Duck’s not dead: Does restocking with captive bred individuals affect the genetic integrity of wild mallard (Anas platyrhynchos) population?

Biological Conservation 152 (2012) 231–240

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Duck’s not dead: Does restocking with captive bred individuals affect the genetic integrity of wild mallard (Anas platyrhynchos) population? Dagmar Cˇízˇková a, Veronika Javu˚rková b, Jocelyn Champagnon c,d,e, Jakub Kreisinger b,⇑ a

Institute of Vertebrate Biology, Academy of Sciences of the Czech Republic, Kveˇtná 8, 603 65 Brno, Czech Republic Department of Zoology, Faculty of Science, Charles University in Prague, Vinicˇná 7, 128 44 Prague 2, Czech Republic c Office National de la Chasse et de la Faune Sauvage, CNERA Avifaune Migratrice, La Tour du Valat, Le Sambuc, 13200 Arles, France d Centre de Recherche de la Tour du Valat, Le Sambuc, 13200 Arles, France e Centre d’Ecologie Fonctionnelle et Evolutive UMR 5175 – CNRS, 1919 Route de Mende, 34293 Montpellier Cedex 5, France b

a r t i c l e

i n f o

Article history: Received 13 December 2011 Received in revised form 6 April 2012 Accepted 9 April 2012 Available online 23 June 2012 Keywords: Genetic introgression Hybridization Major histocompatibility complex mtDNA Restocking Waterfowl

a b s t r a c t The genetic integrity of natural populations can be threatened through large-scale introduction of farmed stocks with different genetic or geographic origin. Huge numbers of farm-reared mallard (Anas platyrhynchos, Anatidae) have been introduced into the wild in many European countries since 1970. Czech breeding facilities currently produce around 200–300,000 ducks annually, exceeding wild numbers by around 10 times. Such facilities, however, were founded with hybrid ducks from outside the Czech Republic. Three types of DNA markers, two neutral (14 microsatellite DNA loci and the mitochondrial DNA control region) and one under selection (MHC class I locus), were used to genotype mallards from six Czech breeding facilities (n = 131) and seven wild nesting localities (n = 139). We found marked genetic divergence between wild and captive-bred populations, the latter having significantly lower genetic diversity. Released captive-bred mallards were integrated into breeding wild population through hybridization mediated by high frequency nesting. Overall, our data suggest that release of captive-bred individuals threatens the genetic integrity of wild population. Massive restocking may also be undesirable as regards public health. Waterfowl are known reservoirs of transmittable pathogens and large-scale restocking could alter immune defence gene frequencies in wild population. We propose the establishment of a national genetic monitoring programme for breeding facilities. Ó 2012 Published by Elsevier Ltd.

1. Introduction Genetic variability and adaptations to local conditions are key components of genetic integrity that predetermine the viability of natural populations (Edmands, 1999; McGinnity et al., 2003; Frankham, 2005), whose maintenance is of severe concern for biodiversity conservation (Sutherland et al., 2006; Frankham, 2005; Randi, 2008). Introgressive hybridization with genetically distant individuals, such as closely related subspecies or species, is a frequent cause of deterioration of the genetic integrity of indigenous populations. This type of genetic swamping may lead to extinction by hybridization of evolutionarily significant units (reviewed in Rhymer and Simberloff, 1996; Mank et al., 2004; Mun´oz-Fuentes et al., 2007). Human activities are an important source of these threats as they mediate the geographic ranges of many exotic organisms, or feral populations of domesticated strains, that have the potential to invade gene pools of indigenous populations (Mank et al., 2004; Mun´oz-Fuentes et al., 2007; Randi, 2008). ⇑ Corresponding author. Tel./fax: +420 221951845. E-mail address: [email protected] (J. Kreisinger). 0006-3207/$ - see front matter Ó 2012 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.biocon.2012.04.008

In addition, wild-living species of economic importance, such as game or angling species, are often subject to intensive management aimed at increasing population numbers and assuring sustainable levels of hunting and fishing (Lahti et al., 2009). This may, however, have undesirable consequences as regards genetic conservation, e.g. massive restocking with individuals originating from phylogeographically distant populations, or from different subspecies, may significantly affect the genetic composition and variability of indigenous populations (Barilani et al., 2005; Mehner et al., 2009; Barbanera et al., 2010, reviewed in Laikre et al. (2010)). Though the potential risks of massive restocking has been the subject of intensive research in several species (Barilani et al., 2005; Mehner et al., 2009; Barbanera et al., 2010), this issue has still not been sufficiently studied in many other cases (reviewed in Laikre et al. (2010)), e.g. for the European mallard (Anas platyrhynchos, Anatidae). The mallard is one of the most widespread waterfowl game species in the world (Cramp and Simmons, 1978). Mallard populations have been managed in Europe for decades, including protection of breeding areas, improvement of nesting opportunities and release of (semi)artificially reared individuals (Veselovsky´, 1954; Fišer et al., 1989). The latter has become widespread in many European countries since the 1970s (Fišer

