Temporal changes and effective population size of an Italian isolated and supportive-breeding managed northern pike (Esox lucius) population

Fisheries Research 96 (2009) 139–147

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Temporal changes and effective population size of an Italian isolated and supportive-breeding managed northern pike (Esox lucius) population L. Lucentini a,∗ , A. Palomba a , L. Gigliarelli a , G. Sgaravizzi a , H. Lancioni a , L. Lanfaloni a , M. Natali b , F. Panara a a b

Dipartimento di Biologia Cellulare e Ambientale, Università degli Studi di Perugia, Via Pascoli, 06123 Perugia, Italy Provincia di Perugia, Ufficio Programmazione e Gestione Fauna Ittica, Via Palermo 21/C, 06129 Perugia, Italy

a r t i c l e

i n f o

Article history: Received 11 January 2008 Received in revised form 11 October 2008 Accepted 13 October 2008 Keywords: Esox lucius Effective population size Fish management Conservation genetics Population genetics

a b s t r a c t Genetic analyses allowed management and conservative programmes specifically designed for biodiversity conservation purposes. To date, no information is available for the Ne definition of any Italian northern pike population, because of their wide, highly fragmented, distribution. Low gene flow levels may have caused some of the health population problems emerging from professional fisheries data, underlining a marked reduction of these populations. This paper reports the first estimate of genetic Ne of the Lake Trasimeno’s northern pike, by means of microsatellites data, over the last four decades compared with five other significant Italian populations. Ne values were evaluated through specific software and  estimator. The Ne results confirm that, despite a marked reduction between 1966 and 1997 and a less than optimal status, the Trasimeno population shows a positive trend in its health status, presumably due to proper management programmes. Considerations on the biological conservation and management of this pike population are also reported. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Biodiversity conservation is the main objective of evolutionary and conservation biologists. Genetic variation is one of the three levels of biodiversity recommended by the World Conservation Union (IUNC) for conservation programmes (Frankham et al., 2004) and a number of studies demonstrated the importance of genetic analysis in finite wild populations and threatened species (Pinto et al., 2002; Larsen et al., 2005; Hansen et al., 2006). Genetic diversity of species or populations is the result of their evolutionary history and its decrease may reduce their adaptability and surviving potential in a changing environment (Frankham et al., 2004). Further, genetic drift, inbreeding and gene flow reduction could impoverish the genetic diversity of small and isolated populations. In fact, conservation biologists attribute a great importance to the knowledge of population size, because small populations could be seriously affected by environmental instability, stochastic demographic change, loss of reproductive habitat, and inbreeding depression (Paschke et al., 2002; Frankham et al., 2004). In particular, inbreeding can affect demographic rates, thus increasing the probability of extinction, directly or

∗ Corresponding author. Fax: +39 0755855736. E-mail address: [email protected] (L. Lucentini). 0165-7836/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fishres.2008.10.007

through a magnification of non-genetic factors (Miller and Waits, 2003). Northern pike (Esox lucius Linnaeus, 1758) has a wide distribution in the northern hemisphere and it is the only Esocid present in Europe. Although it is not considered an endangered species, recent studies indicate a decline of some North-European (Kovàcs et al., 2001; Westin and Limburg, 2002; Jacobsen et al., 2005) and Italian populations (Lorenzoni et al., 2002; Lucentini et al., 2006). Northern pike is a large, top predator and one of the most economically important freshwater fish for both recreational and commercial fisheries; it is also relevant for the conservation and management of freshwater ecosystems (Senanan and Kapuscinski, 2000; Kovàcs et al., 2001). The effects of overfishing, habitat reduction and fragmentation, anthropic perturbations and climate changes, could constitute a risk for this species in Italy (Lorenzoni et al., 2002; Lucentini et al., 2006). Genetic studies with allozymes (Healy and Mulcay, 1980; Seeb et al., 1987), mitochondrial DNA (Brzuzan et al., 1998; Maes et al., 2003; Nicod et al., 2004) and microsatellite markers (Miller and Kapuscinski, 1996, 1997; Hansen et al., 1999; Senanan and Kapuscinski, 2000; Aguilar et al., 2005; Jacobsen et al., 2005; Laikre et al., 2005; Lucentini et al., 2006) revealed a low genetic polymorphism in this species. Microsatellites detected higher polymorphism with respect to other techniques (Laikre et al., 2005; Lucentini et al., 2006). They were used as a molecular approach

