Introduction

Wildlife ranching, where wild animals are managed in fenced areas, is practiced in various regions around the world, including North America, Europe and Africa [1–5]. Whether wildlife ranching is beneficial for conservation is often debated [6–8]. While wildlife ranching preserves habitat that would otherwise be converted for other land-use types [9], it also comes with the pitfalls of small population sizes and fragmentation of populations due to fencing that could result in loss of genetic diversity of many species, not just those of economic interest on the ranch [5, 10]. Furthermore, wildlife ranching often involves intensive breeding of wildlife, or intentional genetic manipulation [5], for a single, or multiple, traits. This can exacerbate the fixation or loss of alleles at a rate much higher than would occur through drift or natural selection [11]. Consequently, the frequency of alleles that are identical by descent increases in the population, thus increasing the risk of inbreeding and inbreeding depression [12]. Deleterious alleles that are in linkage disequilibrium with the genes underlying the desired traits will also be inherited by offspring and increase in frequency in the population [11]. Such intentional genetic manipulation of wildlife may decrease the evolutionary potential of species and reduce the conservation value that may be provided by privately-owned wildlife populations [5, 13].

Few studies have been done comparing genetic diversity of species on intensively managed wildlife ranches to more open or natural systems in state protected areas (reserves and transfrontier- or national parks) and extensive game ranches. A comprehensive study of populations of red deer (Cervus elaphus) on ranches and in a national park in Spain found no significant differences in genetic diversity [2]. In South Africa, Grobler et al. [14] found significantly lower allozyme diversity in impala (Aepyceros melampus) populations in smaller, managed reserves compared to a large population, although these reserves were not intensively breeding impala. In a study of blue and black wildebeest (Connochaetes taurinus and C. gnou), Grobler et al. [15] found no significant difference in genetic diversity between ranch and reference populations of these species, although this was not the aim of the study. Finally, Grobler et al. [16] used polymorphic allozyme loci and found substantially lower genetic diversity in a ranch population of Cape buffalo (Syncerus caffer caffer) compared to reference samples from the Kruger National Park (KNP). However, the ranch population was established with only two bulls and six cows from Addo Elephant National Park (AENP), which is now known to have relatively low genetic diversity [16, 17]. This indicates that the low diversity of the ranch population was likely due to founder effect, given that 25 years (~3.5 buffalo generations) had passed between establishment of the population and sampling.

At the time of the Grobler et al. [16] study, private ranches in South Africa could only be stocked with disease-free buffalo from AENP and zoological gardens around the world. This was due to the prevalence of bovid diseases in other potential source populations, such as KNP or Hluhluwe-iMfolozi Park (HiP), that are controlled in South Africa through strict veterinary regulations [18]. In the 1890s and early 1900s rinderpest and foot-and-mouth disease epidemics resulted in the loss of an estimated 95% of the buffalo population in southern Africa [18]. During the 1980s and 1990s, outbreaks of bovine tuberculosis and Corridor disease in the buffalo populations of South Africa (excluding AENP) prompted South African National Parks (SANParks) to start a disease-free breeding programme for KNP buffalo which was carried out between 1999 and 2007 [19]. The goal was to maintain a disease-free population outside disease-affected areas that would represent the high genetic diversity of KNP. Two of these disease-free KNP-derived populations are now maintained in Mokala National Park (MNP) and the nearby breeding centre Graspan (GNP). Consequently, the wildlife ranching industry had a new source of disease-free buffalo with presumably high genetic diversity.

Concurrently, the wildlife ranching industry in South Africa experienced rapid growth, not only due to the popularity of disease-free buffalo, but also due to the breeding, selling and eventual hunting of rare phenotypes of various antelope species, such as exotic colour variations, unique horn morphology and increased horn length [5, 20, 21]. Buffalo is one of the most expensive species and are generally bred to obtain exceptional horn length and “spread”, with buffalo of East African origin being more expensive and sought after than southern African individuals due to their apparent superior phenotype in this regard [5].

