Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling

Wielstra et al. Frontiers in Zoology 2013, 10:13http://www.frontiersinzoology.com/content/10/1/13

RESEARCH Open Access

Tracing glacial refugia of Triturus newts based onmitochondrial DNA phylogeography and speciesdistribution modelingBen Wielstra1,2*, Jelka Crnobrnja-Isailović3,4, Spartak N Litvinchuk5, Bastian T Reijnen1, Andrew K Skidmore2,Konstantinos Sotiropoulos6, Albertus G Toxopeus2, Nikolay Tzankov7, Tanja Vukov4 and Jan W Arntzen1

Abstract

Introduction: The major climatic oscillations during the Quaternary Ice Age heavily influenced the distributionof species and left their mark on intraspecific genetic diversity. Past range shifts can be reconstructed with the aidof species distribution modeling and phylogeographical analyses. We test the responses of the different membersof the genus Triturus (i.e. the marbled and crested newts) as the climate shifted from the previous glacial period(the Last Glacial Maximum, ~21 Ka) to the current interglacial.

Results: We present the results of a dense mitochondrial DNA phylogeography (visualizing genetic diversity withinand divergence among populations) and species distribution modeling (using two different climate simulations) forthe nine Triturus species on composite maps.

Conclusions: The combined use of species distribution modeling and mitochondrial phylogeography provides insightin the glacial contraction and postglacial expansion of Triturus. The combined use of the two independent techniquesyields a more complete understanding of the historical biogeography of Triturus than both approaches would on theirown. Triturus newts generally conform to the ‘southern richness and northern purity’ paradigm, but we also find moreintricate patterns, such as the absence of genetic variation and suitable area at the Last Glacial Maximum (T. dobrogicus),an ‘extra-Mediterranean’ refugium in the Carpathian Basin (T. cristatus), and areas where species displaced one anotherpostglacially (e.g. T. macedonicus and western T. karelinii). We provide a biogeographical scenario for Triturus, showingthe positions of glacial refugia, the regions that were postglacially colonized and the areas where species displaced oneanother as they shifted their ranges.

Keywords: Colonization, Contact zone, Historical biogeography, Ice Age, Introgression, Quaternary

IntroductionThe Quaternary Ice Age (~2.59 Ma-present) is associ-ated with large climatic oscillations [1-4]. Long, cold anddry glacial cycles are alternated by relatively short, warmand wet interglacials. The transition between the twotakes place in a geological blink of an eye [5,6]. Theclimatic oscillations have a major impact on speciesdistribution patterns [1]. Generally, impacts of glacial cyclesare more extreme further away from the equator (and at

* Correspondence: [email protected] Biodiversity Center, P. O. Box 9517, 2300 RA, Leiden, TheNetherlands2University of Twente, Faculty of Geo-Information Science and EarthObservation – ITC, P.O. Box 6, 7500 AA, Enschede, The NetherlandsFull list of author information is available at the end of the article

© 2013 Wielstra et al.; licensee BioMed CentraCommons Attribution License (http://creativecreproduction in any medium, provided the or

higher elevations): areas at a higher latitude (and eleva-tion) become inhospitable, whereas climate at a lowerlatitude (and elevation) remains habitable [1,7]. Popula-tions at higher latitude may cope with climate changein situ, through adaptation or phenotypic plasticity, oralternatively they may track suitable habitat [6,8].However, a more likely outcome is that such popula-tions go extinct [8-10]. At lower latitudes, populationscan endure glacial periods relatively unimpaired, in socalled glacial refugia [2,10,11]. During subsequent in-terglacials, species can reclaim their former distribution,by rapidly recolonizing the large tracts of still uninha-bited, but freshly habitable land from glacial refugia [1].

l Ltd. This is an Open Access article distributed under the terms of the Creativeommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andiginal work is properly cited.

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Table 1 Systematics of the genus Triturus

Species Authority

Marbled newts

Triturus marmoratus (Latreille, 1800)

Triturus pygmaeus (Wolterstorff, 1905)

Crested newts

Triturus cristatus (Laurenti, 1768)

Triturus carnifex (Laurenti, 1768)

Triturus macedonicus (Karaman, 1922)

Triturus dobrogicus (Kiritzescu, 1903)

Triturus karelinii * (Strauch, 1870)

eastern species

central species

western species

*The taxon traditionally referred to as T. karelinii actually comprises threedistinct species for which the taxonomy still has to be worked out fully [22,23].For now these three species are referred to as the western, central and easternT. karelinii species.

Wielstra et al. Frontiers in Zoology 2013, 10:13 Page 2 of 14http://www.frontiersinzoology.com/content/10/1/13

The fluctuating climate during the Quaternary has leftits mark on patterns of genetic diversity [1-3,5,12]. Po-pulations persisting in glacial refugia have a relativelylong and stable demographical history compared to thosein areas claimed postglacially. As a result, populations inglacial refugia are characterized by high levels of geneticdiversity, whereas populations established after the mostrecent glacial cycle typically show little genetic variation.This is the concept – devised from a northern hemisphereperspective – of ‘southern richness and northern purity’[1]. Furthermore, species displacement after postglacialsecondary contact regularly coincides with introgression ofgenetic material (especially of cytoplasmic DNA) [12,13].By uncovering the spatial structuring of genetic line-ages between glacial refugia and along recolonizationroutes, and by detecting mismatches between geneticmarkers and species boundaries, phylogeographicalsurveys provide insights in past distribution rearrange-ment [9].Independent from genetics, species distribution mo-

deling can be applied to answer historical biogeographicalquestions [14]. Species distribution modeling involves theapproximation of the ecological requirements of a species,based on the range of environmental conditions experi-enced at known localities [15]. The constructed model canthen be extrapolated on current climate layers, to deter-mine the species’ potential distribution. Similarly, themodel can be projected on climatological reconstructionsof the past. Niches evolve over time, questioning thevalidity of predicting past distributions based on presentday models. However, considering the relatively shorttime period spanning the shift from the last glacialcycle to the present day, niche conservatism is a realis-tic assumption [16,17]. Comparison of present and pastpotential distribution provides information on rangeshifts.The distribution of amphibians is tightly linked to

environmental conditions and thus has to follow suit inthe face of rapid climate change [18-21]. The marbledand crested newts (genus Triturus) are a group of nineclosely related species [22,23] (Table 1). Triturus newtsare distributed across most of Europe and adjacent Asia(Figure 1). They are found in regions generally regardedas important glacial refugia, such as the Iberian, Italianand Balkan Peninsulas, Anatolia, Caucasia and thesouthern Caspian basis [3,9,10]. On the other hand,Triturus newts occupy large tracts of land which wouldhave been uninhabitable during glacial periods, particu-larly temperate Eurasia [24]. Thus, within this singlemodel system, we expect to observe varying responses toglaciation.We first conduct a dense phylogeographical survey:

employing mitochondrial DNA, we determine geographicalgenetic structuring (i.e. diversity within and divergence

among populations) for each of the different Triturusspecies. We also delineate areas where mitochondrial DNAhas been asymmetrically introgressed. Subsequently, we ap-proximate the distributions of the different Triturus speciesat the Last Glacial Maximum (~21Ka) using species distri-bution modeling. The outcome of the two independentapproaches is visualized on composite maps, which showthe nine different species side by side. Our hypothesis isthat those areas predicted suitable at the Last Glacial Max-imum based on species distribution models are also theones showing the highest genetic diversity. Vice versa, weexpect areas suggested to have been unsuitable at the timeare genetically depleted. We summarize the signatures ofpast distribution dynamics as inferred from mitochondrialDNA phylogeography and species distribution modeling ona map.

MethodsGenetic approachGenerally the identification of Triturus newts is straight-forward based on geography (Figure 1). For individualsoccurring near the contact zone more care should betaken. The Triturus individuals included were in the firstplace identified based on morphology [26,27] and for asubset nuclear genetic data confirming their identity [23](Arntzen et al., submitted; Wielstra et al., unpublisheddata).We included genetic data (658 bp of subunit 4 of the

NADH dehydrogenase gene complex; ND4) for 2470Triturus newts, representing 493 populations (Figure 2and Additional file 1). A large proportion of these indi-viduals (n = 1795) was taken from previous studies (pub-lished [25,28-32] or submitted [Arntzen et al.]; seeAdditional file 1 for details). The remainder (n = 675)

Figure 1 The distribution of the genus Triturus. Species mostly meet at parapatric contact zones, but note the area of sympatry of T.marmoratus and T. cristatus in western and central France. This map is adapted from [25].


was newly sequenced for the current paper, using theprimers in Table 2 and following the protocol outlinedin [33]. Sequences were manually aligned and identicalones merged into haplotypes using MacClade 4.08 [34].Our purpose was 1) to test for mitochondrial DNAintrogression between species, and 2) to infer the spatialgenetic variation within each species.To detect which individuals retain their ‘original’

mitochondrial DNA and which possess introgressedmitochondrial DNA (derived from one of the other spe-cies), we first assigned mitochondrial DNA haplotypes tospecies by conducting a Neighbor Joining analysis inMEGA 5.05 [35]. Calotriton asper was used as outgroup(sequence taken from [28]; GenBank accession number:GU982378).To infer interspecific geographical structuring, we first

excluded introgressed mitochondrial DNA (as it doesnot properly reflect the evolutionary history of either‘host’ or ‘donor’). Subsequently, we determined – foreach species – 1) the genetic diversity within populationsand 2) the genetic distance among populations. Themean number of pairwise differences among haplotypes(π), as determined with Arlequin 3.5 [36], was used as ameasure of genetic diversity within populations. To de-termine the genetic distance among populations we used

Alleles in Space 1.0 [37]. Alleles in Space connects thepopulations in a network, based on Delaunay triangula-tion. Subsequently, the program produces values ofaverage genetic distance among the populations that areconnected by the network (the proportion of mismatchednucleotide sites; Zi). These values are positioned at the mid-points of the connections in the network. For the measureof genetic diversity within populations we only includedpopulations for which more than one sequence was avai-lable (π will always be zero for populations with only onesequence).We interpolated the values for π and Zi across geo-

graphical space using inverse distance weighting in thespatial analyst extension of ArcGIS (www.esri.com) [38].The output for each species was cropped according toits distribution range (Figure 1). For both π and Zi, wecompiled a single composite map from the nine crops(i.e. one for each Triturus species). We used a colorscheme running from blue to red to reflect low to highvalues of π and Zi. Using a single scale for all Triturusspecies facilitates comparison among them, but runsthe risk that variation in genetically poor species isovershadowed by that of genetically rich species.Hence, we additionally apply a species specific scaleto better express intraspecific structuring. This way

http://www.esri.com/

Figure 2 Maps showing the Triturus localities used for the mitochondrial DNA phylogeography and species distribution modeling. Theinset shows part of the localities used in the genetic analyses (white circles) and the additional ones used for species distribution modeling (blackcircles). Two cut-outs (A and B) show more details on introgressed mitochondrial DNA: populations containing foreign mitochondrial DNA arelabeled with a colored star (with the color denoting the ‘donor’ species) and populations containing two mitochondrial DNA types (of which onemostly, but not exclusively, is of the original species) as a black star; populations containing original mitochondrial DNA are again labeled withwhite circles. The colors used correspond to Figure 1. Details on localities can be found in Dataset S1 and S2.


we managed to visualize 1) regions of relatively highand low intraspecific genetic diversity and 2) therelative genetic divergence among populations withinspecies.

Species distribution modeling approachWe composed a database of 4532 Triturus localities(incorporating the 493 populations included in thegenetic analysis; see Figure 2 and Additional file 2). Thisdatabase is based on [39] and updated in the light of thetaxonomic developments [23] (see Table 1). For most spe-cies the majority of localities concern accurate locations,

but in particularly for the wide ranging T. cristatus wemostly only had the name of the nearest village of a lo-cality or digitized atlas data to our disposal. Species dis-tribution models are still expected to perform relativelyaccurately if spatial error in localities is introduced andthe method we use (Maxent, see below) is robustagainst such errors [40]. We used the bioclimatic vari-ables available at a 2.5 arcminute resolution (c. 5 ×5 km) from the WorldClim database version 1.4 [41] tocalibrate our species distribution models. In order toprevent model overfitting, which would negatively influ-ence transferability [16,42], we minimized multicolinearity

Table 2 Primers used for amplification and sequencing ofthe ND4 mitochondrial DNA fragment

Species or groups of species with their forwardand reverse primers

Source

T. marmoratus

MARF1: CACCTGTGATTACCTAAAGCTCATGTAGAAGC This study

ND4R2: CCCTGAAATAAGAGAGGGTTTAA [33]

T. pygmaeus

PYGF1: CACCTCTGATTGCCTAAAGCCCACGTAGAGGC This study

ND4R2: CCCTGAAATAAGAGAGGGTTTAA [33]

T. karelinii group of crested newts

KARF4: AGCGCCTGTCGCCGGGTCAATA [33]

KARR1: AACTCTTCTTGGTGCGTAG [33]

Other crested newts

KARF4: AGCGCCTGTCGCCGGGTCAATA [33]

DOBR2: GTGTTTCATAACTCTTCTTGGT [33]


among data layers by selecting a subset that showed aPearson’s correlation of r < 0.7. Furthermore, focusing onclimate layers that are deemed biologically meaningfulbased on life history knowledge of the model system yieldsthe most appropriate species distribution models [43]. Vari-ation in the availability of standing water during the breed-ing season appears to have driven the ecological radiationamong Triturus newts [25]. Taking these two points intoconsideration, we included a set of layers that encompassesseasonal variation in evaporation and precipitation, in casubio10 =mean temperature of warmest quarter, bio11 =mean temperature of coldest quarter, bio15 = precipitationseasonality, bio16 = precipitation of wettest quarter,and bio17 = precipitation of driest quarter. Bioclimaticvariables are also available from the WorldClim data-base for the Last Glacial Maximum (~21 Ka). Thesedata are derived from the Paleoclimate ModellingIntercomparison Project phase 2 [44]; [http://pmip2.lsce.ipsl.fr/] and based on two climate simulations: theModel for Interdisciplinary Research on Climate version3.2 (MIROC) and the Community Climate SystemModel version 3 (CCSM) [45].Species distribution models were created with Maxent

3.3.3 k [46]. We restricted the feature type to hingefeatures as this produces smoother model fits, so forcingmodels to be focused on key trends rather than potentialidiosyncrasy in the data. This approach facilitates ex-trapolation to a different time (or place) [47]. The envir-onmental range covered by the pseudo-absence data,used to discriminate presence data from background,should neither be too narrow nor too broad [48-50].Too narrow a range results in complex models that donot generalize well, whereas too broad a range results intoo simple models that focus on coarse-scale and neglectfine-scale variation. A practical solution is to focus on

area that is potentially accessible to the species of inter-est if it were not for abiotic factors, i.e. an area wherespread would not be hampered by major physicalbarriers. However, competition could lead to further ex-clusion of the target species, even though the area wouldbe suited in the absence of such biotic interactions [51].Taking these considerations into account, we restrictedthe area from which pseudo-absence was drawn to thedistribution of the entire genus Triturus. This area wasbroadly defined as a 200 km buffer zone [48] aroundknown Triturus localities (Additional file 2).The species distribution models were tested for statis-

tical significance against a null model derived fromrandom localities [52]. For each tested species distribu-tion model, we created a null distribution of 99 AUCvalues. These AUC values were derived from speciesdistribution models, based on as many random localitiesas used for the tested species distribution model. TheAUC value of the tested species distribution model wastreated as a 100th value and deemed statistically significantif it ranked higher than the 95th value (i.e. above the 95%confidence interval). Random point data were created withENMTools 1.3 [53]; [http://enmtools.blogspot.com/]. Thenull model approach prevents interpreting model qualitybased on an arbitrary AUC threshold and precludes therequirement to set aside part of the localities for modeltesting [52].The species distribution models were projected on the

current and Last Glacial Maximum climate layers. Com-posite maps were created for each set of climate layers(in the same way as explained for the genetic approach).Maxent provides predicted probability values betweenzero and one and we used a color scheme running fromblue to red to reflect these values.

ResultsThe 2470 Triturus sequenced newts comprise 315 hap-lotypes (see Additional file 1 for details and Additionalfile 3 for GenBank accession numbers). The NeighborJoining phylogeny shows that haplotypes group in ninereciprocally monophyletic lineages, corresponding tospecies (Figure 3). A large number of individuals (n = 408; c.16.5%) that belong to one species, possess mitochondrialDNA characteristic of another (Figure 2 and Additional file 1).This mitochondrial DNA introgression is mostly restricted tonear the contact zones; only T. pygmaeus, T. macedonicus,T. cristatus and central T. karelinii have extended rangesin which foreign mitochondrial DNA is present (derivedfrom T. marmoratus for the first one and from westernT. karelinii for the other three).The number of populations included to determine gen-

etic diversity within (π) and genetic divergence among(Zi) populations was 44 and 48 out of 52 populationsfor T. carnifex, 60 and 88 out of 104 for T. cristatus,

http://pmip2.lsce.ipsl.fr/

http://pmip2.lsce.ipsl.fr/

http://enmtools.blogspot.com/

Figure 3 A Neighbor Joining phylogeny for the Triturus ND4 haplotypes. The Triturus haplotypes cluster into nine monophyleticmitochondrial DNA lineages, corresponding to species and colored as in Figure 1. Significantly supported branches (≥ 80%, based on a thousandbootstrap replicates) are denoted with an asterisk. The Calotriton asper outgroup used to root the phylogeny is not shown.


29 and 40 out of 44 for T. dobrogicus, 55 and 79 out of79 for western T. karelinii, 13 and 16 out of 24 for centralT. karelinii, 27 and 31 out of 31 for eastern T. karelinii, 32and 37 out of 72 for T. macedonicus, 34 and 36 outof 36 for T. marmoratus and 49 and 50 out of 51 forT. pygmaeus (populations containing asymmetricallyintrogressed mitochondrial DNA were excluded com-pletely and populations represented by only a single indi-vidual were additionally excluded in the calculation of π).Measures of genetic diversity within and genetic divergenceamong populations can be found in Additional file 1 and 4.Composite maps visualizing genetic structuring in eachspecies are depicted in Figures 4 and 5. Because Alleles inSpace places the values for genetic divergence among popu-lations at the midpoint of the network connecting the pop-ulations, information falling outside the current range islost in Figure 5 (e.g. in the case of two allopatric popula-tions). Uncropped maps for each species, showing a morecomprehensive picture per species but at the cost of con-ciseness, can be found in Additional file 5. Triturus species

generally show considerable spatial variation in their ge-netic composition (reflected by ‘warm’ and ‘cold’ areas inFigures 4 and 5).Species distribution models perform significantly bet-

ter than random (Additional file 6). Composite mapsdepicting the predicted suitability of each species’ distri-bution range at the Last Glacial Maximum, based on thetwo different climate simulations (MIROC and CCSM),are provided in Figure 6. As species were not necessarilybound to their current ranges through time, projectionsfor each species on a wider area (roughly the distributionof the entire genus Triturus) are provided in Additionalfile 7. The ranges of all Triturus species are predicted tohave been restricted at the Last Glacial Maximum. Acomposite map showing species distribution modelsprojected on present day climate layers is provided inFigure 6. Predicted suitability of the distribution rangeof each species under current conditions shows a con-siderable overlap with the sketched outlines of theirranges (Figure 1).

Figure 4 The geographical distribution of genetic variation for the different Triturus species. These are composite maps for all nineTriturus species. For each species, the genetic variation within each population (π) was determined and subsequently interpolated across itsdistribution range. In Figure 4a we use a single scale for all Triturus species (allowing direct comparison among species) whereas in Figure 4b weuse a species specific scale (better expressing genetic structure in genetically relatively poor species). Warmer colors refer to a higher geneticdiversity. The insets show the situation for T. cristatus (left) and T. marmoratus (right) in their area of sympatry (shown in dark gray on themain maps).


DiscussionBefore we discuss our results, we first consider wherecare should be taken when interpreting genetic orspecies distribution modeling results in isolation andexplain how interpretation of the results of the two inde-pendent techniques together at least partially negatesthese problems. Then we identify the probable glacialrefugia and postglacially colonized area for each Triturusspecies. Finally, we summarize our scenario regardingthe biogeographical response of Triturus newts to theclimate shift from the previous glacial period to thecurrent interglacial on a map.

Considerations and combination of the genetic andspecies distribution modeling resultsA high value for genetic diversity within populations(Figure 4) can result from two distinct processes:

postglacial secondary contact from distinct sourcepopulations or long term presence in situ throughoutglacial-interglacial cycles (potentially enforced by a lowconnectivity between populations due to e.g. xeric envi-ronment) [9]. To separate these two processes, phyloge-netic relationships among haplotypes provide some insight.For example, the obvious ‘hotspot’ found in T. cristatus(SE Poland) likely reflects the comingling of haplotypesbelonging to the two distinct clades present in this spe-cies (Figure 3; Additional file 1). On the other hand, thehotspot found in eastern T. karelinii (SE Azerbaijan)likely represents standing genetic variation, given the lackof phylogeographic structure among the haplotypes in-volved. Species distribution modeling can further help todistinguish both processes by establishing whether anarea became suitable only recently or whether it hascontinuously been so on the long term.

Figure 5 The genetic (dis)similarity among populations within the different Triturus species. These are composite maps for all nine Triturusspecies. For each species, the genetic divergence among populations (Zi) was determined and subsequently interpolated across its distributionrange. In Figure 5a we use a single scale for all Triturus species (allowing direct comparison among species) whereas in Figure 5b we use aspecies specific scale (better expressing genetic structure in genetically relatively poor species). Warmer colors refer to a higher geneticdivergence. The insets show the situation for T. cristatus (left) and T. marmoratus (right) in their area of sympatry (shown in dark gray on themain maps).


Maps showing genetic divergence among populations(Figure 5) reflect a different aspect of geographicalgenetic structuring. On the one hand, areas with a highgenetic overturn among populations (e.g. the southernpart of the range of T. macedonicus) show manyhotspots. Such areas would be expected to have beenhabitable on the long term. On the other hand, genetic-ally homogenous areas, due to high levels of gene flowand/or demographic expansion stand out as cold areas(e.g. both of these processes likely determine the absenceof genetic structuring among T. dobrogicus populations).These two processes are more difficult to disentangle:whereas demographic expansion would particularly beexpected in areas that became suitable postglacially,gene flow could reduce genetic structuring both in-side and outside of glacial refugia. In some casesgeographical barriers to gene flow stand out, such as

the Greater Caucasus mountain range separatingeastern T. karelinii on its northern and southernside. Such barriers reflect climatically unsuitable re-gions and can be identified as such in the speciesdistribution models.The reliability of genetic diversity values for popula-

tions depends on sample size, and some populations arerelatively poorly sampled in our study. The genetic turn-over between populations provides additional insightthat counters the adverse effects of low sample sizes. Ifpopulations within an area are genetically diverse, butsample sizes per population are low, genetic diversityper population might be underestimated due to chanceeffects in sampling. However, these effects are randomper population. Therefore, the more populations that arebeing compared within that area, the less likely it is thatactual genetic diversity remains hidden by chance. For

Figure 6 The predicted suitability of each Triturus species’ range at the Last Glacial Maximum (MIROC and CCSM model) and at thepresent. This is a composite map for all nine Triturus species. For each species, its species distribution model was projected on MIROC (a) andCCSM (b) Last Glacial Maximum climate layers and on current climate layers (c), cut according to its current distribution range. Warmer colorsrefer to a higher predicted suitability. The insets show the situation for T. cristatus (left) and T. marmoratus (right) in their area of sympatry (shownin dark gray on the main map).


this same reason determining the genetic turnover be-tween populations is less susceptible to sample size tobegin with: low sample size per population adds up to abig sample sizes within a region. So, in short, if multiplepopulations within a region are found to be genetically

similar, even though samples sizes per population arelow, together they provide a good indication that geneticdiversity in the region really is low. We thus recommendexploring genetic diversity within populations and gen-etic divergence among populations together to improve


the estimate of spatial genetic variation, especially whensample sizes are low.We present the results of a single (mitochondrial) gen-

etic marker. Although we consider our results indicative(and use the species distribution modeling results as anindependent check), it should be taken into account thata single marker may not accurately reflect specieshistory [54]. In fact, we explicitly exploit the mismatchbetween species history and mitochondrial DNA in thecase of asymmetrically introgressed mitochondrial DNA.Asymmetrically introgressed mitochondrial DNA maysuggest that species displaced one another (e.g. [29,55]).For Triturus, the similarity of the introgressed mito-chondrial DNA to that present in the ‘donor’ speciessuggests a recent (postglacial) transference across thespecies boundary. Given the climatic oscillations duringthe Quaternary Ice Age, the Triturus newt species arepresumed to have been in periodic contact through time.However, we did not identify ancient mitochondrialDNA introgression events (as in e.g. [56-58]). A poten-tial explanation is that introgressed mitochondrial DNAwas limited to areas of postglacial expansion and never

Figure 7 Biogeographical scenario for Triturus in response to the lastapproximate positions of glacial refugia (dark shades) and postglacially coloroutes). Areas where species displaced one another (containing asymmetricdobrogicus, neither the genetic nor species distribution modeling approachtext for further details.

penetrated those areas that repeatedly acted as glacial re-fugia. In effect, any mitochondrial DNA that becameintrogressed during an interglacial would be erased bythe next glacial period. Species distribution modeling al-lows us to test this hypothesis.A disadvantage of species distribution models is that

they cannot distinguish between potential and realizeddistribution: areas that had suitable conditions at theLast Glacial Maximum may not have actually beeninhabited. Genetic information can narrow down theposition of glacial refugia (e.g. both France and theCarpathian Basin are suggested to have had suitable con-ditions during the Last Glacial Maximum for T. cristatusbut only the latter shows the high genetic variationexpected to be present in a former glacial refugium). An-other issue is that species distribution models are basedon the climatic conditions currently experienced by aspecies, but the actual conditions under which theycould occur might be broader (e.g. no suitable condi-tions were predicted to have been present at the LastGlacial Maximum for T. dobrogicus, but yet it didsurvive to the present day).

glacial to current interglacial climate shift. Shown are the inferrednized area (light shades; arrows reflect approximate colonizationally introgressed mitochondrial DNA) are shown in grey. For T.manages to recover a refugium (reflected by question marks). See


Glacial refugia and area postglacially colonized for eachTriturus speciesThe two marbled newt species illustrate the different ef-fect of glaciations on species with a relatively southernand relatively northern distribution. The northern T.marmoratus is predicted to have withdrawn its range atthe Last Glacial Maximum more extensively than thesouthern T. pygmaeus (especially according to theCCSM climate simulation; Figure 6). In T. marmoratusgenetic structuring is highest in the south of its range,but for T. pygmaeus it is not (Figures 4 and 5). The pres-ence of T. marmoratus mitochondrial DNA in thenorthern part of the range of T. pygmaeus suggests thatin this area the former was replaced by the latter[31,59,60]. This is confirmed by the distribution modelsprojected on Last Glacial Maximum climate data outsideof the current species ranges (Additional file 7): at thetime, area predicted suitable for T. marmoratus (but notfor T. pygmaeus) was present in the northern part of thearea currently occupied by T. pygmaeus. Striking is thatboth T. marmoratus and T. pygmaeus mitochondrialDNA occurs throughout this area (Figure 2). Although aspecies will often simply show foreign mitochondrial DNAwhere it replaced another [13] see below for examplesin Triturus, exceptions similar to the T. marmoratus-T. pygmaeus case are known (e.g. [61]).The species distribution models based on the two cli-

mate simulations agree that most of the range of T.cristatus was unsuitable at the Last Glacial Maximum(Figure 6). Suitable area was suggested to be present atthe Last Glacial Maximum in both the Carpathian Basinand in France. The genetic data suggest that only theCarpathian Basin acted as a glacial refugium at the LastGlacial Maximum as genetic diversity here is high(Figures 3, 4, 5). This patterns corresponds to the geo-graphical distribution of genetic variation based on nu-clear DNA markers found in previous studies (MHCgenes, allozymes and microsatellites [62,63]). This alsosuggests that the remainder of T. cristatus’ current range(including France) was colonized post-glacially, as thespecies is genetically depleted outside of the CarpathianBasin. Although T. cristatus shows asymmetricallyintrogressed western T. karelinii mitochondrial DNAsouth of the Danube river, relatively distinct T. cristatushaplotypes also occur here (Figures 2 and 3, Additionalfile 1), suggesting a complex pattern of both range ex-pansion at the cost of western T. karelinii but also longterm presence south of the Danube.For T. carnifex there is a discrepancy between the two

climate simulations, with MIROC showing a moreextensive predicted distribution at the Last Glacial Ma-ximum than CCSM. Particularly CCSM suggests a moreconsiderable range reduction and no suitable area in thecurrent northern Balkan range (Figure 6). The genetic

data better correspond to the modeled distributionbased on MIROC, as it shows distinct genetic clustersdistributed on both sides of the Adriatic Sea and highgenetic diversity in Italy (Figures 3, 4, 5), supporting longterm presence in the Balkans and a stable population inItaly (see [32] for more detail). Both climate simulationsagree that T. carnifex colonized the part of its range eastand north of the Alps postglacially. The T. carnifexmitochondrial DNA introgressed into T. cristatus wherethe two species meet suggests that Balkan T. carnifexwere involved in this range expansion.Compared to the other Triturus species, intraspecific

genetic diversity (i.e. phylogenetic depth) is low in T.dobrogicus (Figure 3). Triturus dobrogicus shows a‘starburst’ phylogeny – a large number of similar haplo-types – suggesting a demographic expansion after abottleneck. At the Last Glacial Maximum, the range ofT. dobrogicus is predicted to have been unsuitable(Figure 6). This is in line with a population bottleneck.However, the species distribution models do not make itpossible to pinpoint a potential refugium: the entirecurrently occupied range was predicted unsuitable at theLast Glacial Maximum. Suitable area was also notavailable outside the current range (Additional file 7).Evidently, T. dobrogicus must have had a refugium some-where and given its specialization on river floodplains(e.g. [64]) this was likely positioned within its currentrange. Furthermore, compared to the other Triturusspecies T. dobrogicus does not show reduced geneticdiversity of nuclear DNA (allozymes [65]). This suggeststhat perhaps the actual ecological niche of T. dobrogicusis broader than is currently realized (and used to con-struct the species distribution models). Another optionwould be that the niche of T. dobrogicus has evolved ina brief time interval to adapt to the environmental shift[66]. We consider this less likely as T. dobrogicus musthave survived multiple glacial-interglacial cycles in situ.The western part of the current range of T. macedonicus

is predicted to have been suitable during the Last GlacialMaximum (Figure 6). This does not fully correspond tomitochondrial DNA data, which shows structuring in thesouthern part of the range, including the southeast, but notin the northwest (Figures 4 and 5). Genetic variation in T.macedonicus is the highest of all Triturus newts (Figure 3).In a large part of its range, T. macedonicus possesseswestern T. karelinii mitochondrial DNA (Figure 2). Thiscan be explained by T. macedonicus displacing western T.karelinii there after the Last Glacial Maximum as the areaonly became unsuitable for T. macedonicus after the LastGlacial Maximum (Figure 6; see [29] for a detailedanalysis).In western T. karelinii, a basal split separates a clade

occurring in European Turkey and extreme southeasternBulgaria (Figure 3). The species distribution modeling


approach suggests suitable area was present here duringthe Last Glacial Maximum (Figure 6). The geneticdistinction of this clade is expressed by the ‘barrier’ inFigure 5 surrounding the populations in which it isrepresented. The other clade shows extensive structuringin Asiatic Turkey, suggesting the region acted as arefugium. Suitability at the Last Glacial Maximum is con-firmed by the species distribution modeling approach. Asingle sub-clade derived from (i.e. nested within) AsiaticTurkey has recently colonized the Balkan part of therange; it shows a starburst phylogeny in line with demo-graphic growth during a range expansion [12]. The spe-cies distribution modeling approach agrees that the areaonly became hospitable after the Last Glacial Maximum.Central T. karelinii shows a fragmented distribution at

the Last Glacial Maximum (Figures 6 and 7). This isreflected by an east west divide (the ‘barrier’ in Figure 5)between two distinct clades separated by a basal split(Figure 3), which are currently in secondary contact (the‘warm’ spot in Figure 4). Central T. karelinii containswestern T. karelinii mitochondrial DNA in northwesternAsiatic Turkey (Figure 2), suggesting postglacial speciesdisplacement (see [23] for a detailed analysis).For eastern T. karelinii, the southern Caspian basin

and western Georgia (Colchis) are suggested to have har-bored suitable area at the Last Glacial Maximumaccording to species distribution models (Figure 6).Genetic diversity is particularly high along the southernCaspian Sea shore (Figures 3, 4, 5). Relatively recently asingle nested clade colonized the Caucasus and Crimeafrom the Caspian part of the range [28]. However, thisclade is still distinct (reflected by a relatively deep co-alescence; see Figure 3), suggesting that colonizationhappened during a previous interglacial and thus thatthe clade persisted here in a glacial refugia during one orseveral glacial cycles.

Historical biogeographical scenarioWe provide a historical biogeographical scenario for thegenus Triturus in Figure 7, summarizing glacial refugia,areas of postglacial expansion and regions where speciesdisplaced one another. The genetic and species distribu-tion modeling results provide insight into the distribu-tion dynamics of Triturus in response to climate changeduring the Quaternary Ice Age. Triturus conforms to thegeneral pattern of the Iberian, Italian and BalkanPeninsulas functioning as glacial refugia [1,67]. Further-more, several regions that are increasingly appreciatedas glacial refugia are also identified as such for Triturus:Anatolia, the southern Caspian Basin and the Caucasus re-gion (e.g. [10,68]). The novel idea of extra-Mediterraneanrefugia, positioned more to the north [69], also applies toTriturus: the glacial refugium of T. cristatus was situated inthe Carpathian Basin.

Similarly, areas typically identified as having beenpostglacially colonized from glacial refugia are predictedas such for Triturus: T. marmoratus, T. carnifex and (toa lesser extent) ‘eastern T. karelinii’ extended theirranges into temperate Europe from their southerlypositioned refugia. However, postglacial expansion isbest exemplified by T. cristatus. The contemporaryrange of T. cristatus outside its glacial refugium in theCarpathian Basin encompasses a huge (c. 4.75 millionkm2) postglacially acquired area, stretching all the wayfrom western Europe to Scandinavia and central Russia.This gives an indication of the speed with which postgla-cial colonization can be accomplished.

ConclusionsThe combination of phylogeography and species distri-bution modeling aids locating of glacial refugia [70]. Thetwo approaches to visualize intraspecific geographicalgenetic structuring together illustrate which areas aregenetically rich and thus suspected to reflect long terminhabited area and which areas are genetically poor andthus probably only recently became habitable (Figures 4and 5). Additionally, asymmetrically introgressed mito-chondrial DNA suggests where species displaced oneanother upon post-glacial secondary contact (Figure 2).Independently, the two reconstructions of potential dis-tribution at the Last Glacial Maximum (based on theMIROC and CCSM climate simulations) provide an in-dication of which areas were habitable at the time andwhich were unsuitable for Triturus (Figure 6). We pro-vide a qualitative comparison of the results of the twoindependent techniques (summarized in Figure 7). Weanticipate that future developments in the synthesis ofphylogeography and species distribution modeling willmake it possible to integrate the two approaches into asingle analysis even further.

Additional files

Additional file 1: Genetic sampling. Details on the Triturus populationsincluded in the genetic analysis.

Additional file 2: Locality data. Details on the Triturus populationsused for species distribution modeling.

Additional file 3: Haplotype list. A list of ND4 haplotypes with theirGenBank accession numbers.

Additional file 4: Genetic distances. Values of average geneticdistance among populations (Zi) within each Triturus species.

Additional file 5: Full genetic similarity maps The genetic (dis)similarity among populations within the different Triturus species,not cut according to the current species ranges.

Additional file 6: Model performance. The AUC values for the speciesdistribution models of each Triturus species, tested against null models.

Additional file 7: Full species distribution models. The speciesdistribution model of each Triturus species projected for Last GlacialMaximum and current climate conditions, not cut according to thecurrent species ranges.

http://www.biomedcentral.com/content/supplementary/1742-9994-10-13-S1.xls




http://www.biomedcentral.com/content/supplementary/1742-9994-10-13-S5.pdf


http://www.biomedcentral.com/content/supplementary/1742-9994-10-13-S7.pdf


Competing interestsThe authors declare that they have no competing interests.

Authors’ contributionsBW, AKS, AGT and JWA designed the study. BW, JCI, SNL, BR, KS, NT, TV andJWA collected data. BW analyzed the data. BW drafted the manuscript. Allauthors read and approved the final manuscript.

AcknowledgementsD. Canestrelli generously shared at the time unpublished sequence data. M.P.Miller provided advice on the use of Alleles in Space. J. van Alphen, W. Babik,W. Beukema, S. Bogaerts, S. Branković, Henrik Bringsøe, D. Canestrelli, D.Cogălniceanu, A. Crottini, G. Džukić, G. Espregueira Themudo, E. Froufe, A.Geraldes, Ö. Güclü, I. Haxhiu, D. Jovic, M. Kalezić, H.G. Kami, A. Loureiro, A.Maletzky, Đ. Milošević, N. Moin, O. Mozaffari, P. Mikulíček, B. Naumov, K.Olgun, M. Pabijan, N.A. Poyarkov, B. Safaei, J.F. Schmidtler, M. Slijepcevic, A.Urošević, N. Üzüm, J. Vörös, B. Vrenozi and A. Zuiderwijk aided with thecollection of samples and locality data. The PMIP 2 Data Archive is supportedby CEA, CNRS and the Programme National d’Etude de la Dynamique duClimat (PNEDC). We acknowledge the international modeling groups forproviding their data for analysis and the Laboratoire des Sciences du Climatet de l’Environnement (LSCE) for collecting and archiving the model data. JCIwas supported by a DAPTF Seed Grant 2003 and Grant 173025 of theMinistry of Education and Science of the Republic of Serbia. TV wassupported by Grant 173043 of the Ministry of Education and Science of theRepublic of Serbia.

Author details1Naturalis Biodiversity Center, P. O. Box 9517, 2300 RA, Leiden, TheNetherlands. 2University of Twente, Faculty of Geo-Information Science andEarth Observation – ITC, P.O. Box 6, 7500 AA, Enschede, The Netherlands.3Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, Niš18000, Serbia. 4Institute for Biological Research, University of Belgrade,Bulevar Despota Stefana 142, Beograd 11000, Serbia. 5Institute of Cytology,Russian Academy of Sciences, Tikhoretsky pr. 4, St. Petersburg 194064, Russia.6Department of Biological Applications and Technology, School of Scienceand Technology, University of Ioannina, GR-45110 University Campus ofIoannina, Ioannina, Greece. 7National Museum of Natural History, TsarOsvoboditel blvd. 1, Sofia 1000, Bulgaria.

Received: 9 February 2013 Accepted: 11 March 2013Published: 20 March 2013

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doi:10.1186/1742-9994-10-13Cite this article as: Wielstra et al.: Tracing glacial refugia of Triturusnewts based on mitochondrial DNA phylogeography and speciesdistribution modeling. Frontiers in Zoology 2013 10:13.

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Additional file 1. Genetic sampling.

ID mitochonLocality N π** Source of sequenceLatitude Longitude

Triturus carnifexZMA9105‐678 Tdob02 Austria: Etzmandorf 48,650 15,750 1 n.a. 2ZMA9117‐338 Tdob02 Austria: Haidlhof 47,967 16,167 5 n.a. 2ZMA9117‐339 Tdob02 2ZMA9117‐687 Tdob02 2ZMA9117‐688 Tdob02 2ZMA9117‐689 Tdob02 2ZMA9129‐380 Tdob02 Austria: Kleinmeiseldorf 48,667 15,750 11 n.a. 2ZMA9129‐381 Tdob02 2ZMA9122‐679 Tdob02 2ZMA9122‐680 Tdob02 2ZMA9122‐681 Tdob02 2ZMA9122‐682 Tdob02 2ZMA9122‐683 Tdob02 2ZMA9122‐684 Tdob02 2ZMA9122‐685 Tdob02 2ZMA9122‐686 Tdob02 2ZMA9122‐690 Tdob02 2ZMA9133‐382 Tdob15 Austria: Lackenbach 47,600 16,450 2 n.a. 2ZMA9133‐383 Tdob15 2ZMA9089‐788 Tcar01 Croatia: Belovar Moravce 45,850 16,167 1 n.a. 2ZMA9155‐876 Tcar03 Croatia: Sinac 44,817 15,367 1 n.a. 2N2‐1 Tcar15 Croatia: Svetvinčenat 45,067 13,873 5 0 8N2‐2 Tcar15 8N2‐3 Tcar15 8N2‐4 Tcar15 8N2‐5 Tcar15 8ZMA9252‐318 Tcar06 Italy: Acerra, Napoli 40,850 14,250 3 0 1ZMA9252‐319 Tcar06 1ZMA9252‐320 Tcar06 1SII28‐1 Tcar33 Italy: Alberobello 40,824 17,235 3 0 8SII28‐2 Tcar33 8

Coordinates

SII28‐3 Tcar33 8SI6 Tcar24 Italy: Alviano 42,619 12,243 7 1,3333 8SI7‐1 Tcar25 8SI7‐2 Tcar25 8SI19‐7 Tcar06 8SI19‐8 Tcar06 8SI19‐9 Tcar06 8SI20‐1 Tcar30 8ZMA7553‐71 Tcar04 Italy: Bobinaco 42,350 13,400 6 0 2ZMA7553‐72 Tcar04 2ZMA7553‐73 Tcar04 2ZMA7553‐74 Tcar04 2ZMA7553‐75 Tcar04 2ZMA7553‐76 Tcar04 2CI1‐1 Tcar16 Italy: Busa de San Piero 45,802 12,141 3 0 8CI1‐2 Tcar16 8CI1‐3 Tcar16 8SI13‐1 Tcar28 Italy: Campo di Mele 41,386 13,520 6 0 8SI13‐2 Tcar28 8SI13‐3 Tcar28 8SI13‐4 Tcar28 8SI13‐5 Tcar28 8SI13‐6 Tcar28 8SI17‐18 Tcar27 Italy: Campo Imperatore 42,423 13,621 7 0 8SI17‐19 Tcar27 8SI17‐20 Tcar27 8SI17‐21 Tcar27 8SI17‐22 Tcar27 8SI17‐23 Tcar27 8SI17‐24 Tcar27 8SV23 Tcar06 Italy: Cecita 39,376 16,509 6 0 8SV24 ‐1 Tcar06 8SV24 ‐2 Tcar06 8SV24 ‐3 Tcar06 8SV24 ‐4 Tcar06 8SV24 ‐5 Tcar06 8SI14‐1 Tcar29 Italy: Circeo 41,342 13,039 5 0 8

SI14‐2 Tcar29 8SI15‐1 Tcar29 8SI15‐2 Tcar29 8SI15‐3 Tcar29 8SII21‐1 Tcar31 Italy: Conza 40,855 15,305 7 0 8SII21‐2 Tcar31 8SII21‐3 Tcar31 8SII21‐4 Tcar31 8SII21‐5 Tcar31 8SII21‐6 Tcar31 8SII21‐7 Tcar31 8SI17‐1 Tcar27 Italy: Doganella 41,759 12,791 7 0,5714 8SI19‐1 Tcar06 8SI19‐2 Tcar06 8SI19‐3 Tcar06 8SI19‐4 Tcar06 8SI19‐5 Tcar06 8SI19‐6 Tcar06 8CII7‐1 Tcar18 Italy: Donega 44,939 9,238 5 0,6 8CII7‐2 Tcar18 8CII8‐1 Tcar20 8CII8‐2 Tcar20 8CII8‐3 Tcar20 8SIV25‐1 Tcar02 Italy: Due Uomini 39,551 16,020 15 0,5143 8SIV25‐2 Tcar02 8SIV25‐3 Tcar02 8SIV25‐4 Tcar02 8SIV26‐1 Tcar02 8SIV26‐2 Tcar02 8SIV26‐3 Tcar02 8SIV26‐4 Tcar02 8SIV26‐5 Tcar02 8SIV27‐1 Tcar32 8SIV27‐2 Tcar32 8SIV27‐3 Tcar32 8SIV27‐4 Tcar32 8SIV27‐5 Tcar32 8

SIV27‐6 Tcar32 8ZMA9106‐474 Tcar05 Italy: Farma 43,133 11,167 4 0,5 2ZMA9106‐475 Tcar06 2ZMA9106‐476 Tcar05 2ZMA9106‐477 Tcar05 2ZMA9107‐601 Tcar08 Italy: Florence 43,750 11,250 12 1,5455 2ZMA9107‐780 Tcar08 2ZMA9107‐781 Tcar08 2ZMA9107‐782 Tcar05 2ZMA9107‐783 Tcar06 2ZMA9107‐787 Tcar08 2SI2‐1 Tcar23 8SI2‐2 Tcar23 8SI2‐3 Tcar23 8SI3‐2 Tcar06 8SI3‐3 Tcar06 8SI19‐15 Tcar06 8ZMA9108‐405 Tcar02 Italy: Fuscaldo 39,417 16,033 10 0 3ZMA9108‐406 Tcar02 2ZMA9108‐407 Tcar02 2ZMA9108‐408 Tcar02 2ZMA9108‐409 Tcar02 2ZMA9108‐410 Tcar02 2ZMA9108‐411 Tcar02 2ZMA9108‐412 Tcar02 2ZMA9108‐413 Tcar02 2ZMA9108‐414 Tcar02 2SI3‐1 Tcar06 Italy: Greve in Chianti 43,588 11,302 8 0,9286 8SI4‐1 Tcar05 8SI4‐2 Tcar05 8SI4‐3 Tcar05 8SI4‐4 Tcar05 8SI4‐5 Tcar05 8SI4‐6 Tcar05 8SI5 Tcar08 8SI16 Tcar27 Italy: Jenne 41,877 13,187 7 0,4762 8SI17‐2 Tcar27 8

SI17‐3 Tcar27 8SI17‐4 Tcar27 8SI17‐5 Tcar27 8SI18‐1 Tcar28 8SI18‐2 Tcar28 8SIV26‐6 Tcar02 Italy: Laghicello 39,417 16,086 6 0 8SIV26‐7 Tcar02 8SIV26‐8 Tcar02 8SIV26‐9 Tcar02 8SIV26‐10 Tcar02 8SIV26‐11 Tcar02 8CI2‐1 Tcar17 Italy: Le Poscole 45,600 11,387 5 0 8CI2‐2 Tcar17 8CI2‐3 Tcar17 8CI2‐4 Tcar17 8CI2‐5 Tcar17 83711 Tcar11 Italy: Maceratoio del Rettore 45,488 7,907 5 1 13712 Tcar11 13713 Tcar12 13714 Tcar12 13715 Tcar13 1CIII10‐3 Tcar21 Italy: Minucciano 44,170 10,205 2 0 8CIII10‐4 Tcar21 8CII9‐3 Tcar17 Italy: Montevecchia 45,703 9,375 5 0 8CII9‐4 Tcar17 8CII9‐5 Tcar17 8CII9‐6 Tcar17 8CII9‐7 Tcar17 8CIII10‐1 Tcar21 Italy: Mulino di Pianoro 44,353 11,321 5 0 8CIII10‐2 Tcar21 8CIII11‐1 Tcar21 8CIII11‐2 Tcar21 8CIII11‐3 Tcar21 8SI11 Tcar27 Italy: Navegna 42,152 13,042 11 0 8SI17‐8 Tcar27 8SI17‐9 Tcar27 8SI17‐10 Tcar27 8

SI17‐11 Tcar27 8SI17‐12 Tcar27 8SI17‐13 Tcar27 8SI17‐14 Tcar27 8SI17‐15 Tcar27 8SI17‐16 Tcar27 8SI17‐17 Tcar27 8CII9‐1 Tcar17 Italy: Piampaludo 44,458 8,575 2 0 8CII9‐2 Tcar17 8ZMA9145‐292 Tcar05 Italy: Pisa 43,717 10,400 4 0 2ZMA9145‐293 Tcar05 2ZMA9145‐294 Tcar05 2ZMA9145‐295 Tcar05 23728 Tcar12 Italy: Poirino 44,909 7,836 1 n.a. 1CII3‐1 Tcar18 Italy: Porto Caleri 45,093 12,325 4 0 8CII3‐2 Tcar18 8CII3‐3 Tcar18 8CII4 Tcar18 8SI9‐1 Tcar27 Italy: Rocca di mezzo 42,157 13,635 5 0,4 8SI9‐2 Tcar27 8SI10 Tcar04 8SI17‐6 Tcar27 8SI17‐7 Tcar27 8SI1‐1 Tcar06 Italy: Roccalbegna 42,742 11,503 8 0,5 8SI1‐2 Tcar06 8SI19‐10 Tcar06 8SI19‐11 Tcar06 8SI19‐12 Tcar06 8SI19‐13 Tcar06 8SI19‐14 Tcar06 8SI20‐2 Tcar30 8SI19‐16 Tcar06 Italy: Rufeno 42,776 11,884 2 2 8SI20‐3 Tcar30 8SI8 Tcar26 Italy: San Quirico 42,439 13,837 7 0 8SI22‐16 Tcar26 8SI22‐17 Tcar26 8SI22‐18 Tcar26 8

