Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. esc Fenker Jessica Tropics Monsoonal Australian complex taxa the co-distributed across among Structure Phylogeographic of Predictors ntr,evrnetlhtrgniy pce’tat n itrclboegahcbrir a influence may barriers biogeographic diversification. historical drive and can persistence taxa traits and co-distributed species’ isolation across heterogeneity, population structure environmental as phylogeographic such turn, of processes depth In and microevolutionary scale how geographic reveal the in Differences Abstract 2 Australia ACT, 1 * Fenker Jessica Aus- complex title: the Running across taxa co-distributed Tropics among Monsoonal Structure tralian Phylogeographic populations of of stable Predictors scale more geographic and the larger with predict associated not is D. does species Tajima’s distance negative within persistence. temperature less by structuring hence, and both phylogeographic isolation and, heterozygosity with finer-scale individual of taxa, that mean strength higher implies climate. all had The and This for lineages specialization divergence localized factors. However habitat genetic family, limiting structure. landscape-scale taxonomic phylogeographic by as of the isolation acting predictor of prevalent strongest precipitation was independent the is there and is distance lineages, environment by indicate Within by isolation Results taxa. of Isolation lineages. between strength phylogeographic processes differ intraspecific the microevolutionary structure of but whether phylogeographic depth distance, test and diver- of factors then scale these We predictors genetic geographic whether landscape whether taxa. the and that climatically generalist test environment, in habitat and/or differences then and versus resistance explain and topographically distance, lineages rock-related lineages by the within between isolation intraspecific or from to genera resolve taxa related across first is lizard differ We predictors lineages co-distributed within explore Australia. in space we across of analyses, structure extensive gence genomic (AMT) phylogeographic Using landscape tropics of and persistence. monsoonal depth statistics and heterogeneity, complex summary and isolation environmental genetic for scale turn, population potential the In of the of combination influence diversification. a may and drive microevo- barriers how data persistence biogeographic reveal SNP can and historical taxa and isolation co-distributed traits population across species’ structure as phylogeographic such of depth processes and lutionary scale geographic the in Differences Abstract 2020 26, October 3 2 1 orsodn uhr [email protected] author: Corresponding h utainNtoa University National Australian The Victoria Museum University National Australian eateto cecs uem itra ebun,VC Australia VIC, Melbourne, Victoria, Museums Sciences, of Department iiino clg vlto,Rsac colo ilg,Asrla ainlUiest,Canberra, University, National Australian Biology, of School Research Evolution, & Ecology of Division 1* rdcoso hlgorpi tutr nteAMT the in Structure Phylogeographic of Predictors enroG Tedeschi G. Leonardo , 1 enroG Tedeschi G. Leonardo , 1 aeMelville Jane , 1 aeMelville Jane , 1 2 n ri Moritz Craig and , 2 n ri Moritz Craig and , 1 3 Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. smr egahclylclzdi aawt oe ipra srne B) agrlclpplto size population local mutation-drift larger from IBD), departure structuring using (stronger phylogeographic assessed dispersal size whether species, lower population test stable habitat-generalist with also more taxa We or vs. heterozygosity) in average McRae, rock-restricted variation. localized (higher Fortin, across trait geographically (isolation Balkenhol, vary broader more barriers relationships Spear, representing is geographic these 2006; genera, how or Schwartz, across and & het- 2014), or environmental 2010), Hayden, phylo- Bradburd, 1943), McKelvey, have Scribner, & Wright, Cushman, species & Wang (IBD); within (IBR); most distance dispersal (IBE); which resistance whether by environment by in explore (isolation by We fauna distance (isolation scales. geographic reptile temporal erogeneity by tropical and limited a spatial is 2019). to varying al., lineages with methods et but Vasconcellos & genetics structuring, 2014; Pelletier landscape geographic Sork, 2019; past & apply al., through Riordan, we et persistence Gugger, Here (Myers population Ortego, and barriers 2010; 2020), al., dispersal Pelissier, et physiological presence, & (Bell 2018; the Karger and climates to Leugger, al., geographic related Hagen, et of Yannic, limitation (Singhal 2018; past, dispersal Carstens, distance the extrinsic 1943), by in Wright, isolation or 2015; currently using Heck, Van estimated & microevolutionary limitation, While two Holderegger, groups, of Strien, dispersal among 2019). extinction bridge Rabosky, intrinsic and a & include speciation provides Singhal of (Harvey, processes formation rates microevolution with species concerned and with broadly macro is associated macroevolution – dynamics approaches population research the isolated structure of largely view phylogeographic spatial influence Bradburd, broad barriers & A (Wang climatic 2018). het- divergence Knowles, and environmental & adaptive geographical by Sukumaran, for how shaped Huang, informing potential is (Li, Oliveira to differentiation predict 2005; genetic addition help how al., in can Understanding et 2014), Buckley, limitation (Funk 2009). environ- Riginos, dispersal populations al., habitats), and 2017; of et patchy erogeneity persistence Brumfield, Pease spatially and/or & 2018; to isolation al., Ribas taxa influence specialization et Aleixo, of 2014) (Harvey, Treml, (e.g. structure interactions phylogeographic & traits the Blomberg, biotic species that across and of follows structure heterogeneity combination It phylogeographic mental a complexes). strong how species a 2012). eventually parapatric on al., as will (or et depends manifests per- Rosenblum or species the isolates 2012; for of ephemeral, all of al., potential range et are and persistence the (Etienne the lineages high process- model where and intraspecific speciation speciation protracted isolation process whether the the Population extended to under of species central an result full is a is to transition isolates is Speciation today population observed initiation. of we speciation sistence that promote forms that life factors of variety immense The limitation environmental barriers, geographic distance, Introduction by isolation flow, persistence. gene hence, structure, and, Population higher populations had stable phylogeographic not more lineages finer-scale does and that both localized larger distance implies Keywords with more with by This associated However, D. isolation taxa, is Tajima’s of species all negative structure. within strength less for structuring phylogeographic The and divergence heterozygosity of individual genetic factors. scale mean limiting landscape-scale geographic Isolation as of the strength climate. acting the predictor predict and precipitation but specialization strongest habitat distance, and family, the by taxonomic temperature isolation is the prevalent of environment was independent by there is We distance lineages, by Within phylogeographic isolation taxa. of and of predictors generalist taxa. scale landscape between habitat geographic that the differ indicate environment, versus in Results structure and/or rock-related differences lineages. explain between resistance phylogeographic lineages intraspecific or distance, of within depth genera by processes genetic microevolutionary across isolation whether whether differ to test test then then factors related and these population is complex lineages whether lineages of climatically intraspecific of and within resolve and combination depth first space and topographically a We across scale the and Australia. the divergence from of data of taxa (AMT) predictors SNP tropics lizard explore extensive monsoonal co-distributed we analyses, Using in genomic structure landscape phylogeographic persistence. and statistics and summary isolation genetic for potenti 2 Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. hmielfrorsuy ae npirpyoegahcaaye n ecp o aecsso known of cases rare for (except and analyses phylogeographic prior making on S1), Based Material Supplementary study. 