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to measure the effective population size (Ne ) of two North American (Miller and Kapuscinski, 1997; Aguilar et al., 2005) and one Danish (Larsen et al., 2005) population. The effective population size Ne refers to an ideal population showing the same homozygosity and genetic drift increase rate as the current population considered (Rieman and Allendorf, 2001; Frankham et al., 2004). Managing genetic diversity is of the utmost importance in a conservation programme. The Ne estimate is the key parameter for determining population structure, rates of genetic variation, fixation of deleterious alleles and inbreeding. Ne is strongly affected by several parameters such as sex ratio, family size, number of individuals per generation and generation overlap (Frankham et al., 2004; Nomura, 2005). In general, all these factors determine marked deviations from the ideal conditions. Furthermore, census population size (N) is very difficult to measure for most wild species and is usually greater than Ne . To estimate Ne several genetic methods have been suggested, usually classified as temporal methods since they are based on multiple comparisons across generations. For these reasons, according to the genetic property of interest, different concepts of Ne have been proposed such as the inbreeding effective size Nei and the variance effective size Nev (Wang, 2005). The former is the most widely used: it predicts the heterozygosity decrease rate or the homozygosity increase rate (inbreeding); the latter measures the change variance in gene frequency resulting from one generation of genetic sampling. These methods are not expected to produce similar results (Leberg, 2005); in particular, they can differ greatly in populations that are not in equilibrium (Wang, 2005). The  estimator of Xu and Fu (2004), based on sample homozygosity under the singlestep, stepwise mutation model, is important and shows a high efficiency, reduced bias and mean square error (MSE). The stepwise mutation model has been widely used with microsatellites, because mutation leads to expansion or contraction of a repeated number in a stepwise fashion. The new estimator of Jorde and Ryman (2007) for genetic drift and effective size, weights alleles differently and should be particularly useful for microsatellite loci, especially when many alleles occur at low frequencies. The northern pike of Lake Trasimeno (Central Italy) is a closed, self-reared population. In 2006 the total capture of the fisheries cooperatives in Lake Trasimeno was about 5300 kg with a mean weight of 0.8 kg. In a comparison with historical data, there has been a marked reduction in the last 40 years. Nevertheless, a previous study (Lucentini et al., 2006) underlined a good health status of this population compared with other populations from Italy and northern Europe. Materials from an historical collection of juvenile specimens (about 10–15 cm) captured in 1966, and DNA extracted from adult samples of 1997 and 2006, were used to study the genetic variability and Ne evaluation of Trasimeno pike during the last four decades. Data were compared with those of five other Italian populations.

2. Materials and methods and study area 2.1. Study area Northern pike is widely distributed in Italy with several highly fragmented populations. The Alps separate the Italian pike populations from Northern Europe ones, while the Apennines separate the Northern and Central Italian populations. Lake Trasimeno – a laminar lake located in Central Italy in the upper part of the Tiber drainage – is classified as a Special Protection Zone (IT5210070). The peculiar geomorphology and hydrology make this biome a closed lake. Its fish populations have been isolated for several centuries. In the last decades, the lake has been greatly modified by climate

changes and agricultural and tourist impact, with negative effects on the existent fauna and flora biocenoses. In particular, the reduction of cane-brake (Phragmites australis Cavill Trin. ex Steud), the main spawning site of northern pike, added to climate warming and high fishery pressure, may be the cause of the pike population decline. This scenario has underlined the opportunity of a supportive breeding programme by the Province of Perugia in the “Centro Ittiogenico del Trasimeno” (CIT), a specialized structure in which artificial reproduction is carried out with breeders captured directly in the lake and re-introduced immediately after squeezing. Every year the entire breeder stock is changed and no allochtonous juvenile dissemination or allochtonous breeders are employed. 2.2. Northern pike samples and DNA extraction The main northern pike sampling site was Lake Trasimeno; three different temporal samples were analysed (Table 1). The first, relating to 1966 (T-66), was obtained from an historical collection conserved at CIT. One-year-old northern pike specimens were preserved at room temperature in formaldehyde/absolute alcohol (at unknown ratio). Specimens were gently drained with soft paper and some scales (20 ca.) and a small piece of pectoral fin (10 mg ca.) were removed and dried at 37 ◦ C for 10 prior to DNA extraction. The second and third Trasimeno stocks were collected at CIT in 1997 (T-97) and 2006 (T-06) during the spawning period (from early February to mid-March). Furthermore, for 420 samples (222 females and 198 males) – randomly chosen from breeders utilised at CIT in 2006 for the artificial spawning – total length, sex determination and a few scales were recorded and a scales analysis was conducted with a Laica stereomicroscope. The aim was to define the mean age of breeders, a parameter needed to define Ne (Miller and Kapuscinski, 1997). As already mentioned, the breeders were randomly drawn daily from the lake for 1 month, and several hundred breeders (about 600) were squeezed each year. Based on the above, it can be assumed that the northern pike samples of the CIT are representative of the breeder stock of the entire lake. Samples of 2006 analysed in this paper are not the same as those of Lucentini et al. (2006); in that research a sample of 50 pike of different ages was analysed, mostly sampled in February 2004. To ensure that samples of 2006 were different from those of 1997, a sub-sample of 50 individuals was randomly chosen from all the samples that were not born in 1997 (based on the scale analysis conducted for the entire T-06 sample, 420 individuals). Other samples, used to check the Ne estimations, were captured in lakes of Central and Northern Italy and a total of five Italian pike populations were used as output; two of them were located in the upstream tract of the Po River basin (Lakes Maggiore and Segrino) separated by the Apennines from the other three (Lakes of Chiusi, Piediluco and Bolsena), located in the Tiber drainage. Samples from these populations were provided by local authorities, fisheries co-operatives or colleagues and ranged from 11 to 50 individuals (Table 1). DNA was extracted from preserved and fresh scales and caudal fin pieces as previously reported (Lucentini et al., 2006), then stored at −20 ◦ C. To verify the results obtained through conservative samples, for a sub-sample of 10 individuals, tests were also performed on DNA extracted from internal tissues (liver and muscle) of animals destined to human nutrition. DNA concentration was estimated in a 1.0% agarose gel electrophoresis in presence of Mass Ruler DNA Ladder Mix (Fermentas, Burlington, Canada) and by spectrophotometric assay (GeneQuant, GE, Chalfont St Giles, Buckinghamshire). Samples size reported in Table 1 refers to those actually selected after these controls.

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Table 1 Sample size and lake location. Site Tiber drainage (Central Italy) Trasimeno Lake T-66 T-97 T-06

Latitude, longitude

Surface (km2 )/elevation (m a.s.l.)

