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Genetic diversity and landscape genetic structure of otter (Lutra lutra) populations in Europe

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Genetic diversity and landscape genetic structure of otter

(Lutra lutra) populations in Europe

Nadia Mucci Johanna Arrendal Hermann Ansorge Michael Bailey Michaela Bodner

Miguel Delibes Ainhoa Ferrando Pascal Fournier Christine Fournier Jose´ A. Godoy

Petra Hajkova Silke Hauer Thrine Moen Heggberget Dietrich Heidecke Harri Kirjavainen Hans-Heinrich Krueger Kirsti Kvaloy Lionel Lafontaine Jo´ zsef Lanszki Charles Lemarchand Ulla-Maija Liukko Volker Loeschcke Gilbert Ludwig Aksel Bo Madsen Laurent Mercier

Janis Ozolins Momir Paunovic Cino Pertoldi Ana Piriz Claudio Prigioni Margarida Santos-Reis

Teresa Sales Luis Torsten Stjernberg Hans Schmid Franz Suchentrunk Jens Teubner

Risto Tornberg Olaf Zinke Ettore Randi

Abstract Eurasian otter populations strongly declined and partially disappeared due to global and local causes (habitat destruction, water pollution, human persecution) in parts of their continental range. Conservation strategies, based on reintroduction projects or restoration of dispersal corridors, should rely on sound knowledge of the historical or recent consequences of population genetic structuring. Here we present the results of a survey performed on 616 samples, collected from 19 European countries, geno- typed at the mtDNA control-region and 11 autosomal

microsatellites. The mtDNA variability was low (nucleo- tide diversity = 0.0014; average number of pairwise dif- ferences = 2.25), suggesting that extant otter mtDNA lineages originated recently. A star-shaped mtDNA net- work did not allow outlining any phylogeographic infer- ence. Microsatellites were only moderately variable (Ho = 0.50; He = 0.58, on average across populations), the average allele number was low (observed Ao = 4.9, range 2.5–6.8; effective Ae = 2.8; range 1.6–3.7), sug- gesting small historical effective population size. Extant

N. Mucci E. Randi (&)

Laboratory of Genetics, Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Via Ca` Fornacetta 9, 40064 Ozzano Emilia, Bologna, Italy

J. Arrendal

Department of Animal Ecology, Evolutionary Biology Centre, Uppsala University, Norbyv 18D,

752 36 Uppsala, Sweden
H. Ansorge

Senckenberg Museum of Natural History Goerlitz, PF 300154, 02806 Goerlitz, Germany

M. Bailey

Department of Zoology, Trinity College Dublin, College Green, Dublin 2, Ireland

M. Bodner

Stadtplatz 23, 3943 Schrems, Austria

M. Delibes J. A. Godoy A. Piriz

Estacio´ n Biolo´ gica de Don˜ ana, CSIC,

Avda Ame´rico Vespucio s/n, 41092 Sevilla, Spain

A. Ferrando

Departament de Biologia Cellular, de Fisiologia i d’Immunologia, Universitat Auto` noma de Barcelona,

08193 Cerdanyola del Valle`s, Spain

P. Fournier C. Fournier

Groupe de Recherche et d’Etude pour la Gestion de l’Environnement, Route de Pre´chac, 33730 Villandraut, France

P. Hajkova

Institute of Vertebrate Biology, Academy of Sciences of the

Czech Republic, Kvetna 8, 603 65 Brno, Czech Republic
S. Hauer

Institute of Zoology, Martin Luther University Halle (Saale), Halle (Saale), Germany

T. M. Heggberget

Norwegian Institute for Nature Research, 7485 Trondheim, Norway

D. Heidecke

Institute of Biology/Zoology, Martin-Luther-Universita¨t

Halle-Wittenberg, Domplatz 4, 06108 Halle (Saale), Germany

otters likely originated from the expansion of a single refugial population. Bayesian clustering and landscape genetic analyses however indicate that local populations are genetically differentiated, perhaps as consequence of post-glacial demographic fluctuations and recent isolation. These results delineate a framework that should be used for implementing conservation programs in Europe, particu- larly if they are based on the reintroduction of wild or captive-reproduced otters.
Keywords Eurasian otter Mitochondrial DNA Microsatellites Bayesian clustering