1C; our (Figure for structure ideal phylogeographic of them scales varying sequencing, generalist the by eslce pce o pce opee)ta r ieydsrbtdars h o n n Kimber- and which End and Top ( coordinates, geckos the GPS include precise across These ( with lizards. distributed skinks sampling of inae), widely genetic families extensive and are genera geographic different that had represented complexes) also which species regions, (or ley species selected We data Sequencing geographic the in Methods taxa and among Material microevolutionary lineages. differences phylogeographic whether explain of tested parameters, depth genetic 3) and divergence scale genetic within-lineage and explain in distance/resistance/environment); require- best reflected (habitat by features processes, tested ecologies environmental (Isolation 1) (families), what lineages we explored taxa lineages, 2) among within varies phylogeographic condition; environmental (IBD) with species. or lineages sampling specialization within ment) within landscape-scale ecological structure limitation to and and phylogeographic dispersal data opportunity of limits whether scale SNP the dispersal the obtained provides intrinsic shaping newly how structure in Using phylogeographic test heterogeneity to of environmental lineages scale with interact within temporal Kimberley genetics and drier landscape the spatial apply in the 2018). change al., in climate et variation topography. past (Potter This west End to and 2017). Top and responses al., climate mesic End et demographic (Oliver Top more to variable limestones the the karst relating more isolated than of were and differences regions 2018) there regional 2016, mesic Carlia, al., more also For et (Rosauer the in are islands in older on There Phylogeographic concentrated much including Kimberley, 1C). also are 1C). are lineages (Figure (Figure phylogeographic species) genera ranges Short-range separate generalist other as geographic more reclassified the broader now (e.g. in span not taxa taxa or typically some other (whether In units in lineages phylogeographic whereas 2018). the scales, al., 2017); phylogeographic spatial et 2016) this al., 2020) Potter fine et al., Nielsen, et very 2011; Silva Laver, Oliver at Li, Afonso 2018; 2018; al., Oliver, & occur et & Moritz Glor, can Doughty, 2018; Laver, structure Chapple, al., 2010; et groups Ritchie, Laver al., other geckos; Melville, et for rock-specialist Fujita (notably 2017; boundaries 2014; species Oliver, al., of & et assessments Date, (Catullo while Rosauer, Melville, strong 2020), progress 2018; 2019; in al., revealed et al., still Doughty consistently et are 2017; Oliver complexes have al., 2019; species Doughty, et fauna Silva cryptic & (Afonso cases, Horner, lizard revised some taxonomically In AMT been subsequently species. the have recognized Keogh, taxonomically of represent within & intrusion), structure analyses Doughty, phylogeographic arid more Lanfear, phylogeographic Ord is (Catullo, multilocus and End species plains Recent (Figure 2011). Top terrestrial Gulf regions Cooper, The within the both & divergence in Potter, (e.g. 2011). allopatric Eldridge, plateaus barriers Melville, 2014; of major edaphic & driver separate and plains potential Shoo, climatic river a Harmon, with Large Donnellan, along Smith, 1A). McGuire, and, (Fujita, 2008; (Figure. 1A) Mackey, distinct Kimberley Edwards, Woinarski, biogeographically Australia the 2010; & most AMT than al., in Lee the the mesic et being region 2010; Bowman within latter 1A; fire-prone areas Moritz, the seasons, (Figure distinct with & most Peninsula dry York 2007), biogeographically and the Traill, Cape three wet & and is identified Nix, Kimberley across and studies End, rainfall Top Previous 1B), contrasting the — strongly (Figure 2010). of has al., temperatures areas increases AMT et warm Aridity or (Bowman The consistently 1). plateaus 1A). (Figure sandstone against (Figure woodland disjunct savanna set south by flat to of characterized regions region north by rich from separated biologically relief the and topographic of high diverse fauna relatively a lizard is the to AMT approach The comparative this apply (AMT). We Tropics D). Monsoonal Tajima’s Australian absolute lower (i.e. equilibrium Carlia .binoei H. cnia)addaos( dragons and Scincidae) ; Diporiphora nld pce ihdffrn aia eurmnsad rmpirmultilocus prior from and, requirements habitat different with species include ) rgn,Sihe l,21) and 2011); al., et Smith dragons, Diporiphora 3 gmde.Albut All Agamidae). ; Gehyra Heteronotia and Heteronotia Gehyra Heteronotia eks oize al., et Moritz geckos, hnaethose are than Carlia (represented Gekkon- ; skinks, Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. 07 ostoe l,21) eal fteSPgntpn a eacse nGogse l 21)and (2018) the al. radiation, et Gehyra Georges older in the accessed within be and genus, can each genotyping from Stow, SNP Ferr˜ao, & samples Magnusson, the For Lima, of koira Fraga, (2018). Details (De Dale al., studies 2019). and et genetic al., Wells Melville landscape contigs (Jane et for populations 75bp Rossetto and between distributed 2017) 2017; admixture al., randomly detecting et for within Unmack valuable SNPs proven 2017; has identify and to sequenc- 2001), S1). as reduction platforms (Jaccoud, lineages restriction-enzyme Mat. Next-Generation-Sequencing uses parapatrically-distributed (Suppl. (DArT), range on Technology and taxonomically Array known ing Diversity revised closely-related the method, recently treat detection across been We SNP have localities Our they 2020). sampled not al, of or et whether spread Battey units, geographic phylogeographic (see and taxon number each (135 the of samples 579 maximize of to subset a individuals selected we identification, lineage lia for mtDNA using introgression) mtDNA aa efis efre rnia oriaeaayi Po)bsdo h eei itnemti bet- to matrix analyses — different distance two them genetic ran among the then admixture SNP We on for conStruct the clusters. based test discrete using and (PCoA) identify samples clusters to analysis among verify distance divergence coordinate Euclidean and principal using considered structure individuals a species ween population performed diver- the the of first with each visualize we meta-populations in To data, identified within S1). been diversity Mat. previously confound have (Suppl. as not here lineages, to isolated important historically among is gence SNPs it filtered analyses we landscape-scale For data, clustering our population of and quality design the Sample ensure To fragment. position the restriction ( the contains the replicates with using the data technical calling resulting to SNP across The algo- related final repeatability identified locus. clustering the by base each are and fast variant for markers DART (SilicoDArT) reproducibility a SNP SNP the of Then, of per using index fragments clusters. an Next, restriction into criteria calculate of sequence. aggregated selection will presence/absence the stringent are that of using sequences cluster rest each filtered distance, in quality the Hamming are to a sequences region with First, rithm barcode SNPs. the call compares and that reads map to pipelines clsb eetpplto xaso Sakn 94.Mne et eepromdt aclt p-value. a spatial calculate large to with at performed increase reduced were package to also tests R is Mantel expected the but 1994). is using 1997) (Slatkin, slope slopes (Rousset, expansion visualized IBD We rate population The migration recent above. by and/or identified scales size the as using population lineages) individuals” local intraspecific of decreasing or pair (species each by taxa between calculated the model individuals, alleles linear among shared a the distance of fitted from genetic proportion we and regions the distance or minus geographic lineages “1 among Euclidean varies log (IBD) pair-wise distance between mitochondrial by Isolation using whether S1). studies investigate Mat. To previous Suppl. in in more “genetics” supported details or and (more well one “landscape” sequencing are in Combining multilocus them by groups cases, between models These most observed the 1). is in described of (Table admixture and, within low performance DNA analyses populations if predictive these above even the to the ‘lineages’, refer compared as of will and structuring we 7, on, strong and here with From approach 1 species replicates. this between 50 K with essence, cross-validation a In Forrunning with whereas 2018). clusters. distance), inferred, al., genetic are by et populations within lation isolated (Bradburd panmixia which rate estimated assumes against a null-model, is methods) layer a each distance as within with IBD where