43◦ 06 N, 12◦ 08 E

123.0/256

Sampling date

Sample size

1966 1997 2006

21 18 50 50

Chiusi Lake

43◦ 11 N, 11◦ 56 E

3.87/251

2005

Piediluco Lake

42◦ 30 N, 10◦ 17 E

1.7/375

2005

30

Bolsena Lake

42◦ 35 N, 11◦ 56 E

113.5/305

2003

32

46◦ 06 N, 08◦ 45 E

212.0/193

2002

20

45◦ 49 N, 09◦ 16 E

0.35/370

2004

11

Po drainage (Northern Italy) Maggiore Lake Segrino Lake

2.3. Microsatellite polymorphism analysis Chosen DNA was used in the PCR amplifications of the seven microsatellites loci already selected on the basis of their polymorphism (Lucentini et al., 2006): Elu19, Elu51, Elu76, Elu78, Elu87, Elu276 (Miller and Kapuscinski, 1996, 1997) and EluB38INRA (Launey et al., 2003). Fifty nanograms of DNA were amplified using Ready-To-Go PCR Beads (GE, Chalfont St Giles, Buckinghamshire), 25 pmol of each primer in a total volume of 25 ␮l (Lucentini et al., 2006). Forward primers were marked using fluorochromes NED, FAM and VIC (Applied Biosystems, Foster City, CA, USA), the amplicons were run on a ABI PRISM 310 automatic sequencer (Applied Biosystems, Foster City, CA, USA) in presence of 500-LIZ standard (Applied Biosystems, Foster City, CA, USA) and raw data were analysed through Gene Mapper 4.0 (Applied Biosystems, Foster City, CA, USA). Alleles were designated according to their size and filed in a Microsoft Excel sheet as a binary two-column format. Using Convert 1.31 (Glaubitz et al., 2003), the data matrix was transformed in Arlequin 3.1 (Excoffier et al., 2005) and Genepop 3.3 (Raymond and Rousset, 1995) formats, from which most of the other formats, as that of F-STAT 2.9.3.2 (Goudet, 1995) can be derived. 2.4. Data analysis 2.4.1. Samples characterization To evaluate genotyping errors, and to characterise allelic dropout or misprinting, all the experiments were replicated as suggested for non-destructive samples (Hoffman and Amos, 2005; Roon et al., 2005). For these purposes, data were analysed through Micro-Checker 2.2.3 (Van Oosterhout et al., 2004) that estimates a large allele dropout and null allele frequency for each locus. Linkage disequilibrium (LD) was tested for each pair of loci within each population using Fisher’s exact tests (Raymond and Rousset, 1995), with unbiased P-values derived by a Markov chain method by means of Arlequin 3.11. LD multiple significance tests were set using the sequential Bonferroni procedure. Since the P-value of the exact test could be affected by sample size, its effect was evaluated on the proportion of significant LD P-values by drawing 30 random samples, 10 for each stock, from the entire specimen set. Random samples were drawn without replacement and the proportion of significant LD P-values was compared with the one obtained for each population. Programme packages were used to calculate allele frequencies and to estimate the expected (He ) and observed (Ho ) heterozygosity and allelic richness A (Gum et al., 2003; Kalinowski, 2004). Wright’s indexes (Fit, Fis, Fst) (Weir and Cockerham, 1984) were calculated using F-STAT. To provide confidence intervals (CI) of

the estimated parameter, significance values for each locus were determined by bootstrapping over samples and significance values over all loci were calculated by jackknifing over loci. Levels of genetic differentiation were also assessed analysing Fst between the three stocks through a Pearson correlation test. Using Arlequin 3.01 the allele frequencies for each locus within populations were computed and two AMOVA analyses were carried out for the three temporal samples and for the eight actual Italian samples. These frequencies were also tested for deviation of observed genotype frequencies from those expected under the Hardy–Weinberg equilibrium (HWE), giving the Weir and Cockerham (1984) estimates of Fis. F-STAT was also used to test the significance of differences in the average values of A, Ho and He among stocks (1000 permutations, two-sided test under the null hypothesis of no difference). 2.4.2. Evaluation of the number of generations in an interval The estimation of the generation number in an interval between two sampling times was carried out following the suggestions of Miller and Kapuscinski (1997). For a sub-sample of 420 spawners (222 females and 198 males) randomly caught in the lake, the age was determined through the microscopic scalimetric method. The mean age of spawners, and consequently the mean generation length that is 1 year more, were determined. Finally, subdividing the number of years separating samples by the estimate mean of generation length, the number of generations in each interval was obtained. 2.4.3. Ne estimations To evaluate changes in Ne , the three Trasimeno samples (T-66, T-97 and T-06) were analysed for the genetic signature of a population bottleneck. Allele frequency distortions and heterozygosity excess were tested through Bottleneck (Cornuet and Luikart, 1996). Population bottleneck, revealed by the presence of fewer rare alleles, causes heterozygosity disequilibrium that could be evaluated through the Wilcoxon signed-rank test (Aguilar et al., 2005). The temporal Ne estimation was performed on the basis of the new estimator of Jorde and Ryman (2007) for genetic drift and effective size using sampling plan I by means of TempoFs. This sampling plan was appropriate because all samples were derived from the same population, because sampling was non-lethal and usually performed after squeezing. The individuals could therefore contribute to future generations. This estimator weights alleles differently and should be particularly useful for microsatellite loci, especially when many alleles occur at low frequencies. Xu and Fu’s formula (2004) Ne = /(4) was applied as an alternative method to estimate the number of breeders in Lake Trasimeno assuming the absence of population substructure. The

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Table 2 Per locus genetic diversity population statistics: for each sampling period are reported the per locus  averages across the sampling period (near the locus name), the alleles dimension (bp) and frequencies: the allelic richness (A), the gene diversity (GD) and the alleles number (n). A, GD and n values are reported also across loci. Elu19 (0.02)

