Global Warming Will Affect the Genetic Diversity and Uniqueness of Lycaena Helle Populations

Global Change Biology (2011) 17, 194–205, doi: 10.1111/j.1365-2486.2010.02233.x

Global warming will affect the genetic diversity and uniqueness of Lycaena helle populations

JAN CHRISTIAN HABEL*w z,DENNISRO¨ DDERz,THOMASSCHMITTz and GABRIEL NE` VE§ *Institute of Ecology and Environmental Chemistry, Leuphana University Lu¨neburg, D-21335 Lu¨neburg, Germany, wMuse´e National d’Histoire Naturelle, Section Zoologie des Inverte´bre´s, L-2160 Luxembourg, zDepartment of Biogeography, Trier University, D-54296 Trier, Germany, §Aix-Marseille Universite´, CNRS IRD UMR 6116-IMEP, F-13331 Marseille Cedex 3, France

Abstract The climate warming of the postglacial has strongly reduced the distribution of cold-adapted species over most of Central Europe. Such taxa have therefore become extinct over most of the lowlands and shifted to higher altitudes where they have survived to the present day. The lycaenid butterﬂy Lycaena helle follows this pattern of former widespread distribution and later restriction to mountain areas such as the European middle mountains. We sampled 203 individuals from 10 populations representing six mountain ranges (Pyrenees, Jura, Massif Central, Morvan, Vosges and Ardennes) over the species’ western distribution. Allozyme and microsatellite polymorphisms were analysed to study the genetic status of these highly fragmented populations. Both molecular marker systems revealed a strong genetic differentiation among the analysed populations, coinciding with the orographic structure and highly restricted gene ﬂow among them. The large-scale genetic differentiation is more pronounced in allozymes (FCT: 0.326) than in microsatellites (RCT: 0.113), but microsatellites show a higher resolution on the regional scale (RSC: 0.082) compared with allozymes (FSC: n.s.). For both analytical tools, we found private alleles occurring exclusively in a single mountain area. The highly fragmented and isolated occurrence of populations is supported by the distribution pattern of potentially suitable climate suggested by species distribution models. Model projections under two climate warming scenarios predict a decline of climatically suitable areas, which will result in the extinction of most of the populations showing unique genetic characteristics. Keywords: allozymes, climate change, climate envelope, ecological niche modelling, fragmentation, Lycaenidae, microsatellites, mountain regions

Received 25 August 2009; revised version received 18 December 2009 and accepted 10 March 2010

nowski, 2000). For more than four decades, allozyme Introduction electrophoresis has been applied to assess effects of The number of studies addressing the genetic structure of ecosystem changes (Hedrick, 1999), for example, in populations has increased considerably over the last studies on past climate oscillations (see Schmitt, 2007, decade (Hedrick, 2001; Storfer et al., 2007; see Schmitt, 2009 for recent reviews). However, more recently devel- 2007, 2009 for a recent review). This process has been oped analytical techniques using hypervariable, non- accelerated by the development of powerful molecular coding DNA have become more widely used in techniques and new statistical methods, which offer a landscape genetics allowing high levels of resolution variety of sophisticated analyses (e.g. Haig, 1998; Zane of genetic structures even over small spatial and short et al., 2002; Excoffier & Heckel, 2006). Meanwhile, neutral temporal scales (Storfer et al., 2007). For example, micro- and nonneutral, dominant and codominant markers offer satellites have currently become a popular tool in a broad choice of ways of analysing different temporal population and conservation genetics (e.g. Collevatti and spatial scales in the fields of phylogeography, ecology et al., 2001; Gao et al., 2002; Gaiotto et al., 2003; Dutech and conservation (Allendorf & Luikart, 2007). et al., 2004), despite the difficulty of identifying work- Codominant marker systems, such as allozymes or able loci in some taxonomic groups such as Lepidoptera microsatellites, represent suitable tools for estimating (Ne`ve & Meglećz, 2000; Ne`ve, 2009). This marker is recent and past population structures (Hedrick & Kali- particularly useful for the estimation of the effective

population size (Ne), genetic structures – even among Correspondence: J. C. Habel, Muse´e National d’Histoire Naturelle neighbouring populations – and recent genetic effects of Luxembourg, 25, Rue Mu¨nster, L-2160 Luxembourg, e-mail: environmental changes (cf. Conte et al., 2003; Keller & [email protected] Largiade`r, 2003; Keller et al., 2005). However, data