SI22‐19 Tcar26 8SI22‐20 Tcar26 8SI22‐21 Tcar26 8CI13‐1 Tcar16 Italy: San Severino Marche 43,303 13,074 8 1,2857 8CI13‐2 Tcar16 8SI31‐1 Tcar06 8SI31‐2 Tcar06 8SI31‐3 Tcar06 8SI31‐4 Tcar06 8SI31‐5 Tcar06 8SI31‐6 Tcar06 8SV24 ‐6 Tcar06 Italy: San Pietro in Guarano 39,339 16,352 9 0 8SV24 ‐7 Tcar06 8SV24 ‐8 Tcar06 8SV24 ‐9 Tcar06 8SV24 ‐10 Tcar06 8SV24 ‐11 Tcar06 8SV24 ‐12 Tcar06 8SV24 ‐13 Tcar06 8SV24 ‐14 Tcar06 8CI12 Tcar22 Italy: Senigallia 43,688 13,203 5 0,4 8CI13‐3 Tcar16 8CI13‐4 Tcar16 8CI13‐5 Tcar16 8CI13‐6 Tcar16 8SI32 Tcar26 Italy: Sepino 41,391 14,569 1 n.a. 8SI33‐1 Tcar26 Italy: Sessano 41,626 14,336 9 0 8SI33‐2 Tcar26 8SI33‐3 Tcar26 8SI33‐4 Tcar26 8SI34‐1 Tcar26 8SI34‐2 Tcar26 8SI34‐3 Tcar26 8SI34‐4 Tcar26 8SI34‐5 Tcar26 8CII5‐1 Tcar19 Italy: Stagno Bargone 44,319 9,486 5 0 8CII5‐2 Tcar19 8

CII5‐3 Tcar19 8CII6‐1 Tcar19 8CII6‐2 Tcar19 8SIII29 Tcar34 Italy: Taverna Magnano 40,051 16,124 6 0 8SIII30 ‐1 Tcar34 8SIII30 ‐2 Tcar34 8SIII30 ‐3 Tcar34 8SIII30 ‐4 Tcar34 8SIII30 ‐5 Tcar34 8CI13‐7 Tcar16 Italy: Terra del sole 44,185 11,956 5 0 8CI13‐8 Tcar16 8CI13‐9 Tcar16 8CI13‐10 Tcar16 8CI13‐11 Tcar16 8SI22‐1 Tcar26 Italy: Torre Palermo 41,834 16,038 15 0 8SI22‐2 Tcar26 8SI22‐3 Tcar26 8SI22‐4 Tcar26 8SI22‐5 Tcar26 8SI22‐6 Tcar26 8SI22‐7 Tcar26 8SI22‐8 Tcar26 8SI22‐9 Tcar26 8SI22‐10 Tcar26 8SI22‐11 Tcar26 8SI22‐12 Tcar26 8SI22‐13 Tcar26 8SI22‐14 Tcar26 8SI22‐15 Tcar26 8N1 ‐1 Tcar01 Slovenia: Komen 45,818 13,750 5 0 8N1 ‐2 Tcar01 8N1 ‐3 Tcar01 8N1 ‐4 Tcar01 8N1 ‐5 Tcar01 8ZMA9132‐312 Tcar01 Slovenia: Kramplje 45,733 14,500 8 0 3ZMA9132‐313 Tcar01 2ZMA9132‐314 Tcar01 2

ZMA9132‐315 Tcar01 2ZMA9132‐618 Tcar01 2ZMA9132‐619 Tcar01 2ZMA9132‐785 Tcar01 2ZMA9132‐786 Tcar01 2ZMA9136‐396 Tcar07 Switzerland: Locarno 46,167 8,800 5 0,8 2ZMA9136‐397 Tcar14 2ZMA9136‐749 Tcar07 2ZMA9136‐750 Tcar07 2ZMA9136‐751 Tcar07 2

Triturus cristatus19‐1 Tcar35 Austria: Ameisensee 47,550 13,450 7 n.a. 119‐3 Tcar35 119‐4 Tcar35 119‐10 Tcar35 119‐12 Tcar35 119‐13 Tcar35 119‐15 Tcar35 1ZMA9143‐691 Tcri02 Austria: Ottenstein 48,467 14,283 1 n.a. 229‐3 Tcar36 Austria: Sommeregg 47,833 13,117 6 n.a. 129‐5 Tcar36 129‐9 Tcar36 129‐11 Tcar36 129‐13 Tcar36 129‐18 Tcar36 14754 Tcri33 Bulgaria: Borovan 43,409 23,769 5 4,6667 14755 TkarC20 14756 Tcri34 14757 TkarC20 14758 Tcri35 14783 Tcri07 Bulgaria: Borovitsa 43,590 22,770 3 0 14784 Tcri07 14785 Tcri07 14733 TkarC66 Bulgaria: Chiren 43,324 23,588 5 n.a. 14734 TkarC20 14735 TkarC20 1

4736 TkarC66 14737 TkarC66 12391 TkarC20 Bulgaria: Lilyache 43,317 23,539 2 n.a. 32392 TkarC20 32616 Tcri10 Bulgaria: Montana 43,416 23,222 3 3,3333 12617 Tcri10 14485 Tcri34 12612 TkarC20 Bulgaria: Parshevitsa 43,139 23,471 7 n.a. 12613 TkarC20 12614 TkarC20 12615 TkarC20 12636 TkarC20 12637 TkarC20 12638 TkarC20 14487 TkarC20 Bulgaria: Tosheva yama cave 43,265 23,346 1 n.a. 1ZMA9200‐967 Tcri01 England: Canterbury 51,283 1,083 5 0 2ZMA9200‐970 Tcri01 2ZMA9200‐971 Tcri01 2ZMA7802‐594 Tcri01 4ZMA7802‐595 Tcri01 2ZMA9144‐583 Tcri01 England: Peterborough 52,583 ‐0,250 5 0,4 2ZMA9199‐968 Tcri01 2ZMA9199‐969 Tcri25 2ZMA9199‐972 Tcri01 2ZMA9199‐973 Tcri01 23694 Tcri02 France: Autreppes 49,934 3,900 5 0 13695 Tcri02 13696 Tcri02 13697 Tcri02 13698 Tcri02 13722MAY Tcri02 France: Arles 43,683 4,633 1 n.a. 1ZMA9072‐258 Tcri02 France: Mayenne 48,300 ‐0,617 56 0,0357 2ZMA9072‐260 Tcri02 2ZMA9072‐261 Tcri02 2ZMA9072‐262 Tcri02 2ZMA9072‐263 Tcri02 2ZMA8048‐302 Tcri02 2

ZMA8048‐303 Tcri02 2ZMA8048‐304 Tcri02 2ZMA9068‐324 Tcri02 2ZMA9069‐479 Tcri02 2ZMA9069‐480 Tcri02 2ZMA9069‐481 Tcri02 2ZMA9069‐482 Tcri02 2ZMA9070‐552 Tcri02 2ZMA9070‐553 Tcri02 2ZMA9070‐554 Tcri02 2ZMA9070‐555 Tcri02 2ZMA9070‐556 Tcri02 2ZMA9070‐557 Tcri02 2ZMA9070‐558 Tcri02 2ZMA9070‐559 Tcri02 2ZMA9070‐560 Tcri02 2ZMA9069‐561 Tcri02 2ZMA9071‐562 Tcri02 2ZMA9072‐563 Tcri02 2ZMA9072‐564 Tcri02 2ZMA9072‐565 Tcri02 2ZMA9072‐566 Tcri02 2ZMA9072‐567 Tcri02 2ZMA9072‐568 Tcri02 2ZMA9072‐569 Tcri02 2ZMA9073‐570 Tcri02 2ZMA9073‐571 Tcri02 2ZMA9073‐572 Tcri02 2ZMA9073‐573 Tcri02 2ZMA9073‐574 Tcri02 2ZMA9073‐575 Tcri02 2ZMA9073‐576 Tcri02 2ZMA9073‐577 Tcri02 2ZMA9073‐578 Tcri02 2ZMA9070‐579 Tcri02 2ZMA9070‐580 Tcri02 2ZMA9070‐581 Tcri02 2

ZMA9070‐582 Tcri02 2ZMA9070‐596 Tcri02 2ZMA9070‐597 Tcri02 2ZMA9073‐598 Tcri02 2ZMA9073‐599 Tcri01 2ZMA9070‐600 Tcri02 21750 Tcri02 11752 Tcri02 11756 Tcri02 31757 Tcri02 11758 Tcri02 11759 Tcri02 11760 Tcri02 1ZMA9265‐259 Tcri02 France: St.‐Lô 49,117 1,083 1 n.a. 23721MAY Tcri02 France: Valliguières 44,000 4,583 2 0 13723MAY Tcri02 14803 Tcri01 Germany: Braunschweig 52,270 10,587 5 0 14804 Tcri01 14805 Tcri01 14807 Tcri01 14808 Tcri01 14382 Tcri01 Moldova: Gershunovka 47,717 29,167 1 n.a. 12241 Tcri15 Moldova: Kishenev 1 47,019 28,786 1 n.a. 14373 Tcri27 Moldova: Kishenev 2 47,017 28,883 2 2 14374 Tcri11 14381 Tcri01 Moldova: Oknitsa 48,133 28,633 1 n.a. 14377 Tcri24 Moldova: Slobodzeya 46,717 29,683 2 0 14378 Tcri24 14383 Tcri24 Moldova: Tiraspol' 46,833 29,650 1 n.a. 15021 Tcri38 Netherlands: Heeze 51,393 5,535 3 0 15022 Tcri38 15023 Tcri38 14809 Tcri36 Poland: Kopanina 52,557 16,538 1 n.a. 1ZMA9135‐584 Tcri01 Poland: Limanowa 49,717 20,417 10 0 2ZMA9135‐585 Tcri01 2ZMA9135‐586 Tcri01 2ZMA9135‐587 Tcri01 2

ZMA9135‐588 Tcri01 2ZMA9135‐589 Tcri01 2ZMA9135‐590 Tcri01 2ZMA9135‐591 Tcri01 2ZMA9135‐592 Tcri01 2ZMA9135‐593 Tcri01 25049 Tcri39 Poland: Tłumaczów quarry 50,558 16,434 10 0 15050 Tcri39 15051 Tcri39 15052 Tcri39 15053 Tcri39 15054 Tcri39 15055 Tcri39 15056 Tcri39 15057 Tcri39 15058 Tcri39 15015 Tcri41 Poland: Trzebciny 53,625 18,152 1 n.a. 14810 Tcri01 Poland: Zatwarnica przy potoku Hulskie 49,235 22,527 3 10 14811 Tcri11 14812 Tcri37 11427 Tcri16 Romania: Bogdana 47,033 23,017 3 0,6667 11428 Tcri16 11429 Tcri01 14340 Tcri11 Romania: Brădătel 47,491 26,178 5 0,4 14341 Tcri11 14342 Tcri11 14343 Tcri11 14344 Tcri24 13686 Tcri12 Romania: Budeni 45,768 26,839 5 0,4 13687 Tcri15 13688 Tcri15 13689 Tcri15 13690 Tcri15 14319 Tcri01 Romania: Ciceu 46,404 25,790 3 0 14320 Tcri01 14321 Tcri01 1ZMA9094‐372 Tcri04 Romania: Cimpeni 46,383 23,083 10 0,2 2

ZMA9094‐373 Tcri01 3ZMA9094‐374 Tcri01 2ZMA9094‐375 Tcri01 2ZMA9094‐376 Tcri01 2ZMA9094‐706 Tcri01 2ZMA9094‐707 Tcri01 2ZMA9094‐708 Tcri01 2ZMA9094‐709 Tcri01 2ZMA9094‐710 Tcri01 21367 Tcri01 Romania: Craciunesti 46,867 27,533 3 0,6667 11368 Tcri01 11369 Tcri17 11347 Tcri01 Romania: Huta‐Certeze 47,917 23,483 3 0 11348 Tcri01 11349 Tcri01 14346 Tcri11 Romania: Iorcani 47,361 26,527 4 0,6667 14347 Tcri24 14348 Tcri11 14349 Tcri24 11634 Tcri12 Romania: Jitia 45,583 26,733 3 0 11635 Tcri12 11636 Tcri12 14296 Tcri01 Romania: Mănăşturel 47,190 23,927 5 0 14297 Tcri01 14298 Tcri01 14299 Tcri01 14300 Tcri01 11063 Tcri01 Romania: Marghita 47,350 22,333 3 0,6667 11064 Tcri16 11065 Tcri16 14338 Tcri12 Romania: Penteleu 45,560 26,356 2 0 14339 Tcri12 14997 Tcri40 Romania: Peştiş2 47,092 22,390 2 0 14998 Tcri40 11118 Tcri01 Romania: Picâturile 44,483 23,867 3 0 11119 Tcri01 11120 Tcri01 1

4272 Tcri01 Romania: Pomi 47,694 23,320 10 0,3556 14273 Tcri01 14274 Tcri01 14275 Tcri01 14276 Tcri22 14277 Tcri01 14278 Tcri22 14279 Tcri01 14280 Tcri01 14281 Tcri01 15009 Tcri01 Romania: Săcărâmb 45,971 23,047 3 0 15010 Tcri01 15011 Tcri01 15000 Tcri01 Romania: Sânpetru de Câmpie 46,771 24,278 2 0 15001 Tcri01 14322 Tcri01 Romania: Sânzieni 46,104 26,128 14 2,2088 14323 Tcri23 14324 Tcri01 14325 Tcri23 14326 Tcri01 14327 Tcri01 14328 Tcri01 14329 Tcri01 14330 Tcri23 14331 Tcri01 14332 Tcri23 14333 Tcri23 14334 Tcri01 14335 Tcri26 12453 Tcri01 Romania: Sighisoara 46,207 24,759 3 0,6667 12454 Tcri01 12455 Tcri18 1ZMA9156‐369 Tcri01 Romania: Sinaia 45,333 25,550 10 0,2 2ZMA9156‐370 Tcri01 2ZMA9156‐371 Tcri01 2ZMA9156‐698 Tcri01 2ZMA9156‐699 Tcri01 2

ZMA9156‐700 Tcri01 2ZMA9156‐701 Tcri01 2ZMA9156‐702 Tcri05 2ZMA9156‐703 Tcri01 2ZMA9156‐704 Tcri01 24999 Tcri01 Romania: Şoimuş 47,298 23,203 1 n.a. 14301 Tcri01 Romania: Strâmba 47,248 24,653 10 0 14302 Tcri01 14303 Tcri01 14304 Tcri01 14305 Tcri01 14306 Tcri01 14307 Tcri01 14308 Tcri01 14309 Tcri01 14310 Tcri01 1ZMA9163‐348 Tcri03 Romania: Tîrgoviste 44,933 25,450 10 2,6 2ZMA9163‐349 Tcri01 2ZMA9163‐350 Tcri01 2ZMA9163‐351 Tcri01 2ZMA9163‐352 Tcri01 2ZMA9163‐353 Tcri01 2ZMA9163‐354 Tcri01 2ZMA9163‐693 Tcri01 2ZMA9163‐777 Tcri01 2ZMA9163‐778 Tcri01 24350 Tcri24 Romania: Tolici 47,098 26,450 10 0,5556 14351 Tcri24 14352 Tcri24 14353 Tcri24 14354 Tcri11 14355 Tcri11 14356 Tcri11 14357 Tcri24 14358 Tcri11 14359 Tcri11 11706 Tcri01 Romania: Turț 47,983 23,200 3 0 1

1707 Tcri01 11708 Tdob14 15008 Tcri01 Romania: Valea Ierii 46,650 23,355 1 n.a. 1ZMA9166‐341 Tcri03 Romania: Videle 44,267 25,517 11 2,9818 2ZMA9166‐342 Tcri01 2ZMA9166‐343 Tcri03 2ZMA9166‐344 Tcri20 2ZMA9166‐345 Tcri20 2ZMA9166‐346 Tcri03 2347 Tcri03 2ZMA9166‐694 Tcri21 2ZMA9166‐695 Tcri03 2ZMA9166‐696 Tcri03 2ZMA9166‐697 Tcri03 2ZMA9167‐355 Tcri01 Romania: Virfuri 46,283 22,467 9 0 2ZMA9167‐356 Tcri01 2ZMA9167‐357 Tcri01 2ZMA9167‐358 Tcri01 2ZMA9167‐359 Tcri01 2ZMA9167‐712 Tcri01 2ZMA9167‐713 Tcri01 2ZMA9167‐714 Tcri01 2ZMA9167‐715 Tdob02 25002 Tcri01 Romania: Visuia 46,839 24,305 3 0 15003 Tcri01 15004 Tcri01 14360 Tcri01 Russia: Verkh Kvazhva 58,383 56,383 2 0 14361 Tcri01 14362 Tcri01 Russia: Shusher 56,667 47,267 2 0 14363 Tcri01 14368 Tcri01 Russia: Sharapova Okhota 54,967 37,417 2 0 14369 Tcri01 14371 Tcri01 Russia: Dzerdzhynsk 56,233 43,450 1 n.a. 14375 Tcri28 Russia: Chur 57,117 52,983 1 n.a. 14379 Tcri01 Russia: Sperovo 60,900 36,567 2 1 14380 Tcri29 14389 Tcri01 Russia: Faldino 54,067 37,633 1 n.a. 1

4390 Tcri30 Russia: Bagrationovsk 54,367 20,683 1 n.a. 14391 Tcri01 Russia: Marienburg 59,567 30,067 1 n.a. 14392 Tcri31 Russia: Konechki 57,733 27,850 2 0 14393 Tcri31 14394 Tcri32 Russia: Dyagilevka 54,033 44,917 1 n.a. 14396 Tcri01 Russia: Volkhonshchino 52,750 45,200 1 n.a. 14397 Tcri04 Russia: Karmalka River 55,200 51,867 1 n.a. 14398 Tcri01 Russia: Borok 58,067 38,267 1 n.a. 14399 Tcri01 Russia: Saralovsky uchastok VKGZ 55,283 49,250 1 n.a. 1ZMA9093‐464 TkarC20 Serbia: Bor 44,033 22,133 4 n.a. 2ZMA9093‐465 TkarC20 2ZMA9093‐752 TkarC20 2ZMA9093‐753 TkarC20 24786 TkarC24 Serbia: Duboka 44,569 21,766 5 n.a. 14787 TkarC24 14788 TkarC64 14789 TkarC24 14790 TkarC64 1ZMA9119‐439 TkarC20 Serbia: Jabukovac 44,354 22,389 5 0 2ZMA9119‐440 Tcri06 2ZMA9119‐441 TkarC20 2ZMA9119‐754 Tcri06 2ZMA9119‐755 Tcri06 2ZMA9128‐462 Tdob18 Serbia: Kladovo 44,674 22,512 2 n.a. 2ZMA9128‐463 TkarC20 2ZMA9130‐446 TkarC20 Serbia: Klokocevac 44,333 22,200 5 n.a. 2ZMA9130‐447 TkarC20 2ZMA9130‐760 TkarC20 2ZMA9130‐761 TkarC20 2ZMA9130‐762 TkarC20 22703 Tdob02 Serbia: Kruščica 44,937 21,479 5 0 12704 Tcri09 12705 Tcri09 12706 Tdob02 12707 Tcri09 12667 Tcri09 Serbia: Leskovački Potok 44,887 21,548 2 0 12668 Tcri09 1

ZMA9138‐933 TkarC20 Serbia: Lukovo 43,800 21,850 1 n.a. 2ZMA9139‐789 TkarC64 Serbia: Milanovac 44,183 21,600 7 n.a. 2ZMA9139‐790 TkarC64 2ZMA9139‐791 TkarC24 2ZMA9139‐792 TkarC24 2ZMA9139‐793 TkarC24 2ZMA9139‐883 TkarC64 2ZMA9139‐884 TkarC24 22767 Tcri09 Serbia: Potok lug 44,895 21,488 5 0 12768 Tcri09 12769 Tdob02 12770 Tcri09 12771 Tcri09 14504 TkarC20 Serbia: Sikole 44,177 22,313 4 n.a. 14505 TkarC20 14506 TkarC20 14507 TkarC20 1ZMA9159‐452 TkarC20 Serbia: Štubik 44,292 22,358 5 0 2ZMA9159‐756 Tcri07 2ZMA9159‐757 Tcri07 2ZMA9159‐758 Tcri07 2ZMA9159‐759 Tcri07 24813 TkarC20 Serbia: Trnavac 44,003 22,345 3 n.a. 14814 TkarC67 14815 TkarC67 12735 Tdob02 Serbia: Vršački Breg 45,117 21,367 4 n.a. 12736 Tdob02 12737 Tdob18 12738 Tdob20 13793 TkarC61 Serbia: Zlot 44,011 21,967 7 n.a. 13794 TkarC61 13795 TkarC62 13826 TkarC62 13827 TkarC62 13828 TkarC20 13829 TkarC20 1ZMA9091‐478 Tcri02 Switzerland: Biel 47,150 7,267 1 n.a. 2

4930 Tcri01 Ukraine: Bronytsya1 49,429 23,413 3 0 14931 Tcri01 14932 Tcri01 14388 Tcri01 Ukraine: Cherkasskie Tishki 50,117 36,417 1 n.a. 14385 Tcri11 Ukraine: Domanintsy 48,633 22,333 1 n.a. 14981 Tcri01 Ukraine: Honcharivka 48,866 25,023 2 0 14982 Tcri01 12252 Tcri11 Ukraine: Komsomolskoye lake 48,821 22,782 4 n.a. 14983 Tcri11 Ukraine: Lyucha 48,382 24,905 1 n.a. 12253 Tcri01 Ukraine: Mala Ugolka 48,199 23,635 3 0 12254 Tcri01 12255 Tcri01 14967 Tcri01 Ukraine: Myluvannya 48,975 24,939 3 0 14968 Tcri01 14969 Tcri01 14955 Tcri01 Ukraine: Rakhynya 49,014 24,032 3 0 14956 Tcri01 14957 Tcri01 14992 Tcri01 Ukraine: Rakovets 49,635 24,040 3 0 14993 Tcri01 14994 Tcri01 14984 Tcri01 Ukraine: Rosilna 48,796 24,338 3 0 14985 Tcri01 14986 Tcri01 14387 Tcri01 Ukraine: Russkaya Lozovaya 50,167 36,317 1 n.a. 14384 Tcri11 Ukraine: Uzhgorod 1 48,633 22,283 1 n.a. 14950 Tcri11 Ukraine: Vyshkivsky Pass 48,701 23,635 3 0 14951 Tcri11 14952 Tcri11 1

Triturus dobrogicusZMA9102‐384 Tdob16 Austria: Drösing 48,550 16,917 6 0,6 2ZMA9102‐385 Tdob02 2ZMA9102‐386 Tdob02 2ZMA9102‐387 Tdob02 2ZMA9102‐388 Tdob16 2ZMA9102‐389 Tdob16 2

ZMA8041‐307 Tdob14 Austria: Tadten 47,767 17,000 5 0,4 2ZMA8041‐308 Tdob14 2ZMA8041‐309 Tdob02 2ZMA8041‐310 Tdob14 2ZMA8041‐311 Tdob14 2ZMA9101‐837 Tdob09 Bosnia and Herzegovina: Donja Čadjavica 44,750 19,100 8 0,5714 2ZMA9101‐838 Tdob11 2ZMA9101‐839 Tdob11 2ZMA9101‐840 Tdob11 2ZMA9101‐841 Tdob09 2ZMA9101‐842 TkarC27 2ZMA9101‐843 Tdob09 2ZMA9101‐844 Tdob09 2ZMA9170‐639 Tdob11 Bosnia and Herzegovina: Vrsani 44,830 19,020 8 0,8214 2ZMA9170‐640 Tdob09 2ZMA9170‐641 Tdob09 2ZMA9170‐725 Tdob11 2ZMA9170‐726 Tdob09 2ZMA9170‐727 Tdob11 2ZMA9170‐728 Tdob02 2ZMA9170‐729 Tdob11 24778 Tcri07 Bulgaria: Harlets 43,711 23,842 5 n.a. 14779 Tcri07 14780 Tcri07 14781 Tcri07 14782 Tcri07 12620 Tdob14 Bulgaria: Kudelin 44,197 22,673 1 n.a. 12621 Tdob11 Bulgaria: Srebarna 44,121 27,082 2 0 12622 Tdob11 1ZMA9160‐459 Tdob14 Bulgaria: Svistov 43,617 25,350 2 3 2ZMA9160‐748 Tdob17 22618 Tdob14 Bulgaria: Vidin 44,007 22,924 2 0 12619 Tdob14 1ZMA9103‐880 Tdob02 Croatia: Dugo Selo 45,817 16,250 1 n.a. 3ZMA9146‐638 Tdob12 Croatia: Podgorac 45,483 18,200 2 2 2ZMA9146‐670 Tdob09 2ZMA9165‐861 Tdob02 Croatia: Vedropolje 45,349 16,569 1 n.a. 2

ZMA9172‐637 Tdob11 Croatia: Zupanja 45,083 18,700 7 1,1429 2ZMA9172‐677 Tdob11 2ZMA9172‐720 Tdob11 2ZMA9172‐721 Tdob09 2ZMA9172‐722 Tdob07 2ZMA9172‐723 Tdob10 2ZMA9172‐724 Tdob11 2ZMA9083‐512 Tdob02 Hungary: Alap 46,800 18,683 4 1,5 2ZMA9083‐513 Tdob14 2ZMA9083‐514 Tdob02 2ZMA9083‐744 Tdob05 2ZMA9084‐337 Tdob02 Hungary: Albertirsza 47,233 19,600 1 n.a. 2ZMA9101‐426 Tdob14 Hungary: Körmend 47,017 16,600 1 n.a. 2ZMA9141‐360 Tdob03 Hungary: Öcsöd 46,933 20,383 7 0,2857 2ZMA9141‐361 Tdob03 2ZMA9141‐362 Tdob03 2ZMA9141‐363 Tdob03 2ZMA9141‐364 Tdob03 2ZMA9141‐365 Tdob03 2ZMA9141‐692 Tdob02 2ZMA9161‐335 Tdob14 Hungary: Szolnok 47,167 20,167 1 n.a. 21073 Tcri13 Romania: Amarasti de Jos 43,983 24,100 3 n.a. 11074 Tcri14 11075 Tcri13 11672 Tdob14 Romania: Babesti 47,967 23,100 3 0 11673 Tdob14 11674 Tdob14 12374 Tdob22 Romania: Briala 45,2 27,98 1 n.a. 11126 Tcri12 Romania: Ciorasti 45,435 27,305 2 n.a. 11128 Tcri12 12427 Tdob02 Romania: Giurgeni 44,742 27,868 3 2,6667 12428 Tdob17 12429 Tdob11 12376 Tdob01 Romania: Macin 45,251 28,121 5 1 32377 Tdob21 12378 Tdob02 12379 Tdob01 1

2380 Tdob01 12463 Tcri19 Romania: Mihai Bravu 44,136 26,023 3 n.a. 12464 Tcri03 12465 Tcri20 1993 Tdob14 Romania: Piscolt 47,583 22,300 3 0 1994 Tdob14 1995 Tdob14 1ZMA9152‐366 Tcri01 Romania: Sebis 46,367 22,100 6 1,3333 2ZMA9152‐367 Tdob02 2ZMA9152‐368 Tcri01 2ZMA9152‐716 Tdob02 2ZMA9152‐717 Tcri01 2ZMA9152‐718 Tdob11 2ZMA9167‐711 Tdob02 Romania: Virfuri 46,283 22,467 1 n.a. 2ZMA9171‐336 Tdob14 Romania: Zimnicea 43,650 25,350 2 0 2ZMA9171‐705 Tdob14 2ZMA9090‐296 Tdob02 Serbia: Beograd 44,833 20,500 6 0,6667 2ZMA9090‐297 Tdob07 2ZMA9090‐298 Tdob02 2ZMA9090‐299 Tdob02 2ZMA9090‐300 Tdob02 2ZMA9090‐301 Tdob02 2ZMA9097‐620 Tdob08 Serbia: Debrc 44,600 19,867 28 1,731 2ZMA9097‐621 TkarC27 2ZMA9097‐622 Tdob11 2ZMA9097‐623 Tdob09 2ZMA9097‐624 Tdob11 2ZMA9097‐625 Tdob11 2ZMA9097‐626 Tdob01 2ZMA9097‐634 Tdob10 2ZMA9097‐635 Tdob10 2ZMA9097‐636 TkarC27 2ZMA9097‐637 Tdob11 2ZMA9097‐666 TkarC20 2ZMA9097‐667 TkarC27 2ZMA9097‐668 Tdob11 2ZMA9097‐669 Tdob11 2

ZMA9097‐671 TkarC27 2ZMA9097‐672 Tdob01 2ZMA9097‐673 Tdob01 2ZMA9097‐735 TkarC27 2ZMA9097‐736 TkarC27 2ZMA9097‐737 Tdob11 2ZMA9097‐738 TkarC27 2ZMA9097‐739 Tdob01 2ZMA9097‐740 TkarC27 2ZMA9097‐741 Tdob11 2ZMA9097‐742 Tdob10 2ZMA9097‐743 Tdob11 2ZMA9097‐784 Tdob01 2ZMA9104‐415 Tdob03 Serbia: Ecka 45,287 20,450 14 1,6154 2ZMA9104‐416 Tdob18 2ZMA9104‐417 Tdob14 2ZMA9104‐418 Tdob14 2ZMA9104‐419 Tdob14 2ZMA9104‐420 Tdob14 2ZMA9104‐421 Tdob03 2ZMA9104‐422 Tdob14 2ZMA9104‐423 Tdob18 2ZMA9104‐424 Tdob17 2ZMA9104‐425 Tdob02 2ZMA9104‐510 Tdob14 2ZMA9104‐746 Tdob02 2ZMA9104‐747 Tdob14 2ZMA9111‐610 Tdob11 Serbia: Glusci 44,858 19,541 11 0,5455 2ZMA9111‐611 Tdob11 2ZMA9111‐612 Tdob11 2ZMA9111‐613 Tdob11 2ZMA9111‐614 Tdob11 2ZMA9111‐615 Tdob11 2ZMA9111‐730 Tdob11 2ZMA9111‐731 Tdob11 2ZMA9111‐732 Tdob11 2ZMA9111‐733 Tdob11 2

ZMA9111‐734 Tdob01 2ZMA9120‐845 Tdob02 Serbia: Jamena 44,871 19,069 10 1,0667 2ZMA9120‐846 Tdob06 2ZMA9120‐847 Tdob02 2ZMA9120‐848 Tdob02 2ZMA9120‐849 Tdob02 2ZMA9120‐850 Tdob02 2ZMA9120‐851 Tdob06 2ZMA9120‐852 Tdob06 2ZMA9120‐853 Tdob06 2ZMA9120‐854 Tdob02 2ZMA9142‐453 Tdob17 Serbia: Opovo 45,037 20,443 8 1,8214 2ZMA9142‐454 Tdob14 2ZMA9142‐455 Tdob03 2ZMA9142‐456 Tdob18 2ZMA9142‐457 Tdob14 2ZMA9142‐458 Tdob14 2ZMA9142‐508 Tdob14 2ZMA9142‐509 Tdob02 2ZMA9153‐427 Tdob03 Serbia: Senta 45,917 20,100 14 1,2528 2ZMA9153‐428 Tdob14 2ZMA9153‐429 Tdob03 2ZMA9153‐430 Tdob03 2ZMA9153‐431 Tdob02 2ZMA9153‐432 Tdob14 2ZMA9153‐433 Tdob04 2ZMA9153‐434 Tdob03 2ZMA9153‐435 Tdob14 2ZMA9153‐436 Tdob02 2ZMA9153‐437 Tdob14 2ZMA9153‐438 Tdob03 2ZMA9153‐511 Tdob04 2ZMA9153‐745 Tdob14 22800 Tdob14 Serbia: Stevanove Ravnice 44,828 21,304 3 2,6667 12801 Tdob17 12803 Tdob01 12751 Tdob01 Serbia: Trbušac 44,673 19,830 5 2,6 1

2752 Tdob01 12753 Tdob11 12754 Tdob23 12755 Tdob08 14791 Tdob24 Serbia: Žabari 44,358 21,197 5 1,2 14792 Tdob24 14793 Tdob02 14794 Tdob26 14795 Tdob24 12338 Tdob24 Ukraine: Chop 48,448 22,248 1 n.a. 14426 Tdob02 Ukraine: Orlovka 45,317 28,450 1 n.a. 14432 Tdob02 Ukraine: Tsyurupinsk 46,650 32,750 3 3,3333 14433 Tdob25 14434 Tdob14 12341 Tdob14 Ukraine: Uzhgorod 2 48,616 22,281 3 0 12342 Tdob14 12343 Tdob14 14436 Tdob02 Ukraine: Vilkovo 45,400 29,583 1 n.a. 1

Triturus karelinii west2601 TkarC40 Bulgaria: Alepu 42,348 27,714 4 0,5 52602 TkarC40 52603 TkarC40 52604 TkarC41 52490 TkarC20 Bulgaria: Alexandrovo 42,601 25,093 5 0 52491 TkarC20 12492 TkarC20 52493 TkarC20 52494 TkarC20 52598 TkarC52 Bulgaria: Bata 42,463 24,198 2 0 52599 TkarC52 54264 TkarC19 Bulgaria: Belopoltsi 41,524 25,796 1 n.a. 52630 TkarC53 Bulgaria: Bivoliyane 41,57 25,511 1 n.a. 52605 TkarC20 Bulgaria: Bolata 43,384 28,471 3 0 52606 TkarC20 52633 TkarC20 52572 TkarC20 Bulgaria: Boyana lake 42,636 23,27 2 0 5

2573 TkarC20 52600 TkarC20 Bulgaria: Byaga 42,079 24,393 1 n.a. 54267 TkarC53 Bulgaria: Chukovo 41,489 25,457 4 1,1667 54268 TkarC53 54269 TkarC19 54270 TkarC20 52569 TkarC47 Bulgaria: Dendrariuma 42,628 23,228 3 2 52570 TkarC48 52571 TkarC49 52577 TkarC20 Bulgaria: Dolni Koriten 42,473 22,6 2 0 52578 TkarC20 54266 TkarC20 Bulgaria: Fakia 42,233 27,130 1 n.a. 52586 TkarC36 Bulgaria: Gabrene 41,375 22,965 1 n.a. 52584 TkarC19 Bulgaria: Gaytaninovo 41,451 23,738 1 n.a. 52594 TkarC51 Bulgaria: Gintsi 43,033 23,132 2 1 52595 TkarC20 54796 TkarC20 Bulgaria: Golesh 43,046 22,947 5 0 14797 TkarC20 14798 TkarC20 14799 TkarC20 14800 TkarC20 12575 TkarC20 Bulgaria: Iliya 42,075 22,797 2 1 52576 TkarC19 5ZMA9125‐377 TkarC20 Bulgaria: Karlovo 42,633 24,817 4 0,5 5ZMA9125‐378 TkarC34 5ZMA9125‐379 TkarC20 5ZMA9125‐719 TkarC20 52596 TkarC20 Bulgaria: Karlukovo 43,176 24,078 2 0 52597 TkarC20 52587 TkarC20 Bulgaria: Kolarovo 41,355 23,126 6 0 52588 TkarC20 52589 TkarC20 52590 TkarC20 52591 TkarC20 54484 TkarC20 12624 TkarC20 Bulgaria: Kolets 41,86 25,318 1 n.a. 52634 TkarC20 Bulgaria: Kykrina 43,107 24,865 2 0 5

2635 TkarC20 5ZMA9134‐442 TkarC20 Bulgaria: Levski 43,417 25,133 9 0 3ZMA9134‐443 TkarC20 3ZMA9134‐444 TkarC20 3ZMA9134‐445 TkarC20 3ZMA9134‐769 TkarC20 3ZMA9134‐770 TkarC20 2ZMA9134‐771 TkarC20 2ZMA9134‐772 TkarC20 2ZMA9134‐773 TkarC20 22628 TkarC20 Bulgaria: Lozenska Mt. 42,586 23,449 1 n.a. 54488 TkarC41 Bulgaria: Malko Tarnovo 41,989 27,525 1 n.a. 12627 TkarC20 Bulgaria: Novo Selo 42,177 22,679 1 n.a. 54486 TkarC20 Bulgaria: Orlovets 43,333 25,716 1 n.a. 12623 TkarC20 Bulgaria: Ostar Kamak 41,878 25,853 5 0 54729 TkarC20 14730 TkarC20 14731 TkarC20 14732 TkarC20 12585 TkarC19 Bulgaria: Paril 41,435 23,69 1 n.a. 52629 TkarC20 Bulgaria: Petarch 42,844 23,147 1 n.a. 52625 TkarC41 Bulgaria: Primorsko 42,267 27,755 1 n.a. 5ZMA9149‐515 TkarC20 Bulgaria: Rakovski 42,267 24,967 4 0 3ZMA9149‐766 TkarC20 3ZMA9149‐767 TkarC20 3ZMA9149‐768 TkarC20 34271 TkarC19 Bulgaria: Sedlovina 41,635 25,426 1 n.a. 5ZMA9154‐460 TkarC20 Bulgaria: Sevlievo 43,467 25,217 2 0 5ZMA9154‐461 TkarC20 52579 TkarC20 Bulgaria: Shumnatitsa 42,297 23,626 3 0 52580 TkarC20 52581 TkarC20 54265 TkarC43 Bulgaria: Slivovo 42,209 26,994 1 n.a. 52574 TkarC20 Bulgaria: Slokoshtitsa 42,253 22,695 2 0 52626 TkarC20 52582 TkarC50 Bulgaria: Stara Kresna 41,797 23,211 2 0 52583 TkarC50 5

4483 TkarC20 Bulgaria: Sungurlare 42,786 26,751 1 n.a. 12631 TkarC20 Bulgaria: Urvich 42,558 23,427 1 n.a. 52592 TkarC20 Bulgaria: Zverino 43,094 23,580 3 0 52593 TkarC20 52632 TkarC20 52075 TkarC19 Greece: Alistrati 41,077 23,973 6 0,5333 52076 TkarC19 52850 TkarC55 52851 TkarC55 52852 TkarC55 52853 TkarC55 5ZMA9096‐815 TkarC19 Greece: Dafnochori 40,950 22,800 3 0 3ZMA9096‐816 TkarC19 3ZMA9096‐891 TkarC19 32840 TkarC54 Greece: Katratzides 41,120 26,147 5 0 52841 TkarC54 52842 TkarC54 52843 TkarC54 52844 TkarC54 5ZMA9127‐808 TkarC56 Greece: Kentriko 41,167 22,900 13 0,5385 2ZMA9127‐809 TkarC19 2ZMA9127‐810 TkarC56 2ZMA9127‐811 TkarC56 2ZMA9127‐812 TkarC19 2ZMA9127‐813 TkarC20 2ZMA9127‐814 TkarC19 2ZMA9127‐885 TkarC19 2ZMA9127‐886 TkarC19 5ZMA9127‐887 TkarC19 5ZMA9127‐888 TkarC19 5ZMA9127‐889 TkarC19 5ZMA9127‐890 TkarC19 52855 TkarC56 Greece: Mylochori 41,106 22,9 5 0,4 52856 TkarC56 52857 TkarC19 52858 TkarC56 52859 TkarC56 5

2846 TkarC19 Greece: Saint Kosmas 41,084 24,669 4 0 52847 TkarC19 52848 TkarC19 52849 TkarC19 52860 TkarC19 Greece: Zarkadia 41,018 24,646 1 n.a. 5ZMA9088‐867 TkarC36 Macedonia: Bansko 41,383 22,767 1 n.a. 5ZMA9092‐794 TkarC19 Macedonia: Bigla 41,933 22,667 5 0 5ZMA9092‐795 TkarC19 5ZMA9092‐796 TkarC19 5ZMA9092‐892 TkarC19 5ZMA9092‐895 TkarC19 23733 TkarC19 Macedonia: Garnikovo 41,277 22,060 10 0 53734 TkarC19 53735 TkarC19 53736 TkarC19 53737 TkarC19 53738 TkarC19 53739 TkarC19 53740 TkarC19 53741 TkarC19 53742 TkarC19 5ZMA9140‐797 TkarC20 Macedonia: Mitrašinci 41,750 22,767 12 0,5303 3ZMA9140‐798 TkarC19 3ZMA9140‐799 TkarC19 3ZMA9140‐800 TkarC19 3ZMA9140‐801 TkarC20 3ZMA9140‐802 TkarC19 2ZMA9140‐803 TkarC19 2ZMA9140‐804 TkarC19 2ZMA9140‐805 TkarC20 2ZMA9140‐806 TkarC20 2ZMA9140‐807 TkarC20 2ZMA9140‐894 TkarC19 2ZMA9086‐390 TkarC27 Serbia: Aranđelovac 44,284 20,669 9 0,7778 3ZMA9086‐391 TkarC28 3ZMA9086‐392 TkarC20 3ZMA9086‐393 TkarC20 3

ZMA9086‐394 TkarC20 3ZMA9086‐395 TkarC28 2ZMA9086‐763 TkarC20 5ZMA9086‐764 TkarC20 2ZMA9086‐765 Tkarc27 2ZMA9099‐931 TkarC20 Serbia: Djurinci 44,494 20,653 3 0 5ZMA9099‐932 TkarC20 54054 TkarC20 5ZMA9113‐935 TkarC20 Serbia: Gornja Sabanta 43,900 21,000 1 n.a. 5ZMA9115‐817 TkarC20 Serbia: Grivac 43,967 20,667 9 0,2222 2ZMA9115‐818 TkarC27 2ZMA9115‐819 TkarC20 2ZMA9115‐820 TkarC20 2ZMA9115‐821 TkarC20 5ZMA9115‐822 TkarC20 5ZMA9115‐823 TkarC20 5ZMA9115‐824 TkarC20 5ZMA9115‐893 TkarC20 53782 TkarC24 Serbia: Grza 43,897 21,646 1 n.a. 5617 TkarC20 Serbia: Guberevac 43,852 20,771 5 0 5ZMA9116‐631 TkarC20 5ZMA9116‐632 TkarC20 5ZMA9116‐648 TkarC20 5ZMA9116‐779 TkarC20 23341 TkarC57 Serbia: Ljuberadja 43,032 22,395 2 1 53342 TkarC20 5ZMA9150‐825 TkarC20 Serbia: Resavica Pecina 44,050 21,600 3 0 5ZMA9150‐826 TkarC20 5ZMA9150‐882 TkarC20 5ZMA9157‐827 TkarC20 Serbia: Sisevac 43,957 21,585 4 1,1667 3ZMA9157‐828 TkarC23 3ZMA9157‐874 TkarC24 3ZMA9157‐875 TkarC24 3322 TkarC20 Serbia: Trešnja 44,600 20,583 10 0,5357 2323 TkarC20 2ZMA9164‐649 Tdob19 2ZMA9164‐650 TkarC35 5

ZMA9164‐651 TkarC20 5ZMA9164‐653 TkarC35 5ZMA9164‐654 Tdob19 2ZMA9164‐655 TkarC20 2ZMA9164‐656 TkarC35 2ZMA9164‐657 TkarC20 2ZMA9169‐868 TkarC20 Serbia: Vitanovac 43,733 20,800 5 0 5ZMA9169‐869 TkarC20 5ZMA9169‐870 TkarC20 5ZMA9169‐871 TkarC20 5ZMA9169‐934 TkarC20 54009 TkarC20 Serbia: Vlasi 42,999 22,638 2 0 54010 TkarC20 53952 TkarC20 Serbia: Zli Do 42,422 22,453 10 0 53953 TkarC20 53954 TkarC20 53955 TkarC20 53956 TkarC20 53957 TkarC20 53958 TkarC20 53959 TkarC20 53960 TkarC20 53961 TkarC20 51801 TkarC12 Turkey: Bergama 39,021 27,107 1 n.a. 51879 TkarC18 Turkey: Bigadiç 39,351 28,217 3 0 31880 TkarC18 31881 TkarC18 31788 TkarC18 Turkey: Bozdağ 38,367 28,102 5 0 31789 TkarC18 31790 TkarC18 31791 TkarC18 31792 TkarC18 32564 TkarC18 Turkey: Büyük Kalecik 38,687 30,455 5 0 52565 TkarC18 52566 TkarC18 52567 TkarC18 52568 TkarC18 5

1813 TkarC03 Turkey: Çan 40,006 26,937 12 2,4242 31814 TkarC03 31815 TkarC12 31816 TkarC12 31817 TkarC03 31818 TkarC03 51819 TkarC03 51820 TkarC03 51821 TkarC03 51822 TkarC12 51823 TkarC12 51824 TkarC03 51839 TkarC12 Turkey: Çavuşköy 40,255 28,107 1 n.a. 51802 TkarC12 Turkey: Dikili 39,176 26,822 5 0,4 31803 TkarC12 31804 TkarC17 31805 TkarC12 31812 TkarC12 32861 TkarC12 Turkey: Efes 37,947 27,349 3 0 52862 TkarC12 52863 TkarC12 51825 TkarC12 Turkey: Gönen 40,217 27,618 6 0 51826 TkarC12 51827 TkarC12 51828 TkarC12 51829 TkarC12 51838 TkarC12 5ZMA9121‐483 TkarC32 Turkey: Karacabey 40,250 28,300 3 2 5ZMA9121‐484 TkarC65 2ZMA9121‐485 TkarC33 52360 TkarC01 Turkey: Keşan 40,917 26,633 13 6,7692 32361 TkarC01 32362 TkarC01 32363 TkarC01 32364 TkarC01 52365 TkarC01 52366 TkarC19 3

2367 TkarC01 52368 TkarC01 52369 TkarC01 52370 TkarC01 52371 TkarC01 5K27 TkarC19 5K31 TkarC12 Turkey: Klaros 38,000 27,183 2 0 5K33 TkarC12 5K41 TkarC12 Turkey: Mersinbeleni 37,650 27,683 4 0 5K42 TkarC12 5K43 TkarC12 5K44 TkarC12 51869 TkarC12 Turkey: Mustafa Kemalpaşa 40,069 28,352 5 0,6 31870 TkarC15 31871 TkarC12 31872 TkarC12 31873 TkarC15 3

Triturus karelinii central1847 TkarB15 Turkey: Abanta Gölu 40,607 31,286 3 0 31848 TkarB15 34475 TkacC02 1ZMA9082‐402 TkarB16 Turkey: Adapazari 40,767 30,483 6 0 3ZMA9082‐403 TkarC05 3ZMA9082‐404 TkarC05 3ZMA9134‐774 TkarC02 3ZMA9134‐775 TkarC05 3ZMA9134‐776 TkarB16 3K12 TkarC05 Turkey: Arifiye 40,710 30,360 4 n.a. 5K13 TkarC05 5K14 TkarC05 5K15 TkarC05 5K20 TkarB12 Turkey: Aşağıçaylı 41,933 33,767 1 n.a. 1ZMA7564‐116 TkarB13 Turkey: Bartın 41,617 32,333 6 6,7333 2ZMA7564‐118 TkarB12 2ZMA7564‐121 TkarB17 2ZMA7564‐122 TkarB12 2

ZMA7564‐123 TkarB13 2ZMA7564‐124 TkarB12 21844 TkarC05 Turkey: Demirbey 40,856 30,488 1 n.a. 51849 TkarC09 Turkey: Gebze 40,890 29,504 9 n.a. 31850 TkarC05 31851 TkarC10 31852 TkarC05 31853 TkarC05 51865 TkarC05 51866 TkarC05 51867 TkarC10 51868 TkarC11 31933 TkarB13 Turkey: Karakoç 41,487 32,142 9 6 31934 TkarB13 31935 TkarB12 31936 TkarB12 31937 TkarB12 31938 TkarB13 11939 TkarB12 11940 TkarB12 11941 TkarB12 11948 TkarB10 Turkey: Cebeci 41,201 34,036 5 0 31949 TkarB10 31950 TkarB10 31951 TkarB10 31952 TkarB10 3K03 TkarB01 Turkey: Gürbulak 40,967 39,633 1 n.a. 11928 TkarB17 Turkey: Hacılar 41,491 32,101 5 1 11929 TkarB12 11930 TkarB17 11931 TkarB12 11932 TkarB18 11882 TkarB10 Turkey: Kalecik 40,077 33,341 7 0 31883 TkarB10 31884 TkarB10 31885 TkarB10 31886 TkarB10 3

K10 TkarB10 1K11 TkarB10 11845 TkarB14 Turkey: Karasu 41,076 30,757 2 0 31846 TkarB14 31960 TkarB07 Turkey: Kavak 41,110 36,017 5 0 31961 TkarB07 31962 TkarB07 31963 TkarB07 31964 TkarB07 3K22 TkarC58 Turkey: Okluca 40,233 29,850 4 n.a. 5K23 TkarC58 5K24 TkarC58 5K25 TkarC58 51841 TkarC05 Turkey: Orhangazi 40,554 29,325 3 n.a. 31842 TkarC05 31843 TkarC07 31985 TkarB04 Turkey: Reşadiye 40,441 37,476 8 1,0357 31986 TkarB06 31987 TkarB04 31988 TkarB05 31990 TkarB04 3K04 TkarB19 1K05 TkarB05 1K06 TkarB05 1K34 TkarC02 Turkey: Safibey 40,467 30,233 3 n.a. 5K35 TkarC02 5K36 TkarC05 5K37 TkarB20 Turkey: Seben 40,410 31,570 4 0,6667 1K38 TkarC02 5K39 TkarB15 1K40 TkarB15 12078 TkarB02 Turkey: Şebinkarahisar 40,286 38,126 5 0 32079 TkarB02 32080 TkarB02 32081 TkarB02 32082 TkarB02 3K16 TkarB10 Turkey: Tosya 41,017 34,033 4 0 6