decays has layers, population relatedness a two-dimensional which with from visualized individual at cal- were sampled was each proportions and for admixture structure, portions The population the 1-7. considering between cluster, (K) plot. a complexity to model belonging with individual culated an of probability the for 147 , ConStruct and Diporiphora, adegenet dartR nana Babr,Co,&Rlh 2018). Ralph, & Coop, (Bradburd, ldssprtl Fgr 3,tesqecswr rcse yporeayDr analytical DArT proprietary by processed were sequences the S3), (Figure separately clades akg Gue,Umc,Bry ere,21)i Sui RtdoCr em 2015). Team, Core (RStudio RStudio in 2018) Georges, & Berry, Unmack, (Gruber, package nescniuu n iceepten fpplto tutr yetmtn netypro- ancestry estimating by structure population of patterns discrete and continuous infers akg Jmat&Amd 01.Tesrnt fIDwsmaue ihnec of each within measured was IBD of strength The 2011). Ahmed, & (Jombart package 214 Gehyra n 83 and Heteronotia jtools > 8) alrt ( rate call 98%), FastStructure Ln,21) n etdfrdffrne costx (skinks, taxa across differences for tested and 2019), (Long, 4 FastStructure o h N cenn,fcsn nsailyunique spatially on focusing screening, SNP the for ) ConStruct samdlbsdcutrn ehdta looks that method clustering model-based a is < 0 isn aa,adrmvdsingletons, removed and data), missing 10% Rj tpes rthr,21)and 2014) Pritchard, & Stephens, (Raj, eue pta aa(osdrn Iso- (considering data spatial used we FastStructure PropShared adrelated (and australis, function Distruct Car- Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. n eitnesrae;te,loigfrteitrcinbtentoo h aaes n atyloigat looking lastly and datasets; the seven of ran two between We variables, interaction variables. environmental the distance, distance geographic for with including looking individuals model, then, Manion, associating full among surfaces; Ferrier, a models dissimilarity resistance first (GDM; on nonlinear genetic and species: part between modelling each of in relationship for dissimilarity rate tests analysis the generalized independent the our calculates using in based method variation we (2019), This the divergence, 2007). al. genetic Richardson, et and to & movement) Myers contribute Elith, of IBR of ease and framework greater IBE, the indicate IBD, values where how higher values, investigate resistance (where To resistances. to surface as according conductance evapotranspiration them averages a and the and as slope identify paths rivers, used to software all theory was the for circuit in SDM looks above vary used This the We described SDMs 2013). features S1). (Table Mohapatra, landscape the SDMs four & for the most Shah, using values in by variables (AUC) of path important effective curve lineage be most Kimberley the to result the appear under the for AMT and except area the SDM, performance, The each model normalized. for good replicates and indicating magna bootstrap 0.71, file 25 and asci limited with 0.95 were records, an between points allowing location as background variables, the over-prediction, exported around correlated avoid radius was even To degrees retained model. 3 each we a for (2011), to predictors al. required et the Elith determine Following data 2012). automate Schapire, to & va- github.com/DanRosauer/phylospatial) genetically WorldClim in at 19 of (available modeling all locality script and included the We preparation 2.7 on S1). Python Material based the Supplementary independently, (Figure running the lineages AMT in riables, and the details of species complete gradient (see all aridity samples allows for north/south identified SDMs which general created the organisms, we across Finally, the conditions 1A). dry for po- and available Global mesic resistance. water/productivity the higher contrasting the indicating values represents greater (https://cgiarcsi.community/data/global- rock-dependent and with 2019 for CGIARC-CSI, 1, aridity-and-pet-database) dispersal from to recogni- inhibit obtained 0 to or was from helping species Evapotranspiration normalized cell, generalist tential were grid habitat files each package of asci for dispersal the All altitude facilitate species. the using from as could for that 2019), regions shapefile coefficient areas intervening weighting (CGIAR-CSI, river flat the and ze a altitude from cells obtained obtained from resistance was We represent slope high Slope species. 2019). as can extracted the rivers poten- We rivers of major and resistance. and some considered evapotranspiration low Slope for and slope, (SDM). (1997), dispersal rivers, features Australia models resulting to divergence: landscape Geoscience distribution barriers The four species physiological species resolution. considered on and We on ˜1km surfaces physical 1. based Table at resistance suitability in square of environmental indicated effect tial grid are the taxon resolution were each test that same for records to the used occurrence individuals excluded within of we or numbers models (no-connectivity) to (IBR) of Resistance relate islands value by to in The Isolation variable our environmental species. over-sampling above. an per avoid discussed as To locality modelling, later dissimilarity each used generalized for was using variables point divergence distribution these genetic per extracting Driest variable of and environmental Precipitation each Seasonality, Quarter) Precipitation Warmest Quarter, and Driest and Quarter Quarter by Wettest (Isolation of Temperature individuals Mean resolution. (r among 30-s the at uncorrelated using difference using 2006) variables eight al., these genetic and et among (Hijmans correlation analysis on WorldClim for from IBD tested features variables We 19 the environmental the downloaded of of we value IBE), effect Environment; slope the the To investigate with region. To generalized non-normal) the a were performed of weighting. variables we variable as group our error environment lizard as and 1-standard dominant preference polygons (glm; habitat the hull model precipitation, convex as the the linear of locations, from influence extracted distribution the 2006) for To species Parra, variableaccount generalists. environmental & each dominant habitat Cameron, from the (Hijmans, vs values, WorldClim created rainfall specialists from annual region, rock mean the between on of focused and we below), variation climatic (more represent environments agamids), and geckos, iha U f06 TbeS) ohpeiiainadtmeauerltdt h esnlt of seasonality the to related temperature and precipitation Both S1). (Table 0.61 of AUC an with , < .)vrals(nulMa eprtr,Ma ira ag,Isothermality, Range, Diurnal Mean Temperature, Mean (Annual variables 0.7) rGS10 ArcGIS ER,21)and 2011) (ESRI, 5 ENMTools aEtv3.3 MaxEnt akg Wre ta. 09,keeping 2019), al., et (Warren package R rdcieapoc Pilp,Dudik, (Phillips, approach predictive Circuitscape raster Diporiphora MaxEnt (Hijmans, (McRae, to Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. rm00 o06 Tbe1 iue3 iueS) oee,IDsoe i o ie infiatyacross significantly differ all not (glms; did precipitation mean slopes annual IBD individual to However, Mean varying relation S9). estimates in slope Figure or with generalists, 3; IBD, habitat strong Figure p vs. was 1; there rock-dependent (Table general, genera, 0.68 In lizard done. to also 0.07 (IBD) was distance data from geographic the Euclidean of p and exploration (glm; genetic visual between lineages correlation all significant for a showed tests Mantel The Distance by Isolation and diversity Genetic binoei u itrclyioae ouain htcudcnon oelnsaegntcaaye u ol not could but analyses divergent less genetic multiple landscape include comprise These some still here. confound within could sampling al, variation could here the on with that defined resolved using focused as populations confidently mostly studies lineages isolated studies, genetic these historically previous landscape of in but subsequent some recognized from However, Our varied lineages lineages. S1). species isolated these Mat. per historically identified (Suppl. supported lineages datasets discrete these intraspecific of different and divergent number three The deeply S5-S8). to had Figure one Mat. species Suppl. more most the had in studies, available the lineages prior across no from patterns post-filtering, coincident expected non-spatial and, largely As individuals in partitioning 23 data. reflected After was missing lineages, 1. size Table of in sample given 6% mean are than specialization lineages, habitat major and into occurrence of samples region the lineage, per for Values 3,729 for