calculated for each species, generating phylomorphospace plots that provide a detailed visualization of the relationship between morphology and phylogeny.\u003c/p\u003e \u003cp\u003ePhylogenetic signal in head shape variation (\u003cem\u003ei.e\u003c/em\u003e., Procrustes coordinates from combined datasets) was assessed using the \u003cem\u003egeomorph\u003c/em\u003e package (Adams \u0026amp; Ot\u0026aacute;rola-Castillo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), based on the multivariate \u003cem\u003eK\u003c/em\u003e statistic calculated from the previously generated phylogeny (Adams, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A \u003cem\u003eK\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;1 indicates weak or no phylogenetic signal, \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 suggests a signal consistent with a Brownian motion model, and \u003cem\u003eK\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1 indicates a stronger-than-expected phylogenetic signal. To test for the presence of phylogenetic signal (\u003cem\u003ei.e., K\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0), the species order in the phylogenetic tree was randomly permuted 10,000 times, and the \u003cem\u003eK\u003c/em\u003e value was recalculated for each permutation. The observed \u003cem\u003eK\u003c/em\u003e value was then compared to the distribution of \u003cem\u003eK\u003c/em\u003e values generated under the null model. The hypothesis of phylogenetic signal was accepted if the observed \u003cem\u003eK\u003c/em\u003e value was significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the null distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ecological traits data\u003c/h2\u003e \u003cp\u003eHabitat use data were obtained from the literature (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and categorized into two groups: terrestrial (\u003cem\u003ei.e.\u003c/em\u003e, species that move and feed exclusively on land) and semiaquatic (\u003cem\u003ei.e.\u003c/em\u003e, species that move and feed in riverbeds or lentic/lotic water bodies). Additionally, the classification proposed by Heptinstall et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was used to group MHLC species according to their trophic niche (\u003cem\u003ei.e.\u003c/em\u003e, specialist or generalist) and diet type (\u003cem\u003ei.e.\u003c/em\u003e, vertebrate or invertebrate).\u003c/p\u003e \u003cp\u003eA phylogenetic generalized least squares (PGLS) analysis with 10000 permutations was performed using the \u003cem\u003ecaper\u003c/em\u003e package (Orme et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to evaluate whether head size variation, based on centroid size, and head shape covaried with ecological factors such as habitat use, trophic niche, and diet. Independent models were developed for each explanatory variable to assess their predictive power on head shape and facilitate result interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ancestral state reconstruction of ecological traits\u003c/h2\u003e \u003cp\u003eTo reconstruct the evolutionary history of key ecological traits (\u003cem\u003ei.e.\u003c/em\u003e, trophic niche, habitat use, and diet) specifically within the MHLC, we pruned the time-calibrated phylogeny to include the MHLC species and five selected outgroup taxa (\u003cem\u003eNerodia sipedon\u003c/em\u003e, \u003cem\u003eThamnophis rufipunctatus\u003c/em\u003e, \u003cem\u003eT. cyrtopsis\u003c/em\u003e, \u003cem\u003eT. marcianus\u003c/em\u003e, and \u003cem\u003eT. hammondii\u003c/em\u003e) for which ecological trait data were available in the literature (Rossman et al., 1996; Heimes, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Heptinstall et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAncestral state reconstructions were performed on this pruned tree using the \u003cem\u003ephytools\u003c/em\u003e R package (Revell, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Three models of discrete character evolution, based on Markov chains, were evaluated: Equal Rates (\u003cem\u003eER)\u003c/em\u003e, assumes the same probability of trait gain and loss. Symmetric (\u003cem\u003eSYM)\u003c/em\u003e, assumes equal forward and reverse transition rates, and All Rates Different (\u003cem\u003eARD\u003c/em\u003e), allows different rates for trait gain and loss. The best-fitting model was selected based on the \u003cem\u003eAIC\u003c/em\u003e values.\u003c/p\u003e \u003cp\u003eAll statistical and comparative analyses were performed in the R environment v4.3.3 (R CoreTeam, 2024). The time-calibrated phylogeny generated in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e was used as the basis for niche evolution, PGLS, and ancestral state reconstruction analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Phylogenetic framework, molecular dating, and biogeographic reconstruction\u003c/h2\u003e \u003cp\u003eThe final matrix consisted of 2,553 bp, including 1,051 bp for \u003cem\u003eCytb\u003c/em\u003e, 773 bp for ND4, and 727 bp for \u003cem\u003eDNAH3\u003c/em\u003e. Maximum Likelihood (ML) analyses were conducted using the best-fitting substitution models for each partition: \u003cem\u003eCytb\u003c/em\u003e (HKY\u0026thinsp;+\u0026thinsp;F+G4), \u003cem\u003eND4\u003c/em\u003e (TN\u0026thinsp;+\u0026thinsp;F+G4), and \u003cem\u003eDNAH3\u003c/em\u003e (TNe\u0026thinsp;+\u0026thinsp;G). The MHLC was recovered as a monophyletic group with high support, most phylogenetic nodes showed high robustness, with bootstrap support values greater than 80%. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Molecular dating\u003c/h2\u003e \u003cp\u003eMolecular clock analyses delineate two distinct temporal phases in the diversification of the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The initial phase marks the clade's origin during the Late Miocene, dated at approximately 5.62 Mya (95% HPD: 3.23\u0026ndash;8.31 Mya). This timeframe aligns with a principal period of intense volcanic activity and significant changes in landscape structure and elevation in the central region of the TMVB. The second and most intensive phase of speciation transpired considerably later, spanning the Middle to Late Pleistocene. This latter phase coincides with the final episode of TMVB formation, characterized by the emergence of large stratovolcanoes (\u0026gt;\u0026thinsp;3500 m) over the last 1.5\u0026nbsp;million years, some of which remain active. Divergence times for all extant species fall within this Pleistocene interval: \u003cem\u003eT. errans\u003c/em\u003e at 1.29 Mya (95% HPD: 0.49\u0026ndash;2.23), \u003cem\u003eT. scaliger\u003c/em\u003e at 1.31 Mya (0.66\u0026ndash;2.01), \u003cem\u003eT. scalaris\u003c/em\u003e at 0.93 Mya (0.70\u0026ndash;1.18), \u003cem\u003eT. sumichrasti\u003c/em\u003e at 0.60 Mya (0.21\u0026ndash;1.21), \u003cem\u003eT. exsul\u003c/em\u003e at 0.53 Mya (0.04\u0026ndash;1.28), \u003cem\u003eT. bogerti\u003c/em\u003e at 0.43 Mya (0.13\u0026ndash;0.75), \u003cem\u003eT. conanti\u003c/em\u003e at 0.21 Mya (0.04\u0026ndash;0.39), and \u003cem\u003eT. godmani\u003c/em\u003e at 0.07 Mya (0.004\u0026ndash;0.16). Consequently, while the MHLC originated during late Neogene orogeny, its contemporary species richness is temporal and spatially congruent with Pleistocene climatic fluctuations and the pronounced geological dynamism of the TMVB as detailed below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Ancestral area reconstruction\u003c/h2\u003e \u003cp\u003eThe ancestral area reconstruction, based on the best-fit model (\u003cem\u003eDIVALIKE\u0026thinsp;+\u0026thinsp;J\u003c/em\u003e), indicates that the diversification of the MHLC appears to have been driven primarily by the formation of geographic barriers, leading to allopatric isolation, alongside colonization events that that promoted the formation of new lineages. This pattern is further supported by inferred vicariance events, predominantly associated with lowland valleys and basins that fragmented once-continuous distributions. A key event separated \u003cem\u003eT. errans\u003c/em\u003e (SMOc) from \u003cem\u003eT. exsul\u003c/em\u003e (SMOr), likely mediated by the lowlands of the Chihuahuan Desert province. Additional vicariance includes the separation between the two lineages of \u003cem\u003eT. scalaris\u003c/em\u003e across the Mexico Valley Basin lowlands; the divergence between \u003cem\u003eT. scaliger\u003c/em\u003e populations in the Chihuahuan and TMVB provinces; and the split within \u003cem\u003eT. sumichrasti\u003c/em\u003e between populations north and south of the SMOr (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analysis identifies the TMVB as the most probable ancestral area and the primary center of diversification for the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). From this central region, our results infer a pattern of predominantly unidirectional dispersal events: northwards into the SMOc and SMOr by the ancestor of \u003cem\u003eT. errans\u003c/em\u003e and \u003cem\u003eT. exsul\u003c/em\u003e; eastward along the TMVB axis and the southern SMOr by the ancestors of \u003cem\u003eT. scalaris\u003c/em\u003e and \u003cem\u003eT. sumichrasti\u003c/em\u003e; and southwards into the SMS by the ancestor of the \u003cem\u003eT. conanti\u003c/em\u003e, \u003cem\u003eT. bogerti\u003c/em\u003e, and \u003cem\u003eT. godmani\u003c/em\u003e subclade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Climatic niche characterization and evolution\u003c/h2\u003e \u003cp\u003eAfter cleaning the original dataset following the previously mentioned criteria, 268 presence records were obtained for the eight species within the MHLC: \u003cem\u003eT. bogerti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eT. conanti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11), \u003cem\u003eT. errans\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26), \u003cem\u003eT. exsul\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17), \u003cem\u003eT. godmani\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16), \u003cem\u003eT. scalaris\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;107), \u003cem\u003eT. scaliger\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63), and \u003cem\u003eT. sumichrasti\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18). These records were used in subsequent analyses. Of the 357 models generated for each species, the optimal models were selected based on the previously established statistical criteria: \u003cem\u003eT. bogerti\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;q, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;244.181), \u003cem\u003eT. conanti\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;p, \u003cem\u003epROC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;247.897), \u003cem\u003eT. errans\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;lq, pROC\u0026thinsp;=\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;609.052), \u003cem\u003eT. exsul\u003c/em\u003e (Set3, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;lq, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;190.211), \u003cem\u003eT. godmani\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;p, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;334.279), \u003cem\u003eT. scalaris\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;lqp, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1877.40), \u003cem\u003eT. scaliger\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;lp, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1447.151), and \u003cem\u003eT. sumichrasti\u003c/em\u003e (Set1, \u003cem\u003eRM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2, \u003cem\u003eFC\u003c/em\u003e\u0026thinsp;=\u0026thinsp;lq, \u003cem\u003epROC\u0026thinsp;=\u0026thinsp;p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, \u003cem\u003eAICc\u003c/em\u003e\u0026thinsp;=\u0026thinsp;380.567). All models yielded statistically significant values, indicating that the predictions generated for each species were robust enough for further analyses.\u003c/p\u003e \u003cp\u003eThe percentage contribution of climatic variables for each MHLC species is detailed in Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. Temperature was the primary factor influencing species distribution, contributing between 51.2% and 98.9% to the models, except for \u003cem\u003eT. sumichrasti\u003c/em\u003e, where precipitation-related variables had the greatest influence (71.2%) (Supplementary Data Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current potential distribution of MHLC species exhibits a disjunct pattern, restricted to higher elevations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This distribution is likely shaped by climatically unsuitable conditions in the surrounding lowlands, where warm, dry climates dominated by xeric vegetation limit habitat availability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Niche breadth, overlap, and similarity.