RESEARCH ARTICLE

Efficiency of markers and methods for detecting hybridsand introgression in stocked populations

Nuria Sanz Æ Rosa M. Araguas Æ Raquel Fernandez ÆManuel Vera Æ Jose-Luis Garcıa-Marın

Received: 28 September 2007 / Accepted: 5 March 2008 / Published online: 16 March 2008

� Springer Science+Business Media B.V. 2008

Abstract Detection of hybridization and introgression in

wild populations that have been supplemented by hatchery

fish is necessary during development of conservation and

management strategies. Initially, allozyme data and more

recently highly polymorphic microsatellite markers have

been used to obtain this information. We used both markers

to assess the effectiveness of four assignment methods

(STRUCTURE, NEWHYBRIDS, BAPS and GeneClass) to detect

hatchery introgression in wild stocked populations. Simu-

lations of hybrid genotypes from real parental data revealed

that the number and type of markers used with STRUCTURE,

NEWHYBRIDS and BAPS can identify as admixed most first

and second generation hybrids as well as first generation

backcrosses. In wild populations, introgression rates esti-

mated from different markers and methods were correlated.

However, slight disagreements were observed at both

population and individual levels. Overall, the fully

Bayesian (STRUCTURE, NEWHYBRIDS and BAPS) performed

better than partially Bayesian (GeneClass) assignment

tests. In wild collections, BAPS analyses were limited

because of the lack of a native baseline. In all cases, the

efficiency of methods was reduced as introgression

increased.

Keywords Allozymes � Assignment methods �Brown trout � Introgression � Microsatellites

Introduction

Hybridizations resulting from introductions of exotic indi-

viduals are eroding genetic integrity and threatening

extinction at many levels (species, subspecies and locally

adapted populations; Frankham et al. 2002). Some manage-

ment programs focus on identifying and protecting pure

native gene pools. Others deal with situations where pure

exogenous or introgressed fish are present by selective

removal of non-native or introgressed individuals or by

translocation of pure ones (Allendorf et al. 2004). In all

cases, identification of native and hybridized individuals is a

primary concern in developing conservation and manage-

ment strategies. Thus genetic information becomes essential

to identify hybrids and to estimate levels of introgression in a

population (Ferguson et al. 1995). Beyond first generation

(F1) hybrids, it is difficult to decide when an individual is no

longer viewed as a hybrid but rather as a member of one of

the two parental populations undergoing introgression

(Rhymer and Simberloff 1996). Moreover, the identification

of hybrids can be particularly challenging when the hybrid-

izing groups are closely related, and divergence between

them is based primarily on allele frequency differences rather

than on unique (diagnostic) alleles.

Advances in molecular genetics in recent years have

provided a range of tools to detect hybridization and

introgression (Hewitt 2001). In contrast to protein-coding

loci, highly polymorphic markers such as microsatellites

are capable of more fine-scale discrimination required for

distinguishing conspecific cultured and native populations

(Aurelle et al. 2002). The capability of these markers for

individual genotyping has shifted the focus for monitoring

releases on wild populations from populations to assign-

ment tests of individuals (Hansen et al. 2001a). Computer

simulations suggested that model-based Bayesian statistical

N. Sanz (&) � R. M. Araguas � R. Fernandez � M. Vera �J.-L. Garcıa-Marın

Laboratori d’Ictiologia Genetica, Universitat de Girona,

Facultat de Ciencies, Campus Montilivi s/n, 17071 Girona, Spain

e-mail: nuria.sanz@udg.es

123

Conserv Genet (2009) 10:225–236

DOI 10.1007/s10592-008-9550-0

techniques are more effective than most frequent likeli-

hood-based methods when genetic data from wild

populations before hybridization are not available (Vaha

and Primmer 2006). Additionally, some analyses allow the

classification of individuals into distinct hybrid classes

(F1, F2, or backcrosses classes), which is especially

important for accurately documenting introgression

(Anderson and Thompson 2002). Simulated studies sug-

gested that at least 48 loci with an average pairwise

FST = 0.21 between hybridizing populations would be

necessary for reliable F2 and backcrosses as well as F1

identification (Vaha and Primmer 2006). However, these

simulated conditions are sometime unrealistic. Further-

more, such a large number of loci is not always available

where population genetics studies on fish species often

involved 6–10 microsatellite loci (Koskinen et al. 2004).

Even in salmonids, where a large number of microsatellites

have been described (Gharbi et al. 2006), practical limi-

tations have restricted the number of loci used in genetic

analyses (Paterson et al. 2004; Lerceteau-Kohler and

Weiss 2006). Reduced equipment expenses suggest that

allozyme genotyping could be still used to monitor

hatchery releases. Among studies that have evaluated the

efficiency of the different assignment methods on wild

stocked populations (Hansen et al. 2001a, b; Manel et al.