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et al., 1989; Hu˚da et al., 2001; Laikre et al., 2006; Champagnon et al., 2009). The yearly production of breeding facilities (BF) is currently estimated at some three millions in Europe (Laikre et al., 2006, 2010; Champagnon et al., 2009; Champagnon, 2011), which is comparable to estimates of the wild mallard population (WP) in this geographic region, i.e. approx. 8 million (Delany and Scott, 2006). The ratio of restocked vs. wild mallards in the Czech Republic represents an extreme example, with approximately 200–300,000 BF individuals released per year (Hu˚da et al., 2001; Procházka, 2011, pers. comm.). This exceeds the Czech WP by around 5–10 times (Hudec, 1994). Importantly, breeding of released BF individuals with wild birds has been observed under natural conditions (Horˇák, 2008, pers. comm.). The suitability of such BF stocks for restocking WP is questionable, however, as in several European countries, individuals from non-local populations were used to establish such stocks and, hence, the genetic status of BF mallards is usually unclear (Tocˇka, 1972; Hu˚da et al., 2001; Champagnon, 2011). Of further conservation concern is the fact that farm bred individuals may have decreased genetic diversity compared to WP due to the pronounced effect of genetic drift and inbreeding (e.g. Earnhardt et al., 2004; Theodorou and Couvet, 2004). Moreover, relaxed selection on traits that affect fitness under natural conditions may also have contributed to a phenotype shift in the BF population (e.g. Bryant and Reed, 1999; Lahti et al., 2009). Finally, artificial selection and hybridization with domestic mallard breeds has taken place in some countries, including the Czech Republic, in order to modify important economic phenotypic traits. These include clutch size, body mass and reproductive behaviour (Tocˇka, 1972; Hu˚da et al., 2001). The most common domestic breed used for this purpose is the Khaki Campbell, which originated through hybridization among the Indian Runner (of Asian origin), the Rouen and Orpington (both of European origin), and the wild mallard (Vašák and Procházka, 2008). Altogether, a massive introduction of BF individuals into the wild may be detrimental for WP as it could lead to disruption of local adaptations and loss of genetic diversity, potentially resulting in a decrease in viability (e.g. Frankham, 1996; Sexton et al., 2002; Lavergne and Molofsky, 2007). It is somewhat surprising that the consequences of such a largescale natural experiment have not been addressed by detailed research (but see Baratti et al., 2009; Champagnon et al., 2010). Our paper represents the first attempt to fill this gap. We aim to assess the genetic impact of restocking Czech WP with BF mallards. In particular, we attempt to estimate divergence and differences in genetic diversity between Czech BF and WP mallards and to assess the degree of introgression of the BF genotype into the WP and viceversa. We use both selectively neutral markers (microsatellites and mitochondrial control region) and a locus under selection (MHC class I locus) to provide a robust assessment of the potential risks associated with the restocking of WP of mallard. 2. Methods 2.1. Biological sampling 2.1.1. Breeding facility (BF) population We sampled from six facilities based all over the Czech Republic (Fig. A1). Together, they produce approx. 100–150,000 (i.e. 30– 50%) of the birds introduced into the wild yearly. DNA samples were extracted from 5-day-old embryos (10–32 samples per facility, 131 BF samples) and stored in 96% ethanol. 2.1.2. Wild population (WP) Non-invasive genetic material was collected from mallard nests (i.e. feathers from the nest lining or membranes from hatched eggs: Kreisinger et al., 2010), dried and stored in paper wraps. Only

one sample from each nest was used. This type of sample has previously been shown to provide DNA of sufficient quality for microsatellite genotyping (Pearce et al., 1997; Kreisinger et al., 2010). Our analysis included 139 WP samples collected at seven localities (14–33 samples per locality, Fig. A1). All samples were collected in 2010 and DNA was extracted using DNeasy kits (Qiagen) following the manufacturer’s instructions. 2.2. Genotyping 2.2.1. Microsatellites We used 14 microsatellites developed for mallard (Maak et al., 2003; Denk et al., 2004; Huang et al., 2005; Table 1) in three multiplex sets. Multiplex Polymerase Chain Reaction (PCR) was performed for each DNA sample in 10 ll reaction volume: 1 Qiagen Multiplex PCR Master Mix (Qiagen), 4 ll of DNA solution (approx. 20–30 ng per reaction), and 0.2 lM of each primer. Forward primers were fluorescently labelled. The thermal profile consisted of 15 min at 95 °C, followed by 35 cycles at 94 °C (30 s), 60 °C (90 s), and 72 °C (60 s), with a final extension at 60 °C (30 min). PCR products were sized using ABI PRISM 3100 (Applied Biosystems) and LIZ500 (Applied Biosystems) as standard. Alleles were scored using GENEMARKER (SoftGenetics). 2.2.2. MHC I Out of the five classic MHC class I loci identified in mallard, UDA and UAA loci were expressed, with the latter having a substantially higher expression level (Mesa et al., 2004; Moon et al., 2005). Exon 3 (276 bp) of the UAA locus represents a marker under selection as it is directly involved in the immune response (Wallny et al., 2006; Moon et al., 2005). In order to achieve specific amplification of the UAA locus (given the extremely high sequence similarity between the paralogs), we designed a nested PCR with a U2 outer specific primer (50 GTGCTCTTCTCTGGCTCCAT30 ) at the TAP2 locus, adjacent to the UAA gene (for details on all MHC primers see Fig. B1). The reverse primer L2 (50 CCTGGTCACCTCACAGCATTGAC30 ) was placed into the conservative intron region between UAA exons 2 and 3. Twenty microliters of PCR reaction included: 1U of Long PCR Enzyme mix (Fermentas), 1 Long PCR buffer with MgCl2, 0.5 lM of each primer, 0.2 mM dNTPs and 0.5–5 ll of DNA, and was run at 94 °C (1.5 min) followed by 15 cycles of 95 °C (15 s), 58 °C (30 s), 68 °C (3 min) and 20 cycles using the same conditions but adding 1.5 s of auto-extension and 68 °C (10 min) of final extension. Due to the substantial length of the resulting PCR product (3424 bp), this PCR was not suitable for most of the non-invasive DNA samples. Therefore, we designed a new outer specific primer, U3 (50 CAAAAGAGAAGCAGCCATGAGA30 ), at the intergenic region between the TAP2 and UAA loci, which provided a shorter U3 + L2 product (1322 bp). Fifteen microliters of PCR included: 1 Qiagen Multiplex PCR Master Mix (Qiagen), 0.5 lM of each primer and 0.5–5 ll of DNA, and was run at 94 °C (15 min) followed by 42 cycles of 94 °C (30 s), 61 °C (90 s), 72 °C (90 s) and 72 °C (10 min). The outer PCR products were purified using ExoSap (USB), diluted 0–5 (depending on the strength of the amplification) and used as a template for the inner PCR of UAA exon 3. We used different inner primers to check for the presence of null alleles: iF1 (50 CCTCATGCTCACCTCTCCTCTCCAG30 ), iR5 (50 CCCCTGTCCCTCTTTGTGTTTCAGG30 ), iF2 (50 TCATGCTCACCTTTCCTCTC30 ), iR2 (50 TTGTTTTTCAGGTTCTCACA30 ), iF3 (50 CCTCTCCAGCACGTCCTTC30 ), iR3 (50 GGCTGTGACCTCCTCGA30 ). Fluorescently labelled primers (iF2 + iR2 (255 bp) and iF3 + iR3 (210 bp)) were used to perform capillary electrophoresis single-strand conformation polymorphism analysis (SSCP; for principles see, for example, Bryja et al., 2005). Fifteen microliters of PCR included: 1 Qiagen Multiplex PCR Master Mix (Qiagen), 0.5 lM of each primer and 1.5 ll of