T-66 T-97 T-06

Alleles (bp) 110

117

124

128

134

137

144

148

153

157

170

0.05 0.00 0.02

0.60 0.00 0.00

0.24 0.08 0.12

0.00 0.03 0.06

0.00 0.00 0.04

0.10 0.03 0.00

0.00 0.56 0.32

0.00 0.00 0.10

0.00 0.31 0.27

0.00 0.00 0.02

0.02 0.00 0.05

Total across samples EluB38INRA (0.01)

Alleles (bp)

T-66 T-97 T-06

169

173

177

0.50 0.19 0.36

0.45 0.75 0.62

0.05 0.06 0.02

Total across samples Elu51 (0.04)

T-66 T-97 T-06

3

129

0.10 0.03 0.11

0.10 0.33 0.23

0.07 0.00 0.01

0.38 0.00 0.01

0.07 0.03 0.14

0.02 0.03 0.07

0.19 0.47 0.31

0.07 0.11 0.12

Total across samples Alleles (bp)

0.19 0.17 0.02

0.07 0.00 0.01

Total across samples Elu78 (0.07)

T-66 T-97 T-06

135

137

140

146

0.00 0.00 0.02

0.36 0.31 0.16

0.07 0.42 0.21

0.14 0.28 0.29

0.43 0.00 0.32

T-66 T-97 T-06

137

142

147

154

0.43 0.11 0.16

0.17 0.36 0.28

0.17 0.28 0.40

0.24 0.25 0.15

0.00 0.00 0.01

T-66 T-97 T-06

0.81 0.67 0.81

8 6 8

7.43

0.76

8

0.66 0.45 0.23

4 3 4

3.59

0.45

4

A

GD

n

3.99 3.00 4.62

0.68 0.68 0.75

4 3 5

4.38

0.70

5

A

GD

n

4.00 4.00 4.38

0.72 0.73 0.72

4 4 5

4.21

0.72

5

Alleles (bp) 120

123

126

129

132

138

144

156

172

177

0.17 0.00 0.03

0.00 0.08 0.05

0.21 0.33 0.25

0.17 0.31 0.28

0.05 0.17 0.15

0.17 0.06 0.00

0.19 0.00 0.01

0.02 0.06 0.09

0.00 0.00 0.15

0.02 0.00 0.00

Total across samples

7.85 6.00 6.73

4.00 3.00 3.00

Total across samples Elu276 (0.03)

n

n

Alleles (bp) 134

GD

GD

Total across samples Elu87 (0.02)

A

A

Alleles (bp) 130

1

0.48

127

0.50 0.72 0.87

0.67

2.76

123

0.24 0.1 0.10

8.75

3 3 3

120

T-66 T-97 T-06

5 5 9

0.55 0.41 0.49

117

159

0.59 0.61 0.81

2.98 3.00 2.62

115

153

4.84 5.00 7.96

n

111

147

n

GD

Alleles (bp)

142

GD

A

109

Elu76 (0.04)

A

A

GD

n

7.69 6.00 7.05

0.86 0.77 0.82

8 6 8

8.94

0.82

10

Average across loci

A

GD

n

T-66 T-97 T-06

5.05 4.29 5.19

0.69 0.62 0.66

5.14 4.29 6.00

Total across samples

4.84

0.66

scaled estimator  was evaluated as reported by Xu and Fu (2004). The mutation rate  values were calculated as suggested by Frankham et al. (2004) for each locus and for each pair of sampling periods (1966–1997; 1966–2006 and 1997–2006), then the

average was calculated across sampling period for each locus. Ne was calculated across loci and for each locus. The mutation rate  calculated for each locus for the three Trasimeno samples allowed the Xu and Fu’s formula (2004) to be applied to other five northern

L. Lucentini et al. / Fisheries Research 96 (2009) 139–147

pike populations. The Ne values calculated for these populations were then compared with those obtained for the Lake Trasimeno population. 2.4.4. Estimation of N On the basis of the obtained Ne values, it was possible to evaluate the adult census size N using the Ne /N ratio R. A captures data census was conducted with the fishery cooperatives operating on Lake Trasimeno in 2006. The data concerned the entire captured fish stock and information about fish weight. By subdividing capture as expressed in kg by the mean weight of the fish, an indication of the total number of fish captured in 2006 was obtained. On this basis, and using the Ne values evaluated for T-06, two Ne /N ratios were calculated and used to evaluate the N values for the other two samples (T-66 and T-97) and for the other five populations. 3. Results The 7 microsatellite loci investigated exhibited polymorphism and 46 alleles were detected for the three samples (Table 2). The mean numbers of alleles (n) were 4.29 for T-97, 5.14 for T-66 and 6.00 for T-06. Allelic richness (A) across loci and per population ranged from 4.29 (T-97) to 5.19 (T-06), with an average value of 4.84 across loci and sampling times (Table 2). Distributions of A did not show significant statistical differences between sampling times (p = 0.67). The allele frequency per locus and sampling times showed differences and eight alleles were sample-specific: two alleles were present only in T-66 (Elu276 allele 177; Elu19 allele 117) and six alleles were present only in T-06 (Elu276 allele 172; Elu78 allele 130; Elu19 alleles 134,148 and 157 and Elu87 allele 154). Null alleles may be present at Elu276 locus for T-66 and Elu19, Elu51 and Elu276 for T-06, as suggested by the general excess of homozygotes for most allele size classes (CI = 95%) (Table 3). No evidence of scoring error due to stuttering or of large allele dropout