2002), none have compared the efficiency of methods and

markers simultaneously to assess introgression.

In Spain, extensive interbreeding between native Med-

iterranean brown trout (Salmo trutta) and hatchery fish of

North-European origin have created a valuable source

material for evaluating the efficiency of different methods

and markers to assess introgression. We selected six Ibe-

rian brown trout collections having contrasting levels of

hatchery releases to compare the efficiency of the assign-

ment methods provided by STRUCTURE, NEWHYBRIDS, BAPS

and GeneClass in detecting introgression in wild popula-

tions. Based on 19 allozyme and 9 microsatellite loci, we

simulated genotypes from real parental data to assess the

power of methods to detect F1, F2 and first generation

backcrosses with each kind of marker.

Material and methods

Collections and genetic diversity

The brown trout collections (Table 1) included one never

stocked population (Manyanet), and five collections

potentially affected by hatchery releases. Fish collected by

electroshocking in 1993 and previously analyzed for allo-

zyme variation (Araguas et al. 2004) were selected for

comparative microsatellite analyses. Legal restrictions on

lethal sampling to a maximum of 25 fish per sample and Ta

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226 Conserv Genet (2009) 10:225–236

123

the availability of fish limited numbers of some collections,

as typified in many endangered species (Frankham et al.

2002). A restricted sample length within a 15–20 cm range

approximated a common age class of 1–2 years for all fish.

Published allozyme data from the Baga hatchery stock

(Garcıa-Marın et al. 1991; Araguas et al. 2004), were

included in the analyses to represent the pure hatchery

population. These hatchery fish were no longer preserved

and a new collection representing random descendents of

the earlier one (Baga Hatchery personnel, personal com-

munication) was obtained for microsatellite analysis. Exact

probability test on the allele frequencies at the diagnostic

LDH-C* locus showed no significant differences between

these two samples (P = 0.838).

Araguas et al. (2004) included genotypes for the 19

variable loci involving the selected collections. For

microsatellite genotyping, DNA was extracted using the

Chelex-proteinase K procedure (Estoup et al. 1996). Nine

microsatellite loci were amplified: Str15, Str73 (Estoup

et al. 1993), Str591INRA (Presa and Guyomard 1996),

Ssa85 (O’Reilly et al. 1996), Ssa408 (Cairney et al. 2000),

SsHaeIII14.20 (Goodier unpublished, Genbank Accession

no. U10050), SsoSL417 (Slettan et al. 1995), SsoSL438

(Slettan et al. 1996), SSsp2213 (Paterson et al. 2004).

Forward primers were end-labelled with fluorescence dye.

PCR reactions (25 ll) contained 1 ll of DNA extraction,

1.25 units of Amplitaq Gold� DNA polymerase (Applied

Biosystems), 2.5 ll BufferGold, 2 ll dNTP MIX 10 mM,

3 ll-2.5 ll MgCl2 25 mM and were performed on a

GENEAMP�PCR SYSTEM 9700 (Applied Biosystems).

The heating cycle parameters used for a multiplex

involving the Str73, Ssa85, SsoSL438, Str591INRA and

SsHaeIII14.20 loci were initial denaturation at 95�C for

10 min; following by 35 PCR cycles (denaturation at 95�C

for 1 min, annealing at 54�C for 1 min, and extension at

72�C, for 2 min). For a second multiplex reaction,

including Str15, SsoSL417, Ssa408 and SSsp2213 loci,

temperature cycling conditions were initial denaturation

step at 95�C for 10 min, nine touchdown cycles (denatur-

ation at 95�C for 30 s, annealing at 65–57�C for 45 s,

extension at 72�C for 45 s) and 25 PCR cycles (denatur-

ation at 95�C for 1 min, annealing at 58�C for 1 min and

extension at 72�C for 2 min). In all cases, final extension

was at 72�C for 30 min. Genotypes were visualized using

an Applied Biosystem 3130 Genetic Analyzer.

Genetic variability within samples was estimated by the

mean unbiased expected heterozygosity (HS) and the allelic

richness (A) using Pop100gene (http://www.montpellier.

inra.fr/URLB/pop100gene/pop100gene.html) and FSTAT

(Goudet 1995) programs. Genotypic distributions were tes-

ted for conformance to Hardy-Weinberg expectations by

the exact probability test implemented in GENEPOP (Raymond

and Rousset 1995). Nei’s estimators of gene diversity and

differentiation assessed genetic differentiation among sam-

ples. In addition, Weir and Cockerham estimators with 1,000

permutations evaluated FST values and their significance for

all population pairs. All these estimates were computed using

FSTAT.

