Ferreiraetal2006

Wildl. Biol. Pract., June 2006, 2(1): 17-25.

DOI: 10.2461/wbp.2006.2.4

ORIGINAL PAPER GENETIC STRUCTURE OF THE WILD BOAR (SUS SCROFA L.) POPULATION IN PORTUGAL E. Ferreira1, L. Souto1, A.M.V.M. Soares1 & C. Fonseca1* Departamento de Biologia, Universidade de Aveiro, Campus Universitรกrio de Santiago, 3810-193, Aveiro, Portugal. * Corresponding author: Prof. Dr. Carlos M.M.S. Fonseca Fax:+351 234 426408; E-mail: cfonseca@bio.ua.pt

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Keywords Allele frequencies; Game species; Multiplex amplification; Portugal.

Abstract The main objective of this study was the assessment of the genetic structure and level of variability in the Portuguese wild boar population. A total of 65 wild boar blood samples were collected all over the continental territory, during 2002/03 and 2003/04 hunting seasons. A set of six microsatellite markers, developed for domestic pig, was used. Loci SW986 and SW828 presented a small number of alleles for the Portuguese population, whereas other loci, like SW1701 and SW1517, presented a high degree of polymorphism. From the six analysed loci, four presented significant deviation from Hardy-Weinberg equilibrium conditions, suggesting the existence of genetic structure in the population. Samples were divided into North, Centre and South groups according to the position of wild boar capture location in relation to rivers Douro and Tejo. All the FST estimates were statistically significant and the highest FST value was 0.08 (P<0.001), referring to the distance between Northern and Central groups. FCA analysis was also performed. The resulting bi-dimensional diagram suggests structuring of the Portuguese wild boar population.

Introduction According to Morais [1], wild boar (Sus scrofa L.) was once very abundant in Portugal, as well as in the rest of Europe. However, in Portugal, at the beginning of the XX century, the species was confined to some mountain areas near the national border with Spain, and to some former royal hunting areas [2]. During the last four decades, Portuguese wild boar population exhibited an outstanding increase in terms of number and distribution area [2]. Considered as a threatened and non-hunting species in the recent past [3], nowadays the wild boar is very common, widespread and the most important large game species in Portugal. The number of animals harvested annually has increased from 423 in 1989/1990 to approximately 8254 individuals in 2000/2001 [4] confirming that the wild boar population is increasing and its distribution range is expanding all over the country [1,5].

However, in Portugal, specific studies addressing the biology and ecology of wild boar populations are lacking. The increasing population size might lead to a conflict between human activities and wild boar populations. Some cultivated plants are presently the main food items in wild boar diet [6,7,8], and the impact of this ungulate on agricultural and forest systems is broadly known [9,10,11].

Presently, the knowledge of species genetic diversity and structure is one of the most important aspects in wildlife population management. Several genetic markers are being used for assessing conservation issues related with several species [12,13,14,15].

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Microsatellites were recently used to evaluate the genetic impact of reintroductions and demographic decline in the wild boar in Italy [12]. These authors stress the importance of those markers for the development of conservation and management strategies. Another example was the forensic application of microsatellite markers in wild boar poaching detection [16].

Time of divergence between ancestral forms of wild boar populations was estimated as being much larger than the estimated for its domestication. Several studies point to a greater proximity between domestic breeds and wild boar populations from the same continent, than from wild populations from Europe and Asia [17,18,19]. A high amount of data on pig genome is now available and the linkage map of this species presents several thousands of known loci [20]. Several markers developed for the pig were already successfully applied in wild suiform species [21].

The primary aim of this study was the assessment of the genetic structure level of the wild boar population in Portugal.

Methods Sampling Wild boar sampling was performed all over the continental territory (Figure 1), during the 2002/03 and 2003/04 hunting seasons. A total of 65 blood samples were collected into K3EDTA 5ml tubes. A brief characterization of the hunted individuals was performed, including geographical location of the capture, sex and age class.