194 r 2010 Blackwell Publishing Ltd GLOBAL WARMING AND GENETIC UNIQUENESS 195 obtained from microsatellite analyses should be inter- lar marker systems and species distribution modelling. preted with caution, as several severe shortcomings of Our selected model organism has suffered severely this marker system, such as the frequent presence of from the advancing temperature increase combined null alleles, have been noted (Chapuis et al., 2008). with land-use changes; today most of the populations Therefore, the most important observable differences of central Europe therefore occur as isolated remnants between allozymes and microsatellites are (i) the much (Finger et al., 2009). This may lead to strong genetic drift higher level of polymorphisms in microsatellites, (ii) effects (cf. Lande, 1995; Hedrick & Kalinowski, 2000), their significantly higher level of heterozygosity and often even combined with reductions in population size (iii) a two to four orders of magnitude higher mutation and individual fitness (Bouzat et al., 1998; Madsen et al., rate in microsatellites (Estoup et al., 1998; Streiff et al., 2000; Chapman et al., 2009). 1998; Gao et al., 2002). Both methods, then, have their We combine the analysis of allozymes and microsa- specific strengths and therefore should be applied in tellites to study the population genetic constitution at combination. Nevertheless, in most cases our knowl- the westernmost distribution of this butterfly species edge about population genetic structures of species is with species distribution modelling for its past, present based on a single genetic marker system, although some and possible future distribution patterns. We analysed studies even showed that various molecular markers 203 individuals from 10 populations scattered from may result in conflicting patterns (Vandewoestijne & the Pyrenees to the Ardennes and representing six Baguette, 2002; Garcia-Paris et al., 2003; Veith et al., mountain systems and modelled distributions based 2008). on 458 presence localities. The population genetic ana- In addition to genetic analyses, species distribution lyses performed assess (i) the recent genetic status of models (SDMs) allow spatial assessments of areas these highly isolated populations and (ii) potential potentially suitable for a species and the connectivity differences between these two molecular marker patterns of these areas (Guisan & Zimmermann, 2000; systems. Via species distribution modelling, we evalu- Jeschke & Strayer, 2008; Ro¨dder et al., 2010). SDMs rely ate (iii) the consequences of our genetic results for on the assumption that environmental conditions are conservation. the primary drivers of the target species’ distribution and that the range of this species fits with those conditions (Arau´ jo & Pearson, 2005). These models have Materials and Methods frequently been applied to predict changes in species’ potential distributions under current, past and Study species future climate scenarios (Arau´ jo et al., 2004; Arau´ jo The Violet Copper L. helle (Denis & Schiffermu¨ ller, 1775) is a & Guisan, 2006; Heikkinen et al., 2006; Hijmans & boreo-montane butterfly of the Palearctic. This lycaenid spe- Graham, 2006; Pearman et al., 2008). cies was widely distributed over Central Europe during the The application of SDMs is of particular interest in late glacial and early postglacial period and declined in the combination with genetic analyses, preferably those wake of rising temperatures during the postglacial warming based on more than one single marker system for the over major parts of the European lowlands (Habel et al., 2010). Isolated remnant populations have remained restricted to understanding of past, present and future changes in mountain areas like the lower mountains of Western and ranges. As a recent altitudinal and latitudinal range Central Europe (Nunner, 2006; Bachelard & Fournier, 2008), shift due to global warming is a generally accepted but some populations also occur in lowland wet meadows in phenomenon (Parmesan & Yohe, 2003), predictive tools some countries of Central Europe (Skorka et al.,2007). The are highly relevant, but so far do not take into account habitats of L. helle are cool and damp grasslands with sheltered the intraspecific diversity. However, the preservation of stands, marshes, clearings in forests and along streams, genetic diversity and unique lineages within species in springs and bogs with sheltered warm pockets (Reál, 1962; a rapidly changing world is considered as a key aspect Bachelard & Descimon, 1999) and specific vegetation struc- in conservation biology (Allendorf & Luikart, 2006). tures (Turlure et al., 2009); another requirement is the existence Therefore, the combination of SDMs and genetic ana- of the exclusive larval food plant, Polygonum bistorta. For these lyses allows the delimitation of particularly endangered reasons, the habitats of L. helle are rather scattered (cf. Finger et al., 2009), a fact which is further aggravated by the species’ genetic lineages within species of interest, and thus sedentariness (cf., Bink, 1992; Baguette & Ne`ve, 1994; Agnes, allow the planning of appropriate conservation 2000; Bauerfeind et al. 2008). L. helle has been strongly declin- actions. ing all over Europe during the last decades (Van Swaay & To address the question of such genetic consequences Warren, 1999). Therefore, the species was included in the of climate change, we selected the highly endangered Natura 2000 directive of Europe. The initiated conservation Lycaenid butterfly Lycaena helle to analyse different efforts showed some local positive effects (e.g. in the Ar- temporal and spatial scales with two different molecu- dennes), but are still invisible in the overall genetic structures r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 194–205 196 J. C. HABEL et al.