In this study, we aimed to: (i) Determine how successful the SANParks disease-free breeding programme was in maintaining the high genetic diversity of KNP buffalo; (ii) Investigate whether the genetic diversity of buffalo under intensive management regimes on private ranches was significantly different compared to their source populations in national parks; (iii) Determine how the relatedness and inbreeding levels of buffalo on private ranches compared to those in national parks and; (iv) Characterize the genetic composition of private ranch buffalo populations. We discuss how the results relate to the general breeding practices on private ranches and what the implications are for the conservation and management of Cape buffalo and other wildlife species that are popular in wildlife ranching. Finally, we make recommendations regarding the genetic management of Cape buffalo in national parks in southern Africa.

Materials and methods

Samples and ethics statement

This study was performed in collaboration with SANParks (Project code: HARC1227) and the Ministry of Environment and Tourism, Namibia. Samples from MNP (Northern Cape, South Africa, GPS: -29.162613, 24.321083), GNP (located near MNP in the Northern Cape) and AENP (Eastern Cape, South Africa, GPS: -33.483474, 25.750269) were obtained from SANParks, as blood stored in ethylenediaminetetraacetic acid (EDTA). Samples from Waterberg Plateau National Park (WPP, Namibia, GPS: -20.352369, 17.337493) and 12 private wildlife ranches (P001 –P012) were previously obtained by the Veterinary Genetics Laboratory (VGL) at the University of Pretoria. All samples were collected between 2008 and 2015. Thus, within one buffalo generation (5–7.5 years), with 99% of the samples collected between 2011 and 2015 (Table 1). Private buffalo owners send samples to the VGL for individual genotyping and parentage analysis. The number of samples from each locality is shown in Table 1. Permission was obtained from the Namibian Department of Environmental Affairs and Tourism and each private ranch owner to use the genotype data of their samples in this study. Data from the private ranches, such as sample and ranch names, have been anonymized for this study. Ranch locations have also been omitted as this may compromise the anonymity of the participating ranches. The location of the ranches was not important for the analyses and conclusions of the study, except to say that all ranches are located in South Africa. This project was approved by the Animal Ethics Committee of the University of Pretoria (code: ec005-16).

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Table 1. Sample information and selected summary statistics of Cape buffalo from the 16 localities included in this study.

https://doi.org/10.1371/journal.pone.0236717.t001

Results

Genetic diversity of national parks and private ranches

In this study, Graspan (GNP) and Mokala National Park (MNP) were used as “benchmark” localities in terms of genetic diversity due to the Kruger National Park (KNP) origin of these buffalo. Addo Elephant National Park (AENP) had the fewest number of alleles, despite having the seventh largest sample size in the study. Nine of 11 private alleles were present in the private ranch samples, with the remaining two private alleles present in GNP and MNP (Table 1).

The allelic richness (A_R) analysis showed that AENP had significantly lower genetic diversity than any other population in this study (Fig 1A, S3 Table). WPP and P003 had significantly lower A_R than the mean across all the populations, whereas P001, P002 and P006 had significantly higher A_R than the mean (Fig 1A). When the mean was calculated without AENP, only P001 remained with significantly higher A_R. Despite its low A_R, AENP showed no significant population-level inbreeding (F_IS, Fig 1B). Conversely, P006 showed a low, but significant F_IS value, with an excess of homozygotes, despite having the highest A_R. P007 showed an excess of heterozygotes, with an F_IS significantly less than zero. The observed and expected heterozygosity (Fig 1C) values had large standard deviations, but mirrored the inbreeding coefficients, where a lower observed than expected heterozygosity corresponded to a positive inbreeding coefficient and vice versa.

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Fig 1. Genetic diversity indices of the 16 sampling localities analysed in the present study.