\u003c/h2\u003e \u003cp\u003eSpecies within the MHLC are predominantly climatic specialists, exhibiting narrow niche breadths: \u003cem\u003eT. bogerti\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.33), \u003cem\u003eT. conanti\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27), \u003cem\u003eT. errans\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.41), \u003cem\u003eT. exsul\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18), \u003cem\u003eT. godmani\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40), \u003cem\u003eT. scalaris\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.36), \u003cem\u003eT. scaliger\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24), and \u003cem\u003eT. sumichrasti\u003c/em\u003e (\u003cem\u003eI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20).\u003c/p\u003e \u003cp\u003eConsequently, pairwise climatic niche overlap is remarkably low, with 92.2% of comparisons (26 of 28) showing null to minimal overlap (\u003cem\u003eSchoener's D\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.2; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Data Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Niche similarity tests indicate that this prevalent pattern is driven by climatic niche divergence (CND). In 92.2% of species pairs, niche similarity did not exceed random expectations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), supporting the hypothesis that lineages have diverged into distinct climatic spaces.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSchoener D\u003c/em\u003e index ecological niche overlap values for MHLC species. Letter \u003cem\u003eD\u003c/em\u003e indicates evidence of divergence in the climate niche in at least one of the paired comparisons, while letter C indicates evidence of climate niche conservatism\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOverlap\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSchoener\u0026acute;s D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eT. bogerti\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT. conanti\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eT. errans\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eT. exsul\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eT. godmani\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eT. scalaris\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eT. scaliger\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eT. sumichrasti\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. bogerti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3274(D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1001 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0593 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0796 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1042 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1723 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. conanti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1490 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1479 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1227 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1407 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14851 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. errans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1954 (C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0425 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0883 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2759 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0237 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. exsul\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0001 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0597 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0049 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. godmani\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1706 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1181 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0670 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. scalaris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0782 (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0508 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. scaliger\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0193 (D)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. sumichrasti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the two remaining pairwise comparisons (7.14%), which involve the sister species \u003cem\u003eT. errans\u003c/em\u003e and T. exsul, niche overlap was low (\u003cem\u003eD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19). Critically, niche similarity tests for this pair revealed evidence of phylogenetic climatic niche conservatism (PCNC) in at least one direction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating they have retained similar ancestral climatic preferences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This finding contrasts with the general pattern within the clade and is notable given the species' current allopatric distributions in the Sierra Madre Occidental and Sierra Madre Oriental, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Climatic niche evolution\u003c/h2\u003e \u003cp\u003eThe best-fit evolutionary model for the data was the \u0026ldquo;\u003cem\u003eWN\u0026rdquo;\u003c/em\u003e model, indicating that climatic niche variation within the MHLC is independent of phylogenetic relationships. This result is consistent with the previously observed patterns of PCNC and CND. Notably, \u003cem\u003eT. errans\u003c/em\u003e and \u003cem\u003eT. exsul\u003c/em\u003e, which exhibited evidence of CND, showed a similar pattern across both principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Geometric morphometry\u003c/h2\u003e \u003cp\u003eThe regression analysis of the lateral view did not reveal a significant dependence between size and shape (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.021, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;110, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.418, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062, \u003cem\u003er\u003c/em\u003e\u0026sup2;= 