Ardennes A1 A2

Vosges V Morvan MV J Jura M2

M3 M1 M4 Alps Massif Central

P Pyrenees 100 km

Fig. 1 Geographic locations of the 10 sampling sites of Lycaena helle over the western European study area. Names of localities are as in Table 1.

Table 1 Overview of all sampling localities, areas, numbers of individuals, names of localities and capture dates

Region Abbreviation Name of locality Coordinates N; E N Date of sampling

Pyrenees P F-Quillane 42.54; 2.10 19 V-1991 Jura J F-Le Russey 47.09; 6.43 19 31-V-1991 Massif Central M1 F-Col de Montcineyre 45.28; 2.53 15 12-VI-1991 M2 F-Espinasse 46.02; 2.42 37 12-VI-1991 M3 F-Lac de Montcineyre 45.28; 2.53 13 12-VI-1991 M4 F-Mareuge 42.28; 1.48 15 V-1991 Morvan MV F-Lavault de Fre´toy 47.06; 4.02 24 30-V-1991 Vosges V F-Brochotte Xonrupt 48.04; 6.55 13 V-1991 Ardennes A1 F-Pont Collin 50.00; 4.49 17 V-1991 A2 B-Arlon 49.39; 5.43 31 V-1991

For the geographic locations see Fig. 1. B, Belgium; F, France. as revealed by a comparison of microsatellites analysed for collected butterﬂies could not be used for allozyme electro- samples collected in 1991 and 2005–2006, however, slight shifts phoresis as only one leg per individual was dissected as a in allele frequencies were observed. These more recently nonlethal sampling method (Habel et al., 2010).

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Table 2 Conditions of electrophoresis for different enzymes analysed for Lycaena helle

Homogenate Enzyme EC No. Number of loci Buffer applications Running time (min)

AAT 2.6.1.1 2 TC 3 60 ME 1.1.1.40 1 TC 3 40 G6PDH 1.1.1.49 1 TC 3 60 ACON 4.2.1.3 1 TC 3 45 MDH 1.1.1.37 2 TC 3 40 PK 2.7.1.40 1 TC 2 45

PepPhePro 3.4.11/13 1 TG 2 20 PGI 5.3.1.9 1 TG 1 40 PGM 5.4.2.2 1 TG 1 40 6PGDH 1.1.1.44 1 TM 3 60 IDH 1.1.1.42 2 TM 3 60 APK 2.7.3.3 1 TM 1 30

All buffers were run at 200 V. For more details see Karl et al. (2009). TC, Tris-citrate pH 8.2; TG, Tris-glycine pH 8.5; TM, Tris-maleic acid pH 7.0 (adjusted from TM pH 7.8).