A Allelic richness, where the mean is shown by the dashed line and the mean without AENP is shown by the dotted line. B Inbreeding coefficient, where an asterisk (*) indicates significant deviation from zero (dotted line). C Observed (H_O) and expected (H_E) heterozygosity. Vertical bars indicate 95% confidence intervals on plots A and B, and standard deviation on plot C.

https://doi.org/10.1371/journal.pone.0236717.g001

Effective population size (N_e) estimates were similar for most localities (range: 3.3–48.8), as well as for GNP-MNP and all private ranches combined (88.7 and 81.9, respectively) (S1 Fig, S3 Table). AENP had an N_e estimate of 24.2 (S3 Table), although this was not significantly lower than GNP-MNP, based on (slightly) overlapping 95% confidence intervals (S1 Fig).

Relatedness and individual inbreeding

The pairwise relatedness (r) values had an L-shaped distribution, with a slight increase in frequency of values around r = 0.5 in all localities, albeit to varying degrees (Fig 2). Mean pairwise relatedness was generally low, where P003 (0.110) and AENP (0.096) had the highest and GNP (0.044) and MNP (0.049) had the lowest mean relatedness (Fig 2, S4 Table). The positive, and relatively high, skewness values confirmed the distributions were right-tailed and highly asymmetrical (range: 1.75–3.31, S4 Table). A normal distribution has a skewness of zero and kurtosis of three. The kurtosis values of GNP and MNP were the highest at 14.64 and 12.56 (S4 Table), meaning their relatedness distributions had the most extreme tails, i.e. their tails were the “thinnest”. As stated by Bradley [39]: “The coefficients of skewness and kurtosis of an L-shaped distribution increase rapidly as the long positive tail of the distribution becomes thinner and thinner…”. In other words, GNP and MNP had the lowest proportion of close relatives, while those localities with lower kurtosis values, such as P003 (3.49) and AENP (5.14), had a larger proportion of close relatives.

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Fig 2. Pairwise relatedness distributions per locality.

Density histograms were constructed with ggplot2 v3.3.0 [40] in R, with 30 bins for all histograms. The dashed line on each plot indicates the mean relatedness, with the text inset showing the value of the mean. Note that the y-axes scales are independent between plots to aid in visualization of the distribution for each locality, due to the large range of sample sizes in this data set.

https://doi.org/10.1371/journal.pone.0236717.g002

Indeed, this was illustrated by the strong negative correlation between kurtosis of the relatedness distributions and the proportion of pairwise relatedness values ≥ 0.25 (Fig 3). The proportion of close relatives, as estimated by the proportion of r values ≥ 0.25, was not significantly higher in private ranches (mean = 10.0%) compared to national parks (mean = 8.1%) (p-value = 0.2, one-tailed t-test). However, private ranches generally had a proportion of close relatives approximately twice the value seen in GNP (4.8%) and MNP (4.2%), while this value in AENP (14.5%) was approximately three times higher than the latter two (S4 Table). In general, males were more related to each other than females, except for P003 and P011, although this difference was not significant when the mean was calculated across all localities (p-value = 0.077, one-tailed t-test) (S5 Table).

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Fig 3. Scatterplot showing the correlation between kurtosis and proportion of relatedness values ≥ 0.25.

Pearson’s correlation coefficient (R) and the associated p-value are shown.

https://doi.org/10.1371/journal.pone.0236717.g003

Individual inbreeding (F) estimates were substantially higher in AENP (mean F = 0.156) compared to the other localities, except for P006 which had a mean F of 0.112 (Fig 4, S4 Table). The higher levels of F in these two localities was also evident in their distributions in Fig 4, with larger interquartile ranges and higher 75% quantiles than all other localities. The remainder of the localities had relatively low inbreeding estimates, with means ranging between 0.023 and 0.097 (S4 Table).

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Fig 4. Box and whisker plots showing the distribution of individual inbreeding (F) estimates.