Spatial genetic structure Landscape genetics


The Eurasian otter (Lutra lutra) is a top predator living in a variety of aquatic habitats, including rivers, lakes, lagoons, coastal wetlands and marine shores (Kruuk 2006). It is considered a flagship species and an indicator of habitat quality (Bifolchi and Lode` 2005), although otters can breed well also in relatively degraded and less productive streams and wetlands, as long as enough prey is available (Ruiz-Olmo et al. 2001). The species was widely distributed across the Eurasian continent and in parts of North Africa, reaching China, Japan, Indonesia, Malaysia and India (Foster-Turley and Santiapillai 1990). Recently and more markedly during the second half of the last century, many otter populations strongly declined in several European countries, due to a combination of global and local causes.

Otters were hunted for fur, or persecuted because they were considered a pest to fish farming and fishery. In some countries a bounty was paid until the 1970s, when the species was finally legally protected. Habitat destruction, such as channeling and mining in or around river beds, dam construction, wetland reclamation and the destruction of riparian forests contributed to eradicate otter populations. Freshwater pollution also destroyed otter populations by killing their prey, or by bioaccumulation of organochlorines and heavy metals (MacDonald and Mason 1994). Now the species is fully protected by the IUCN, CITES and Bern conventions, and by national laws in almost all the Euro- pean countries.

Strict protection led some otter populations to expand and recover naturally (Kruuk 2006). At the same time, where healthy populations survived, active conservation programs were designed aiming at improving habitat con- nectivity and sustaining natural dispersal through restored ecological corridors (Reuther 1994). Reintroduction pro- grams of captive-reproduced or relocated wild otters have been planned where natural colonization was no longer possible due to the eradication or isolation of the remaining populations. Reintroduction projects were carried out in southern Sweden (Sjo¨ a˚sen 1996), Switzerland (Weber et al.

1991), Spain (Saavedra and Sargatal 1998) and the Neth- erlands (Van Ewijk et al. 1997). Those projects were realized before any information on otter population genetic structure was available (an exception is the recent rein- troduction in The Netherlands, from where the otter dis- appeared after 1989; Koelewijn and Jansman 2007), generating unplanned consequences. Thus, for instance, the

H. Kirjavainen

Department of Biology, University of Joensuu, P.O. Box 111,

80101 Joensuu, Finland
H.-H. Krueger

Aktion Fischotterschutz e. V, Otter-Zentrum, Sudendorfallee 1,

29386 Hankensbuttel, Germany
K. Kvaloy

Norwegian Institute for Nature Research, Tungasletta 2,

7485 Trondheim, Norway
L. Lafontaine

Re´seau Loutre Francophone, BP1, 29670 Locquenole, France

J. Lanszki

Department of Nature Conservation, University of Kaposva´r, P.O.B. 16, 7401 Kaposvar, Hungary

C. Lemarchand

Ecole Nationale Ve´te´rinaire de Lyon—UMR INRA ENVL 1233,

1, avenue Bourgelat, 69280 Marcy l’Etoile, France
U.-M. Liukko

Finnish Environment Institute, P.O. Box 140, 00251 Helsinki, Finland

V. Loeschcke C. Pertoldi

Department of Biological Sciences, Ecology and Genetics, Aarhus University, Ny Munkegade, Building 1540,

8000 Aarhus C, Denmark
G. Ludwig

Department of Biological and Environmental Science, University of Jyva¨skyla¨, P.O.B. 35, Jyvaskyla 40014, Finland

A. B. Madsen

Department of Wildlife Ecology and Biodiversity, National Environmental Research Institute, University of Aarhus, Kalo, Rønde, Denmark

L. Mercier

Otter Reintroduction Centre, Hunawihr, France

J. Ozolins

State Forest Service, 13 Janvara Iela 15, Riga 1932, Latvia

M. Paunovic

Institute for Biological Research, 29 Novembra 142, Beograd 11000, Serbia and Montenegro

Otter Trust managed a first reintroduction program into lowland English rivers (Wayre 1991), releasing captive- bred otters which showed mitochondrial DNA (mtDNA) haplotypes of non-European origin (Randi et al. 2005). Wild-captured and captive-reproduced otters originating from northern Norway and Sweden were relocated to southern Sweden without any prior knowledge on any possible phylogeographic structure in Scandinavia (Arrendal et al. 2004).