Sampling Statistics

Two hundred and three individuals from 10 populations in six Distortions of our results obtained by microsatellites through mountain areas were collected. The individuals originated stutter bands, large allele dropout or null alleles (cf. Selkoe & from the Pyrenees (one population), Massif Central (four Toonen, 2006) were tested using the programme Micro-Checker populations), Jura (one population), Morvan (one population), (Van Oosterhout et al., 2004). Allelic richness and mean allele Vosges (one population) and Ardennes (two populations), all numbers were calculated using FSTAT (Goudet, 1995). F- and caught in summer 1991 (Fig. 1, Table 1). The individuals were R-statistics, hierarchical genetic variance analyses (AMOVAs), netted in the field, frozen in liquid nitrogen and stored at observed and expected heterozygosities, tests on Hardy–Wein- 80 1C until analysis. berg equilibrium and linkage disequilibrium were calculated with ARLEQUIN 3.1 (Excoffier et al., 2005). Phenograms using the neighbour joining algorithm (Saitou & Microsatellite analysis Nei, 1987) based on Chord standard distances (Cavalli-Sforza & Edwards, 1967) were constructed with TREEMAKER.Nodesup- We selected five previously developed polymorphic microsa- port was assessed by means of 1000 bootstrap replicates. tellite loci (LheF12, LheB06, LheE12, Lhe03, Lhe14, see Habel Variance analyses were carried out on three hierarchical 0 et al. 2008). The forward primer of each pair was 5 end- levels: within populations, among populations within regions labelled with the fluorescent phosphoramidites FAM (LheE12 and among regions. For the microsatellites, the specific analo- and LheB06), HEX (Lhe03) and TET (Lhe14 and LheF12). gous R-statistics based on allele lengths and the stepwise Details of the analytical procedures are described in Habel mutation model (SMM) were used (cf. Balloux & Lugon- et al. (2008) and Finger et al. (2009). Molecular analysis and the Moulin, 2002). The STRUCTURE software (Pritchard et al., 2000) interpretation of electropherograms were performed by the was used to assess the highest probabilities for the respective same person in the same laboratory for all samples. differentiation for both analytical methods. For each run, the burn-in and simulation lengths were 100 000 and 300 000, respectively. We used the batch run function to carry out a Allozyme analysis total of 100 runs – 10 each for one to 10 clusters, i.e. K 5 1–10. For allozyme electrophoresis, the same individuals as for the microsatellites were used. Half of the abdomen of each in- SDM dividual was homogenised in Pgm buffer by ultrasound and centrifuged at 17 000 g for 5 min. For allozyme electrophoresis, Species records. In this study, 458 presence localities over the we used cellulose acetate plates applying standard protocols West European distributional range of L. helle provided the (Richardson et al., 1986; Hebert & Beaton, 1993). We analysed a basis for SDM computation. We either visited these localities total of 16 allozyme loci previously established for the con- ourselves or used data provided by colleagues. L. helle records generic species Lycaena tityrus (Karl et al., 2009) with most of were not randomly distributed over the species’ range, these loci being polymorphic in other butterfly species (e.g. suggesting some degree of sample selection bias which may Besold et al., 2008; Schmitt & Haubrich, 2008). Running con- violate SDM assumptions since clumped records may lead to ditions are given in Table 2. an overrepresentation in the feature space (Phillips, 2008). To r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 194–205 198 J. C. HABEL et al. account for this, we extracted all ‘bioclimatic’ values from the influence on the butterfly’s occurrence (Bauerfeind et al., 2008; records and performed a cluster analysis based on Euclidean Turlure et al., 2009; Goffart et al., 2010). distances, from which resulting classes were blunted at a threshold, leaving 200 classes. Only one record per class was Model algorithm. We used MAXENT 3.3.1 for SDM computation used for further processing. This method reduces the amount (http://www.cs.princeton.edu/ shapire/Maxent; downloaded of duplicate information in the feature space and thus the May 25, 2009; Phillips et al., 2006; Phillips & Dudı´k, 2008). In impact of samples clumped in the geographic space. numerous comparative studies, Maxent has achieved better Calculations were performed with XLSTAT 2008 (Addinsoft, results than other presence only methods (Elith et al., 2006; http://www.xlstat.com; downloaded July 1, 2007). Heikkinen et al., 2006; Wisz et al., 2008). The Maxent algorithm estimates geographic distributions of species from environ- Climate data. Information on current climate was obtained mental conditions as extracted from species records and from from the Worldclim database, version 1.4, which is based on random background data by finding the maximum entropy