K17 TkarB10 6K18 TkarB10 6K19 TkarB10 6ZMA9118‐954 TkarC29 Turkey: Yakacık 40,917 29,2 8 n.a. 5ZMA9118‐955 TkarC10 5ZMA9118‐956 TkarC30 5ZMA9118‐957 TkarC09 5ZMA9118‐958 TkarC11 5ZMA9118‐959 TkarC09 5ZMA9118‐960 TkarC31 5ZMA9118‐961 TkarC09 51840 TkarC05 Turkey: Yenişehir 40,268 29,605 1 n.a. 52103 TkarB01 Turkey: Yomra 40,868 39,857 1 n.a. 3

Triturus karelinii east2412 TkarA01 Azerbaijan: Avyarud 38,500 48,600 5 6,2 32413 TkarA01 32414 TkarA02 32415 TkarA06 32416 TkarA07 34470 TkarA01 Azerbaijan: Azfilial 38,667 48,783 2 1 14471 TkarA19 14467 TkarA01 Azerbaijan: Moshkhan 38,483 48,833 3 1,3333 14468 TkarA19 14469 TkarA02 12211 TkarA09 Georgia: Chiantba Lake 41,914 45,299 5 0 12212 TkarA09 12213 TkarA09 12214 TkarA09 12215 TkarA09 12196 TkarA09 Georgia: Chilitba lake 41,795 44,633 1 n.a. 12221 TkarA08 Georgia: Ikalto 41,951 45,395 5 0,6 12222 TkarA09 12223 TkarA08 12224 TkarA09 12225 TkarA09 12173 TkarA09 Georgia: Khekordzi 41,809 44,498 1 n.a. 1

2168 TkarA09 Georgia: Kobuleti 41,822 41,814 5 0 32169 TkarA09 32170 TkarA09 32171 TkarA09 32172 TkarA09 34445 TkarA18 Georgia: Machara 42,983 41,050 3 0 14446 TkarA18 14447 TkarA18 12157 TkarA09 Georgia: Pichori river valley 42,134 41,841 5 0 12158 TkarA09 12159 TkarA09 12160 TkarA09 12161 TkarA09 12162 TkarA09 Georgia: Poti 42,187 41,727 5 0 12163 TkarA09 12164 TkarA09 12165 TkarA09 12166 TkarA09 12193 TkarA09 Georgia: Satovle 41,786 44,545 3 0 12194 TkarA09 12195 TkarA09 12226 TkarA09 Georgia: Telavi 41,903 45,475 5 0,6 32227 TkarA08 32228 TkarA09 32229 TkarA09 32230 TkarA08 32207 TkarA09 Georgia: Tsodoreti 41,729 44,572 4 0 32208 TkarA09 32209 TkarA09 32210 TkarA09 32485 TkarA04 Iran: Alandan 36,233 53,467 3 0 32486 TkarA04 32487 TkarA04 3RMNH RenA 469 TkarA03 Iran: Qu’Am Shahr 36,436 52,803 2 1 3RMNH RenA 469 TkarA04 32129 TkarA12 Russia: Anapa 44,851 37,361 4 0 12130 TkarA12 1

2131 TkarA12 12132 TkarA12 12133 TkarA12 Russia: Anapskaya 44,883 37,438 3 0 12134 TkarA12 12135 TkarA12 12138 TkarA14 Russia: Cape Malyi Utrish 44,716 37,467 5 0 32139 TkarA14 32140 TkarA14 32141 TkarA14 32142 TkarA14 32409 TkarA12 Russia: Charnoreinje 43,917 40,650 1 n.a. 14465 TkarA09 Russia: Ersi 41,983 47,983 2 0 14466 TkarA09 12143 TkarA12 Russia: Goriachiy Kluch 44,563 39,032 1 n.a. 12144 TkarA12 Russia: Kamennomostskiy 44,285 40,123 3 0 12145 TkarA12 12146 TkarA12 14477 TkarA09 Russia: Mikhgerg 41,633 47,983 2 0 14478 TkarA09 12150 TkarA12 Russia: Nalchik*** 43,458 43,521 5 0,6 32151 TkarA11 32152 TkarA12 32153 TkarA11 32154 TkarA11 32136 TkarA14 Russia: Novorossiysk 44,755 37,819 2 6 12137 TkarA12 12147 TkarA12 Russia: Psebay 44,071 40,847 3 0 12148 TkarA12 12149 TkarA12 14454 TkarA12 Russia: Yablonovskiy 44,900 38,950 2 0 14455 TkarA12 12112 TkarA16 Ukraine: Alushta 44,682 34,384 5 0 62113 TkarA16 62114 TkarA16 62115 TkarA16 62116 TkarA16 62119 TkarA16 Ukraine: Chatyr‐Dag 44,688 34,234 5 0 1

2120 TkarA16 12121 TkarA16 12122 TkarA16 12123 TkarA16 12105 TkarA16 Ukraine: Nikita 44,538 34,243 5 0 32106 TkarA16 32107 TkarA16 32108 TkarA16 32109 TkarA16 3

Triturus macedonicus3248 Tmac11 Albania: Bajzë 42,303 19,441 5 4,8 53249 Tmac12 53250 Tmac11 53251 Tmac12 53252 Tmac12 53274 Tmac05 Albania: Bejar 40,429 19,850 5 12 53275 Tmac05 53276 Tmac27 53277 Tmac27 53494 Tmac05 53269 Tmac04 Albania: Fushë‐Krujë 41,529 19,682 5 3 53270 Tmac14 53271 Tmac04 53272 Tmac14 53273 Tmac15 53278 Tmac21 Albania: Gracen 41,157 19,949 5 0 53279 Tmac21 53280 Tmac21 53281 Tmac21 53282 Tmac21 53660 Tmac06 Albania: Kavajë 41,168 19,556 5 1 53661 Tmac06 53662 Tmac06 53663 Tmac07 53664 Tmac04 53263 Tmac08 Albania: Kolsh 42,072 20,326 5 0,6 5

3264 Tmac08 53265 Tmac09 53266 Tmac09 53267 Tmac09 53681 Tmac12 Albania: Koplik 42,197 19,451 5 3,2 53682 Tmac11 53683 Tmac11 53684 Tmac11 53685 Tmac11 53472 Tmac04 Albania: Lushnjë 41,000 19,664 5 2,4 53473 Tmac23 53474 Tmac04 53475 Tmac04 53476 Tmac23 53519 Tmac22 Albania: Pogradec 40,931 20,640 5 0,4 53520 Tmac22 53521 Tmac22 53522 Tmac22 53523 Tmac24 5ZMA9100‐872 Tdob11 Bosnia and Herzegovina: Donja – Gornja Čadjavica 44,800 19,033 2 n.a. 2ZMA9100‐873 TkarC27 5ZMA9112‐829 Tdob09 Bosnia and Herzegovina: Gornja Čadjavica 44,750 19,083 9 n.a. 2ZMA9112‐830 Tdob11 2ZMA9112‐831 TkarC27 2ZMA9112‐832 TkarC27 2ZMA9112‐833 Tdob13 2ZMA9112‐834 Tdob11 2ZMA9112‐835 Tdob13 2ZMA9112‐836 Tdob11 2ZMA9112‐881 Tdob13 2ZMA9162‐855 TkarC27 Bosnia and Herzegovina: Tavna Monastire 44,600 19,067 9 n.a. 2ZMA9162‐856 TkarC27 5ZMA9162‐857 TkarC27 5ZMA9162‐858 TkarC27 5ZMA9162‐859 TkarC27 5ZMA9162‐927 TkarC20 2ZMA9162‐928 TkarC27 2

ZMA9162‐929 TkarC27 2ZMA9162‐930 TkarC20 2ZMA9168‐896 TkarC27 Bosnia and Herzegovina: Visegrad 43,783 19,333 14 n.a. 5ZMA9168‐897 TkarC27 5ZMA9168‐898 TkarC20 5ZMA9168‐899 TkarC27 5ZMA9168‐900 TkarC27 2ZMA9168‐901 TkarC27 2ZMA9168‐902 TkarC27 2ZMA9168‐903 TkarC27 2ZMA9168‐904 TkarC27 2ZMA9168‐905 TkarC27 2ZMA9168‐906 TkarC20 2ZMA9168‐907 TkarC27 2ZMA9168‐908 TkarC27 2ZMA9168‐909 TkarC20 2ZMA9085‐602 Tmac01 Greece: Ano Kaliniki 40,853 21,450 11 0,1818 3ZMA9085‐603 Tmac01 2ZMA9085‐604 Tmac03 5ZMA9085‐605 Tmac01 2ZMA9085‐658 Tmac01 5ZMA9085‐659 Tmac01 5ZMA9085‐660 Tmac01 2ZMA9085‐661 Tmac01 2ZMA9085‐674 Tmac01 2ZMA9085‐675 Tmac01 5ZMA9085‐676 Tmac01 53768 TkarC19 Greece: Archangelos 41,050 22,291 5 n.a. 53769 TkarC19 54256 TkarC19 54257 TkarC19 54258 TkarC19 52104 Tmac02 Greece: Asprangeli 39,825 20,725 1 n.a. 33702 Tmac02 Greece: Elafotopos 39,900 20,669 4 0,5 53703 Tmac02 53704 Tmac28 53705 Tmac02 5

3710 Tmac02 Greece: Elati 39,835 20,740 1 n.a. 53775 Tmac29 Greece: Kerameia 39,562 22,081 7 0 53776 Tmac29 53777 Tmac29 53778 Tmac29 53779 Tmac29 53780 Tmac29 53781 Tmac29 52820 Tmac16 Greece: Kounoupena 39,683 19,764 4 3 52821 Tmac17 52822 Tmac18 52823 Tmac19 5616 TkarC20 Greece: Livadia 41,283 23,017 1 n.a. 53706 Tmac02 Greece: Negrades 39,847 20,663 3 0 53707 Tmac02 53708 Tmac02 52815 Tmac13 Greece: Paliambela 38,909 20,97 5 0 52816 Tmac13 52817 Tmac13 52818 Tmac13 52819 Tmac13 53745 TkarC59 Greece: Pefkodasos 41,030 22,580 10 0 53746 Tmac01 53747 TkarC19 53748 TkarC59 53749 TkarC59 53750 Tmac01 53751 Tmac01 53752 Tmac01 53753 TkarC59 53754 TkarC19 52835 Tmac01 Greece: Prespa 40,831 21,121 5 2 52836 Tmac01 52837 Tmac20 52838 Tmac01 52839 Tmac01 52825 Tmac25 Greece: Seli 40,567 22,022 5 0,4 5

2826 Tmac26 52827 Tmac26 52828 Tmac26 52829 Tmac26 53770 Tmac30 Greece: Trilofos 40,358 22,477 5 0 53771 Tmac30 53772 Tmac30 53773 Tmac30 53774 Tmac30 53709 Tmac29 Greece: Vryta 40,800 21,919 1 n.a. 52830 Tmac01 Greece: Zygos‐Mikro 39,823 21,261 5 0 52831 Tmac01 52832 Tmac01 52833 Tmac01 52834 Tmac01 53381 TkarC19 Kosovo: Doganovic 42,216 21,189 5 n.a. 53382 TkarC19 53383 TkarC19 53384 TkarC20 53385 TkarC19 52682 Tmac08 Kosovo: Karagač 42,640 20,309 4 0 52683 Tmac08 52684 Tmac08 52685 Tmac08 53669 Tmac08 Kosovo: Kijevo 42,568 20,728 5 0 53670 TkarC20 53671 TkarC45 53672 Tmac08 53673 Tmac08 53650 TkarC44 Kosovo: Prepolac 43,009 21,233 5 n.a. 53651 TkarC44 53652 TkarC44 53653 TkarC20 53654 TkarC20 53211 TkarC20 Kosovo: Pristina 42,666 21,172 5 n.a. 53212 TkarC20 53213 TkarC20 5

3214 TkarC20 53215 TkarC20 53377 Tmac08 Kosovo: Xërze 42,341 20,573 1 n.a. 53320 Tmac08 Macedonia: Debreste 41,502 21,293 5 0 53321 Tmac08 53322 Tmac08 53323 Tmac08 53324 Tmac08 52784 TkarC19 Macedonia: Dojran 41,241 22,718 3 n.a. 52786 TkarC19 52787 TkarC19 53317 Tmac01 Macedonia: Gorno Srpci 41,072 21,219 5 0 53529 Tmac01 53530 Tmac01 53531 Tmac01 53532 Tmac01 53601 Tmac01 Macedonia: Gostivar 41,817 20,899 4 0 53602 Tmac01 53603 Tmac01 53605 Tmac01 5ZMA9124‐860 TkarC39 Macedonia: Karbinci 41,767 22,233 1 n.a. 53334 Tmac10 Macedonia: Kolibari 41,580 20,951 5 0,6 53335 Tmac08 53336 Tmac08 53337 Tmac08 53338 Tmac10 53405 Tmac22 Macedonia: Leskoec 40,962 20,885 5 0,4 53406 Tmac22 53407 Tmac22 53408 Tmac22 53409 Tmac20 53535 Tmac03 Macedonia: Loznani 41,221 21,446 5 0 53536 Tmac03 53537 Tmac03 53538 Tmac03 53539 Tmac03 52907 TkarC19 Macedonia: Petrovac 41,939 21,615 4 n.a. 5

2908 TkarC19 52910 TkarC19 52911 TkarC19 53667 Tmac03 Macedonia: Prilep 41,419 21,611 2 0 53668 Tmac03 5ZMA9147‐862 TkarC19 Macedonia: Probistip 41,983 22,167 5 n.a. 5ZMA9147‐863 TkarC19 5ZMA9147‐864 TkarC19 5ZMA9147‐865 TkarC19 2ZMA9147‐866 TkarC19 53318 TkarC19 Macedonia: Rugince 42,148 21,977 5 n.a. 53319 TkarC19 53545 TkarC19 53546 TkarC19 53547 TkarC19 53327 Tmac08 Macedonia: Vrbjani 41,413 20,816 5 0 53583 Tmac08 53584 Tmac08 53585 Tmac08 53586 Tmac08 53245 Tmac11 Montenegro: Bjeloši 42,374 18,907 2 0 53246 Tmac11 53147 Tmac11 Montenegro: Donji Lokanj 41,900 19,267 5 0 53148 Tmac11 53149 Tmac11 53150 Tmac11 53151 Tmac11 52686 Tmac11 Montenegro: Kućišta 42,460 18,854 1 n.a. 53371 TkarC20 Serbia: Blato 43,301 20,730 4 n.a. 53372 TkarC20 53373 TkarC20 53374 TkarC20 53674 TkarC20 Serbia: Delimedje 43,091 20,265 5 n.a. 53675 TkarC20 53676 TkarC20 53677 TkarC20 53678 TkarC20 5

ZMA9098‐910 TkarC20 Serbia: Divcibare 44,100 19,933 11 n.a. 2ZMA9098‐911 TkarC20 2ZMA9098‐912 TkarC20 5ZMA9098‐913 TkarC20 5ZMA9098‐914 TkarC20 5ZMA9098‐915 TkarC20 5ZMA9098‐916 TkarC20 5ZMA9098‐917 TkarC20 2ZMA9098‐918 TkarC20 2ZMA9098‐919 TkarC20 2ZMA9098‐920 TkarC20 24259 TkarC20 Serbia: Dojni Dusnik 43,159 22,127 5 n.a. 54260 TkarC20 54261 TkarC20 54262 TkarC20 54263 TkarC20 53492 TkarC20 Serbia: Drežnik 43,774 19,992 2 n.a. 53493 TkarC20 53895 TkarC27 Serbia: Gornji Varbes 43,204 21,963 8 n.a. 53896 TkarC20 53897 TkarC27 53898 TkarC27 53899 TkarC27 53900 TkarC20 53901 TkarC37 53902 TkarC20 5ZMA9114‐448 TkarC20 Serbia: Grcak 43,467 20,950 8 n.a. 2ZMA9114‐449 TkarC20 5ZMA9114‐450 TkarC20 5ZMA9114‐451 TkarC20 5ZMA9114‐627 TkarC20 2ZMA9114‐628 TkarC20 2ZMA9114‐629 TkarC20 2ZMA9114‐630 TkarC20 53856 TkarC63 Serbia: Jezero 43,583 21,900 8 n.a. 53857 TkarC20 53858 TkarC63 5

3859 TkarC20 53860 TkarC63 53861 TkarC63 53862 TkarC63 53863 TkarC63 5ZMA9122‐921 TkarC20 Serbia: Karan 43,888 19,926 6 n.a. 5ZMA9122‐922 TkarC20 2ZMA9122‐923 TkarC20 5ZMA9122‐924 TkarC20 5ZMA9123‐925 TkarC20 5ZMA9123‐926 TkarC20 23783 TkarC60 Serbia: Krivi Vir 43,801 21,761 8 n.a. 53784 TkarC60 53785 TkarC60 53786 TkarC60 53787 TkarC60 53788 TkarC60 53789 TkarC60 53790 TkarC60 54003 TkarC20 Serbia: Ljuberadja 43,032 22,395 2 n.a. 54004 TkarC20 52656 TkarC27 Serbia: Ljubovija 44,200 19,400 5 n.a. 52657 TkarC27 52658 TkarC27 52659 TkarC27 52660 TkarC27 5ZMA9137‐606 TkarC20 Serbia: Lucane 42,417 21,717 12 n.a. 2ZMA9137‐607 TkarC38 5ZMA9137‐608 TkarC20 2ZMA9137‐609 TkarC20 2ZMA9137‐642 TkarC20 2ZMA9137‐643 TkarC20 2ZMA9137‐644 TkarC39 2ZMA9137‐645 TkarC20 5ZMA9137‐646 TkarC20 5ZMA9137‐662 TkarC20 2ZMA9137‐663 TkarC20 5

ZMA9137‐664 TkarC20 23693 TkarC46 Serbia: Promaja 42,678 22,357 11 n.a. 53903 TkarC46 53904 TkarC46 53905 TkarC46 53906 TkarC20 53907 TkarC46 53908 TkarC20 53909 TkarC46 53910 TkarC20 53911 TkarC46 53912 TkarC20 52517 TkarC42 Serbia: Rataje 43,477 21,106 1 n.a. 53831 TkarC63 Serbia: Rtanj 43,772 21,932 10 n.a. 53832 TkarC63 53833 TkarC63 53834 TkarC20 53835 TkarC63 53836 TkarC63 53837 TkarC20 53838 TkarC63 53839 TkarC20 53840 TkarC20 5ZMA9158‐398 TkarC20 Serbia: Stanisinci 43,517 20,883 4 n.a. 5ZMA9158‐399 TkarC20 5ZMA9158‐400 TkarC20 2ZMA9158‐401 TkarC20 52955 TkarC20 Serbia: Todorovce 42,875 21,866 5 n.a. 52956 TkarC20 52957 TkarC20 52958 TkarC20 52959 TkarC20 54017 TkarC20 Serbia: Vlasi 42,999 22,638 2 n.a. 54018 TkarC20 52533 TkarC20 Serbia: Vrtovac 43,404 22,450 5 n.a. 52534 TkarC20 52535 TkarC20 5

2536 TkarC20 52537 TkarC20 53796 TkarC20 Serbia: Zlot 44,011 21,967 8 n.a. 53797 TkarC20 53806 TkarC20 53807 TkarC20 53819 TkarC20 53820 TkarC61 53821 TkarC20 53822 TkarC62 5

T. marmoratusZMA9095‐518 Tmar01 France: Confolens 46,007 0,730 1 n.a. 3ZMA7617‐179 Tmar04 France: Mayenne 48,3 ‐0,617 39 0,6505 2ZMA9266‐264 Tmar03 2ZMA9266‐265 Tmar03 2ZMA9267‐266 Tmar01 2ZMA9267‐267 Tmar01 2ZMA9267‐268 Tmar01 2ZMA9268‐269 Tmar01 2ZMA9266‐270 Tmar01 2ZMA9266‐271 Tmar03 2ZMA 8049‐305 Tmar01 2306 Tmar01 2ZMA9074‐325 Tmar01 2ZMA9074‐326 Tmar01 2ZMA9074‐466 Tmar01 2ZMA9074‐467 Tmar01 2ZMA9074‐468 Tmar01 2ZMA9074‐469 Tmar01 2ZMA9075‐519 Tmar01 2ZMA9076‐520 Tmar01 2ZMA9076‐521 Tmar01 2ZMA9076‐522 Tmar01 2ZMA9077‐523 Tmar01 2ZMA9077‐524 Tmar01 2ZMA9078‐525 Tmar01 2

ZMA9078‐526 Tmar01 2ZMA9079‐527 Tmar01 2ZMA9079‐528 Tmar01 2ZMA9079‐529 Tmar01 2ZMA9079‐530 Tmar01 2ZMA9080‐531 Tmar01 2ZMA9080‐532 Tmar01 2ZMA9074‐533 Tmar01 2ZMA9074‐534 Tmar01 2ZMA9074‐539 Tmar01 2ZMA9074‐540 Tmar05 2ZMA9081‐936 Tmar03 2ZMA9081‐937 Tmar01 2ZMA9081‐938 Tmar01 2ZMA9074‐939 Tmar01 2ZMA9151‐535 Tmar01 France: Rochechouart 45,794 0,775 4 0 2ZMA9151‐536 Tmar01 2ZMA9151‐537 Tmar01 2ZMA9151‐538 Tmar01 2M1695 Tmar18 Portugal: Aldeia de João Pires 40,108 ‐7,141 3 0 7M2842 Tmar18 7M2843 Tmar18 7M1697 Tmar18 Portugal: Aldeia Stª Margarida 40,051 ‐7,259 5 0,4 7M1698 Tmar18 7M1699 Tmar18 7M1700 Tmar28 7M1701 Tmar18 7M1339 Tpyg03 Portugal: Areia 3 39,508 ‐7,934 5 10 7M1340 Tmar24 7M1341 Tmar18 7M1342 Tpyg03 7M1343 Tpyg44 7M2626 Tmar18 Portugal: Carepa 39,622 ‐7,771 5 0 7M2627 Tmar18 7M2628 Tmar18 7M2629 Tmar18 7M2630 Tmar18 7

M1530 Tmar04 Portugal: Castelo Mendo 40,586 ‐6,94 5 0 7M1531 Tmar04 7M1532 Tmar04 7M1533 Tmar04 7M1534 Tmar04 7M1739 Tmar29 Portugal: Coimbra 40,205 ‐8,407 3 5,3333 7M1740 Tmar29 7M1742 Tmar16 7M2063 Tmar18 Portugal: Fratel 39,618 ‐7,736 5 0 7M2656 Tmar18 7M2657 Tmar18 7M2658 Tmar18 7M2659 Tmar18 7M1628 Tmar18 Portugal: Idanha‐a‐Nova 39,934 ‐7,192 4 0 7M1629 Tmar18 7M1630 Tmar18 7M1633 Tmar18 7M1902 Tmar18 Portugal: Madeirã 39,94 ‐8,037 5 3,6 7M1903 Tmar18 7M1904 Tmar16 7M1905 Tmar18 7M1906 Tmar18 7M1973 Tmar02 Portugal: Mezio 40,939 ‐7,807 5 1,8 7M1974 Tmar15 7M1975 Tmar02 7M1976 Tmar15 7M1977 Tmar02 7M1568 Tmar04 Portugal: Mogadouro 41,341 ‐6,606 5 0 7M1569 Tmar04 7M1570 Tmar04 7M1571 Tmar04 7M1572 Tmar04 7M1191 Tmar18 Portugal: Monte do Conde 40,168 ‐7,25 3 0,6667 7M1201 Tmar18 7M1202 Tmar27 7M3644 Tmar04 Portugal: Nelas 40,488 ‐7,839 10 2,2444 7M3645 Tmar04 7

M3646 Tmar04 7M3647 Tmar19 7M3648 Tmar04 7M3665 Tmar20 7M3666 Tmar20 7M3667 Tmar20 7M3668 Tmar20 7M3669 Tmar20 7M1952 Tmar12 Portugal: Nespereira de Baixo 40,76 ‐8,367 5 0 7M1953 Tmar12 7M1954 Tmar12 7M1956 Tmar12 7M1972 Tmar12 7M302 Tmar25 Portugal: Pedra do Altar 39,706 ‐7,819 4 5 7M303 Tmar18 7M304 Tmar26 7M305 Tmar18 7M2851 Tmar10 Portugal: Porto 41,152 ‐8,639 5 3,6 7M2852 Tmar10 7M2853 Tmar11 7M2854 Tmar10 7M2855 Tmar10 7M5696 Tmar07 Portugal: Rebordelo 41,711 ‐7,110 15 0,4 1M5697 Tmar07 1M5698 Tmar07 1M5699 Tmar07 1M5700 Tmar07 1M5701 Tmar07 1M5702 Tmar07 1M5703 Tmar07 1M5704 Tmar07 1M5705 Tmar07 1M5706 Tmar07 1M5707 Tmar07 1M5708 Tmar07 1M5709 Tmar41 1M5710 Tmar07 1

M237 Tmar13 Portugal: Saide 40,45 ‐8,323 5 3,6 7M239 Tmar04 7M240 Tmar14 7M242 Tmar14 7M243 Tmar14 7M3708 Tmar17 Portugal: Santa Catarina 39,45 ‐9,009 4 0 7M3709 Tmar17 7M3710 Tpyg03 7M3711 Tmar17 7M311 Tmar16 Portugal: Santos 39,601 ‐7,958 5 5,2 7M312 Tmar17 7M313 Tmar16 7M314 Tmar17 7M315 Tmar18 7M1280 Tmar18 Portugal: São João do Deserto 40,138 ‐7,189 5 0 7M1281 Tmar18 7M1282 Tmar18 7M1283 Tmar18 7M1284 Tmar18 7M2383 Tpyg03 Portugal: Senhora do Almurtão 39,904 ‐7,159 5 n.a. 7M2384 Tpyg03 7M2385 Tpyg03 7M2386 Tpyg03 7M2387 Tmar18 7M549 Tmar07 Portugal: Serra do Gerês 41,817 ‐8,035 5 1,6 7M555 Tmar07 7M556 Tmar07 7M557 Tmar08 7M558 Tmar09 7M1765 Tmar16 Portugal: Sicó 40,018 ‐8,525 5 0,8 7M1766 Tmar30 7M1767 Tmar30 7M1768 Tmar30 7M1769 Tmar30 7M1770 Tmar16 Portugal: Soure 40,018 ‐8,622 10 3,8222 7M1771 Tmar31 7M1772 Tmar31 7

M1773 Tmar32 7M1774 Tmar33 7M1802 Tmar33 7M1803 Tmar32 7M1804 Tmar31 7M1805 Tmar32 7M1806 Tmar30 7M2792 Tmar21 Portugal: Torreira 40,759 ‐8,706 3 0 7M2793 Tmar21 7M3477 Tmar21 7M2136 Tmar18 Portugal: Vilar de Boi 39,67 ‐7,735 5 0,4 7M2137 Tmar18 7M2138 Tmar23 7M2139 Tmar18 7M2140 Tmar18 7M2747 Tmar04 Portugal: Vila Velha de Ródão 39,669 ‐7,668 4 3,3333 7M2748 Tmar22 7M2749 Tpyg43 7M2750 Tmar18 7M1718 Tmar06 Spain: Cabreiras 42,442 ‐7,903 5 0,4 7M1719 Tmar01 7M1720 Tmar01 7M1721 Tmar01 7M1722 Tmar01 7ZMA7614‐161 Tmar02 Spain: El Berrueco 40,9 ‐3,883 8 0 2ZMA7614‐162 Tmar02 2ZMA7614‐163 Tmar02 2ZMA7614‐164 Tmar02 2ZMA7614‐165 Tmar02 2ZMA7614‐166 Tmar02 2ZMA7614‐167 Tmar02 2ZMA9274‐980 Tmar02 3M2976 Tmar39 Spain: Mirador de la Curota 42,974 ‐8,974 15 0,4952 1M2977 Tmar39 1M2978 Tmar39 1M2980 Tmar39 1M2981 Tmar40 1

M2982 Tmar39 1M2983 Tmar39 1M2984 Tmar39 1M2985 Tmar39 1M2986 Tmar39 1M2987 Tmar39 1M2988 Tmar39 1M2989 Tmar39 1M2990 Tmar39 1M2992 Tmar40 1ZMA9272‐974 Tmar01 Spain: Santillana del Mar 43,389 ‐4,103 2 0 1ZMA9272‐976 Tmar01 1M3698 Tmar04 Spain: Serradilla del Llano 40,49 ‐6,34 5 0 7M3699 Tmar04 7M3700 Tmar04 7M3701 Tmar04 7M3702 Tmar04 7

T. pygmaeusP3022 Tpyg14 Portugal: Alqueidão 39,536 ‐8,575 5 0,4 7P3023 Tpyg12 7P3024 Tpyg12 7P3025 Tpyg12 7P3026 Tpyg12 7P3225 Tpyg12 Portugal: Assentiz ‐ Fungalvaz 39,591 ‐8,507 5 0 7P3226 Tpyg12 7P3227 Tpyg12 7P3228 Tpyg12 7P3229 Tpyg12 7P1857 Tpyg35 Portugal: Calvão 40,469 ‐8,703 3 2,6667 7P1858 Tpyg36 7P1860 Tpyg37 7P1285 Tmar37 Portugal: Esperança 39,168 ‐7,176 8 0 7P1286 Tpyg23 7P1287 Tmar38 7P1288 Tpyg23 7P1289 Tpyg23 7

P1290 Tpyg23 7P1301 Tpyg23 7P1308 Tmar38 7P1313 Tpyg03 Portugal: Gavião 1 39,473 ‐7,937 8 0 7P1314 Tpyg03 7P1315 Tpyg03 7P1316 Tmar18 7P1317 Tpyg03 7P1319 Tmar34 7P1333 Tmar18 7P1334 Tmar18 7P247 Tmar16 Portugal: Gavião 2 39,455 ‐7,926 5 n.a. 7P249 Tmar16 7P251 Tmar18 7P253 Tmar16 7P255 Tmar16 7P1622 Tpyg03 Portugal: Granja 38,293 ‐7,253 5 1,8 7P1623 Tpyg24 7P1624 Tpyg25 7P1625 Tpyg25 7P1627 Tpyg03 7P7199 Tpyg56 Portugal: Mil Brejos 2 38,129 ‐8,253 5 0,4 1P7200 Tpyg56 1P7201 Tpyg57 1P7202 Tpyg56 1P7203 Tpyg56 1P1807 Tpyg09 Portugal: Mira 40,392 ‐8,786 4 1,5 7P1808 Tpyg38 7P1809 Tpyg09 7P1810 Tpyg39 7P1610 Tpyg12 Portugal: Mitra 38,536 ‐8,001 5 0 7P1611 Tpyg12 7P1612 Tpyg12 7P1613 Tpyg12 7P1614 Tpyg12 7P2836 Tpyg03 Portugal: Mora 38,958 ‐8,141 4 1,3333 7P2838 Tpyg03 7

P2839 Tpyg12 7P2841 Tmar18 7P1312 Tpyg03 Portugal: Mourão 38,408 ‐7,31 2 0 7P1616 Tpyg03 7P1118 Tpyg03 Portugal: Nisa 39,55 ‐7,589 5 0 7P1122 Tmar35 7P3179 Tpyg03 7P3186a Tmar36 7P3186b Tpyg03 7P2631 Tpyg09 Portugal: Praia de Vagueira 40,572 ‐8,754 10 0,4 7P2632 Tpyg09 7P2633 Tpyg09 7P2634 Tpyg09 7P2635 Tpyg09 7P2636 Tpyg10 7P2637 Tpyg09 7P2638 Tpyg09 7P2639 Tpyg09 7P2640 Tpyg09 7P1882 Tpyg09 Portugal: Quiaios 40,24 ‐8,8 4 4,5 7P1883 Tpyg40 7P1885 Tpyg41 7P1886 Tpyg42 7P3216 Tpyg13 Portugal: Rexaldia 2 39,573 ‐8,527 4 0 7P3217 Tpyg13 7P3218 Tpyg13 7P3222 Tpyg13 7P2816 Tpyg15 Portugal: Rio Maior 39,341 ‐8,909 6 0 7P2817 Tpyg15 7P2818 Tpyg15 7P2819 Tpyg15 7P2820 Tpyg15 7P2821 Tpyg15 7P1649 Tpyg03 Portugal: Rosmaninhal 39,751 ‐7,092 5 0 7P1650 Tpyg03 7P1651 Tpyg03 7P1667 Tpyg03 7

P1668 Tpyg03 7P116 Tpyg12 Portugal: Serra de Stº António 39,509 ‐8,724 5 0 7P117 Tpyg12 7P118 Tpyg12 7P119 Tpyg12 7P120 Tpyg12 7P3573 Tpyg20 Portugal: Sintra 1 38,79 ‐9,422 2 2 7P3574 Tpyg21 7P3577 Tpyg21 Portugal: Sintra 2 38,786 ‐9,386 4 1,5 7P3578 Tpyg21 7P3579 Tpyg22 7P3580 Tpyg21 7P1335 Tpyg29 Portugal: Sovrete 39,268 ‐7,284 3 5,3333 7P1337 Tpyg30 7P1338 Tpyg31 7P3600 Tpyg11 Portugal: Tomar 39,603 ‐8,417 5 0,8 7P3601 Tpyg12 7P3602 Tpyg12 7P3603 Tpyg12 7P3604 Tpyg12 7P2796 Tpyg32 Portugal: Valado dos Frades 39,592 ‐9,006 5 1,8 7P2799 Tpyg09 7P2800 Tpyg33 7P2801 Tpyg09 7P2803 Tpyg34 7P2001 Tpyg03 Portugal: Velada 39,575 ‐7,701 5 0 7P2002 Tpyg03 7P2003 Tpyg03 7P2004 Tpyg03 7P2005 Tpyg03 7P1082 Tpyg26 Portugal: Vila do Bispo 37,076 ‐8,906 5 2 7P1083 Tpyg27 7P1084 Tpyg27 7P1085 Tpyg27 7P1086 Tpyg28 7P1260 Tmar18 Portugal: Zebreira 39,926 ‐7,089 4 0 7P1263 Tpyg03 7

P1271 Tpyg03 7P1691 Tmar29 7P2185 Tpyg03 Portugal: Zebreira West 39,839 ‐7,11 5 0 7P2186 Tpyg03 7P2187 Tpyg03 7P2188 Tpyg03 7P2189 Tmar18 7P3136 Tpyg16 Spain: Aceitunas 40,151 ‐6,321 4 0 7P3137 Tpyg16 7P3138 Tpyg16 7P3139 Tpyg16 7P3149 Tpyg03 Spain: Alcuescar 39,087 ‐6,358 5 0 7P3150 Tpyg03 7P3151 Tpyg03 7P3152 Tpyg03 7P3153 Tpyg03 7ZMA9087‐940 Tpyg07 Spain: Archidona Loja 37,1 ‐4,383 10 0,4 2ZMA9087‐941 Tpyg07 2ZMA9087‐942 Tpyg07 2ZMA9087‐943 Tpyg07 2ZMA9087‐944 Tpyg07 2ZMA9087‐947 Tpyg07 2ZMA9087‐948 Tpyg07 2ZMA9087‐949 Tpyg07 2ZMA9087‐950 Tpyg07 2ZMA9087‐951 Tpyg02 2P6323 Tpyg48 Spain: Barrancos ‐ Aroche 2 38,070 ‐6,935 18 1,0523 1P6324 Tpyg03 1P6325 Tpyg12 1P6326 Tpyg03 1P6327 Tpyg03 1P6328 Tpyg03 1P6329 Tpyg03 1P6330 Tpyg03 1P6331 Tpyg03 1P6332 Tpyg12 1P6333 Tpyg03 1

P6334 Tpyg12 1P6336 Tpyg03 1P6337 Tpyg12 1P6338 Tpyg03 1P6339 Tpyg12 1P6340 Tpyg03 1P6341 Tpyg03 1P6421 Tpyg12 Spain: Calanas 2 37,654 ‐6,863 24 0,5181 1P6422 Tpyg12 1P6423 Tpyg52 1P6424 Tpyg50 1P6425 Tpyg12 1P6426 Tpyg12 1P6427 Tpyg12 1P6428 Tpyg12 1P6429 Tpyg50 1P6430 Tpyg12 1P6431 Tpyg52 1P6432 Tpyg12 1P6433 Tpyg12 1P6434 Tpyg50 1P6435 Tpyg12 1P6436 Tpyg50 1P6437 Tpyg12 1P6438 Tpyg12 1P6439 Tpyg12 1P6440 Tpyg12 1P6441 Tpyg12 1P6442 Tpyg52 1P6443 Tpyg12 1P6444 Tpyg12 1P6771 Tpyg45 Spain: Canamero ‐ Grosan 39,343 ‐5,454 13 0,4615 1P6772 Tpyg45 1P6773 Tpyg45 1P6774 Tpyg45 1P6775 Tpyg03 1P6776 Tpyg45 1

P6777 Tpyg45 1P6778 Tpyg03 1P6779 Tpyg45 1P6780 Tpyg45 1P6781 Tpyg45 1P6782 Tpyg03 1P6783 Tpyg03 1P3665 Tpyg12 Spain: Casar de Palomero 1 40,274 ‐6,236 5 n.a. 7P3666 Tmar20 7P3667 Tmar20 7P3668 Tmar20 7P3669 Tmar20 7P7125 Tpyg53 Spain: Cerra Hierro 37,953 ‐5,623 14 1,8132 1P7126 Tpyg54 1P7127 Tpyg53 1P7128 Tpyg02 1P7129 Tpyg53 1P7130 Tpyg03 1P7131 Tpyg55 1P7132 Tpyg54 1P7133 Tpyg53 1P7134 Tpyg53 1P7135 Tpyg53 1P7136 Tpyg53 1P7137 Tpyg02 1P7138 Tpyg55 1P4186 Tpyg12 Spain: Donana 36,851 ‐6,380 15 0,6667 1P4187 Tpyg12 1P4188 Tpyg12 1P4189 Tpyg12 1P4190 Tpyg12 1P4191 Tpyg12 1P4192 Tpyg12 1P4202 Tpyg12 1P4204 Tpyg12 1P4206 tpyg46 1P4208 Tpyg12 1

P4210 Tpyg03 1P4212 Tpyg47 1P4214 Tpyg12 1P4216 Tpyg12 1P3105 Tpyg17 Spain: El Bronco 40,209 ‐6,326 5 1 7P3106 Tpyg18 7P3110 Tpyg16 7P3112 Tpyg16 7P3114 Tpyg16 7P6377 Tpyg12 Spain: El Portil 37,245 ‐7,097 21 0 1P6378 Tpyg12 1P6379 Tpyg12 1P6380 Tpyg12 1P6381 Tpyg12 1P6382 Tpyg12 1P6383 Tpyg12 1P6384 Tpyg12 1P6385 Tpyg12 1P6386 Tpyg12 1P6387 Tpyg12 1P6388 Tpyg12 1P6389 Tpyg12 1P6390 Tpyg12 1P6391 Tpyg12 1P6392 Tpyg12 1P6393 Tpyg12 1P6394 Tpyg12 1P6395 Tpyg12 1P6396 Tpyg12 1P6397 Tpyg12 1P6398 Tpyg49 Spain: El Villar 37,210 ‐6,703 23 1,004 1P6399 Tpyg50 1P6400 Tpyg12 1P6401 Tpyg51 1P6402 Tpyg12 1P6403 Tpyg49 1P6404 Tpyg49 1

P6405 Tpyg49 1P6406 Tpyg12 1P6407 Tpyg51 1P6408 Tpyg49 1P6409 Tpyg51 1P6410 Tpyg49 1P6411 Tpyg49 1P6412 Tpyg51 1P6413 Tpyg49 1P6414 Tpyg49 1P6415 Tpyg12 1P6416 Tpyg12 1P6417 Tpyg49 1P6418 Tpyg51 1P6419 Tpyg12 1P6420 Tpyg51 1P6448 Tpyg03 Spain: Embalse de Aracena 37,924 ‐6,486 21 0,7619 1P6449 Tpyg03 1P6450 Tpyg03 1P6451 Tpyg03 1P6452 Tpyg12 1P6453 Tpyg03 1P6454 Tpyg03 1P6455 Tpyg03 1P6456 Tpyg12 1P6457 Tpyg12 1P6458 Tpyg03 1P6459 Tpyg03 1P6460 Tpyg12 1P6461 Tpyg03 1P6462 Tpyg03 1P6463 Tpyg12 1P6464 Tpyg03 1P6465 Tpyg03 1P6466 Tpyg03 1P6467 Tpyg03 1P6468 Tpyg03 1

P6515 Tpyg03 Spain: Estacion de Belalcazar ‐ Sta Eufamia 38,654 ‐5,012 21 0 1P6516 Tpyg03 1P6517 Tpyg03 1P6518 Tpyg03 1P6519 Tpyg03 1P6522 Tpyg03 1P6523 Tpyg03 1P6524 Tpyg03 1P6525 Tpyg03 1P6526 Tpyg03 1P6527 Tpyg03 1P6528 Tpyg03 1P6529 Tpyg03 1P6530 Tpyg03 1P6531 Tpyg03 1P6532 Tpyg03 1P6533 Tpyg03 1P6534 Tpyg03 1P6535 Tpyg03 1P6536 Tpyg03 1P6537 Tpyg03 1P7156 Tpyg03 Spain: Llerena 38,138 ‐6,018 13 0 1P7157 Tpyg03 1P7158 Tpyg03 1P7159 Tpyg03 1P7160 Tpyg03 1P7161 Tpyg03 1P7162 Tpyg03 1P7163 Tpyg03 1P7164 Tpyg03 1P7165 Tpyg03 1P7166 Tpyg03 1P7167 Tpyg03 1P7168 Tpyg03 1P6503 Tpyg03 Spain: Llerena ‐ Higueira de la Serena 38,668 ‐5,679 12 0 1P6504 Tpyg03 1P6505 Tpyg03 1

P6506 Tpyg03 1P6507 Tpyg03 1P6508 Tpyg03 1P6509 Tpyg03 1P6510 Tpyg03 1P6511 Tpyg03 1P6512 Tpyg03 1P6513 Tpyg03 1P6514 Tpyg03 1P3140 Tpyg16 Spain: Pedro Muñoz 3 40,255 ‐6,341 4 0 7P3141 Tpyg16 7P3143 Tpyg16 7P3144 Tpyg16 7ZMA7676‐272 Tpyg06 Spain: Puerto de Galiz 36,683 ‐5,450 13 1,0256 2ZMA7676‐273 Tpyg06 2ZMA7676‐274 Tpyg06 2ZMA7676‐275 Tpyg06 2ZMA7676‐276 Tpyg06 2ZMA7676‐277 Tpyg02 2ZMA7676‐278 Tpyg02 2ZMA7676‐279 Tpyg06 2ZMA7676‐280 Tpyg06 2ZMA7676‐281 Tpyg06 2ZMA9148‐945 Tpyg07 2ZMA9087‐946 Tpyg07 2ZMA9148‐952 Tpyg06 2ZMA7552‐77 Tpyg02 Spain: Rio Alberite 36,4 ‐5,650 20 0,2 3ZMA7552‐78 Tpyg02 2ZMA7552‐79 Tpyg02 2ZMA7552‐80 Tpyg02 2ZMA7552‐81 Tpyg02 2ZMA7552‐82 Tpyg02 2ZMA7552‐83 Tpyg02 2ZMA7552‐84 Tpyg02 2ZMA7552‐85 Tpyg02 2ZMA7552‐86 Tpyg02 2ZMA7552‐87 Tpyg02 2

ZMA7552‐88 Tpyg02 2ZMA7552‐89 Tpyg02 2ZMA7552‐90 Tpyg02 2ZMA7552‐91 Tpyg02 2ZMA7552‐92 Tpyg02 2ZMA7552‐93 Tpyg02 2ZMA7552‐94 Tpyg02 2ZMA7552‐95 Tpyg02 2ZMA7618‐180 Tpyg07 2P3126 Tpyg16 Spain: Stª Cruz de Paniagua 40,191 ‐6,327 5 0,8 7P3127 Tpyg16 7P3128 Tpyg16 7P3129 Tpyg19 7P3130 Tpyg03 7P6343 Tpyg12 Spain: Ste Barbara 37,789 ‐7,217 15 0 1P6344 Tpyg12 1P6345 Tpyg12 1P6346 Tpyg12 1P6347 Tpyg12 1P6348 Tpyg12 1P6349 Tpyg12 1P6350 Tpyg12 1P6351 Tpyg12 1P6352 Tpyg12 1P6353 Tpyg12 1P6354 Tpyg12 1P6355 Tpyg12 1P6356 Tpyg12 1P6357 Tpyg12 1ZMA7677‐282 Tpyg01 Spain: Venta del Charco 38,2 ‐4,267 10 2,3778 3ZMA7677‐283 Tpyg05 2ZMA7677‐284 Tpyg05 2ZMA7677‐285 Tpyg05 2ZMA7677‐286 Tpyg07 2ZMA7677‐287 Tpyg03 2ZMA7677‐288 Tpyg05 2ZMA7677‐289 Tpyg01 2

ZMA7677‐290 Tpyg08 2ZMA7677‐291 Tpyg05 2ZMA7615‐168 Tpyg04 Spain: Villalba 40,633 ‐4,000 11 0,5091 2ZMA7615‐169 Tpyg04 2ZMA7615‐170 Tpyg03 2ZMA7615‐171 Tpyg03 2ZMA7615‐172 Tpyg04 2ZMA7615‐173 Tpyg03 2ZMA7615‐174 Tpyg03 2ZMA7615‐175 Tpyg03 2ZMA7615‐176 Tpyg04 2ZMA7615‐177 Tpyg03 2ZMA7615‐178 Tpyg03 2

* = Haplotypes correspond to Appendix S4. Introgressed haplotypes are highlighted** = Introgressed mitochondrial DNA was excluded in the calculation of π and π was not determined for populations for which only one individual was sequenced (*** = Erroneously listed as from Psebay in Wielstra et al. (2010) Cryptic crested newt diversity at the Eurasian transition: The mitochondrial DNA phylogeography of

Individuals are identified with a code that refers to complete specimens (ID starting with ZMA) or tail tips (remaining samples). All material is stored at the Netherla

Source of sequence:1 = This study2 = Arntzen JW, Wallis GP, Wielstra B (submitted) Spatial patterns of nuclear, mitochondrial and morphological transition in Triturus newts3 = Wielstra B, Espregueira Themudo G, Güclü Ö, Olgun K, Poyarkov NA, Arntzen JW (2010) Cryptic crested newt diversity at the Eurasian transition: The mitochond4 = Wielstra B, Arntzen JW (2011) Unraveling the rapid radiation of crested newts (Triturus cristatus superspecies) using complete mitogenomic sequences. BMC Ev5 = Wielstra B, Arntzen JW (2012) The shifting interchange of two species of Triturus newts deduced from asymmetrically introgressed mitochondrial DNA. BMC Ev6 = Ivanović A, Üzüm N, Wielstra B, Olgun K, Litvinchuk SN, Kalezić ML, Arntzen JW (2012) Is mitochondrial DNA divergence of Near Eastern crested newts (Triturus7 = Espregueira Themudo G, Nieman AM, Arntzen JW (2012) A comparison of interspecific gene flow estimates among differentiated regions of the Triturus marmo8 = Canestrelli D, Salvi D, Maura M, Bologna M, Nascetti G (2012) One species, three Pleistocene evolutionary histories: Phylogeography of the crested newt Trituru

Concerning haplotypes from source 3, it should be noted that:TkarA05 = TkarA04; TkarA10 = TkarA09; TkarA13 = Tkar12; TkarA15 = TkarA14; TkarA17 = TkarA16; TkarB03 = TkarB02; TkarB08 & TkarB09 = TkarB07; TkarB11 = TKa(Further subdivision of these identical ND4 haplotypes is due to an additionally sequenced ND2 segment in the original publication.)Concerning haplotypes from source 8, it should be noted that:N1 = Tcar01; S25 & S26 = Tcar02; S10 = Tcar04; S4 = Tcar05; S1, S3, S19, S23, S24 & S31 = Tcar06; S5 = Tcar08; C9 = C2; C13 = C1; C4 & C7 = C3; C6 = C5; C11 = C10; S(Further subdivision of these identical ND4 haplotypes is due to an additionally sequenced ND2 segment in the original publication.)

(noted as n.a.).f Near Eastern Triturus newts. Mol Phylogenet Evol 56: 888‐896.

nds Centre for Biodiversity ‐ Naturalis, The Netherlands.

rial DNA phylogeography of Near Eastern Triturus newts. Mol Phylogenet Evol 56: 888‐896.vol Biol 11: 162ol Biol 12: 161 karelinii group) reflected by differentiation of skull shape? Zool Anz: doi: 10.1016/j.bbr.2011.03.031oratus / T. pygmaeus hybrid zone. Mol Ecol 21: 5324–5335.us carnifex . PLoS ONE 7: e41754

arB10; TkarC04 = TkarC03; TkarC06 & TkarC08 = TkarC05; TkarC13, TkarC14 & TkarC16 = TkarC12; TkarC21, TkarC22 & TkarC26 = TkarC20; TkarC25 = TkarC24

S22, S32, S33 & S34 = S8; S11, S16 & S17 = S9; S18 = S13; S15 = S14; S30 = S29

Additional file 2. Locality data.