Reintroduction projects should respect the IUCN guidelines prescribing that ‘‘the source population of rein- troduced animals is genetically as similar as possible with formerly resident genotypes’’ (IUCN 1998). Thus, infor- mation on genetic structure of natural otter populations, as well as the identification of the genetic origins of otters in captivity, should be mandatory before any reintroduction plan is implemented. The intra-specific taxonomy of otter populations is uncertain, because the species exhibits unusually low levels of mtDNA variation, and shows almost no mtDNA geographic structure (Effenberger and Suchen- trunk 1999; Mucci et al. 1999; Cassens et al. 2000; Arrendal et al. 2004; Ferrando et al. 2004; Ketmaier and Bernardini

2005; Pe´rez-Haro et al. 2005; Finnegan and Ne´ill 2009;

Stanton et al. 2009). Autosomal microsatellites are poly- morphic in otters, but the populations studied so far showed little geographical differentiation also at the nuclear level (Dallas et al. 1999; Pertoldi et al. 2001; Dallas et al. 2002; Randi et al. 2003; Arrendal et al. 2004; Hajkova et al. 2007; Janssens et al. 2008). The scope of published studies was limited by restricted geographical sampling collections. Hence, it is still difficult to evaluate the genetic structure of otter populations in Europe. Two main questions should be answered: (1) do extant natural otter populations show any global phylogeographic differentiation; and (2) did recent anthropogenic demographic fluctuations generate genetic disequilibria and local genetic sub-structuring?

In an effort to answer to these questions, we here present results obtained from a large set of genotypic data, including an alignment of mtDNA sequences (1580 bp

long) and multilocus genotypes determined at 11 autoso- mal microsatellites in 616 otter samples collected from 19 natural populations across the species’ distribution in Europe. These data were analyzed using population and landscape genetic approaches aiming at: (1) reconstructing the main patterns of otter genetic differentiation across Europe, and (2) describing detailed otter population structuring at local geographical scale. A broad scale sur- vey across Europe should shed light on eventual phyloge- ographic structuring of otter populations, and landscape genetic analyses should detect the consequences of recent demographic fluctuations. This information could help in reconstructing the still largely unknown historical bioge- ography of the species, thus providing guidelines to design sound restoration programs.

Materials and methods
Sample collection
In this study we used a total of 616 distinct otter genotypes. Most of them were determined from 589 tissue samples, preserved in 90% ethanol or Longmire buffer (Longmire et al. 1997) at -20°C, which were collected between 2000 and 2007 from 19 European countries (Table 1). The geographical locations of 535 of these samples (originating from Portugal, Spain, France, Ireland, Germany, Czech Republic, Slovakia, Serbia-Montenegro, Finland, Sweden, Norway and Italy) were mapped using ARCVIEW GIS 3.1 (Fig. 1). The locations of the other 54 samples (collected in England, Ireland, Austria, Denmark, Hungary and Latvia- Belarus) were not precisely known, and thus were not mapped. Additionally, 27 genotypes were obtained from about 200 faecal samples collected in southern Italy (mainly within and around the Pollino National Park; Calabria and Basilicata regions). Otters completely disap- peared from north and central Italy before the end of the

1980s, surviving only in the southern regions from where

C. Pertoldi

Mammal Research Institute, Polish Academy of Sciences, Waszkiewicza 1c, 17-230 Bialowiez_ a, Poland

C. Prigioni

Department of Animal Biology, Pavia University, Piazza Botta 9, 27100 Pavia, Italy

M. Santos-Reis T. S. Luis

Centro de Biologia Ambiental/Departamento de Biologia Animal, Faculdade de Cieˆncias, Universidade de Lisboa, Campo Grande, Ed. C2, 1749-016 Lisbon, Portugal

T. Stjernberg

Finnish Museum of Natural History, Zoological Museum, University of Helsinki-Finland, Helsinki, Finland