to 7,472 52,693 to from for 150,178 reduced SNPs from of lineages), number four average the filtering, stringent After clustering effects and for differentiation tested Population also We covariate. a mean with as on Results size values group range D lizard of Tajima’s and relationship Suppl. preference using the genus; habitat regression (glm) influence of each linear could regressed for generalized D) we chronograms Tajima’s a by BEAST performed lineages, inferred DNA all (as mitochondrial expansion across the range mean structure against from S1-S4) phylogeographic (obtained Figure age Mat. deeper N and or local Ralph, area (Battey, (or finer-scale lizards lineage dispersal abundant to lower highly modest translate these whether is assuming for test space statistics, scale reasonable To summary across seems 2020). these as individuals Kern, on sizes of & population structuring sampling effective spatial local quasi-random of large our effects to confounding that reduce note to We expected fluctuations. mean (N population higher sizes from where arise population persistence, effective about local species N larger information within complementary to structuring related us phylogeographic provide is of measures These depth above). and ( heterozygosity scale stability observed geographic population dartR mean and the estimated restriction we to dispersal (complexes), relate size, (population lineages) processes within microevolutionary how explore the To and permutations. variable 50 diversity phylogeographic the with response quantified of matrix the and predictors GDM as table, Population-level a site-pair divergence using GDM genetic a significance used in and We predictors importance as 1). predictor’s datasets Table resistance in and environmental details geographic, (more alone dataset each > e nulse data unpublished n oengtv aiasDvle osrne eitosfo uainditeulbim scould as equilibrium, mutation-drift from deviations stronger to values D Tajima’s negative more and , 0.05). Gehyra akg Gue,Umc,Bry ere,21) swl stesrnt ie lp)o B (as IBD of slope) (i.e. strength the as well as 2018), Georges, & Berry, Unmack, (Gruber, package E(oize l,21)and 2016) al., et (Moritz TE N24ars e iegs n rm1574t 007for 10,017 to 185,704 from and lineages) ten across (N=214 ConStruct Ho ), e ieg aisapoiaey3-od ewe .0 o012(al ) xetfor Except 1). (Table 0.192 to 0.007 between 30-fold, approximately varies lineage per eyagemina Gehyra rps(e iue2fra xml with example an for 2 Figure (see graphs < .5 up.Mt 1.A atltssaepoet yeIerr(ag 03 a 2013) (Wang, error I type to prone are tests Mantel As S1). Mat. Suppl. 0.05; Ho alamunda Carlia B lp n aiasDa rdco aibe.Koigta recent that Knowing variables. predictor as D Tajima’s and slope IBD , and Diporiphora .koira G. Ho Pte ta. 08 Spl a.S1). Mat. (Suppl. 2018) al., et (Potter Oie ta. 2020), al., et (Oliver n aiasDvalues. D Tajima’s and 6 N17ars ih iegs,fo 8,8 o11,531 to 182,688 from lineages), eight across (N=147 e ,IDsoet oe ipra n/rsalrlocal smaller and/or dispersal lower to slope IBD ), ioihr bilineata Diporiphora e PCo Ho n oesal ouain ttelandscape the at populations stable more and ) n aiasD(aia 99,uigthe using 1989), (Tajima, D Tajima’s and ) A, alaamax; Carlia fastStructure Heteronotia .nana G. aafrteohrseisare species other the for data n ihro h pta or spatial the of either and and Mrt ta. 2018), al., et (Moritz 4 N8;trelineages). three (N=83; .perplexa D. Carlia N15across (N=135 Ho Fne et (Fenker ealso we , Ho H. Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. ainb itneo slto yRssac.A B ersnsa neato ewe pce risand traits species between Iso- interaction an than represents divergence IBE genetic As landscape-scale Resistance. of by predictor Isolation stronger or physiological a Distance and by as scales. size lation emerged spatial body Environment finer as intra-lineage at by such observed sampling attributes, Isolation in intensive history variation more natural three-fold as additional the well of as of inclusion tolerances, causes from broader into represent come insight implicitly could More which IBD genera, traits. among ecological differences in consistent no divergence distributed were there widely only Similarly, the 2019), surprising. widely-dispersed especially al., is on and et found Oliver taxa, specialization, 2019; be these al., even habitat can et lineages the (Melville rock-specialist to platforms the related sandstone lapistola of open not most and was of rocks slopes, flat populations IBD While aridity. the or by genus represented the (Georges limitation, studying admixture dispersal for 2017). and Intrinsic al., resolution structure through et population higher available Melville and much datasets divergence 2018; provide scale genetic al., SNPs smaller on et features the capture, environmental with exon of Compared or these influence AMT. within sequencing structuring the phylogeographic multilocus in strong traditional complexes for evidence species previous lizard extend tropical and support datasets SNP Our results other (p 0.005); specialization Discussion habitat = or p occurrence 52.351; of (AIC= region or values genera, D D Tajima’s Tajima’s either (mostly negative clades with Older less same. had the across now remained sampling by lineages impacted potentially localised such be more removed still we could 2020), D in al. Tajima’s 4; reduction et and ( possible (Battey IBD for taxa metapopulations of control than composite estimates of (to rather our covariate slope lineages taxa a isolated the some as historically or in D 0.081) As Tajima’s p= 0.049). add 24.025; = we (AIC= When D 0.212). = Tajima’s Ho p either phylogeographic -56.6; of and (AIC= scale size IBD the range higher predicted between limitation had relationship dispersal significant lineages or restricted stability geographically size, structure, population whether for Testing structure phylogeographic of predictors genetic Population aetergntcdvrec iie yrssac ufc,weefu r eae ocs-itneSDM; cost-distance to related are eight four lineages, where 12 remaining surface, the resistance For and lineages. by 25 limited environmental of australis with 13 divergence Gehyra for combination genetic variables Except in important effects. their (always most combined have distance the alone, did Geographic of as IBD one models. distances was neither the variables), genetic to contrast lineages, in strongly variation By within contributing the divergence 2). those ( of genetic (Table much lineages or of taxa as two alone, predictor explained across for (IBE) ever important varied Environment alone, most by variables IBR the Isolation environmental nor was lineages. principal between IBR, within the explained or divergence while together IBD genetic resistance with in and combination variation environment, in of distance, 96% by and isolation 24% including models dispersal GDM of The limits as IBR and IBE, mean IBD, again, p 1); (glm; (Table values specialization negative habitat most the or exhibited genera fluctuations. AMT the population of recent region reflecting Gulf likely 1) for Table value -1.376; positive slightly the eeoohabinoei Heteronothia ihrneepnin h eaierltosi fhtrzgst n ag iermie infiat(p significant remained size range and heterozygosity of relationship negative the expansion) range with .sobria D. r soitdwt h oesasl itiue alrc sapet Oie ta. 09.That 2019). al., et (Oliver escarpments rock tall distributed sparsely more the with associated are I oslope. to KIM alamunda Carlia and ioihr sobria Diporiphora .lapistola G. TE ; Ho e up.Mt noadFgr 1)adrpae et.Ecuigteetaxa, these Excluding tests. repeated and S10) Figure and Info Mat. Suppl. see hnteaoetx r xldd aiasDvle i o ie across differ not did values D Tajima’s excluded, are taxa above the When . eyagemina Gehyra ; ioihr bilineata Diporiphora otepeec fariver, a of presence the to I and KIM > Gehyra 0.05). l iegssoe eaieTjm’ aus(008to (-0.058 values D Tajima’s negative showed lineages all , eyaaustralis Gehyra aesrne B Fgr ) u hr sn relationship no is there but 4), (Figure IBD stronger have ) 7 .koira G. , > .perplexa D. Ho 0,05). AC=-564 .0) u hr a no was there but 0.005), = p -75.664; = (AIC eeooi binoei Heteronotia i o aesrne B hngeneralists than IBD stronger have not did , Heteronotia ,ciai aibe eeicue among included were variables climatic ), ; eyagemina Gehyra eksand geckos A oevapotranspiration, to NA6 Ho a o eae aridity, related not was , eyakoira Gehyra alaamax Carlia .koira G. , .nana G. and nthe in G. Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. ta. 07,ti a o bevdi u odsrbtdtx,epaiigaanta h iegneis divergence the that again emphasizing taxa, co-distributed our Aleixo, in (Harvey, other observed divergence while be and not Finally, diversity could was genetic speciation. limitation, this predict potential dispersal 2017), can and than the preference al., structure more ecological whereas et that phylogeographic persistence, structure, shown fine-scale population phylogeogaphic have Thus, of studies fine-scale population influence. development of effective no to determinants local had key as larger IBD to stability of lower points geographically population structure) strength that This lineage greater is lineages. cryptic here and with within result et taxa size equilibrium promising (Li removing A scales mutation-drift (when two 2017). from and the al., heterozygosity connect deviations et that higher Seeholzer, processes had Harvey, the lineages 2018; discern al., restricted to et challenging dyna- proved Singhal (microevolutionary) has 2018; level it al., 2019), the population al. by on divergence et generated clear (Harvey genetic presumably mics differentiation not on are is influence of patterns lineages bigger macroevolutionary stages within a While earlier have structure to could the 2018). features related lineages, al., landscape other is et necessarily and for (Singhal not IBD environmental Yet, does and as 2018). This graphs, al., lineages, dispersal. IBD et phylogeographic intrinsic Singhal the new 2000; limiting best of (Avise, not the formation are generally rivers) in was (e.g. and taxa IBR, result variation distributed differentiation or widely topographic on IBD for as role that, with (such shows small combination graphs a in IBD play the or gemina; into features alone look landscape IBE, deeper that some Rather, A in revealing predictor. taxa. differentiation predictor, genetic co-distributed best of our predictor the of significant never a was is (IBR) it suitability et cases, habitat (Vasconcellos in structure variation genetic spatial and While distribution (Potter regions stability species determined climatic affected in long-term events in interac- species/lineages climate 2019). variation biotic spatial past the al., be are and wherein of 2014), work should 2018), resilience al., present and al., et the the et Riginos 2015), in influence 2017; Zhang, considered al., could the differentiation & et not that genetic Yang, Aleixo, not factors (Harvey, competition, Song, of Other is as driver Cushman, detail. This Yang, such principal more here. 2014; in tions, a examined AMT al., as the et lineages reported in Cushman the is investigated were physio- 2017; condition of variables and al., climatic seasonality half 2007) et in in about al., (Bothwell variation variation et within seasonal Spatial Woinarski distance time abilities. regimes; first genetic (Figure river dispersal AMT of the further the in predictors and across changes significant rainfall organisms (e.g. & of for Sinervo, geographic seasonality barriers (Camargo, strong on time logical the impact and Additionally, direct space 2003). ectotherm in has Vitt, variation for 1B) & climate expected Pianka important to is 2010; result responsive are Jr, This are Swillen, Sites 2014). Vanoverbeke, traits constraints Hoffmann, ecological Orsini, environmental & which 2017; Hangartner, in the al., Sexton, lizards, et 2013; that (Bothwell Meester, findings De patterns landscape-scale & differentiation Mergeay, supports on neutral influences This genome-wide important of lizards. as drivers emerged these is precipitation 2016) of and al., temperature diversification et both (Zamudio in variation patterns Spatial phylogeographic and genetics landscape here. both elusive and traits families species lizard between different while requirements: to dragon, ecological why Average belong different generalist unclear - they have is tat diversity - it and heterogeneity genetic and Gekkonidae), environmental common their and to in explaining (Agamidae responses little in have similar variables species exhibit might These important Range. they most Daytime distantly as Average the and out Temperature Yet, fall Annual species. variables same generalist the and have rock-specialists here, between But 2018). patterns al., related genetic et landscape genetic Robertson in landscape 2017; Peery, of differences compa- & determinant Previous Mladenoff, main that lizards. Reid, the infer AMT 2019; Heteronotia be we these al., can in 2015), et characteristics divergence (Myers Crawford, Ib´a˜nez, genomic species-specific patterns & Paz, of that Lips, driver showed 2019; principal studies al., the rative et be (Myers could adaptation landscape local the of elements abiotic ioihr magna Diporiphora iue 6adS) hr ssbtnilpplto tutr ihnlnae slnsaefeatures landscape as lineages within structure population substantial is there S7), and S6 Figures Gkoia)hv ieetdtriat fpplto tutr.Smlry hr sn overall no is there Similarly, structure. population of determinants different have (Gekkonidae) I and KIM .nana G. sancunl okrltdgco vrl,teepce association expected the Overall, gecko. rock-related nocturnal, a is 4 eyanana4 Gehyra iegssaeawdl ypti itiuinadalso and distribution sympatric widely a share lineages 8 .magna D. I sadyiehabi- daytime a is KIM .bilineata D. Gehyra and and G. Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. n ie ewrsdiegntccnetvt n iest nafudto iainte ( & tree G., riparian T. foundation a Whitham, in gradients M., diversity Precipitation L. West: and Evans, American ). connectivity the genetic I., in drive lizards. E. ecosystems networks Hersch-Green, river riparian rainforest threatened and A., Conserving montane S. (2017). Woolbright, (2010). in J. A., G. C. change S. Allan, Moritz, Cushman, & climate M., E., to H. S. Bothwell, Williams, resilience B., indicate J. MacKenzie, isolation J., Ecology on and C. Molecular structure Hoskin, persistence spatial M., Tonione, continuous of L., of Patterns J. Effects Parra, place: C., the R. is Bell, Space (2020). data. D. genetic A. population Kern, of & analysis L., P. Ralph, C., Battey, Tropical (2000). (2017). C. C. Moritz, J. & Avise, of M., species M. sister of Coelho, history C., Fernandes, demographic Australia. and S., northwestern diversification Potter, generalist: G., climate J. vs. Bragg, specialist A., C. A. Silva, Afonso on advice for supported was Brennan Museum, research CM. References Ian This Australian to tissues. and South Fellowship the Museum, comments Laureate to Territories ARC access for for Northern an Laver Museum by Museum, Australian Rebecca Australian Western and the and Museum, thank Catullo Queensland also Renee We thank 2. figure to scholarship. PhD like the would for CAPES We local – N´ıvel on barriers. Superior de Coordena¸c˜ao focus physical Pessoal thanks Aperfei¸coamento de and should JF de physiological studies influence future of these and seasonality as divergence strong seasons Acknowledgements genetic wet The for and 2016). promote propensity dry species- Mason, across features the of & restriction impact environmental Bell, examination dispersal also and (Zamudio, Further could biogeographic populations AMT occurrence. AMT which between the of the elucidate isolation in region to structure and and phylogeographic help persistence of restriction should driver habitat characteristics strongest family, the specific than phylogeogra- be ex- fine-scale to rather promoting and seems in lizards, intrinsic stability adaptation and what Population Local size explore species. structure. population to within of phic structuring us importance phylogeographic the allows indicate fine-scale datasets 2013). statistics of SNP genetic development Matute, well-sampled the & influence and Rabosky factors taxa 2014; trinsic co-occurring al., on et study Price Our 2018; al., integration et better Li a 2017; promise developments al., Conclusion recent et but Seeholzer, (Harvey, limited, macroe- fields still into microevolution are both biotic versa) incorporating of and vice Methods specialization investigated. (and physiological be analyses as should volutionary such bloking) dimensions, ecological range other (e.g. of interactions effects that and species-specific utai:Eiec rmeooia ih oesadamlilcspyoeyo prli odes(Anura: toadlets northern Uperoleia of of phylogeny boundaries multi-locus biogeographical a The and (2014). S. models J. niche Myobatrachidae). phylogeographic Keogh, ecological & linking from P., for Evidence Doughty, organisms R., Australia: Lanfear, model A., as R. Lizards Catullo, (2010). W. genetic