The lower and upper hinges of the box indicate the 25% and 75% quantiles, respectively, with the horizontal line in the box showing the median. The lower whisker shows the smallest observation greater than or equal to the lower hinge minus 1.5 times the interquartile range and the upper whisker shows the largest observation less than or equal to the upper hinge plus 1.5 times the interquartile range. Points indicate outliers beyond the whiskers. The turquoise squares show the mean.

https://doi.org/10.1371/journal.pone.0236717.g004

Population differentiation

The majority of the 120 population pairs were significantly differentiated based on the D_JOST and F_ST analyses (Table 2). These two metrics were significantly correlated, Spearman’s rho = 0.90, p-value < 2.2e^-16. Differentiation estimates were deemed significant if the 95% confidence interval did not encompass zero (S6 Table). Only four pairs were not significantly differentiated based on F_ST, namely GNP-MNP, P003-P006, P003-P008 and P003-P011, while ten pairs were not significantly differentiated based on D_JOST (Table 2). In terms of the national parks, GNP and MNP were more similar to each other than to any other population and their differentiation (D_JOST) was not significant, while AENP and WPP were also more similar to each other than to any other populations, but were still significantly differentiated. Both results were unsurprising given the shared origin of GNP and MNP buffalo and that AENP provided founders for WPP. The mean F_ST across all populations was 0.077 and the mean D_JOST was 0.116. Between the four national parks, the mean F_ST was 0.147 and mean D_JOST was 0.184. The corresponding values among the private buffalo populations only were 0.055 and 0.098. AENP was the most differentiated from the private ranches, with a D_JOST of 0.203 (mean calculated across P001 –P012), followed by WPP (0.161), MNP (0.089) and finally GNP (0.080).

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Table 2. Pairwise D_JOST (above diagonal) and F_ST (below diagonal) values for the 16 sampling localities in this study.

https://doi.org/10.1371/journal.pone.0236717.t002

The STRUCTURE analysis of the full data set revealed K = 2 as the most likely (based on DeltaK), while the analysis with the relatives removed data set revealed K = 3 as the most likely number of genetic clusters (based on both the log likelihood of the data and DeltaK) (S2 Fig). STRUCTURE assumes Hardy-Weinberg equilibrium (HWE), and linkage equilibrium between loci, within populations [35]. In the full data set, five private ranches did not conform to HWE, after Bonferroni corrections (S7 Table). However, in the relatives removed data set, all localities conformed to HWE (S7 Table). In the full data set, some pairs of loci were not in linkage equilibrium after Bonferroni corrections were performed, although there was no pair consistently in linkage disequilibrium (S8 Table). However, in the relatives removed data set, all pairs of loci were in linkage equilibrium in all localities (S8 Table). The relatives removed data set therefore satisfied the assumptions of STRUCTURE and thus we concluded that K = 3 represents the most likely number of genetic clusters in this data set.

The individual assignment plots at both K = 2 and K = 3 showed that the private ranches predominantly constituted buffalo originating from AENP and GNP-MNP, as expected given the known history of the private ranch populations (S3 Fig). However, at K = 3, some localities showed a signal from a third, unsampled gene pool. P001 and P002, in particular, showed a substantial contribution from this third gene pool, while P004 and P011 also had a fairly strong contribution. In the national parks, WPP showed a not insignificant signal from this third gene pool, but clustered predominantly with AENP, as expected, while AENP was mostly homogeneous. GNP and MNP clustered together and showed some variation contributed from AENP and the third gene pool (S3 Fig).

The discriminant analysis of principal components (DAPC) at K = 3 (the optimum K as determined above) showed distinct clustering of samples corresponding to those identified in the STRUCTURE analysis, namely an AENP cluster, GNP-MNP cluster and a third, unidentified gene pool “Other” (S4 Fig).

Discussion

In this study, we evaluated and compared the genetic diversity, relatedness and inbreeding, as well as characterized the structure of disease-free Cape buffalo (Syncerus caffer caffer) in private ranches and national parks in southern Africa. Disease-free buffalo populations in southern Africa could be important reservoirs of genetic diversity for the species if future disease outbreaks occur on the scale of those of the 1890s/1900s [19]. The unique population histories of each sampling locality offered an opportunity to investigate how recent (GNP and MNP) and older (AENP) population contractions, as well as admixture of different gene pools (WPP and private ranches) affected the genetic diversity of the populations. Furthermore, given the more intensive management of buffalo populations on private ranches, in general, as compared to national parks, it was important to determine whether these practices may have affected the genetic diversity, and how the relatedness and inbreeding distributions of these populations compare against more natural or less managed populations.