143

was found (CI = 95%). The genotyping error was always lower than 2.0%. The moderate error was confirmed by the HWE data and by LD absence between loci on a total of 21 comparisons obtained for data pooled according to population after Bonferroni corrections (significance level 0.01) (data not shown). Exact tests for genotypic linkage disequilibrium, evaluated separately for the three samples, were significant at a 0.01 level for 15 out of 63 comparisons: 2 for T66 and 13 for T-06. The analysis conducted separately for the three samples but for a random subsample of 10 individuals of each population were significant at a 0.01 level for eight comparisons: 2 for T-66, 2 for T-97 and 4 for T-06, suggesting an influence of samples size on LD evaluation. The genetic diversity (GD) estimates across loci indicated a decline between T-66 and T-97 and an increase between T-97 and T-06, although some loci showed a constant (Elu87) or an inverse trend (EluB38INRA and Elu76) (Table 2). He values were 0.63 (T-97), 0.67 (T-06) and 0.71 (T-66) globally across loci, while those of Ho were 0.54 (T-06), 0.70 (T-97) and 0.76 (T-66) (Table 3). The HWE was evaluated on the basis of the Weir and Cockerham (1984) estimates of Fis per locus and globally across loci (Table 3). Deviations from HWE were not detected for the three sampling periods. The per locus estimate showed a different response to the HWE by of each locus. The case of Elu276 was particularly evident; the P values were always ≤0.05 (Table 3). Wright’s index Fst ranged from 0.03 (T-97 vs. T-06) to 0.16 (T-66 vs. T-97) (Table 4). The AMOVA analysis performed for the three temporal samples showed that 90.77% of the variance could be attributed to intra-sample variation and, consequently, only 9.23% could be due to variations existing among the three samples (Table 5A). The AMOVA analysis performed for the current Trasimeno sample together with the other populations showed that 72.76% of the variance could be attributed to intra-sample variation and, consequently, 27.24% could be due to variations existing among the eight populations (Table 5B).

Table 3 For each sampling period are reported the observed (Ho ) and expected (He ) heterozygosity and the HWE P-values (P) for each locus and across loci. The presence of null alleles is also reported. Ho

He

P

Null-allele presence

T-66 Elu19 EluB38 Elu51 Elu76 Elu78 Elu87 Elu276 Across loci

0.76 0.76 0.71 0.95 0.71 0.81 0.62 0.76

0.62 0.58 0.81 0.67 0.69 0.73 0.87 0.71

0.66 0.12 0.06 0.00 0.07 0.37 0.02 0.19

No No No No No No Yes

T-97 Elu19 EluB38 Elu51 Elu76 Elu78 Elu87 Elu276 Across loci

0.61 0.50 0.83 0.44 0.61 1.00 0.89 0.70

0.64 0.41 0.67 0.49 0.71 0.74 0.78 0.63

0.14 0.73 0.00 0.32 0.03 0.24 0.05 0.21

No No No No No No No

T-06 Elu19 EluB38 Elu51 Elu76 Elu78 Elu87 Elu276 Across loci

0.55 0.60 0.57 0.26 0.66 0.60 0.55 0.54

0.81 0.49 0.81 0.23 0.75 0.72 0.84 0.67

0.00 0.27 0.00 1.00 0.00 0.12 0.00 0.20

Yes No Yes No No No Yes

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Table 4 Fst per pair of sampling period. According to Hartl and Clark (1997) Fst values were differently classified: Fst lower than 0.05 indicated little differentiation (◦ ), if ranging from 0.05 to 0.15 it indicated moderate differentiation (§), from 0.15 to 0.25: great differentiation (§§) and if higher than 0.25 very great differentiation. Related Pvalues were always 0.05.

T-66 T-97 T-06

T-66

T-97

T-06

0.00 0.16§§ 0.11§

0.00 0.03◦

0.00

Table 5 AMOVA results. Averages over seven loci for the three temporal samples (A) and for temporal and geographical samples (B). Source of variation

Sum of squares

Variance components

Percentage variation

Among populations Within populations

28.79 390.65

0.23 2.31

9.23 90.77

Total

419.44

2.55

Among populations Within populations

221.38 645.01

0.79 2.13

Total

866.39

2.93

A

B 27.24 72.76

The three samples did not show evidence of a recent population bottleneck; the results of the Wilcoxon signed-rank test for heterozygosity excess were not significant. Allele frequency distribution distortions emerged for the T-97 and T-06 samples showing a proportion decrease of rare alleles (shifted mode), and was not present in T-66 sample (L-shaped distribution) (Fig. 1). The mean age of parents and, consequently, the number of generations separating each pair of sampling periods were evaluated on the basis of the mean parent age of the already-mentioned homogeneous sample of spawners randomly chosen. The mean parent age, calculated by weighting both sexes in the same manner, was 4.5. The three samples were separated by 7 (T-97/T-66), 2 (T06/T-97) and 9 (T-06/T-66) generations. The per locus  averages across the sampling period ranged from 0.01 (EluB38INRA) to 0.07 (Elu78) (Table 2), with an average of 0.04. Applying Xu and Fu’s (2004) formula, the Ne evaluations were carried out for the three sampling periods, ranging from 16.64 (T97) to 32.37 (T-06) (Fig. 2A). The application of this formula to the other five northern pike populations allowed the Ne values that ranged from 9 (Lake Segrino) to 38 (Lake Maggiore) to be evaluated (data not shown). Using these values, two estimates of Ne were