weather conditions recorded from 1950 to 2000 with a grid distribution (Phillips et al., 2006). Since the area from which cell resolution of 30 arc sec (Hijmans et al., 2005; http:// background data is obtained reflects ideally those regions www.worldclim.org). In order to assess the predictive ability accessible to the target species (Phillips, 2008), we restricted of our model, we used paleoclimate simulations describing the background samples to an area enclosed by a minimum climatic conditions as they are calculated to have been during polygon enclosing all presence records. Areas with climate the last glacial maximum (ca. 21 000 BP). We used General conditions not analogous to those represented by background Circulation Model (GCM) simulations from the Community data may lead to uncertainties in model predictions (Phillips Climate System Model (CCSM; http://www.ccsm.ucar.edu/; et al., 2006). The Maxent algorithm automatically allows an Kiehl & Gent, 2004) with a resolution of 2.5 arc minutes identification of the degree of uncertainty when projecting developed by R. J. Hijmans; the downscaling process of the models (‘clamping’). In our SDM, the degree of ‘clamping’ was original CCSM data was described by Peterson and Nya´ri removed from the model prediction using the ‘fade by (2007). clamping’ option, which reduces the prediction within each For projections under future global warming scenarios, a grid cell by the difference between clamped and nonclamped set of different families of emission scenarios was formulated values at that cell. by the IPCC based on the future production of greenhouse It has been suggested that ensemble model predictions may gases and aerosol precursor emissions. Scenarios A2a and B2a enhance the reliability and robustness of SDM results (Arau´ jo describe two possible demographic, politico-economic, social & New, 2007). Therefore, we computed 100 models, each and technological futures. Scenario B2a emphasizes more trained with randomly chosen 75% of the 200 distribution environmentally conscious, more regionalized solutions to records used for model training. Subsequently, we integrated the requirements of economic, social and environmental sus- all results into a map indicating the average Maxent value per tainability. Scenario A2a also emphasizes regionalized solu- grid cell (past and future projections of the models were tions to economic and social development, but it is less processed in the same manner). The 25% previously removed environmentally conscious. Climate change simulations for records were used for model evaluation by means of calcula- both scenarios based on the CCCma and HADcm3 models tion of the area under the curve (AUC) in receiver operating (Flato et al., 2000; Gordon et al., 2000) for the year 2080 with a characteristic curves (Swets, 1988), a threshold-independent spatial resolution of 2.5 arc minutes were obtained via the index widely used in ecological modelling (Jeschke & Strayer, Worldclim homepage. 2008), whereby AUC values may range from 0.5 (random Each climate data set comprises minimum and maximum accuracy) to 1.0 (perfect discrimination). temperatures and mean precipitation per month. Based on these, we used DIVA-GIS 5.4 to compute 19 so-called bioclim variables (Busby, 1991), which are better suited for species Results distribution modelling than monthly values (Busby, 1991; Beaumont et al., 2005). Since multicollinearity of predictor Genetic analyses variables may hamper the reliability of SDM results (Heikki- nen et al., 2006), we excluded highly intercorrelated variables Three of the 16 allozyme loci analysed were poly- 2 (Pearsons’ correlation coefficient; R 40.75). The final variable morphic (Pgi, Me and Apk). The highest number of set comprised the annual mean temperature (Bio1), the mean alleles was observed in Pgi, where there were six; the monthly temperature range (Bio2), isothermality (Bio3), the mean number of alleles per locus was 1.73 ( 1.44 SD). maximum temperature of the warmest month (Bio5), the The five microsatellite loci were all polymorphic and minimum temperature of the coldest month (Bio6), the tem- yielded from 10 to 34 alleles (mean: 23 10 SD), with a perature annual range (Bio7), the mean temperature of the wettest quarter (Bio8), the mean temperature of the driest total number of 114 alleles over all loci and populations. quarter (Bio9), the annual precipitation (Bio12), the precipita- Sixty of these alleles (F12: 15 of 34, B06: 21 of 32, He12: 7 tion seasonality (Bio15), the precipitation of the driest quarter of 16, He14: 9 of 24 and He03: 8 of 10) and two out of the (Bio17), the precipitation of the warmest quarter (Bio18) and 13 alleles of the polymorphic allozyme loci (one allele the precipitation of the coldest quarter (Bio19). All these each of Pgi and Apk) were restricted to one of the six variables were selected due to their confirmed or probable mountain ranges. The cumulative frequencies per