0.017). In contrast, the dorsal (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.876, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;110, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.766, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003er\u0026sup2;\u003c/em\u003e= 0.151) and ventral (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.453, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;110, \u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.825, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003er\u0026sup2;\u003c/em\u003e= 0.038) views showed a significant influence of size on shape. However, in both cases, size explains only approximately 15.19% and 3.86% of the variability in head shape, respectively. This suggests that size has a relatively limited effect on shape and that other factors not considered in this analysis, such as diet or microhabitat use (see below), might be better predictors of variation in these structures.\u003c/p\u003e \u003cp\u003eThe first two axes of the Principal Component Analysis (PCA) explained 89% of the total variation in mean head shape among MHLC species (PC1\u0026thinsp;=\u0026thinsp;67%, PC2\u0026thinsp;=\u0026thinsp;22%). The major axis of variation (PC1) described a morphological continuum associated with anteroposterior and dorsoventral elongation of the head and widening of the posterior maxilla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Notably, this continuum separated species according to their habitat use (see below). PC2 reflected secondary shape changes related to the widening of the occipital region and anterior maxilla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the phylomorphospace encompassing total head shape variation revealed common patterns. Species occupying the same biogeographic province tended to cluster together, exhibiting similarities in head shape that appear independent of their phylogenetic relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, semi-aquatic species formed a distinct cluster, separating from terrestrial species along PC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe multivariate \u003cem\u003eK\u003c/em\u003e analysis indicated a low phylogenetic signal in mean head shape variation among MHLC species (\u003cem\u003eKmult\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.318, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.691), suggesting that head shape variation is primarily influenced by factors independent of phylogenetic relationships. Instead, other factors, such as habitat use, may play a more significant role in shaping head morphology in the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Ecological traits\u003c/h2\u003e \u003cp\u003eThe phylomorphospace clustering pattern was consistent with the PGLS results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The PGLS analyses revealed that habitat use had a significant positive covariation with head size (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.053, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0074, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.67). In contrast, neither trophic niche (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3111, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5972, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.1092) nor diet (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.304, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.119, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.2476) were significantly associated with this morphological variable. Regarding head shape, the analyses detected no significant associations with any of the ecological variables tested: habitat use (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4893, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5423, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.07), trophic niche (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1193, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8864, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.01), or diet (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3204, \u003cem\u003eDF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6638, \u003cem\u003er\u0026sup2;\u003c/em\u003e = 0.05). Consistent with these results, species with semi-aquatic habits (\u003cem\u003eT. bogerti\u003c/em\u003e, \u003cem\u003eT. conanti\u003c/em\u003e, \u003cem\u003eT. sumichrasti\u003c/em\u003e, and \u003cem\u003eT. godmani\u003c/em\u003e) exhibited negative values along PC1, indicating a tendency toward larger heads characterized by a broader posterior region, greater dorsoventral depth, an expanded posterior maxilla, and larger parietal scales. Conversely, terrestrial species (\u003cem\u003eT. errans\u003c/em\u003e, \u003cem\u003eT. exsul\u003c/em\u003e, \u003cem\u003eT. scalaris\u003c/em\u003e, and \u003cem\u003eT. scaliger\u003c/em\u003e) occupied positive values along PC1, displaying relatively smaller and dorsoventrally narrower heads with a more anteroposteriorly elongated maxilla and smaller parietal scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Ancestral state reconstruction of ecological traits\u003c/h2\u003e \u003cp\u003eThe best-fitting evolutionary model for microhabitat use was the \"\u003cem\u003eER\u003c/em\u003e\" model, this suggests that colonizing a semi-aquatic niche was not inherently more difficult than abandoning it and returning to a fully terrestrial life within the MHCL. For trophic niche and diet, the \"\u003cem\u003eARD\u003c/em\u003e\" model was best-fitting, this implies that the likelihood of becoming a specialist or having an invertebrate-based diet is different and lower than the probability of becoming a generalist and feeding on vertebrates.