The high allelic richness, effective population size (N_e), low inbreeding coefficient (F_IS) and low relatedness in GNP and MNP indicated that the disease-free breeding programme appeared to have been designed in a robust enough manner to prevent a significant founder effect. The observed heterozygosity of GNP and MNP (0.66 and 0.61, respectively) was slightly lower than that previously estimated in the north of KNP (0.71–0.75), but overlapped with the distribution of heterozygosity throughout the entire park (0.62–0.75) [17, 41–44]. Thus, short of a direct comparative study between GNP-MNP and KNP using the same loci, we can conclude that the disease-free breeding programme has likely maintained the high genetic diversity of KNP buffalo in the GNP and MNP populations.

The older population contraction (due to the disease outbreaks in the 1890s/1900s) in AENP was reflected in the significantly lower allelic richness (A_R), the low heterozygosity, low N_e and high individual inbreeding (F) estimates observed in this population. The observed heterozygosity in AENP (0.40) was slightly lower than previously reported (0.48) by O'Ryan et al. [17], who used seven loci compared to 11 used here. There was no overlap of loci between the studies. While the observed reduction in heterozygosity of AENP may be due to the different loci used, it is likely that the genetic diversity of this population decreased in the last two decades, given the severity of the known population contraction that occurred and the fact that there has been no gene flow into this population between O'Ryan et al. [17] and the current study (Pers. Comm. D. Zimmerman 2015).

The admixed populations in this study (WPP and the private ranches) had genetic diversity statistically equivalent to that of GNP and MNP, although WPP and P003 had significantly lower A_R than the mean across all populations. However, the significantly higher A_R in WPP, compared to AENP, the population from which the majority of its founders originated, was most likely a consequence of unique variation contributed by the non-AENP/Czech Republic zoo/East African buffalo. This highlighted the positive effect that even a few breeding migrants (whether natural or human-mediated) can have on the genetic diversity of a population, however the risk of outbreeding depression should also be taken into account when mixing individuals from isolated populations [49].

Inbreeding coefficients were low for all populations, with only P006 and P007 significantly deviating from zero, in opposite directions. P006 showed an excess of homozygosity (low, but significantly positive F_IS) and P007 showed an excess of heterozygosity (significantly negative F_IS). The latter result for P007 was most likely a result of the small sample size from this population (N = 17) making statistical inferences less robust, as indicated by the large confidence intervals around the F_IS estimate. P006 was an anomaly, because it had high genetic diversity, low mean relatedness, high kurtosis and a low proportion of relatedness values ≥ 0.25, but still had a significantly positive F_IS value and a relatively high mean individual inbreeding value (F = 0.112). One potential explanation for these observations follows. On private ranches with intensive management, stud breeding is often practiced, where a single male is generally the only breeding bull in a breeding group. This breeding strategy may have been implemented for several consecutive generations in the P006 population without sufficient turnover of cows in the breeding group, thus resulting in significant inbreeding. The high genetic diversity and low relatedness could be a result of recent introductions of newly acquired buffalo that were sampled and included in this study before they could contribute their genetic variation to the population, and thus before they could have reduced the inbreeding levels in P006. The fact that this F_IS deviation and high individual F values were not seen in any of the other private ranches could be due to a faster turnover of breeding bulls and/or cows, or that the samples included in this study were more randomly sampled from those populations.

Given the high genetic diversity of the private ranches, the relatively low estimates of N_e were somewhat surprising. The private ranches had N_e estimates close to those estimated for AENP and WPP. The N_e in AENP was only 3% of the population census size, while the N_e of WPP was only 4.2% of the census size. Thus, while the Czech Republic zoo buffalo may have increased the genetic diversity of WPP, the effect of the population decline that its main source population (AENP) experienced had not been completely negated. For comparison, the N_e of GNP-MNP was 18% of their combined population census size, which is comparable to the N_e/N_c ratios reported for KNP by van Hooft et al. [44] and O'Ryan et al. [17] of 26% and 10–30%, respectively. The low N_e estimated for the private ranches may reflect the management approaches on these ranches. However, the N_e estimates in this study should be interpreted with caution, particularly for the private ranches, since these populations are atypical and do not necessarily satisfy the assumptions of typical population genetics models.

