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Genetic structure of fragmented southern populations of African Cape buffalo (Syncerus caffer caffer).

Smitz N, Cornélis D, Chardonnet P, Caron A, de Garine-Wichatitsky M, Jori F, Mouton A, Latinne A, Pigneur LM, Melletti M, Kanapeckas KL, Marescaux J, Pereira CL, Michaux J - BMC Evol. Biol. (2014)

Bottom Line: African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases.We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes.The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases).

View Article: PubMed Central - PubMed

ABSTRACT

Background: African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the "genetic health" of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites.

Results: The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness (A r ) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (P a ) of 11), low differentiation, and an absence of an inbreeding depression signal (mean F IS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift (F IS = 0.062, mean A r = 6.160, P a = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century.

Conclusions: We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study.

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Clusters inferred with STRUCTURE software, after the Evanno correction (K = 3). The cluster membership of each sample is shown by the colour composition of the vertical lines, with the length of each colour being proportional to the estimated membership coefficient. The spatial representation is shown in Figure 4. A. Representation of the 3 clusters identified with STRUCTURE; B. Representation of the cluster membership of each sample within each sampling localities.
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Fig3: Clusters inferred with STRUCTURE software, after the Evanno correction (K = 3). The cluster membership of each sample is shown by the colour composition of the vertical lines, with the length of each colour being proportional to the estimated membership coefficient. The spatial representation is shown in Figure 4. A. Representation of the 3 clusters identified with STRUCTURE; B. Representation of the cluster membership of each sample within each sampling localities.

Mentions: The STRUCTURE 2.3 software output was interpreted using the ΔK method, as described by Evanno [55]. The highest ΔK was for K = 3 (Figure 3 and Additional file 5: Figure S3), suggesting the existence of three clusters in our dataset (ΔK = 262.2). These clusters were considered as different “populations” in the subsequent analyses. The proportion of each cluster within every sampled locality is represented in Figure 4. The first cluster –N- (in blue in Figure 4) mainly appeared in samples collected in the northern section of the study area (all samples of Nyakasanga and Mana Pools, a large part of the samples from Niassa, Marromeu, Victoria Falls, Okavango Delta and Chobe, as well as from Hwange, to a lesser extent). The second cluster –C- (in green on Figure 4) appears in the central section of the study area, and is represented in very high proportions in the sets of samples from Kruger, Sengwe, Manguana, Limpopo and Hwange. The third cluster –S- (in red on Figure 4) essentially includes samples from Hluhluwe-iMfolozi, although residual shared loci were observed at other sampling localities.Figure 3


Genetic structure of fragmented southern populations of African Cape buffalo (Syncerus caffer caffer).

Smitz N, Cornélis D, Chardonnet P, Caron A, de Garine-Wichatitsky M, Jori F, Mouton A, Latinne A, Pigneur LM, Melletti M, Kanapeckas KL, Marescaux J, Pereira CL, Michaux J - BMC Evol. Biol. (2014)

Clusters inferred with STRUCTURE software, after the Evanno correction (K = 3). The cluster membership of each sample is shown by the colour composition of the vertical lines, with the length of each colour being proportional to the estimated membership coefficient. The spatial representation is shown in Figure 4. A. Representation of the 3 clusters identified with STRUCTURE; B. Representation of the cluster membership of each sample within each sampling localities.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4232705&req=5

Fig3: Clusters inferred with STRUCTURE software, after the Evanno correction (K = 3). The cluster membership of each sample is shown by the colour composition of the vertical lines, with the length of each colour being proportional to the estimated membership coefficient. The spatial representation is shown in Figure 4. A. Representation of the 3 clusters identified with STRUCTURE; B. Representation of the cluster membership of each sample within each sampling localities.
Mentions: The STRUCTURE 2.3 software output was interpreted using the ΔK method, as described by Evanno [55]. The highest ΔK was for K = 3 (Figure 3 and Additional file 5: Figure S3), suggesting the existence of three clusters in our dataset (ΔK = 262.2). These clusters were considered as different “populations” in the subsequent analyses. The proportion of each cluster within every sampled locality is represented in Figure 4. The first cluster –N- (in blue in Figure 4) mainly appeared in samples collected in the northern section of the study area (all samples of Nyakasanga and Mana Pools, a large part of the samples from Niassa, Marromeu, Victoria Falls, Okavango Delta and Chobe, as well as from Hwange, to a lesser extent). The second cluster –C- (in green on Figure 4) appears in the central section of the study area, and is represented in very high proportions in the sets of samples from Kruger, Sengwe, Manguana, Limpopo and Hwange. The third cluster –S- (in red on Figure 4) essentially includes samples from Hluhluwe-iMfolozi, although residual shared loci were observed at other sampling localities.Figure 3

Bottom Line: African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases.We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes.The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases).

View Article: PubMed Central - PubMed

ABSTRACT

Background: African wildlife experienced a reduction in population size and geographical distribution over the last millennium, particularly since the 19th century as a result of human demographic expansion, wildlife overexploitation, habitat degradation and cattle-borne diseases. In many areas, ungulate populations are now largely confined within a network of loosely connected protected areas. These metapopulations face gene flow restriction and run the risk of genetic diversity erosion. In this context, we assessed the "genetic health" of free ranging southern African Cape buffalo populations (S.c. caffer) and investigated the origins of their current genetic structure. The analyses were based on 264 samples from 6 southern African countries that were genotyped for 14 autosomal and 3 Y-chromosomal microsatellites.

Results: The analyses differentiated three significant genetic clusters, hereafter referred to as Northern (N), Central (C) and Southern (S) clusters. The results suggest that splitting of the N and C clusters occurred around 6000 to 8400 years ago. Both N and C clusters displayed high genetic diversity (mean allelic richness (A r ) of 7.217, average genetic diversity over loci of 0.594, mean private alleles (P a ) of 11), low differentiation, and an absence of an inbreeding depression signal (mean F IS = 0.037). The third (S) cluster, a tiny population enclosed within a small isolated protected area, likely originated from a more recent isolation and experienced genetic drift (F IS = 0.062, mean A r = 6.160, P a = 2). This study also highlighted the impact of translocations between clusters on the genetic structure of several African buffalo populations. Lower differentiation estimates were observed between C and N sampling localities that experienced translocation over the last century.

Conclusions: We showed that the current genetic structure of southern African Cape buffalo populations results from both ancient and recent processes. The splitting time of N and C clusters suggests that the current pattern results from human-induced factors and/or from the aridification process that occurred during the Holocene period. The more recent S cluster genetic drift probably results of processes that occurred over the last centuries (habitat fragmentation, diseases). Management practices of African buffalo populations should consider the micro-evolutionary changes highlighted in the present study.

Show MeSH
Related in: MedlinePlus