DNA Extraction In the laboratory, a fraction of each collected sample was stored as a bloodstain in FTA® cards (Whatman), and kept at room temperature. The remaining portion of the blood was stored in 1.5ml tubes at -20 ºC. FTA® cards present several advantages, such as a pre-extraction of the DNA that will remain in the card until being washed with the proper eluent. These cards also allow the degradation of pathogens present in the blood and also the long-term storage of samples at room temperature. Only a small amount of FTA® card is needed for the DNA extraction procedure.

Total DNA was re-extracted from blood samples in FTA® cards, following the general Chelex® procedure [22]. Extraction was performed in a total volume of 200 µl, using a 1 to 2 mm2 card punch. Samples were kept at 4 ºC for immediate amplification, or stored at -20 ºC for later use.

Microsatellite Genotyping Six markers were selected, from an available set of 91 microsatellites [23], on account of their known polymorphism, chromosome location, annealing temperature, size range, fluorescence type [24] and performance on standard amplification. This selection was performed with the aim of multiplex amplification of several markers.

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Fig. 1. Sampled municipalities (in grey) and location of Douro, Vouga, Mondego and Tejo rivers.

The selected markers were amplified in multiplex PCR reactions, in the following sets: (1) S0008, SW986, SW1129 and (2) SW1701, SW1517, SW828. Both sets were amplified with the Quiagen Multiplex PCR Kit® – see also Souto et al [25].

Amplification was performed following manufacturer conditions, with each primer at 0.2 µM and 2.5 to 5 µl of Chelex extract in a final volume of 25 µl. Qsolution® (2.0 µl) was also added to each PCR reaction. Cycling conditions were, for both microsatellite sets: 15 minutes at 95 ºC; 30 cycles of 30 seconds at 94 ºC, 3 minutes at 58 ºC and 60 seconds at 72 ºC; final extension at 60 ºC for 30 minutes.

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Electrophoresis was performed on an ABI PRISMTM 310 Capillary Sequencer.

Allele sizing was achieved with GeneScan速 3.1.2, using Gene ScanTM -500 TAMRATM Size Standard (Applied Biosystems) for set1, and Gene ScanTM -500 ROXTM Size Standard for set2. The set of samples was typed for the six chosen markers.

Data analysis In an attempt to assess the possible level of genetic structuring in the Portuguese wild boar population, allele frequencies, expected/observed heterozigosity, and deviations from Hardy-Weinberg equilibrium (HWE) were estimated for all markers, based on the overall set of samples, using ARLEQUIN version 2.000 software [26].

The 65 samples were separated into three groups. The Portuguese main rivers, Douro and Tejo, were used as criteria for the geographical allocation of the 65 samples: North group (n = 20) North of Douro; South group (n = 15) South of Tejo; Centre group (n = 30) between Douro and Tejo. ARLEQUIN was used to estimate allele frequencies, as well as expected and observed heterozigosity and to assess the Hardy-Weinberg equilibrium deviation, for the three groups herein defined. Genetic differentiation between groups (FST) was estimated according to Weir and Cockerham [27]. The validation of the previously defined groups was evaluated using a 2-D Factorial Correspondence Analysis performed with GENETIX v4.05 [28].

Results Allele Frequencies Loci SW986 and SW828 (Table 1) were found to be the least polymorphic surveyed markers in the Portuguese wild boar population, while SW1701 and SW1517 were the most polymorphic. Alleles from the less polymorphic marker (SW828) were detected in individuals from all over the country, although presenting variations in regional frequencies.

For all the markers, the most frequent alleles appear to be distributed throughout the three regions. Considering the Portuguese wild boar population as a whole, four out of the six typed markers (S0008, SW1701, SW1129 and SW1517) showed significant deviations from the Hardy-Weinberg equilibrium (HWE). When taking the three geographic groups into account, the marker S0008 was still in disequilibrium for the Centre population.

Genetic Distance Genetic distances between the considered geographical groups were estimated using FST (Table 2). The highest distance value (FST = 0.08) was detected between the North and the Centre groups. FST estimates were always highly significant (P<0.001).

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Table 1. Allele frequencies for the 6 typed loci for the Portuguese wild boar population.