\u003c/p\u003e \u003cp\u003eThe ancestral states reconstruction indicates a terrestrial habitat use as the most probable condition for the common ancestor of the MHLC and for the genus \u003cem\u003eThamnophis\u003c/em\u003e in general (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Within the MHLC, semi-aquatic habits evolved independently on two occasions: once in the clade comprising \u003cem\u003eT. bogerti\u003c/em\u003e, \u003cem\u003eT. godmani\u003c/em\u003e, and \u003cem\u003eT. conanti\u003c/em\u003e, and a second time in \u003cem\u003eT. sumichrasti\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding a vertebrate-based diet was reconstructed as the ancestral condition for the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), aligning with the predominant feeding habit reported for the genus, which primarily targets small vertebrates such as fish, anurans, and lizards. In contrast, a less common invertebrate-based diet evolved independently at least twice within the group, specifically in \u003cem\u003eT. exsul\u003c/em\u003e and \u003cem\u003eT. scaliger\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eConcurrently, a generalist trophic niche was inferred as the most likely ancestral state for the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), representing a distinct strategy from the more specialized piscivorous or anuran-based diets typical of many \u003cem\u003eThamnophis\u003c/em\u003e species. From this vertebrate generalist ancestor, a derived trophic specialization towards more typical \u003cem\u003eThamnophis\u003c/em\u003e diets appears to have evolved independently in \u003cem\u003eT. scaliger\u003c/em\u003e, \u003cem\u003eT. exsul\u003c/em\u003e which feed almost exclusively on annelids, and in \u003cem\u003eT. sumichrasti\u003c/em\u003e, whose diet consists almost exclusively of anurans.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eOur integrative analysis supports the hypothesis that MHLC diversification resulted from ecological specialization and environmental heterogeneity of the Mexican highlands. Results suggest that the MHLC began to diversify in south-central Mexico during the Late Miocene, approximately 5.62\u0026nbsp;million years ago. These estimates align with previous Natricidae studies (Guo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; McVay et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hallas et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although these studies did not focus specifically on the MHLC or include all eight species analyzed here, their consistent findings support the hypothesis that the orogenic and climatic changes of the Late Miocene in this region may have been a key factor in lineage diversification across our study group and multiple co-distributed taxa, as we detail below.\u003c/p\u003e \u003cp\u003eBy the Late Miocene, the main mountain ranges of Mexico (\u003cem\u003ei.e.\u003c/em\u003e, SMOc, SMOr, and SMS) had largely reached their present configuration (Ferrari et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ferrari et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, intense volcanic activity in the central-eastern region of the current TMVB was beginning to shape the first low-elevation volcanoes, restructuring local ecosystems through climatic and topographic changes (Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arce et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fossil evidence suggests that these environmental shifts were associated with the replacement of tropical affinity plant communities (\u003cem\u003ee.g., Cedrela\u003c/em\u003e and \u003cem\u003eTerminalia\u003c/em\u003e), with temperate affinity communities (\u003cem\u003ee.g., Pinus\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e) (Casta\u0026ntilde;eda-Posadas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The synchrony of these geological and climatic processes makes it difficult to estimate their individual effects, but their combined influence likely created a dynamic landscape of isolated high-elevation habitats and shifting connectivity, providing a favourable scenario for the MHLC radiation (Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Sky-Island Dynamic (SI-D) model provides a plausible conceptual framework to explain the Pleistocene diversification event observed in the MHLC. This model posits that glacial-interglacial cycles drove repeated expansions and contractions of temperate habitats (McCormack et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; K\u0026ouml;rner, 2019). During cooler glacial periods, populations could disperse across now-inhospitable lowland corridors, connecting adjacent mountain ranges. Subsequent interglacial warming forced range contractions to higher elevations, isolating populations on mountain peaks and fostering allopatric divergence. An exemplary case of this process is provided by the pygmy rattlesnake \u003cem\u003eCrotalus ravus\u003c/em\u003e, endemic to the TMVB highlands. Its phylogeographic history, reconstructed from genetic, genomic and paleoclimatic data, aligns with population structure patterns predicted under the SI-D (Cisneros-Bernal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond this integrative case, the same biogeographic signature is recurrent among other Mexican highland endemic taxa from different lines of evidence, documented in reptiles like \u003cem\u003ePhrynosoma orbiculare\u003c/em\u003e (Bryson et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), \u003cem\u003eBarisia imbricata\u003c/em\u003e (Bryson \u0026amp; Riddle, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the \u003cem\u003eCrotalus triseriatus\u003c/em\u003e complex (Bryson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003ec), and mammals such as \u003cem\u003eRomerolagus diazi\u003c/em\u003e (Osuna et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and \u003cem\u003ePeromyscus hylocetes\u003c/em\u003e (Le\u0026oacute;n-Tapia et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The recurrence of this pattern across independent lineages underscores that the SI-D model reflects a generalizable biogeographic process that has profoundly shaped the evolutionary history of the region's temperate biota, including the MHLC.\u003c/p\u003e \u003cp\u003ePhylogenetic climatic niche conservatism (PCNC) appears to have promoted diversification primarily through vicariance supporting the previous approach. Pyron et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) suggest that PCNC can drive speciation when environmental changes isolate populations into refugia, reducing gene flow and enabling divergence through genetic drift. The sister species \u003cem\u003eT. errans\u003c/em\u003e and \u003cem\u003eT. exsul\u003c/em\u003e, which inhabit the SMOc and SMOr respectively, show significant niche similarity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) despite their current allopatry (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As Kozak and Wiens (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) argue, disjunct montane distributions in sister lineages often reflect historical connectivity, with ancestors likely occupying lower elevations under more favorable climatic conditions. This scenario of vicariance driven by PCNC is analogous to that proposed for other taxa with disjunct distributions across these mountain ranges, such as \u003cem\u003eCrotalus pricei\u003c/em\u003e (Bryson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003eb), where paleoclimatic models suggest that during glacial cycles, \u003cem\u003eQuercus\u003c/em\u003e forests might have formed a dispersal corridor between the SMOc and SMOr.