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Table 3 Parameters of genetic diversity for all Lycaena helle populations analysed: total number of alleles (A), allelic richness (AR), percentage of expected heterozygosity (He) and observed heterozygosity (Ho) as well as frequency and number of private alleles.

Frequency of Number of

Region Site Marker AARHo[%] He[%] private alleles private alleles

Pyrenees P MS 3.84 2.98 47.5 64.3 5.6 1 AZ 1.25 1.25 3.6 5.2 4.6 1 Massif Central M1 MS 6.85 4.04 68.9 75.4 10.4 8 AZ 1.25 1.21 5.8 7.6 0 0 M2 MS 8.44 4.02 59.0 72.1 8.9 9 AZ 1.25 1.18 5.8 6.3 0 0 M3 MS 5.27 3.69 59.7 72.6 18.3 6 AZ 1.13 1.12 6.5 6.9 0 0 M4 MS 4.83 3.19 52.3 67.5 18.9 6 AZ 1.13 1.12 7.2 6.7 0 0 Morvan MV MS 3.65 2.66 39.8 58.8 14.3 4 AZ 1.25 1.15 2.9 3.3 0 0 Jura J MS 5.00 3.18 52.4 60.5 9.1 5 AZ 1.18 1.16 15.8 11.5 0 0 Vosges V MS 3.47 2.59 43.0 61.5 20.4 6 AZ 1.10 1.04 1.4 2.8 0 0 Ardennes A1 MS 5.43 4.59 63.3 81.2 13.0 8 AZ 1.46 1.20 4.8 6.5 0 0 A2 MS 8.63 4.25 54.8 77.2 8.6 13 AZ 1.45 1.29 4.2 5.0 3.4 1 Mean (S.D.) MS 5.54 3.52 54.1 69.1 12.8 6.6 (1.86) (0.69) (9.1) (7.7) (5.07) (3.20) Mean (S.D.) AZ 1.25 1.17 5.8 6.2 0.8 0.2 ( 0.12) ( 0.07) ( 3.9) ( 2.4) ( 1.71) ( 0.42)

MS, microsatellites; AZ, allozymes.

population of these regionally restricted alleles ranged in the microsatellites than in the allozymes (U-tests: all from 0% to 4.6% in allozymes and from 1.0% to 56.4% in Po0.05; Table 3). microsatellites. The genetic differentiation among all analysed popu-

No linkage disequilibrium was observed for any pair lations was very high for allozymes (FST: 0.336, of loci after Bonferroni correction in either of the two Po0.001) compared with the microsatellite data (RST: marker systems. Therefore, further analyses were per- 0.166, Po0.001); as R- and F-statistics throughout formed using standard algorithms in population genet- showed similar values in microsatellites, only the re- ics. Significant deviations from HWE were detected in sults of R-statistics are given. A hierarchical variance the microsatellite loci LheB06 and Lhe14, but not in analysis showed strong genetic differentiation among the other three microsatellite and the allozyme loci. This the six mountain areas for both molecular marker heterozygote deficiency in these two microsatellite systems (allozymes: FCT: 0.325, microsatellites: RCT: loci was caused by the presence of null alleles (cf. Habel 0.113, both Po0.001). This strong genetic differentiation et al., 2008). We, therefore, calculated all analyses among regions is accompanied by a strong genetic without the two loci affected by null alleles; however, differentiation among the populations within regions the exclusion of these loci did not change any of the (i.e. the Massif Central and Ardennes), which is detect- results. Consequently, all following analyses presented able with microsatellites (RST: 0.082, Po0.001), but not below were based on the complete microsatellite with allozymes (FST: 0.015, n.s.). data set. A neighbour joining phenogram based on genetic We calculated six parameters of genetic diversity: distances (Cavalli-Sforza & Edwards, 1967) revealed mean number of alleles per locus, allelic richness, ob- similar genetic groupings for both marker systems served and expected heterozygosity as well as the (Fig. 2). Comparable results were obtained for Baye- numbers and frequencies of private alleles in the poly- sian structure analyses of the microsatellite data set, morphic loci. All these values were significantly higher with almost all L. helle individuals collected in one r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 194–205 200 J. C. HABEL et al.

(a) (b) V A2

A1 M1 M2 M4 68 92 M3 M2 58 M4 73 53 MV M3 51 75 94 A1 M1 83 J A2

P P

Cafalli-Sforza & Cafalli-Sforza & V Edwards (1987) MV J Edwards (1987) 0.03 0.04

Fig. 2 Neighbour-joining phenograms based on Cavalli-Sforza & Edwards (1967) distances with bootstrap values (derived from 1000 replicates) of 10 populations of Lycaena helle from its western European distribution area. (a) Phenogram obtained for 16 allozyme loci, (b) phenogram constructed using ﬁve microsatellite loci. Abbreviations of localities coincide with Table 1 and Fig. 1.