Assessing the power of markers and methods to detect

hybrids by simulated genotypes

Simulations based on observed parental data tested the

power of assignment methods to detect first (F1) and sec-

ond (F2) generation hybrids, and backcrosses between F1

and parental native (Bnat). Backcrosses of F1 with parental

hatchery (Bhat) were excluded because of minimal survival

in the wild of released hatchery fish (Arias et al. 1995;

Poteaux et al. 2001; Araguas et al. 2004). Based on the

official stocking records, the collection from Manyanet was

used as a pure native source to simulate 250 parental native

individuals. We also generated 250 individuals from the

Baga collection to be used as a parental hatchery. All real

individuals used to simulate both parental source popula-

tions to the pure native or pure hatchery were confirmed by

admixture analysis with STRUCTURE. We generated 50 of

each F1, F2 and backcrosses of F1 with parental native

classes from 50 parental native (Manyanet) and 50 parental

hatchery (Baga) individuals randomly selected from the

above simulated genotypes. All simulations were done with

HYBRIDLAB (Nielsen et al. 2006) software. These simulated

data were used to carry out admixture analysis using

STRUCTURE (Pritchard et al. 2000), NEWHYBRIDS (Anderson

and Thompson 2002), BAPS (Corander and Marttinen 2006)

and GeneClass (Piry et al. 2004). Running conditions for

these programs were as used with wild samples (see

below). The proportion of hybrid individuals (F1, F2,

Bnat) in the simulated data set that were correctly identi-

fied as hybrids (Vaha and Primer 2006) evaluated the

efficiency of all methods. Following Barilani et al. (2005;

2007), we used initial threshold q-values of 0.10 (STRUC-

TURE) and posterior probability of 0.9 (NEWHYBRIDS) to

assign individuals to parental or admixed classes. In BAPS

we initially used 20 runs of a trained clustering to group all

simulated individuals into two partitions (K = 2) using the

hatchery Baga collection as reference individuals. Then,

the resulting clusters were used as a basis for running an

admixture analysis to identify hybrid genotypes (F1, F2

and Bnat) as admixed. This approach was similar to the one

performed with STRUCTURE. An alternative analysis with

BAPS used 20 runs of a trained clustering of all simulated

individuals using the original Baga collection as reference

and five as a maximum number of clusters. In the optimal

partition, clusters were assigned to native or hybrid classes

based on their Kullback-Leibler divergence from the Baga

fish. The most divergent cluster and Baga fish were then

Conserv Genet (2009) 10:225–236 227

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used as pre-defined native and hatchery baselines for

admixture analysis of all simulated individuals.

Identifying genetic admixture and evaluating

introgression in wild populations

In addition to Hardy–Weinberg deviations, the presence of

several genetically homogeneous groups (K) in each wild

collection was checked by the Bayesian Markov Chain

Monte Carlo (MCMC) approach method of STRUCTURE. For

each sample, we selected the optimal K values according to

Garnier et al. (2004). A burn-in period of 10,000 steps

followed by 10,000 Monte Carlo replicates was simulated

with K = 1, 2, 3, 4. Each simulation was further replicated

20 times to verify the consistence of the estimated K.

The presence of the LDH-C*90 allele frequency, fixed in

hatchery stocks and absent in native populations of the

Mediterranean rivers (Garcıa-Marın et al. 1991; Poteaux

et al. 2001) was the primary evidence of genetic intro-

gression in the studied populations. The MCMC method of

STRUCTURE was also used to estimate individual admixture

coefficients (the proportion of the genome of individual

fish derived from native and hatchery trout, respectively)

by allozymes and microsatellites. The average proportion

of hatchery ancestry (q) was calculated for each sample

incorporating data from Baga sample as hatchery baseline

and assuming an admixture model with two populations

(hatchery and native) where hatchery individuals were

forced to be non-admixed. Because of the high differenti-

ation reported between Iberian native populations and

hatchery stock (Table 1), we used a model of independent

allele frequencies. STRUCTURE was run with a burn-in period

of 50,000 steps followed by 200,000 Monte Carlo repli-

cates. For each sample, the 95% confidence intervals of

each estimated q value were calculated according to

Hansen et al. (2001b).

In addition, hybridization between native and hatchery

fish was examined by the Bayesian statistical method

implemented in the NEWHYBRIDS. This method estimates

the posterior probability (P) that each individual in a wild

sample falls into different parental (native (Pnat) or

hatchery (Phat)) or hybrid classes (F1, F2, Bnat). Fol-

lowing results from simulated data, individuals were

assigned to one of the five genotypic classes if P C 0.5. For

each data set, which included the wild population and the

Baga hatchery genotypes, posterior probabilities were

evaluated after 100,000 iterations of the Monte Carlo

Markov Chains. The program ran without any prior infor-

mation about the hybrid status of collected individuals and

populations, and with the ‘Uniform’ prior option for both

mixing proportions and allele frequencies.