The L-shaped relatedness distributions indicated that most buffalo from each sampling locality were unrelated. This was a somewhat surprising result given the observed social structure that exists in unmanaged buffalo herds, where females mate with one or a few (in large herds) dominant bulls [45, 46], and the genetic evidence from herds in KNP that supports this hypothesis [44]. Therefore, the expectation was that there would be a relatively high proportion of close relatives in each locality, and particularly in private ranches, due to the breeding strategies employed. However, the high kurtosis values and low proportions of close relatives showed that this was not the case. There was a slight increase in the frequency of first-order relatives (r = ~0.5) indicating some family structure was present, albeit to a lower extent than expected.

These observations may be explained by several individual factors, or a combination thereof. First, the expected ecological and genetic patterns of buffalo herds may not hold true in the case of private ranches, and even in some smaller natural populations, due to the breakdown of natural demographic processes and social structure. Second, the samples included in this study may have originated from multiple herds within each locality (unfortunately, sampling information at this scale was not available). This would explain why most buffalo within localities were unrelated, as well as the lower-than-expected proportion of close relatives in each locality. The increase in frequency of relatedness values around r = 0.5 could thus be even more pronounced in individual herds than was observed here. Lastly, the relatively frequent introduction of buffalo on private ranches (immigrants) and removal of other buffalo (emigrants) would result in lower overall relatedness and lower proportions of close relatives than expected in these populations. Only a small number of breeding migrants per generation are necessary to affect the genetic diversity and relatedness of a population [17]. This may also explain the unexpected relatedness results in GNP-MNP, which showed some evidence of (indirect) gene flow from an unsampled population- likely via their source population, KNP (see discussion of STRUCTURE results below).

Females were expected to be more related to each other than males, given their lower inter-herd migration levels (5–20% per generation in females vs 100% in males) [44]. Again, the results indicated that this was not the case for the localities in this study, where relatedness was generally higher within males rather than females, although overall this difference was not significant. Perhaps the non-conformity of private ranches to this sex-biased relatedness hypothesis is not entirely surprising, given that the ranches do not represent a natural system in the way that AENP does, for example. Thus, not seeing this sex-biased relatedness pattern in a more natural population such as AENP was more surprising but could again be explained by considering that samples may have originated from multiple herds.

Interestingly, samples from P003 may, in fact, represent a single herd, as the relatedness parameters of this locality most closely matched the expectations discussed above. P003 had the highest mean relatedness (0.115), the lowest skewness and kurtosis (thus the most even relatedness distribution), the highest proportion of close relatives (20.5%) and a substantially higher mean relatedness between females (0.158) than between males (0.095), while still maintaining low individual inbreeding levels (mean = 0.083).

While D_JOST is generally a more accurate measure of genetic differentiation than F_ST [33], we predominantly compare F_ST here, as most previous buffalo studies did not compute D_JOST, except Smitz et al. [42], and the two metrics were significantly and strongly correlated in this data set. The genetic differentiation between AENP and GNP/MNP (D_JOST = 0.289/0.303, F_ST = 0.255/0.257) was, to the best of our knowledge, the highest recorded (in terms of F_ST) between any two buffalo populations in southern Africa. It was substantially higher than the differentiation found between AENP and KNP (F_ST = 0.131) by O'Ryan et al. [17]. It was also higher than the differentiation found between Hluhluwe-iMfolozi Park (HiP) and KNP (D_JOST = 0.213, F_ST = 0.141) by Smitz et al. [42] and between AENP and HiP (F_ST = 0.227) estimated by O'Ryan et al. [17].