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Table 1 Summary of descriptive statistics for individual loci and for WP (wild population) and BF (breeding facilities). Locus name

WP

BF

FST

Marker

AR

Ho

He

FIS

AR

Ho

He

FIS

APL21 APL121 APL141 APL261 APL231 APH022 APH082 APH0132 APH0172 APH0182 APH0212 APH0202 APH0232 CAUD0133

msat msat msat msat msat msat msat msat msat msat msat msat msat msat

18.657 30.492 39.247 17.000 7.891 12.854 14.767 14.548 10.896 4.000 15.895 9.870 55.881 18.936

0.731 0.758 0.792 0.596 0.703 0.519 0.773 0.406 0.579 0.311 0.645 0.698 0.793 0.727

0.892 0.918 0.948 0.858 0.734 0.806 0.832 0.732 0.827 0.386 0.883 0.815 0.968 0.907

0.181 0.175 0.165 0.306 0.042 0.357 0.071 0.446 0.301 0.194 0.270 0.144 0.181 0.199

15.533 17.442 18.654 13.000 6.908 5.845 9.908 6.991 6.908 3.000 12.833 7.999 29.505 10.982

0.802 0.626 0.600 0.513 0.427 0.295 0.748 0.145 0.351 0.473 0.651 0.634 0.814 0.374

0.855 0.753 0.819 0.829 0.521 0.447 0.781 0.592 0.667 0.432 0.720 0.694 0.927 0.468

0.062 0.169 0.268 0.383 0.180 0.342 0.043 0.756 0.475 -0.096 0.096 0.086 0.122 0.202

0.020 0.101 0.035 0.002 0.043 0.098 0.039 0.012 0.061 0.003 0.066 0.047 0.021 0.191

Mean UAA Control region

msat MHC I mtDNA

16.554 83.000 32.000

0.615 0.453 –

0.789 0.959 0.900

0.216 0.528 –

10.088 26.890 7.000

0.491 0.444 –

0.625 0.674 0.431

0.216 0.295 –

0.055 0.120 0.167

msat = Microsatellite locus published in (1) Denk et al. (2004), (2) Maak et al. (2003), or (3) Huang et al. (2005); MHC I = Major Histocompatibility Complex class I; mtDNA = mitochondrial DNA; AR = Allelic/haplotype richness Ho = observed heterozygosity and He = expected heterozygosity (or haplotype diversity in the case of the control region); FIS = inbreeding coefficient (significant values ); and FST = fixation index.

DNA (outer PCR). The thermal profile consisted of 15 min at 94 °C, followed by 35 cycles at 94 °C (30 s), 52 °C (90 s), 72 °C (60 s), and 72 °C (10 min). From 0.5 to 2.5 ll of PCR product were multiplexed and mixed with 0.5 ll of 600 LIZ Size Standard (Applied Biosystems) and 12 ll of Hi–Di formamide, and denatured at 95 °C for 5 min. Electrophoresis was run at 18 °C in 5% CAP using ABI PRISM 3130 (Applied Biosystems). The data were analysed using GENEMAPPER v. 3.7 (Applied Biosystems). Because of the very high polymorphism shown by SSCP, all WP samples were bidirectionally sequenced with iF1 + iR5 (262 bp) using the same PCR conditions as mentioned above for the inner PCRs except for an annealing temperature of 45 °C. A minimum of two PCRs representing a given SSCP pattern were sequenced in order to ensure more homogenous BF samples consisting of only a few distinct SSCP patterns. Homozygous individuals were used directly for subsequent analysis. We were able to confirm the alleles of most heterozygotes by combining SSCP and sequence information. In some cases (i.e. perfect quality of the sequence and SSCP pattern), when a heterozygous SSCP pattern consisted of a known allele (sequenced before) and an unknown allele, we obtained the unknown allele’s sequence by subtracting the known sequence from the heterozygote sequence. In most of these cases, however, and when both alleles were unknown, the iF1 + iR5 PCR product (made with proofreading Phusion polymerase, Finnzymes) was cloned (CloneJet PCR Cloning Kit, Fermentas). Those clones that had an SSCP pattern matching the iF1 + iR5 SSCP heterozygote pattern (the same PCR conditions as for sequencing, fluorescently labelled primers) were then sequenced.