\u003c/p\u003e \u003cp\u003eIn contrast, climatic niche divergence (CND) was the predominant pattern across the MHLC and could be associated with inferred dispersal events from the TMVB to other mountain ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The evolution of distinct climatic preferences in lineages such as \u003cem\u003eT. scalaris\u003c/em\u003e, \u003cem\u003eT. scaliger\u003c/em\u003e, \u003cem\u003eT. sumichrasti\u003c/em\u003e, \u003cem\u003eT. godmani\u003c/em\u003e, \u003cem\u003eT. conanti\u003c/em\u003e and \u003cem\u003eT. bogerti\u003c/em\u003e likely facilitated their colonization of novel environments in the TMVB, SMOr and SMS respectively. This link between CND and dispersal is consistent with the hypothesis that CND can enable lineages to exploit new ecological opportunities presented by heterogeneous landscapes (Pyron \u0026amp; Burbrink, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pyron et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The results of Hidalgo-Licona et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) support this hypothesis, as their characterizing of the climatic niches of \u003cem\u003eT. scalaris\u003c/em\u003e and \u003cem\u003eT. scaliger\u003c/em\u003e suggest that climatic segregation and specialization in these species may have been shaped by climatic heterogeneity of the TMVB and climatic fluctuations of the Neogene-Quaternary.\u003c/p\u003e \u003cp\u003eBoth processes, PCNC and CND, may have contributed to \u003cem\u003ein situ\u003c/em\u003e evolution, also potentially shaping the divergent patterns observed in the MHLC at the morphological level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Simpson (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e1953\u003c/span\u003e) originally conceptualized this phenomenon under the ecological opportunity hypothesis, which posits that environmental shifts facilitate increased morphological variation and species diversification (Burbrink \u0026amp; Pyron, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Deepak et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe decoupling of morphology variation from phylogeny into de MHLC combined with its significant covariation with microhabitat use provides further evidence for diversification driven by ecological opportunity. Critically, the ancestral state reconstruction supports this interpretation by revealing that semiaquatic habits evolved independently at least twice within the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), concurrently with the evolution of the distinctive larger, dorsoventrally deeper head morphology observed in these lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This pattern of convergence, also documented in other Natricidae snakes, particularly among semiaquatic and terrestrial species by Deepak et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), arguing that repeated evolution of functional adaptations for locomotion and prey capture across different environments results from adaptive responses to ecological pressures, rather than shared ancestry. Furthermore, the inference of a generalist vertebrate diet as the ancestral condition for the MHLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, c), from which derived specializations (\u003cem\u003ee.g.\u003c/em\u003e, annelid-based diets in \u003cem\u003eT. scaliger\u003c/em\u003e and \u003cem\u003eT. exsul\u003c/em\u003e) evolved independently, underscores a dynamic history of trophic niche partitioning that likely complemented divergence in habitat use.\u003c/p\u003e \u003cp\u003eThe evolutionary and biogeographic history of the MHLC, marked by SI-D, dispersal/ vicariance, niche conservatism/divergence, and ecomorphological variation, exemplifies a broader macroevolutionary pattern in the Mexican highlands. Similar signatures, have been documented across multiple lines of evidence (\u003cem\u003ee.g.\u003c/em\u003e, morphological, ecological, and molecular) in other Mexican montane taxa, include reptiles such as \u003cem\u003ePituophis\u003c/em\u003e (Bryson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003ea; Hidalgo-Licona et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), \u003cem\u003eCrotalus\u003c/em\u003e (Cisneros-Bernal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Caballero-Vi\u0026ntilde;as et al., 2025), \u003cem\u003eSceloporus\u003c/em\u003e (Leach\u0026eacute; et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); mammals like \u003cem\u003eNelsonia\u003c/em\u003e (Le\u0026oacute;n-Tapia, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and birds such as \u003cem\u003eArremon\u003c/em\u003e (Moreno-Contreras et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and \u003cem\u003eAphelocoma\u003c/em\u003e (McCormack et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The recurrence of this pattern across disparate vertebrate groups strongly suggests that the complex topography and environmental heterogeneity of the Mexican Highlands have been a key driver of biological diversification across multiple scales.\u003c/p\u003e \u003cp\u003eHowever, it is important to recognize some cautions of this study. First, while the SI-D model provides a plausible explanation for the observed patterns of structuring and divergence, its validation requires more integrative phylogeographic approaches. Future studies incorporating genetic, genomic, morphological, and ecological data would enable more robust biogeographic and demographic reconstructions, clarifying how the documented patterns relate to the SI-D model, TMVB volcanism, and Neogene-Quaternary climatic oscillations\u003c/p\u003e \u003cp\u003eSecond, our ecomorphological inferences are based on correlations between morphology and microhabitat use, which, though suggestive, require direct biomechanical testing (\u003cem\u003ee.g.