These levels of genetic differentiation between buffalo populations in South African protected areas are two to four times higher than the differentiation observed between Cape buffalo populations across Africa. AENP and HiP are more differentiated from KNP, than KNP is from populations in the East African countries of Kenya and Tanzania (F_ST = 0.078–0.100) [42, 43]. Incredibly, southern African Cape buffalo (S. c. caffer) populations are more differentiated from each other than KNP buffalo are from forest buffalo (S. c. nanus) (F_ST = 0.116–0.155), a different subspecies [43]. This amount of intraspecific genetic differentiation within the single subspecies of Cape buffalo populations in southern Africa (and particularly South Africa) is indicative of the highly fragmented nature of South Africa’s protected areas and is another example of the human-induced population fragmentation and consequent reproductive isolation experienced by more than 25% of species in the world [47]. Thus, if past subdivision of South African buffalo populations was minimal, which is likely, as suggested by O'Ryan et al. [17], then action should be taken to re-establish gene flow [48], either through the establishment of natural corridors between populations or through translocations (e.g. from the disease-free GNP-MNP to AENP). Potential concerns about outbreeding depression and/or genomic swamping of the AENP buffalo population following this proposed genetic supplementation could be alleviated by considering the current support for an estimated short time since isolation (~100 years) [17, 49] and by conducting regular genetic monitoring of the population. Furthermore, recent studies showed local genes, and alleles related to local adaptation, were maintained in the recipient population after genetic supplementation, while the benefits of genetic rescue were still observed [50–52].

The results obtained with the STRUCTURE analyses showed that the removal of close relatives assisted in the more accurate identification of the most likely number of genetic clusters present in the data set, by satisfying the assumptions of the models implemented. The results suggested that these approaches were effective in mitigating the biases introduced into STRUCTURE analyses by the sampling of close relatives [53].

The individual assignment plots at K = 3 illustrated the significant genetic differentiation between AENP and GNP-MNP. Apparent contributions to each other between these two clusters are most likely a result of shared ancestral polymorphism and not recent gene flow, given the small proportion of the contributions. At K = 3, a contribution from a third gene pool (dark green) was prevalent in many individual buffalo in P001, P002, P004 and P007, and in a few individuals in the WPP population. The DAPC analysis also clustered a large proportion of individuals into this third gene pool. This third gene pool may have originated from one of two broadly defined regions: East Africa or the northern parts of southern Africa. We define East Africa here as the area north-north-east of Tanzania, inclusive. Thus, Tanzania, Kenya, Burundi, Rwanda, Uganda and the southern parts of South Sudan, Ethiopia and Somalia. The support for an East African origin is that WPP had four buffalo potentially of Tanzanian or Kenyan origin introduced into its population via a Czech Republic zoo. Additionally, numerous private ranches in South Africa breed with East African buffalo, although the definition of East Africa may differ from ranch to ranch.

The northern parts of southern Africa are here defined as the “northern cluster” identified by Smitz et al. [42] and consists of northern Botswana, northern Zimbabwe, Angola and central and northern Mozambique (excluding Gorongosa National Park). Smitz et al. [42] identified two additional clusters; central (comprising southern Zimbabwe, southern Mozambique and northern South Africa, which included KNP- the source of GNP-MNP buffalo) and a southern cluster consisting mainly of buffalo from the isolated Hluhluwe-iMfolozi Park in the east of South Africa. The authors found evidence of the northern cluster gene pool in KNP, indicating gene flow between the northern cluster and KNP. It is thus conceivable that the signal of the third gene pool identified in the present study originated from northern Botswana/Zimbabwe buffalo and was also present in some of the buffalo that formed part of the disease-free breeding programme of KNP and therefore is present in both GNP-MNP and the private ranches (which source their buffalo from GNP-MNP, AENP and elsewhere). Some private ranches may also have independently introduced buffalo from this northern cluster to their properties, thus giving a more extensive signature of this third gene pool in the private ranch populations.

Both the above-mentioned scenarios are likely and may be occurring at the same time given the extensive translocations of buffalo in southern Africa. A more comprehensive data set consisting of Cape buffalo samples from across the subspecies’ range, including private ranches, may reveal a more detailed genetic history regarding the origin of buffalo on private ranches and the natural genetic structure that exists across the continent.