As suggested by BAPS results on simulated data, we used

BAPS to identify a native cluster based on the largest

Kullback-Leibler divergence from the Baga reference

collection in the optimal partition after 20 runs of the

individual trained clustering of all wild fish. In this case,

runs were set to a maximum of seven partitions (the six

wild collections plus Baga stock). The putative native

cluster and the Baga fish were then used as pre-defined

baselines for the admixture analysis of all wild fish.

Finally, individuals were classified into groups of wild

or hatchery trout using the assignment test provided by

GeneClass 2 with the six wild and the hatchery populations

as reference samples. We used a modified version of the

assignment test where population allele frequencies were

estimated using a Bayesian approaches (Rannala and

Mountain 1997). We also used the ‘simulation option’ of

Paetkau et al. (2004) for assessing the probability of each

individual being rejected from the reference populations.

Results

Genetic diversity within and among samples

On average, population diversity was higher for microsat-

ellite (A = 4.973, range 3.647–6.965; HS = 0.597, range

0.378–0.773) than for allozyme loci (A = 1.525, range

1.301–1.853; HS = 0.122, range 0.062–0.239). Both kinds

of markers indicated the highest level of genetic variability

in Filia. However, the lowest value of variability was

observed in Cavallers by microsatellites but in Manyanet

by allozymes (Table 1). Significant and large genetic dif-

ferentiation was detected among wild populations and

between pooled wild populations and hatchery stocks using

either allozyme or microsatellite loci. In the allozyme

analyses, those differences remained large when the diag-

nostic LDH-C locus was removed from the analyses

(Table 2). None of the nine microsatellite loci showed

fixed alleles to distinguish hatchery from native fish.

However, some of the alleles detected in a high frequency

in Baga hatchery stock (Str-73*144, *146, Str-

591INRA*148, SsHaeIII-14.20*312, *322, *324, SsoSL-

417*177, *183, SsoSL-438*105) were observed only in

wild samples in concurrence with specific hatchery alleles

for allozyme loci (G3PDH-2*50; LDH-C*90, sMDH-

A2*120, sMDH-B1, 2*80).

Performance of markers and methods by simulated

hybrid populations

At an initial threshold q-value of 0.10 (0.10 B q B 0.9

admixed) STRUCTURE correctly identified as admixed most

of the simulated hybrid individuals by both allozymes and

microsatellites genotypes: 100% of F1, 98% of F2, and

96% of Bnat (Fig. 1). However, a proportion of 12–4%

228 Conserv Genet (2009) 10:225–236

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(allozymes–microsatellites) of parental hatchery individu-

als showed q \ 0.9 and could not be distinguished from

hybrid genotypes. The use of a threshold q-value of 0.2

increased the efficiency of detecting hatchery fish up to 98–

100% (allozymes–microsatellites), but then decreased the

efficiency for hybrids since some F2 and Bnat individuals

were misclassified as parental hatchery and parental native

respectively (Fig. 1). Therefore we selected the threshold

of 0.1 to be used in the analyses of wild collections.

Analysis with NEWHYBRIDS, using the posterior probability

value of 90% as a threshold for assigning individuals and

regardless of specific hybrid class, correctly identified as

hybrid 100% of simulated genotypes from F1 but failed on

identify a proportion of hybrid individuals from F2

(14–8%, allozymes–microsatellites) and Bnat (30–34%,

allozymes–microsatellites). The efficiency of this method

increased with a posterior probability of 50%, though still

a proportion of 8–4% (allozymes–microsatellites) of F2

genotypes were misclassified as parental hatchery fish and

6–10% (allozymes–microsatellites) of Bnat as parental

native specimens (Fig. 1).

Analyses with BAPS using the trained clustering with a

maximum of two partitions largely failed on the identifi-

cation of the simulated hybrid genotypes, which were

mostly considered native (98–98% of F1, 80–84% of F2,

100–100% of Bnat, allozymes–microsatellites). The

alternative BAPS analysis using a maximum of five parti-

tions to detect a native source for admixture analysis

identified five clusters in the optimal trained analysis on

simulated allozyme genotypes. The first one grouped all

simulated hatchery specimens and four F2 genotypes to the

reference fish from Baga. The most divergent cluster from

the Baga fish incorporated 38 simulated native fish and one

Bnat fish. A third cluster included the rest of native indi-

viduals and 28 hybrid individuals. The other two clusters

grouped individuals despite of their specific hybrid class.