2.2.3. Control region Mitochondrial DNA was amplified using the L78 (Sorenson and Fleischer, 1996) and H774 (Sorenson et al., 1999) primers. A PCR mixture of 15 ll volume included 1 Qiagen Multiplex PCR Master Mix (Qiagen), 0.5 lM of each primer, and 2 ll of DNA. The thermal profile consisted of 15 min at 94 °C, followed by 35 cycles at 94 °C (30 s), 55 °C (90 s), 72 °C (60 s), and a final extension at 72 °C (10 min). Primer L78 was used for unidirectional sequencing (BigDyeÒ Terminator v. 3.1 Cycle Sequencing Kit, Applied Biosystems) and sequences were cropped to 521 bp to omit ends with low quality. Sequences of lower quality were also sequenced with the other second primer (H774).

2.3. Data analysis 2.3.1. Descriptive genetic marker statistics and genetic diversity indices Standard genetic diversity indices for microsatellite and MHC data, including allelic richness and haplotype richness for mtDNA (e.g. El Mousadik and Petit, 1996), number of alleles per locus, observed (Ho) and expected (He) heterozygosity, and haplotype diversity were calculated using CERVUS 3 (Kalinowski et al., 2007) and FSTAT v. 2.9.3. (Goudet, 1995). We assessed substitution distances between MHC alleles using the model selection and distance calculation tools in MEGA 5.05 (Tamura et al., 2011). Nucleotide diversity (p) and number of segregating sites (S) for MHC data and the control region were calculated using DNASP v. 5. (Librado and Rozas, 2009), while the number of private alleles was estimated for a standardized population sample (random subsample size g = 90) based on rarefaction analysis implemented in ADZE 1.0 (Szpiech et al., 2008). Estimates of null allele frequencies and tests of heterozygous deficiency and heterozygous excess were performed in GENEPOP 3.4 (Raymond and Rousset, 1995) using the Markov chain method. 2.3.2. Population structuring pattern Single and multilocus F-statistics (Wright, 1951; Weir and Cockerham, 1984) were computed using FSTAT v. 2.9.3. We estimated the proportion of genetic variation that accounted for differences between wild and domestic populations through hierarchical Analysis of Molecular Variance (AMOVA; Excoffier et al., 1992), as implemented in ARLEQUIN 3.01 (Excoffier et al., 2005), using an option for calculating with sequence distances (Kimura’s two parameter model) for the control region. For microsatellite data, genetic difference among individuals and between populations was visualized through Factorial Correspondence Analysis (FCA) using GENETIX v. 4.05.2 (Belkhir et al., 1996–2004). Population structure, individual assignments and admixture proportions were analysed using Bayesian clustering techniques. STRUCTURE 2.1 (Pritchard et al., 2000; Falush et al., 2003) was used to test the level of genetic structuring between WP and BF population for both microsatellite and MHC genotypes. We used 106 iterations with a burn-in period of 105 and admixture ancestry models with correlated allelic frequencies. Ten independent runs were

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performed assuming existence of 1–10 population clusters (K). DK statistics based on the rate of change in the log likelihood of data between successive K values (Evanno et al., 2005) was performed in STRUCTURE HARVESTER v. 0.6.5 software (Earl, 2011). HYBRIDLAB v. 1.0 (Nielsen et al., 2006) was used to evaluate the power of the analyses to identify hybrids between WP and BF and to classify them appropriately to a given hybrid class. Initially, we randomly selected 50 individuals from the WP and BF and assigned them to WP and BF clusters according to qi and qj thresholds >0.98. HYBRIDLAB was used to generate 100 random genotypes for each of six hybrid category based on the allele frequencies corresponding to these individuals. The same STRUCTURE analysis described above was performed on the simulated dataset. We also estimated the migration rates between BF and WP as a measure of gene flow using BAYESASS v. 1.3 (Wilson and Rannala, 2003) with 3  106 iterations and a burn-in period of 106. 2.3.3. Bottleneck detection M-ratio statistics (Garza and Williamson, 2001) were used to test for any population bottlenecks in WP and BF. This approach assumes that the number of alleles at a given microsatellite locus decline faster compared to the allele size range in populations affected by a bottleneck. We compared single locus M-ratio estimates for the WP and BF population using the paired t-test. We then computed the M-ratio critical value (Mc) at a = 0.05 using CRITICAL_M (http://swfsc.noaa.gov/textblock.aspx?Division=FED& id=3298). The following parameters were defined to constrain the Critical M simulation: (1) 1-ps (i.e. proportion of mutations that do not correspond to the single step mutation model) was set to 0.1 or 0.2; (2) Dg (mean size of multi-step mutations) was set at 3.5, as recommended by Garza and Williamson (2001); and (3) h (4Nel; Ne = pre-bottleneck population size, l = mutation rate) ranged between 1 and 20. Mitochondrial sequences were analysed by D and F statistics, as proposed by Fu and Li (1993), and implemented in DNASP v. 5. As we assume selective neutrality for mitochondrial DNA, these tests were used to detect any bottleneck at WP vs. BF. 2.3.4. Analysis of selection on MHC MEGA 5.05 was used to compute synonymous (dS) and nonsynonymous (dN) distances between alleles (using the Nei and Gojobori method with Jukes–Cantor correction) in order to obtain the dN/dS value used for detection of positive selection. The Z test (Nei and Kumar, 2000) was employed to test for significance of the result. In order to detect the direction of selection at individual codons, we used the fixed effects likelihood method, implemented in the HYPHY package (Kosakovsky Pond et al., 2005) from the Datamonkey webserver (Kosakovsky Pond and Frost, 2005) with the p value set at 0.25. The overall selection pattern on MHC sequences was obtained using HYPHY Evolutionary fingerprinting analysis. Both HYPHY analyses were performed with Single BreakPoint analysis output (SBP tree), i.e. taking the recombination into account. 2.3.5. Phylogenetic analysis A phylogenetic network was constructed for 35 different haplotypes of the mitochondrial control region using the median-joining algorithm (Bandelt et al., 1999) included in NETWORK v. 4.6.0.0 (Fluxus-engineering.com). Transversions were weighted three times higher than transitions (transversion/transition ratio estimated by MEGA 5.05 was 13:1) and the value of epsilon was set at 20 (chosen empirically from values 10, 20 and 30). Maximum parsimony (MP) was employed as a post-processing option to clean up the resulting network. A network of 96 MHC sequences was constructed using a median-joining algorithm and Foulds, Hendy and Penny’s greedy FHP distance calculation method (Foulds et al., 1979) using default settings.