H. Schmid O. Zinke

Zurich Zoo, Zu¨ richbergstrasse 221, 8044 Zurich, Switzerland

F. Suchentrunk

Research Institute of Wildlife Ecology, University of Veterinary

Medicine Vienna, Savoyenstrasse 1, 1160 Vienna, Austria
J. Teubner

Landesumweltamt Brandenburg, Naturschutzstation

Zippelsfo¨ rde, 16827 Zippelsforde, Germany
R. Tornberg

Faculty of Science, Department of Biology, University of Oulu, Oulu, Finland

Table 1 Origin and number of the otter (Lutra lutra) samples used for mtDNA sequencing and microsatellite (STR) genotyping
Country Samples mtDNA STR

1 Portugal




2 Spain




3 France




4 England




5 Ireland




6 Denmark




7 Germany




8 Austria




9 Czech Republic



10 Slovakia




11 Hungary




12–13 Serbia and Montenegro



14–15 Latvia and Belarus




16 Finland




17 Sweden




18 Norway




19 Italy




Samples from Serbia and Montenegro (n = 8), and from Latvia and

Belarus (n = 6) were pooled

the samples used in this study were obtained (Prigioni et al.


Tissue samples derived mainly from found-dead or trapped otters collected from regions where the species is more abundant. Therefore, sampling was not homogenous across Europe, but most of the more widespread popula- tions are represented. Natural otter populations are expanding in Spain and Portugal (Ruiz-Olmo et al. 2001). Our samples derive mainly from central and south-western regions (Extremadura, Andalucia, Faro, Beja, Setubal, Evora, Lisboa, Santarem, Portalegre) where the species is particularly abundant. Only a few individuals were col- lected from northern Spain. We did not get samples from central and eastern Spain where otters are rare or absent. French samples derived from the Atlantic west coast provinces (Bretagne, Pays de la Loire, Poitou–Charentes, Aquitaine). Only a few samples were collected from the Central Massif. Otters recently expanded westwards throughout most of eastern Germany (Reuther and Roy

2001) and we obtained a large number of samples from the whole range (Saxony, Brandenburg and Mecklenburg). Sampling was done fairly evenly within the distribution of the species in Czech Republic, Slovakia, Finland, Sweden and Norway. We collected samples also from southern

Fig. 1 Origin of the otter samples in Europe. Four individuals from Ireland and the samples collected in England, Austria, Denmark, Hungary and Latvia-Belarus are not mapped because their geographical locations were not available. Rectangles identify those populations which were analyzed separately in

GENELAND (see: Results and

Fig. 5)

Sweden where otters were reintroduced in the past 20 years (Sjo¨ a˚sen 1996). We did not use additional samples from East Anglia (UK) because those populations originated from the reintroduction of otters by the Otter Trust breeding centre (Jefferies et al. 1986). Otters are wide- spread in north-west Austria where they are in contact with the Czech population. A disjunct expanding otter popula- tion is distributed in south-east Austria (Reuther 1994). Our samples originated from both populations, although no detailed geographic information was made available. The Danish population is restricted to northern and western Jutland (Madsen 1996) from where the samples were col- lected. Only a few individuals were available from Serbia and Montenegro, Latvia, Belarus and England.
DNA extraction, amplification and genotyping
Total DNA was extracted using a guanidine-thiocyanate and silica beads protocol (Gerloff et al. 1995). DNA tied to silica particles was cleaned using sequential washings and finally eluted in a TE buffer (10 mM TrisHCl, pH 8; 0.1 mM EDTA). Most of the published mtDNA studies are based on partial sequences of the control-region, which in otters show unusually low sequence variation. In order to search for additional mutations in other mtDNA regions, we sequenced ca. 2000 bp, including the 30 end of the cytocrome b (CYB;

65 bp), the threonine tRNA (tRNA-Thr; 68 bp), the proline tRNA(tRNA-Pro; 66 bp), the entire control-region (CR;

1090 bp), the phenilalanine tRNA (tRNA-Phe; 69 bp) and

the initial 50 region of the 12 ribosomal RNA gene (12S RNA; 464 bp), which was PCR-amplified in 95 samples, selected to represent all the sampling locations, using the external primers LlucybL996 (50 -CCT TAC CCT AAC CTG AAT CGG) and 12SH51 (50 -CTA GAG GGA TGT AAA GCA CCG). Amplifications were performed in a 9700 ABI thermal cycler using the following protocol: (94°C 9 20 ), 40 cycles at (94°C 9 4000 ) (50°C 9 4000 ) (72°C 9 10 ), and a final extension at 72°C for 100 . Clean sequences of ca.