Frequencies

Frequencies

Locus Allele Portugal South Centre North S0008

SW1701

SW828

175 179 181 183 185 187 191 193 195 HO HE 90 92 106 108 110 112 114 120 122 124 126 128 132 HO HE 211 217 221 HO HE

n = 65

n = 15 n = 30 n = 20

0.338 0.015 0.054 0.031 0.200 0.208 0.085 0.015 0.054

0.333 0.067 0.167

0.500

0.100

Locus Allele Portugal South Centre North SW986

135 147 149 151 159 HO HE

0.100 0.008 0.046 0.154 0.115 0.023 0.185 0.100 0.131 0.038 0.054 0.038 0.008

0.050 0.025 0.167 0.325 0.067 0.325 0.133 0.075 0.050 0.067 0.050 0.050 SW1129 139 0.8667 0.6333 0.7500 141 0.8299 0.7124 0.8013 149 153 0.133 0.050 0.150 155 0.017 157 0.017 0.125 159 0.100 0.167 0.175 167 0.167 0.167 HO 0.100 HE 0.267 0.250 0.025 0.167 0.050 0.125 SW1517 118 0.033 0.217 0.075 126 0.017 0.100 132 0.033 0.033 0.100 134 0.125 136 0.017 138

0.8462 0.8915

0.8000 0.8333 0.9000 0.8713 0.8418 0.9013

0.331 0.054 0.615

0.467 0.100 0.433

0.3846 0.5274

0.4667 0.3000 0.4500 0.6621 0.3672 0.5372

0.7231 0.7940

0.050 0.133 0.200 0.067

0.167 0.050 0.783

0.475 0.025 0.500

140 142 144 146 148 154 156 HO HE

n = 65

n = 15 n = 30 n = 20

0.015 0.277 0.623 0.077 0.008

0.233 0.500 0.267

0.4615 0.5330

0.6667 0.3667 0.4500 0.6460 0.4458 0.5859

0.062 0.431 0.023 0.123 0.177 0.077 0.085 0.023

0.033 0.367 0.033 0.133 0.167 0.133 0.067 0.067

0.6154 0.7624

0.7333 0.6333 0.5000 0.8414 0.8232 0.6282

0.015 0.008 0.023 0.323 0.054 0.077 0.162 0.100 0.115 0.015 0.092 0.008 0.008

0.067

0.7539 0.8358

0.7333 0.7000 0.8500 0.6989 0.8243 0.8680

0.033 0.533 0.067 0.033 0.133 0.133

0.05

North

0.06

0.067 0.333 0.033 0.150 0.233 0.067 0.100 0.017

0.017 0.017 0.367 0.067 0.083 0.183 0.050 0.117 0.017 0.067 0.017

0.075 0.625 0.075 0.100 0.050 0.075

0.025 0.100 0.025 0.100 0.250 0.150 0.100 0.025 0.200 0.025

Table 2. Genetic distances (FST, P < 0.001) between groups.

Centre

0.025 0.375 0.550 0.050

0.017

HO: observed heterozigosity.

HE: expected heterozigosity (Hardy-Weinberg equilibrium).

Bold HO: significant departures from expected HWE heterozigosity (P < 0.05).

South

0.017 0.233 0.733

Centre

0.08

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Factorial Correspondence Analysis Factorial Correspondence Analysis (FCA) was also applied for assessing the degree of structuring. FCA is a canonical analysis that results in a powerful method for recovering maximum information on the genetic relationships among individuals within and between populations using n–dimensional space. Samples were plotted in a 2D diagram (Figure 2), where the two main factors (axes in the diagram) explain 15.31% of the total inertial value.

214

2

110

Axe 2 (7,15%)

217

1

132

6 134

0

158 -2

-1

0

1

Axe 1 (8,16%)

Fig. 2. 2D Factorial Correspondence Analysis for the three defined geographic groups. North – grey filled rhombus; Centre – black squares; South – white filled circles. Figures stand for sample identification.