3. Results 3.1. Genetic diversity and loci description 3.1.1. Microsatellites The number of alleles per microsatellite locus was 3–56 (Table 1) and total number of alleles detected 288. Allelic richness was 1.21 times higher and He 1.67 times higher in WP compared to BF population (paired t-tests: t = 3.72, d.f. = 13, p = 0.0026 and t = 4.11, d.f. = 13, p = 0.0012, respectively). Rarefaction analysis confirmed that the mean number of unique alleles per locus (i.e. those occurring only in the WP or BF) was significantly higher in WP compared to BF population (mean ± 95% CI range of unique alleles per locus for WP and BF for g = 90 was 8.2 (95% CI = 4.2–12.3) and 1.5 (95% CI = 0.4–2.0), respectively (Fig. 1)). Both WP and BF population exhibited significant deficiency of heterozygotes at almost all loci, as indicated by significant FIS values in GENEPOP tests (Table 1). Mean FIS was the same for both WP and BF (paired ttests: t = 0.10, d.f. = 13, p = 0.9228). When deleting admixed individuals, or individuals that corresponded to WP and BF genotype but were sampled in BF and WP (see STRUCTURE results), the difference between Ho and He was reduced. FIS, however, remained significant for most loci (results not shown). As null alleles tend to be distributed stochastically across loci, it is highly improbable that heterozygote deficiency, which affected the vast majority of loci in the set, was caused by null alleles. Instead, we propose a combined effect of population substructure (i.e. between individual WP localities and individual BF; note the significant values of FSC in Table 2) and breeding of related individuals within WP localities and BF. In the case of BF, inbreeding is probably a result of artificial selection and the small size of the breeding flocks. There is insufficient data on population dynamics within the WP allowing us to give a clearer explanation; however, a high proportion of genetically related individuals within localities were detected, at least for females (Kreisinger et al., unpublished data).

3.1.2. MHC I We successfully genotyped 129 samples (98%) from BF and 99 samples (71%) from WP. Lower amplification success for WP was probably due to the lower quality of DNA obtained from the noninvasive samples used for the rather long PCR amplification (1322 bp). Comparison of SSCP genotypes revealed approx. 11% of samples as homozygous with iF2 + iR2 primers but heterozygous with iF3 + iR3, thus suggesting the presence of null alleles for iF2 + iR2 primers. Sequencing with iF1 + iR5 showed that approx. 6% of all samples assessed as homozygous with iF3 + iR3 SSCP genotyping were actually heterozygous. In future, therefore, we recommend multiplexing PCRs of iF3 + iR3 and iF1 + iR5 for SSCP screening and primers iF1 + iR5 for sequencing. We found 99 different UAA exon 3 alleles in 270 individuals. Sixty-three allele sequences were confirmed through sequencing of at least two independent PCRs. Forty-eight alleles were sequenced only once, but sequencing of PCR artefacts in clones was prevented by choosing clones according to their SSCP pattern. We were unable to obtain a sequence for three alleles. The sequences were deposited in the GenBank database under accession codes JN810912–JN811007. We found no defective sequences, except for a single allele containing a stop codon. Seven alleles contained 3 bp deletions (not changing the reading frame). The allele sequences contained 90 unique amino acid sequences (see Fig. C1). Minimum and maximum evolutionary divergences between sequences, measured as Kimura’s two-parameter distances adjusted to account for varying substitution rates between sites (c = 0.36), were 0.4% and 48.1%, respectively.

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Fig. 1. Rarefaction curves on number of alleles unique for WP (black circles) and BF (white circles). Estimates were undertaken separately for (A) microsatellites (mean number of unique alleles per locus ±95% confidence intervals), (B) control region, and (C) UAA locus of MHC I.

Analysis of selection revealed that purifying selection dominates evolution of the MHC sequences (dN = 0.115 ± 0.020; dS = 0.200 ± 0.032; dN/dS = 0.575; Z test p value p = 0.012 for HA:dN > dS). When tested for each codon separately, a pattern typical for MHC was found, i.e. the presence of both positively and negatively selected codons (17 and 29 codons, respectively; Fig. C1). Overall analysis of selection (Evolutionary fingerprinting method) showed three significantly different classes of codon: (1) strong positive selection (dN = 4.87, dS = 0.68, dN/dS = 7.12; 7.9% of codons), (2) ‘‘quick’’ negative selection (dN = 1.12, dS = 2.02, dN/dS = 0.55; 32.2%), and (3) ‘‘slow’’ negative selection (dN = 0.39, dS = 0.49, dN/dS = 0.8; 59.9%).