1822 bp were obtained directly from the PCR products, with the PCR primers, the forward primers LLU-dL225 (50 -CCC AAG ACT CAA GGA AGA GGC), OTT-D3L (50 -ACA ACA TTT ACT GTG CCT GCC C), OTT-D4L (50 -CAT CTG GTT CTT ACT TCA GGG CC), and the reverse primer OTT-D5H (50 -ACA AGT GGT GGG AGA GAG AAG CG) using an ABI 3130XL automated sequencer. Sequences were analyzed using SEQUENCING ANALYSIS 5.3 and SEQSCAPE 2.5 (Applied Biosystems). A final alignment of 1580 bp was obtained using BIOEDIT 7.0.9 ( BioEdit/bioedit.html) after the removal of a variable length repeated region (242 bp long). Additionally, a shorter frag- ment 479 bp long, including the final 30 end of the CYB (65 bp), tRNA-Thr (68 bp), tRNA-Pro (66 bp) and the initial part of the CR (280 bp), was sequenced in 15 faecal

individual genotypes from southern Italy using primers LlucybL996 and H16498 (50 -CCTGAACTAGGAACCA- GATG-30 ).

Multilocus genotypes of 616 samples were obtained by PCR amplifications of the following 11 autosomal micro- satellites: Lut435, Lut453, Lut604, Lut701, Lut715, Lut733, Lut782, Lut818, Lut832, Lut833 (Dallas and Piertney 1998) and Lut902 (Dallas et al. 1999). Amplifi- cations were performed using the following protocol: (94°C 9 20 ), 35 cycles at (94°C 9 3000 ) (60°C 9 3000 ) (72°C 9 10 ), and a final extension at 72°C for 100 . Alleles and genotypes were identified using an ABI 3130XL sequencer and the software GENEMAPPER 4.0 (Applied Biosystems).
Analyses of the mtDNA sequences
The sequences were aligned with a mtDNA sequence of the European otter (GenBank NC_011358), and the haplotypes were identified using COLLAPSE 1.0 (http://crandalllab.byu. edu/Computer.aspx). Haplotype diversity (h), average pairwise nucleotide substitutions (k), and other statistics were computed using DNASP 5.00.07 (Rozas et al. 2003). Unrooted networks were drawn to infer haplotype relation- ships with the median-joining network procedure as imple- mented in NETWORK (Bandelt et al. 1999; http:// The distribu- tion of observed pairwise haplotype substitutions (mismatch distribution) was computed with ARLEQUIN 3.1.1 (Excoffier et al. 2005;, and a population-expansion test was performed using the sum of square deviation (SSD) between the observed and the expected mismatch, and the Harpending’s raggedness index (R; Schneider and Excoffier 1999). Tajima’s D (Tajima

1989) was computed using the segregating sites method in

Analyses of microsatellite variation
Population genetic analyses were performed in two ways. First, we analyzed pre-defined groups corresponding to the sampled countries, which could, admittedly, include a number of genetically distinct, but unknown, biological populations. The few samples from Serbia and Montene- gro, and from Latvia and Belarus were aggregated in population genetic analyses. The software GENALEX 6.1 (Peakall and Smouse 2006) was used to compute, for each of the pre-defined groups, the observed and effective average number of alleles per locus (Ao and Ae) and the average expected and observed heterozygosity (He and Ho). The software GENETIX 4.03 (Belkhir et al. 2001) was used to test for departure from Hardy–Weinberg equilibrium (HWE) through the values of the fixation index FIS (Wright

1969; the probability to obtain simulated FIS values higher than the observed was evaluated after 1,000 random per- mutations of alleles within individuals) and Factorial Correspondence Analyses (FCA; Benzecri 1973) plotting individual multilocus genotypes in 2- or 3-D Cartesian spaces. A Principal Component Analysis (PCA) of differ- entiation among the sampled populations was performed with PCA-GEN 1.2. ( pcagen.htm).