made using Jorde and Ryman (2007) unbiased estimator (Fig. 2B). The temporal estimate of actual Ne was 29 with a 95% confidence interval of 17–122, while for 1997, Ne was 13 with a 95% confidence interval of 8–38. As reported above, the total capture of the fisheries cooperatives in Lake Trasimeno in 2006 was 5300 kg with a mean weight of 0.8 kg, amounting to over 6600 fish. On this basis, using the temporal Ne estimation, the mean value of R was 0.006. A similar value (0.005) was obtained using Xu and Fu (2004) Ne value. Using these R values, and the Ne values obtained for T-97 and T-66, the census size N was equal to 6600 (T-66) and 3400 (T-97) (Fig. 2A) for the Xu and Fu (2004) derived Ne and 2167 (T-97) and 4833 (T-06) for the Ne based on the Jorde and Ryman (2007) estimator. 4. Discussion Climate changes, high habitat fragmentation and rivalry with recently introduced species have led to the reduction of several Italian northern pike populations (Lorenzoni et al., 2002; Lucentini et al., 2006). This tendency has been partially balanced by stocking activities, either by using individuals often of unknown origin, or through traditional supportive breeding practice. In the latter case, offspring obtained from a limited number of wild spawners, chosen on the basis of favourable morphological characteristics, have been released with their offspring into the basin. As previously reported (Wang and Ryman, 2001) the aim of this practice is to increase N without the introduction of exogenous genes, but in isolated populations it could cause a reduction of Ne . A recent study of northern pike genetic diversity in Italy suggested that the Trasimeno population preserved most of its original genetic structure with respect to other investigated Italian populations (Lucentini et al., 2006). This could be ascribed to the fact that the CIT since 1970 has been carrying out a conservation programme of northern pike in Lake Trasimeno through traditional supportive breeding. In 1990 a robust supportive breeding activity was started in which at least 600 spawners per year for artificial reproduction and repopulation. To reduce heavy mortality in the early life stages, the hatched larvae were reared for 2 months in appropriate external basins of about 2500 m2 , fed with living Daphnia magna specimens, and then released in the lake as juveniles (5–8 cm long). Several findings suggest the importance of Lake Trasimeno as a natural refuge for this species during the last ice age (Maes et al., 2003; Nicod et al., 2004). For these reasons, Lake Trasimeno is a good model to investigate temporal changes in allele frequency on an isolated, small, northern pike population managed by supportive breeding. In addition, the knowledge of this population’s Ne might be a fundamental parameter to assess the risk of extinction for small and

Fig. 1. Allele frequency distribution for the three stocks of Lake Trasimeno: white bars refer to T-66, black bars to T-97 and grey bars to T-06. Mode-shift analysis underlined a normal L-shaped distribution for T-66 and shifted mode for T-97 and T-06 samples.

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Fig. 2. Histograms showing the effective population size Ne and the here derived census size (N) in the three stocks. Ne was calculated on the basis of the Xu and Fu (2004) formula (A) and of the Jorde and Ryman (2007) estimator (B). Ne values across loci were reported with their S.D. indicated as bars.

isolated pike populations (Frankham et al., 2004) and to define breeding programmes (Nomura, 2005). For this aim, two historical and one contemporary tissue samples were studied by means of microsatellite marker. This molecular marker detected an appreciable number of alleles, including rare alleles present at frequencies as low as 5%, that were useful to analyse historical tissue or specimen collections, and, therefore, suitable for the Ne definition in fish (Miller and Kapuscinski, 1997; Aho et al., 2006; Hansen et al., 2006; Araki et al., 2007). The three sampling periods (T-66, T-97 and T-06) investigated represented the recent history of this species in Lake Trasimeno. The analysis reveals appreciable values of genetic diversity, particularly considering that northern pike is characterised by a very low genetic polymorphism (Brzuzan et al., 1998; Senanan and Kapuscinski, 2000; Launey et al., 2003; Nicod et al., 2004; Aguilar et al., 2005; Jacobsen et al., 2005; Laikre et al., 2005; Larsen et al., 2005; Lucentini et al., 2006). The evaluation of possible genotyping errors underlined that the T-66 sample did not show signs of amplification difficulties that might be caused by the crosslinking due to formaldehyde preservation. The herewith reported  values show a variation range (0.01–0.07), similar to that reported by Miller and Kapuscinski (1997) (0.04–0.099), even though data from these authors furnished an average  values (0.06) greater than that here reported (0.03). These  values are very high and this could be attributed to biological and genetic characteristics of northern pike. Spawn-

ing is characterised by multiple fertilisations, by several males, for each female egg deposition, multiple paternity is common in this species. It has been reported that species with more intense sexual selection, revealed by higher levels of extra-pair paternity, have significantly elevated mutation rates, accounting for more than 10% of the variance. According to Møller and Cuervo (2003) sexual selection may be driving the evolution of genetic variability, presumably through an increased mutation rate. Furthermore, the genetic characteristics of northern pike are peculiar, particularly the high resolving power of microsatellites in comparison with the other molecular markers tested in this species. Furthermore, these values did not differ markedly from those reported for silver crucian carp (0.012) (Liu et al., 2008), for pink salmon (0.008) (Steinberg et al., 2002) or for barn swallows for which several values greater than 0.04 were found (Brohede et al., 2004). In human, a 37-repeat of a dinucleotide locus usually shows a  value of 0.01 (Lai and Sun, 2003). The absence of significant LD for pairs of loci out of 21 comparisons across the three samples, suggests that most of the seven loci were not associated in a very strong LD. This suggests a sampling from random mating and homogeneous populations, and also that any heterozygote deficit, if present, could be due to locus-specific events, such as linkage with genes under selective pressure or null alleles (Pinto et al., 2002). Fst analysis reveals the existence of a variable differentiation among the three sampling periods (Hartl and Clark, 1997). The values suggest that T-97 and T-06 are more similar