Three clusters were identified in simulated microsatellite

genotypes. One of them joined all the simulated hatchery

parental fish and one F2 genotype to the reference fish from

Baga. A second cluster grouped all the simulated native

fish with one F1 and 13 Bnat, while the third one clustered

the remained hybrid fish. Using Baga fish and the most

divergent cluster as reference hatchery and native baselines

for admixture analysis of all simulated individuals, most of

the F1, F2 and Bnat fish were identified as admixed.

However, still the 12–4% (allozymes–microsatellites) of

F2 genotypes were misclassified as parental hatchery fish

and 14–30% (allozymes–microsatellites) of Bnat as

parental native specimens (Fig. 1).

Analysis of simulated hybrid genotypes carried out by

GeneClass assigned most of simulated hybrids to a hybrid

class (F1, F2 or Bnat). The simulation option of GeneClass

rejected all simulated parentals from the other parental

class. GeneClass rejected almost all simulated hybrids

from both hatchery and native parental classes, except for a

proportion of 16% of F2 genotypes that were not rejected

from parental hatchery and a proportion of 6% of Bnat that

were not rejected from parental native, using allozymes.

Genetic admixture and evaluation of introgression in

wild populations

Hardy–Weinberg test of allozyme loci revealed significant

deviation in two of the 46 tests, at the 5% significance level

and after Bonferroni sequential adjustment (locus MEP-1

in Manyanet and locus ME in Conangles). Only one out of

Table 2 Genetic differences among collections

Locus FST among

wild samples

FST between pooled

wild samples and

hatchery stock

Allozymes

sAAT-4 0.017 0.127

CK-A1 0.115 0.167

bGALA-2 0.214 0.339

GAPDH-2 0.011 0.000

bGLUA 0.083 0.405

G3PDH-2 0.000 0.222

GPI-A 0.005 0.000

IDHP-2 0.160 0.048

IDHP-3 0.109 0.069

LDH-A2 0.351 0.770

LDH-C 0.230 0.871

sMDH-A2 0.065 0.123

sMDH-B1 0.127 0.307

ME 0.276 0.307

MEP-1 0.132 0.019

MEP-3 0.079 0.052

MPI-2 0.092 0.218

PEPLT 0.016 0.000

SOD-1 0.018 0.001

Average 0.189 0.411

Average without LDH-C 0.185 0.327

Microsatellites

Ssa85 0.262 0.082

Ssa408 0.240 0.052

SsHaeIII14.20 0.234 0.206

SsoSL417 0.259 0.101

SsoSL438 0.310 0.223

SSsp2213 0.221 0.082

Str15 0.290 0.282

Str73 0.109 0.382

Str591INRA 0.393 0.343

Average 0.261 0.192

Conserv Genet (2009) 10:225–236 229

123

the 54 tests was significant for microsatellite loci (locus

Str591INRA in Cardos). Thus, apparently all collections

were consistent with a single random mating unit. How-

ever, following Garnier et al. (2004), STRUCTURE suggested

two genetically homogeneous groups (K = 2) in Conan-

gles, Cavallers and Nicolau from both allozymes and

microsatellites. In all three populations, the occurrence of

two units persisted when individuals from Baga hatchery

were included in the analysis.

The LDH-C*90 allele provided evidence of hatchery

genes in all stocked populations other than Cavallers.

However, the proportion of hatchery genes estimated by

the fully Bayesian methods confirmed the hatchery releases

made in Cavallers as well as in all other stocked locations,

and indentified Manyanet as a pure native population at

that time (Fig. 2, Table 3). STRUCTURE suggested a large

proportion of native genes remaining in all collections

except Filia and similar results were detected by

NEWHYBRIDS and BAPS. All individual fish that were more

likely to be of hybrid or hatchery origin according to

NEWHYBRIDS had moderate or high hatchery ancestry esti-

mations in STRUCTURE and BAPS analyses. Agreement

(a)

(a)

(a)

(b)

(b)

(b)

Fig. 1 Admixture analyses of

simulated genotypes (phat,parental hatchery; Pnat,parental native; F1, first

generation hybrid; F2, second

generation hybrid; Bnat,backcrosses of F1 with parental

native). For STRUCTURE and BAPS

each individual is represented as

a vertical bar partitioned into

two segments according to the

proportion of hatchery (black)

and native (white) genome. For

NEWHYBRIDS each individual is

represented as a vertical bar

partitioned into segments whose

length is proportional to the

likelihood of belonging to a

parental hatchery (black),

parental native (white) or hybrid

(grey) class. (a) Allozymes and

(b) microsatellites

230 Conserv Genet (2009) 10:225–236

123

between the three methods was higher from allozyme data

than from microsatellite genotypes (Table 4). Using

microsatellites, some of the native fish indicated by

STRUCTURE and NEWHYBRIDS in Conangles, Cavallers and

Nicolau were assigned to hybrid class by BAPS. These

discrepancies reflect limited capability of BAPS to generate

a native microsatelllite baseline because higher FST values

observed among wild samples than between wild samples

(a) (b)Fig. 2 Plots of individual

admixture coefficients (q)

estimated by STRUCTURE

including their 90% probability.