(a) –500 SDM –520 12345678910 –540 In our models, we received ‘good’ to ‘excellent’ AUC –560 values following previously given deﬁnitions (Swets, –580 1988), suggesting that our SDM output was of high –600 quality (Fig. 4a). The mean minimum training presence –620 was 0.06 and the lowest 10% training presence was 0.31

Means of estimated –640

log probabilities of data (Fig. 4b). Maxent allows the user to trace the relative –660 –680 contribution of each variable to the model. In the 100 merged models, the ‘mean temperature of the wettest (b) –2000 quarter’ had the highest explanatory power, followed –2200 12345678910 by the ‘maximum temperature of the warmest month’ –2400 and the ‘precipitation of the warmest quarter’ (Fig. 4c). –2600 Our model accurately predicts the current distribu- –2800 tion of L. helle (Fig. 5a) if compared with the observed –3000 current distribution of the species (Kudrna, 2002). The –3200 projection of our model onto climate conditions, as

Means of estimated –3400 BP log probabilities of data expected for 21 000 , suggests a much broader poten- –3600 –3800 tial distribution over major parts of Europe (Fig. 5b), whereas projections onto future scenarios suggest an Fig. 3 Means of the estimated log probability of data (Pritchard almost entire loss of suitable habitats above the mini- et al., 2000; Evanno et al., 2005) of Structure simulations (10 mum 10% training threshold. Only marginally suitable 5 independent runs for each K 1–10 of all Lycaena helle indivi- areas may be available in 2080 (Figs 5c and d). duals) using (a) allozymes and (b) microsatellites.

Discussion mountain area assigned with high probability values Coinciding differentiation patterns among mountain to their true group. Saturation of probabilities was groups mostly reached for K 5 6 (Fig. 3b). Bayesian structure analyses did not yield suitable results for allozymes, Both molecular marker systems consistently group the and a plateau of probabilities was reached for K 5 3 populations into six clusters if applying analyses at the (Fig. 3a). population level (i.e. neighbour joining phenograms);

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Fig. 4 Model performance in terms of training area under the curve (AUC) and test AUC (a), and minimum training presence (Min train) and minimum 10% training presence (10% train) (b) and variable importance (c) in 100 Maxent models. Abbreviations in (c) are Bio1, annual mean temperature; Bio2, mean monthly temperature range; Bio3, isothermality; Bio5, maximum temperature warmest month; Bio6, minimum temperature coldest month; Bio7, temperature annual range; Bio8, mean temperature wettest quarter; Bio9, mean temperature driest quarter; Bio12, annual precipitation; Bio15, precipitation seasonality; Bio17, precipitation driest quarter; Bio18, precipitation warmest quarter; Bio19, precipitation coldest quarter.

these groups mirror the mountain regions of their origin. scale (cf. Scribner et al., 1994; Tessier et al., 1995), as is However, the allozyme approach in this case mostly fails the case for L. helle among populations within the for individual based analyses, in which the more poly- mountain areas. However, even allozymes can be sui- morphic and highly divers microsatellites perform quite table markers for local studies if they show above well. The signal lacking in allozymes for this analysis average genetic diversities, as frequently observed in might be due to the low number of polymorphic loci (3) butterﬂies, e.g. Polyommatus coridon (Krauss et al., 2004), in combination with the comparatively low number of Erebia epiphron (Schmitt et al., 2005) or Erebia medusa alleles, as exempliﬁed by many allozyme studies (e.g. (Schmitt et al., 2000), but also in the brown trout Salmo Habel et al., 2005; Schmitt et al., 2006; Haubrich & trutta (Crozier & Ferguson, 1986; Moren et al., 1995). Schmitt, 2007; Schmitt & Haubrich, 2008). As in our analyses, other studies often also showed mostly concordant (e.g. Aagaard et al., 2002; Hammouti Loss of genetic uniqueness as a result of climate warming et al., 2010) or only slightly deviating results (e.g. Joyce & Pullin, 2001; Kropf et al., 2002; 2003) obtained from The retreat of L. helle from a wide and mostly contin- two molecular markers. However, also differing pic- uous distribution throughout Europe (Fig. 5b) to higher tures among markers were observed in some cases, elevations during the postglacial warming and the with allozymes often showing lower resolution than subsequent isolation into different mountain systems DNA-based markers (e.g. Vandewoestijne & Baguette, (Fig. 5a) caused distinct evolutionary processes and 2002; Garcia-Paris et al., 2003; Veith et al., 2008). Thus, resulted in seven to nine morphologically distinguish- microsatellites often only have a better resolution than able subspecies (Meyer, 1982; Bozano & Weidenhoffer, moderately polymorphic allozymes on a more local 2001). The comparison with other data sets on butter- r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 194–205 202 J. C. HABEL et al.