Locality ReferenceLatitude Longitude

T. carnifexAustria: Achleiten 47,817 13,217 24Austria: Bad Ischl 47,700 13,617 24Austria: Eichgraben 48,172 15,984 GBIF‐dAustria: Etzmandorf 48,650 15,750 1Austria: Graz 47,083 15,417 4Austria: Gunskirchen 48,100 13,917 24Austria: Haidlhof 47,967 16,167 1Austria: Hartberg 47,250 16,033 24Austria: Kainisch 47,550 13,850 24Austria: Klagenfurt 46,617 14,317 8Austria: Kleinmeiseldorf 48,667 15,750 1Austria: Klosterneuburg 48,300 16,317 8Austria: Koppl 47,800 13,133 24Austria: Lackenbach 47,600 16,450 1Austria: Lienz 46,833 12,767 8Austria: Linz 48,300 14,283 8Austria: Lonitzberg 48,036 15,043 GBIF‐cAustria: Micheldorf 47,867 14,117 24Austria: Ried 47,867 13,100 24Austria: Seewalchen 47,950 13,550 24Austria: Strobl 47,717 13,450 24Austria: Ternberg 47,950 14,367 24Austria: Zecherl 47,967 13,233 24Croatia: Belovar Moravce 45,850 16,167 1Croatia: Istarske Toplice 45,367 13,850 7Croatia: Lički Osik 44,600 15,417 7Croatia: Mrkpol 45,300 14,850 1Croatia: Plitvice 44,883 15,617 8Croatia: Salakovci 45,050 14,083 7Croatia: Sinac 44,817 15,367 1Croatia: Svetvinčenat 45,067 13,873 23Croatia: Vrelo 45,248 15,018 GBIF‐bCroatia: Žumberak 45,783 15,483 7Czech Republic: Mašovice 48,867 15,983 25Czech Republic: Rapšach 48,883 14,900 8

Coordinates

Hungary: Köszeg 47,383 16,550 8Hungary: Öriszentpéter 46,833 16,417 8Italy: Acerra 40,850 14,250 1Italy: Agnone 41,800 14,367 GBIF‐bItaly: Alberobello 40,824 17,235 23Italy: Alviano 42,619 12,243 23Italy: Ancona 43,616 13,519 GBIF‐aItaly: Arcade 45,775 12,206 23Italy: Avezzano 42,033 13,433 21Italy: Benevento 41,133 14,767 16Italy: Bobinaco 42,350 13,400 1Italy: Bologna 44,500 11,333 4Italy: Busa de San Piero 45,802 12,141 23Italy: Cameri 45,500 8,667 21Italy: Campa di Segni 41,667 13,000 4Italy: Campo di Mele 41,386 13,520 23Italy: Campo Imperatore 42,423 13,621 23Italy: Caramagna 44,793 7,713 22Italy: Caselette 45,100 7,483 21Italy: Caseletto Spartano 40,150 15,617 21Italy: Cecita 39,376 16,509 23Italy: Circeo 41,342 13,039 23Italy: Colfiorito 43,017 12,883 21Italy: Conza 40,855 15,305 23Italy: Doganella 41,759 12,791 23Italy: Donega 44,939 9,238 23Italy: Due Uomini 39,551 16,020 23Italy: Farma 43,133 11,167 1Italy: Florence 43,750 11,250 1Italy: Fuscaldo 39,417 16,033 1Italy: Gorizia 45,933 13,617 4Italy: Greve in Chianti 43,588 11,302 23Italy: Jenne 41,877 13,187 23Italy: La Sneira 45,492 7,892 23Italy: Laghicello 39,417 16,086 23Italy: Le Poscole 45,600 11,387 23Italy: Lenola 41,417 13,467 21Italy: Maceratoio del Rettore 45,488 7,907 23Italy: Massa Marittima 43,035 10,865 22Italy: Matera 40,667 16,600 21Italy: Minucciano 44,170 10,205 23

Italy: Modena 44,650 10,933 4Italy: Mondovi 44,383 7,817 8Italy: Monta 44,852 9,489 22Italy: Monte Prat 46,248 12,990 22Italy: Monte Sant'Angelo 41,700 15,967 21Italy: Montevecchia 45,703 9,375 23Italy: Mulino di Pianoro 44,353 11,321 23Italy: Muzzana del Turgnano 45,786 13,121 22Italy: Navegna 42,152 13,042 23Italy: Pedace 39,283 16,333 8Italy: Piampaludo 44,458 8,575 23Italy: Pietranico 42,283 13,933 4Italy: Pisa 43,717 10,400 1Italy: Poirino 44,909 7,836 23Italy: Porto Caleri 45,093 12,325 23Italy: Riva del Garda 45,900 10,850 8Italy: Rocca di mezzo 42,157 13,635 23Italy: Roccalbegna 42,742 11,503 23Italy: Roncofreddo 44,050 12,317 21Italy: Rufeno 42,776 11,884 23Italy: San Pietro in Guarano 39,339 16,352 23Italy: San Quirico 42,439 13,837 23Italy: San Severino Marche 43,303 13,074 23Italy: Senigallia 43,688 13,203 23Italy: Sepino 41,391 14,569 23Italy: Sessano 41,626 14,336 23Italy: Siena 43,333 11,317 4Italy: Sormano 45,883 9,250 21Italy: Stagno Bargone 44,319 9,486 23Italy: Susa 45,133 7,050 8Italy: Taverna Magnano 40,051 16,124 23Italy: Terra del sole 44,185 11,956 23Italy: Tirano 46,183 10,167 8Italy: Torre Palermo 41,834 16,038 23Italy: Vellai 46,039 11,939 8Italy: Venice 45,433 12,333 4Italy: Verona 45,433 10,983 4Italy: Villa Literno 41,103 14,250 GBIF‐bSlovenia: Komen 45,818 13,750 23Slovenia: Kramplje 45,733 14,500 1Slovenia: Matena 45,967 14,517 12

Slovenia: Radenci 46,642 16,041 7Slovenia: Štanjel 45,817 13,833 7Slovenia: Turjanci 46,617 16,067 7Switzerland: Locarno 46,167 8,800 1

T. cristatusAustria: Ameisensee 47,550 13,450 1Austria: Bad Zell 48,317 14,700 24Austria: Bürmoos 47,983 12,933 24Austria: Egg 47,433 9,900 8Austria: Haibach 48,417 13,883 24Austria: Ottenstein 48,467 14,283 1Austria: Reichersberg 48,333 13,350 24Austria: Sommeregg 47,833 13,117 1Belarus: Bakov 53,500 31,233 4Belarus: Brest 52,100 23,700 4Belarus: Drisvyaty 55,600 26,683 4Belarus: Dron'ki 51,650 29,983 4Belarus: Galyi Bor 52,383 26,767 4Belarus: Germanovichi 55,417 27,733 4Belarus: Guta 52,783 24,600 4Belarus: Horsk 52,083 27,200 4Belarus: Kamenyuki 52,550 23,800 4Belarus: Khoiniki 51,883 29,967 4Belarus: Khupin 52,050 28,117 4Belarus: Khvoensk 52,050 27,933 4Belarus: Kobuzi 54,450 27,100 4Belarus: Kolodishchi 53,950 27,767 4Belarus: Lelchitsy 51,783 28,333 4Belarus: Lemeshevichi 52,100 26,267 4Belarus: Lipsk 52,833 26,100 4Belarus: Luskinopol' 55,000 30,167 4Belarus: Maloe Stakhovo 54,250 28,450 4Belarus: Minsk 53,900 27,567 4Belarus: Mosty Lake 52,217 30,917 4Belarus: Mozyr' 52,050 29,267 4Belarus: Radoshkovichi 54,167 27,250 4Belarus: Simonichi 51,883 28,100 4Belarus: Slobodka 53,117 29,117 4Belarus: Smorgon 54,483 26,417 4Belarus: Staryi Dvorets 53,250 24,117 4

Belarus: Tonezh‐Ivanovaya Sloboda 51,833 27,833 4Belarus: Turov 52,067 27,733 4Belarus: Ugly 53,917 26,633 4Belarus: Vitebsk 55,183 30,183 4Belarus: Yazyl' 52,983 27,967 4Belarus: Zapol'e 53,450 24,450 4Belarus: Zasulie 53,567 26,817 4Belarus: Zhidche 51,950 25,950 4Bulgaria: Barsia 43,197 23,149 5Bulgaria: Borovan 43,409 23,769 1Bulgaria: Borovitsa 43,590 22,770 1Bulgaria: Chiren 43,324 23,588 1Bulgaria: Gromshin 43,509 23,479 5Bulgaria: Ledenika 43,191 23,481 5Bulgaria: Lilyache 43,317 23,539 1Bulgaria: Montana 43,416 23,222 1Bulgaria: Palilula 43,432 23,373 5Bulgaria: Parshevitsa 43,139 23,471 1Bulgaria: Stoyanovo 43,265 23,348 5Bulgaria: Tosheva yama cave 43,265 23,346 1Czech Republic: Čertoryje 49,450 17,383 12Czech Republic: Chlumec 49,050 15,467 12Czech Republic: Horní Újezd 49,150 15,833 12Czech Republic: Hostim 49,017 15,900 12Czech Republic: Jindřichov 50,100 12,400 26Czech Republic: Kurovice 49,283 17,500 25Czech Republic: Moravský Krumlov 49,033 16,317 12Czech Republic: Nová Říše 49,150 15,583 25Czech Republic: Nova Sedlice 49,903 18,001 GBIF‐oCzech Republic: Pastviny 50,067 16,550 25Czech Republic: Sedlec 49,167 16,117 12Czech Republic: Suchdol 50,136 14,383 GBIF‐iCzech Republic: Třebohostice 50,033 14,733 25Czech Republic: Týnec 48,783 17,017 8Denmark: Allindelille Fredskov 55,500 11,760 GBIF‐jDenmark: Done Myre 55,160 14,880 GBIF‐jDenmark: Dover 56,700 8,430 GBIF‐jDenmark: Fredensborg 55,980 12,380 GBIF‐jDenmark: Frederiksdal Skov 55,760 12,410 GBIF‐jDenmark: Hoerup By 54,900 9,860 GBIF‐jDenmark: Kastrup at Tystrup 55,310 11,560 GBIF‐j

Denmark: Kirkeby Skov 55,080 10,530 GBIF‐jDenmark: Langebaek Vesterskov 54,980 12,080 GBIF‐jDenmark: Lemvig 56,530 8,300 GBIF‐jDenmark: Lundby Krat 56,960 10,000 GBIF‐jDenmark: Lundegaard at Praestoe 55,100 12,060 GBIF‐jDenmark: Maidalen 55,080 15,080 GBIF‐jDenmark: Oester Thoreby 54,750 11,830 GBIF‐jDenmark: Orup 55,200 12,080 GBIF‐jDenmark: Peterslund 56,680 8,660 GBIF‐jDenmark: Reberbanen 55,050 9,360 GBIF‐jDenmark: Salling 56,630 9,030 GBIF‐jDenmark: Skodsborg 55,810 12,550 GBIF‐jDenmark: Soroe 55,410 11,560 GBIF‐jDenmark: St. Rise 54,850 10,380 GBIF‐jDenmark: Store Donse Dam 55,880 12,400 GBIF‐jDenmark: Svindinge 55,200 10,680 GBIF‐jDenmark: Tinghoej 55,650 8,460 GBIF‐jDenmark: Voldum Lake 57,060 8,780 GBIF‐jEngland: Canterbury 51,283 1,083 1England: Oxford 51,767 ‐1,250 16England: Peterborough 52,583 ‐0,250 1Estonia: Karde 58,833 26,267 4Estonia: Kiddijerw 58,783 26,550 4Estonia: Laitsna 59,433 25,900 4Estonia: Rakvere 59,350 26,367 4Estonia: Tartu 58,367 26,717 4Finland: Anttola 61,583 27,633 8Finland: Nilsiä 63,200 28,083 8France: 001 47,790 ‐3,424 GBIF‐gFrance: 002 47,610 ‐3,064 GBIF‐gFrance: 003 48,330 ‐2,704 GBIF‐gFrance: 004 48,510 ‐2,344 GBIF‐gFrance: 005 47,970 ‐2,344 GBIF‐gFrance: 006 47,430 ‐2,344 GBIF‐gFrance: 007 48,150 ‐1,984 GBIF‐gFrance: 008 47,970 ‐1,984 GBIF‐gFrance: 009 47,250 ‐1,984 GBIF‐gFrance: 010 48,510 ‐1,624 GBIF‐gFrance: 011 48,330 ‐1,624 GBIF‐gFrance: 012 48,150 ‐1,624 GBIF‐gFrance: 013 47,610 ‐1,624 GBIF‐g

France: 014 47,430 ‐1,624 GBIF‐gFrance: 015 47,250 ‐1,624 GBIF‐gFrance: 016 46,710 ‐1,624 GBIF‐gFrance: 017 49,050 ‐1,264 GBIF‐gFrance: 018 48,330 ‐1,264 GBIF‐gFrance: 019 47,970 ‐1,264 GBIF‐gFrance: 020 47,790 ‐1,264 GBIF‐gFrance: 021 47,610 ‐1,264 GBIF‐gFrance: 022 47,430 ‐1,264 GBIF‐gFrance: 023 47,070 ‐1,264 GBIF‐gFrance: 024 46,710 ‐1,264 GBIF‐gFrance: 025 46,350 ‐1,264 GBIF‐gFrance: 026 48,690 ‐0,904 GBIF‐gFrance: 027 48,150 ‐0,904 GBIF‐gFrance: 028 47,970 ‐0,904 GBIF‐gFrance: 029 47,790 ‐0,904 GBIF‐gFrance: 030 47,610 ‐0,904 GBIF‐gFrance: 031 47,070 ‐0,904 GBIF‐gFrance: 032 46,890 ‐0,904 GBIF‐gFrance: 033 46,710 ‐0,904 GBIF‐gFrance: 034 49,230 ‐0,544 GBIF‐gFrance: 035 48,870 ‐0,544 GBIF‐gFrance: 036 48,510 ‐0,544 GBIF‐gFrance: 037 48,150 ‐0,544 GBIF‐gFrance: 038 47,970 ‐0,544 GBIF‐gFrance: 039 47,790 ‐0,544 GBIF‐gFrance: 040 47,610 ‐0,544 GBIF‐gFrance: 041 47,430 ‐0,544 GBIF‐gFrance: 042 47,250 ‐0,544 GBIF‐gFrance: 043 46,530 ‐0,544 GBIF‐gFrance: 044 46,170 ‐0,544 GBIF‐gFrance: 045 49,230 ‐0,184 GBIF‐gFrance: 046 48,690 ‐0,184 GBIF‐gFrance: 047 48,510 ‐0,184 GBIF‐gFrance: 048 48,150 ‐0,184 GBIF‐gFrance: 049 47,970 ‐0,184 GBIF‐gFrance: 050 47,790 ‐0,184 GBIF‐gFrance: 051 47,610 ‐0,184 GBIF‐gFrance: 052 47,250 ‐0,184 GBIF‐gFrance: 053 47,250 ‐0,184 GBIF‐gFrance: 054 46,170 ‐0,184 GBIF‐g

France: 055 49,590 0,176 GBIF‐gFrance: 056 49,050 0,176 GBIF‐gFrance: 057 48,870 0,176 GBIF‐gFrance: 058 48,690 0,176 GBIF‐gFrance: 059 48,510 0,176 GBIF‐gFrance: 060 48,330 0,176 GBIF‐gFrance: 061 48,150 0,176 GBIF‐gFrance: 062 47,610 0,176 GBIF‐gFrance: 063 45,990 0,176 GBIF‐gFrance: 064 49,230 0,536 GBIF‐gFrance: 065 49,050 0,536 GBIF‐gFrance: 066 48,870 0,536 GBIF‐gFrance: 067 48,690 0,536 GBIF‐gFrance: 068 48,150 0,536 GBIF‐gFrance: 069 47,430 0,536 GBIF‐gFrance: 070 47,070 0,536 GBIF‐gFrance: 071 46,890 0,536 GBIF‐gFrance: 072 46,710 0,536 GBIF‐gFrance: 073 46,530 0,536 GBIF‐gFrance: 074 45,990 0,536 GBIF‐gFrance: 075 49,230 0,896 GBIF‐gFrance: 076 49,050 0,896 GBIF‐gFrance: 077 48,690 0,896 GBIF‐gFrance: 078 48,510 0,896 GBIF‐gFrance: 079 48,330 0,896 GBIF‐gFrance: 080 48,150 0,896 GBIF‐gFrance: 081 47,970 0,896 GBIF‐gFrance: 082 47,790 0,896 GBIF‐gFrance: 083 47,610 0,896 GBIF‐gFrance: 084 46,890 0,896 GBIF‐gFrance: 085 46,710 0,896 GBIF‐gFrance: 086 46,530 0,896 GBIF‐gFrance: 087 46,350 0,896 GBIF‐gFrance: 088 49,770 1,256 GBIF‐gFrance: 089 48,690 1,256 GBIF‐gFrance: 090 47,970 1,256 GBIF‐gFrance: 091 47,790 1,256 GBIF‐gFrance: 092 46,890 1,256 GBIF‐gFrance: 093 46,710 1,256 GBIF‐gFrance: 094 46,530 1,256 GBIF‐gFrance: 095 50,670 1,616 GBIF‐g





France: 260 46,350 5,937 GBIF‐gFrance: 261 45,990 5,937 GBIF‐gFrance: 262 45,810 5,937 GBIF‐gFrance: 263 45,450 5,937 GBIF‐gFrance: 264 45,270 5,937 GBIF‐gFrance: 265 45,090 5,937 GBIF‐gFrance: 266 49,410 6,297 GBIF‐gFrance: 267 49,050 6,297 GBIF‐gFrance: 268 47,250 6,297 GBIF‐gFrance: 269 47,070 6,297 GBIF‐gFrance: 270 46,890 6,297 GBIF‐gFrance: 271 46,710 6,297 GBIF‐gFrance: 272 46,170 6,297 GBIF‐gFrance: 273 45,450 6,297 GBIF‐gFrance: 274 49,050 6,657 GBIF‐gFrance: 275 48,870 6,657 GBIF‐gFrance: 276 47,430 6,657 GBIF‐gFrance: 277 47,070 6,657 GBIF‐gFrance: 278 48,870 7,017 GBIF‐gFrance: 279 47,610 7,017 GBIF‐gFrance: 280 49,050 7,377 GBIF‐gFrance: 281 48,690 7,377 GBIF‐gFrance: 282 47,790 7,377 GBIF‐gFrance: 283 47,610 7,377 GBIF‐gFrance: 284 48,690 7,737 GBIF‐gFrance: 285 48,510 7,737 GBIF‐gFrance: 286 48,543 4,052 GBIF‐gFrance: 287 48,437 0,344 GBIF‐gFrance: 288 47,453 ‐0,686 GBIF‐gFrance: 289 47,050 2,167 GBIF‐gFrance: 290 48,835 4,799 GBIF‐gFrance: 291 45,725 5,459 GBIF‐gFrance: 292 45,569 3,183 GBIF‐gFrance: 293 47,627 5,695 GBIF‐gFrance: 294 46,561 ‐1,811 GBIF‐gFrance: 295 46,825 6,199 GBIF‐gFrance: 296 45,180 5,132 GBIF‐gFrance: 297 47,193 3,634 GBIF‐gFrance: 298 48,790 5,802 GBIF‐gFrance: 299 47,684 ‐3,265 GBIF‐gFrance: 300 50,443 1,851 GBIF‐g

France: 301 48,569 4,689 GBIF‐gFrance: 302 48,074 ‐0,292 GBIF‐gFrance: 303 48,948 3,987 GBIF‐gFrance: 304 46,447 4,251 GBIF‐gFrance: 305 47,537 ‐1,423 GBIF‐gFrance: 306 47,659 ‐1,390 GBIF‐gFrance: 307 47,611 1,894 GBIF‐gFrance: 308 49,393 4,719 GBIF‐gFrance: 309 49,326 ‐1,703 GBIF‐gFrance: 310 47,412 2,913 GBIF‐gFrance: 311 49,142 6,692 GBIF‐gFrance: 312 47,628 7,536 GBIF‐gFrance: 313 45,620 4,729 GBIF‐gFrance: 314 47,259 ‐1,140 GBIF‐gFrance: 315 50,801 2,184 GBIF‐gFrance: 316 45,435 ‐0,748 GBIF‐gFrance: 317 50,389 3,265 GBIF‐gFrance: 318 45,360 4,828 GBIF‐gFrance: 319 50,439 3,624 GBIF‐gFrance: 320 47,381 ‐2,191 GBIF‐gFrance: 321 47,360 ‐1,497 GBIF‐gFrance: 322 47,219 6,092 GBIF‐gFrance: 323 47,603 5,485 GBIF‐gFrance: 324 46,868 5,824 GBIF‐gFrance: 325 47,536 5,965 GBIF‐gFrance: 326 47,461 5,831 GBIF‐gFrance: 327 47,346 6,220 GBIF‐gFrance: 328 49,097 4,040 GBIF‐gFrance: 329 47,788 0,396 GBIF‐gFrance: 330 47,351 0,085 GBIF‐gFrance: 331 46,526 ‐0,754 GBIF‐gFrance: 332 47,074 ‐0,726 GBIF‐gFrance: 333 49,685 4,403 GBIF‐gFrance: 334 46,644 2,734 GBIF‐gFrance: 335 48,554 0,009 GBIF‐gFrance: 336 49,260 4,138 GBIF‐gFrance: 337 48,956 4,941 GBIF‐gFrance: 338 46,904 4,611 GBIF‐gFrance: 339 44,997 2,942 GBIF‐gFrance: 340 47,281 5,805 GBIF‐gFrance: 341 50,625 3,212 GBIF‐g

France: 342 45,731 ‐0,631 GBIF‐gFrance: 343 49,927 4,431 GBIF‐gFrance: 344 45,873 2,827 GBIF‐gFrance: 345 49,422 0,498 GBIF‐gFrance: 346 46,398 1,622 GBIF‐gFrance: 347 47,984 1,034 GBIF‐gFrance: 348 47,315 ‐1,256 GBIF‐gFrance: 349 48,512 3,834 GBIF‐gFrance: 350 47,996 0,697 GBIF‐gFrance: 351 47,938 0,390 GBIF‐gFrance: 352 46,204 2,389 GBIF‐gFrance: 353 46,402 5,786 GBIF‐gFrance: Aigurande 46,433 1,833 8France: Ambleteuse 50,767 1,617 1France: Autreppes 49,934 3,900 1France: Arles 43,683 4,633 8France: Mayenne 48,300 ‐0,617 1France: Saint‐Chély‐d'Apcher 44,800 3,267 8France: Saint‐Vivien‐de‐Médoc 45,433 ‐1,033 8France: St.‐Lô 49,117 1,083 1France: Valliguières 44,000 4,583 8France: Vic‐sur‐Cère 44,983 2,617 8France: Vizille 45,083 5,767 8Germany: Amt Neuhaus 53,319 10,906 GBIF‐hGermany: Aspach 49,000 9,424 GBIF‐hGermany: Babensham 48,083 12,283 24Germany: Bergisch Gladbach 50,963 7,136 GBIF‐hGermany: Bergkamen 51,628 7,635 GBIF‐hGermany: Berlin 52,583 13,250 4Germany: Bischofwiesen 47,633 12,950 24Germany: Böblingen 48,687 9,018 GBIF‐hGermany: Braunschweig 52,270 10,587 1Germany: Buxtehude 53,461 9,696 GBIF‐hGermany: Cologne 50,924 6,962 GBIF‐hGermany: Crawinkel 50,792 10,781 GBIF‐hGermany: Dietzenbach 50,013 8,773 GBIF‐hGermany: Gedern 50,439 9,185 GBIF‐hGermany: Gießen 50,576 8,647 GBIF‐hGermany: Glücksburg 54,829 9,617 GBIF‐hGermany: Gustrow 53,800 12,167 GBIF‐bGermany: Hamburg 53,550 10,000 GBIF‐b

Germany: Hannover 52,374 9,706 GBIF‐hGermany: Haus Ortlohn 51,367 7,700 GBIF‐bGermany: Heidelberg 49,417 8,633 4Germany: Heilbronn 49,139 9,211 GBIF‐hGermany: Isny 47,692 10,039 GBIF‐iGermany: Kiel 54,333 10,067 4Germany: Kochel 47,650 11,383 24Germany: Lengthal 48,183 12,800 24Germany: Ludwigsburg 48,892 9,193 GBIF‐iGermany: Mainz 49,959 8,173 GBIF‐hGermany: Mainz‐Laubenheim und Bodenheim 49,947 8,318 GBIF‐hGermany: Northeim 51,695 9,987 GBIF‐hGermany: Osterode am Harz 51,742 10,245 GBIF‐hGermany: Roibach 48,033 12,800 24Germany: Rothenburg ob der Tauber 49,387 10,169 GBIF‐hGermany: Saulgau 47,993 9,562 GBIF‐iGermany: Schwabstedt 54,403 9,250 GBIF‐hGermany: Schwalbach 50,141 8,541 GBIF‐hGermany: Schwalmstadt 50,932 9,217 GBIF‐hGermany: Stuttgart 48,775 9,179 GBIF‐iGermany: Surheim 47,867 12,967 24Germany: Ulm 48,400 10,000 GBIF‐iGermany: Unna‐Mühlhausen 51,630 7,638 GBIF‐hGermany: Waldsolms 50,421 8,523 GBIF‐hGermany: Welzheim 48,875 9,634 GBIF‐iGermany: Winnenden 48,873 9,390 GBIF‐iGermany: Wuppertal 51,266 7,174 GBIF‐hGermany: Zeulenroda‐Triebes 50,643 11,967 GBIF‐hLatvia: Bauska 56,400 24,183 4Latvia: Bene 56,483 23,067 4Latvia: Ikskile 56,833 24,500 4Latvia: Ilgas Skolas 56,033 26,083 4Latvia: Irlava 56,883 23,000 4Latvia: Jekabpils 56,500 25,867 4Latvia: Jurmala 56,967 23,783 4Latvia: Kalnai 57,017 22,650 4Latvia: Katlakalna 56,867 24,183 4Latvia: Koknese 56,650 25,450 4Latvia: Krustkalny Nature Reserve 56,783 26,167 4Latvia: Laicene 57,233 22,733 4Latvia: Lielvarde 56,717 24,800 4

Latvia: Riga 56,967 24,067 4Latvia: Rucava 56,167 21,150 4Latvia: Sigulda 57,150 24,850 4Latvia: Siltere Nature Reserve 57,667 22,350 4Latvia: Smiltene 57,433 25,900 4Latvia: Teychi Nature Reserve 56,600 26,517 4Lithuania: Antalepte Reservoir 55,633 25,950 4Lithuania: Ignalina 55,333 26,167 4Lithuania: Kamanos Nature Reserve 56,300 22,650 4Lithuania: Kaunas 54,900 23,917 4Lithuania: Klaipeda 55,733 21,283 4Lithuania: Kurtuvenai Regional Park 55,783 23,033 4Lithuania: Obelu Ragas 55,267 26,033 4Lithuania: Pasiliu Miskas 55,683 24,667 4Lithuania: Rasys Lake 55,217 25,700 4Lithuania: Siesarcio ez. 55,217 25,483 4Lithuania: Tauragnai 55,450 25,817 4Lithuania: Trok Lakes 54,633 24,933 4Lithuania: Utena 55,500 25,600 4Lithuania: Uzupe 55,483 24,017 4Lithuania: Vandziogala 55,117 23,967 4Lithuania: Vilnius 54,683 25,283 4Lithuania: Vyziai 55,417 25,983 4Lithuania: Zarasai 55,733 26,250 4Luxembourg: Bandels 49,850 6,020 GBIF‐fLuxembourg: Drai Brecken 49,640 5,930 GBIF‐fLuxembourg: Etang de la carriere 49,770 6,220 GBIF‐fLuxembourg: Kalbuerg 50,090 5,900 GBIF‐fLuxembourg: Laemkaul 49,790 6,380 GBIF‐fLuxembourg: Leng 49,770 5,890 GBIF‐fLuxembourg: Mare (Weiergewan) 49,540 6,310 GBIF‐fLuxembourg: Mare Loorwis 49,510 6,040 GBIF‐fLuxembourg: Oberdonven 49,650 6,390 GBIF‐fLuxembourg: Pesch 49,650 6,090 GBIF‐fLuxembourg: Scheiergrond 49,510 5,880 GBIF‐fMoldova: Bendery 46,833 29,483 4Moldova: Etulia 45,533 28,450 4Moldova: Gershunovka 47,717 29,167 1Moldova: Grushka 48,133 28,550 4Moldova: Gura‐Bykului 46,933 29,467 4Moldova: Ivancha 47,283 28,850 4

Moldova: Kantemir 46,483 28,267 4Moldova: Karbuna 46,717 28,950 4Moldova: Kishenev 1 47,019 28,786 1Moldova: Kishenev 2 47,017 28,883 1Moldova: Kolbasnoe 47,783 29,217 4Moldova: Malayeshty 47,100 29,600 4Moldova: Oknitsa 48,133 28,633 1Moldova: Redensky Les Nature Reserve 47,317 28,050 4Moldova: Slobodzeya 46,717 29,683 1Moldova: Tiraspol' 46,833 29,650 1Moldova: Vornicheny 47,150 28,433 4Moldova: Vulkaneshty 47,117 28,233 1Netherlands: Doetinchem 51,967 6,283 16Netherlands: Heeze 51,393 5,535 1Netherlands: Hoensbroek 50,917 5,917 16Netherlands: St. Michielsgestel 51,633 5,350 16Netherlands: Winterswijk 51,967 6,733 16Norway: Aase 59,553 5,344 GBIF‐kNorway: Åkerdam 59,434 11,173 GBIF‐kNorway: Årabrot 59,441 5,241 GBIF‐kNorway: Årstad Ø. 59,673 11,379 GBIF‐kNorway: Årudsetra 59,782 9,696 GBIF‐kNorway: Bånntjernvn. 59,961 10,698 GBIF‐kNorway: Bekkedalsåsen 61,664 11,262 GBIF‐kNorway: Benkehøgda 60,658 9,919 GBIF‐kNorway: Bergdam (grunn) SV f. skole 59,617 5,383 GBIF‐kNorway: Bergsrud 59,726 10,899 GBIF‐kNorway: Bjørnebråte 59,811 11,358 GBIF‐kNorway: Bjørnestad 59,596 10,861 GBIF‐kNorway: Blanktjørndalen 63,812 10,177 GBIF‐kNorway: Blomlitjørna 60,244 5,409 GBIF‐kNorway: Botne 59,481 10,278 GBIF‐kNorway: Butjenndolpa 60,012 12,319 GBIF‐kNorway: Dæhlie 59,773 11,163 GBIF‐kNorway: Dalatjørn 59,727 6,079 GBIF‐kNorway: Damvt. 59,728 9,159 GBIF‐kNorway: Dobbeltdammen 59,412 8,379 GBIF‐kNorway: Drammen omegn 59,756 10,155 GBIF‐kNorway: Edelsvoll 59,795 10,482 GBIF‐kNorway: Eksberget 60,737 10,965 GBIF‐kNorway: Fiskeløystj. 59,325 9,130 GBIF‐k

Norway: Fiskhøltj. 59,420 8,682 GBIF‐kNorway: Fløter N. gård 59,498 11,020 GBIF‐kNorway: Fritzøe flyplass 59,052 10,066 GBIF‐kNorway: Gjerpen 59,221 9,612 GBIF‐kNorway: Grøftedam (lang) 59,617 5,383 GBIF‐kNorway: Grøftedam (rund) 59,617 5,383 GBIF‐kNorway: Grøstad Pukkverk 59,820 10,298 GBIF‐kNorway: Grunndalsåsen Ø 59,998 10,446 GBIF‐kNorway: Halum 59,368 11,426 GBIF‐kNorway: Haug 61,172 11,483 GBIF‐kNorway: Hauketj. 60,558 10,051 GBIF‐kNorway: Haver 59,648 10,678 GBIF‐kNorway: Heistadmoen 60,803 10,806 GBIF‐kNorway: Helgøya 60,981 10,790 GBIF‐kNorway: Henni 61,091 10,520 GBIF‐kNorway: Hestmarkdammen 63,412 10,838 GBIF‐kNorway: Hisdal‐Årland 60,398 5,686 GBIF‐kNorway: Hof 59,554 10,038 GBIF‐kNorway: Holt 59,803 10,683 GBIF‐kNorway: Holum skog 59,982 10,940 GBIF‐kNorway: Hove, Ånøya 63,234 10,124 GBIF‐kNorway: Høvik 59,896 10,537 GBIF‐kNorway: Hyggen mellom 59,716 10,363 GBIF‐kNorway: Hystad 59,234 11,089 GBIF‐kNorway: Igletjern 59,977 9,503 GBIF‐kNorway: Igultjern SØ f. Persvollsætra 60,481 10,153 GBIF‐kNorway: Jerpatj 59,136 9,115 GBIF‐kNorway: Kåbøl nord 59,496 10,835 GBIF‐kNorway: Koladalstj. 59,674 6,439 GBIF‐kNorway: Kongsrudtjern 59,976 11,144 GBIF‐kNorway: Kraftledningsdammen 63,061 9,282 GBIF‐kNorway: Kroktjernshaugen 60,417 10,336 GBIF‐kNorway: Langsethdammen 59,673 10,535 GBIF‐kNorway: Langsjølungen 59,884 11,808 GBIF‐kNorway: Lauar lille 59,567 9,661 GBIF‐kNorway: Leiketjørn 60,060 9,198 GBIF‐kNorway: Lemyrtj. 60,449 11,912 GBIF‐kNorway: Lisæterkollen 59,843 11,007 GBIF‐kNorway: Lokmyrtj. N. 63,380 10,292 GBIF‐kNorway: Lomtjørna 63,625 10,904 GBIF‐kNorway: Møklegard 59,185 10,851 GBIF‐k

Norway: Narmo 60,859 11,111 GBIF‐kNorway: Norheimsund prestegård 60,346 6,181 GBIF‐kNorway: Okseauget 59,950 12,109 GBIF‐kNorway: Oppegårdskollen 60,116 10,905 GBIF‐kNorway: Opperud gård 60,172 10,351 GBIF‐kNorway: Ørebukta 59,426 10,683 GBIF‐kNorway: Østensjøvatnet 59,892 10,831 GBIF‐kNorway: Roastad 59,547 10,681 GBIF‐kNorway: Sæterhaugen 60,183 11,621 GBIF‐kNorway: Seljord 59,491 8,557 GBIF‐kNorway: Seterbergrøys 59,186 11,701 GBIF‐kNorway: Skjolden Ø. 59,574 11,131 GBIF‐kNorway: Skogen‐Sagen gårder 60,819 11,297 GBIF‐kNorway: Skoledammen 59,097 11,400 GBIF‐kNorway: Slitu gård 59,564 11,260 GBIF‐kNorway: Småtjørnan S. 59,857 8,987 GBIF‐kNorway: Solberg 60,656 11,205 GBIF‐kNorway: Solberg 59,532 11,438 GBIF‐kNorway: Spydeberg 59,614 11,081 GBIF‐kNorway: Stortj.‐Bergtj.Dalvola 63,356 11,890 GBIF‐kNorway: Svarttj. SØ f. Engelsåstrøa 63,364 10,542 GBIF‐kNorway: Tokerud N. 59,672 10,729 GBIF‐kNorway: Tomb vollgrav 59,320 10,813 GBIF‐kNorway: Tretj. 63,476 11,250 GBIF‐kNorway: Tveiter 60,076 11,108 GBIF‐kNorway: Tverrved 59,310 10,499 GBIF‐kNorway: Vehus 60,144 9,073 GBIF‐kNorway: Vestgård 59,375 11,639 GBIF‐kNorway: Vettal S. 60,097 11,291 GBIF‐kNorway: Ytraland 59,311 5,234 GBIF‐kPoland: 001 50,008 20,421 GBIF‐pPoland: 002 50,031 19,370 GBIF‐pPoland: 003 50,106 20,361 GBIF‐pPoland: 004 50,167 19,250 GBIF‐pPoland: 005 50,167 19,750 GBIF‐pPoland: 006 50,167 20,250 GBIF‐pPoland: 007 50,167 20,750 GBIF‐pPoland: 008 50,167 22,250 GBIF‐pPoland: 009 50,182 21,428 GBIF‐pPoland: 010 50,500 19,250 GBIF‐pPoland: 011 50,500 19,750 GBIF‐p

Poland: 012 50,500 20,250 GBIF‐pPoland: 013 50,500 20,750 GBIF‐pPoland: 014 50,500 21,250 GBIF‐pPoland: 015 50,500 22,250 GBIF‐pPoland: 016 50,500 22,750 GBIF‐pPoland: 017 50,715 19,497 GBIF‐pPoland: 018 50,726 19,285 GBIF‐pPoland: 019 50,833 19,250 GBIF‐pPoland: 020 50,833 19,750 GBIF‐pPoland: 021 50,833 20,250 GBIF‐pPoland: 022 50,833 20,750 GBIF‐pPoland: 023 50,833 21,250 GBIF‐pPoland: 024 50,833 22,250 GBIF‐pPoland: 025 51,046 18,751 GBIF‐pPoland: 026 51,071 20,738 GBIF‐pPoland: 027 51,167 15,250 GBIF‐pPoland: 028 51,167 15,750 GBIF‐pPoland: 029 51,167 16,250 GBIF‐pPoland: 030 51,167 16,750 GBIF‐pPoland: 031 51,167 17,250 GBIF‐pPoland: 032 51,167 17,750 GBIF‐pPoland: 033 51,167 18,750 GBIF‐pPoland: 034 51,167 19,250 GBIF‐pPoland: 035 51,167 19,750 GBIF‐pPoland: 036 51,167 21,750 GBIF‐pPoland: 037 51,430 21,569 GBIF‐pPoland: 038 51,500 16,750 GBIF‐pPoland: 039 51,500 18,750 GBIF‐pPoland: 040 51,500 19,250 GBIF‐pPoland: 041 51,500 19,750 GBIF‐pPoland: 042 51,500 20,250 GBIF‐pPoland: 043 51,500 20,750 GBIF‐pPoland: 044 51,500 21,750 GBIF‐pPoland: 045 51,513 20,031 GBIF‐pPoland: 046 51,833 15,750 GBIF‐pPoland: 047 51,833 16,750 GBIF‐pPoland: 048 51,833 17,750 GBIF‐pPoland: 049 51,833 18,750 GBIF‐pPoland: 050 51,833 19,250 GBIF‐pPoland: 051 51,833 19,750 GBIF‐pPoland: 052 51,833 20,250 GBIF‐p



Poland: 135 50,500 17,250 GBIF‐pPoland: 136 50,500 18,750 GBIF‐pPoland: 137 50,167 18,750 GBIF‐pPoland: 138 50,833 18,250 GBIF‐pPoland: 139 50,500 18,250 GBIF‐pPoland: 140 50,167 18,250 GBIF‐pPoland: 141 50,833 23,750 GBIF‐pPoland: 142 50,833 23,250 GBIF‐pPoland: 143 50,500 23,250 GBIF‐pPoland: 144 50,167 23,250 GBIF‐pPoland: 145 52,500 14,750 GBIF‐pPoland: 146 53,833 14,750 GBIF‐pPoland: 147 53,500 14,750 GBIF‐pPoland: 148 53,167 14,750 GBIF‐pPoland: 149 53,833 23,250 GBIF‐pPoland: 150 53,167 23,250 GBIF‐pPoland: 151 54,167 19,750 GBIF‐pPoland: 152 54,167 19,250 GBIF‐pPoland: 153 54,167 21,750 GBIF‐pPoland: 154 54,167 21,250 GBIF‐pPoland: 155 54,167 22,750 GBIF‐pPoland: 156 54,167 22,250 GBIF‐pPoland: 157 54,167 23,250 GBIF‐pPoland: 158 49,777 20,971 GBIF‐pPoland: 159 49,458 20,042 GBIF‐pPoland: 160 49,586 21,336 GBIF‐pPoland: 161 50,677 18,964 GBIF‐pPoland: 162 50,775 18,790 GBIF‐pPoland: 163 50,929 18,758 GBIF‐pPoland: 164 50,881 18,917 GBIF‐pPoland: 165 50,785 18,539 GBIF‐pPoland: 166 51,976 14,895 GBIF‐pPoland: 167 52,123 14,784 GBIF‐pPoland: Kopanina 52,557 16,538 1Poland: Lanckorona 49,900 19,483 16Poland: Limanowa 49,717 20,417 1Poland: Mogielnica 51,700 20,683 16Poland: Tłumaczów quarry 50,558 16,434 1Poland: Trzebciny 53,625 18,152 1Poland: Zatwarnica przy potoku Hulskie 49,235 22,527 1Romania: Bogdana 47,033 23,017 16

Romania: Bradatel 47,491 26,178 1Romania: Budeni 45,768 26,839 1Romania: Bumbesti‐Jiu 45,167 23,383 1Romania: Carpen 44,350 23,267 2Romania: Ciceu 46,404 25,790 1Romania: Cimpeni 46,383 23,083 1Romania: Ciorasti 45,435 27,305 1Romania: Craciunesti 46,867 27,533 1Romania: Crasna 45,583 26,217 1Romania: Drihiu 47,183 22,667 1Romania: Filiaşi 44,550 23,533 2Romania: Găloleşti 45,567 27,000 1Romania: Ghertensiş 45,383 21,750 8Romania: Halmaşd 47,100 22,350 1Romania: Hurezani 44,833 23,150 1Romania: Huta‐Certeze 47,917 23,483 1Romania: Iorcani 47,361 26,527 1Romania: Jitia 45,583 26,733 1Romania: Manasturel 47,189 23,927 1Romania: Marghita 47,350 22,333 1Romania: Obrejita 45,433 27,133 8Romania: Orşova 44,717 22,400 8Romania: Penteleu 45,560 26,356 1Romania: Peştiş 47,092 22,390 1Romania: Picâturile 44,483 23,867 1Romania: Picior de Munte 44,783 25,383 1Romania: Podari 44,250 23,783 2Romania: Pomi 47,694 23,320 1Romania: Radovan 44,167 23,433 2Romania: Săcărâmb 45,971 23,047 1Romania: Sânpetru de Câmpie 46,771 24,279 1Romania: Sanzieni 46,114 26,128 1Romania: Sebis 46,367 22,100 1Romania: Sighisoara 46,207 24,759 1Romania: Simnicu de Sus 44,383 23,800 2Romania: Sinaia 45,333 25,550 1Romania: Şoimuş 47,298 23,203 1Romania: Solotvina 47,933 23,883 4Romania: Stramba 47,248 24,653 1Romania: Tasnad 47,467 22,600 1Romania: Tîrgoviste 44,933 25,450 1

Romania: Tolici 47,098 26,450 1Romania: Turț 47,983 23,200 1Romania: Valea Ierii 46,650 23,355 1Romania: Videle 44,267 25,517 1Romania: Vidra 45,917 26,933 1Romania: Vipereşti 45,233 26,467 16Romania: Virfuri 46,283 22,467 1Romania: Visuia 46,839 24,305 1Russia: Abramovka 54,200 47,700 4Russia: Akshuat 53,683 47,450 4Russia: Alatyr' 54,850 46,567 4Russia: Aleshkino 53,950 48,433 4Russia: Altukhovo 52,683 34,350 4Russia: Andreyevka 54,683 46,400 4Russia: Andreyevka 54,050 44,033 4Russia: Antonovka 54,517 46,267 4Russia: Araslanovo 55,483 48,200 4Russia: Ardatov 55,250 43,100 4Russia: Argash 53,950 46,250 4Russia: Argazinskoe Reservoir 55,367 60,467 4Russia: Atemar 54,183 45,400 4Russia: Avdeyevka 52,783 32,800 4Russia: Bagrationovsk 54,367 20,683 1Russia: Balakhna 56,483 43,617 4Russia: Baryshskaya Sloboda 54,583 46,800 4Russia: Begeshka 57,200 53,267 4Russia: Berezniki 59,400 56,800 8Russia: Bereznyak 56,117 50,817 4Russia: Berkuty 56,967 54,100 4Russia: Beryozovka 52,433 34,133 4Russia: Besovo Lake 54,867 38,383 4Russia: Birsk 55,417 55,533 4Russia: Bochikha 55,733 43,417 4Russia: Bolgar 54,983 49,017 4Russia: Bologoe 57,883 34,100 4Russia: Bol'shaya Elkhovka 54,283 45,317 4Russia: Bol'shoe Karpovo 57,400 46,100 4Russia: Bol'shoe Mares'evo 54,367 45,683 4Russia: Bol'shoe Stanichnoe 54,250 46,983 4Russia: Bol'shoy Chilim 54,650 47,333 4Russia: Bol'shoy Saltan 55,633 49,900 4

Russia: Borisov Forestry 55,417 36,067 4Russia: Borisovka 50,600 36,017 4Russia: Bornukovo 55,367 44,800 4Russia: Borok 58,067 38,267 1Russia: Bryansky Les Nature Reserve 52,483 33,850 4Russia: Bun'kovo 57,033 40,750 4Russia: Burepolom 57,983 47,150 4Russia: Byrgynda 55,883 53,450 4Russia: Chamzinka‐Komsomol'sky 54,417 45,817 4Russia: Chavash Varmane National Park 54,883 47,533 4Russia: Chelyabinsk 55,133 61,333 4Russia: Chemodanovka 53,250 45,250 4Russia: Chepurnovka 53,967 43,733 4Russia: Cheremenetskiy Nature Reserve 58,617 29,950 4Russia: Cherki‐Dyurtele 55,083 48,350 4Russia: Cherneya 56,250 28,533 4Russia: Chernogolovka 56,000 38,383 4Russia: Chernovo 56,900 45,583 4Russia: Chernozer'e 56,433 44,850 4Russia: Chernukha 55,600 43,750 4Russia: Chirikovo 54,500 47,717 4Russia: Chistopol' 55,367 50,617 4Russia: Chumakino 54,150 46,333 4Russia: Chur 57,117 52,983 1Russia: Dal'nepesochnaya 55,100 42,050 4Russia: Dal'niy 53,917 45,000 4Russia: Devushkino 57,017 44,817 4Russia: Dimitrovgrad 54,217 49,600 4Russia: Dmitreiv‐L'govsky 52,117 35,100 4Russia: Dmitrovka 53,767 46,300 4Russia: Doksha 56,817 53,750 4Russia: Domodedovo 55,433 37,750 4Russia: Dorokhovo 55,533 36,367 4Russia: Dranichnoe 57,033 45,417 4Russia: Drenevo 57,617 41,150 4Russia: Dumino 56,717 40,317 4Russia: Dvortsy 54,617 36,000 4Russia: Dyagilevka 54,033 44,917 1Russia: Dzerdzhynsk 56,233 43,450 1Russia: Egor'evsk 55,367 39,033 4Russia: Elisenvaara 61,400 29,767 4

Russia: Elizavetino 59,500 29,750 4Russia: Enikeyevo 56,400 46,450 4Russia: Ermolovka 54,017 46,867 4Russia: Faldino 54,067 37,633 1Russia: Gainy 60,317 54,317 4Russia: Galich'ya Gora Nature Reserve 52,600 38,933 4Russia: Glazkovo 57,767 40,983 4Russia: Glotovka 53,933 46,700 4Russia: Glubokoe Lake 55,750 36,500 4Russia: Gmyrino 58,100 29,100 4Russia: Golitsino 55,617 37,000 4Russia: Golitsyno 53,600 43,983 4Russia: Gol'tsovka 53,600 45,133 4Russia: Gomselga 62,050 33,933 4Russia: Gorki 56,300 41,017 4Russia: Gorodets 54,783 35,050 4Russia: Gremyachiy 53,000 47,683 4Russia: Gusev 54,583 22,200 4Russia: Ichalki Forestry 55,433 44,550 4Russia: Idritsa 56,333 28,900 4Russia: Inyshka 55,183 60,100 4Russia: Inzhavino 52,317 42,483 4Russia: Ivanovka 54,850 48,783 4Russia: Ivanovo 57,000 41,000 4Russia: Ivanovskoe 55,017 35,117 4Russia: Izhevsk 56,833 53,200 4Russia: Kabanovo 55,750 38,950 4Russia: Kalikino 57,167 40,933 4Russia: Kalinikha 56,817 45,433 4Russia: Kaliningrad 54,717 20,517 4Russia: Kaluga 54,500 36,333 4Russia: Kambarka 56,267 54,217 4Russia: Karabash 54,683 52,567 4Russia: Karakulino 56,050 53,417 4Russia: Karmalka River 55,200 51,867 1Russia: Karmanovo 56,700 37,200 4Russia: Kazaklar 56,250 49,200 4Russia: Kazakovo 55,767 42,767 4Russia: Kazan' 55,800 49,083 4Russia: Kazan' State University Biological Station 55,717 48,700 4Russia: Khakhaly 56,667 44,717 4