Most of the samples included in the North cluster are located in the area comprised

by X(-2.0) and Y(-1.1); most of the Centre samples are located between (or close to) X(0.1) and Y(-1.1); South samples show a more scattered distribution, partially overlapping the Centre ones. Samples 6, 214, 217 and 132, 134, which do not overlap with Centre ones, correspond to the most Southern locations, Baixo Alentejo and Algarve, respectively. Some samples from the South and North groups are scattered among the Centre samples, generally corresponding to animals obtained in the vicinity of the Centre region.

Discussion The assessment of the genetic structure level in the wild boar population in Portugal was the main objective of this study. For this purpose, microsatellite markers were applied due to their polymorphism degree, fast mutation rate and ease of use. In fact, it was possible to amplify and run the six markers in only two sets thus corroborating the advantages of this time and resource saving approach.

Significant deviations from the HWE were found in 4 out of 6 loci, for the overall Portuguese population. This might suggest the existence of some level of population structure [29]. Portuguese wild boar samples were separated into three geographic groups, divided by the rivers Douro and Tejo, which might constitute two major geographic barriers. Differences in allele’s composition, number and frequencies

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were found among the regions. The relevance of these observations was tested using different methods. Genetic distance was estimated with an FST estimator. It is generally accepted that FST values between 0-0.05 indicate low genetic differentiation; values between 0.05-0.15 correspond to moderate differentiation; values between 0.15-0.25 stand for high differentiation and values over 0.25 indicate very high genetic differentiation [29]. When comparing South and Centre groups, the distance estimate was at the 0.05 threshold. However, estimated distances between these two groups and the North group were slightly larger (0.06 and 0.08, respectively). According to Balloux and Lugon-Moulin [30], the level of statistical significance of these estimates might present an equal or even greater importance than the absolute value of the estimate. In fact, the low estimates (always bellow 0.15) must not be neglected, considering that values as low as 0.05 can have biological meaning. Although based on six markers only, the current estimates were highly significant. Therefore, based on our results, one cannot exclude the existence of biologically meaningful structure on the wild boar population in Portugal.

Possibly, although both rivers Douro and Tejo act as topological obstacles to dispersion, they are not complete geographical barriers, allowing some introgression of animals from both sides of each river. The higher genetic distance values between the North and the other geographical groups can be related with the increased difficulty in crossing the rocky edges of Douro river, which present much higher slopes than those of Tejo river.

In the 2D-Factorial correspondence analysis, the percentage of the total inertial value explained is approximately 15%. This percentage is comparable to other previously performed intraspecific [31] and even interspecific analysis [32]. Results from our analysis also suggest the differentiation between wild boars from the North and from the remaining groups. The individuals from the North group, which are scattered among the Centre ones, generally correspond to boundary locations.

Differentiation between Centre and South was not clear, considering that several individuals from both groups were scattered across overlapping areas in the diagram.

Samples from the southernmost locations are displaced from the â&#x20AC;&#x153;mixedâ&#x20AC;?

South/Centre cloud of samples, in the 2D-FCA graph. This observation complements the highly significant FST value between the groups. Diagram analysis suggests that Tejo, despite being the major Iberian river, does not constitute a suitable geographical barrier to be used as a criterion for defining a Southern subpopulation. Notwithstanding, it is possible to detect a smaller Southern group, surely defined by other factor(s). Samples 110 and 158, which are positioned in opposite extremes of the diagram, were obtained from animals captured in a fenced hunting area (Tapada de Mafra). The origin of these individuals is not clear since wild boar restocking (from unknown origin) was reported more than once in Tapada de Mafra (R. Paiva, personal communication).

Although not clearly stating the existence of highly structured sub-populations of wild boar in Portugal, our data suggest a significant differentiation between the North and the South of the country. Nonetheless, results question the role of the main rivers as geographical boundaries. New approaches must be taken, eventually including more individuals and more markers and using other methods of analysis, in order to allow the assessment of genetic structure without forcing artificial or semi-artificial grouping of individuals.

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Acknowledgements We would like to thank to Dr. Max F. Rothschild (The USDA Supported US Pig Genome Coordination Project) for providing the primer set IX. We also would like to thank all those people who helped collecting samples and to CNCP (Confederação Nacional dos Caçadores Portugueses), for financial support.

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