Allelic richness and He were higher in WP compared to BF (Table 1). The number of unique alleles for BF and WP for g = 90 was 14.35 and 67.64, respectively (Fig. 1). Similarly, WP showed higher nucleotide diversity (0.122 vs. 0.076) and number of segregating sites (0.580 vs. 0.428) than BF. There was a significant heterozygote deficit in both BF and WP (p < 0.001), with FIS = 0.295 and 0.528, and estimated frequency of null alleles 10% and 25%, respectively. We expect actual null allele frequencies to be lower, however, due to the contribution of inbreeding/population substructure to the heterozygote deficiency (see results for neutral markers). It is probable that null allele frequency is higher in WP compared to BF due to the higher probability of allelic drop-out during the long

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Table 2 Hierarchical analysis of molecular variance (AMOVA) at individual sampling sites (i.e. breeding localities in the case of WP and individual breeding facilities in the case of BF) nested within WP and BF. Significant values of FST, FSC and FCT (evaluated by permutation test) are indicated by an asterisk. Marker

Source of variation

d.f.

Sum of squares

Variance component

Percent of variation explained

Fixation indices

Microsatellites

BF vs. WP Between indiv. BF + WP sampling sites Within individual sampling sites Total

1 11 527 539

85.61 120.05 2400.50 2606.16

0.27 0.16 4.56 4.98

5.5 3.13 91.38

FCT: 0.055 FSC: 0.033 FST: 0.086

MHC I

BF vs. WP Between indiv. BF + WP sampling sites Within individual sampling sites Total

1 11 443 455

12.12 12.50 159.82 184.43

0.05 0.02 0.36 0.43

11.26 5.23 83.51

FCT: 0.113 FSC: 0.059 FST: 0.165

Control region

BF vs. WP Between individ. BF + WP sampling sites Within individual sampling sites Total

1 11 250 262

27.94 50.03 238.66 316.63

0.17 0.18 0.95 1.31

13.35 13.79 72.86

FCT: 0.133 FSC: 0.159 FST: 0.271

outer PCR amplification (1322 bp) in the case of quality non-invasive DNA samples. The phylogenetic network presented in Fig. D1 summarises the differences in nucleotide and population composition of MHC alleles in BF vs. WP.

3.1.3. Control region We found 35 different haplotypes in both BF and WP, of which 27 had not yet been described (accession numbers JN811008– JN811042). We found no evidence of coamplified nuclear mtDNA in our data (similar to Kulikova et al., 2004, 2005). Haplotype richness and diversity were higher in WP compared to BF (Table 1). Nucleotide diversity was 0.007 and 0.002, and number of segregating sites 0.065 and 0.019, for WP and BF, respectively. The number of unique alleles for BF and WP at g = 90 was 2.60 and 23.71, respectively (Fig. 1). The phylogenetic relationships between haplotypes and difference in haplotype composition between BF and WP are visualized in Fig. D1.

3.2. Pattern of population structuring All genetic markers used in this study showed significant patterns of population structuring between WP and BF samples, indicating limited gene flow between BF and WP. FST values suggest a moderate level of population differentiation between WP and BF for microsatellites (FST = 0.055; 99% CI range = 0.026–0.094), MHC I (FST = 0.120), and for the control region (FST = 0.167). FCA of microsatellite genotypes shows clustering of WP and BF individuals into two relatively separate groups (Fig. 2). Based on this analysis, a higher inter-individual genetic variation is also apparent in WP genotypes. A relatively low proportion of total genetic variation was explained by the two main axes (>5%). Nevertheless, when the gravity centroids of BF and WP samples were compared, the first two factors accounted for 100% of total inertia, suggesting differentiation between these two populations. AMOVA revealed that a significant proportion of genetic variability (corresponding to 5.5%, 13.3%, and 13.4% in the case of microsatellites, MHC I, and control region, respectively) was explained at the level BF vs. WP (permutation based on p values <0.001). Moreover, significant structuring was found between individual BF and between WP localities (i.e. within groups) and between all sampling sites (see Table 2 for further details). Bayesian clustering analysis performed on microsatellite data and additional analysis of DK statistics (Evanno et al., 2005) also revealed the existence of two population clusters (Fig. E1). The majority of samples belonging to the first population cluster originated from BF (hereafter BF cluster), while most of the samples from the second cluster originated from WP (hereafter WP cluster; Fig. 3).

Based on a simulation performed in HYBRIDLAB, an assignment probability corresponding to q < 0.20 was selected as an appropriate cut-off criterion to identify hybrid origin of a given individual for STRUCTURE analysis. This criterion was sufficiently robust to prevent false positive discovery of hybrids (only 1.5% of ‘‘simulated pure genotypes’’ were incorrectly identified as hybrids and 6.1% of simulated hybrid genotypes between WP and BF were identified as non-admixed individuals). Nevertheless, the chance of correct identification of a hybrid genotype varied considerably between individual hybrid categories and was high for F1 and F2 hybrids, yet relatively low for backcrosses (Fig. E2). Only one individual out of all BF samples (0.7%) corresponded to the WP cluster. Based on a q < 0.8 cut-off criterion, 6 (4.6%) individuals from BF samples were identified as hybrids between the WP and BF clusters. WP samples contained 18 genotypes (13% of WP samples) that were assigned to the BF cluster and 7 (5% of WP samples) individuals that were identified as hybrids between the WP and BF clusters. Based on the frequency that WP and BF genotypes occur in the wild population (0.845 and 0.155, respectively, assuming conservatively that all detected hybrids are F1) and on the Hardy–Weinberg law, the expected proportion of F1 hybrids is 26.2%. This estimate is significantly higher than the proportion of all hybrids detected in WP samples (binomial test: p < 0.0001). The distribution of BF genotypes within WP samples appears to be spatially non-random. While BF genotypes were absent at some localities, they dominated over WP genotypes at one locality (Fig. 3). Interestingly, the pattern of genetic differentiation based on single locus MHC I data and STRUCTURE analysis are similar to that for microsatellites (Fig. 3). In line with these results, BAYESASS analysis suggests considerable asymmetry in the gene flow between BF and WP, with an

Fig. 2. Factorial correspondence analysis of mallard microsatellite genotypes for WP (light characters) and BF (black characters).