In conclusion, we echo O'Ryan et al. [17] in recommending that the AENP population be augmented with buffalo of KNP origin to increase its genetic diversity- one breeding bull per generation (every ~7.5 years), to prevent genomic swamping and maintain unique diversity and local adaptation (if present) in AENP. This is more feasible now than in 1998, given that disease-free populations of KNP buffalo now exist in GNP and MNP and have high genetic diversity. Likewise, the population in WPP, with moderate genetic diversity and most of its founders from AENP, would benefit from the introduction of disease-free buffalo from GNP-MNP (at a similar frequency and rate to AENP) to firstly increase and then maintain genetic diversity. Disease-free buffalo populations (including private ranches, AENP, GNP-MNP and WPP) represent an important insurance policy for the species in southern Africa. Confirming that GNP, MNP and the private ranch populations maintained high genetic diversity was thus an important result. However, it is equally important to continually monitor genetic diversity of these populations, as the impact on genetic diversity of their recent establishment (i.e. founder effect) and fragmentation has likely not yet manifested. Furthermore, we found that genetic diversity, relatedness and inbreeding levels did not appear to be affected at the present time by the breeding strategies employed for buffalo on private ranches in this study, except perhaps for P006. It should be cautioned, however, that the genetic diversity on private ranches is highly dependent on the management practices on each ranch and the exchange of buffalo between ranches. If not managed actively and adequately, genetic diversity could be lost due to breeding practices, small population sizes (genetic drift) and inbreeding. We also showed that private ranch buffalo were predominantly of AENP and GNP-MNP origin, but that there was substantial contribution from a third, unsampled gene pool, most likely representing buffalo from East Africa, or the northern parts of southern Africa.

Private ranches contain a significant proportion of individuals of different wildlife species, not only in South Africa, but also in other countries such as Namibia, the USA and Spain [2, 5]. The present study is the first in southern Africa to evaluate the genetic diversity, at a large scale, of one of these species, the Cape buffalo, on private ranches compared to their source populations in national parks. Given that genetic diversity is a key component of biodiversity [54] and private ranches harbour a significant proportion of certain wildlife species, there is a clear case for similar studies to be conducted for other popular wildlife ranching species. This will require cooperation between private ranch owners, researchers and conservation authorities. The goal should be to catalogue the genetic diversity contained in private wildlife populations, as compared to national parks, for the benefit of the species. Furthermore, there appears to be no unified guidelines for private ranchers to assist in the genetic management of wildlife on their ranches in southern Africa to ensure sustainable use and long-term survival of the species [5]. Additional studies and appropriate guidelines are particularly pertinent given the increased pressures on extensive areas for ranching and conservation and on wildlife populations due to human expansion, anthropogenic activities and climate change [55, 56].

Future research on African buffalo could include investigating at a finer scale how specific ranching approaches (e.g. intensive vs. extensive, or particular breeding protocols) affect the genetic diversity, relatedness and inbreeding of populations and individual herds. Additionally, researchers could take advantage of the relative affordability of generating genome-wide data to investigate which loci might be under selection in private ranches and natural populations, to explore the demographic history of the species to investigate how it may have responded to historical climate change, to estimate divergence times of different populations, and to inspect what signatures historical population declines may have left in the genomes of individuals from different populations.

Supporting information

Acknowledgments

We acknowledge the contribution of Amy Clarke, previously of the Veterinary Genetics Laboratory in genotyping many of the samples over numerous years. We would like to thank Dave Zimmerman of SANParks for providing detailed population histories of the AENP, GNP and MNP buffalo populations. We also thank Mark Jago (previously of the Namibia Ministry of Environment and Tourism (MET)) and Malan Lindeque (Namibia MET), for providing a detailed population history of the WPP buffalo population and the MET for releasing this information for use in this research, as well as for providing samples. We thank the owners of the private ranches included in this study for their permission to publish the data in anonymized form.