J. population Jr, studies. discrete Sites speciation and & and continuous B., Inferring Sinervo, (2018). A., P. L. Camargo, Ladiges, P. Ralph, . . & . space. M., D., across M. G. structure Crisp, Coop, G., S., L. G. Cook, Bradburd, R., J. tropics. Brown, monsoon F., Australian M. the Braby, 10.1111/j.1365-2699.2009.02210.x of K., Biogeography G. Brown, (2010). S., Y. J. M. D. Bowman, oeua Ecology Molecular ora fBiogeography of Journal , hlgorpy h itr n omto fspecies of formation and history the Phylogeography: 19 , 26 Genetics oeua Ecology Molecular 5124.di 10.1111/j.1365-294X.2010.04676.x doi: 2531–2544. , oeua Ecology Molecular 1) 1453.di 10.1111/mec.14281 doi: 5114–5132. (19), , 210 Genetics 1,3–2 o:10.1534/genetics.118.301333 doi: 33–52. (1), , 19 , , 41 26 , 1) 3250–3270. (16), 215 4,659–672. (4), 1) 4045–4058. (15), 193–214. , 9 ora fBiogeography of Journal avr nvriyPress. University Harvard . , 37 ouu angustifolia Populus Carlia 2,2126 doi: 201–216. (2), knsfrom skinks Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. avy .G,Seozr .F,Sih .T,Rbsy .L,Cev,A . rmed .T (2017). birds. T. World R. New Brumfield, in & M., rates A. speciation Cuervo, and L., D. differentiation Rabosky, genetic T., population B. genetic between Smith, predicts F., association association G. Positive Habitat Seeholzer, G., (2017). M. T. Harvey, R. birds. Brumfield, amazonian & in C., divergence 10.1086/693856 C. population Ribas, of and A., analysis diversity Aleixo, facilitate to G., package M. r Harvey, An dartR: sequencing. (2018). 10.1111/1755-0998.12745 genome A. doi: representation Georges, reduced 691–699. & from F., generated O. data Berry, SNP J., P. (2018). Unmack, J. B., Information. P. Water Gruber, Unmack, Surface National (1997). . . . Australia. Geoscience J., M. problematic Young, the M., for delimitation Adams, 10.1111/mec.14925 species D., and phylogeography (Chelidae: White, into turtles life B., short-necked new (2005). G. breathe W. Pauly, markers F. SNP B., Allendorf, Genomewide & Gruber, S., A., Amish, S., Georges, persistence D. Pilliod, and ( A., Diversification frogs B. Ecology spotted Maxell, Molecular S., Columbia (2010). P. of Corn, C. ( structure S., gecko Population M. Moritz, Blouin, bynoe’s & C., the W. C., Funk, of S. biogeography Australia-wide Donnellan, a, interface: Gekkonidae). arid-monsoonal J. to the modelling McGuire, dissimilarity at K., generalized M. Using Fujita, (2007). assessment. biodiversity K. regional Richardson, in diversity & beta , of J., (2012). patterns predict Elith, B. and A. G., analyse Phillimore, Manion, & S., A., record. Ferrier, Purvis, fossil Sciences N., the Biological P. with B: Pearson, agreement Society T., to Royal Aze, closer the phylogenies T., molecular Stadler, brings B., explanation Diversity-dependence Haegeman, statistical S., A R. (2011). 10. Etienne, J. release C. Yates, desktop: & ArcGIS E., (2011). Y. ESRI. Chee, Australia: M., north-western Dudik, ecologists. in T., for barriers Hastie, MaxEnt Biogeographic J., of S. (2011). Phillips, J. J., S. Elith, nomenclature. Cooper, of & standardisation S., in and overview Potter, delimitation an species. B., Species new D. four (2018). M. of R. Eldridge, description Palmer, the & Patterns with P., Tropics, Contrasting Oliver, Monsoonal R., Australian Ambush. (2017). Pratt, in J. Wait L., A. Tedeschi, That the G., Stow, Those Bourke, & and P., Forage M., Doughty, Actively Ferrao, land- That E., complex Snakes W. in Amazonian Magnusson, flow Heredity for P., Flow Gene A. Gene Lima, of (2006). R., K. Fraga, M. De (2014). Schwartz, modeling. J. & causal G. J., with Allan, Hayden, hypotheses 10.1086/506976 S., multiple . . Testing K. . river McKelvey, B., scapes: and A., gradients Honchak, climatic S. S., by Ferrier, driven Cushman, jointly M., is L. tree foundation Evans, riparian N., a networks. Meneses, in connectivity T., genetic Max, Landscape A., S. Cushman, (2019). CGIAR-CSI. 13 eyanana Gehyra 3,2224 o:10.1111/j.1472-4642.2007.00341.x doi: 252–264. (3), , clgclApplications Ecological 108 Evolution 5,5454 o:10.1093/jhered/esx051 doi: 524–534. (5), Sumt:Gkoia) ope:cytcaddvretmrhlgcleouini the in evolution morphological divergent and cryptic complex: Gekkonidae). (Squamata: , 14 iia lvto Database Elevation Digital 2,4346 o:10.1111/j.1365-294X.2005.02426.x doi: 483–496. (2), , iest n Distributions and Diversity 64 2321.di 10.1111/j.1558-5646.2010.00993.x doi: 2293–2314. , Emydura , 24 5,10–04 o:10.1890/13-1612.1 doi: 1000–1014. (5), , fesenAustralia. eastern of ) 279 niomna ytm eerhIsiue CA Institute, Research Systems Environmental 13) 3010.di 10.1098/rspb.2011.1439 doi: 1300–1309. (1732), osrimfrSailInformation. Spatial for Consortium . aaluteiventris Rana utainJunlo Zoology of Journal Australian 10 , 17 1,43–57. (1), mrcnNaturalist American mrcnNaturalist American oeua Ecology Molecular ssrnl ffce ytelandscape. the by affected strongly is ) oeua clg Resources Ecology Molecular Zootaxa iest n Distributions and Diversity , , , , , 59 4403 27 190 168 4,270–272. (4), 2) 1551.doi: 5195–5213. (24), eeooi binoei Heteronotia 5,6168 doi: 631–648. (5), 201–244. , 4,4649 doi: 486–499. (4), . rceig of Proceedings ora of Journal , 18 (3), ; Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. oiz .M,Pat .C,Bn,S,Buk,G,Bag .G,Duhy . lvr .M (2018). M. P. Oliver, lizards Australian . of . . complex P., Doughty, species G., a J. in Bragg, sympatry G., and Bourke, divergence, S., size Bank, (2016). body ( S. C., diversity, Australia. Donnellan, R. lineage of Pratt, Cryptic tropics K. . . M., . monsoonal Parris, P., C. the Doughty, Moritz, across G., gecko . Bourke, . a . R., in Agudo, L., Ecology endemism D., K. Molecular nested in Rosauer, reveals Date, platform K., phylogeography DArTseq Smith M. Multilocus the Fujita, A., of M., Kilian, Application C. Moritz, L., SNPs: using Hodkinson, vertebrates. admixture K., on and research Boysen, hybridization phylogeographic Identifying L., (2017). M. M. Haines, J., genus the Melville, in lizards dragon of revision Taxonomic (2019). 78 P. Doughty, & P., and Phylogenetics Horner, Diporiphora Molecular S., K. (2011). Date, S. J., A. Melville, J. Ii, savannas. tropical & Australia’s E., Evolution in R. lizards and Glor, dragon Phylogenetics of J., diversification N. and S. origins Chapple, Evolutionary Evolution G., guide. E. user Ritchie, 4 J., Circuitscape Melville, (2013). K. T. data. Mohapatra, scientific & B., social . V. of Shah, presentation H., and B. Analysis McRae, jtools: macroevo- (2019). impact A. processes Microevolutionary J. Long, (2018). L. L. Knowles, & phy- J., patterns. Sukumaran, Statistical lutionary P., J. barrier: Huang, Carpentarian J., Li, Australia’s across Divergence ( wren (2008). Australian fairy 10.1111/j.1558-5646.2008.00543.x V. in red-backed S. diversity the Edwards, Trans-biome of logeography & (2017). Y., M. J. P. Lee, Oliver, & F., 10.1016/j.ympev.2017.07.015 D. Rosauer, doi: V., (Diplodactylidae: S. lizards grass-specialist Nielsen, J., R. specialized Laver, two data. in SNP diversity endemic genome-wide of ( Tropics patterns of and Monsoonal 153. analysis Origins Australian the the (2018). from M. for P. lineages Oliver, lizard tools & P., New Doughty, J., R. 1.3-1: Laver, adegenet (2011). geno- I. independent Ahmed, Bioinformatics information & sequence T., for technology Jombart, state solid a Arrays: 1.4. Version Diversity typing. layers (2001). climate D. global Jaccoud, Worldclim Org (2006). Worldclim. J. Http://Www. Parra, Database: & WorldClim S., Cameron, R., Hijmans, Controls Demographic (2019). R. Isolation: Hijmans, Reproductive Beyond (2019). Process. D.L. Speciation Rabosky, the & on S., Singhal, G., M. Harvey, Sciences of Academy National the of Proceedings yr,E . u,A . eaa . o,C . ai aok,A . eo-sia,J,... . . J., Lemos-Espinal, R., A. Rabosky, Davis generate L., barriers biogeographic C. vicariant Cox, not M., and heterogeneity snakes. Gehara, 10.1111/mec.15182 desert-adapted Environmental in T., structure (2019). population A. community-wide T. Xue, F. A., Burbrink, E. Myers, Gehyra 35.di 10.24199/j.mmv.2019.78.02 doi: 23–55. , uli cd Research Acids Nucleic ). Evolution Rpii:Aaia)fo h utainmnonltropics. monsoonal Australian the from Agamidae) (Reptilia: , 27 , M vltoayBiology Evolutionary BMC 25 2) 0037.di 10.1093/bioinformatics/btr521 doi: 3070–3071. (21), atr egahcDt nlssadModeling and Analysis Data Geographic raster: , 72 6,15–36 o:10.1111/mec.13511 doi: 1354–1366. (6), 1,5–6 o:10.1111/evo.13380 doi: 54–66. (1), nulRve fEooy vlto,adSystematics and Evolution, Ecology, of Review Annual , 58 , 29 2,2720 o:10.1016/j.ympev.2010.11.025 doi: 257–270. (2), 4,2e25 o:10.1093/nar/29.4.e25 doi: 25e–225. (4), Strophurus oa oit pnScience Open Society Royal auu melanocephalus Malurus , 18 , 114 ). 