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Fig. 3. Posterior probability assignment of individual samples to WP (light bars) and BF (dark bars) population clusters based on analysis in STRUCTURE using (A) 14 polymorphic microsatellites and (B) MHC data, with no prior information on sample origin. The white horizontal lines correspond to an 80% cut-off for distinguishing pure and admixed individuals (cut-off value defined by simulation, see Fig. E1). Seven different WP sampling sites (1–7) and 6 BF sampling sites (A–F) of Czech mallard are included (for details on sample origin, see Fig. A1).

estimated migration rate from BF to WP of 0.0664 (posterior 95% CI range = 0.0437–0.0926). The migration rate in the opposite direction was estimated to be lower by one order (mean = 0.0045, posterior 95% CI range = 0.0001–0.0143). 3.3. Detection of population bottlenecks The mean M-ratio for all microsatellite loci was 0.721 and 0.818 for BF and WP, respectively. M-ratio values for individual loci tended to be higher in WP compared to BF (paired t-tests: t = 2.10, d.f. = 13, p = 0.0555). We did not find evidence for a bottleneck in WP, the mean M-ratio for WP being higher compared to Mc over the whole range of parameters used for Critical M simulation. M-ratio analysis did, however, suggest a bottleneck in BF as there was a marginally non-significant difference between the observed M-ratio (0.721) and Mc if 1-ps was assumed to be 0.2 (Mc range for Dg between 1 and 20: 0.713–0.685), and a significant difference if 1-ps was set at 0.1 (Mc range for Dg between 1 and 20: 0.799– 0.742). For mitochondrial sequences, both Fu and Li’s tests revealed a bottleneck in the BF population (D and F statistics significant, p < 0.05), but not for WP (D and F statistics not significant, p > 0.1). 4. Discussion This study showed that Czech wild mallard populations can be clearly distinguished from BF mallards using three different genetic markers: microsatellites, the MHC class I locus and mitochon-

drial DNA. Based on AMOVA, the proportion of genetic variation accounting for the difference between BF and WP varied between 5.5% and 13.4%, depending on type of genetic marker (Table 2). STRUCTURE and FCA analyses revealed the existence of two distinct genetic clusters corresponding to the BF and WP genotype (Figs. 2 and 3). The number of ducks used for establishment of breeding facilities has usually been low (tens to hundreds; Hu˚da et al., 2001) and breeding flocks kept at relatively low numbers from then on. Moreover, introduction of wild individuals into captive breeding programs has been limited due to the risk of pathogen transmission, though the exchange of individuals between different breeding facilities has been supported (Hu˚da et al., 2001). Hence, genetic differentiation between WP and BF may have arisen simply because of a founder effect and long-term bottleneck, together with subsequent genetic isolation of BF from WP. Our data are consistent with this scenario. M-ratio statistics and Fu and Li’s method both suggest that BF, but not WP, was affected by a population bottleneck for both nuclear and mitochondrial markers. In addition, the introgression of the WP genotype into recent BF was proved to be limited. According to the STRUCTURE results, the occurrence of individuals assigned to the WP genetic cluster, or individuals of admixed origin in BF samples, is low (Fig. 3). Furthermore, BAYESASS analysis show that migration from WP to BF occurs rarely compared to migration in the opposite direction, suggesting high asymmetry in gene flow between BF and WP. Nevertheless, it is worth noting that, aside from the effect of bottleneck and genetic drift, differences between WP and BF may also have arisen due to the fact that individuals used for

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establishment of BF did not originate from wild Czech stocks and that they were hybridized with domesticated mallard strains of exotic origin, such as the Khaki Campbell (Tocˇka, 1972; Hu˚da et al., 2001). This mutually non-exclusive explanation, however, needs to be confirmed by future research. Our results suggest that the distinct WP mallard genotype still persists in natural populations (Figs. 2 and 3). This finding is surprising, considering that the number of BF individuals released in the Czech Republic each year exceeds estimates of the wild population by 5–10 times, and that this practice is common to many other European countries. For many other species, such as the American black duck (Anas rubripes, Mank et al., 2004), the red-legged partridge (Alectoris rufa, Barbanera et al., 2010) or the tiger salamander (Ambystoma californiense, Riley, 2003), restocking or incidental hybridization of wild population with genetically distinct individuals at intensities comparable to those mentioned above has often had severe consequences for genetic composition. In several cases, this has resulted in a phenotype shift in the indigenous population (e.g. McGinnity et al., 2009). On the other hand, despite clear population structuring between BF and WP, our data indicate that introgression of the BF genotype into WP is in progress. In particular, STRUCTURE analysis indicates that the contribution of the BF genetic cluster to the WP genetic pool was relatively high, with 13% of wild breeding individuals assigned to the BF cluster, and dominated over individuals from the WP cluster at one breeding locality (Fig. 3). At the phenotypic level, Champagnon et al. (2010) showed that considerable changes in the morphology of bill lamellae in European mallards has emerged during the past 30 years. Under the assumption that selection on morphology of feeding apparatus is relaxed in BF, the described change might have been caused by introgressive hybridization of WP with individuals of BF origin. Direct evidence for this possibility is, however, lacking. Interestingly, the proportion of individuals in WP identified as WP  BF hybrids was lower than expected, possibly indicating a reduced potential of the BF genotype to invade the WP genetic pool. As suggested by Champagnon et al. (2010), and according to our MHC data, individuals originating from BF differ from wild birds in several phenotypic traits that might reduce their fitness under natural conditions (Champagnon et al., 2012). A mutually non-exclusive explanation for the relative lack of WP  BF hybrids may be found in a barrier to panmixia between WP and BF. Indeed, a study of game and wild mallard strains by Cheng et al. (1978) showed both preference and higher reproductive success for males pairing with females of their own strain. Alternatively, the relative lack of hybrids may have arisen from a lower dispersal potential of BF individuals (Champagnon et al., 2012). Reduced flying ability is a common phenotypic defect of mallards originating from BF in the Czech Republic (Hu˚da et al., 2001). Should this trait be reduced to some extent in F1 hybrids, high migration rates could also cause the observed hybrid deficiency. Decreased genetic diversity in captive-bred populations is common as a limited number of individuals is used for their establishment. In addition, the effect of genetic drift and inbreeding is more pronounced in captive breeds (e.g. Earnhardt et al., 2004; Theodorou and Couvet, 2004). We found that BF showed a considerable decrease in genetic diversity compared to WP based on all markers used. Standard measures of genetic diversity in BF, such as He and allelic richness, show a 17% and 39% of decrease, respectively, compared to WP (Table 1). This discrepancy is even more pronounced when comparing numbers of unique alleles detected in BF and WP (Fig. 1). This implies that massive restocking of wild mallard populations with BF individuals may, under certain circumstances, reduce genetic diversity and consequently evolutionary potential of WP. This has previously been shown for example for supportive breeding of brown trout (Salmo