Second, we used untrained Bayesian clustering (with software STRUCTURE; Pritchard et al. 2000) to split the sam- pling groups into a number of sub-populations that could correspond to natural genetically distinct groups (see details below). The geographic structure of the subpopulations was further investigated through landscape genetic analyses (with the software GENELAND 3.1.5; http://www2.imm.dtu. dk/*gigu/Geneland/#; Guillot et al. 2005). The genetic structure of pre-defined and new sub-populations was described using: (1) the number of microsatellite loci in which a departure from HWE was observed; (2) the fixation index FIS; (3) the average FST values among sub-popula- tions; (4) an estimation of isolation-by-distance (IBD) through a Mantel test of correlation between genetic and geographic distance matrices (latitude and longitude were used to assess the geographical distance among individuals); and (5) the Paetkau et al. (2004) population assignment test.

Bayesian inference of population structure
The genetic structure of the sampled populations was inferred using the multilocus genotypes and the Bayesian clustering procedures implemented in STRUCTURE 2.2.1 ( STRUCTURE was designed to identify the number K of distinct genetic populations (clusters) included in the sample, assuming HWE and linkage equilibrium within each population, and to assign the individuals to the inferred clusters. Burn-in periods of 50,000 steps followed by 500,000 Monte Carlo iterations were used to obtain convergence of the parameter values. Explorative analyses were performed first with K from 1 to 18 using all the samples, then splitting the sample into six distinct geographical subgroups (see below). All simulations were independently replicated four times for each K, using the ‘‘admixture’’ and the ‘‘independent’’ allele frequency models (Falush et al. 2003). The number of populations K was set at the value that maximised the increase in the posterior probability of the data LnP(D) according to the formula [LnP(D)k - LnP(D)k–1] (Garnier et al. 2004). Individual samples were assigned to the clus- ters using only genetic information, and ignoring sampling locations (options usepopinfo = 0, popflag = 1). For each K, the coefficients of individuals membership qi were used to assign probabilistically the genotypes to one cluster (the

population of origin), or to more than one cluster if they were admixed. Averaged coefficients of membership across the four replicates were obtained by CLUMPP 1.0 (Jakobsson and Rosenberg 2007; http://rosenberglab.bioinformatics. The software DISTRUCT 1.1 (Rosenberg 2004; was used to plot the graphical representations of the qi values.

Landscape genetic analyses
Based on the results of the first STRUCTURE analyses, and taking into account the geographical locations of the sampled populations, we identified six main subgroups (506 samples; see Results), which were analysed with GENELAND to determine simultaneously the population genetic structure and the geographical distributions of the clusters. This procedure uses information on genotypes and geographical locations to infer the spatial structure of the samples, assuming that spatial proximity should be a-priori related to genetic proximity. Genetic and geographic dis- tances among individual genotypes were used to maximize the posterior probability to obtain the optimal number of clusters K and their spatial locations. The density distri- bution of the number of populations after a burn-in period of 50,000 iterations was used to select the optimal K value. Then, five replicates of 100,000 MCMC iterations (with thinning = 1,000) using the ‘‘independent’’ allele fre- quency model were run. At optimal K values, the posterior mode of population membership was used to assign the individuals to the subpopulations. The geographical dis- tributions of the subpopulations were reconstructed from the plottings of the posterior probability of each individual to have origins in each of the K clusters.

The mtDNA network

The long mtDNA alignment (95 individuals, 1,580 nucle- otides) showed 20 distinct haplotypes, defined by 20 polymorphic sites (19 transitions and one indel, including

15 singletons and only four parsimony informative sites).

No mutations were detected in the CYB, tRNA-Thr and tRNA-Phe genes. One diagnostic transition and one indel characterized the tRNA-Pro of the Italian samples. There were five transitions (0.8% sequence divergence), one transversion and three transitions (1.5% sequence diver- gence), respectively in the first (593 bp) and in the final part (255 bp) of the CR. Finally, nine transitions (1.9% sequence divergence) were found in the 12S rRNA gene (465 bp). Mitochondrial DNA diversity was high, with

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