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 Universitário de Santiago, 3810-193,
Aveiro, Portugal.
* Corresponding author:
Prof. Dr. Carlos M.M.S. Fonseca
Fax:+351 234 426408; E-mail: [email protected]
1
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].
18
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.
19
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.
20
Electrophoresis was performed on an ABI PRISMTM 310 Capillary Sequencer.
Allele sizing was achieved with GeneScan® 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).
21
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
22
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
23
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 “mixed”
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.
24
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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