trutta, Hansen et al., 2000) or Atlantic salmon (Salmo salar, McGinnity et al., 2009). Natural selection may, to some extent, mitigate differences that arise due to the stochastic effect of genetic drift or inbreeding (van Oosterhout et al., 2006; Alcaide et al., 2008). Consequently, one could argue that selectively neutral markers may not be suitable for highlighting issues associated with restocking of WP as the genetic differentiation between WP and BF may be maintained lower, and the level of genetic diversity in BF higher, at loci under selection. This was not the case in our study, however, as significant loss of genetic diversity in BF, and a considerable level of genetic differentiation, was also detected at the MHC class I locus, a gene involved in the immune response and shown to be under strong selection pressure in our population (Figs. 1 and 3). Hence, our results are consistent with several previous studies that imply random genetic drift to be the main force underlining the pattern of allele distribution in isolated populations (reviewed in Biedrzycka and Radwan (2008)). Population genetic patterns detected at the MHC I locus provide further evidence that massive introduction of BF individuals into the wild is undesirable. This type of management could change allelic frequencies and reduce allelic diversity of loci associated with fitness, which could decrease WP viability through disruption of local adaptations. Moreover, as natural populations of mallard represent an important pool of pathogens potentially transmittable to humans (Fereidouni et al., 2010), introgressive hybridization of BF individuals with WP (potentially changing the immunocompetence of WP) is also controversial from an epidemiological point-of-view. We propose that further research be undertaken to evaluate the effects of restocking at larger geographic scales, as this practice remains widespread in many European countries. Genotypes of historic WP that have not been affected by admixture with BF individuals should be included in further analyses in order to obtain more precise estimates of the level of introgression. Finally, differences in traits associated with fitness (e.g. Champagnon et al., 2010) between WP and BF individuals should be analysed in detail in order to assess the potential of BF genotypes to invade WP.

5. Conclusions and management implications Our results represent a first insight into the population genetics of wild European mallards as regards the large-scale release of captive bred individuals. We detected considerable genetic differentiation between BF and WP populations, and reduced genetic diversity in the BF population on both selectively neutral genetic markers and markers under selection. Moreover, we confirmed introgression of BF genotypes into the wild population. These findings allow us to identify two potential threats to the viability of wild population: (1) a decrease in genetic diversity, and (2) the introduction of maladapted genotypes at fitness-related loci. We believe that these risks could be substantially decreased if appropriate management of BF is established. We propose, therefore, that the following operative protocol be adopted. As a first step, the BF genetic pool should be regularly enriched with WP genotypes. At the same time, rigorous control of BF genetic composition should be established, both in order to monitor the effectiveness of the previous step and the quality of individuals produced. Last, but not least, a restriction on BF mallard release should also be considered. Czech legislation dealing with the release of animals into nature (Act No. 114/1992 Coll. on Nature and Landscape Protection, Act No. 449/2001 Coll. On Hunting, and the International Agreement on the Conservation of African–Eurasian Migratory Waterbirds) imposes restrictions on the release of animals that are interspecific

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hybrids or hybrids with a domestic species. Enforcement in the case of mallards, however, is rather problematic (see also Laikre et al., 2010) as the general requirement of genetic quality of released individuals is not defined. The desirable markers described in this paper, however, could be used for such evaluation of bred mallards in future. Acknowledgements We thank Tomas Albrecht, Pavel Munclinger, and two anonymous reviewers for valuable comments on earlier drafts of the manuscript. We are grateful to Jan Zimmel, Jan Lávicˇka, Jirˇí Hájek and Alois Kopecˇny´ for providing genetic samples. This research was supported by Grant No. 0021620828 of the Ministry of Education of the Czech Republic and by a research Grant of the Academy ˇ R) No. AV0Z60930519. Supof Sciences of the Czech Republic (AS C port was also provided by an internal grant from Charles University (Projects GA UK Nos. 56007/2007/B-Bio and 151607/2007/B-Bio) and from the Grant Agency of the AS CˇR (Project No. A6093403). J.K. was supported by Research Centre Project No. LC06073 and VAV by Grant SP2D 3-60-08 and institutional resources of Ministry of Education, Youth and Sports of the Czech Republic for the support of science and research. J.C. was funded by a doctoral grant from the Office National de la Chasse et de la Faune Sauvage (ONCFS), with additional funding from a research agreement between ONCFS and the Tour du Valat.

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