Russia: Kholuy 56,583 41,883 4Russia: Khopyor Nature Reserve 51,117 41,617 4Russia: Kil'dyushevo 54,767 48,700 4Russia: Kil'mez' 56,950 51,067 4Russia: Kil'mez' 57,017 51,333 4Russia: Kirov 58,617 49,717 4Russia: Kiselevka 53,450 47,200 4Russia: Kishert' 57,367 57,250 4Russia: Kivat' 53,467 47,683 4Russia: Kizhi Island 62,083 35,217 4Russia: Klyuchevaya 57,633 36,117 4Russia: Kochkurovo 54,750 45,000 4Russia: Kolesnikovo 55,967 53,583 4Russia: Kolomna 55,083 38,783 4Russia: Komarovka 53,283 47,717 4Russia: Kondopoga 62,200 34,283 4Russia: Konechki 57,733 27,850 1Russia: Korovino 54,167 48,917 4Russia: Kotroks 54,283 44,050 4Russia: Kotyakovo 54,300 46,700 4Russia: Krasnaya Polyana 52,567 47,233 4Russia: Krasnaya Sludka 58,183 56,450 4Russia: Krasnogorskiy 57,500 42,850 4Russia: Krasnokamsk 58,083 55,767 4Russia: Krasnoufimsk 56,600 57,767 4Russia: Krasnyi Bor 54,000 47,250 4Russia: Krasnyi Gulyai 54,033 48,333 4Russia: Krasnyi Yar 54,233 48,550 4Russia: Krivipolyanskoe Forestry 53,033 41,917 4Russia: Krutets 54,050 42,650 4Russia: Krutyshki 54,867 38,150 4Russia: Kudymkar 59,017 54,650 4Russia: Kulikovo 56,967 41,167 4Russia: Kungur 57,433 56,950 4Russia: Kurgal'skiy Nature Reserve 59,783 28,117 4Russia: Kursk 51,750 36,183 4Russia: Kur'ya 58,017 56,000 4Russia: Kuzino 61,500 39,133 4Russia: Kuznechnoe 61,150 29,850 4Russia: Kuznetsovka 54,650 56,633 4Russia: Kyilud 56,900 52,450 4

Russia: Kyshtym 55,717 60,533 4Russia: Laishevo 55,400 49,533 4Russia: Lakhdenpokh'ya 61,533 30,217 4Russia: Leonovo 55,900 36,733 4Russia: Lesnichestvo 54,367 44,433 4Russia: Lesunovo 55,667 43,117 4Russia: Levocha 58,833 35,033 4Russia: L'gov 51,650 35,283 4Russia: Lichadeyevo 55,383 43,417 4Russia: Lipin Bor 60,267 37,967 4Russia: Lobnya 56,000 37,500 4Russia: Lubyany 56,033 51,417 4Russia: Luga 58,750 29,883 4Russia: Luzhki 54,867 37,550 4Russia: Malaya Purga 56,600 53,033 4Russia: Malmyzh 56,517 50,683 4Russia: Malye Kabany 55,667 49,250 4Russia: Malye Klyari 55,200 48,917 4Russia: Malyi Makatelem 55,083 43,667 4Russia: Mamadysh 55,717 51,383 4Russia: Marienburg 59,567 30,067 1Russia: Matyushino 55,617 49,017 4Russia: Mayna 54,117 47,617 4Russia: Medvedsky Forest 57,400 50,017 4Russia: Melet' 56,667 50,783 4Russia: Mezhdurech'e 53,783 45,983 4Russia: Migalikha 55,750 43,867 4Russia: Mikhaylovskoe 55,817 36,350 4Russia: Mikhnevo 55,117 37,967 4Russia: Mishnevo 53,983 36,350 4Russia: Molochnitsa 54,250 42,883 4Russia: Molvino 53,767 48,467 4Russia: Mordovsky Nature Reserve 54,733 43,233 4Russia: Morsovo 53,750 42,317 4Russia: Mozhayskaya 59,717 30,150 4Russia: Mugreevskiy 56,567 42,333 4Russia: Mukhtolovo 55,467 43,200 4Russia: Mytniki 55,783 36,167 4Russia: Nechkino 56,683 53,733 4Russia: Neklyudovo 56,433 43,983 4Russia: Nikol'skoe 54,833 48,500 4

Russia: Nikol'skoe na Cheremshane 54,050 49,167 4Russia: Nikulino 53,167 47,067 4Russia: Nizhne‐Irginskaya Oak Forest 56,883 57,533 4Russia: Nizhniy Novgorod 56,317 44,000 4Russia: Novaya Beden'ga 54,517 48,333 4Russia: Novaya Esineyevka 53,183 44,017 4Russia: Novaya Petrovka 54,883 43,967 4Russia: Novaya Pyrma 54,017 45,450 4Russia: Novaya Sakhcha 54,467 50,033 4Russia: Novaya Shomokhta 57,467 43,367 4Russia: Novokashpirsky 53,050 48,417 4Russia: Novosursk 54,233 46,450 4Russia: Novoul'yanovsk 54,167 48,367 4Russia: Novyi 56,833 54,067 4Russia: Novyi Mostyak 52,717 47,433 4Russia: Obrezki 54,833 45,367 4Russia: Odintsovo 55,667 37,300 4Russia: Oktyabr'sky 55,833 48,833 4Russia: Oktyabr'sky 54,900 46,217 4Russia: Oktyabr'sky 56,567 53,967 4Russia: Olonets 60,983 32,983 4Russia: Orekhovvo‐Zuevo‐Podosinki 55,567 38,850 4Russia: Orenburg 51,783 55,100 4Russia: Orlovka 54,867 44,250 4Russia: Ostorozhnoe‐Galkino 54,800 35,750 4Russia: Osuyskoe 57,233 34,867 4Russia: Otyasskoe Forestry 53,233 41,650 4Russia: Pal'niki 58,317 56,900 4Russia: Panovka 55,883 49,367 4Russia: Panskaya Sloboda 54,117 48,500 4Russia: Pargola 60,050 31,317 4Russia: Pasha 60,400 33,050 4Russia: Pavlovka 52,683 47,133 4Russia: Pavlovskaya Sloboda 55,800 37,100 4Russia: Pechmen' 56,833 56,017 4Russia: Pechory 57,817 27,600 4Russia: Pelya‐Khovanskaya 54,567 44,950 4Russia: Penza 53,200 45,000 4Russia: Perevoznoe 56,883 53,883 4Russia: Perm' 58,000 56,233 4Russia: Pervyi Mys Island 57,283 55,417 4

Russia: Petrodvorets 59,867 30,000 4Russia: Petrozawodsk 61,800 34,333 4Russia: Pizhma 57,550 47,183 4Russia: Plyos 57,450 41,500 4Russia: Plyuskovo 54,700 35,550 4Russia: Podkurovka 53,950 48,250 4Russia: Pogorelovka 53,483 40,633 4Russia: Polessk 54,867 21,117 4Russia: Polevskoy Plant 56,467 60,183 4Russia: Polotnyanya Zavod 54,733 36,017 4Russia: Polushkino 55,583 36,600 4Russia: Polyakovo 56,167 51,900 4Russia: Pribylovo 60,433 28,717 4Russia: Privolzhskaya Lesostep' Nature Reserve 52,950 46,633 4Russia: Prosnitsa 58,450 50,233 4Russia: Radishchevo 52,850 47,833 4Russia: Ragusha River 59,267 33,933 4Russia: Raifsky Les 55,917 48,717 4Russia: Rameno 53,233 48,350 4Russia: Resseta River 53,800 35,267 4Russia: Rezvan' 54,517 36,100 4Russia: Russkoe Timoshkino 53,417 46,833 4Russia: Russky Turek 57,050 50,250 4Russia: Rustay 56,500 44,817 4Russia: Ryazan 54,600 39,700 4Russia: Rybachiy 55,167 20,833 4Russia: Sabaevo 53,983 45,717 4Russia: Sadovaya 53,133 47,667 4Russia: Salmi 61,367 31,850 4Russia: Saltyganovo 55,183 49,100 4Russia: Saltykovka 55,750 37,917 4Russia: Samara 53,233 50,167 4Russia: Saralovsky uchastok VKGZ 55,283 49,250 1Russia: Saransk 54,183 45,150 4Russia: Sarapul 56,467 53,817 4Russia: Saratov 51,517 46,033 8Russia: Sarov 54,950 43,283 4Russia: Sauzbash 55,950 53,783 4Russia: Savino 56,600 41,217 4Russia: Sedel'nitsy 57,150 40,550 4Russia: Semenovka 53,750 44,583 4

Russia: Semirodniki 53,633 47,033 4Russia: Semyonov 56,783 44,483 4Russia: Semyonovka 51,883 43,350 4Russia: Sengiley 53,950 48,783 4Russia: Sergiev Posad 56,317 38,133 4Russia: Shapki 59,600 31,167 4Russia: Sharapova Okhota 54,967 37,417 1Russia: Shebekino District 50,417 36,917 4Russia: Shemordan 56,183 50,400 4Russia: Sherstki 57,967 47,450 4Russia: Shimorskoe 55,333 42,017 4Russia: Shingarino 54,100 43,900 4Russia: Shiya River 55,883 51,433 4Russia: Shlykovo 54,017 37,150 4Russia: Shul'gino 59,250 34,850 4Russia: Shumbut 55,550 50,667 4Russia: Shusher 56,667 47,267 1Russia: Shuyka 57,950 46,967 4Russia: Shuyskoe 59,367 41,033 4Russia: Simkino 54,167 46,133 4Russia: Slavkino 52,967 47,183 4Russia: Smolensk 54,783 32,050 4Russia: Sobolikha 56,833 43,350 4Russia: Sokol 57,050 53,383 4Russia: Sokury 55,600 49,417 4Russia: Sopokha 62,367 33,900 4Russia: Sortavala 61,700 30,683 4Russia: Sosnovyi Bor 53,533 46,583 4Russia: Sovetsk 57,583 48,950 4Russia: Sperovo 60,900 36,567 1Russia: Srednee Kechevo 56,533 53,217 4Russia: Srednyaya Tyzhma 56,283 51,683 4Russia: Srednyaya Yulyuzan' 53,017 45,950 4Russia: Staraya Andreyevka 52,833 46,650 4Russia: Staraya Besovka 54,283 49,983 4Russia: Staraya Khanineyevka 53,683 47,167 4Russia: Staraya Kulatka 52,717 47,600 4Russia: Staraya Maina 54,600 48,917 4Russia: Staraya Malykla 54,233 49,833 4Russia: Starodevich'e 54,600 43,783 4Russia: Starourazayevo 56,033 54,183 4

Russia: Starye Bygi 57,183 53,833 4Russia: Starye Maklaushi 54,450 47,467 4Russia: Starye Turdaki 53,917 45,483 4Russia: Sudosevo 54,150 45,767 4Russia: Surnarskoe Forestry 56,250 50,050 4Russia: Surskoe 54,467 46,700 4Russia: Svet 53,850 46,200 4Russia: Svyatoe Boloto Lake 53,617 41,867 4Russia: Sylva 58,017 56,767 4Russia: Tambov 52,717 41,417 4Russia: Taneyevka 52,883 44,900 4Russia: Tel'ma‐Perkhurovo 55,367 40,033 4Russia: Terbuny 52,133 38,300 4Russia: Terekhovo 58,067 33,333 4Russia: Togliatti 53,483 49,500 4Russia: Tokari 61,100 34,400 4Russia: Tolstik 57,500 49,350 4Russia: Tsentral'no‐Chernozyomnyi Nature Reserve 51,567 36,100 4Russia: Tula 54,167 37,550 4Russia: Tushna 54,017 48,550 4Russia: Tyulyachi 55,883 50,217 4Russia: Tyum‐Tyum 56,967 50,433 4Russia: Ubezhitsy 56,050 43,400 4Russia: Ufa 54,667 55,717 4Russia: Uglovka 58,250 33,500 4Russia: Ulu‐Telyak 54,933 56,983 4Russia: Ul'yanovsk 54,317 48,367 4Russia: Upravlenchesky Gorodok 53,333 50,200 4Russia: Urzhum 57,117 50,000 4Russia: Urzhumskoe 54,283 47,850 4Russia: Vasil'sursk 56,133 46,000 4Russia: Vel'yaminovo 55,217 37,933 4Russia: Verbilki 56,517 37,567 4Russia: Verkh Kvazhva 58,383 56,383 1Russia: Verkhneye Sviyazhskoe 53,617 47,750 4Russia: Verkhnyaya Tereshka 52,900 47,383 4Russia: Veshkaima 54,050 47,117 4Russia: Vichuga 57,217 41,933 4Russia: Vikshitsa 62,250 33,917 4Russia: Voldynskoe 56,333 37,483 4Russia: Volkhonschina 53,250 42,100 1

Russia: Volkhonshchino 52,750 45,200 4Russia: Vologda 59,233 39,900 4Russia: Volosovo 59,433 29,500 4Russia: Vorob'yovka 54,067 45,300 4Russia: Voronezh Nature Reserve 51,900 39,650 4Russia: Voskresenskoe 56,850 56,883 4Russia: Vruda 59,417 29,233 4Russia: Vyal'e Lake 59,050 30,200 4Russia: Vyartsilya 62,183 30,700 4Russia: Vyritsa 59,400 30,317 4Russia: Vyrypaevka 53,833 46,550 4Russia: Vysha 53,850 42,450 4Russia: Vysokovo 57,383 43,900 4Russia: Vytebet' River 53,850 35,633 4Russia: Vytegra River 61,100 36,317 4Russia: Yadrin 55,933 46,200 4Russia: Yarkovsky Forestry 52,867 40,133 4Russia: Yaroslavl' 57,617 39,850 4Russia: Yatmas‐Duzay 56,000 50,817 4Russia: Yoshkar‐Ola 56,633 47,867 4Russia: Yukhmachi 54,617 49,967 4Russia: Yukhnov 54,750 35,233 4Russia: Yusup‐Alan 56,433 50,133 4Russia: Zarskoier Selo 59,750 30,333 4Russia: Zelenodol'sk 55,850 48,517 4Russia: Zhigulyovsky Nature Reserve 53,033 49,067 4Russia: Zhilino 54,517 56,383 4Russia: Zhizhala River 55,100 34,750 4Russia: Znamenskoe 54,550 45,883 4Russia: Zorinskie Bolota 51,167 36,367 4Russia: Zvenigorod 55,717 36,867 4Serbia: Bor 44,033 22,133 1Serbia: Duboka 44,569 21,766 1Serbia: Jabukovac 44,354 22,389 1Serbia: Klokocevac 44,333 22,200 1Serbia: Kruščica 44,937 21,479 1Serbia: Leskovački Potok 44,887 21,548 1Serbia: Milanovac 44,183 21,600 1Serbia: Miroč 44,483 22,333 7Serbia: Potok lug 44,895 21,488 1Serbia: Sikole 44,177 22,313 1

Serbia: Štubik 44,292 22,358 1Serbia: Trnavac 44,003 22,345 1Serbia: Vršački Breg 45,117 21,367 1Serbia: Zaječar 43,900 22,267 8Serbia: Zlot 44,011 21,967 1Slovakia: Hor Štubna 48,550 20,483 8Slovakia: Prešov 48,983 21,250 25Slovakia: Revúca 48,683 20,117 12Slovakia: Ulič 48,950 22,417 36Slovakia: Žilina‐Krasňany 49,200 18,883 12Sweden: Alingsås 57,930 12,535 GBIF‐mSweden: Alnarp 55,659 13,082 GBIF‐nSweden: Alvhem 58,006 12,156 GBIF‐mSweden: Aneboda 57,123 14,551 GBIF‐nSweden: Ängelholm 56,243 12,862 GBIF‐nSweden: Asige 56,883 12,756 GBIF‐mSweden: Askim 57,633 11,936 GBIF‐mSweden: Åstorp 56,135 12,944 GBIF‐nSweden: Bjällum 58,341 13,621 GBIF‐nSweden: Borgholm 56,876 16,659 GBIF‐qSweden: Edsberg 59,119 14,854 GBIF‐lSweden: Eskilstuna 59,366 16,508 GBIF‐qSweden: Fyledalen 55,555 13,859 GBIF‐nSweden: Gullmarsfjorden 58,402 11,589 GBIF‐mSweden: Gussemåla 57,151 15,963 GBIF‐nSweden: Håkanryd 56,096 14,536 GBIF‐nSweden: Hässleholms 56,158 13,764 GBIF‐nSweden: Hindås 57,704 12,446 GBIF‐mSweden: Hisingen 57,767 11,883 GBIF‐mSweden: Höllviksnäs 55,404 12,935 GBIF‐nSweden: Höör 55,933 13,541 GBIF‐nSweden: Huddinge 59,172 18,107 GBIF‐qSweden: Hunneberg 58,330 12,440 GBIF‐mSweden: Indal 62,583 17,100 8Sweden: Kållandsö 58,655 13,149 GBIF‐nSweden: Kinnekulle 58,581 13,398 GBIF‐mSweden: Landskrona 55,870 12,830 GBIF‐nSweden: Linnebacka 58,718 11,217 GBIF‐nSweden: Mölle 56,285 12,503 GBIF‐mSweden: Mora 61,000 14,533 8Sweden: Mörrum 56,198 14,750 GBIF‐n

Sweden: Näverkärr 58,353 11,370 GBIF‐mSweden: Naverstad 58,771 11,557 GBIF‐mSweden: Nordingrå 62,933 18,283 8Sweden: Oskarshamn 57,264 16,449 GBIF‐mSweden: Östra Tommarp 55,536 14,236 GBIF‐mSweden: Övedskloster 55,686 13,636 GBIF‐nSweden: Övrahammar 57,393 16,582 GBIF‐mSweden: Påboda 56,419 16,069 GBIF‐nSweden: Persnäs 57,066 16,929 GBIF‐nSweden: Räften 55,726 13,295 GBIF‐nSweden: Ramlösa 56,022 12,756 GBIF‐nSweden: Ronneby 56,210 15,278 GBIF‐lSweden: Röstånga 56,002 13,286 GBIF‐nSweden: Skaftö 58,255 11,483 GBIF‐lSweden: Södermanland 59,033 16,753 GBIF‐mSweden: Stockholm 59,333 18,050 4Sweden: Sundsvall 62,391 17,306 GBIF‐nSweden: Svedala 55,508 13,234 GBIF‐nSweden: Täby 59,444 18,069 GBIF‐qSweden: Undersåker 63,317 13,250 8Sweden: Uppsala 59,857 17,645 GBIF‐lSweden: Valdemarsvik 58,203 16,601 GBIF‐nSweden: Västerstad 55,785 13,677 GBIF‐nSwitzerland: Biel 47,150 7,267 1Switzerland: Fläsch 47,033 9,517 8Switzerland: Martigny 46,100 7,067 8Switzerland: Noville 46,381 6,899 GBIF‐bUK: 001 50,601 ‐3,551 GBIF‐eUK: 002 50,704 ‐3,134 GBIF‐eUK: 003 50,709 ‐2,285 GBIF‐eUK: 004 50,759 ‐2,721 GBIF‐eUK: 005 50,822 ‐0,006 GBIF‐eUK: 006 50,852 ‐0,867 GBIF‐eUK: 007 50,872 ‐2,525 GBIF‐eUK: 008 50,89 ‐2,001 GBIF‐eUK: 009 50,931 ‐1,217 GBIF‐eUK: 010 50,962 ‐0,008 GBIF‐eUK: 011 50,977 ‐2,714 GBIF‐eUK: 012 51,006 ‐0,938 GBIF‐eUK: 013 51,046 ‐1,679 GBIF‐eUK: 014 51,061 ‐0,574 GBIF‐e

UK: 015 51,098 ‐0,072 GBIF‐eUK: 016 51,14 ‐4,146 GBIF‐eUK: 017 51,158 ‐0,233 GBIF‐eUK: 018 51,172 0,336 GBIF‐eUK: 019 51,203 ‐2,358 GBIF‐eUK: 020 51,233 ‐0,146 GBIF‐eUK: 021 51,26 ‐2,652 GBIF‐eUK: 022 51,293 0,317 GBIF‐eUK: 023 51,334 ‐1,964 GBIF‐eUK: 024 51,338 ‐1,427 GBIF‐eUK: 025 51,361 ‐1,268 GBIF‐eUK: 026 51,407 0,299 GBIF‐eUK: 027 51,426 ‐2,864 GBIF‐eUK: 028 51,437 0,527 GBIF‐eUK: 029 51,452 ‐2,444 GBIF‐eUK: 030 51,466 ‐0,632 GBIF‐eUK: 031 51,545 0,448 GBIF‐eUK: 032 51,56 ‐1,062 GBIF‐eUK: 033 51,588 ‐2,529 GBIF‐eUK: 034 51,6 ‐3,445 GBIF‐eUK: 035 51,609 ‐2,29 GBIF‐eUK: 036 51,609 ‐3,003 GBIF‐eUK: 037 51,64 ‐3,869 GBIF‐eUK: 038 51,698 ‐0,481 GBIF‐eUK: 039 51,716 0,534 GBIF‐eUK: 040 51,751 ‐3,425 GBIF‐eUK: 041 51,766 ‐2,225 GBIF‐eUK: 042 51,787 ‐1,422 GBIF‐eUK: 043 51,821 ‐0,331 GBIF‐eUK: 044 51,847 0,054 GBIF‐eUK: 045 51,859 1,115 GBIF‐eUK: 046 51,865 ‐1,935 GBIF‐eUK: 047 51,878 ‐2,292 GBIF‐eUK: 048 51,901 0,522 GBIF‐eUK: 049 51,935 ‐1,469 GBIF‐eUK: 050 51,956 ‐0,255 GBIF‐eUK: 051 51,964 ‐2,036 GBIF‐eUK: 052 52,003 ‐1,89 GBIF‐eUK: 053 52,038 0,185 GBIF‐eUK: 054 52,057 ‐1,418 GBIF‐eUK: 055 52,077 ‐0,203 GBIF‐e

UK: 056 52,082 ‐3,146 GBIF‐eUK: 057 52,112 0,919 GBIF‐eUK: 058 52,143 1,435 GBIF‐eUK: 059 52,146 ‐1,271 GBIF‐eUK: 060 52,148 ‐2,001 GBIF‐eUK: 061 52,189 ‐3,436 GBIF‐eUK: 062 52,206 ‐2,417 GBIF‐eUK: 063 52,222 ‐1,422 GBIF‐eUK: 064 52,243 1,245 GBIF‐eUK: 065 52,278 ‐1,714 GBIF‐eUK: 066 52,291 ‐0,864 GBIF‐eUK: 067 52,306 ‐1,886 GBIF‐eUK: 068 52,327 ‐2,442 GBIF‐eUK: 069 52,345 1,481 GBIF‐eUK: 070 52,358 0,152 GBIF‐eUK: 071 52,371 ‐1,339 GBIF‐eUK: 072 52,393 ‐2,594 GBIF‐eUK: 073 52,407 ‐3,618 GBIF‐eUK: 074 52,422 ‐1,496 GBIF‐eUK: 075 52,453 ‐0,111 GBIF‐eUK: 076 52,462 ‐1,632 GBIF‐eUK: 077 52,478 0,649 GBIF‐eUK: 078 52,497 ‐0,381 GBIF‐eUK: 079 52,557 ‐1,934 GBIF‐eUK: 080 52,574 0,36 GBIF‐eUK: 081 52,59 ‐0,673 GBIF‐eUK: 082 52,595 ‐1,116 GBIF‐eUK: 083 52,608 ‐0,309 GBIF‐eUK: 084 52,642 ‐2,222 GBIF‐eUK: 085 52,661 0,512 GBIF‐eUK: 086 52,687 ‐2,445 GBIF‐eUK: 087 52,717 ‐3,042 GBIF‐eUK: 088 52,731 0,901 GBIF‐eUK: 089 52,737 1,109 GBIF‐eUK: 090 52,801 ‐0,603 GBIF‐eUK: 091 52,822 ‐1,926 GBIF‐eUK: 092 52,864 ‐2,893 GBIF‐eUK: 093 52,88 0,75 GBIF‐eUK: 094 52,908 ‐4,533 GBIF‐eUK: 095 52,922 ‐0,774 GBIF‐eUK: 096 52,957 ‐1,704 GBIF‐e

UK: 097 52,999 ‐1,18 GBIF‐eUK: 098 53,006 ‐1,933 GBIF‐eUK: 099 53,046 ‐1,554 GBIF‐eUK: 100 53,065 ‐1,738 GBIF‐eUK: 101 53,093 ‐2,168 GBIF‐eUK: 102 53,126 ‐3,263 GBIF‐eUK: 103 53,148 ‐4,386 GBIF‐eUK: 104 53,178 ‐2,973 GBIF‐eUK: 105 53,199 ‐1,506 GBIF‐eUK: 106 53,219 ‐4,269 GBIF‐eUK: 107 53,256 ‐3,399 GBIF‐eUK: 108 53,267 ‐4,547 GBIF‐eUK: 109 53,288 ‐2,705 GBIF‐eUK: 110 53,301 ‐0,051 GBIF‐eUK: 111 53,307 ‐1,595 GBIF‐eUK: 112 53,313 ‐4,232 GBIF‐eUK: 113 53,378 0,086 GBIF‐eUK: 114 53,429 ‐1,107 GBIF‐eUK: 115 53,458 ‐0,921 GBIF‐eUK: 116 53,519 ‐2,441 GBIF‐eUK: 117 53,541 ‐2,075 GBIF‐eUK: 118 53,573 ‐0,189 GBIF‐eUK: 119 53,641 ‐3,006 GBIF‐eUK: 120 53,669 ‐0,639 GBIF‐eUK: 121 53,76 ‐0,788 GBIF‐eUK: 122 53,802 ‐0,557 GBIF‐eUK: 123 53,827 ‐2,39 GBIF‐eUK: 124 53,856 ‐2,154 GBIF‐eUK: 125 53,987 ‐1,009 GBIF‐eUK: 126 54,084 ‐1,427 GBIF‐eUK: 127 54,16 ‐0,392 GBIF‐eUK: 128 54,26 ‐3,129 GBIF‐eUK: 129 54,302 ‐2,924 GBIF‐eUK: 130 54,388 ‐0,616 GBIF‐eUK: 131 54,437 ‐2,848 GBIF‐eUK: 132 54,485 ‐1,538 GBIF‐eUK: 133 54,521 ‐3,468 GBIF‐eUK: 134 54,541 ‐1,988 GBIF‐eUK: 135 54,574 ‐1,383 GBIF‐eUK: 136 54,665 ‐2,157 GBIF‐eUK: 137 54,708 ‐2,698 GBIF‐e

UK: 138 54,736 ‐4,422 GBIF‐eUK: 139 54,799 ‐1,456 GBIF‐eUK: 140 54,882 ‐3,325 GBIF‐eUK: 141 54,915 ‐2,963 GBIF‐eUK: 142 54,977 ‐2,703 GBIF‐eUK: 143 55,069 ‐2,235 GBIF‐eUK: 144 55,249 ‐1,921 GBIF‐eUK: 145 55,47 ‐2,951 GBIF‐eUK: 146 55,564 ‐2,002 GBIF‐eUK: 147 55,807 ‐4,555 GBIF‐eUK: 148 55,903 ‐4,241 GBIF‐eUK: 149 55,986 ‐4,566 GBIF‐eUK: 150 56,119 ‐4,654 GBIF‐eUK: 151 56,274 ‐3,455 GBIF‐eUK: 152 56,415 ‐2,949 GBIF‐eUK: 153 57,214 ‐3,739 GBIF‐eUK: 154 57,482 ‐4,478 GBIF‐eUK: 155 57,575 ‐3,589 GBIF‐eUkraine: Aleksandriya 48,667 33,117 4Ukraine: Bakhmach 52,183 32,817 4Ukraine: Belichi 50,467 30,350 4Ukraine: Berdichev 49,900 28,600 4Ukraine: Beregomet 48,200 25,433 4Ukraine: Bilki 48,317 23,167 4Ukraine: Bogdan 48,033 24,367 4Ukraine: Bol'shaya Poboyna 48,833 27,000 4Ukraine: Boroniava 48,157 23,378 1Ukraine: Borovichi 52,183 33,367 4Ukraine: Bratskoe 47,867 31,567 4Ukraine: Bratslav 48,817 28,950 4Ukraine: Bronytsya 49,429 23,413 1Ukraine: Bryukhovichi 49,583 24,567 4Ukraine: Burshtyn Hydroelectric Power Station 49,283 24,633 4Ukraine: Bushtyna 48,033 23,333 4Ukraine: Cherkasskie Tishki 50,117 36,417 1Ukraine: Cherneshchina 50,083 35,217 4Ukraine: Chernovtsy 48,283 25,950 4Ukraine: Chernyi Potok 48,397 22,997 1Ukraine: Chinadievo 48,517 22,883 4Ukraine: Chortkov 49,017 25,783 4Ukraine: Delovoe 47,967 24,183 4

Ukraine: Delyatin 48,633 24,583 4Ukraine: Demidovka 48,450 29,350 4Ukraine: Derazhnya 49,267 27,433 4Ukraine: Dobrovody 48,767 30,383 4Ukraine: Dobrynov 49,033 24,700 4Ukraine: Dolishny Shopot 48,017 25,300 4Ukraine: Eskhar 49,800 36,567 4Ukraine: Gaidary 49,633 36,317 4Ukraine: Galich 49,133 24,733 4Ukraine: Gayvoron 48,350 29,883 4Ukraine: Glebovichi 49,633 24,233 4Ukraine: Gorgany Nature Reserve 48,500 24,000 4Ukraine: Honcharivka 48,866 25,023 1Ukraine: Irpen' 50,533 30,267 4Ukraine: Ivangorod 48,783 29,817 4Ukraine: Ivano‐Frankovsk 48,917 24,717 4Ukraine: Izyum 49,183 37,267 8Ukraine: Izyum 49,183 36,217 4Ukraine: Kamenka 49,017 23,567 4Ukraine: Kamenka 48,033 25,933 4Ukraine: Kamenskoe 48,283 22,933 1Ukraine: Kamieniec‐Podolski 48,683 26,600 4Ukraine: Kanevsky Nature Reserve 49,750 31,500 4Ukraine: Kelechin 48,617 23,400 4Ukraine: Kharkov 49,983 36,217 4Ukraine: Khitreyki 50,083 23,767 4Ukraine: Khmel'nitsky 49,417 26,983 4Ukraine: Khristinovka 48,817 29,983 4Ukraine: Kiev 50,433 30,517 4Ukraine: Klavdievo 50,583 30,017 4Ukraine: Kolenkovtsy 48,400 26,133 4Ukraine: Kolochava 48,433 23,733 4Ukraine: Kolomiya 48,533 25,033 4Ukraine: Komsomolskoye lake 48,821 22,782 1Ukraine: Koncha Zaspa 50,250 30,533 4Ukraine: Korosnoe 49,700 24,533 4Ukraine: Korostov 49,000 23,417 4Ukraine: Kosovo 48,317 25,100 4Ukraine: Kostoev 49,983 24,033 4Ukraine: Krasilov 49,667 27,000 4Ukraine: Kremenets 50,100 25,733 4

Ukraine: Krutilov 49,233 26,200 4Ukraine: Kuz'mino 48,550 22,550 4Ukraine: Ladyzhin 48,667 29,250 4Ukraine: Lesniki 49,450 24,883 4Ukraine: Lesnoe 50,100 36,267 4Ukraine: Lesnoe 46,483 29,350 4Ukraine: Lipetskaya Polyana 48,333 23,367 4Ukraine: Lipnyazhka 48,450 31,067 4Ukraine: Loknetsa 51,817 25,833 4Ukraine: Lugi 48,833 24,033 4Ukraine: Luzhany 48,367 25,783 4Ukraine: Lvov 49,833 24,033 4Ukraine: Lyuben' Veliky 49,717 23,717 4Ukraine: Lyucha 48,382 24,905 1Ukraine: Maidan1 51,533 27,867 4Ukraine: Maidan2 49,117 23,250 4Ukraine: Mala Ugolka 48,199 23,635 1Ukraine: Malaya Slavuta 50,300 26,867 4Ukraine: Malaya Ugol'ka 48,283 23,617 4Ukraine: Malintsy 48,417 26,283 4Ukraine: Malyi Bereznyi 48,850 22,467 4Ukraine: Marshintsy 48,200 26,300 4Ukraine: Mayaki 46,417 30,267 4Ukraine: Medenichi 49,433 23,750 4Ukraine: Mezhgor'e 48,533 23,500 4Ukraine: Mikulichin 48,400 24,600 4Ukraine: Minai 48,597 22,293 1Ukraine: Mirgorod 49,967 33,617 4Ukraine: Miropol'e 51,033 35,283 4Ukraine: Mlinovtsy 49,683 25,133 4Ukraine: Mokvin 51,017 26,533 4Ukraine: Morshin 49,183 23,883 4Ukraine: Mostis'ka 49,800 23,150 4Ukraine: Motovilovka 49,817 27,800 4Ukraine: Mukachevo 48,467 22,750 4Ukraine: Myluvannya 48,975 24,939 1Ukraine: Naraev 49,533 24,783 4Ukraine: Nevitskoe 48,683 22,400 4Ukraine: Nezhin 51,033 31,883 4Ukraine: Nezhukhov 49,283 23,800 4Ukraine: Nikolaev 49,517 23,967 4

Ukraine: Nizhankovichi 49,700 22,817 4Ukraine: Novo‐Arkhangel'sk 48,667 30,800 4Ukraine: Novodnestrovsk 48,617 27,367 4Ukraine: Odessa 46,383 30,750 4Ukraine: Olevsk 51,233 27,650 4Ukraine: Ol'khovoe 48,467 30,167 4Ukraine: Ostap'e 49,367 26,033 4Ukraine: Ozyornaya 49,633 25,333 4Ukraine: Pereginskoe 48,700 24,183 4Ukraine: Pervomaisk 48,050 30,850 4Ukraine: Pikovets 48,750 31,283 4Ukraine: Pilyava 49,600 27,467 4Ukraine: Podluzhnoe 50,933 26,317 4Ukraine: Podpoloz'e 48,750 23,033 4Ukraine: Pogorelovka 48,533 25,950 4Ukraine: Pokotilovo 48,467 30,700 4Ukraine: Polapy 51,317 23,950 4Ukraine: Poltava 49,600 34,550 4Ukraine: Polyana 48,617 22,983 4Ukraine: Poroshkovo 48,683 22,750 4Ukraine: Potoki 48,750 31,133 4Ukraine: Pyatnitskoe 49,933 36,817 4Ukraine: Radzivilov 50,117 25,267 4Ukraine: Rakhov 48,067 24,200 4Ukraine: Rakhynya 49,014 24,032 1Ukraine: Rakovets 49,635 24,040 1Ukraine: Rava‐Russkaya 50,233 23,617 4Ukraine: Rosilna 48,796 24,338 1Ukraine: Rostoch'e Nature Reserve 49,933 23,750 4Ukraine: Rozhnyatov 48,933 24,167 4Ukraine: Rukhotin 48,517 26,200 4Ukraine: Russkaya Lozovaya 50,167 36,317 1Ukraine: Sarny 48,933 29,917 4Ukraine: Savranka River 48,100 29,683 4Ukraine: Semyonovka 47,950 31,067 4Ukraine: Shepetovka 50,183 27,067 4Ukraine: Shirokiy Lug 48,228 23,761 1Ukraine: Shirokoye 48,217 23,098 1Ukraine: Skiby 51,233 24,117 4Ukraine: Slovechno 51,383 28,350 4Ukraine: Snyatyn 48,450 25,567 4

Ukraine: Solotvino 48,700 24,417 4Ukraine: Sosnovka 49,117 26,717 4Ukraine: Starogutskoe Forestry 52,317 33,817 4Ukraine: Stary Sambor 49,450 23,000 4Ukraine: Storozhinets 48,167 25,733 4Ukraine: Strakholes'e 51,267 30,233 4Ukraine: Strel'tsovka 49,300 39,867 4Ukraine: Sumovka 48,483 29,483 4Ukraine: Svityaz' 51,467 23,850 4Ukraine: Syanki 49,017 22,900 4Ukraine: Sykhov 49,217 24,033 4Ukraine: Tal'yanki 48,800 30,567 4Ukraine: Teresva 48,017 23,733 4Ukraine: Torun' 48,667 23,567 4Ukraine: Tsunov 49,517 25,100 4Ukraine: Turka 49,150 23,033 4Ukraine: Turyi Remety 48,718 22,582 1Ukraine: Tyachev 48,033 23,583 4Ukraine: Ukrainsky Stepnoy Nature Reserve 50,333 34,600 4Ukraine: Uman' 48,750 30,233 4Ukraine: Velikiy Bychkov 47,974 24,053 1Ukraine: Vereshchitsa 49,533 23,750 4Ukraine: Veselovka 48,617 29,850 4Ukraine: Vinnitsa 49,250 28,383 4Ukraine: Voinilov 49,133 24,483 4Ukraine: Volovets 48,717 23,217 4Ukraine: Vorob'evka 52,200 33,067 1Ukraine: Voronyaki 49,783 24,900 4Ukraine: Vydrichi 51,633 24,767 4Ukraine: Vyshkivsky Pass 48,701 23,635 1Ukraine: Vystupovichi 51,567 29,083 4Ukraine: Yaduty 51,367 34,333 4Ukraine: Yanov 49,217 25,717 4Ukraine: Yaremcha 49,450 24,550 4Ukraine: Yaski 46,517 30,083 4Ukraine: Yavorov 49,950 23,383 4Ukraine: Yurkovka 48,583 30,033 4Ukraine: Zales'e 50,600 30,850 4Ukraine: Zatoka 49,850 23,683 4Ukraine: Zhirovka 49,733 24,067 4Ukraine: Znamenka 48,717 32,667 4

Ukraine: Znob'‐Novgorodskoe 52,267 33,600 4Ukraine: Zubrovitsa Nature Reserve 48,067 25,467 4Ukraine: Zvenigorod 49,733 24,267 4Ukraine: Zvinyachka 49,583 26,250 4Urkaine: Domanintsy 48,633 22,333 1Urkaine: Uzhgorod 48,633 22,283 1

T. dobrogicusAustria: Drösing 48,550 16,917 1Austria: Tadten 47,767 17,000 1Austria: Vienna 48,183 16,450 26Bosnia and Herzegovina: Brcko 44,867 18,817 8Bosnia and Herzegovina: Donja Čadjavica 44,750 19,100 1Bosnia and Herzegovina: Vrsani 44,830 19,020 1Bulgaria: Archar 43,817 22,917 2Bulgaria: Durankulak lake 43,672 28,547 3Bulgaria: Harlets 43,711 23,842 1Bulgaria: Kudelin 44,197 22,673 1Bulgaria: Oryahovo 43,733 23,967 2Bulgaria: Silistra 44,117 27,267 8Bulgaria: Srebarna 44,121 27,082 1Bulgaria: Svistov 43,617 25,350 1Bulgaria: Vidin 44,007 22,924 1Croatia: Dugo Selo 45,817 16,250 1Croatia: Jasenovac 45,267 16,917 8Croatia: Mlaka 45,235 17,025 7Croatia: Orle 45,683 16,233 7Croatia: Podgorac 45,483 18,200 1Croatia: Slavonski Brod 45,167 18,017 7Croatia: Varaždin 46,317 16,333 8Croatia: Vedropolje 45,349 16,569 8Croatia: Zupanja 45,083 18,700 1Czech Republic: Lanžhot 48,667 16,950 26Czech Republic: Nové Mlýny 48,850 16,733 27Hungary: Alap 46,800 18,683 1Hungary: Albertirsza 47,233 19,600 1Hungary: Baracska 47,271 18,727 27Hungary: Bataszek 46,200 18,824 1Hungary: Cégénydányád 47,933 22,500 1Hungary: Dráva mente 46,225 17,063 27Hungary: Dunaföldvár 46,638 16,541 27

Hungary: Fehér‐tó 46,471 20,624 27Hungary: Felső‐Babád 47,301 19,227 27Hungary: Fülöpháza 46,883 19,400 25Hungary: Kapszeg‐tó 47,489 19,064 27Hungary: Kis‐Balaton 46,649 17,163 27Hungary: Körmend 47,017 16,600 1Hungary: Külső‐tó 46,913 17,864 27Hungary: Kunpeszér 47,067 19,285 27Hungary: Mura 46,417 16,717 27Hungary: Nagy‐tó 46,993 17,606 27Hungary: Nyíregyháza 47,933 21,717 1Hungary: Öcsöd 46,933 20,383 1Romania: Razboinita 45,222 29,165 1Hungary: Sátoraljaújhely 48,434 21,639 27Hungary: Szolnok 47,167 20,167 1Moldova: Kagul 45,917 28,200 4Romania: Amarasti de Jos 43,983 24,100 1Romania: Andrid 47,517 22,350 1Romania: Ariciu 45,367 27,517 1Romania: Babesti 47,967 23,100 1Romania: Briala 45,200 27,980 1Romania: Bukarest 44,433 26,117 8Romania: Ciorasti 45,435 27,305 1Romania: Giurgeni 44,742 27,868 1Romania: Ghertensiş 45,383 21,750 8Romania: Histria 44,550 28,767 3Romania: Macin 45,251 28,121 1Romania: Mahmudia 45,123 29,172 27Romania: Mihai Bravu 44,136 26,023 1Romania: Obrejita 45,433 27,133 1Romania: Oltenița 44,067 26,633 8Romania: Piscolt 47,583 22,300 1Romania: Prahova 44,817 25,867 1Romania: Sebis 46,367 22,100 1Romania: Virfuri 46,283 22,467 1Romania: Vlahii 44,200 27,867 3Romania: Zimnicea 43,650 25,350 1Serbia: Beograd 44,833 20,500 1Serbia: Debrc 44,600 19,867 1Serbia: Ecka 45,287 20,450 1

Serbia: Glusci 44,858 19,541 1Serbia: Hrastovača 46,161 19,655 7Serbia: Ivanovo 44,723 20,705 20Serbia: Jamena 44,871 19,069 1Serbia: Jasenovo 44,940 21,310 7Serbia: Kikinda 45,673 20,474 7Serbia: Kladovo 44,674 22,512 1Serbia: Kovačica 45,141 20,561 1Serbia: Negotin 44,236 22,554 20Serbia: Novi Kneževac 46,050 20,100 7Serbia: Opovo 45,037 20,443 1Serbia: Orid 44,715 19,801 7Serbia: Ostrovo 45,567 20,330 4Serbia: Radojevo 45,750 20,804 7Serbia: Ravenica 44,717 19,967 7Serbia: Senta 45,917 20,100 1Serbia: Smederovo 44,650 20,933 2Serbia: Sremska Mitrovica 44,933 19,687 7Serbia: Stevanove Ravnice 44,828 21,304 1Serbia: Svetozar Miletic 45,854 19,189 20Serbia: Trbušac 44,673 19,830 7Serbia: Vatin 45,235 21,249 7Serbia: Vrbovac 44,937 19,501 7Serbia: Žabari 44,358 21,197 1Slovakia: Beša 48,533 21,950 27Slovakia: Čunovo 48,017 17,200 36Slovakia: Devínske Jazero 48,283 16,950 25Slovakia: Domica 48,467 20,467 27Slovakia: Leles 48,467 22,017 36Slovakia: Malý Kiarov 48,100 19,433 27Slovakia: Maňa 48,133 18,267 36Slovakia: Rusovce 48,050 17,150 25Slovakia: Šúr 48,233 17,233 36Slovakia: Svätá Mária 48,450 21,817 36Slovakia: Torozlín 48,150 18,200 27Slovakia: Veľký Blh 48,433 20,100 27Slovakia: Veškovce 48,550 22,100 36Ukraine: Beregovo 48,217 22,633 4Ukraine: Batevo 48,367 22,400 1Ukraine: Bobovoe 48,083 22,883 4Ukraine: Chervenyovo 48,467 22,533 4

Ukraine: Chop 48,448 22,248 1Ukraine: Chorny Potok 48,183 22,917 1Ukraine: Dertsen 48,355 22,650 1Ukraine: Drisina 48,350 22,667 1Ukraine: Gat' 48,317 22,633 4Ukraine: Izmail 45,350 28,850 1Ukraine: Kherson 46,650 32,600 1Ukraine: Kliucharki 48,416 22,640 1Ukraine: Krasnaya Khatka 46,583 32,433 4Ukraine: Leski 45,467 29,483 4Ukraine: Mukachevo 48,433 22,700 1Ukraine: Minai1 48,575 22,273 1Ukraine: Minai2 48,600 22,283 1Ukraine: Nizhneye Solotvino 48,533 22,433 4Ukraine: Novosel'skoe 45,350 28,600 4Ukraine: Orlovka 45,317 28,450 1Ukraine: Pokrovka 46,500 31,700 4Ukraine: Reni 45,433 28,300 1Ukraine: Shalanki 48,228 22,902 1Ukraine: Svoboda 48,383 22,350 4Ukraine: Tsyurupinsk 46,650 32,750 1Ukraine: Uzhgorod 2 48,616 22,281 1Ukraine: Vary 48,117 22,717 4Ukraine: Vilkovo 45,400 29,583 1Ukraine: Vinogradov 48,117 23,033 4Ukraine: Zaluzh'e 48,367 22,867 4Ukraine: Zapson' 48,283 22,483 4

T. macedonicusAlbania: Klos 41,500 20,095 15Albania: Bajram Curri 42,350 20,083 15Albania: Bajzë 42,303 19,441 1Albania: Ballaban 40,417 20,134 15Albania: Bejar 40,429 19,850 1Albania: Belsh 40,983 19,883 15Albania: Bilisht 40,623 20,987 15Albania: Bruc 41,692 20,053 15Albania: Bёrxullё 41,383 19,700 15Albania: Cajё 41,883 20,500 15Albania: Dhrovian 39,887 20,217 15Albania: Divjakё 40,991 19,524 15

Albania: Fllakё 41,369 19,506 15Albania: Fushë‐Krujë 41,529 19,682 1Albania: Gjerbёs 40,624 20,251 15Albania: Gosё 41,089 19,636 15Albania: Gracen 41,157 19,949 1Albania: Kakrukё 40,538 20,134 15Albania: Kala e Dodёs 41,817 20,433 15Albania: Kavajë 41,168 19,556 1Albania: Kolsh 42,072 20,326 1Albania: Konispol 39,655 20,176 15Albania: Koplik 42,197 19,451 1Albania: Krutje 40,867 19,717 15Albania: Kuç 40,171 19,837 15Albania: Kulme 41,771 19,816 15Albania: Kёlcyrё 40,308 20,187 15Albania: Labinot‐Mal 41,201 20,151 15Albania: Leskovik 40,151 20,592 15Albania: Libohovё 40,025 20,258 15Albania: Librazhd 41,174 20,309 15Albania: Lukovё 39,989 19,908 15Albania: Lushnjë 41,000 19,664 1Albania: Maliq 40,704 20,693 15Albania: Manёz 41,433 19,583 15Albania: Mbrostar 40,752 19,574 15Albania: Mifol 40,604 19,487 15Albania: Ndroq 41,267 19,650 15Albania: Pogradec 40,931 20,640 1Albania: Progěr 40,700 20,933 2Albania: Pёrrenjas 41,070 20,543 15Albania: Sauk 41,288 19,820 15Albania: Selcё 42,507 19,123 15Albania: Shishtavec 41,983 20,600 15Albania: Sopik 39,782 20,070 1Albania: Spille 41,090 19,467 15Albania: Tale 41,682 19,614 15Albania: Theth 42,405 19,764 15Albania: Vau Dejёs 42,000 19,633 15Albania: Velipojё 41,865 19,430 15Albania: Vermosh 42,592 19,688 15Albania: Vithkuq 40,522 20,576 15Bosnia and Herzegovina: Donja – Gornja Čadjavica 44,800 19,033 1

Bosnia and Herzegovina: Dubrava 44,533 18,667 7Bosnia and Herzegovina: Gornja Čadjavica 44,750 19,083 1Bosnia and Herzegovina: Sarajevo 43,867 18,433 16Bosnia and Herzegovina: Tavna Monastire 44,600 19,067 1Bosnia and Herzegovina: Visegrad 43,783 19,333 1Greece: Ano Kaliniki 40,853 21,450 1Greece: Archangelos 41,050 22,291 1Greece: Asprangeli 39,825 20,725 1Greece: Elafotopos 39,900 20,669 1Greece: Elati 39,835 20,740 1Greece: Farmáki 40,367 20,850 17Greece: Kerameia 39,562 22,081 1Greece: Kounoupena 39,684 19,764 1Greece: Livadia 41,283 23,017 1Greece: Negrades 39,847 20,663 1Greece: Paliambela 38,909 20,970 1Greece: Pefkodasos 41,030 22,580 1Greece: Prepsa 40,831 21,121 1Greece: Seli 40,567 22,022 1Greece: Trilofos 40,358 22,477 1Greece: Vasilitsa 40,055 21,070 18Greece: Vryta 40,800 21,919 1Greece: Zygos‐Mikro 39,823 21,261 1Kosovo: Doganovic 42,216 21,189 1Kosovo: Karagač 42,640 20,309 1Kosovo: Kijevo 42,568 20,728 1Kosovo: Novo Brdo 42,617 21,417 7Kosovo: Prepolac 43,009 21,233 1Kosovo: Pristina 42,666 21,172 1Kosovo: Radevo 42,573 21,060 7Kosovo: Xërze 42,341 20,573 1Macedonia: Debreste 41,502 21,293 1Macedonia: Dojran 41,241 22,718 1Macedonia: Ezerani 41,028 21,022 7Macedonia: Gorno Srpci 41,072 21,219 1Macedonia: Gostivar 41,817 20,899 1Macedonia: Karbinci 41,767 22,233 1Macedonia: Kočani 41,933 22,400 8Macedonia: Kolibari 41,580 20,951 1Macedonia: Kostobačilo 41,050 20,800 7Macedonia: Leskoec 40,962 20,885 1