1,18 o:10.1186/s12862-018-1236-8 doi: 1–8. (1), 11 . oeua hlgntc n Evolution and Phylogenetics Molecular 2) 6328–6333. (24), Oedura spp.). oeua Ecology Molecular ). . ora fBiogeography of Journal , Evolution 4 7.di 10.1098/rsos.161061 doi: (7). eor fMsu Victoria Museum of Memoirs RPcaeVrin201) 2.0 Version Package (R , , h aueConservancy Nature The 62 0 3.10-3.21. 50, , 28 1) 1733.doi: 3117–3134. (12), 2) 5544.doi: 4535–4548. (20), vial from Available , , 115 45 Molecular 1,142– (1), 62–70. , . , Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. a,A,Sehn,M,&Picad .K 21) atTUTR:Vrainlifrneo population of inference Variational FastSTRUCTURE: (2014). sets. K. data J. SNP Pritchard, large & in M., structure Stephens, A., Raj, America of evolution Competition States the birds. from United and decoupled of the Drosophila are rates in of Role speciation isolation Macroevolutionary reproductive the (2013). intrinsic R. of D. and Matute, & L., Speciation, D. S. Rabosky, Gupta, Ecological . Diversity-Dependence, . . P., 10.1146/annurev-ecolsys-110512-135800 Alstrom, T., (2013). Macroevolution. D. L. Tietze, in Pleistocene songbirds. Himalayan S., D. of U. (2018). diversification Johansson, Rabosky, C. the D., slows Moritz, C. filling Buchanan, & Niche M., J., (2014). D. E. K. Hooper, Roycroft, D., T. F., Price, D. savanna. Rosauer, Inferring tropical G., a tips: J. across the 10.1111/mec.14441 diversification Bragg, at drive T., Phylogenomics changes A. climatic (2016). Xue, lizard, C. S., tropical Moritz, Potter, & a K., in 10.1111/mec.13546 Bi, history doi: M., demographic B. 1367–1380. their Peter, and G., J. lineages Bragg, S., Potter, (2003). J. L. Press. 3.3. Vitt, MaxEnt & genetic R., (2012). population E. R. Pianka, predict Schapire, latitude & and M., Dudik, size S., range Phillips, Geographical survey. (2018). global C. a B. in Carstens, structure & A., Wayne, T. Pelletier, . . . J., Rodzen, W., differentiation. ( Buermann, 294X.2009.04112.x in generating deer E., mule history in California J. life factors of McCormack, genetics P., and historical Landscape J. ecology Pollinger, (2009). of H., K. role R. A. Freedman, the M., Testing K. (2015). Pease, J. approach. A. phylogeographic trait-based Crawford, A & landscape: , R., and a suitability across K. variation niche Lips, genetic climatic structuring R., of Ibanez, Influence A., (2014). Paz, L. oaks. V. Californian Sork, southern & among ge- C., patterns 41 by population hybridization E. isolation of on Riordan, and overlap Drivers F., adaptation geographical P. by (2013). Gugger, isolation J., L. limitation, M. Ortego, Meester, dispersal C. De by Moritz, & Isolation & J., wild: Mergeay, P., colonization. the Doughty, I., in J., Swillen, differentiation netic R. J., Vanoverbeke, Ellis, L., J., Orsini, the Fenker, of G., revision Australia. radiation. L. taxonomic northern lizard Integrative Tedeschi, from Australian Gekkonidae) convergence: (2019). M., tropical and M. A. Crypsis C. a Prasetya, Moritz, (2020). in & M., diversification G., P. L. ecological Tedeschi, Oliver, and C., R. Persistence Pratt, Biology J., rocks: a Evolutionary R. the Laver, in BMC S., off variation Bank, genetic Costa, and G., explain On L. . resistance . Ashman, . river M., O., P. and D. Oliver, Mesquita, distance T., by F. isolation Burbrink, lizard. suitability, M., whiptail Climatic Gehara, Brazilian A., (2018). V. Sao-Pedro, C. A., G. P. Martinez, F., E. Oliveira, 24 1) 8510.di 10.1111/jbi.12334 doi: 1895–1908. (10), 1) 7333.di 10.1111/mec.13275 doi: 3723–3737. (14), oeua Ecology Molecular nulRve fEooy vlto,adSystematics and Evolution, Ecology, of Review Annual Heredity , ilg Letters Biology 19 , , 1,11.di 10.1186/s12862-019-1408-1 doi: 1–15. (1), Genetics 22 110 iad:wnost h vlto fdiversity of evolution the to windows Lizards: , 120 2) 9359.di 10.1111/mec.12561 doi: 5983–5999. (24), 3) 55–55.di 10.1073/pnas.1305529110 doi: 15354–15359. (38), PeerJ 3,2125 o:10.1038/s41437-017-0017-2 doi: 251–265. (3), , , 197 14 , oeua Ecology Molecular 2020 2,5359 o:10.1534/genetics.114.164350 doi: 573–589. (2), 1,15 o:10.1098/rsbl.2017.0566 doi: 1–5. (1), 1,15.di 10.7717/peerj.7971 doi: 1–56. (1), 12 dcieshemionus Odocoileus rceig fteNtoa cdm fSciences of Academy National the of Proceedings T&TLb-eerh rneo University Princeton Labs-Research, T & AT alaamax Carlia oeua Ecology Molecular , 18 9,14–82 o:10.1111/j.1365- doi: 1848–1862. (9), eyaaustralis Gehyra Nature . oeua Ecology Molecular :Terlso clgcland ecological of roles The ): Vl ) nvo California of Univ 5). (Vol. , , 509 ora fBiogeography of Journal 44 , 27 79) 222–225. (7499), 1,4152 doi: 481–502. (1), 2,5052 doi: 520–532. (2), ru (Squamata: group oeua Ecology Molecular , 25 (6), . , Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. il,P . aok,D .(09.Tprts hlgne n iesfiain htaew estimating, we are What diversification: and phylogenies rates, estimates? Tip the 210X.13153 are (2019). Consider- L. good landscapes: D. how Rabosky, in and Isolation-by-distance & O., (2015). P. J. Title, H. Heck, Van genetics. & landscape R., for Holderegger, ations for V., surfaces J. resistance M. of phenotypic Use Strien, analysis. constrained (2010). and K. of 10.1111/j.1365-294X.2010.04657.x parameterization Scribner, & Evidence for doi: H., Considerations B. 3576–3591. McRae, (2011). studies: J., genetic M. J. Fortin, landscape N., Melville, Balkenhol, F., & S. P., Spear, lizards. L. agamid of Shoo, complex J., species 5646.2010.01211.x cryptic L. a Harmon, in populations. evolution L., stable and K. growing Smith, in Australia’s disequilibrium across Linkage Rabosky, test (1994). comparative . A M. . . Slatkin, speciation? O., of P. rate Title, radiation. the I., vertebrate predict Holmes, structure diverse distance: R., population most M. or Does Marchan-Rivadeneira, environment R., (2018). by M. L. isolation D. Grundler, Genetic H., Huang, common? (2014). S., most A. Singhal, is A. flow Hoffmann, gene & of B., pattern S. R. Which Hangartner, for P., development J. Sexton, integrated RStudio: by http://www.rstudio.com isolation (2015). from under Retrieved F-statistics Team. from Core flow RStudio gene of Restore estimation (2019). and differentiation D. P. Genetic distance. Wilson, (1997). delimitation. & F. provenance M., Rousset, species Merwe, der for van framework . . H., . genomics-era 538–548. McPherson, D., a A., T. Renew: Kilian, Hether, and J., M., Bragg, K. Patterns M., Hardwick, Explains Rossetto, Model S., Speciation Roches, Ephemeral Des An Scales. W., Rosalia: Time 9171-x J. Santa Across Meets Brown, Diversification Goldilocks J., of (2012). A. J. (2018). B. L. C. Sarver, Harmon, Moritz, B., . E. . uncertain . Rosenblum, P., sidesteps Doughty, Australia, S., Kimberley, Donnellan, the J., in D. diversity Coates, taxonomy. evolutionary K., for P. M. planning conservation Blom, (2016). Real-world M., C. Byrne, Australia. Moritz, F., of D. . End . Rosauer, Top . the G., Gillespie, from S., example Donnellan, an S., Conservation Catalano, priorities: W. Funk, G., conservation Bourke, and K., . hotspots P. . Phylogeography, . M. M., Blom, S. F., ( D. Haig, Rosauer, species I., Northwest. frog M. Pacific two Paez-Vacas, for US J., both connectivity the M. of predicts from Adams, drivers ) capacity in A., variation C. Dispersal Regional Pearl, (2018). A., C. M. (2014). Murphy, A. M., J. E. fishes. Robertson, Treml, reef & in and P., richness preference species S. habitat 10.1086/676505 and landscape, Blomberg, structure of M., genetic effects Y. population Genetic Buckley, (2017). C., Z. Riginos, M. species. Peery, turtle & co-occurring J., three D. on demography Mladenoff, N., B. Reid, Genetics osrainLetters Conservation , 204 , 145 39.di 10.1016/j.biocon.2016.05.002 doi: 83–93. , 4,1219–1228. (4), Heredity , ehd nEooyadEvolution and Ecology in Methods 11 oeua Ecology Molecular mrcnNaturalist American 148,11.di 10.1111/conl.12438 doi: 1–10. (12438), , vltoayBiology Evolutionary 114 1,2–7 o:10.1038/hdy.2014.62 doi: 27–37. (1), Evolution oeua Ecology Molecular 13 , 27 , , 192 1) 3242–3256. (16), Evolution 68 1,11.di 10.1111/evo.12258 doi: 1–15. (1), 4,4247 o:10.1086/699515 doi: 432–447. (4), , 39 mrcnNaturalist American 2,2521 o:10.1007/s11692-012- doi: 255–261. (2), , , , 65 26 10 Sui,Ic,Bso,MA Boston, Inc., RStudio, 4,9692 o:10.1111/j.1558- doi: 976–992. (4), 3,781–798. (3), 6,8184 o:10.1111/2041- doi: 821–834. (6), aapretiosa Rana oeua Ecology Molecular etrto Ecology Restoration Genetics , 184 , and 137 1,5–4 doi: 52–64. (1), .luteiventris R. 1,331–336. (1), , Biological 19 , 27 , (17), 42 (3), . Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. ausfrteATrgo;adC eeaie iermdlbtencaedph(ae nnme of number on (based depth clade between model linear millimetres) Generalized (in C) rainfall and and with Celsius) Map degrees region; A) (in AMT temperature (AMT). the monthly Tropics Average Kimberley, for Monsoonal — B) regions values York; Australian principal Cape three the the and of from End location Top data the highlighting with evapotranspiration, and figure rivers, elevation, Introductory 1. FIGURE and conceptual substantial writing legends to contributed Figure contributed authors CM All manuscript. and final analysis. JM data the and approved conceptualization writing. and with and contributed LT analysis or advice. data (Dryad editorial conceptualization, repository article. the led public of JF appropriate end be an the manuscript at in the included archived Contributions Should be be Author will 1. will DOI Material data results Supplementary the the the and at supporting Figshare) available traits, data are the Species’ results accepted, the phylogeography: supporting in data The Phenotypes (2016). Accessibility A. Data N. Mason, & diversification. C., , vertebrate R. and variation, Bell, environmental R., structure. paleo-environmental K. Harnessing genetic Zamudio, (2020). intraspecific L. predict Pellissier, to & https://doi.org/10.1111/eva.12986 N., data 1526–1542. D. genetic Karger, genetic and F., of Leugger, drivers modeling O., and Hagen, diversity G., Genetic Yannic, (2015). P. Zhang, 10.1007/s11258-015-0479-3 & doi: J., 925–937. Yang, X., Song, of A., differentiation S. Cushman, J., Yang, distance. by Isolation (1943). (2007). S. Wright, B. Din- genotyping- Traill, prospects & by future H., revealed and Nix, processes ecological scales B., . Mackey, values, . spatial . Z., different C. at J. N., Woinarski, flow Huron, gene 10.7717/peerj.5462 Contrasting L., doi: (2018). (e5462). Beaumont, Package). J. Dale, in L., (Software & by-sequencing J., Baumgarten, ENMTools S. M., Wells, Cardillo, (2019). N., , R. Matzke, L., nage, environment. D. by Isolation Warren, a (2014). S. variation: G. 10.1111/mec.12938 genetic Bradburd, doi: spatial & J., on I. heterogeneity isolation. Wang, ecological landscape and of geographic effects quantifying for full 340-3411. approach the of regression divergence Examining matrix (2019). genomic multiple C. D. (2013). and Cannatella, structure I.J. & Wang, population T., M. shapes savanna. Rodrigues, Cerrado change M., Neotropical climate the E. in Ortiz, Historical treefrogs N., J. instability: Weber, by R., Isolation G. Colli, M., M. Vasconcellos, threatened Percichthyidae: another confirm (Teleostei: to blackfishes data (2017). Evolution river allozyme B. L. and of Beheregaray, sequence & species between A., conflict T. candidate key Raadik, a M., resolve Adams, P., SNPs M. Genome-wide Hammer, J., Sandoval-Castillo, J., P. Unmack, 113 doi:10.5281/zenodo.3268814 2) 8041–8048. (29), , 109 1–2.di 10.1016/j.ympev.2017.02.013 doi: 415–420. , euui soongorica Reaumuria scau armatus Isocladus . asvl oorplmrhcNwZaadmrn isopod. marine Zealand New polymorphic colour massively a , fteInrMnoi lta nChina. in plateau Mongolia Inner the of Genetics oeua Ecology Molecular abra N Press. e ANU Canberra: . , 28 14 2,114–138. (2), URL:Https://Github.Com/Danlwarren/ENMTools. rceig fteNtoa cdm fSciences of Academy National the of Proceedings h aueo otenAsrla tsnatural It’s Australia: Northern of Nature The , 28 Gadopsis oeua Ecology Molecular 7,14–74 o:10.1111/mec.15045 doi: 1748–1764. (7), vltoayApplications Evolutionary ). oeua hlgntc and Phylogenetics Molecular ln Ecology Plant , 23 Evolution 2) 5649–5662. (23), , PeerJ 216 13(6), , 67-12, , (7), , 6 Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. IR,adtesaitclsgicneo h ulGMmdl ihtopriua aibe htbs ex- best significant that statistically variables represent particular bold in two values with Model model, models. GDM full the full of (p the (per- each surface in of resistance proportion divergence and significance the genomic (IBE) plained statistical climate demonstrating (IBD), the analyses, distance and geographic modelling by (IBR), dissimilarity explained divergence generalized genomic most of (Loc) from centage) in SNPs Results used of 2. (N) number section). TABLE samples Methods specialization; the of habitat in number (IBR); information ( on models (more heterozygosity data Observed slope resistance including filtering; the after paper, in and our used before in samples included and lineages analyses, each Lizard for 1. indicated TABLE are values Significance legends Table preferences. habitat different heterozygosity observed represent against regression. Symbols age clade and groups. area estimate. clade distribution point lineage the mean is uncer- of ( the circle plot represent the Regression Slopes where requirements 4. estimate, relationship. habitat FIGURE IBD IBD and slope details) stronger more show the for values along Methods Higher (width) environmen- see tainty generalists). same proxy; agamids), a vs. and as geckos specialists rainfall (skinks, (rock annual taxa mean across analyses. (using plotted all condition graphs tal across distance by dataset Isolation our clade Colours 3. in lizard FIGURE C) found break. skink Analysis; species Gulf AMT distinct Coordinate the three the Principal represent of for B) Colours east analysis plot; present structure distance lizard lineages by Species four known preferences. habitat the all 2. different for includes FIGURE represent values) Symbols This (logkm2 clade. area lizard paper. distribution different the represent clade in and included trees) mtDNA genera ultrametric of substitutions Ho < .5 aus hddvle ersn h otsgicn results. significant most the represent values Shaded values. 0.05) ,Tjm’ ausadioainb itne(B)soevle.Clusrpeetdffrn lizard different represent Colours values. slope (IBD) distance by isolation and values D Tajima’s ), Ho 15 ,Tjm’ ausadioainb itne(IBD) distance by isolation and values D Tajima’s ), ConStruct nlssadD) and analysis alaamax Carlia FastStructure hr )Isolation A) where , analysis. Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary.

Genetic distance (1−propShared alleles) Temperature (°C) 100 110 120 130 −10

PCoA Axis 2 ( 23 %) 10 20 30 40 50 60 70 80 90

0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0 0 0 J FM A MJ A SOND −10 Euclidean Geog A ETE 5 GULF raphic distance PCoA Axis1(43.3%) Months 10 0 15 Rainfall (mm) B 10 0 20 40 60 80 100 120 140 160 180 200 220 240 260 WTE

Clade Geographic Range 12 9 6 16 B 0.1 D F astStructure C ConStruct -spatialanalysis 0.2 amax_WTE Clade Depth Carlia amaxgroup 0.3 amax_GULF 0.4 p=0.3577 r2= -0.0021 0.5 C Species group Habitat preference amax_ETE Skinks Carlia Geckos Heteronotia Geckos Gehyra Dr generalist rock-related agons Diporiphora Posted on Authorea 26 Oct 2020 — The copyright holder is the author/funder. All rights reserved. No reuse without permission. — https://doi.org/10.22541/au.160372863.34740745/v1 — This a preprint and has not been peer reviewed. Data may be preliminary. monsoonal-tropics of-phylogeographic-structure-among-co-distributed-taxa-across-the-complex-australian- Table2.xlsx file Hosted monsoonal-tropics of-phylogeographic-structure-among-co-distributed-taxa-across-the-complex-australian- Table1.xlsx file Hosted D A vial at available at available 10 11 12 13 0.1 0.2 0.3 0.4 0.5 Clade Age Mean Lineage Area 0.0 9 0.00 0.00 0.05 0.05

Obers Oberse 0.2 Lizard groups Lizard IBD slope

e

v v

ed Heterozygosity ed Heterozygosity 0.10 0.10 0.4 P−v 0.15 0.15 P−v alue =0.800 alue =0.005 https://authorea.com/users/370133/articles/488881-predictors- https://authorea.com/users/370133/articles/488881-predictors- Geckos Gehyra Geckos Heteronotia Agamids Diporiphora Skinks Carlia 0.6 0.20 0.20 E B 10 11 12 13 0.0 0.1 0.2 0.3 0.4 0.5 9 P−v alue =0.081 0.0 −1.0 −1.0

Tajima’s D values Tajima’s D values 0.2 Mean annualrainfall −0.5 −0.5 IBD slope 17 P−v alue =0.194 0.4 0.0 0.0 F C 10 11 12 13 0.0 0.2 0.4 9 0.6 Drier Wetter 0.2 0.2 0.0

IBD slope IBD slope 0.4 0.4 P−v P−v 0.2 alue =0.212 alue =0.029 Habitat specialization Habitat 0.6 0.6 IBD slope Species group 0.4 Habitat preference rock-related Skinks Carlia Geckos Heteronotia Geckos Gehyra Dr generalist agons Diporiphora 0.6 Generalists Rock Specialists