Individuals were ranked from

highest to lowest q value and

rank was plotted against 1 - q.

A 1 - q value of 1 denotes a

pure native brown trout,

whereas a value of 0 denotes a

pure hatchery trout. K, Number

of populations estimated by

STRUCTURE. White symbols

indicate individuals assigned to

parental native, parental

hatchery and hybrid class (s,

parental native, h, parental

hatchery, D, F1, r, F2, e,

Bnat) by NEWHYBRIDS with

P [ 0.5. Black filled symbols

indicate individuals assigned

with P \ 0.5. (a) Allozymes

and (b) microsatellites

Conserv Genet (2009) 10:225–236 231

123

and hatchery fish (Table 2). Agreement between STRUCTURE

and NEWHYBRIDS was better using microsatellite than

allozymes. Some of the admixed fish indicated by STRUC-

TURE in Filia, Conangles, Cavallers and Nicolau were

assigned to native class by NEWHYBRIDS with allozyme

data. As suggested for the simulation results, these dis-

crepancies could reflect some Bnat fish originating in these

locations. Additionally, it is also possible that NEWHYBRIDS

identified as parental native some wild admixed fish

beyond second generation hybrid in these moderately int-

rogressed populations. Multilocus based methods failed to

identify the hybrid origin of some individuals genotyped as

LDH-C*90/*90 or *90/*100.

The partially Bayesian method of GeneClass assigned

most individuals to their original sample. Using allozymes,

some fish from stocked populations were misassigned, but

only a few specimens from Filia, Conangles and Nicolau

were considered of hatchery origin and assigned to the

Baga stock (Table 4). In fact, the simulation option rejec-

ted a large proportion of wild trout of the hatchery stock in

all samples but Filia.

Discussion

A common management strategy to maintain or improve

freshwater fisheries is the extensive release of cultured fish

(Brown and Day 2002). However, these activities mandate

development of conservation and management strategies to

identify remaining pure native and hybridized populations

(Ferguson et al. 1995). Comparisons between microsatel-

lites and allozymes in hybrid identification have indicated a

higher efficiency of microsatelllites when hatchery strains

and wild fish are weakly differentiated (Aurelle et al. 2002;

Corujo et al. 2004), but microsatellites appear to be less

effective when differentiation is large (Poteaux et al.

2001). In addition, simulations have reported comparable

efficiencies of STRUCTURE and NEWHYBRIDS (Vaha and

Primmer 2006) and STRUCTURE and BAPS (Latch et al.

2006).

The present study analyzed the efficiency of classifica-

tion methods and different allelic markers in the context of

the introduction of evolutionary divergent non-native

stocks. This practice is wide-spread where native cultured

stocks have not been developed (Cowx 1999; Brown and

Day 2002). Our simulated data showed that any combina-

tion of markers and methods were equally useful in

detecting hatchery releases (Fig. 1) and estimates of

introgression in wild collections were highly correlated

(r [ 0.9, P \ 0.01, Tables 3, 4). However, at the individ-

ual level, the simulated genotypes indicated that

STRUCTURE, NEWHYBRIDS and BAPS erroneously classified

some F2 and Bnat specimens respectively into hatchery or

native genotypic class. The empirical data indicated those

errors accumulated in the most introgressed populations,

where some hatchery fish could be considered admixed fish

originated in the wild by STRUCTURE and some Bnat wild

fish as native by NEWHYBRIDS. The combinations allo-

zymes-NEWHYBRIDS and microsatellites-BAPS resulted in

questionable results in some introgressed populations

(Table 4). With regard to discrepancies being larger in the

Filia collection, where Hardy–Weinberg test and STRUC-

TURE suggested a single panmictic unit, NEWHYBRIDS may

lead to erroneous conclusions in those populations where

Table 3 Proportion of relative ancestries in wild collections

Allozymes Microsatellites

LDH-C STRUCTURE (C.I.) NEWHYBRIDS (S.E.) BAPS (S.E.) STRUCTURE (C.I.) NEWHYBRIDS (S.E.) BAPS (S.E.)

Manyanet 1.000 0.995 (0.975–1.000) Pnat: 0.999 (0.000) 1.000 (0.000) 0.995 (0.984–1.000) Pnat: 0.999 (0.000) 1.000 (0.000)

Cardos 0.960 0.955 (0.897–0.995) Pnat: 0.923 (0.009) 0.965 (0.022) 0.989 (0.956–1.000) Pnat: 0.983 (0.001) 0.940 (0.027)

Filia 0.571 0.518 (0.379–0.649) Pnat: 0.323 (0.029) 0.368 (0.080) 0.595 (0.470–0.701) 0.188 (0.074)

F2: 0.233 (0.012) F2: 0.664 (0.038)

Phat: 0.339 (0.044) Phat: 0.195 (0.034)

Conangles 0.941 0.803 (0.683–0.901) Pnat: 0.797 (0.021) 0.744 (0.058) 0.941 (0.864–0.995) Pnat: 0.891 (0.021) 0.551 (0.062)

Cavallers 1.000 0.895 (0.958–0.815) Pnat: 0.892 (0.008) 0.914 (0.032) 0.889 (0.806–0.964) Pnat: 0.716 (0.040) 0.627 (0.052)

Bnat: 0.100 (0.006) F2: 0.175 (0.022)

Bnat: 0.105 (0.007)

Nicolau 0.821 0.820 (0.731–0.901) Pnat: 0.851 (0.013) 0.849 (0.050) 0.891 (0.781–0.974) Pnat: 0.727 (0.028) 0.576 (0.049)

Bnat: 0.190 (0.018)

The LDH-C*100 native allele frequencies are from Araguas et al. (2004). Using STRUCTURE, native ancestry (1 – q) is indicated. C.I., 95%

confidence interval. Using NEWHYBRIDS, the samples were assigned to one (with P [ 0.9) or more than one (with P [ 0.1) of the following

genotypic classes (Pnat, parental native; Phat, parental hatchery; F1, first generation hybrid; F2, second generation hybrid; Bnat, backcrosses of

F1 with Pnat. In BAPS, the average admixture coefficient with respect to the native baseline is indicated. S.E., standard error

232 Conserv Genet (2009) 10:225–236

123

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Conserv Genet (2009) 10:225–236 233

123

extensive hybridization is beyond second generation as

with assignment of Filia fish only to purebreds and F2

classes (Fig. 2). STRUCTURE analyses on wild populations

resulted in more robust results using either allozymes or

microsatellites. Admixture analyses using BAPS require the

identification of all source populations (Corander and

Marttinen 2006) but in stocking activities the ancestral

composition of the recipient wild population is usually

unknown. Our approach generating a native baseline from

an initial trained clustering of all wild fish worked well

with allozyme data, but overestimated hatchery ancestry in

some samples (Conangles, Cavallers and Nicolau) with

microsatellites. The distinct pattern of FST values between

wild samples and hatchery fish observed using allozymes

or microsatellites (Table 2), generated a good native

baseline for allozymes because of the larger divergence

between wild collections and Baga fish. Better agreement

between BAPS results and those obtained in STRUCTURE and

NEWHYBRIDS were observed from individual trained clus-

tering of each wild collection using a maximum of three

partitions (hatchery, hybrid and native). In these BAPS

analyses, the hybrid and native clusters were identified

according with intermediate (hybrid) and large (native)

Kullback-Leibler divergence from Baga collection

(Table 4).

In simulated analyses, GeneClass distinguished among

fish from distinct random mating units. However, because

native and hybrid fish (F1, F2 and Bnat) were both rejected

from the hatchery baseline, it may be impossible to suspect

hatchery releases in stocked populations once Hardy–

Weinberg equilibrium is attained (Table 4). Therefore, it is

not surprising that GeneClass considered as native most of

the fish from the wild stocked collections, and only fish

from the hatchery or having strong hatchery ancestry were

separated from the rest. Even including the hatchery

baseline, GeneClass approaches have not detected admixed

fish in random mating populations (Hansen et al. 2001a, b;

Manel et al. 2002).

The detection by STRUCTURE of two distinct units in

Conangles and Cavallers concurs with the stocking records

at these localities (Table 1), and in Nicolau, confirmed the

downstream migration of fish from an exogenous hatchery-

like population established upstream (Garcıa-Marın et al.

1998). STRUCTURE represents then, a complement to clas-

sical Hardy–Weinberg tests when historical records of the

populations do not fully agree with adjusted Hardy–

Weinberg proportions. We checked the efficiency of

Hardy–Weinberg test and STRUCTURE to detect departures

from panmixia by generating 25 random introductions of

one hatchery fish from the Baga stock into pure (e.g. Ma-

nyanet), moderately admixed (10% hatchery genes) and

more introgressed (25% hatchery genes) populations. The

two admixed populations were generated using HYBRIDLAB

(Nielsen et al. 2006) and the Manyanet and Baga samples

as parental populations. From simulated F3 individuals, a

sample of 24 fish was randomly collected to add a hatchery

specimen. This rate of hatchery introduction by a single

release (4%) agrees with estimates observed among Med-

iterranean brown trout populations (Araguas et al. 2004).

In the native population, the presence of the hatchery

specimen was detected in all replicates either using allo-

zymes or microsatellites and exact probabilities Hardy–

Weinberg test or STRUCTURE. The hatchery specimen

introduced in the moderately introgressed population was

never noticed by Hardy–Weinberg deviations, and only

STRUCTURE detected introduction in 13–10 (allozymes–

microsatellites) out of 25 replicates. In the most intro-

gressed population, introductions were unnoticed by any

marker or method combination. Vaha and Primmer (2006)

showed that the efficiency to detect purebreds remained at

the same level in the simulation of a low (1%) and higher

(10%) hybridization scenario. Our results demonstrate that

the increase of the level of hybridization decreases the

efficiency to detect purebred hatchery fish, even in a situ-

ation of 10% of hatchery genes. With regard to ongoing

introductions reducing divergence between wild popula-

tions and hatchery stocks, the ability to identify hybrid fish

generated by future releases is compromised (Berry et al.

2004).

Diagnostic markers

Diagnostic genetic tags between stocks can be of major

importance for the rehabilitation of natural populations

(Ferguson et al. 1995). The fixed allele differences at the

LDH-C locus between wild Spanish populations and

exogenous hatchery stocks have been used definitively to

assess the survival and reproductive success of released

hatchery fish (Arias et al. 1995; Araguas et al. 2004).

However, this diagnostic locus suggested a native gene

pool in the yearly stocked population in Cavallers, where

all multilocus analyses reflected the genetic impact of those

hatchery releases. This observation in Cavallers suggests

caution in using a single diagnostic locus to exclude

introgression in native populations despite a significant

correlation between the LDH-C*90 allele frequency and

the admixture level estimated by STRUCTURE or BAPS from

both allozymes and microsatellites. Actually, a non-

destructive DNA based RFLP technique identifies quickly

and cheaply the LDH-C locus genotypes (McMeel et al.

2001). Because results obtained from such DNA analyses

agree with historical records on hatchery releases, the

LDH-C*90 allele frequency will persists as a primary

estimator of hatchery effects on Mediterranean brown trout

populations. When results do not match stocking records or

when such official records are doubtful, microsatellite

234 Conserv Genet (2009) 10:225–236

123

based analyses from the same DNA extractions are

warranted.

Conclusions

Simulated data indicated that allozymes and microsatellite

genotyping could identify hybrids and introgression at

similar efficiencies with all statistical methods assessed.

However, analyses on stocked wild populations indicated a

limited capability of GeneClass to detect admixtures and a

lower discriminatory capability of allozymes compared to

NEWHYBRIDS. In wild collections, BAPS analyses were lim-

ited because of the lack of a native baseline. STRUCTURE

using microsatellite data appears to be the best combination

for wild studies. Our number of microsatellite loci and

level of population divergence between wild populations

and hatchery stocks approached acceptable conditions (12

loci when FST is around 0.21, Vaha and Primmer 2006). In

fact, 6–10 microsatellite loci usually are involved in fish

population genetics (Koskinen et al. 2004), but this number

of loci could be enough even when population divergences

are reduced (Hauser et al. 2006). In the particular case of

Mediterranean brown trout, the pattern of population

divergences among wild collections (FST = 0.261,

Table 2) suggests that the nine microsatellite loci used also

would be suitable to detect introgression of hatchery stocks

originated from native Mediterranean populations.

The strong allozymic genetic divergence between native

Mediterranean brown trout populations and the hatchery

stocks originated from Atlantic populations (FST = 0.411,

Table 2) limited the extension of our conclusions on this

kind of marker to other fish species. Mediterranean and

Atlantic populations represent evolutionary divergence of

lineages (Cortey et al. 2004), and FST value of 0.411, or

0.327 excluding LDH-C (Table 2), are unusual among co

specific freshwater fish populations (Ward et al. 1994). As

indicated, this large FST value at allozyme loci improved

identification of a native baseline in BAPS analyses.

Finally, our results indicate that introgression by ongo-

ing stocking practices reduces efficiency for detecting

future stocking activities. This discriminatory loss is

especially dangerous for fisheries without historical records

on stocking practices because admixed populations may be

used as sources for the development of native hatchery

strains. Moreover, this insensitivity also excludes man-

agement options intended to eliminate hybridized

populations (Allendorf et al. 2004), because practically all

wild specimens can be potentially admixed in some degree.

Acknowledgements This research meets the objectives of the REN-

2003–05931/GLO project of the Spanish MCYT. R. Fernandez and M.

Vera were fellows of the Spanish MCYT. We thank FM Utter for English

revision and for valuable suggestions to improve this manuscript.

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