Macedonia: Lesnovo 42,013 22,230 7Macedonia: Loznani 41,221 21,446 1Macedonia: Mislesevo 41,185 20,709 7Macedonia: Petrovec 41,939 21,615 1Macedonia: Prilep 41,419 21,611 1Macedonia: Probistip 41,983 22,167 1Macedonia: Rugince 42,148 21,977 1Macedonia: Stracin 42,150 22,030 7Macedonia: Vrbjani 41,413 20,816 1Montenegro: Bjeloši 42,374 18,907 1Montenegro: Doni Štoj 41,900 19,329 1Montenegro: Erakovice 42,537 19,035 19Montenegro: Jankovića Krš 42,384 19,030 19Montenegro: Kućišta 42,460 18,854 1Montenegro: Smrdan 42,445 19,166 19Montenegro: Ubao 42,467 19,354 19Serbia: Berovacko jezero 43,027 22,600 1Serbia: Berovičko 43,027 22,559 7Serbia: Blato 43,301 20,730 1Serbia: Borovsko polje 42,970 22,721 1Serbia: Bukovac 44,167 19,967 7Serbia: Delimedje 43,091 20,265 1Serbia: Divcibare 44,100 19,933 1Serbia: Dojni Dusnik 43,159 22,127 1Serbia: Drežnik 43,774 19,992 1Serbia: Gornji Varbes 43,204 21,963 1Serbia: Grcak 43,467 20,950 1Serbia: Grdelica 42,874 22,066 7Serbia: Jezero 43,583 21,900 1Serbia: Jezero Ploce 43,283 22,142 1Serbia: Karan 43,888 19,926 1Serbia: Kesino Jezero 42,786 22,245 7Serbia: Krivi Vir 43,801 21,761 1Serbia: Kruševac 43,583 21,350 7Serbia: Ljuberadja 43,032 22,395 1Serbia: Ljubovija 44,200 19,400 1Serbia: Lucane 42,417 21,717 1Serbia: Pcinja 42,330 21,900 20Serbia: Preševo 42,306 21,656 7Serbia: Promaja 42,678 22,357 1Serbia: Rataje 43,477 21,106 1

Serbia: Rtanj 43,772 21,932 1Serbia: Stanisinci 43,517 20,883 1Serbia: Todorovce 42,875 21,866 1Serbia: Vlasi 42,999 22,638 1Serbia: Vranje 42,606 21,856 1Serbia: Vrtovac 43,404 22,450 1Serbia: Zlot 44,011 21,967 1

T. karelinii westBulgaria: Alepu 42,348 27,714 1Bulgaria: Alexandrovo 42,601 25,093 1Bulgaria: Balyia 43,127 22,995 5Bulgaria: Bankya 42,717 23,133 2Bulgaria: Bata 42,463 24,198 1Bulgaria: Belopoltsi 41,524 25,796 1Bulgaria: Bivoliyane 41,570 25,511 1Bulgaria: Bolata 43,384 28,471 1Bulgaria: Boyana Lake 42,636 23,270 1Bulgaria: Byaga 42,079 24,393 1Bulgaria: Chukovo 41,489 25,457 1Bulgaria: Dendrariuma 42,628 23,228 1Bulgaria: Dobrich 43,567 27,833 3Bulgaria: Dolna Dikanya 42,450 23,117 2Bulgaria: Dolni Koriten 42,473 22,600 1Bulgaria: Dzhurovo 42,967 24,050 2Bulgaria: Ezero 42,763 25,363 5Bulgaria: Fakia 42,233 27,130 1Bulgaria: Gabrene 41,375 22,965 1Bulgaria: Gaytaninovo 41,451 23,738 1Bulgaria: Gintsi 43,033 23,132 1Bulgaria: Golesh 43,046 22,947 1Bulgaria: Iliya 42,075 22,797 1Bulgaria: Kalotina 43,000 22,867 2Bulgaria: Kamchya 42,900 26,933 4Bulgaria: Karlovo 42,633 24,817 1Bulgaria: Karlukovo 43,176 24,078 1Bulgaria: Kazichene 42,667 23,400 2Bulgaria: Kolarovo 41,355 23,126 1Bulgaria: Kolets 41,860 25,318 1Bulgaria: Kostenets 42,300 23,850 2Bulgaria: Kykrina 43,107 24,865 1

Bulgaria: Levski 43,417 25,133 1Bulgaria: Lozenska Mt. 42,586 23,449 1Bulgaria: Makotsevo 42,700 23,800 2Bulgaria: Malko Tarnovo 41,989 27,525 1Bulgaria: Nisovo 43,655 26,085 5Bulgaria: Novo Selo 42,177 22,679 1Bulgaria: Orlovets 43,333 25,716 1Bulgaria: Ostar Kamak 41,878 25,853 1Bulgaria: Paril 41,435 23,690 1Bulgaria: Petarch 42,844 23,147 1Bulgaria: Primorsko 42,267 27,755 1Bulgaria: Rakovski 42,267 24,967 1Bulgaria: Razgrad 43,486 26,466 5Bulgaria: Samokov 42,333 23,500 2Bulgaria: Sedlovina 41,635 25,426 1Bulgaria: Sevlievo 43,467 25,217 1Bulgaria: Shezna 42,898 27,266 5Bulgaria: Shumnatitsa 42,297 23,626 1Bulgaria: Slivovo 42,209 26,994 1Bulgaria: Slokoshtitsa 42,253 22,695 1Bulgaria: Smochevo 42,150 23,100 2Bulgaria: Stara Kresna 41,797 23,211 1Bulgaria: Sungurlare 42,786 26,751 1Bulgaria: Urvich 42,558 23,427 1Bulgaria: Varbishka Mt. 42,933 26,683 1Bulgaria: Varvara 42,103 27,890 5Bulgaria: Zimevitsa 43,069 23,259 1Bulgaria: Zlatni Pyasatsi 43,300 28,033 3Bulgaria: Zverino 43,094 23,580 1Greece: Alistrati 41,077 23,973 1Greece: Dafnochori 40,950 22,800 1Greece: Katratzides 41,120 26,148 1Greece: Kentriko 41,167 22,900 1Greece: Komotini 41,117 25,433 6Greece: Mylochori 41,106 22,900 1Greece: Saint Kosmas 41,084 24,669 1Greece: Thessaloniki 40,633 22,950 6Greece: Zarkadia 41,018 24,646 1Macedonia: Bansko 41,383 22,767 1Macedonia: Berovo 41,717 22,867 7Macedonia: Bigla 41,933 22,667 1

Macedonia: Garnikovo 41,277 22,060 1Macedonia: Mitrašinci 41,750 22,767 1Macedonia: Stracin 42,150 22,030 7Macedonia: Visoka Čuka 41,283 22,333 7Serbia: Aranđelovac 44,284 20,669 1Serbia: Belanovica 44,233 20,400 8Serbia: Berovacko jezero 43,027 22,600 1Serbia: Borovsko polje 42,970 22,721 1Serbia: Djurinci 44,494 20,653 1Serbia: Gornja Sabanta 43,900 21,000 1Serbia: Grivac 43,967 20,667 1Serbia: Grza 43,897 21,646 1Serbia: Guberevac 43,852 20,771 1Serbia: Kruševac 43,583 21,350 7Serbia: Ljuberadja 43,032 22,395 1Serbia: Ponor 44,243 19,940 7Serbia: Rajkovici 44,187 19,985 7Serbia: Rataje 43,477 21,106 7Serbia: Resavica Pecina 44,050 21,600 1Serbia: Sisevac 43,957 21,585 1Serbia: Trešnja 44,600 20,583 1Serbia: Vitanovac 43,733 20,800 1Serbia: Vlasi 42,999 22,638 1Serbia: Zli Do 42,422 22,453 1Turkey: Babeski 41,433 27,083 9Turkey: Bergama 39,021 27,107 1Turkey: Biga 40,233 27,233 10Turkey: Bigadiç 39,351 28,217 1Turkey: Bozdağ 38,367 28,102 1Turkey: Buldan 38,067 28,783 9Turkey: Büyük Kalecik 38,687 30,455 1Turkey: Çan 40,006 26,937 1Turkey: Çavuşköy 40,255 28,107 1Turkey: Çorlu 41,167 27,783 9Turkey: Demirköy 41,817 27,767 9Turkey: Dikili 39,176 26,822 1Turkey: Efes 37,947 27,349 1Turkey: Gönen 40,217 27,618 1Turkey: Habibler 41,133 28,833 10Turkey: Halkali 41,033 28,767 1Turkey: Hayrabolu 41,217 27,100 9

Turkey: Karacabey 40,250 28,300 1Turkey: Keşan 40,917 26,633 1Turkey: Klaros 38,000 27,183 1Turkey: Kula 38,550 28,650 10Turkey: Lake Sülüklü 38,565 27,533 13Turkey: Lapseki 40,345 26,688 9Turkey: Lüleburgaz 41,400 27,350 9Turkey: Mersinbeleni 37,650 27,683 1Turkey: Mustafa Kemalpaşa 40,069 28,352 1Turkey: Pehlivanköy 41,367 26,967 9Turkey: Saray 41,450 27,917 9Turkey: Sarayiçi 41,683 26,550 9Turkey: Uzunköprü 41,267 26,683 9Turkey: Yamanlar Dagi 38,533 27,233 11

T. karelinii centralTurkey: Abanta Gölu 40,607 31,286 1Turkey: Adapazari 40,767 30,483 1Turkey: Arifiye 40,710 30,360 1Turkey: Aşağıçaylı 41,933 33,767 9Turkey: Bartın 41,617 32,333 1Turkey: Bolu 40,700 31,467 29Turkey: Cebeci 41,201 34,036 1Turkey: Demirbey 40,856 30,488 1Turkey: Dranaz pass 41,607 34,834 14Turkey: Düzce 40,850 30,983 29Turkey: Erbaa 40,700 36,617 16Turkey: Gebze 40,890 29,504 1Turkey: Gürbulak 40,967 39,633 9Turkey: Hacılar 41,491 32,101 1Turkey: İznik 40,423 29,733 10Turkey: Kalecik 40,077 33,341 1Turkey: Karabük 41,133 32,617 11Turkey: Karakoç 41,487 32,142 1Turkey: Karasu 41,076 30,757 1Turkey: Kavak 41,110 36,017 1Turkey: Korgan 40,833 37,350 10Turkey: Koyulhisar 40,300 37,830 9Turkey: Kuşçular 41,583 35,867 11Turkey: Kuşçular Köyü 41,067 35,400 28Turkey: Mekece 40,450 30,050 14

Turkey: Niksar 40,583 36,950 9Turkey: Okluca 40,233 29,850 1Turkey: Orhangazi 40,554 29,325 1Turkey: Reşadiye 40,441 37,476 1Turkey: Safibey 40,467 30,233 1Turkey: Seben 40,410 31,570 1Turkey: Şebinkarahisar 40,286 38,126 1Turkey: Şerefiye 40,150 37,767 16Turkey: Seydiler 41,700 33,700 9Turkey: Şile 41,167 29,617 10Turkey: Suşehri 40,163 38,095 9Turkey: Taşlıyayla 40,600 31,683 10Turkey: Tokat 40,333 36,583 16Turkey: Tosya 41,017 34,033 9Turkey: Ulubey 40,867 37,750 10Turkey: Yakacık 40,917 29,200 1Turkey: Yenişehir 40,268 29,605 1Turkey: Yomra 40,868 39,857 1

T. karelinii eastAzerbaijan: Almu 38,767 48,550 4Azerbaijan: Anitino 39,083 48,550 4Azerbaijan: Avyarud 38,500 48,600 1Azerbaijan: Azfilial 38,667 48,783 1Azerbaijan: Beslanjkend 40,733 48,400 4Azerbaijan: Chukhuryurt 40,733 48,633 4Azerbaijan: Digya 40,917 48,450 4Azerbaijan: Isti‐Su 38,433 48,767 4Azerbaijan: Kamyshovka 38,633 48,867 4Azerbaijan: Katekh 41,650 46,567 4Azerbaijan: Khizy 40,900 49,117 4Azerbaijan: Kumbashi 38,950 48,817 4Azerbaijan: Lenkaran 38,755 48,823 1Azerbaijan: Lerik 38,767 48,417 1Azerbaijan: Moshkhan 38,483 48,833 1Azerbaijan: Mt. Talysh 38,700 48,300 4Azerbaijan: Mukhtadir 41,650 48,783 4Azerbaijan: Shemakha 40,633 48,633 4Azerbaijan: Turianchay Nature Reserve 40,733 47,550 4Azerbaijan: Zakataly Nature Reserve 41,783 46,600 4Georgia: "544" Lake near Turtle Lake 41,700 44,767 4

Georgia: Adigeni 41,683 42,700 4Georgia: Adzhametskiy Nature Reserve 42,133 42,783 4Georgia: Akhmeta Nature Reserve 42,217 45,250 4Georgia: Anaklia 42,400 41,567 4Georgia: Arali 41,633 42,811 1Georgia: Ateni Canyon 41,917 44,100 4Georgia: Batumi 41,633 41,667 4Georgia: Bebesyry Lake 42,683 41,583 4Georgia: Betania 41,583 44,233 4Georgia: Bursa River 41,933 45,817 4Georgia: Chiantba Lake 41,914 45,299 1Georgia: Chilitba lake 41,795 44,633 1Georgia: Didimitarbi 41,750 43,583 4Georgia: Ertatsminda Lake 41,867 44,317 4Georgia: Gagry 43,333 40,250 4Georgia: Gumma 43,117 41,033 4Georgia: Ikalto 41,951 45,395 1Georgia: Kakhisi Lake 41,783 43,300 4Georgia: Kazbegskiy Nature Reserve 42,667 44,650 4Georgia: Khekordzi 41,809 44,498 1Georgia: Kobuleti 41,822 41,814 1Georgia: Kutaisi 42,267 42,700 4Georgia: Lagodekhi 41,833 46,267 4Georgia: Liakhvskiy Nature Reserve 42,317 44,283 4Georgia: Lidzava 43,183 40,367 4Georgia: Machara 42,983 41,050 1Georgia: Malaya Ritsa Lake 43,483 40,500 4Georgia: Perevisa 42,250 43,300 4Georgia: Pichori river valley 42,134 41,841 1Georgia: Poti 42,187 41,727 1Georgia: Pskhu Nature Reserve 43,417 40,817 4Georgia: Satovle 41,786 44,545 1Georgia: Senaki 42,267 42,067 4Georgia: Surami 42,033 43,533 4Georgia: Tba 41,800 43,450 4Georgia: Telavi 41,903 45,475 1Georgia: Tetritskaro 41,533 44,467 4Georgia: Tkvarcheli 42,862 41,682 1Georgia: Tskhinval 42,233 43,983 4Georgia: Tsodoreti 41,729 44,572 1Georgia: Znauri 42,183 43,783 4

Georgia: Zugdidi 42,500 41,867 4Iran: Alandan 36,233 53,467 1Iran: Charaz 36,660 51,205 32Iran: Galesh Kalam 37,212 50,259 32Iran: Gorgan 36,767 54,467 30Iran: Gorgan Bay 36,733 53,950 4Iran: Harehdasht 37,981 48,801 32Iran: Jokleh Bandan 37,120 49,730 33Iran: Khasan‐Kyande 37,400 49,883 4Iran: Molla Kela 36,554 51,800 32Iran: Noor Jungle 36,555 53,068 31Iran: Nour 36,556 52,064 32Iran: Qu’Am Shahr 36,436 52,803 1Iran: Tonekabon 36,817 50,867 33Russia: Achishkho Mt. 43,733 40,117 4Russia: Afipsip 45,000 38,783 4Russia: Akhtyrskiy 44,833 38,283 4Russia: Anapa 44,851 37,361 1Russia: Anapskaya 44,883 37,438 1Russia: Bamut 43,133 45,200 4Russia: Barsukovskaya 44,767 41,833 4Russia: Belidzhi 41,867 48,017 1Russia: Besleneyevskaya 44,233 40,700 4Russia: Cape Malyi Utrish 44,716 37,467 1Russia: Charnoreinje 43,917 40,650 1Russia: Derbentskaya 44,767 38,500 4Russia: Dzhibakhni 42,067 47,950 4Russia: Ersi 41,983 47,983 1Russia: Fisht Mt. 43,917 39,867 4Russia: Gerpegem Ridge 44,133 40,750 4Russia: Golubye Ozera 43,233 43,550 4Russia: Goriachiy Kluch 44,563 39,032 1Russia: Gruzinka Ridge 44,783 38,117 4Russia: Guzeripl' 43,833 40,200 4Russia: Il'inskaya 43,367 45,950 4Russia: Kaladzhinskaya 44,300 40,900 4Russia: Kalezh 44,000 39,417 4Russia: Kaluzhskaya 44,767 38,967 4Russia: Kamennomostskiy 44,285 40,123 1Russia: Kharag 41,933 47,817 4Russia: Kharkhni 41,933 47,883 1

Russia: Khosta 43,517 39,867 34Russia: Krasnodar 45,033 38,967 4Russia: Krepostnaya 44,717 38,700 4Russia: Maikop 44,617 40,117 34Russia: Mezmai 44,200 40,000 4Russia: Mikhgerg 41,633 47,983 1Russia: Mineral'nye Vody 44,200 43,133 4Russia: Molodyozhnyi 44,650 39,667 4Russia: Muzhichi 43,050 45,000 4Russia: Nalchik 43,458 43,521 1Russia: Novoprokhladnoe 44,133 40,300 4Russia: Novorossiysk 44,755 37,819 1Russia: Oktyabr'skiy 44,483 39,817 4Russia: Psebay 44,071 40,847 1Russia: Rugun 41,600 48,000 4Russia: Sergei‐Pole 43,700 39,717 4Russia: Shakhan Mt. 44,250 40,283 4Russia: Smolenskaya 44,783 38,800 4Russia: Sochi 43,567 39,750 4Russia: Stavropol' 45,050 41,867 4Russia: Tkhamakha 44,617 38,867 4Russia: Umpyr Pass 43,783 40,700 4Russia: Vel'yaminovka 44,100 39,100 4Russia: Vesyoloe 43,400 40,017 4Russia: Yablonovskiy 44,900 38,950 1Ukraine: Alushta 44,682 34,384 1Ukraine: Aromatnoe 44,783 33,850 4Ukraine: Baydarskaya Yayla 44,417 33,783 4Ukraine: Chatyr‐Dag 44,688 34,234 1Ukraine: Kolkhoznoe 44,483 33,883 4Ukraine: Krasnoles'e 44,817 34,217 4Ukraine: Kuybyshevo 44,633 33,867 1Ukraine: Luchistoe 44,767 34,400 4Ukraine: Nikita 44,538 34,243 1Ukraine: Orlinoe 44,467 33,783 1Ukraine: Prokhladnoe 44,705 34,000 1Ukraine: Rezervnoe 44,467 33,667 4Ukraine: Rodnoe 44,550 33,733 4Ukraine: Schastlivoe 44,567 34,067 4Ukraine: Sebastopol 44,583 33,533 4Ukraine: Simeiz 44,450 34,117 4

Ukraine: Simferopol 44,950 34,100 35Ukraine: Verkhnesadovoe 44,683 33,700 4

T. marmoratusFrance: Confolens 46,007 0,730 1France: Mayenne 48,300 ‐0,617 1France: Rochechouart 45,794 0,775 1France: 001 42,570 2,699 GBIF‐gFrance: 002 42,570 3,057 GBIF‐gFrance: 003 42,930 3,057 GBIF‐gFrance: 004 43,082 1,344 GBIF‐gFrance: 005 43,110 ‐0,544 GBIF‐gFrance: 006 43,110 2,337 GBIF‐gFrance: 007 43,178 0,639 GBIF‐gFrance: 008 43,290 0,536 GBIF‐gFrance: 009 43,290 3,417 GBIF‐gFrance: 010 43,370 2,563 GBIF‐gFrance: 011 43,372 2,254 GBIF‐gFrance: 012 43,402 2,174 GBIF‐gFrance: 013 43,430 2,602 GBIF‐gFrance: 014 43,436 2,167 GBIF‐gFrance: 015 43,457 2,328 GBIF‐gFrance: 016 43,460 2,237 GBIF‐gFrance: 017 43,462 2,336 GBIF‐gFrance: 018 43,466 2,194 GBIF‐gFrance: 019 43,467 2,001 GBIF‐gFrance: 020 43,470 ‐0,544 GBIF‐gFrance: 021 43,522 2,087 GBIF‐gFrance: 022 43,631 2,053 GBIF‐gFrance: 023 43,650 ‐1,264 GBIF‐gFrance: 024 43,650 ‐0,904 GBIF‐gFrance: 025 43,650 0,536 GBIF‐gFrance: 026 43,650 2,337 GBIF‐gFrance: 027 43,650 2,670 GBIF‐gFrance: 028 43,650 3,777 GBIF‐gFrance: 029 43,830 ‐0,544 GBIF‐gFrance: 030 43,830 ‐0,904 GBIF‐gFrance: 031 43,830 0,176 GBIF‐gFrance: 032 43,830 3,777 GBIF‐gFrance: 033 43,830 3,057 GBIF‐gFrance: 034 43,830 3,417 GBIF‐g

France: 035 43,860 0,737 GBIF‐gFrance: 036 44,010 3,777 GBIF‐gFrance: 037 44,010 3,057 GBIF‐gFrance: 038 44,010 3,417 GBIF‐gFrance: 039 44,190 ‐1,264 GBIF‐gFrance: 040 44,190 ‐0,184 GBIF‐gFrance: 041 44,190 0,176 GBIF‐gFrance: 042 44,190 3,057 GBIF‐gFrance: 043 44,370 1,616 GBIF‐gFrance: 044 44,370 1,256 GBIF‐gFrance: 045 44,370 1,616 GBIF‐gFrance: 046 44,370 2,670 GBIF‐gFrance: 047 44,817 1,641 GBIF‐gFrance: 048 44,910 2,337 GBIF‐gFrance: 049 44,910 2,700 GBIF‐gFrance: 050 45,090 ‐0,904 GBIF‐gFrance: 051 45,090 0,536 GBIF‐gFrance: 052 45,090 0,176 GBIF‐gFrance: 053 45,090 0,896 GBIF‐gFrance: 054 45,270 2,337 GBIF‐gFrance: 055 45,270 1,976 GBIF‐gFrance: 056 45,270 3,417 GBIF‐gFrance: 057 45,315 ‐0,175 GBIF‐gFrance: 058 45,332 1,295 GBIF‐gFrance: 059 45,432 2,634 GBIF‐gFrance: 060 45,435 ‐0,748 GBIF‐gFrance: 061 45,450 2,337 GBIF‐gFrance: 062 45,450 ‐0,544 GBIF‐gFrance: 063 45,450 1,616 GBIF‐gFrance: 064 45,450 1,976 GBIF‐gFrance: 065 45,450 3,057 GBIF‐gFrance: 066 45,457 2,210 GBIF‐gFrance: 067 45,575 0,342 GBIF‐gFrance: 068 45,584 0,274 GBIF‐gFrance: 069 45,630 0,176 GBIF‐gFrance: 070 45,630 1,616 GBIF‐gFrance: 071 45,630 1,976 GBIF‐gFrance: 072 45,660 ‐0,206 GBIF‐gFrance: 073 45,663 0,891 GBIF‐gFrance: 074 45,729 0,587 GBIF‐gFrance: 075 45,731 ‐0,631 GBIF‐g

France: 076 45,765 ‐0,626 GBIF‐gFrance: 077 45,810 ‐0,544 GBIF‐gFrance: 078 45,810 0,896 GBIF‐gFrance: 079 45,810 0,176 GBIF‐gFrance: 080 45,810 1,256 GBIF‐gFrance: 081 45,810 1,616 GBIF‐gFrance: 082 45,810 1,976 GBIF‐gFrance: 083 45,810 2,697 GBIF‐gFrance: 084 45,829 ‐0,320 GBIF‐gFrance: 085 45,856 0,597 GBIF‐gFrance: 086 45,867 ‐1,228 GBIF‐gFrance: 087 45,873 2,827 GBIF‐gFrance: 088 45,891 ‐1,259 GBIF‐gFrance: 089 45,910 0,789 GBIF‐gFrance: 090 45,924 ‐1,057 GBIF‐gFrance: 091 45,953 0,900 GBIF‐gFrance: 092 45,983 2,851 GBIF‐gFrance: 093 45,990 2,337 GBIF‐gFrance: 094 45,990 ‐1,264 GBIF‐gFrance: 095 45,990 ‐0,904 GBIF‐gFrance: 096 45,990 0,536 GBIF‐gFrance: 097 45,990 0,900 GBIF‐gFrance: 098 45,990 1,256 GBIF‐gFrance: 099 45,990 1,616 GBIF‐gFrance: 100 46,014 ‐1,015 GBIF‐gFrance: 101 46,170 2,337 GBIF‐gFrance: 102 46,170 ‐1,264 GBIF‐gFrance: 103 46,170 ‐0,184 GBIF‐gFrance: 104 46,170 ‐0,544 GBIF‐gFrance: 105 46,170 0,536 GBIF‐gFrance: 106 46,170 0,896 GBIF‐gFrance: 107 46,170 1,256 GBIF‐gFrance: 108 46,170 2,697 GBIF‐gFrance: 109 46,268 2,567 GBIF‐gFrance: 110 46,350 2,337 GBIF‐gFrance: 111 46,350 ‐1,264 GBIF‐gFrance: 112 46,350 ‐0,544 GBIF‐gFrance: 113 46,350 ‐0,184 GBIF‐gFrance: 114 46,350 ‐0,904 GBIF‐gFrance: 115 46,350 0,896 GBIF‐gFrance: 116 46,350 1,256 GBIF‐g

France: 117 46,350 2,697 GBIF‐gFrance: 118 46,350 2,697 GBIF‐gFrance: 119 46,414 4,759 GBIF‐gFrance: 120 46,522 4,806 GBIF‐gFrance: 121 46,526 ‐0,754 GBIF‐gFrance: 122 46,530 ‐1,264 GBIF‐gFrance: 123 46,530 2,337 GBIF‐gFrance: 124 46,530 ‐0,544 GBIF‐gFrance: 125 46,530 0,896 GBIF‐gFrance: 126 46,530 0,536 GBIF‐gFrance: 127 46,530 1,256 GBIF‐gFrance: 128 46,530 1,616 GBIF‐gFrance: 129 46,530 1,976 GBIF‐gFrance: 130 46,530 2,697 GBIF‐gFrance: 131 46,530 3,057 GBIF‐gFrance: 132 46,548 4,873 GBIF‐gFrance: 133 46,561 ‐1,811 GBIF‐gFrance: 134 46,578 ‐1,615 GBIF‐gFrance: 135 46,592 ‐1,087 GBIF‐gFrance: 136 46,644 2,734 GBIF‐gFrance: 137 46,696 0,510 GBIF‐gFrance: 138 46,702 ‐1,317 GBIF‐gFrance: 139 46,710 ‐1,624 GBIF‐gFrance: 140 46,710 2,337 GBIF‐gFrance: 141 46,710 ‐1,264 GBIF‐gFrance: 142 46,710 ‐0,544 GBIF‐gFrance: 143 46,710 1,616 GBIF‐gFrance: 144 46,710 2,697 GBIF‐gFrance: 145 46,710 3,057 GBIF‐gFrance: 146 46,733 ‐1,569 GBIF‐gFrance: 147 46,856 ‐1,315 GBIF‐gFrance: 148 46,890 ‐1,624 GBIF‐gFrance: 149 46,890 ‐1,264 GBIF‐gFrance: 150 46,890 ‐0,904 GBIF‐gFrance: 151 46,890 ‐0,544 GBIF‐gFrance: 152 46,890 0,896 GBIF‐gFrance: 153 46,890 1,976 GBIF‐gFrance: 154 46,904 4,611 GBIF‐gFrance: 155 46,921 4,474 GBIF‐gFrance: 156 47,070 ‐1,264 GBIF‐gFrance: 157 47,070 ‐1,624 GBIF‐g

France: 158 47,070 ‐0,904 GBIF‐gFrance: 159 47,128 ‐1,485 GBIF‐gFrance: 160 47,250 ‐2,344 GBIF‐gFrance: 161 47,250 2,337 GBIF‐gFrance: 162 47,271 ‐1,179 GBIF‐gFrance: 163 47,276 ‐1,659 GBIF‐gFrance: 164 47,360 ‐1,497 GBIF‐gFrance: 165 47,381 ‐2,191 GBIF‐gFrance: 166 47,430 ‐2,344 GBIF‐gFrance: 167 47,430 2,337 GBIF‐gFrance: 168 47,430 ‐2,704 GBIF‐gFrance: 169 47,430 ‐1,624 GBIF‐gFrance: 170 47,430 ‐1,624 GBIF‐gFrance: 171 47,430 ‐1,264 GBIF‐gFrance: 172 47,430 ‐1,264 GBIF‐gFrance: 173 47,430 ‐0,544 GBIF‐gFrance: 174 47,445 ‐2,450 GBIF‐gFrance: 175 47,450 ‐0,060 GBIF‐gFrance: 176 47,528 ‐1,801 GBIF‐gFrance: 177 47,537 ‐1,423 GBIF‐gFrance: 178 47,543 ‐1,285 GBIF‐gFrance: 179 47,610 ‐1,984 GBIF‐gFrance: 180 47,610 2,337 GBIF‐gFrance: 181 47,610 ‐3,064 GBIF‐gFrance: 182 47,610 ‐2,704 GBIF‐gFrance: 183 47,610 1,616 GBIF‐gFrance: 184 47,610 1,976 GBIF‐gFrance: 185 47,659 ‐1,390 GBIF‐gFrance: 186 47,684 ‐3,265 GBIF‐gFrance: 187 47,788 0,396 GBIF‐gFrance: 188 47,790 ‐3,064 GBIF‐gFrance: 189 47,790 ‐3,424 GBIF‐gFrance: 190 47,790 ‐2,704 GBIF‐gFrance: 191 47,790 2,337 GBIF‐gFrance: 192 47,790 ‐0,544 GBIF‐gFrance: 193 47,790 ‐0,904 GBIF‐gFrance: 194 47,790 0,536 GBIF‐gFrance: 195 47,790 0,896 GBIF‐gFrance: 196 47,790 1,976 GBIF‐gFrance: 197 47,790 2,697 GBIF‐gFrance: 198 47,970 ‐3,424 GBIF‐g

France: 199 47,970 ‐2,704 GBIF‐gFrance: 200 47,970 ‐2,344 GBIF‐gFrance: 201 47,970 ‐1,984 GBIF‐gFrance: 202 47,970 2,337 GBIF‐gFrance: 203 47,970 ‐1,264 GBIF‐gFrance: 204 47,970 ‐0,904 GBIF‐gFrance: 205 47,970 ‐0,544 GBIF‐gFrance: 206 47,970 0,896 GBIF‐gFrance: 207 47,970 0,536 GBIF‐gFrance: 208 47,970 1,256 GBIF‐gFrance: 209 47,970 1,976 GBIF‐gFrance: 210 47,996 0,697 GBIF‐gFrance: 211 48,074 ‐0,292 GBIF‐gFrance: 212 48,150 ‐4,144 GBIF‐gFrance: 213 48,150 ‐3,064 GBIF‐gFrance: 214 48,150 ‐3,424 GBIF‐gFrance: 215 48,150 ‐3,784 GBIF‐gFrance: 216 48,150 ‐2,344 GBIF‐gFrance: 217 48,150 ‐1,264 GBIF‐gFrance: 218 48,150 ‐1,624 GBIF‐gFrance: 219 48,150 ‐0,544 GBIF‐gFrance: 220 48,150 ‐0,184 GBIF‐gFrance: 221 48,150 ‐0,904 GBIF‐gFrance: 222 48,150 0,536 GBIF‐gFrance: 223 48,330 ‐4,504 GBIF‐gFrance: 224 48,330 ‐4,144 GBIF‐gFrance: 225 48,330 ‐3,424 GBIF‐gFrance: 226 48,330 ‐3,784 GBIF‐gFrance: 227 48,330 ‐2,704 GBIF‐gFrance: 228 48,330 ‐1,264 GBIF‐gFrance: 229 48,330 ‐1,624 GBIF‐gFrance: 230 48,330 ‐0,904 GBIF‐gFrance: 231 48,330 ‐0,544 GBIF‐gFrance: 232 48,330 ‐0,184 GBIF‐gFrance: 233 48,330 2,697 GBIF‐gFrance: 234 48,400 ‐0,251 GBIF‐gFrance: 235 48,510 ‐4,144 GBIF‐gFrance: 236 48,510 ‐4,504 GBIF‐gFrance: 237 48,510 ‐3,424 GBIF‐gFrance: 238 48,510 ‐3,064 GBIF‐gFrance: 239 48,510 ‐2,344 GBIF‐g

France: 240 48,510 ‐2,704 GBIF‐gFrance: 241 48,510 ‐1,624 GBIF‐gFrance: 242 48,510 ‐0,904 GBIF‐gFrance: 243 48,510 ‐0,184 GBIF‐gFrance: 244 48,510 0,176 GBIF‐gFrance: 245 48,510 2,697 GBIF‐gFrance: 246 48,554 0,009 GBIF‐gFrance: 247 48,593 ‐0,495 GBIF‐gFrance: 248 48,637 ‐0,828 GBIF‐gFrance: 249 48,690 ‐3,064 GBIF‐gFrance: 250 48,690 ‐3,784 GBIF‐gFrance: 251 48,690 ‐2,344 GBIF‐gFrance: 252 48,690 ‐0,544 GBIF‐gFrance: 253 48,690 ‐0,904 GBIF‐gFrance: 254 48,690 ‐0,184 GBIF‐gFrance: 255 48,690 2,337 GBIF‐gFrance: 256 48,690 1,616 GBIF‐gFrance: 257 48,690 1,976 GBIF‐gFrance: 258 48,690 2,697 GBIF‐gFrance: 259 48,870 ‐1,264 GBIF‐gFrance: 260 48,870 ‐1,624 GBIF‐gFrance: 261 48,870 ‐0,544 GBIF‐gFrance: 262 48,892 ‐1,310 GBIF‐gFrance: 263 49,050 ‐0,544 GBIF‐gFrance: 264 49,050 ‐0,184 GBIF‐gFrance: 265 49,183 ‐1,515 GBIF‐gFrance: 266 49,188 ‐0,898 GBIF‐gFrance: 267 49,281 ‐1,673 GBIF‐gFrance: 268 49,326 ‐1,703 GBIF‐gPortugal: Aldeia de João Pires 40,108 ‐7,141 1Portugal: Aldeia Stª Margarida 40,051 ‐7,259 1Portugal: Areia 3 39,508 ‐7,934 1Portugal: Carepa 39,622 ‐7,771 1Portugal: Castelo Mendo 40,586 ‐6,940 1Portugal: Coimbra 40,205 ‐8,407 1Portugal: Fratel 39,618 ‐7,736 1Portugal: Gerês ‐ Carris 41,817 ‐8,035 1Portugal: Idanha‐a‐Nova 39,934 ‐7,192 1Portugal: Madeirã 39,940 ‐8,037 1Portugal: Mezio 40,939 ‐7,807 1Portugal: Mogadouro 41,341 ‐6,606 1

Portugal: Monte do Conde 40,168 ‐7,250 1Portugal: Nelas 40,488 ‐7,839 1Portugal: Nespereira de Baixo 40,760 ‐8,367 1Portugal: Pedra do Altar 39,706 ‐7,819 1Portugal: Porto 41,152 ‐8,639 1Portugal: Rebordelo 41,711 ‐7,110 1Portugal: Saide 40,450 ‐8,323 1Portugal: Santa Catarina 39,450 ‐9,009 1Portugal: Santos 39,601 ‐7,958 1Portugal: São João do Deserto 40,138 ‐7,189 1Portugal: Senhora do Almurtão 39,904 ‐7,159 1Portugal: Serra do Gerês 41,817 ‐8,035 1Portugal: Sicó 40,018 ‐8,525 1Portugal: Soure 40,018 ‐8,622 1Portugal: Torreira 40,759 ‐8,706 1Portugal: Vila Velha de Ródão 39,669 ‐7,668 1Portugal: Vilar de Boi 39,670 ‐7,735 1Portugal: 001 41,281 ‐8,729 37Portugal: 002 41,801 ‐8,693 37Portugal: 003 41,928 ‐8,680 37Portugal: 004 41,075 ‐8,655 37Portugal: 005 40,986 ‐8,642 37Portugal: 006 40,880 ‐8,631 37Portugal: 007 40,760 ‐8,594 37Portugal: 008 40,800 ‐8,591 37Portugal: 009 41,475 ‐8,585 37Portugal: 010 39,850 ‐8,588 37Portugal: 011 41,528 ‐8,562 37Portugal: 012 41,888 ‐8,549 37Portugal: 013 39,973 ‐8,517 37Portugal: 014 41,930 ‐8,496 37Portugal: 015 41,488 ‐8,493 37Portugal: 016 40,054 ‐8,492 37Portugal: 017 41,704 ‐8,464 37Portugal: 018 41,314 ‐8,462 37Portugal: 019 39,962 ‐8,470 37Portugal: 020 40,831 ‐8,463 37Portugal: 021 41,302 ‐8,449 37Portugal: 022 39,869 ‐8,454 37Portugal: 023 39,820 ‐8,443 37Portugal: 024 40,964 ‐8,432 37

Portugal: 025 41,068 ‐8,430 37Portugal: 026 40,173 ‐8,434 37Portugal: 027 41,729 ‐8,411 37Portugal: 028 39,749 ‐8,423 37Portugal: 029 39,957 ‐8,410 37Portugal: 030 40,111 ‐8,406 37Portugal: 031 40,736 ‐8,399 37Portugal: 032 39,860 ‐8,396 37Portugal: 033 40,990 ‐8,372 37Portugal: 034 41,443 ‐8,356 37Portugal: 035 40,246 ‐8,361 37Portugal: 036 41,322 ‐8,345 37Portugal: 037 40,889 ‐8,343 37Portugal: 038 39,874 ‐8,344 37Portugal: 039 39,677 ‐8,341 37Portugal: 040 41,251 ‐8,305 37Portugal: 041 41,632 ‐8,295 37Portugal: 042 40,790 ‐8,299 37Portugal: 043 41,462 ‐8,278 37Portugal: 044 39,677 ‐8,288 37Portugal: 045 40,428 ‐8,251 37Portugal: 046 39,685 ‐8,246 37Portugal: 047 40,552 ‐8,235 37Portugal: 048 41,587 ‐8,213 37Portugal: 049 40,540 ‐8,222 37Portugal: 050 39,791 ‐8,222 37Portugal: 051 41,332 ‐8,192 37Portugal: 052 40,063 ‐8,206 37Portugal: 053 41,663 ‐8,174 37Portugal: 054 40,250 ‐8,191 37Portugal: 055 40,336 ‐8,190 37Portugal: 056 39,914 ‐8,192 37Portugal: 057 40,311 ‐8,184 37Portugal: 058 41,436 ‐8,162 37Portugal: 059 40,035 ‐8,176 37Portugal: 060 40,584 ‐8,167 37Portugal: 061 41,464 ‐8,129 37Portugal: 062 39,828 ‐8,138 37Portugal: 063 40,355 ‐8,121 37Portugal: 064 40,751 ‐8,112 37Portugal: 065 41,374 ‐8,099 37






Portugal: 271 41,309 ‐6,839 37Portugal: 272 41,636 ‐6,820 37Portugal: 273 41,504 ‐6,809 37Portugal: 274 41,500 ‐6,798 37Portugal: 275 41,581 ‐6,774 37Portugal: 276 40,297 ‐6,813 37Portugal: 277 41,752 ‐6,754 37Portugal: 278 41,263 ‐6,766 37Portugal: 279 41,528 ‐6,742 37Portugal: 280 41,671 ‐6,732 37Portugal: 281 40,435 ‐6,763 37Portugal: 282 41,282 ‐6,730 37Portugal: 283 41,852 ‐6,688 37Portugal: 284 41,493 ‐6,680 37Portugal: 285 41,338 ‐6,683 37Portugal: 286 41,636 ‐6,612 37Portugal: 287 41,772 ‐6,569 37Portugal: 288 41,600 ‐6,503 37Portugal: 289 41,423 ‐6,478 37Portugal: 290 41,579 ‐6,290 37Spain: Cabreiras 42,442 ‐7,903 1Spain: El Berrueco 40,900 ‐3,883 1Spain: Mirador de la Curota 42,974 ‐8,974 1Spain: Serradilla del Llano 40,490 ‐6,340 1Spain: Santillana del Mar 43,389 ‐4,103 1Spain: 001 40,140 ‐6,830 GBIF‐rSpain: 002 40,220 ‐6,710 GBIF‐rSpain: 003 40,230 ‐6,940 GBIF‐rSpain: 004 40,230 ‐6,830 GBIF‐rSpain: 005 40,300 ‐5,770 GBIF‐rSpain: 006 40,310 ‐6,710 GBIF‐rSpain: 007 40,310 ‐6,590 GBIF‐rSpain: 008 40,310 ‐5,650 GBIF‐rSpain: 009 40,310 ‐5,530 GBIF‐rSpain: 010 40,310 ‐5,410 GBIF‐rSpain: 011 40,320 ‐6,820 GBIF‐rSpain: 012 40,320 ‐5,180 GBIF‐rSpain: 013 40,390 ‐6,110 GBIF‐rSpain: 014 40,390 ‐5,890 GBIF‐rSpain: 015 40,390 ‐5,770 GBIF‐rSpain: 016 40,400 ‐6,700 GBIF‐r

Spain: 017 40,400 ‐6,580 GBIF‐rSpain: 018 40,400 ‐6,470 GBIF‐rSpain: 019 40,400 ‐5,650 GBIF‐rSpain: 020 40,400 ‐5,530 GBIF‐rSpain: 021 40,410 ‐5,180 GBIF‐rSpain: 022 40,410 ‐5,060 GBIF‐rSpain: 023 40,480 ‐6,230 GBIF‐rSpain: 024 40,480 ‐6,110 GBIF‐rSpain: 025 40,480 ‐5,890 GBIF‐rSpain: 026 40,480 ‐5,770 GBIF‐rSpain: 027 40,490 ‐6,700 GBIF‐rSpain: 028 40,490 ‐6,580 GBIF‐rSpain: 029 40,490 ‐6,460 GBIF‐rSpain: 030 40,490 ‐5,650 GBIF‐rSpain: 031 40,490 ‐5,420 GBIF‐rSpain: 032 40,490 ‐5,300 GBIF‐rSpain: 033 40,500 ‐5,180 GBIF‐rSpain: 034 40,500 ‐5,070 GBIF‐rSpain: 035 40,500 ‐4,710 GBIF‐rSpain: 036 40,570 ‐6,220 GBIF‐rSpain: 037 40,570 ‐6,110 GBIF‐rSpain: 038 40,570 ‐5,890 GBIF‐rSpain: 039 40,570 ‐5,780 GBIF‐rSpain: 040 40,580 ‐6,580 GBIF‐rSpain: 041 40,580 ‐6,460 GBIF‐rSpain: 042 40,580 ‐5,660 GBIF‐rSpain: 043 40,580 ‐5,540 GBIF‐rSpain: 044 40,580 0,340 GBIF‐rSpain: 045 40,580 0,460 GBIF‐rSpain: 046 40,580 0,580 GBIF‐rSpain: 047 40,590 ‐5,070 GBIF‐rSpain: 048 40,590 ‐4,950 GBIF‐rSpain: 049 40,590 ‐4,830 GBIF‐rSpain: 050 40,590 ‐4,710 GBIF‐rSpain: 051 40,590 ‐4,600 GBIF‐rSpain: 052 40,600 ‐4,360 GBIF‐rSpain: 053 40,660 ‐6,220 GBIF‐rSpain: 054 40,660 ‐6,100 GBIF‐rSpain: 055 40,660 ‐5,900 GBIF‐rSpain: 056 40,660 ‐5,780 GBIF‐rSpain: 057 40,660 0,220 GBIF‐r

Spain: 058 40,670 ‐5,430 GBIF‐rSpain: 059 40,670 ‐5,310 GBIF‐rSpain: 060 40,670 0,340 GBIF‐rSpain: 061 40,670 0,460 GBIF‐rSpain: 062 40,670 0,570 GBIF‐rSpain: 063 40,680 ‐6,810 GBIF‐rSpain: 064 40,680 ‐4,950 GBIF‐rSpain: 065 40,680 ‐4,720 GBIF‐rSpain: 066 40,690 ‐4,600 GBIF‐rSpain: 067 40,690 ‐4,480 GBIF‐rSpain: 068 40,690 ‐4,240 GBIF‐rSpain: 069 40,750 ‐5,900 GBIF‐rSpain: 070 40,750 ‐5,780 GBIF‐rSpain: 071 40,750 0,220 GBIF‐rSpain: 072 40,760 ‐6,450 GBIF‐rSpain: 073 40,760 ‐6,330 GBIF‐rSpain: 074 40,760 0,330 GBIF‐rSpain: 075 40,760 0,450 GBIF‐rSpain: 076 40,760 0,570 GBIF‐rSpain: 077 40,780 ‐4,480 GBIF‐rSpain: 078 40,780 ‐4,360 GBIF‐rSpain: 079 40,840 ‐5,910 GBIF‐rSpain: 080 40,840 ‐5,790 GBIF‐rSpain: 081 40,850 ‐6,450 GBIF‐rSpain: 082 40,850 ‐6,330 GBIF‐rSpain: 083 40,850 0,330 GBIF‐rSpain: 084 40,850 0,450 GBIF‐rSpain: 085 40,850 0,570 GBIF‐rSpain: 086 40,850 0,690 GBIF‐rSpain: 087 40,870 ‐4,250 GBIF‐rSpain: 088 40,870 ‐4,130 GBIF‐rSpain: 089 40,870 ‐4,010 GBIF‐rSpain: 090 40,870 ‐3,890 GBIF‐rSpain: 091 40,880 ‐3,530 GBIF‐rSpain: 092 40,930 ‐5,910 GBIF‐rSpain: 093 40,930 ‐5,790 GBIF‐rSpain: 094 40,940 ‐5,670 GBIF‐rSpain: 095 40,940 ‐5,550 GBIF‐rSpain: 096 40,950 ‐6,800 GBIF‐rSpain: 097 40,950 ‐5,200 GBIF‐rSpain: 098 40,950 ‐5,080 GBIF‐r

Spain: 099 40,950 ‐4,960 GBIF‐rSpain: 100 40,960 ‐3,650 GBIF‐rSpain: 101 41,020 ‐6,200 GBIF‐rSpain: 102 41,020 ‐6,090 GBIF‐rSpain: 103 41,020 ‐5,910 GBIF‐rSpain: 104 41,020 ‐5,800 GBIF‐rSpain: 105 41,030 ‐6,680 GBIF‐rSpain: 106 41,030 ‐6,440 GBIF‐rSpain: 107 41,030 ‐6,320 GBIF‐rSpain: 108 41,030 ‐5,680 GBIF‐rSpain: 109 41,030 ‐5,440 GBIF‐rSpain: 110 41,030 ‐5,320 GBIF‐rSpain: 111 41,040 ‐6,800 GBIF‐rSpain: 112 41,040 ‐5,080 GBIF‐rSpain: 113 41,050 ‐4,010 GBIF‐rSpain: 114 41,050 ‐3,890 GBIF‐rSpain: 115 41,050 ‐3,770 GBIF‐rSpain: 116 41,110 ‐6,200 GBIF‐rSpain: 117 41,110 ‐6,080 GBIF‐rSpain: 118 41,110 ‐5,920 GBIF‐rSpain: 119 41,110 ‐5,800 GBIF‐rSpain: 120 41,120 ‐6,680 GBIF‐rSpain: 121 41,120 ‐6,560 GBIF‐rSpain: 122 41,120 ‐6,440 GBIF‐rSpain: 123 41,120 ‐6,320 GBIF‐rSpain: 124 41,120 ‐5,680 GBIF‐rSpain: 125 41,120 ‐5,560 GBIF‐rSpain: 126 41,120 ‐5,440 GBIF‐rSpain: 127 41,120 ‐5,320 GBIF‐rSpain: 128 41,130 ‐5,080 GBIF‐rSpain: 129 41,140 ‐3,770 GBIF‐rSpain: 130 41,150 ‐3,420 GBIF‐rSpain: 131 41,150 ‐3,300 GBIF‐rSpain: 132 41,200 ‐6,200 GBIF‐rSpain: 133 41,200 ‐6,080 GBIF‐rSpain: 134 41,200 ‐5,920 GBIF‐rSpain: 135 41,200 ‐5,800 GBIF‐rSpain: 136 41,210 ‐6,440 GBIF‐rSpain: 137 41,210 ‐6,320 GBIF‐rSpain: 138 41,210 ‐5,680 GBIF‐rSpain: 139 41,210 ‐5,330 GBIF‐r