\u003c/em\u003e, locomotor performance, predatory efficiency) to confirm adaptive causality. Additionally, ancestral state reconstructions were limited by incomplete taxon sampling (\u003cem\u003ei.e.\u003c/em\u003e, the exclusion of \u003cem\u003eT. lineri\u003c/em\u003e and \u003cem\u003eT. ahumadai\u003c/em\u003e) which may affect the inferred history of trait evolution. Moreover, while our discrete ecological categorizations (\u003cem\u003ei.e.\u003c/em\u003e, semi-aquatic vs. terrestrial) are based on available ecological information (Rossman et al., 1996; Holman, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), they may oversimplify continuous ecological variation and obscure gradual shifts in habitat use (Wiens et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, it should be noted that some species were represented by few individuals after applying the selection criteria for the geometric morphometric analyses. This sampling limitation likely stems from the rarity of these taxa, which have restricted distributions and, consequently, a scarce representation in scientific collections. This scarcity also accounts for the low availability of molecular data for these species. Therefore, the results of the morphometric and molecular analyses for these taxa should be interpreted with caution, as they could be refined in future studies that incorporate a larger number of specimens.\u003c/p\u003e \u003cp\u003eFinally, although our data alling with a pattern of rapid diversification through predominantly Pleistocene speciation in line with the estimates of McVay et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), several critical aspects remain unresolved. Specifically, our study lacks direct estimates of speciation and extinction rates and does not explicitly test whether the observed phenotypic variation reflects true adaptive radiation driven by ecological niche divergence. To rigorously test these evolutionary hypotheses, future studies should combine phylogenomic-scale datasets with high-resolution 3D geometric morphometrics and ecological niche modeling within a formal macroevolutionary framework. While Our results establish an important foundation for such work by revealing cryptic diversity patterns and their potential associations with Mexican highlands complex geography and environmental heterogeneity, a large-scale integrative approach would facilitate the quantification of morphological disparity, tests of evolutionary rate heterogeneity across traits, and explicit assessment of niche divergence patterns, ultimately providing deeper insights into the mechanisms underlying the MHLC exceptional cryptic diversity.\u003c/p\u003e"},{"header":"5 CONCLUSIONS","content":"\u003cp\u003eThe evolutionary history of the MHLC clade has been profoundly influenced by the interplay of climate, geography, and paleoenvironmental dynamics in the Mexican highlands. Our results indicate that the group\u0026rsquo;s diversification in south-central Mexico during the Late Miocene (~\u0026thinsp;5.62 Ma) was spatiotemporally coupled with regional orographic evolution and volcanic activity, which drove the transition from tropical to temperate plant communities (Casta\u0026ntilde;eda-Posadas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mastretta-Yanes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arce et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These environmental shifts likely promoted the observed divergence patterns across multiple scales here analysed (\u003cem\u003ei.e.\u003c/em\u003e, morphological, ecological, and molecular). The MHLC, niche specialization and correlated variation in head morphology among species, corresponding to distinct habitat preferences, suggest that each lineage has evolved unique ecomorphological traits independently of their phylogenetic relationships. Such patterns mirror those documented in other regional endemics, underscoring the role of montane landscapes as key drivers of diversification in Mexican biota.\u003c/p\u003e \u003cp\u003ePleistocene structure and divergence events further align with the synergistic effects of volcanism and Neogene-Quaternary climate fluctuations, consistent with predictions of the Sky-Island Dynamic model. This framework proposes diversification through glacial-interglacial range expansions and contractions, a pattern corroborated by phylogeographic structure in other co-distributed montane taxa. The observed congruence suggests that the Mexican highlands may harbour greater undocumented diversity than currently recognized.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLFHL and OFV initiated and designed the project. LFHL collected original data, performed analyses, and wrote the manuscript with help from OFV, AYCB and UOGV. OFV, AYCB and UOGV advised the theory and methodology.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe extend our sincere gratitude to the curators, technicians, and institutions that generously supported this work through the donation of tissue samples, access to specimens, and use of databases. Special thanks to Guadalupe Guti\u0026eacute;rrez Mayen, H\u0026eacute;ctor Eliosa Le\u0026oacute;n, and Carlos Hern\u0026aacute;ndez Jim\u0026eacute;nez from the Facultad de Ciencias Biol\u0026oacute;gicas at the Benem\u0026eacute;rita Universidad Aut\u0026oacute;noma de Puebla; Omar Hern\u0026aacute;ndez Ord\u0026oacute;\u0026ntilde;ez and V\u0026iacute;ctor Hugo Reynoso Rosales from the Colecci\u0026oacute;n Nacional de Anfibios y Reptiles (UNAM); Gustavo Campillo Garc\u0026iacute;a from the Museo de Zoolog\u0026iacute;a of the Facultad de Ciencias (UNAM); Alejandro Carbajal Saucedo and David Lazcano Villareal from the Facultad de Ciencias Biol\u0026oacute;gicas at the Universidad Aut\u0026oacute;noma de Nuevo Le\u0026oacute;n; An\u0026iacute;bal Helios D\u0026iacute;az de la Vega P\u0026eacute;rez from the Centro Tlaxcala de Biolog\u0026iacute;a de la Conducta at the Universidad Aut\u0026oacute;noma de Tlaxcala; Irene Goyenechea Mayer Goyenechea and Norma Leticia Manr\u0026iacute;quez Moran from the Instituto de Ciencias B\u0026aacute;sicas e Ingenier\u0026iacute;a at the Universidad Aut\u0026oacute;noma del Estado de Hidalgo. We are also deeply grateful to Mar\u0026iacute;a Eugenia Mu\u0026ntilde;iz D\u0026iacute;az de Le\u0026oacute;n from the Departamento de Biolog\u0026iacute;a Comparada of the Facultad de Ciencias (UNAM) for providing the equipment used to quantify the quality and concentration of the DNA samples employed in this study. We thank Brett O. Butler for the translation and suggestions provided for this manuscript. We are deeply grateful to the following individuals who assisted us during fieldwork: Ricardo Palacios Aguilar, Gonzalo Medina Rangel, Mauricio Tepos-Ram\u0026iacute;rez, Mar\u0026iacute;a Concepci\u0026oacute;n Puga y Colmenares Le\u0026oacute;n, Irving Yahan Rojas-Velasco, Liliana Tovar, Carolina Jaramillo Alba, Jos\u0026eacute; Luis Jaramillo Alba, Vianey Heredia-Dom\u0026iacute;nguez, Stephani Rendis Ceja, Crist\u0026oacute;bal Torres-Velasco, Manuel Garc\u0026iacute;a-Rosas, Aldo D\u0026aacute;valos, Florencia Edith Ju\u0026aacute;rez Robles, and Leonardo Mart\u0026iacute;nez. We also thank Luis Canseco M\u0026aacute;rquez, Leonardo Fern\u0026aacute;ndez Badillo, and Ricardo Palacios-Aguilar for providing the photographs included in Fig. 3 (T. conanti, T. sumichrasti, and T. godmani, respectively), as well as Carla Mariana Mendoza Licona for creating the illustrations used in Fig. 6 and 7. Luis F. Hidalgo-Licona (LFHL) and Antonio Y. Cisneros-Bernal (AYCB) would like to thank to the Secretar\u0026iacute;a de Ciencia, Humanidades, Tecnolog\u0026iacute;a e Innovaci\u0026oacute;n (SECIHTI) for their support through the scholarship (CVU: #850195 and # 669772). Additionally, we appreciate the funding provided by DGAPA-PAPIIT (grants IN227720 and IN200624).This article is part of the requirements for LFHL to obtain the Doctoral in Sciences degree at the Posgrado en Ciencias Biol\u0026oacute;gicas, UNAM.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGenetic data are available in GenBank. The ID numbers of each sequence in GenBank are referred to in Table S1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams, D. C. (2014). 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RASP (Reconstruct Ancestral State in Phylogenies): a tool for historical biogeography. \u003cem\u003eMolecular Phylogenetics and Evolution\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 46\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ympev.2015.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.ympev.2015.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"evolutionary-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evol","sideBox":"Learn more about [Evolutionary Biology](http://link.springer.com/journal/11692)","snPcode":"11692","submissionUrl":"https://submission.nature.com/new-submission/11692/3","title":"Evolutionary Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"endemism, ecological niche modelling, geometric morphometry, trans-mexican volcanic belt, ecomorphology, mountains, divergence, phylogenetic signal, phylomorphospace","lastPublishedDoi":"10.21203/rs.3.rs-9023509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9023509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Mexican highlands, a biodiversity hotspot characterized by complex topography and dynamic paleoenvironments, function as \"Sky Islands,\" isolating temperate-adapted species and promoting in situ diversification. This pattern is exemplified by the Mexican Highland Clade (MHLC) of Thamnophis, a genus exhibiting high ecological and phylogenetic diversity in North America. We hypothesize that MHLC diversification is the result of ecological specialization and divergence driven by the environmental heterogeneity of the Mexican highlands. Through an integrative approach, combining phylogenetics, morphological analyses, and ecological niche modelling, we reconstruct the clade’s evolutionary and biogeographic history to identify key drivers of diversification. Our results indicate that MHLC diversification was driven by the geologic and climatic heterogeneity of the highlands, with initial divergence in the Late Miocene (~5.62 Ma) consistent with Trans-Mexican Volcanic Belt activity. These patterns align with other highland-endemic taxa, suggesting shared biogeographic processes. Niche specialization and correlated shifts in head morphology suggest possible adaptive responses to habitat variation, potentially reflecting ecomorphological convergence independent of phylogenetic constraints. These findings highlight the Mexican highlands as a crucial area for evolutionary processes in North America, containing significant undocumented biodiversity and confirming their importance as a global endemism hotspot.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Sky-islands diversification: The case of the alpine garter snakes (Natricidae: Thamnophis), their evolutionary and biogeographic history in the Mexican highlands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 02:33:59","doi":"10.21203/rs.3.rs-9023509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"82411334495941885027872535807885102064","date":"2026-05-02T13:33:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196432080782093861276491284524519012159","date":"2026-05-01T21:24:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T21:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338501008343676877833376922799291873836","date":"2026-03-30T15:40:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-19T11:20:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T02:23:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T02:23:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Biology","date":"2026-03-03T19:55:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evol","sideBox":"Learn more about [Evolutionary Biology](http://link.springer.com/journal/11692)","snPcode":"11692","submissionUrl":"https://submission.nature.com/new-submission/11692/3","title":"Evolutionary Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"52be50ca-0417-4e8f-b50c-fbfe06cbe83c","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"82411334495941885027872535807885102064","date":"2026-05-02T13:33:31+00:00","index":33,"fulltext":""},{"type":"reviewerAgreed","content":"196432080782093861276491284524519012159","date":"2026-05-01T21:24:36+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T02:33:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 02:33:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9023509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9023509","identity":"rs-9023509","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}