Fig. 5 Potential distribution of Lycaena helle under current climate conditions (a), LGM conditions as expected for 21 000 BP (b) and predictions for the near future (2080) assuming IPCC A2a (c) and B2a scenarios (d). Maxent values above the minimum training value are indicated in light grey and those above 10% training presence in dark grey. Species records used for model training are indicated as white dots. Ice caps during the LGM are given as dotted ﬁelds. An overview on all 458 records are given in an electronic appendix.

flies (e.g. Habel et al., 2005; Schmitt et al., 2005; Haubrich sibly reducing their survival probability (cf. Chapman & Schmitt, 2007) support an evolutionary history of et al., 2009). The possible extinction of distinct popula- these differentiation traits not older than the last ice tion groups implies the loss of a large proportion of the age. These taxonomic units are supported by intraspe- genetic diversity, including numerous private alleles, cific uniqueness in both genetic marker systems. Addi- which are exclusively present in these populations and tional microsatellite data on samples collected 2005– are not preserved anywhere else over the entire Eur- 2006 support this finding for within mountain differ- opean range (Finger et al., 2009; Habel et al., 2010). entiation for most of the mountain areas included in this These results obtained from our SDMs are far differ- study (Finger et al., 2009, Habel et al., 2010). The SDM ent from the previous results in the Climatic Risk Atlas projections on global change scenarios suggest an al- of European Butterflies (Settele et al., 2008): ‘Present most complete loss of climatically suitable areas in the distribution can be explained by climatic variables westernmost parts of the distribution of L. helle, except to a moderate extent (AUC 5 0.78). Climate risk cate- for few areas in the Pyrenees, the Massif Central and gory: ‘LR’. One point contributing to these differences parts of the Alps (Figs 5c and d). This negative trend may be the use of different sets of variables (e.g. would have two consequences: (i) losses of entire po- Peterson & Nakazawa, 2008; Ro¨dder & Lo¨tters, 2009; pulations or even lineages and (ii) genetic erosion Ro¨dder et al., 2009). A second point – maybe more processes in many remaining populations, hereby pos- important – is the scale used for modelling. Settele

r 2010 Blackwell Publishing Ltd, Global Change Biology, 17, 194–205 GLOBAL WARMING AND GENETIC UNIQUENESS 203 et al. (2008) used coarser raster data with a resolution of reduce the connectivity among surviving populations 50 50 km. We used more precise species records and thus will increase their individual extinction risk. (o1 km) and climate data with a resolution of 30 arc However, these genetic data will not mirror possible sec (equal to approximately 1 km2). As shown by Seo positive changes achieved by recent habitat restoration et al. (2009) the scale of the species records used and the measures, as genetic structures will eventually be the scale of the environmental predictor highly influence result of population dynamics processes in the actual the predictive power of models. Using too broad-scaled landscapes. Conservation measures should therefore be predictors may lead to an overestimation of the poten- targeted into maintaining or restoring suitable habitat tial distribution of a species of up to 2.89 times by SDMs structure to maintain as much biodiversity as possible. operating on grids above 50 50 km, compared with SDMs operating at 1 km2. Additionally, the model performance may decrease when using too broad scaled Acknowledgements variables. Comparisons of our results with those pre- We acknowledge a grant from the Research Fund of Luxembourg sented by Settele et al. (2008) once more demonstrate (grant number BFR-05/118) and the Natural History Museum these effects. Luxembourg for financial support. The work of D. R. was funded Species do not necessarily face extinction with chan- by the ‘Forschungsinitiative’ of the Ministry of Education, Science, Youth and Culture of the Rhineland-Palatinate state of ging climate conditions, but they may adapt to them, Germany ‘Die Folgen des Global Change fu¨ r Bioressourcen, and the future perspectives of L. helle might be better Gesetzgebung und Standardsetzung’. We are grateful to Henri than predicted by our model. However, such rapid Descimon (Marseille, France) and Philippe Bachelard (Olby, adaptation processes are much more likely in species France) for providing the collected samples and the respective with wide continuous distribution and high genetic authorities for the permissions where necessary. Finally, we thank two anonymous referees for constructive remarks on an diversity and hence a high adaptive capacity (cf. 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