Spain: 140 41,220 ‐5,210 GBIF‐rSpain: 141 41,220 ‐4,730 GBIF‐rSpain: 142 41,240 ‐3,420 GBIF‐rSpain: 143 41,240 ‐3,300 GBIF‐rSpain: 144 41,240 ‐3,180 GBIF‐rSpain: 145 41,290 ‐6,190 GBIF‐rSpain: 146 41,290 ‐6,070 GBIF‐rSpain: 147 41,290 ‐5,930 GBIF‐rSpain: 148 41,290 ‐5,810 GBIF‐rSpain: 149 41,300 ‐6,430 GBIF‐rSpain: 150 41,300 ‐6,310 GBIF‐rSpain: 151 41,300 ‐5,690 GBIF‐rSpain: 152 41,300 ‐5,570 GBIF‐rSpain: 153 41,310 ‐4,730 GBIF‐rSpain: 154 41,320 ‐4,610 GBIF‐rSpain: 155 41,320 ‐4,140 GBIF‐rSpain: 156 41,320 ‐4,020 GBIF‐rSpain: 157 41,320 ‐3,900 GBIF‐rSpain: 158 41,320 ‐3,660 GBIF‐rSpain: 159 41,330 ‐3,420 GBIF‐rSpain: 160 41,330 ‐3,300 GBIF‐rSpain: 161 41,330 ‐3,180 GBIF‐rSpain: 162 41,330 ‐3,060 GBIF‐rSpain: 163 41,380 ‐6,190 GBIF‐rSpain: 164 41,380 ‐6,070 GBIF‐rSpain: 165 41,380 ‐5,930 GBIF‐rSpain: 166 41,380 ‐5,810 GBIF‐rSpain: 167 41,390 ‐6,310 GBIF‐rSpain: 168 41,390 ‐5,570 GBIF‐rSpain: 169 41,400 ‐5,210 GBIF‐rSpain: 170 41,400 ‐4,970 GBIF‐rSpain: 171 41,400 ‐4,730 GBIF‐rSpain: 172 41,410 ‐4,500 GBIF‐rSpain: 173 41,410 ‐4,260 GBIF‐rSpain: 174 41,410 ‐1,620 GBIF‐rSpain: 175 41,410 2,100 GBIF‐rSpain: 176 41,420 ‐3,540 GBIF‐rSpain: 177 41,420 ‐3,420 GBIF‐rSpain: 178 41,420 ‐3,180 GBIF‐rSpain: 179 41,420 ‐2,580 GBIF‐rSpain: 180 41,470 ‐6,190 GBIF‐r

Spain: 181 41,470 ‐6,070 GBIF‐rSpain: 182 41,470 ‐5,930 GBIF‐rSpain: 183 41,470 ‐5,810 GBIF‐rSpain: 184 41,480 ‐5,690 GBIF‐rSpain: 185 41,480 ‐5,460 GBIF‐rSpain: 186 41,480 ‐5,340 GBIF‐rSpain: 187 41,480 ‐0,540 GBIF‐rSpain: 188 41,490 ‐5,100 GBIF‐rSpain: 189 41,490 ‐4,860 GBIF‐rSpain: 190 41,490 ‐4,740 GBIF‐rSpain: 191 41,500 ‐4,620 GBIF‐rSpain: 192 41,500 ‐4,500 GBIF‐rSpain: 193 41,500 ‐4,380 GBIF‐rSpain: 194 41,500 ‐4,020 GBIF‐rSpain: 195 41,500 ‐3,660 GBIF‐rSpain: 196 41,500 ‐1,980 GBIF‐rSpain: 197 41,500 ‐1,740 GBIF‐rSpain: 198 41,500 ‐1,620 GBIF‐rSpain: 199 41,500 ‐1,380 GBIF‐rSpain: 200 41,500 2,100 GBIF‐rSpain: 201 41,500 2,220 GBIF‐rSpain: 202 41,510 ‐3,540 GBIF‐rSpain: 203 41,510 ‐3,060 GBIF‐rSpain: 204 41,510 ‐2,820 GBIF‐rSpain: 205 41,510 ‐2,700 GBIF‐rSpain: 206 41,510 ‐2,580 GBIF‐rSpain: 207 41,510 ‐2,460 GBIF‐rSpain: 208 41,560 ‐6,180 GBIF‐rSpain: 209 41,560 ‐6,060 GBIF‐rSpain: 210 41,570 ‐5,460 GBIF‐rSpain: 211 41,570 ‐5,340 GBIF‐rSpain: 212 41,580 ‐5,100 GBIF‐rSpain: 213 41,580 ‐4,860 GBIF‐rSpain: 214 41,580 ‐4,740 GBIF‐rSpain: 215 41,580 ‐0,780 GBIF‐rSpain: 216 41,590 ‐4,620 GBIF‐rSpain: 217 41,590 ‐4,500 GBIF‐rSpain: 218 41,590 ‐4,380 GBIF‐rSpain: 219 41,590 ‐3,780 GBIF‐rSpain: 220 41,590 ‐3,660 GBIF‐rSpain: 221 41,590 ‐2,340 GBIF‐r

Spain: 222 41,590 1,980 GBIF‐rSpain: 223 41,590 2,100 GBIF‐rSpain: 224 41,590 2,220 GBIF‐rSpain: 225 41,590 2,340 GBIF‐rSpain: 226 41,600 ‐3,540 GBIF‐rSpain: 227 41,600 ‐3,420 GBIF‐rSpain: 228 41,600 ‐3,300 GBIF‐rSpain: 229 41,600 ‐3,180 GBIF‐rSpain: 230 41,600 ‐3,060 GBIF‐rSpain: 231 41,600 ‐2,700 GBIF‐rSpain: 232 41,600 ‐2,580 GBIF‐rSpain: 233 41,600 ‐2,460 GBIF‐rSpain: 234 41,600 2,460 GBIF‐rSpain: 235 41,600 2,580 GBIF‐rSpain: 236 41,650 ‐6,180 GBIF‐rSpain: 237 41,650 ‐6,060 GBIF‐rSpain: 238 41,660 ‐5,340 GBIF‐rSpain: 239 41,670 ‐5,220 GBIF‐rSpain: 240 41,670 ‐4,860 GBIF‐rSpain: 241 41,670 ‐4,740 GBIF‐rSpain: 242 41,670 ‐1,020 GBIF‐rSpain: 243 41,670 ‐0,900 GBIF‐rSpain: 244 41,680 ‐4,620 GBIF‐rSpain: 245 41,680 ‐3,900 GBIF‐rSpain: 246 41,680 ‐3,780 GBIF‐rSpain: 247 41,680 ‐1,980 GBIF‐rSpain: 248 41,680 1,980 GBIF‐rSpain: 249 41,680 2,100 GBIF‐rSpain: 250 41,680 2,220 GBIF‐rSpain: 251 41,690 ‐3,660 GBIF‐rSpain: 252 41,690 ‐3,540 GBIF‐rSpain: 253 41,690 ‐3,420 GBIF‐rSpain: 254 41,690 ‐3,180 GBIF‐rSpain: 255 41,690 ‐3,060 GBIF‐rSpain: 256 41,690 ‐2,820 GBIF‐rSpain: 257 41,690 ‐2,700 GBIF‐rSpain: 258 41,690 ‐2,580 GBIF‐rSpain: 259 41,690 ‐2,460 GBIF‐rSpain: 260 41,690 ‐2,340 GBIF‐rSpain: 261 41,690 2,340 GBIF‐rSpain: 262 41,690 2,460 GBIF‐r

Spain: 263 41,690 2,580 GBIF‐rSpain: 264 41,690 2,700 GBIF‐rSpain: 265 41,690 2,820 GBIF‐rSpain: 266 41,740 ‐5,830 GBIF‐rSpain: 267 41,750 ‐5,710 GBIF‐rSpain: 268 41,750 ‐5,470 GBIF‐rSpain: 269 41,750 ‐5,350 GBIF‐rSpain: 270 41,760 ‐5,110 GBIF‐rSpain: 271 41,760 ‐1,020 GBIF‐rSpain: 272 41,770 ‐3,900 GBIF‐rSpain: 273 41,770 ‐3,780 GBIF‐rSpain: 274 41,770 ‐1,980 GBIF‐rSpain: 275 41,770 ‐1,860 GBIF‐rSpain: 276 41,770 ‐1,740 GBIF‐rSpain: 277 41,770 1,980 GBIF‐rSpain: 278 41,770 2,100 GBIF‐rSpain: 279 41,770 2,220 GBIF‐rSpain: 280 41,780 ‐3,660 GBIF‐rSpain: 281 41,780 ‐3,540 GBIF‐rSpain: 282 41,780 ‐3,300 GBIF‐rSpain: 283 41,780 ‐3,180 GBIF‐rSpain: 284 41,780 ‐3,060 GBIF‐rSpain: 285 41,780 ‐2,940 GBIF‐rSpain: 286 41,780 ‐2,820 GBIF‐rSpain: 287 41,780 ‐2,700 GBIF‐rSpain: 288 41,780 ‐2,580 GBIF‐rSpain: 289 41,780 ‐2,460 GBIF‐rSpain: 290 41,780 ‐2,340 GBIF‐rSpain: 291 41,780 2,340 GBIF‐rSpain: 292 41,780 2,460 GBIF‐rSpain: 293 41,780 2,580 GBIF‐rSpain: 294 41,780 2,700 GBIF‐rSpain: 295 41,780 2,820 GBIF‐rSpain: 296 41,840 ‐6,410 GBIF‐rSpain: 297 41,840 ‐6,290 GBIF‐rSpain: 298 41,840 ‐5,590 GBIF‐rSpain: 299 41,840 ‐5,470 GBIF‐rSpain: 300 41,850 ‐5,110 GBIF‐rSpain: 301 41,850 ‐4,990 GBIF‐rSpain: 302 41,850 ‐4,870 GBIF‐rSpain: 303 41,860 ‐8,100 GBIF‐r

Spain: 304 41,860 ‐7,490 GBIF‐rSpain: 305 41,860 ‐7,370 GBIF‐rSpain: 306 41,860 ‐4,630 GBIF‐rSpain: 307 41,860 ‐3,900 GBIF‐rSpain: 308 41,860 ‐3,780 GBIF‐rSpain: 309 41,860 ‐2,220 GBIF‐rSpain: 310 41,860 ‐2,100 GBIF‐rSpain: 311 41,860 ‐1,980 GBIF‐rSpain: 312 41,860 ‐1,860 GBIF‐rSpain: 313 41,860 ‐1,730 GBIF‐rSpain: 314 41,860 ‐1,610 GBIF‐rSpain: 315 41,860 1,980 GBIF‐rSpain: 316 41,860 2,100 GBIF‐rSpain: 317 41,860 2,220 GBIF‐rSpain: 318 41,870 ‐3,660 GBIF‐rSpain: 319 41,870 ‐3,420 GBIF‐rSpain: 320 41,870 ‐3,300 GBIF‐rSpain: 321 41,870 ‐3,180 GBIF‐rSpain: 322 41,870 ‐3,060 GBIF‐rSpain: 323 41,870 ‐2,940 GBIF‐rSpain: 324 41,870 ‐2,820 GBIF‐rSpain: 325 41,870 ‐2,700 GBIF‐rSpain: 326 41,870 ‐2,580 GBIF‐rSpain: 327 41,870 ‐2,460 GBIF‐rSpain: 328 41,870 ‐2,340 GBIF‐rSpain: 329 41,870 2,340 GBIF‐rSpain: 330 41,870 2,460 GBIF‐rSpain: 331 41,870 2,580 GBIF‐rSpain: 332 41,870 2,700 GBIF‐rSpain: 333 41,870 2,820 GBIF‐rSpain: 334 41,870 2,940 GBIF‐rSpain: 335 41,870 3,060 GBIF‐rSpain: 336 41,870 3,180 GBIF‐rSpain: 337 41,920 ‐6,170 GBIF‐rSpain: 338 41,930 ‐6,530 GBIF‐rSpain: 339 41,930 ‐6,290 GBIF‐rSpain: 340 41,930 ‐0,650 GBIF‐rSpain: 341 41,940 ‐4,990 GBIF‐rSpain: 342 41,940 ‐4,750 GBIF‐rSpain: 343 41,950 ‐8,100 GBIF‐rSpain: 344 41,950 ‐7,970 GBIF‐r

Spain: 345 41,950 ‐7,850 GBIF‐rSpain: 346 41,950 ‐7,730 GBIF‐rSpain: 347 41,950 ‐7,490 GBIF‐rSpain: 348 41,950 ‐7,370 GBIF‐rSpain: 349 41,950 ‐4,510 GBIF‐rSpain: 350 41,950 ‐3,900 GBIF‐rSpain: 351 41,950 ‐3,780 GBIF‐rSpain: 352 41,950 ‐2,100 GBIF‐rSpain: 353 41,950 ‐1,970 GBIF‐rSpain: 354 41,950 ‐1,850 GBIF‐rSpain: 355 41,950 2,100 GBIF‐rSpain: 356 41,950 2,220 GBIF‐rSpain: 357 41,960 ‐8,820 GBIF‐rSpain: 358 41,960 ‐3,660 GBIF‐rSpain: 359 41,960 ‐3,540 GBIF‐rSpain: 360 41,960 ‐3,420 GBIF‐rSpain: 361 41,960 ‐3,300 GBIF‐rSpain: 362 41,960 ‐3,180 GBIF‐rSpain: 363 41,960 ‐3,060 GBIF‐rSpain: 364 41,960 ‐2,940 GBIF‐rSpain: 365 41,960 ‐2,820 GBIF‐rSpain: 366 41,960 ‐2,700 GBIF‐rSpain: 367 41,960 ‐2,580 GBIF‐rSpain: 368 41,960 ‐2,460 GBIF‐rSpain: 369 41,960 ‐2,340 GBIF‐rSpain: 370 41,960 2,340 GBIF‐rSpain: 371 41,960 2,460 GBIF‐rSpain: 372 41,960 2,580 GBIF‐rSpain: 373 41,960 2,820 GBIF‐rSpain: 374 41,960 2,940 GBIF‐rSpain: 375 41,960 3,060 GBIF‐rSpain: 376 41,960 3,180 GBIF‐rSpain: 377 42,010 ‐6,040 GBIF‐rSpain: 378 42,020 ‐6,640 GBIF‐rSpain: 379 42,020 ‐6,400 GBIF‐rSpain: 380 42,020 ‐5,720 GBIF‐rSpain: 381 42,030 ‐7,130 GBIF‐rSpain: 382 42,030 ‐7,010 GBIF‐rSpain: 383 42,030 ‐6,770 GBIF‐rSpain: 384 42,030 ‐5,230 GBIF‐rSpain: 385 42,030 ‐4,990 GBIF‐r

Spain: 386 42,030 ‐4,870 GBIF‐rSpain: 387 42,030 ‐4,750 GBIF‐rSpain: 388 42,040 ‐7,970 GBIF‐rSpain: 389 42,040 ‐7,730 GBIF‐rSpain: 390 42,040 ‐7,610 GBIF‐rSpain: 391 42,040 ‐4,510 GBIF‐rSpain: 392 42,040 ‐4,030 GBIF‐rSpain: 393 42,040 ‐3,910 GBIF‐rSpain: 394 42,040 ‐3,790 GBIF‐rSpain: 395 42,040 ‐1,850 GBIF‐rSpain: 396 42,040 ‐1,730 GBIF‐rSpain: 397 42,040 ‐1,610 GBIF‐rSpain: 398 42,040 ‐1,490 GBIF‐rSpain: 399 42,040 1,850 GBIF‐rSpain: 400 42,040 2,210 GBIF‐rSpain: 401 42,050 ‐8,820 GBIF‐rSpain: 402 42,050 ‐8,700 GBIF‐rSpain: 403 42,050 ‐8,580 GBIF‐rSpain: 404 42,050 ‐3,660 GBIF‐rSpain: 405 42,050 ‐3,540 GBIF‐rSpain: 406 42,050 ‐3,420 GBIF‐rSpain: 407 42,050 ‐3,300 GBIF‐rSpain: 408 42,050 ‐3,180 GBIF‐rSpain: 409 42,050 ‐3,060 GBIF‐rSpain: 410 42,050 ‐2,940 GBIF‐rSpain: 411 42,050 ‐2,820 GBIF‐rSpain: 412 42,050 ‐2,700 GBIF‐rSpain: 413 42,050 ‐2,580 GBIF‐rSpain: 414 42,050 ‐2,460 GBIF‐rSpain: 415 42,050 ‐2,340 GBIF‐rSpain: 416 42,050 2,340 GBIF‐rSpain: 417 42,050 2,460 GBIF‐rSpain: 418 42,050 2,580 GBIF‐rSpain: 419 42,050 2,700 GBIF‐rSpain: 420 42,050 2,820 GBIF‐rSpain: 421 42,050 3,060 GBIF‐rSpain: 422 42,050 3,180 GBIF‐rSpain: 423 42,100 ‐6,040 GBIF‐rSpain: 424 42,100 ‐5,960 GBIF‐rSpain: 425 42,110 ‐6,640 GBIF‐rSpain: 426 42,110 ‐6,520 GBIF‐r

Spain: 427 42,110 ‐6,400 GBIF‐rSpain: 428 42,110 ‐5,360 GBIF‐rSpain: 429 42,120 ‐6,880 GBIF‐rSpain: 430 42,120 ‐6,760 GBIF‐rSpain: 431 42,120 ‐4,750 GBIF‐rSpain: 432 42,120 ‐1,000 GBIF‐rSpain: 433 42,120 ‐0,880 GBIF‐rSpain: 434 42,130 ‐8,090 GBIF‐rSpain: 435 42,130 ‐7,970 GBIF‐rSpain: 436 42,130 ‐7,730 GBIF‐rSpain: 437 42,130 ‐4,510 GBIF‐rSpain: 438 42,130 ‐4,150 GBIF‐rSpain: 439 42,130 ‐4,030 GBIF‐rSpain: 440 42,130 ‐3,790 GBIF‐rSpain: 441 42,130 ‐2,090 GBIF‐rSpain: 442 42,130 1,850 GBIF‐rSpain: 443 42,130 1,970 GBIF‐rSpain: 444 42,130 2,090 GBIF‐rSpain: 445 42,140 ‐8,820 GBIF‐rSpain: 446 42,140 ‐8,700 GBIF‐rSpain: 447 42,140 ‐8,580 GBIF‐rSpain: 448 42,140 ‐3,670 GBIF‐rSpain: 449 42,140 ‐3,540 GBIF‐rSpain: 450 42,140 ‐3,420 GBIF‐rSpain: 451 42,140 ‐3,300 GBIF‐rSpain: 452 42,140 ‐3,180 GBIF‐rSpain: 453 42,140 ‐3,060 GBIF‐rSpain: 454 42,140 ‐2,700 GBIF‐rSpain: 455 42,140 2,330 GBIF‐rSpain: 456 42,140 2,460 GBIF‐rSpain: 457 42,140 2,580 GBIF‐rSpain: 458 42,140 2,700 GBIF‐rSpain: 459 42,140 2,820 GBIF‐rSpain: 460 42,140 2,940 GBIF‐rSpain: 461 42,190 ‐6,270 GBIF‐rSpain: 462 42,190 ‐6,150 GBIF‐rSpain: 463 42,190 ‐6,030 GBIF‐rSpain: 464 42,190 ‐5,970 GBIF‐rSpain: 465 42,200 ‐6,640 GBIF‐rSpain: 466 42,200 ‐5,360 GBIF‐rSpain: 467 42,210 ‐7,240 GBIF‐r

Spain: 468 42,210 ‐7,000 GBIF‐rSpain: 469 42,210 ‐6,880 GBIF‐rSpain: 470 42,210 ‐6,760 GBIF‐rSpain: 471 42,210 ‐5,240 GBIF‐rSpain: 472 42,210 ‐5,120 GBIF‐rSpain: 473 42,210 ‐4,880 GBIF‐rSpain: 474 42,210 ‐4,760 GBIF‐rSpain: 475 42,210 ‐1,120 GBIF‐rSpain: 476 42,210 ‐1,000 GBIF‐rSpain: 477 42,210 ‐0,880 GBIF‐rSpain: 478 42,210 ‐0,760 GBIF‐rSpain: 479 42,220 ‐7,970 GBIF‐rSpain: 480 42,220 ‐7,730 GBIF‐rSpain: 481 42,220 ‐7,610 GBIF‐rSpain: 482 42,220 ‐7,360 GBIF‐rSpain: 483 42,220 ‐4,640 GBIF‐rSpain: 484 42,220 ‐4,030 GBIF‐rSpain: 485 42,220 ‐3,910 GBIF‐rSpain: 486 42,220 ‐1,850 GBIF‐rSpain: 487 42,220 ‐1,730 GBIF‐rSpain: 488 42,220 ‐1,610 GBIF‐rSpain: 489 42,230 ‐8,700 GBIF‐rSpain: 490 42,230 ‐3,670 GBIF‐rSpain: 491 42,230 ‐3,300 GBIF‐rSpain: 492 42,230 ‐3,180 GBIF‐rSpain: 493 42,230 ‐3,060 GBIF‐rSpain: 494 42,230 ‐2,700 GBIF‐rSpain: 495 42,230 ‐2,580 GBIF‐rSpain: 496 42,230 ‐2,450 GBIF‐rSpain: 497 42,230 ‐2,330 GBIF‐rSpain: 498 42,230 2,450 GBIF‐rSpain: 499 42,230 2,580 GBIF‐rSpain: 500 42,230 2,700 GBIF‐rSpain: 501 42,230 2,820 GBIF‐rSpain: 502 42,230 2,940 GBIF‐rSpain: 503 42,230 3,060 GBIF‐rSpain: 504 42,230 3,180 GBIF‐rSpain: 505 42,280 ‐5,850 GBIF‐rSpain: 506 42,290 ‐6,630 GBIF‐rSpain: 507 42,300 ‐7,240 GBIF‐rSpain: 508 42,300 ‐7,000 GBIF‐r

Spain: 509 42,300 ‐6,880 GBIF‐rSpain: 510 42,300 ‐6,760 GBIF‐rSpain: 511 42,300 ‐5,240 GBIF‐rSpain: 512 42,300 ‐5,120 GBIF‐rSpain: 513 42,300 ‐1,240 GBIF‐rSpain: 514 42,300 ‐1,000 GBIF‐rSpain: 515 42,300 ‐0,880 GBIF‐rSpain: 516 42,300 ‐0,760 GBIF‐rSpain: 517 42,310 ‐8,210 GBIF‐rSpain: 518 42,310 ‐8,090 GBIF‐rSpain: 519 42,310 ‐7,970 GBIF‐rSpain: 520 42,310 ‐7,850 GBIF‐rSpain: 521 42,310 ‐7,480 GBIF‐rSpain: 522 42,310 ‐7,360 GBIF‐rSpain: 523 42,310 ‐3,910 GBIF‐rSpain: 524 42,310 ‐3,790 GBIF‐rSpain: 525 42,310 ‐2,210 GBIF‐rSpain: 526 42,310 ‐1,970 GBIF‐rSpain: 527 42,310 ‐1,850 GBIF‐rSpain: 528 42,310 ‐1,600 GBIF‐rSpain: 529 42,310 ‐1,480 GBIF‐rSpain: 530 42,310 ‐1,360 GBIF‐rSpain: 531 42,320 ‐8,820 GBIF‐rSpain: 532 42,320 ‐8,700 GBIF‐rSpain: 533 42,320 ‐8,580 GBIF‐rSpain: 534 42,320 ‐8,450 GBIF‐rSpain: 535 42,320 ‐8,330 GBIF‐rSpain: 536 42,320 ‐3,670 GBIF‐rSpain: 537 42,320 ‐3,550 GBIF‐rSpain: 538 42,320 ‐3,420 GBIF‐rSpain: 539 42,320 ‐3,300 GBIF‐rSpain: 540 42,320 ‐3,180 GBIF‐rSpain: 541 42,320 ‐3,060 GBIF‐rSpain: 542 42,320 ‐2,820 GBIF‐rSpain: 543 42,320 ‐2,700 GBIF‐rSpain: 544 42,320 ‐2,330 GBIF‐rSpain: 545 42,320 2,330 GBIF‐rSpain: 546 42,320 2,580 GBIF‐rSpain: 547 42,320 2,700 GBIF‐rSpain: 548 42,320 2,820 GBIF‐rSpain: 549 42,320 2,940 GBIF‐r

Spain: 550 42,320 3,060 GBIF‐rSpain: 551 42,320 3,180 GBIF‐rSpain: 552 42,320 3,300 GBIF‐rSpain: 553 42,370 ‐6,270 GBIF‐rSpain: 554 42,370 ‐5,980 GBIF‐rSpain: 555 42,370 ‐5,730 GBIF‐rSpain: 556 42,380 ‐5,610 GBIF‐rSpain: 557 42,380 ‐5,370 GBIF‐rSpain: 558 42,390 ‐7,240 GBIF‐rSpain: 559 42,390 ‐7,120 GBIF‐rSpain: 560 42,390 ‐6,870 GBIF‐rSpain: 561 42,390 ‐5,250 GBIF‐rSpain: 562 42,390 ‐5,000 GBIF‐rSpain: 563 42,390 ‐1,240 GBIF‐rSpain: 564 42,390 ‐1,120 GBIF‐rSpain: 565 42,390 ‐1,000 GBIF‐rSpain: 566 42,390 ‐0,870 GBIF‐rSpain: 567 42,390 ‐0,750 GBIF‐rSpain: 568 42,400 ‐7,850 GBIF‐rSpain: 569 42,400 ‐4,640 GBIF‐rSpain: 570 42,400 ‐4,280 GBIF‐rSpain: 571 42,400 ‐4,150 GBIF‐rSpain: 572 42,400 ‐4,030 GBIF‐rSpain: 573 42,400 ‐3,790 GBIF‐rSpain: 574 42,400 ‐2,210 GBIF‐rSpain: 575 42,400 ‐2,090 GBIF‐rSpain: 576 42,400 ‐1,970 GBIF‐rSpain: 577 42,400 ‐1,850 GBIF‐rSpain: 578 42,400 ‐1,720 GBIF‐rSpain: 579 42,400 ‐1,600 GBIF‐rSpain: 580 42,400 ‐1,480 GBIF‐rSpain: 581 42,400 ‐1,360 GBIF‐rSpain: 582 42,410 ‐8,820 GBIF‐rSpain: 583 42,410 ‐8,700 GBIF‐rSpain: 584 42,410 ‐8,570 GBIF‐rSpain: 585 42,410 ‐8,450 GBIF‐rSpain: 586 42,410 ‐3,670 GBIF‐rSpain: 587 42,410 ‐3,550 GBIF‐rSpain: 588 42,410 ‐3,300 GBIF‐rSpain: 589 42,410 ‐3,180 GBIF‐rSpain: 590 42,410 ‐3,060 GBIF‐r

Spain: 591 42,410 ‐2,700 GBIF‐rSpain: 592 42,410 ‐2,450 GBIF‐rSpain: 593 42,410 2,820 GBIF‐rSpain: 594 42,410 2,940 GBIF‐rSpain: 595 42,410 3,060 GBIF‐rSpain: 596 42,460 ‐6,140 GBIF‐rSpain: 597 42,460 ‐5,860 GBIF‐rSpain: 598 42,460 ‐5,740 GBIF‐rSpain: 599 42,470 ‐5,490 GBIF‐rSpain: 600 42,470 ‐5,370 GBIF‐rSpain: 601 42,470 ‐0,630 GBIF‐rSpain: 602 42,480 ‐7,110 GBIF‐rSpain: 603 42,480 ‐6,990 GBIF‐rSpain: 604 42,480 ‐6,870 GBIF‐rSpain: 605 42,480 ‐6,750 GBIF‐rSpain: 606 42,480 ‐5,250 GBIF‐rSpain: 607 42,480 ‐4,760 GBIF‐rSpain: 608 42,480 ‐1,240 GBIF‐rSpain: 609 42,480 ‐1,110 GBIF‐rSpain: 610 42,480 ‐0,990 GBIF‐rSpain: 611 42,490 ‐8,210 GBIF‐rSpain: 612 42,490 ‐8,090 GBIF‐rSpain: 613 42,490 ‐7,840 GBIF‐rSpain: 614 42,490 ‐4,280 GBIF‐rSpain: 615 42,490 ‐4,160 GBIF‐rSpain: 616 42,490 ‐3,790 GBIF‐rSpain: 617 42,490 ‐2,210 GBIF‐rSpain: 618 42,490 ‐2,090 GBIF‐rSpain: 619 42,490 ‐1,840 GBIF‐rSpain: 620 42,490 ‐1,720 GBIF‐rSpain: 621 42,490 ‐1,600 GBIF‐rSpain: 622 42,490 ‐1,480 GBIF‐rSpain: 623 42,490 ‐1,360 GBIF‐rSpain: 624 42,500 ‐8,820 GBIF‐rSpain: 625 42,500 ‐8,700 GBIF‐rSpain: 626 42,500 ‐8,330 GBIF‐rSpain: 627 42,500 ‐3,300 GBIF‐rSpain: 628 42,500 ‐2,940 GBIF‐rSpain: 629 42,500 ‐2,700 GBIF‐rSpain: 630 42,500 ‐2,450 GBIF‐rSpain: 631 42,500 ‐2,330 GBIF‐r








Spain: 919 43,480 ‐7,830 GBIF‐rSpain: 920 43,480 ‐7,580 GBIF‐rSpain: 921 43,490 ‐8,320 GBIF‐rSpain: 922 43,490 ‐8,200 GBIF‐rSpain: 923 43,490 ‐3,560 GBIF‐rSpain: 924 43,540 ‐6,090 GBIF‐rSpain: 925 43,550 ‐6,590 GBIF‐rSpain: 926 43,550 ‐5,660 GBIF‐rSpain: 927 43,550 ‐5,410 GBIF‐rSpain: 928 43,560 ‐7,080 GBIF‐rSpain: 929 43,560 ‐6,960 GBIF‐rSpain: 930 43,560 ‐6,710 GBIF‐rSpain: 931 43,570 ‐8,070 GBIF‐rSpain: 932 43,580 ‐8,320 GBIF‐rSpain: 933 43,580 ‐8,190 GBIF‐rSpain: 934 43,630 ‐5,790 GBIF‐rSpain: 935 43,660 ‐7,820 GBIF‐r

T. pygmaeusPortugal: Alqueidão 39,536 ‐8,575 1Portugal: Assentiz ‐ Fungalvaz 39,591 ‐8,507 1Portugal: Calvão 40,469 ‐8,703 1Portugal: Esperança 39,168 ‐7,176 1Portugal: Gavião 1 39,473 ‐7,937 1Portugal: Gavião 2 39,455 ‐7,926 1Portugal: Granja 38,293 ‐7,253 1Portugal: Mira 40,392 ‐8,786 1Portugal: Mitra 38,536 ‐8,001 1Portugal: Mora 38,958 ‐8,141 1Portugal: Mourão 38,408 ‐7,310 1Portugal: Nisa 39,550 ‐7,589 1Portugal: Praia de Vagueira 40,572 ‐8,754 1Portugal: Quiaios 40,240 ‐8,800 1Portugal: Rexaldia 2 39,573 ‐8,527 1Portugal: Rio Maior 39,341 ‐8,909 1Portugal: Rosmaninhal 39,751 ‐7,092 1Portugal: Serra de Stº António 39,509 ‐8,724 1Portugal: Sintra 1 38,790 ‐9,422 1Portugal: Sintra 2 38,786 ‐9,386 1Portugal: Sovrete 39,268 ‐7,284 1Portugal: Tomar 39,603 ‐8,417 1

Portugal: Valado dos Frades 39,592 ‐9,006 1Portugal: Velada 39,575 ‐7,701 1Portugal: Vila do Bispo 37,076 ‐8,906 1Portugal: Zebreira 39,926 ‐7,089 1Portugal: Zebreira West 39,839 ‐7,110 1Portugal: Mil Brejos 2 38,129 ‐8,253 1Portugal: 001 39,071 ‐9,282 37Portugal: 002 39,055 ‐9,227 37Portugal: 003 39,421 ‐9,227 37Portugal: 004 39,385 ‐9,203 37Portugal: 005 39,066 ‐9,041 37Portugal: 006 39,909 ‐8,881 37Portugal: 007 38,731 ‐8,845 37Portugal: 008 40,085 ‐8,841 37Portugal: 009 38,793 ‐8,834 37Portugal: 010 40,355 ‐8,817 37Portugal: 011 40,254 ‐8,816 37Portugal: 012 40,379 ‐8,807 37Portugal: 013 38,920 ‐8,786 37Portugal: 014 39,318 ‐8,777 37Portugal: 015 39,997 ‐8,765 37Portugal: 016 40,482 ‐8,736 37Portugal: 017 39,858 ‐8,729 37Portugal: 018 39,706 ‐8,720 37Portugal: 019 40,306 ‐8,708 37Portugal: 020 39,926 ‐8,708 37Portugal: 021 40,403 ‐8,698 37Portugal: 022 38,299 ‐8,692 37Portugal: 023 38,396 ‐8,661 37Portugal: 024 39,711 ‐8,644 37Portugal: 025 38,399 ‐8,607 37Portugal: 026 38,114 ‐8,603 37Portugal: 027 40,438 ‐8,588 37Portugal: 028 38,589 ‐8,589 37Portugal: 029 39,754 ‐8,578 37Portugal: 030 38,182 ‐8,577 37Portugal: 031 38,074 ‐8,573 37Portugal: 032 38,134 ‐8,563 37Portugal: 033 38,038 ‐8,556 37Portugal: 034 38,106 ‐8,536 37Portugal: 035 39,139 ‐8,508 37


Portugal: 077 39,445 ‐7,331 37Portugal: 078 39,704 ‐7,314 37Portugal: 079 39,848 ‐7,167 37Portugal: 080 39,697 ‐7,165 37Portugal: 081 39,721 ‐7,101 37Portugal: 082 39,839 ‐6,967 37Portugal: 083 40,013 ‐6,957 37Portugal: 084 39,962 ‐6,932 37Portugal: 085 40,007 ‐6,908 37Spain: Aceitunas 40,151 ‐6,321 1Spain: Alcuescar 39,087 ‐6,358 1Spain: Archidona Loja 37,100 ‐4,383 1Spain: Barrancos ‐ Aroche 2 38,070 ‐6,935 1Spain: Calanas 2 37,654 ‐6,863 1Spain: Canamero ‐ Grosan 39,343 ‐5,454 1Spain: Casar de Palomero 1 40,274 ‐6,236 1Spain: Cerra Hierro 37,953 ‐5,623 1Spain: Donana 36,851 ‐6,380 1Spain: El Bronco 40,209 ‐6,326 1Spain: El Portil 37,245 ‐7,097 1Spain: El Villar 37,210 ‐6,703 1Spain: Embalse de Aracena 37,924 ‐6,486 1Spain: Estacion de Belalcazar ‐ Sta Eufamia 38,654 ‐5,012 1Spain: Llerena 38,138 ‐6,018 1Spain: Llerena ‐ Higueira de la Serena 38,668 ‐5,679 1Spain: Pedro Muñoz 3 40,255 ‐6,341 1Spain: Puerto de Galiz 36,683 ‐5,450 1Spain: Rio Alberite 36,400 ‐5,650 1Spain: Stª Cruz de Paniagua 40,191 ‐6,327 1Spain: Ste Barbara 37,789 ‐7,217 1Spain: Venta del Charco 38,200 ‐4,267 1Spain: Villalba 40,633 ‐4,000 1Spain: 001 36,240 ‐6,050 GBIF‐rSpain: 002 36,610 ‐6,260 GBIF‐rSpain: 003 36,700 ‐6,150 GBIF‐rSpain: 004 36,520 ‐6,260 GBIF‐rSpain: 005 36,330 ‐6,050 GBIF‐rSpain: 006 36,420 ‐6,040 GBIF‐rSpain: 007 36,510 ‐6,040 GBIF‐rSpain: 008 36,520 ‐6,150 GBIF‐rSpain: 009 36,970 ‐6,470 GBIF‐r











Spain: 420 40,600 ‐4,000 GBIF‐rSpain: 421 40,600 ‐3,890 GBIF‐rSpain: 422 40,780 ‐3,890 GBIF‐rSpain: 423 40,690 ‐3,890 GBIF‐r

References:1: Personal database2: Tzankov N, Stoyanov A (2008) Triturus cristatus (Laurenti, 1768): a new species for Bulgaria from its southernmost known localities. Salamandra 44: 153‐3: Gherghel I, Iftime A (2009) On the presence of the Danube crested newt, Triturus dobrogicus , at Durankulak Lake, Bulgaria. North‐West J Zool 5: 209‐213.4: Litvinchuk SN, Borkin LJ (2009) Evolution, systematic and distribution of crested newts ( Triturus cristatus complex) in Russia and adjacent countries (Evrop5: N. Tzankov (in litt.)6: Sotiropoulos K, Legakis A, Polymeni R‐M (1995) A review of the knowledge on the distribution of the genus Triturus (Rafinesque, 1815) in Greece (Caudata7: Collection University of Belgrade8: Arntzen JW (2003) Triturus cristatus superspecies‐crested newt superspecies [German: Triturus cristatus Superspecies‐Kammolch‐Artenkreis]. Handbuch d9: Collection Adnan Menderes University10: Özeti N, Yilmaz I (1994) Tiirkiye Amfibiliri. The Amphibians of Turkey (Izmir).11: Schmidler JJ, Schmidtler JF (1967) Über die Verbreitung der Molchgattung Triturus in Kleinasien. Salamandra 3: 15‐35.12: Mikulíček P, Horák A, Zavadil V, Kautman J, Piálek J (2012) Hybridization between three crested newt species (Triturus cristatus superspecies) in the Czec13: Çiçek K, Ayaz D (2011) New data on facultive paedomorphism of the smooth newt, Lissotriton vulgaris , in Western Anatolia, Turkey. J Freshw Ecol 26: 9914: J.F. Schmidtler (in litt.)15: I. Haxhiu (in litt.)16: Arntzen JW, Wallis GP (1999) Geographic variation and taxonomy of crested newts (Triturus cristatus superspecies): Morphological and mitochondrial da17: Denoël M (2004) Distribution and characteristics of aquatic habitats of newts and Yellow‐bellied Toads in the district of Ioannina (Epirus, Greece). Herpet18: K. Sotiropoulos (in litt.)19: Ćirović R, Vukov TD, Radović D, Džukić G, Kalezić ML (2008) Distribution patterns and environmental determinants of European newts in the Montenegrin20: J. Crnobrnja‐Isailović (in litt.)21: Scillitani G, Picariello O (2000) Genetic variation and its causes in the crested newt, Triturus carnifex (Laurenti, 1768), from Italy (Caudata: Salamandridae22: W. Beukema (in litt.)23: D. Canestrelli (in litt.)24: Maletzky A, Mikulíčel P, Franzen M, Goldschmid A, Gruber H‐J, Horák A, Kyek M (2008) Hybridization and introgression between two species of crested ne25: Mikulíček P, Crnobrnja‐Isailović J, Piálek J (2007) Can microsatellite markers resolve phylogenetic relationships between closely related crested newt spec26: Zavadil V, Piálek J, Klepsch L (1994) Extension of the known range of Triturus dobrogicus : electrophoretic and morphological evidence for presence in the27: J. Vörös (in litt.)28: Demirsoy A (1996) Tiikiye Omurglilari, Amfibiler (Meteksan, Ankara).29: Sparreboom M, Arntzen JW (1987) Über die Amphibien in der Umgebung von Adapazari, Türkei. Herpetofauna 9: 27‐34.30: Kami HG (1997) Rediscovery of the Southern Crested Newt, Triturus (cristatus ) karelini (Salamandridae), from its easternmost locality in Iran. Zool Midd31: B. Safaei (in litt.)32: O. Mozaffari (in litt.)33: H.G. Kami (in litt.)34: Tarkhnishvili DN, Gokhelashvili RK (1999) The amphibians of the Caucasus (Pensoft, Sofia).35: Kuzmin SL (1999) The amphibians of the former Soviet Union (Pensoft, Sofia).

36: P. Mikulíček (in litt.)37: Loureiro A, Ferrand N, Carretero MA, Paulo OS (2008) Atlas dos Anfíbios e Répteis de Portugal (Instituto da Conservação da Natureza e da BiodiversidadeGBIF refers to data obtained via the GBIF Data Portal (data.gbif.org) on 2010‐01‐01, originally supplied by the following data providers:a: UCM = University of Colorado Museum of Natural Historyb: MVZ = Museum of Vertebrate Zoology Berkeleyc: KU = University of Kansas Biodiversity Research Centerd: USNM = Smithsonian National Museum of Natural Historye: UK‐NBN = UK National Biodiversity Networkf: MNHNL = Musée National d'Histoire Naturelle Luxembourgg: SPN‐MNHN = Service du Patrimoine naturel, Muséum national d'Histoire naturelle, Parish: GEO = GEO‐Tag der Artenvielfalti: SMNS = Staatliches Museum für Naturkunde Stuttgartj: ZMUC = Zoological Museum Uiversity of Copenhagenk: VM = Natural History Museum, University of Oslol: EM = Museum of Evolution, Uppsalam: GNM = Gothenburg Natural History Museumn: MZLU = Lund Museum of Zoologyo: MNCN = Museo Nacional de Ciencias Naturales, Madridp: IO‐PAS = Institute of Nature Conservation, Polish Academy of Sciencesq: NRM = Swedish Museum of Natural Historyr: ARM‐INB‐2007‐Anfibios = Ministerio de Medio Ambiente, y Medio Rural y Marino. Dirección General de Medio Natural y Política Forestal. Inventario Nacio

161..peisky Dom Press, St. Petersburg).

a: Salamandridae). Herpetozoa 8: 25‐34.

der Reptilien und Amphibien Europas. Schwanzlurche IIA , eds Grossenbacher K & Thiesmeier B (Aula‐Verlag, Wiebelsheim), pp 421‐514.

ch Republic and Slovakia: comparison of nuclear markers and mitochondrial DNA. Fol Zool 61: 202‐218 9‐103.

ata. Contrib Zool 68: 181‐203.tozoa 17: 49‐64.

n karst area. Biologia 63: 745‐752.

e). Herpetologica 56: 119‐130.

ewts ( Triturus cristatus and T. carnifex ) along contact zones in Germany and Austria: morphological and molecular data. Herpetol J 18: 1‐15.cies (Triturus cristatus superspecies)? Amphib‐Reptilia 28: 467‐474e Czech Republic. Amphib‐Reptilia 15: 329‐335.

dle East 15: 37‐40.

e, Lisboa)

onal de Biodiversidad 2007, Anfibios

Additional file 3. Haplotype list.

Haplotype GenBank Accession Numbers Alternative haplotype code in original publication (if applicable)

Tcri01 GU982383Tcri02 GU982384Tcri03 JQ653320Tcri04 JQ653321Tcri05 JQ653322Tcri06 JQ653323Tcri07 JQ653324Tcri09 JQ653367Tcri10 JQ653368Tcri11 JQ653369Tcri12 JQ653370Tcri13 JQ653371Tcri14 JQ653372Tcri15 JQ653373Tcri16 JQ653374Tcri17 JQ653375Tcri18 JQ653376Tcri19 JQ653377Tcri20 JQ653326Tcri21 JQ653327Tcri22 JQ653378Tcri23 JQ653379Tcri24 JQ653380Tcri25 JQ653328Tcri26 JQ653381Tcri27 JQ653382Tcri28 JQ653383Tcri29 JQ653384Tcri30 JQ653385Tcri31 JQ653386Tcri32 JQ653387Tcri33 JX860853Tcri34 JX860854

Tcri35 JX860855Tcri36 JX860856Tcri37 JX860857Tcri38 JX860858Tcri39 JX860859Tcri40 JX860860Tcri41 JX860861Tcar01 GU982385Tcar02 GU982386Tcar03 JQ653329Tcar04 JQ653330Tcar05 JQ653331Tcar06 JQ653332Tcar07 JQ653333Tcar08 JQ653334Tcar11 JQ653335Tcar12 JQ653336Tcar13 JQ653390Tcar14 JQ653337Tcar15 JQ598120 N2Tcar16 JQ598121 C1Tcar17 JQ598122 C2Tcar18 JQ598123 C3Tcar19 JQ598125 C5Tcar20 JQ598128 C8Tcar21 JQ598130 C10Tcar22 JQ598132 C12Tcar23 JQ598135 S2Tcar24 JQ598139 S6Tcar25 JQ598140 S7Tcar26 JQ598141 S8Tcar27 JQ598142 S9Tcar28 JQ598145 S13Tcar29 JQ598146 S14Tcar30 JQ598152 S20Tcar31 JQ598153 S21Tcar32 JQ598159 S27

Tcar33 JQ598160 S28Tcar34 JQ598161 S29Tcar35 JQ828932Tcar36 JQ828933Tdob01 GU982389Tdob02 GU982390Tdob03 JQ653338Tdob04 JQ653339Tdob05 JQ653340Tdob06 JQ653341Tdob07 JQ653342Tdob08 JQ653343Tdob09 JQ653344Tdob10 JQ653345Tdob11 JQ653346Tdob12 JQ653347Tdob13 JQ653348Tdob14 JQ653349Tdob15 JQ653350Tdob16 JQ653351Tdob17 JQ653352Tdob18 JQ653353Tdob19 JQ653354Tdob20 JQ653391Tdob21 JQ653392Tdob22 JQ653393Tdob23 JQ653394Tdob24 JQ653395Tdob25 JQ828934Tdob26 JX860862Tmac01 GU982387Tmac02 GU982388Tmac03 JQ240240Tmac04 JQ240241Tmac05 JQ240242Tmac06 JQ240243Tmac07 JQ240244

Tmac08 JQ240245Tmac09 JQ240246Tmac10 JQ240247Tmac11 JQ240248Tmac12 JQ240249Tmac13 JQ240250Tmac14 JQ240251Tmac15 JQ240252Tmac16 JQ240253Tmac17 JQ240254Tmac18 JQ240255Tmac19 JQ240256Tmac20 JQ240257Tmac21 JQ240258Tmac22 JQ240259Tmac23 JQ240260Tmac24 JQ240261Tmac25 JQ240262Tmac26 JQ240263Tmac27 JQ240264Tmac28 JQ240265Tmac29 JQ240266Tmac30 JQ240267TkarC01 GU982424TkarC02 GU982425TkarC03 GU982426TkarC05 GU982428TkarC07 GU982430TkarC09 GU982432TkarC10 GU982433TkarC11 GU982434TkarC12 GU982435TkarC15 GU982438TkarC17 GU982440TkarC18 GU982441TkarC19 GU982442TkarC20 GU982443

TkarC23 GU982446TkarC24 GU982447TkarC27 GU982450TkarC28 GU982451TkarC29 JQ240205TkarC30 JQ240206TkarC31 JQ240207TkarC32 JQ240208TkarC33 JQ240209TkarC34 JQ240210TkarC35 JQ240211TkarC36 JQ240212TkarC37 JQ240213TkarC38 JQ240214TkarC39 JQ240215TkarC40 JQ240216TkarC41 JQ240217TkarC42 JQ240218TkarC43 JQ240219TkarC44 JQ240220TkarC45 JQ240221TkarC46 JQ240222TkarC47 JQ240223TkarC48 JQ240224TkarC49 JQ240225TkarC50 JQ240226TkarC51 JQ240227TkarC52 JQ240228TkarC53 JQ240229TkarC54 JQ240230TkarC55 JQ240231TkarC56 JQ240232TkarC57 JQ240233TkarC58 JQ240234TkarC59 JQ240235TkarC60 JQ240236TkarC61 JQ240237

TkarC62 JQ240238TkarC63 JQ240239TkarC64 JQ653355TkarC65 JQ653356TkarC66 JX860864TkarC67 JX860865TkarB01 GU982408TkarB02 GU982409TkarB04 GU982411TkarB05 GU982412TkarB06 GU982413TkarB07 GU982414TkarB10 GU982417TkarB12 GU982419TkarB13 GU982420TkarB14 GU982421TkarB15 GU982422TkarB16 GU982423TkarB17 JQ653357TkarB18 JQ653396TkarB19 JQ653397TkarB20 JQ653398TkarA01 GU982391TkarA02 GU982392TkarA03 GU982393TkarA04 GU982394TkarA06 GU982396TkarA07 GU982397TkarA08 GU982398TkarA09 GU982399TkarA11 GU982401TkarA12 GU982402TkarA14 GU982404TkarA16 GU982406TkarA18 JQ828935TkarA19 JX860863Tmar01 GU982379

Tmar02 GU982380Tmar03 JQ653358Tmar04 JQ653359Tmar05 JQ653360Tmar06 JX043494 1Tmar07 JX043499 3Tmar08 JX043502 4Tmar09 JX043503 5Tmar10 JX043504 6Tmar11 JX043506 7Tmar12 JX043509 8Tmar13 JX043514 9Tmar14 JX043516 11Tmar15 JX043525 13Tmar16 JX043529 14Tmar17 JX043530 15Tmar18 JX043533 16Tmar19 JX043547 17Tmar20 JX043549 18Tmar21 JX043554 19Tmar22 JX043567 21Tmar23 JX043572 23Tmar24 JX043610 24Tmar25 JX043614 26Tmar26 JX043616 27Tmar27 JX043620 28Tmar28 JX043627 29Tmar29 JX043629 30Tmar30 JX043634 31Tmar31 JX043638 32Tmar32 JX043640 33Tmar33 JX043641 34Tmar34 JX043667 39Tmar35 JX043702 43Tmar36 JX043704 44Tmar37 JX043768 52Tmar38 JX043770 54

Tmar39 JQ653399Tmar40 JQ653400Tmar41 JQ653401Tpyg01 GU982381Tpyg02 GU982382Tpyg03 JQ653361Tpyg04 JQ653362Tpyg05 JQ653363Tpyg06 JQ653364Tpyg07 JQ653365Tpyg08 JQ653366Tpyg09 JX043647 35Tpyg10 JX043652 36Tpyg11 JX043657 37Tpyg12 JX043658 38Tpyg13 JX043671 40Tpyg14 JX043685 41Tpyg15 JX043690 42Tpyg16 JX043732 45Tpyg17 JX043730 46Tpyg18 JX043731 47Tpyg19 JX043738 48Tpyg20 JX043753 49Tpyg21 JX043754 50Tpyg22 JX043757 51Tpyg23 JX043769 53Tpyg24 JX043779 55Tpyg25 JX043780 56Tpyg26 JX043783 57Tpyg27 JX043784 58Tpyg28 JX043787 59Tpyg29 JX043798 60Tpyg30 JX043799 61Tpyg31 JX043800 62Tpyg32 JX043801 63Tpyg33 JX043803 64Tpyg34 JX043805 65

Tpyg35 JX043806 66Tpyg36 JX043807 67Tpyg37 JX043808 68Tpyg38 JX043810 69Tpyg39 JX043812 70Tpyg40 JX043814 71Tpyg41 JX043815 72Tpyg42 JX043816 73Tpyg43 JX043568 22Tpyg44 JX043613 25Tpyg45 JQ653402Tpyg46 JQ653403Tpyg47 JQ653404Tpyg48 JQ653405Tpyg49 JQ653406Tpyg50 JQ653407Tpyg51 JQ653408Tpyg52 JQ653409Tpyg53 JQ653410Tpyg54 JQ653411Tpyg55 JQ653412Tpyg56 JQ653413Tpyg57 JQ653414

Additional file 4. Genetic distances.

