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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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Representation of three final competing scenarios tested with approximate Bayesian computation (ABC). This analysis was based on a matrix including individuals displaying a probability of belonging to one of the two clusters over 0.9 (STRUCTURE software). Ni corresponds to effective population size of each cluster, and Ti corresponds to the time expressed in numbers of generations since divergence. The following conditions were considered: T1 < T2, T2 < T3, with 0 being the sampling date. Abbreviations are as follows: C; Central cluster, N; Northern cluster, PP; Posterior probability and their associated 95% confidence interval.
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Fig2: Representation of three final competing scenarios tested with approximate Bayesian computation (ABC). This analysis was based on a matrix including individuals displaying a probability of belonging to one of the two clusters over 0.9 (STRUCTURE software). Ni corresponds to effective population size of each cluster, and Ti corresponds to the time expressed in numbers of generations since divergence. The following conditions were considered: T1 < T2, T2 < T3, with 0 being the sampling date. Abbreviations are as follows: C; Central cluster, N; Northern cluster, PP; Posterior probability and their associated 95% confidence interval.

Mentions: Alternative biogeographic divergence scenarios were inferred and compared using the DIYABC software package. In-depth information about scenario building procedure and alternative competitive scenario representations are available as additional file (Additional file 2: Figure S1A and S1B). Two runs were performed, a first one consisting of all scenarios (Additional file 2: Figure S1A and S1B), and a second one whereby only scenarios having the highest posterior probabilities were taken into consideration. The range and distribution of prior for parameters used to describe these scenarios (effective population size, time of splitting or merging events, and admixture rates) are given as additional file (Additional file 3: Table S2). For the second run, 3,000,000 datasets were simulated for each scenario (Figure 2) by building a reference table from a specified set of prior parameter distributions. A principal component analysis (PCA- DIYABC, Additional file 4: Figure S2) was performed on the first 30,000 simulated datasets to check if the set of scenarios and the prior distributions of their parameters were able to generate datasets similar to the observed dataset. A normalized Euclidean distance between each simulated dataset of the reference table and the observed dataset was calculated to identify the most likely scenario. To estimate the relative posterior probability of each scenario, 1% of the closest simulated datasets was used in a logistic regression. The most likely scenario was the one with the highest posterior probability.Figure 2


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)

Representation of three final competing scenarios tested with approximate Bayesian computation (ABC). This analysis was based on a matrix including individuals displaying a probability of belonging to one of the two clusters over 0.9 (STRUCTURE software). Ni corresponds to effective population size of each cluster, and Ti corresponds to the time expressed in numbers of generations since divergence. The following conditions were considered: T1 < T2, T2 < T3, with 0 being the sampling date. Abbreviations are as follows: C; Central cluster, N; Northern cluster, PP; Posterior probability and their associated 95% confidence interval.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Representation of three final competing scenarios tested with approximate Bayesian computation (ABC). This analysis was based on a matrix including individuals displaying a probability of belonging to one of the two clusters over 0.9 (STRUCTURE software). Ni corresponds to effective population size of each cluster, and Ti corresponds to the time expressed in numbers of generations since divergence. The following conditions were considered: T1 < T2, T2 < T3, with 0 being the sampling date. Abbreviations are as follows: C; Central cluster, N; Northern cluster, PP; Posterior probability and their associated 95% confidence interval.
Mentions: Alternative biogeographic divergence scenarios were inferred and compared using the DIYABC software package. In-depth information about scenario building procedure and alternative competitive scenario representations are available as additional file (Additional file 2: Figure S1A and S1B). Two runs were performed, a first one consisting of all scenarios (Additional file 2: Figure S1A and S1B), and a second one whereby only scenarios having the highest posterior probabilities were taken into consideration. The range and distribution of prior for parameters used to describe these scenarios (effective population size, time of splitting or merging events, and admixture rates) are given as additional file (Additional file 3: Table S2). For the second run, 3,000,000 datasets were simulated for each scenario (Figure 2) by building a reference table from a specified set of prior parameter distributions. A principal component analysis (PCA- DIYABC, Additional file 4: Figure S2) was performed on the first 30,000 simulated datasets to check if the set of scenarios and the prior distributions of their parameters were able to generate datasets similar to the observed dataset. A normalized Euclidean distance between each simulated dataset of the reference table and the observed dataset was calculated to identify the most likely scenario. To estimate the relative posterior probability of each scenario, 1% of the closest simulated datasets was used in a logistic regression. The most likely scenario was the one with the highest posterior probability.Figure 2

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