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to each other with respect to T-66. In fact, the value of 0.03 (T-97 vs. T-06) indicates very little differentiation, while the others ranged from low (0.11, T-66 vs. T-06) to medium (0.16, T-66 vs. T-97) differentiation. Low population differentiation suggests a fairly marked gene flow among all individuals, and also that the population appeared to be panmictic and not subdivided in subpopulations without any apparent genetic drift and inbreeding increase. Such considerations are also confirmed by the AMOVA analysis. The three sampling periods showed that about 91% of the variation is referable to “within population” differences and about 9 % was referable to “among populations” differences (Table 5A). AMOVA performed for current samples having different geographic origins showed that 27% of the variation is among these populations. This value is three times greater then that obtained for historical samples of Trasimeno Lake (Table 5B). This result underlined that even if the population of Trasimeno Lake has changed over four decades, its integrity was successfully preserved, as already reported on the basis of other statistical approaches (Lucentini et al., 2006). Ne is usually considered to be an underestimation of the real natural breeders of a population (Frankham et al., 2004) and, subsequently, the derived N might be smaller than the real census size (Lode and Peltier, 2005; Araki et al., 2007). The underestimation of Ne could be due to many factors such as sex ratio, age structure, or samples characteristics, even though, as reported above, the sampling seemed well structured. When deriving census size values, a possible limitation could be the reported Ne /N ratio of 0.005/0.006 based only on the captured fish and not on a true census size prediction. Nevertheless, a greater error might derive by using the value of 0.03 calculated and applied for northern pike of some North American biomes (Miller and Kapuscinski, 1997; Aguilar et al., 2005). Because no data are available for any European populations, it is possible that climate characteristics of the southern habitat zone could modify the relationships between breeders and the entire population. For example, it is generally assumed that females of northern pike usually are reproductive after 2 years of age. But in the Lake Trasimeno population most 1-year-old females have mature eggs. Furthermore, many marine species of sub-tropical zones show Ne /N ratios whose order of magnitude is fourfold to fivefold greater (Turner et al., 2002). Besides, no general evaluations about freshwater fish Ne /N ratio are known, even though empirically derived Ne /N values are generally lower (Ne /N equal to 0.2 ca.) than those emerging from demographic models (Frankham et al., 2004). Even though there is not a population size census estimation for Lake Trasimeno carried out with the capture–recapture method over defined transepts, professional fishery data indicate that the census size of this population was definitely greater than 6600 individuals. The lack of information on the effort of fisheries did not allow the Nc,t consistency (captured percentage) to be evaluated with respect to Nw,t (wild percentage), the two terms composing the total population (Frankham et al., 2004). Nevertheless, in the absence of any other information, the data from the fisheries provide an important baseline that allows the R ratio to be defined. Although R and the derived N evaluations are only approximate (Rieman and Allendorf, 2001), they were needed to define a population trend over the last four decades confirming the unofficial information on the northern pike trend in the Lake Trasimeno. Furthermore, the Ne estimation method notwithstanding, the decline of census population size between T-66 and T-97 correlates with the fish capture reduction observed from 1966 to 1997. In fact it is generally known, but never demonstrated before, that this population consistency decreased from the sixties to the nineties, when the recruitment of Trasimeno pike population was carried out mainly using traditional supportive breeding. The more recent increase can be related to the increment of both the number of

spawners and the supportive breeding activity started in 1992. The reduction in census size may be ascribed to high fishing pressure, which prevents the increase of census population, in spite of the measurable Ne increment between T-97 and T-06. Other possible causes of size reduction of the Trasimeno population might be related to environmental perturbations (reduction of habitat in relation with annual rains, reduction of mating sites, strong dependence on annual temperature oscillations, etc.) or demographic threats (excessive mortality of fries or competition) (Lorenzoni et al., 2002). These findings agree with the hypothesis that northern pike populations fluctuate yearly and tend to maintain a low effective population size, showing a metapopulation dynamics with frequent extinction and recolonisation events on an evolutionary time scale (Gilpin, 1991; Miller and Kapuscinski, 1997). However, the robust supportive breeding might be sufficient to maintain adequate genetic diversity and appreciable Ne in the Trasimeno pike stock. As suggested for other fish, selecting wild-bred individuals for captive breeding is likely to be advantageous when attempting to minimise inbreeding and drift (Wang and Ryman, 2001). The number of about 600 breeders per year since 1997, when genetic monitoring of this population started, seems to guarantee the maintenance of the effective population size at an adequate level for relatively small and isolated populations (Ballou et al., 1995; Larsen et al., 2005). It is not possible to define how every breeder effectively contributes to reproduction and it is possible that the number of breeders that effectively contribute to successive generations may be lower. Furthermore, it is not only artificial spawning that contributes to northern pike fries. Even if with several difficulties due to habitat degradation and predation, natural reproductive processes are also regularly observed in this lake. In order to understand whether the obtained data can be considered descriptive of the Trasimeno population size, the Xu and Fu (2004) formula was applied to five other populations (data not shown), from five markedly different Italian lakes (Table 1), for which census sizes were not available. By comparing the population sizes that can be derived from these data with those obtained through the Xu and Fu formula, the differences in the Ne values among samples can be considered realistic. In fact, as expected on the basis of lake sizes and previews data about the genetic assessment of these lakes (Lucentini et al., 2006), the Lake Maggiore population was slightly bigger than that of Trasimeno, while the Segrino population was the smallest. In conclusion, this work widens our knowledge of the genetic diversity of one of the most isolated autochthonous northern pike populations of Southern Europe managed with supportive breeding, and defines the actual Ne . A positive influence on the genetic status of the Lake Trasimeno stock could be related to the breeding programme organisation. The introgression is usually imputable to the use of a limited number of individuals, or of just one male, to fertilise several females and to produce thousands of offspring (Aho et al., 2006). In the supportive breeding programme carried out at CIT, approximately 600 breeders with an equal number of males and females were used; the breeders are changed every year. To preserve an adequate level of genetic diversity in the Trasimeno pike population, the maintenance or the increase of the Ne value would be worthwhile. Management options to achieve this objective should deal with: (1) a supplementation of the existing population through a robust supportive breeding based on more than 600 breeders, randomly collected from wild spawners; (2) habitat conservation and restoration, particularly of the riparian vegetation implied in northern pike life cycle and (3) a greater control of both recreational and professional fishing activities. The conservation of the Trasimeno northern pike population is relevant because it is largely made up of autochthonous speci-

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mens and can be preserved as a stocking reserve in repopulation or recruitment programmes for other Central Italy basins (Lucentini et al., 2006). This population is currently declining in the total number of annual captures. Even though there is not a visible bottleneck, the possible future crisis of this population might emerge as allele frequency distortions. This investigation is the first reported contribution regarding the definition of Ne in an Italian managed and conserved wild northern pike population. Hopefully the results will be relevant for researchers fisheries and local authorities presently working on northern pike management and conservation programmes. Acknowledgements This work was supported by the National research project (FISR-M.I.C.E.N.A.) concerning the effects of climate changes in biodiversity conservation in the Mediterranean area. The authors thank the Province of Perugia, particularly to Romano Dolciami and Andrea Mezzetti of the CIT technical team. References Aguilar, A., Banks, J.D., Levine, K.F., Wayne, R.K., 2005. Population genetics of northern pike (Esox lucius) introduced into Lake Davis, California. Can. J. Fish. Aquat. Sci. 62, 1589–1599. Aho, T., Ronn, J., Piironen, J., Bjorklund, M., 2006. Impacts of effective population size on genetic diversity in hatchery Brown trout (Salmo trutta) populations. Aquaculture 253, 244–248. Araki, H., Waples, R.S., Ardren, W.R., Cooper, B., Blouin, M.S., 2007. Effective population size of steelhead tout: influence of variance in reproductive success, hatchery programs, and genetic compensation between life-history forms. Mol. Ecol. 16, 953–966. Ballou, J.D., Gilpin, M., Foose, T.J., 1995. Population Management for Survival and Recovery: Analytical Methods and Strategies in Small Population Conservation. Columbia University Press, New York. Brohede, J., Anders, P., Møller, A.P., Ellegren, H., 2004. Individual variation in microsatellite mutation rate in barn swallows. Mutat. Res. 545, 73–80. Brzuzan, P., Luczynski, M., Kuzniar, P.A., 1998. Mitochondrial DNA variation in two samples of northern pike, Esox lucius L. Aquacult. Res. 29, 521–526. Cornuet, J.M., Luikart, G., 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014. Excoffier, L., Laval, G., Schneider, S., 2005. Arlequin ver. 3.0: an integrated software package for population genetics data analysis. E. B. O. 1, 47–50. Frankham, R., Ballou, J.D., Briscoe, D.A., 2004. A Primer of Conservation Genetics, 1st edition. The Press Syndicate of the University of Cambridge, Cambridge. Gilpin, M., 1991. The genetic effective size of a metapopulation. Biol. J. Linn. Soc. 42, 165–175. Glaubitz, J.C., Rhodes, O.E., DeWoody, J.A., 2003. Prospects for inferring pairwise relationships with single nucleotide polymorphisms. Mol. Ecol. 12, 1039–1047. Goudet, J., 1995. FSTAT (vers. 1.2): a computer programme to calculate F-statistics. J. Heredity 86, 485–486. Gum, B., Gross, R., Rottmann, O., Schroder, W., Kuhn, R., 2003. Microsatellite variation in Bavarian populations of European grayling (Thymallus thymallus): implications for conservation. Conserv. Genet. 4, 659–672. Hansen, M.M., Bekkevold, D., Jensen, L.F., Mensberg, K.D., Nielsen, E.E., 2006. Genetic restoration of a stocked brown trout Salmo trutta population using microsatellite DNA analysis of historical and contemporary samples. J. Appl. Ecol. 43, 669–679. Hansen, M.M., Taggart, J.B., Meldrup, D., 1999. Development of new VNTR markers for pike and assessment of variability at di- and tetranucleotide repeat microsatellite loci. J. Fish Biol. 55, 183–188. Hartl, D.L., Clark, A.G., 1997. Principles of Population Genetics. Sinauer Associates, Sunderland, MA. Healy, J.A., Mulcay, M.F., 1980. A biochemical genetic analysis of the northern pike, Esox lucius L., from Europe and North America. J. Fish Biol. 17, 317–324. Hoffman, J., Amos, W., 2005. Microsatellite genotyping errors: detection approaches common sources and consequences for paternal exclusion. Mol. Ecol. 14, 599–612. Jacobsen, B., Hansen, M.M., Loeschcke, V., 2005. Microsatellite DNA analysis of northern pike (Esox lucius L.) populations: insights into the genetic structure and demographic history of a genetically depauperate species. Biol. J. Linn. Soc. 84, 91–101. Jorde, P.E., Ryman, N., 2007. Unbiased estimator for genetic drift and effective population size. Genetics 177, 927–935. Kalinowski, S.T., 2004. Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conserv. Genet. 5, 1–12.

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