Z i

Latitude Longitude

Triturus carnifex39,484 16,027 0,0007539,484 16,053 0,0007539,417 16,060 0,0000040,853 14,778 0,0018940,203 15,663 0,0083040,201 15,135 0,0064245,275 14,934 0,0018944,942 14,620 0,0037745,400 14,187 0,0018945,080 13,099 0,0207545,456 13,038 0,0188745,443 13,812 0,0018945,448 12,233 0,0056645,810 12,946 0,0207541,118 13,885 0,0018941,096 13,645 0,0056641,364 13,280 0,0037741,632 13,354 0,0013542,017 13,411 0,0009241,772 13,578 0,0022641,123 14,937 0,0075541,613 15,304 0,0000041,345 15,672 0,0075540,134 15,142 0,0056639,464 16,265 0,0064239,801 16,072 0,0045339,714 16,317 0,0094345,792 15,334 0,0000045,776 14,125 0,0000045,834 14,959 0,0000042,114 13,294 0,0024342,015 13,115 0,0005442,251 13,221 0,0018944,885 10,796 0,0018944,937 9,790 0,0018945,652 10,381 0,0000044,555 9,722 0,0049145,321 9,307 0,0030240,438 16,680 0,0113240,943 16,081 0,0150941,329 16,637 0,01132

Coordinates

43,628 14,602 0,0264243,326 15,703 0,0264242,137 14,938 0,0000045,334 15,767 0,0018941,509 14,453 0,0000041,730 15,187 0,0000041,818 12,989 0,0028541,956 12,917 0,0032342,189 12,517 0,0018942,386 12,643 0,0051243,337 16,701 0,0207543,842 16,103 0,0245340,453 15,715 0,0113242,698 12,064 0,0027043,040 12,479 0,0023642,961 12,659 0,0018243,496 13,139 0,0065143,744 12,515 0,0061343,937 12,580 0,0003839,378 16,219 0,0056639,378 16,193 0,0056644,088 9,488 0,0037744,389 9,031 0,0037744,018 9,943 0,0075542,033 14,087 0,0000044,977 11,354 0,0018944,262 10,763 0,0000042,938 11,335 0,0018942,955 11,526 0,0033042,759 11,694 0,0018944,253 14,285 0,0230244,378 13,538 0,0230244,391 12,764 0,0052845,884 10,094 0,0015145,935 9,088 0,0015143,182 11,593 0,0037743,887 11,629 0,0075543,481 11,920 0,0075540,380 14,536 0,0113242,290 13,628 0,0003842,431 13,729 0,0094342,298 13,736 0,0098140,100 16,872 0,0056644,052 11,286 0,0056644,269 11,639 0,0056643,968 11,603 0,0075543,361 11,235 0,00118

42,254 13,518 0,0015142,387 13,511 0,0018945,828 8,354 0,0018945,553 9,019 0,0045345,214 8,573 0,0034043,734 10,825 0,0034644,035 10,861 0,0056643,230 10,952 0,0023643,600 10,039 0,0023643,944 10,303 0,0056644,245 9,846 0,0056643,425 10,784 0,0004743,653 10,851 0,0009443,669 11,276 0,0032241,610 13,113 0,0024341,551 12,915 0,0051244,629 9,362 0,0030245,985 10,471 0,0052845,701 11,764 0,0037744,639 12,141 0,0056644,699 8,907 0,0030244,973 8,241 0,0003845,199 7,872 0,0003844,684 8,206 0,0000042,681 11,873 0,0016842,042 12,271 0,0061342,251 12,147 0,0010141,121 14,410 0,0056641,238 14,293 0,0056644,723 11,823 0,0037745,347 11,856 0,0018941,506 13,928 0,0075542,827 13,237 0,0051942,863 13,348 0,0033042,728 13,058 0,0033039,445 16,186 0,0064239,358 16,431 0,0000041,892 13,986 0,0098142,871 13,456 0,0061343,064 13,520 0,01170

Triturus cristatus53,091 17,345 0,0045652,092 17,293 0,0060851,558 16,486 0,0045647,667 22,767 0,0010147,839 23,260 0,0003047,522 22,827 0,0013246,254 25,959 0,0018546,751 26,120 0,0220446,601 26,289 0,02149

45,610 21,973 0,0121646,015 21,935 0,0136846,688 22,429 0,0015251,671 19,285 0,0030450,138 18,426 0,0030449,125 23,098 0,0212849,065 23,524 0,0212848,761 23,209 0,0000043,550 13,928 0,0141843,637 13,702 0,0136843,503 22,996 0,0055750,609 2,492 0,0015251,338 3,309 0,0030450,664 4,718 0,0015247,426 26,353 0,0007647,230 26,489 0,0007647,295 26,314 0,0007650,142 36,367 0,0000052,117 36,975 0,0000052,092 37,025 0,0000044,600 25,484 0,0164044,375 24,692 0,0179644,708 24,659 0,0019852,567 36,867 0,0000054,517 37,525 0,0000046,981 24,103 0,0000046,711 23,817 0,0000046,920 23,641 0,0000056,159 52,425 0,0030455,242 50,559 0,0015256,200 51,117 0,0015244,289 13,061 0,0136844,243 22,129 0,0152050,442 ‐0,434 0,0018051,933 0,142 0,0003049,792 ‐0,042 0,0014948,709 0,233 0,0000350,200 0,808 0,0015251,283 0,808 0,0000050,200 1,083 0,0015243,950 23,545 0,0126643,946 23,818 0,0131743,413 23,496 0,0052347,806 23,402 0,0003047,554 23,705 0,0000047,442 23,624 0,0003049,092 17,350 0,0015249,513 15,359 0,0045643,838 24,643 0,0219256,892 50,125 0,0015255,975 48,259 0,0000047,950 23,342 0,0000046,708 23,050 0,0011746,842 23,186 0,0010146,517 23,219 0,00015

45,575 5,925 0,0000045,417 5,950 0,0000043,842 4,608 0,0000047,244 23,565 0,0000047,496 23,262 0,0003047,695 23,781 0,0000047,724 24,144 0,0000047,219 24,290 0,0000051,933 0,417 0,0003048,091 23,418 0,0000048,058 23,559 0,0000045,936 26,484 0,0204345,664 26,598 0,0012245,832 26,242 0,0192145,676 26,786 0,0012245,572 26,545 0,0000047,809 10,775 0,0000046,075 9,458 0,0000048,402 22,991 0,0212848,308 22,767 0,0212848,727 22,558 0,0000053,354 26,395 0,0015249,305 24,490 0,0000053,684 25,945 0,0015246,948 25,984 0,0215846,681 17,886 0,0136846,333 22,775 0,0001546,658 22,742 0,0010145,719 25,839 0,0020045,447 25,953 0,0199156,967 25,375 0,0015258,650 28,959 0,0015256,050 24,267 0,0030447,114 27,030 0,0225446,983 26,992 0,0225449,526 2,492 0,0000046,433 26,645 0,0035047,992 22,308 0,0222948,633 22,308 0,0000047,992 22,333 0,0222944,912 21,514 0,0000044,891 21,518 0,0000044,916 21,484 0,0000046,702 17,881 0,0136845,133 25,500 0,0021345,570 25,105 0,0024845,770 25,155 0,0006645,345 24,313 0,0005147,166 23,110 0,0010146,974 23,279 0,0000044,323 22,374 0,0015244,590 21,953 0,0152044,621 21,969 0,0167248,534 21,375 0,0010146,144 21,906 0,01317

47,327 20,948 0,0121649,222 23,723 0,0000049,532 23,727 0,0000049,325 24,036 0,0000045,585 22,008 0,0121652,414 13,563 0,0045651,414 13,511 0,0030444,594 21,923 0,0152046,943 28,160 0,0217846,394 27,813 0,0003046,318 27,186 0,0214857,525 51,825 0,0000058,784 41,917 0,0007659,642 46,475 0,0007646,967 4,242 0,0000046,559 2,833 0,0000046,150 1,983 0,0000348,542 5,584 0,0000050,369 12,435 0,0015249,710 8,927 0,0015249,272 6,401 0,0015251,832 8,061 0,0030445,992 2,008 0,0000347,221 22,362 0,0025347,015 24,116 0,0000046,805 24,292 0,0000047,044 24,479 0,0000045,247 25,903 0,0183946,306 25,275 0,0005145,869 25,670 0,0001544,925 26,125 0,0073244,914 25,937 0,0073246,728 24,706 0,0005146,826 25,222 0,0000052,042 20,550 0,0015253,996 19,418 0,0045647,370 25,416 0,0215851,801 21,605 0,0091251,898 22,048 0,0015249,332 22,970 0,0076055,600 40,434 0,0000055,150 40,542 0,0000057,750 54,683 0,0015256,450 45,359 0,0000055,350 46,092 0,0015255,133 44,184 0,0015246,150 28,208 0,0030445,492 27,600 0,0103647,018 28,835 0,0045646,867 29,283 0,0015246,868 29,235 0,0045646,243 28,261 0,0042657,267 33,742 0,0000056,350 32,634 0,0015245,429 22,298 0,01216

45,163 22,718 0,0136847,925 28,900 0,0000049,150 32,475 0,0000048,942 32,742 0,0000058,567 40,009 0,0007652,948 14,370 0,0030449,175 21,350 0,0212857,934 36,992 0,0007659,484 37,417 0,0007656,517 37,842 0,0000047,812 27,406 0,0215847,747 27,580 0,0220447,500 28,083 0,0005147,576 28,710 0,0212848,442 33,000 0,0228048,417 33,050 0,0228053,950 32,084 0,0015246,127 22,757 0,0000052,427 5,169 0,0003051,988 2,643 0,0033447,367 29,025 0,0228047,275 29,409 0,0228046,925 29,267 0,0015247,368 28,977 0,0212846,775 29,667 0,0000048,500 32,984 0,0228052,933 28,242 0,0015245,227 23,457 0,0000046,089 23,903 0,0005154,050 41,275 0,0015255,919 20,327 0,0000056,596 24,110 0,0030443,941 22,564 0,0000043,972 22,580 0,0015256,075 14,909 0,0003060,234 33,317 0,0007644,037 23,319 0,0121644,419 23,128 0,0136851,434 40,809 0,0000049,734 37,442 0,0228048,509 35,359 0,0179653,392 45,059 0,0015253,409 41,417 0,0000046,311 23,201 0,0000046,177 23,065 0,0001557,150 40,859 0,0000046,371 23,663 0,0000056,792 54,125 0,0015246,489 24,519 0,0005153,975 48,534 0,0015254,017 47,225 0,0000054,658 47,084 0,0015248,934 22,405 0,0146948,934 22,430 0,0146949,028 22,655 0,01469

49,476 21,472 0,0076048,624 24,964 0,0212848,589 24,622 0,0212848,831 24,681 0,0000048,342 23,418 0,0212848,450 23,635 0,0212848,179 25,601 0,0215847,937 25,542 0,0003048,858 23,834 0,0212848,921 24,981 0,0000048,886 24,639 0,0000048,554 26,786 0,0000048,291 24,270 0,0212848,498 23,987 0,0000048,500 26,828 0,0000047,815 24,779 0,0212848,749 23,987 0,0212848,905 24,185 0,0000052,001 22,362 0,0015249,216 24,189 0,0000047,063 22,704 0,0025347,192 22,675 0,0006847,324 22,768 0,0010146,523 24,532 0,00051

Triturus dobrogicus44,917 18,900 0,0012145,050 17,835 0,0021745,216 17,635 0,0028246,183 16,585 0,0015245,583 16,410 0,0000046,417 16,425 0,0015247,309 16,525 0,0015246,709 16,350 0,0030447,684 16,725 0,0018248,075 16,684 0,0038048,159 16,959 0,0019844,831 20,902 0,0025344,593 21,251 0,0038544,596 20,849 0,0019345,226 28,051 0,0042644,997 27,995 0,0032444,971 27,924 0,0040546,575 19,850 0,0013046,359 19,392 0,0021747,017 19,142 0,0011447,200 19,884 0,0015247,050 20,275 0,0028247,083 19,992 0,0013044,717 20,184 0,0026944,479 20,532 0,0041045,240 22,570 0,0015244,513 21,989 0,0025345,556 21,886 0,0020347,309 27,925 0,00304

46,025 31,167 0,0025346,684 26,342 0,0015244,729 19,704 0,0020444,675 19,484 0,0018744,804 19,321 0,0011344,278 21,935 0,0033447,392 16,800 0,0003047,892 18,259 0,0007648,583 19,599 0,0022847,925 20,941 0,0015244,637 19,849 0,0030244,753 20,165 0,0029446,325 22,284 0,0010145,598 21,702 0,0030447,284 17,842 0,0017546,909 17,642 0,0019045,500 19,400 0,0041345,394 19,585 0,0025244,977 18,885 0,0040446,425 20,242 0,0016746,650 21,242 0,0023246,142 21,100 0,0023245,827 21,275 0,0026445,602 20,275 0,0022347,675 17,800 0,0019044,935 20,472 0,0021544,855 20,137 0,0043744,766 19,686 0,0027646,609 25,611 0,0027446,642 25,775 0,0015245,284 28,286 0,0012244,102 22,799 0,0000044,183 22,061 0,0033445,073 19,996 0,0045345,388 19,821 0,0042045,162 20,447 0,0023644,948 19,992 0,0046145,145 22,696 0,0015244,865 19,305 0,0041245,942 18,692 0,0039644,432 27,475 0,0030445,071 28,726 0,0020344,761 28,333 0,0030444,811 19,085 0,0033946,142 18,442 0,0041846,250 17,400 0,0045645,283 18,450 0,0019548,292 22,691 0,0000047,633 27,516 0,0030445,416 17,385 0,0030444,790 19,060 0,0009644,851 19,045 0,0031744,957 18,860 0,0014345,030 28,159 0,0020347,375 21,234 0,00000

47,258 21,342 0,0028246,975 22,200 0,0025346,933 22,384 0,0015247,775 22,700 0,0000047,125 22,784 0,0015244,967 23,909 0,0015245,809 24,225 0,0000048,016 22,274 0,0030448,208 22,674 0,0030444,933 20,874 0,0029847,808 21,208 0,0030445,359 29,017 0,0000048,532 22,265 0,0030447,892 21,224 0,0000046,584 25,540 0,0045644,425 26,665 0,0045643,634 25,350 0,0022843,812 24,137 0,0022843,829 24,137 0,0000045,386 29,916 0,0055745,058 20,877 0,0030044,661 27,531 0,0030443,886 26,216 0,0045643,869 26,216 0,00532

Triturus karelinii western clade42,291 27,422 0,0330542,169 27,620 0,0011442,111 27,328 0,0319142,548 24,508 0,0019042,356 24,605 0,0003842,271 24,296 0,0015242,450 24,892 0,0003842,173 24,680 0,0000042,215 22,687 0,0000042,300 22,566 0,0000042,338 22,574 0,0000039,099 26,965 0,0003039,591 26,880 0,0053739,514 27,022 0,0050741,059 22,850 0,0004741,167 22,784 0,0030441,275 22,834 0,0032742,308 27,735 0,0011442,128 27,640 0,0000044,442 20,626 0,0012544,162 20,661 0,0006844,320 20,618 0,0005742,380 23,912 0,0015242,188 24,010 0,0000044,004 21,593 0,0011443,929 21,293 0,0011443,975 21,300 0,0000041,874 23,682 0,0015241,765 24,066 0,00152

42,221 27,062 0,0015242,099 27,260 0,0334338,859 28,160 0,0000039,019 29,336 0,0000038,527 29,279 0,0000042,164 22,746 0,0007642,126 22,738 0,0007643,835 20,769 0,0000043,892 20,717 0,0001743,910 20,719 0,0001742,363 22,648 0,0000042,448 22,527 0,0000040,162 28,230 0,0006139,710 28,285 0,0021339,803 28,162 0,0015241,287 25,063 0,0011441,360 25,048 0,0000041,562 25,442 0,0011440,236 27,863 0,0000039,784 27,918 0,0015239,619 27,363 0,0000039,186 27,662 0,0015244,167 21,135 0,0006844,092 20,835 0,0006841,264 23,856 0,0010141,578 24,183 0,0025343,775 20,784 0,0000043,859 20,884 0,0000043,817 20,900 0,0000041,563 26,814 0,0313341,453 27,079 0,0102941,256 23,832 0,0010141,216 23,550 0,0025341,395 23,408 0,0015244,034 22,900 0,0005743,759 23,409 0,0000044,325 21,092 0,0005740,567 27,126 0,0298141,103 27,572 0,0319141,472 24,994 0,0015241,748 25,372 0,0015241,122 27,816 0,0319141,271 22,933 0,0032741,365 23,046 0,0015241,261 23,013 0,0017543,383 21,598 0,0007643,815 21,223 0,0015243,465 22,021 0,0022843,899 21,323 0,0015243,850 20,734 0,0001743,927 21,616 0,0011443,934 20,834 0,0001743,876 20,886 0,0000044,004 20,660 0,0001743,970 20,827 0,00000

41,499 26,000 0,0015241,322 25,972 0,0030441,701 25,825 0,0015242,905 24,448 0,0003843,142 24,472 0,0000042,870 24,841 0,0003840,112 27,278 0,0050741,081 24,321 0,0010141,051 24,658 0,0000041,048 24,310 0,0010140,127 25,398 0,0028440,097 25,734 0,0018240,563 26,542 0,0076040,512 25,792 0,0065941,069 25,397 0,0030438,694 27,605 0,0015241,530 25,484 0,0011441,603 25,469 0,0015240,063 24,811 0,0018241,014 23,387 0,0010141,259 28,028 0,0319141,120 27,913 0,0319142,617 24,955 0,0003842,434 25,030 0,0000040,253 28,204 0,0020340,160 28,326 0,0026342,510 26,941 0,0000042,567 27,233 0,0330541,379 22,866 0,0000042,004 22,732 0,0007642,055 22,673 0,0015242,632 23,249 0,0015242,736 23,188 0,0015242,740 23,209 0,0000043,064 23,356 0,0007642,969 23,364 0,0000042,939 23,140 0,0007640,462 26,785 0,0280242,442 23,538 0,0000042,428 23,527 0,0000042,572 23,438 0,0000043,085 27,611 0,0000043,060 26,234 0,0000043,359 27,094 0,0000041,586 23,088 0,0030441,576 23,169 0,0015243,974 21,623 0,0015241,616 23,451 0,0030441,231 23,013 0,0027441,137 22,900 0,0011242,441 22,962 0,0015242,551 22,914 0,0015241,028 22,850 0,0012243,426 26,844 0,0000043,400 25,467 0,00000

41,842 22,717 0,0006341,913 22,782 0,0007642,352 23,013 0,0022841,567 22,767 0,0024141,563 22,866 0,0024142,611 23,360 0,0000042,597 23,349 0,0000041,936 23,004 0,0022841,774 22,989 0,0024141,624 23,475 0,0030441,443 23,714 0,0000042,213 23,220 0,0030444,272 21,127 0,0000044,547 20,618 0,0005742,820 24,138 0,0015243,572 22,590 0,0000043,613 22,839 0,0000043,135 23,829 0,0000042,047 23,419 0,0015242,865 23,425 0,0000042,840 23,515 0,0000038,484 27,228 0,0000037,974 27,266 0,0000038,511 27,145 0,0000042,779 23,889 0,0015242,525 23,824 0,0015241,250 28,803 0,0304040,242 29,075 0,0065942,826 28,113 0,0319141,809 29,161 0,0076042,044 26,424 0,0015242,332 26,302 0,0000042,498 26,873 0,0015243,442 25,175 0,0000043,297 24,606 0,0000043,322 24,648 0,0000042,240 25,473 0,0000041,869 25,586 0,0000042,231 25,206 0,0000042,064 25,143 0,0000041,724 25,682 0,0030441,757 25,640 0,0015242,922 22,893 0,0000043,023 22,793 0,0000042,945 23,047 0,0000042,866 28,093 0,0330543,040 23,040 0,0007641,547 25,654 0,0015241,970 24,856 0,0000044,389 20,661 0,0006842,606 25,785 0,0000042,753 22,498 0,0007642,727 22,424 0,0007643,262 24,999 0,0000043,009 25,113 0,00000

42,854 24,979 0,0000042,659 22,874 0,0000038,157 27,726 0,0015241,254 25,052 0,0011442,593 23,328 0,0015242,178 23,319 0,0015241,092 23,437 0,0022341,019 26,390 0,0313343,548 22,274 0,0000043,472 22,297 0,0015241,582 24,531 0,0015242,967 25,405 0,0000043,375 25,425 0,0000043,016 22,517 0,0007643,039 22,671 0,0007641,514 22,414 0,0006341,605 22,364 0,0000041,330 22,414 0,0030438,588 27,003 0,0003041,114 22,430 0,0000037,799 27,516 0,0000037,825 27,433 0,0000042,178 22,560 0,0015241,850 22,257 0,0015243,078 21,627 0,0000042,505 21,430 0,0015238,009 27,893 0,0015242,736 22,619 0,0000038,169 29,069 0,0015243,070 23,264 0,0000041,665 26,571 0,0030441,305 25,802 0,0034241,507 25,627 0,0011440,151 29,101 0,0051739,792 29,034 0,0060839,460 30,153 0,0060839,475 24,992 0,00152

Triturus karelinii central clade41,487 32,126 0,0069940,947 31,860 0,0187440,951 31,836 0,0257341,554 32,217 0,0070441,550 32,242 0,0083641,049 31,694 0,0252340,509 31,428 0,0005140,780 32,746 0,0091240,244 32,456 0,0278640,687 30,885 0,0015240,589 31,027 0,0020340,922 30,620 0,0030440,842 31,022 0,0045640,847 32,837 0,0093740,776 36,747 0,0039941,039 37,825 0,00304

40,704 38,555 0,0039941,522 34,892 0,0076041,450 36,700 0,0045641,347 31,545 0,0169741,284 31,429 0,0237141,409 33,185 0,0093741,775 33,050 0,0063341,567 33,902 0,0030441,156 35,027 0,0091241,064 35,025 0,0091241,109 34,035 0,0000040,594 34,679 0,0091240,547 33,687 0,0000040,577 38,992 0,0045640,627 38,880 0,0045640,918 39,745 0,0000040,259 35,409 0,0100740,364 37,801 0,0024740,182 35,734 0,0106441,317 33,183 0,00937

Triturus karelinii eastern clade44,610 34,314 0,0000044,613 34,239 0,0000044,685 34,309 0,0000038,584 48,692 0,0109440,067 48,292 0,0117040,150 48,383 0,0068437,552 50,793 0,0068439,035 50,393 0,0076044,819 37,629 0,0045644,736 37,643 0,0045644,800 37,453 0,0091244,767 35,873 0,0076044,783 35,911 0,0076044,867 37,400 0,0000044,786 35,836 0,0076039,210 50,393 0,0098841,808 47,983 0,0068444,784 37,414 0,0091244,699 35,926 0,0076044,627 35,855 0,0076044,101 40,387 0,0000043,450 40,850 0,0106443,634 40,587 0,0106444,424 39,578 0,0000043,773 40,041 0,0106444,659 38,426 0,0045644,640 38,250 0,0091243,994 40,749 0,0000043,527 40,949 0,0106442,005 41,771 0,0000042,403 41,432 0,0076042,585 41,389 0,0076044,178 40,485 0,00000

44,593 39,537 0,0000044,486 39,899 0,0000043,269 39,641 0,0091243,850 39,259 0,0106442,796 42,681 0,0100342,161 41,784 0,0000042,823 42,624 0,0100341,943 46,729 0,0006141,768 46,729 0,0074544,732 38,991 0,0000041,816 45,024 0,0006140,202 47,038 0,0164140,115 46,586 0,0158143,180 38,029 0,0076041,978 41,828 0,0000037,460 50,818 0,0048138,575 48,808 0,0070937,468 50,702 0,0086638,492 48,717 0,0068941,972 43,170 0,0000041,816 43,156 0,0000042,634 44,010 0,0100341,798 44,522 0,0000041,802 44,566 0,0000041,791 44,589 0,0000042,627 44,077 0,0100341,762 44,603 0,0000041,758 44,559 0,0000041,769 44,535 0,0000041,776 43,193 0,0000043,765 42,184 0,0009144,179 41,236 0,0009141,855 44,966 0,0000041,822 44,936 0,0000042,686 44,410 0,0100342,705 44,458 0,0106441,933 45,347 0,0006144,828 38,385 0,0045641,909 45,387 0,0006141,927 45,435 0,0007344,892 38,194 0,0000040,161 45,207 0,0158141,967 46,689 0,0006142,721 45,752 0,0100343,221 42,286 0,0115536,335 53,135 0,0007639,108 50,725 0,00912

Triturus macedonicus40,799 22,301 0,0285740,694 22,529 0,0288840,463 22,250 0,0033440,684 21,971 0,0018240,915 22,250 0,0273639,792 20,217 0,03229

39,874 20,666 0,0003839,765 20,214 0,0319139,367 20,848 0,0319139,824 20,993 0,0258439,366 21,116 0,0091239,693 21,671 0,0273639,236 21,526 0,0334339,960 22,279 0,0015240,065 22,052 0,0018240,195 21,642 0,0285740,710 21,736 0,0287140,338 21,356 0,0001441,994 21,170 0,0076041,799 21,654 0,0060841,225 22,096 0,0015240,680 20,245 0,0161140,416 20,655 0,0295640,165 20,260 0,0219639,863 20,697 0,0003839,868 20,705 0,0003839,830 20,733 0,0000041,287 21,256 0,0060841,362 21,370 0,0076041,147 21,333 0,0015241,243 21,018 0,0060841,458 21,055 0,0000042,079 20,736 0,0060842,455 20,651 0,0000042,193 20,814 0,0060842,472 19,875 0,0124642,356 20,318 0,0009142,188 19,884 0,0133740,327 21,191 0,0015240,842 21,286 0,0016641,715 19,475 0,0100342,049 19,359 0,0024341,863 19,567 0,0106441,017 21,052 0,0063841,188 20,851 0,0094242,417 18,881 0,0000042,382 19,148 0,0072942,339 19,174 0,0072940,952 21,170 0,0015240,897 21,003 0,0053541,072 19,309 0,0152041,029 19,336 0,0152042,550 19,582 0,0106441,801 20,004 0,0094241,615 20,138 0,0115541,343 19,816 0,0100341,497 20,884 0,0006141,615 20,858 0,0060841,699 20,925 0,0066941,461 21,452 0,0076042,035 21,011 0,00000

41,660 21,096 0,0060842,135 19,889 0,0121640,963 21,335 0,0001441,037 21,448 0,0013841,743 20,571 0,0009141,945 20,613 0,0069941,541 21,122 0,0006141,079 19,807 0,0048641,084 19,610 0,0036541,163 19,753 0,0072940,881 20,881 0,0054740,947 20,763 0,0006142,250 19,446 0,0068142,491 20,441 0,0000042,207 20,450 0,0009141,349 19,619 0,0051741,285 20,383 0,0106442,137 19,087 0,0000041,534 19,412 0,0072941,771 19,232 0,0072939,836 20,694 0,0000040,426 19,660 0,0103340,793 19,900 0,0173340,715 19,757 0,0147741,172 20,728 0,0094241,044 20,295 0,0018242,286 19,179 0,0024340,056 19,807 0,0197640,799 19,703 0,0142939,296 20,367 0,0167239,378 20,817 0,0319141,320 21,529 0,0000041,110 21,765 0,0288840,827 21,685 0,0274942,604 20,519 0,0000039,862 20,965 0,0262239,829 21,001 0,0258440,366 20,895 0,0271341,011 21,683 0,02888

T. marmoratus40,402 ‐5,521 0,0106440,197 ‐6,750 0,0076040,695 ‐5,112 0,0030441,121 ‐5,245 0,0030440,916 ‐6,473 0,0000039,996 ‐7,648 0,0030439,879 ‐7,539 0,0041039,823 ‐7,928 0,0053247,159 0,025 0,0005144,703 ‐1,718 0,0000045,845 ‐2,360 0,0005139,565 ‐7,853 0,0076039,620 ‐7,754 0,0000039,563 ‐7,835 0,00760

40,110 ‐7,255 0,0008140,153 ‐7,220 0,0005140,095 ‐7,224 0,0003040,605 ‐8,345 0,0082140,605 ‐8,515 0,0057840,760 ‐8,537 0,0091240,080 ‐7,200 0,0003040,021 ‐7,167 0,0000039,993 ‐7,226 0,0003040,123 ‐7,165 0,0000040,073 ‐8,222 0,0109439,979 ‐8,281 0,0133740,112 ‐8,466 0,0105440,377 ‐7,095 0,0076040,362 ‐7,065 0,0076040,214 ‐7,938 0,0064140,195 ‐8,180 0,0115540,469 ‐8,081 0,0082440,328 ‐7,545 0,0058840,537 ‐7,390 0,0028940,328 ‐8,365 0,0095240,714 ‐7,823 0,0053240,624 ‐8,103 0,0044140,850 ‐8,087 0,0039540,347 ‐7,041 0,0076039,664 ‐7,795 0,0038039,612 ‐7,865 0,0060839,654 ‐7,889 0,0071439,555 ‐7,946 0,0076039,771 ‐7,998 0,0063839,688 ‐7,777 0,0041039,688 ‐7,744 0,0055739,670 ‐7,702 0,0033439,734 ‐8,816 0,0142940,105 ‐8,858 0,0136840,389 ‐8,664 0,0079040,763 ‐7,374 0,0024339,479 ‐8,472 0,0076039,526 ‐8,484 0,0051740,112 ‐8,515 0,0102340,482 ‐8,557 0,0096341,764 ‐7,573 0,0014842,130 ‐7,969 0,0145942,077 ‐7,507 0,0138840,270 ‐7,549 0,0059342,063 ‐8,807 0,0164142,396 ‐8,505 0,0158141,485 ‐8,337 0,0047443,182 ‐6,539 0,0015245,637 ‐4,796 0,0020340,964 ‐6,773 0,0000040,538 ‐6,640 0,0000040,018 ‐8,574 0,0052939,810 ‐8,242 0,0100342,365 ‐5,355 0,01216

41,526 ‐6,858 0,0105442,550 ‐5,607 0,0135840,956 ‐8,503 0,0133741,046 ‐8,223 0,0142941,325 ‐7,459 0,0129741,149 ‐7,025 0,0105442,708 ‐8,439 0,0018240,006 ‐7,150 0,0000040,299 ‐6,741 0,0076042,916 ‐6,003 0,0003039,860 ‐7,464 0,0033441,378 ‐7,921 0,0136843,459 ‐1,608 0,0152042,145 ‐3,993 0,0152039,761 ‐7,448 0,0000039,706 ‐7,547 0,0076040,204 ‐5,909 0,0121639,919 ‐7,176 0,0000039,644 ‐7,736 0,0003039,644 ‐7,702 0,0030439,787 ‐7,414 0,0030439,734 ‐8,767 0,0145940,956 ‐8,673 0,0097341,867 ‐8,840 0,0121641,212 ‐8,992 0,0197639,646 ‐7,753 0,0003039,802 ‐7,430 0,00304

T. pygmaeus36,542 ‐5,550 0,0016236,892 ‐4,917 0,0034136,750 ‐5,017 0,0026137,862 ‐6,899 0,0025537,997 ‐6,711 0,0013437,789 ‐6,675 0,0027638,041 ‐7,235 0,0033437,959 ‐7,735 0,0048638,211 ‐7,753 0,0057838,415 ‐7,627 0,0033438,333 ‐8,127 0,0048638,872 ‐8,764 0,0031738,544 ‐8,197 0,0048638,458 ‐8,820 0,0052438,747 ‐8,071 0,0020337,931 ‐9,146 0,0128437,603 ‐8,580 0,0142938,311 ‐5,651 0,0020638,046 ‐5,821 0,0020638,403 ‐5,849 0,0000037,517 ‐7,157 0,0000037,687 ‐7,675 0,0048640,431 ‐8,745 0,0036740,482 ‐8,770 0,0014440,521 ‐8,729 0,0028439,218 ‐7,230 0,00709

39,510 ‐7,188 0,0040539,460 ‐7,134 0,0030439,512 ‐7,763 0,0000039,409 ‐7,437 0,0040539,371 ‐7,611 0,0040539,651 ‐7,341 0,0000039,066 ‐9,166 0,0045639,064 ‐9,148 0,0034238,788 ‐9,404 0,0026638,788 ‐7,243 0,0030438,838 ‐7,297 0,0040538,472 ‐7,656 0,0030438,351 ‐7,282 0,0015239,551 ‐8,865 0,0030439,425 ‐8,817 0,0030439,467 ‐8,958 0,0030438,902 ‐7,643 0,0060839,541 ‐8,626 0,0045639,550 ‐8,616 0,0000039,582 ‐8,517 0,0045639,150 ‐8,525 0,0030439,439 ‐8,742 0,0033439,247 ‐8,358 0,0023339,523 ‐8,650 0,0003039,951 ‐6,707 0,0015239,619 ‐6,340 0,0015239,419 ‐6,725 0,0000040,200 ‐6,327 0,0015240,223 ‐6,334 0,0006140,232 ‐6,334 0,0009140,059 ‐6,708 0,0015240,091 ‐6,715 0,0015237,161 ‐8,002 0,0124640,265 ‐6,289 0,0045640,242 ‐6,281 0,0054740,249 ‐7,922 0,0018240,414 ‐7,548 0,0033439,128 ‐6,767 0,0030439,839 ‐7,091 0,0000039,795 ‐7,101 0,0000039,883 ‐7,100 0,0000040,355 ‐8,752 0,0059540,316 ‐8,793 0,0043739,695 ‐7,350 0,0000039,738 ‐7,339 0,0000039,555 ‐8,551 0,0048639,588 ‐8,472 0,0051739,570 ‐8,496 0,0009140,198 ‐7,896 0,0040538,579 ‐6,647 0,0011038,506 ‐6,422 0,0007239,524 ‐7,819 0,0000039,563 ‐7,645 0,0000039,875 ‐8,762 0,0049439,916 ‐8,654 0,00494

39,751 ‐7,395 0,0000040,022 ‐8,202 0,0040537,933 ‐9,164 0,0139839,916 ‐8,903 0,0049439,992 ‐8,896 0,0026637,939 ‐6,055 0,0026838,031 ‐6,252 0,0007238,878 ‐6,019 0,0000038,613 ‐6,188 0,0000039,591 ‐9,104 0,0041839,681 ‐9,088 0,0033439,113 ‐7,713 0,0047339,216 ‐8,039 0,0010139,281 ‐8,279 0,0026339,538 ‐8,177 0,0036539,597 ‐8,462 0,0006140,171 ‐6,324 0,0006140,039 ‐6,705 0,0015239,922 ‐8,609 0,0055540,036 ‐8,560 0,0042637,048 ‐6,739 0,0005137,228 ‐6,900 0,0011237,031 ‐6,542 0,0016340,233 ‐6,282 0,0045640,213 ‐6,279 0,0045638,748 ‐6,834 0,0000036,738 ‐7,278 0,0141336,964 ‐7,643 0,0127736,626 ‐6,015 0,0050239,191 ‐9,214 0,0045637,650 ‐4,325 0,0081237,527 ‐5,003 0,0080538,077 ‐4,945 0,0042137,318 ‐5,537 0,0068437,388 ‐6,433 0,0025137,402 ‐6,002 0,0048039,589 ‐8,059 0,0036537,432 ‐6,783 0,0015437,567 ‐6,595 0,0034436,767 ‐5,915 0,0063840,423 ‐7,495 0,0018238,239 ‐7,123 0,0011038,182 ‐7,094 0,0022137,930 ‐7,076 0,0021137,722 ‐7,040 0,0004438,427 ‐4,640 0,0022838,304 ‐5,318 0,0020637,450 ‐6,980 0,0004438,661 ‐5,346 0,0000039,006 ‐5,567 0,0010538,999 ‐5,233 0,0010539,644 ‐4,506 0,0005539,988 ‐4,727 0,0016039,417 ‐4,134 0,0028340,454 ‐5,118 0,00359

39,809 ‐5,845 0,0040939,215 ‐5,906 0,0010539,747 ‐5,888 0,00257

Additional file 5. Full genetic similarity maps. The genetic (dis)similarity among populations within the different Triturus species, not cut according to the current species ranges. For each species, the genetic divergence among populations (Zi) was determined and subsequently interpolated across its distribution range. We use both a single scale for all Triturus species (allowing direct comparison among species) and aspecies specific scale (better expressing genetic structure in genetically relatively poor species). Warmer colors refer to a higher genetic divergence. Single scale T. carnifex

Single scale T. cristatus

Single scale T. dobrogicus

Single scale T. karelinii western species

Single scale T. karelinii central species

Single scale T. karelinii eastern species

Single scale T. macedonicus

Single scale T. marmoratus

Single scale T. pygmaeus

Species specific scale T. carnifex

Species specific scale T. cristatus

Species specific scale T. dobrogicus

Species specific scale T. karelinii western species

Species specific scale T. karelinii central species

Species specific scale T. karelinii eastern species

Species specific scale T. macedonicus

Species specific scale T. marmoratus

Species specific scale T. pygmaeus

Additional file 6. Model performance.

Rank T. marmoratus T. pygmaeus T. carnifex T. cristatus T. dobrogicus T. macedonicus west central east1 0,508 0,529 0,556 0,511 0,565 0,552 0,554 0,541 0,5502 0,515 0,532 0,561 0,512 0,572 0,559 0,558 0,597 0,5523 0,516 0,532 0,568 0,513 0,576 0,562 0,559 0,602 0,5534 0,516 0,533 0,569 0,515 0,576 0,564 0,564 0,602 0,5545 0,517 0,534 0,574 0,515 0,577 0,568 0,566 0,603 0,5626 0,518 0,534 0,574 0,515 0,579 0,568 0,566 0,604 0,5627 0,519 0,536 0,574 0,515 0,579 0,569 0,567 0,609 0,5668 0,519 0,537 0,575 0,516 0,579 0,570 0,567 0,612 0,5699 0,519 0,538 0,576 0,516 0,580 0,572 0,568 0,613 0,57110 0,519 0,539 0,577 0,518 0,581 0,572 0,570 0,613 0,57111 0,519 0,539 0,577 0,518 0,585 0,577 0,572 0,614 0,57312 0,520 0,540 0,577 0,518 0,585 0,578 0,573 0,614 0,57413 0,520 0,540 0,580 0,518 0,585 0,579 0,574 0,616 0,57514 0,521 0,540 0,580 0,518 0,586 0,580 0,574 0,618 0,57615 0,522 0,540 0,581 0,519 0,586 0,580 0,577 0,620 0,57816 0,522 0,541 0,583 0,519 0,587 0,580 0,578 0,631 0,57917 0,522 0,542 0,583 0,520 0,588 0,581 0,579 0,633 0,57918 0,522 0,542 0,583 0,520 0,589 0,581 0,581 0,633 0,57919 0,522 0,542 0,583 0,520 0,589 0,582 0,582 0,634 0,58120 0,522 0,543 0,584 0,520 0,589 0,582 0,583 0,635 0,58221 0,522 0,543 0,584 0,521 0,589 0,583 0,583 0,635 0,58222 0,522 0,543 0,585 0,521 0,589 0,583 0,584 0,636 0,58223 0,523 0,543 0,585 0,521 0,590 0,584 0,584 0,637 0,58324 0,523 0,543 0,586 0,521 0,590 0,584 0,585 0,638 0,58325 0,523 0,544 0,586 0,522 0,590 0,585 0,585 0,638 0,58326 0,523 0,544 0,586 0,522 0,590 0,585 0,585 0,640 0,58427 0,523 0,544 0,586 0,522 0,591 0,585 0,586 0,641 0,58528 0,524 0,544 0,586 0,522 0,592 0,586 0,586 0,641 0,58629 0,524 0,544 0,588 0,522 0,592 0,586 0,587 0,643 0,58630 0,524 0,544 0,590 0,522 0,593 0,586 0,588 0,645 0,58731 0,524 0,545 0,590 0,523 0,593 0,588 0,589 0,647 0,58732 0,524 0,546 0,590 0,523 0,594 0,589 0,591 0,648 0,58733 0,524 0,546 0,591 0,523 0,595 0,589 0,591 0,648 0,58734 0,524 0,546 0,591 0,523 0,595 0,589 0,592 0,649 0,58935 0,524 0,546 0,591 0,523 0,596 0,590 0,592 0,651 0,58936 0,525 0,546 0,592 0,523 0,597 0,590 0,593 0,651 0,58937 0,525 0,546 0,592 0,523 0,597 0,592 0,593 0,651 0,590

T. karelinii

38 0,525 0,546 0,593 0,523 0,597 0,592 0,594 0,652 0,59039 0,525 0,547 0,594 0,523 0,599 0,593 0,594 0,653 0,59040 0,525 0,547 0,596 0,523 0,600 0,593 0,594 0,654 0,59141 0,525 0,547 0,596 0,523 0,600 0,593 0,594 0,654 0,59142 0,525 0,548 0,597 0,524 0,600 0,594 0,594 0,654 0,59143 0,525 0,548 0,598 0,524 0,601 0,594 0,594 0,655 0,59244 0,525 0,548 0,599 0,524 0,601 0,594 0,595 0,655 0,59345 0,525 0,548 0,600 0,524 0,602 0,594 0,596 0,656 0,59546 0,526 0,548 0,600 0,525 0,602 0,594 0,596 0,656 0,59547 0,526 0,549 0,602 0,525 0,602 0,595 0,596 0,659 0,59648 0,526 0,549 0,602 0,525 0,603 0,595 0,597 0,659 0,59649 0,526 0,549 0,603 0,525 0,604 0,595 0,597 0,660 0,59750 0,526 0,549 0,603 0,525 0,604 0,596 0,597 0,660 0,59751 0,526 0,549 0,604 0,525 0,604 0,597 0,598 0,661 0,59852 0,526 0,550 0,605 0,525 0,605 0,597 0,598 0,663 0,59853 0,527 0,550 0,605 0,525 0,605 0,598 0,599 0,664 0,59954 0,527 0,550 0,606 0,525 0,605 0,598 0,600 0,665 0,59955 0,527 0,550 0,608 0,526 0,606 0,598 0,600 0,665 0,59956 0,527 0,550 0,608 0,526 0,607 0,599 0,601 0,667 0,60057 0,527 0,551 0,609 0,526 0,608 0,600 0,601 0,668 0,60158 0,527 0,551 0,609 0,526 0,608 0,602 0,601 0,668 0,60159 0,527 0,552 0,610 0,526 0,608 0,603 0,603 0,670 0,60260 0,527 0,553 0,611 0,526 0,608 0,603 0,605 0,672 0,60261 0,527 0,553 0,611 0,526 0,609 0,604 0,606 0,672 0,60362 0,527 0,553 0,612 0,526 0,609 0,604 0,606 0,674 0,60363 0,528 0,553 0,613 0,526 0,610 0,605 0,607 0,677 0,60364 0,528 0,554 0,613 0,526 0,610 0,605 0,607 0,677 0,60465 0,528 0,554 0,613 0,526 0,611 0,605 0,607 0,678 0,60466 0,528 0,554 0,613 0,526 0,612 0,605 0,607 0,678 0,60567 0,528 0,554 0,614 0,526 0,612 0,605 0,608 0,678 0,60568 0,528 0,554 0,615 0,527 0,613 0,606 0,608 0,679 0,60669 0,528 0,555 0,615 0,527 0,613 0,606 0,610 0,679 0,60770 0,528 0,555 0,617 0,527 0,614 0,606 0,611 0,681 0,60771 0,528 0,555 0,617 0,527 0,614 0,606 0,611 0,682 0,60972 0,529 0,556 0,618 0,527 0,614 0,606 0,611 0,683 0,60973 0,529 0,557 0,618 0,527 0,616 0,606 0,612 0,684 0,61274 0,529 0,557 0,618 0,527 0,617 0,607 0,612 0,686 0,61275 0,529 0,557 0,618 0,528 0,618 0,607 0,614 0,689 0,61376 0,529 0,558 0,618 0,528 0,618 0,609 0,614 0,690 0,61477 0,529 0,558 0,618 0,528 0,620 0,609 0,615 0,695 0,61478 0,529 0,558 0,619 0,528 0,620 0,610 0,615 0,695 0,615

79 0,530 0,558 0,622 0,529 0,621 0,611 0,616 0,695 0,61680 0,530 0,558 0,622 0,529 0,621 0,611 0,616 0,696 0,61681 0,530 0,560 0,623 0,529 0,621 0,612 0,617 0,696 0,61682 0,531 0,560 0,624 0,529 0,622 0,614 0,618 0,697 0,61683 0,531 0,561 0,625 0,529 0,623 0,614 0,619 0,702 0,61784 0,531 0,561 0,625 0,529 0,626 0,614 0,620 0,703 0,61785 0,532 0,561 0,625 0,529 0,626 0,617 0,622 0,705 0,61786 0,532 0,561 0,626 0,530 0,626 0,619 0,622 0,707 0,61787 0,532 0,562 0,627 0,530 0,629 0,619 0,622 0,710 0,61888 0,533 0,562 0,629 0,530 0,629 0,622 0,622 0,710 0,61989 0,533 0,562 0,630 0,531 0,630 0,624 0,623 0,711 0,62090 0,533 0,563 0,631 0,531 0,630 0,625 0,626 0,717 0,62091 0,533 0,564 0,632 0,531 0,631 0,625 0,626 0,720 0,62392 0,534 0,564 0,634 0,531 0,631 0,626 0,628 0,721 0,62493 0,535 0,566 0,637 0,532 0,634 0,626 0,629 0,722 0,62494 0,535 0,569 0,638 0,532 0,640 0,627 0,630 0,724 0,62595 0,536 0,569 0,640 0,532 0,640 0,627 0,634 0,725 0,62796 0,536 0,569 0,640 0,533 0,644 0,627 0,637 0,726 0,62997 0,536 0,570 0,643 0,533 0,649 0,631 0,643 0,730 0,63198 0,537 0,570 0,646 0,534 0,658 0,634 0,646 0,731 0,63999 0,547 0,571 0,666 0,534 0,666 0,642 0,659 0,736 0,643100 0,904 0,963 0,945 0,739 0,974 0,939 0,928 0,943 0,874

Additional file 7. Full species distribution models. The species distribution model of each Triturus species projected for Last Glacial Maximum and current climate conditions, not cut according to the current species ranges. For each species, its ecological niche model was projected on Last Glacial Maximum (both the MIROC and CCSM model) and current climate layers. Warmer colors refer to a higher predicted suitability. MIROC T. carnifex

MIROC T. cristatus

MIROC T. dobrogicus

MIROC T. karelinii western species

MIROC T. karelinii central species

MIROC T. karelinii eastern species

MIROC T. macedonicus

MIROC T. marmoratus

MIROC T. pygmaeus

CCSM T. carnifex

CCSM T. cristatus

CCSM T. dobrogicus

CCSM T. karelinii western species

CCSM T. karelinii central species

CCSM T. karelinii eastern species

CCSM T. macedonicus

CCSM T. marmoratus

CCSM T. pygmaeus

CURRENT T. carnifex

CURRENT T. cristatus

CURRENT T. dobrogicus

CURRENT T. karelinii western species

CURRENT T. karelinii central species

CURRENT T. karelinii eastern species

CURRENT T. macedonicus

CURRENT T. marmoratus

CURRENT T. pygmaeus

Documents

Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling