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Chapter 2 Assessing the impact of the rinderpest bottleneck in KNP and HiP from mitochondrial DNA sequences
Abstract
Disease, whether acute or chronic, has played a major role in the population history of the African buffalo. While subacute diseases such as BTB are not characterised by high rates of mortality, acute diseases such as rinderpest typically result in high mortalities. The rinderpest pandemic of 1890’s, besides causing the death of more than 5 million cattle in South Africa, also caused the eradication of as much as 95 % of the Cape buffalo population in KNP. This dramatic population decline may have resulted in a genetic bottleneck, which in turn may have reduced the level of genetic variation among the buffalo of the park. A reduction in variation may subsequently result in genetic drift, which in turn can negatively affect the ability of the population to sustain variation and adapt to environmental change.
The research presented in this chapter is aimed at quantifying the degree to which the bottleneck has affected genetic variation in KNP and HiP. A 452 basepair region of the Dloop region (HV1) of 161 and 97 animals from KNP and HiP, comprising 30 and 14 herds respectively, was sequenced for this purpose. While 34 haplotypes were identified in KNP (with the mean number of pairwise differences between them being 22.09 ±9.78), only 4 haplotypes could be identified for HiP (mean number of parwise differences between haplotypes = 11.14 ± 5.10). HiP consistently exhibited a reduced level of variation when compared to KNP, reflected by both nucleotide diversities (0.049 vs. 0.025) as well as haplotype diversities (0.92 ±0.009 vs. 0.48 ± 0.05). It is thus apparent that while HiP exhibits signals of a genetic bottleneck, the impact thereof in KNP appears to have been overestimated.
The effect of the bottleneck is also reflected by statistics that describe a change in population size. While KNP seems to be in equilibrium, HiP exhibits strong signals of a population contraction (Fu’s Fs = 24.03, p = 0.01). Other test statistics that indicate remnants of a bottleneck and subsequent population contraction for HiP include the D* and F* statistics of Fu and Li and Tajima’s D statistic. The potential effect of small postrinderpest populations, large-scale removal of animals from both parks and immigration into the parks are discussed. The importance of having access to baseline information regarding the genetic status of the buffalo population is stressed, particularly for making sound discisions regarding conservation management of the two parks.
Introduction
Both ancient and recent demographic events have played a role in the population history of the Cape buffalo. While climatic fluctuations have influenced the geographical distribution of Cape buffalo on a more ancient time scale (Van Hooft et al., 2002), more recently their abundance has been impacted upon quite dramatically by disease. The rinderpest pandemic of the 1890s caused a drastic population crash in Cape buffalo populations throughout sub-Saharan Africa, and resulted in a reduction of up to 95 % of buffalo in the Kruger National Park, South Africa (Stevenson-Hamilton, 1957). A population decline of this magnitude was from the outset regarded as a potential bottleneck which may have affected the population dynamics of the Cape buffalo. As bottlenecks are known to have a detrimental effect on the genetic diversity of populations, a significant reduction in genetic variation of buffalo due to rinderpest is anticipated. Certain potential risks may be associated with a reduced level of genetic variation, such as a compromised ability of a population to sustain and to maintain genetic variation. The latter is of particular importance for a species’ response to environmental and demographic change. Several studies have highlighted the effects of population bottlenecks on a variety of species, and many demonstrated a concomitant reduction in genetic variation. Examples include the northern hairy-nosed wombat (Taylor et al., 1994), the black-footed rock-wallaby (Eldridge et al., 1999), the northern elephant seal (Weber et al., 2000), the common warthog and African elephant (Muwanika et al., 2003), the golden monkey (Su and Shi,1995; Li et al., 2003) and the European lynx (Hellborg et al., 2002).
The importance of understanding the effects of demographic bottlenecks for future management has been stressed by several authors (Hellborg et al., 2002; Harley et al., 2005). A small population size is by default an effect of a bottleneck, and this may result in genetic drift and subsequent loss of variation. Detecting and understanding bottlenecks and their effects is however by no means a simple task, and tools sensitive enough to detect genetic signals of past events are a necessity. The mitochondrial genome is one of the most widely used markers in this regard. Certain of its characteristics such as its mode of inheritance, small effective population size (only one quarter of autosomal loci), make it a sensitive marker for detecting reduction in genetic variation (Hoelzel et al., 1993; Goldsworthy et al., 2000). The molecule is furthermore sensitive to genetic drift and the
effect of population subdivision.
The aim of this chapter is to investigate the extent to which the rinderpest epidemic affected the genetic status and level of variation among the buffalo populations in KNP and HiP. Conservation implications and future perspectives are discussed with regard to the longterm maintenance and sustainability of genetic variation within the two parks.
Materials and Methods
Sampling
HiP
Blood from ninety-seven animals, originating from 14 herds, was used for mtDNA sequence analysis. These herds represent the five major management regions of the park, viz. Manzibomvu, Nqumeni, Masinda, Makhamisa and Mbhuzane areas (Fig. 2.1). Young animals born in the same year were randomly sampled from each herd in order to minimize the chance of sampling full-sibs and maximize the chance of sampling as much diversity as possible.
KNP
Blood from a total of 161 animals, originating from 30 herds occurring throughout the park (Fig. 2.2) was used for mtDNA sequence analysis. At least 5 animals, again primarily young animals were chosen at random from each of the 30 herds.
Genomic amplification and characterisation
DNA was extracted from whole blood (300 [l) using the Roche kit according supplier specifications and used as template for the amplification of a 452 bp region corresponding to HVRI of the D-loop region of the mitochondrial genome (Bastos, submitted). Approximately 200ng of extracted DNA, 1X PCR buffer, 0.2 mM of dNTP, 0.4 [M of each primer (DLH –BOV, 5’CCT GAA GAA AGA ACC AGA TG 3’; Thrl-BOV, 5’ TAA TAT ACT GGT CTT GTA AAC C 3’) and 1 unit of Taq polymerase (Biotools) in a final reaction volume of 50 [l was used to amplify the target region under the following cycling conditions: an initial denaturation step at 950C for 40 seconds, followed by 39 cycles consisting of denaturation at 950C for 20 seconds, annealing at 500C for 30 seconds and extension at 700C for 45 seconds, and a final extension step at 700C for 1 minute. The amplicons were subsequently purified using the Roche High Pure PCR purification kit, according to the instructions of the manufacturer. Forty nanograms of purified amplicon were used for cycle sequencing, with the Bigdye Cycle Sequencing kit (Perkin Elmer) according to the instructions of the manufacturer. The cycle sequencing profile consisted of 25 cycles starting with a denaturation step at 960C for 10 seconds, followed by an annealing step at 500C that lasted for 5 seconds and a final extension step at 600C for 4 minutes. Precipitated amplicons were electrophoresed on an ABI 3100 capillary-based DNA sequencer (Applied Biosystems) and reaction profiles were visualised and edited manually using the program Chromas (Conor McCarthy, School of Biomolecular and Biomedical Science, faculty of science, Griffith University, Brisbane, Queensland, Australia).
Statistical analysis of sequences
Edited sequences were aligned using DNAman (version 4.13, Lynnon Biosoft) and transformed into GDE or Clustal format. In order to account for mutation rate heterogeneity during subsequent analysis, the α parameter of the gamma distribution of rates of mutation across the sites was estimated using Modeltest 3.06 (Posada and Crandall, 1998). General estimates of sequence diversities and molecular evolutionary analyses were conducted using MEGA version 3.0 (Kumar et al., 2004), DnaSP4.00 (Rozas et al., 2003) and Arlequin (Schneider et al., 1997). TCS 1.13 (Clement et al., 2000) was used to construct a minimum spanning network (MSN), depicting the phylogenetic relationships among the respective haplotypes. The program estimates gene genealogies from DNA sequences based on the cladogram estimation method, also known as statistical parsimony (Templeton et al., 1992; Clement et al., 2000). MSN’s are favoured above neighbourjoining trees for intraspecific studies since they allow for multifurcations whilst not assuming that ancestral lineages are extinct (Crandall and Templeton, 1993). The latter implies that haplotypes are not forced to occupy the tip positions.
In order to detect signals of past demographic events, Arlequin was used to determine the distribution of the observed and expected pairwise nucleotide site differences (mismatch distribution), based on a sudden expansion model, while DnaSP 4.00 determined the mismatch distributions based on a stable population (at equilibrium and for no recombination), i.e. population with constant population size (Watterson 1975; Slatkin and Hudson 1991, Rogers and Harpending 1992). The site frequency spectra (SFS) which is a measure of the difference in the frequency of different mutation classes, was determined using DnaSP 4.00. A population that has recently expanded will be characterized by an excess of singleton mutations relative to the expected frequencies under neutrality and stationarity (Fu, 1997; Okello et al., 2005).
Acknowledgements
List of Figures
List of Tables
Preamble
Summary
Disclaimer
CHAPTER 1 GENERAL INTRODUCTION
1. Introduction
1.1 The Genus Syncerus
1.2 Distribution and habitat
1.2.1 KNP
1.2.2 HiP
1.3 Ecology and behaviour
1.4 Buffalo and disease
1.4.1 Foot-and mouth disease (FMD)
1.4.2 Corridor disease (CD)
1.4.3 Bovine Tuberculosis (BTB)
1.4.4 Rinderpest
1.5 Molecular markers as tools for population genetic studies
1.5.1 Microsatellites (Msats)
1.5.1.1 Msat characteristics
1.5.1.2 Msat evolution and homoplasy
1.5.1.3 Homoplasy, FST and gene flow (Nm)
1.5.1.4 Alternatives to FST
1.5.1.5 Direct measurement of gene flow and dispersal
1.5.1.6 Estimating effective population size
1.5.2 Mitochondrial DNA
1.5.2.1 MtDNA Characteristics
1.5.2.2 MtDNA sequence evolution
1.6 The application of Msats and mtDNA markers in population genetic studies
1.7 Conservation genetics
1.8 Aims of this study
1.8.1 Specific objectives were to:
CHAPTER 2 ASSESSING THE IMPACT OF THE RINDERPEST BOTTLENECKIN KNP AND HIP FROM MITOCHONDRIAL DNA SEQUENCES
Abstract
1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
CHAPTER 3 DEVELOPMENT AND EVALUATION OF A MULTIPLEX STR SYSTEM FOR GENOTYPING CAPE BUFFALO
Abstract *
1. Introduction
2. Materials and methods
3. Results and Discussion
4. Conclusions
Appendix A
Appendix B
CHAPTER 4 INTER- AND INTRA-POPULATION STRUCTURE, GENETIC VARIATION AND DISPERSAL OF HIP AND KNP BUFFALO
Abstract
1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
CHAPTER 5 CONCLUDING REMARKS
REFERENCES
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