Fine-scale genetic analysis of the exploited Nile monitor (Varanus niloticus) in Sahelian Africa
© Dowell et al.; licensee BioMed Central. 2015
Received: 18 February 2015
Accepted: 11 March 2015
Published: 28 March 2015
Overexploitation of wildlife populations results in direct consequences, such as extinction and local extirpation, as well as indirect effects including genetic diversity loss and changes in genetic structure. A clear understanding of the underlying genetic patterns of harvested species is necessary for sustainable management. The Nile monitor (Varanus niloticus) is a commercially valuable species in the international leather industry, with the highest levels of exploitation concentrated throughout Sahelian Africa. In this study, we examined the fine-scale genetic patterns of V. niloticus populations in the Sahel, with the expectation that the genetic structure would correspond to riverine drainage basins. The analyses were based on genotypes at 11 microsatellite loci for 318 individuals, spanning three separate watersheds throughout the Sahel.
Our analyses identified four genetic clusters throughout the region, one of which (the westernmost population) exhibited very high levels of genetic differentiation (F ST = 0.47). Contrary to our expectation, the largest genetic break occurred within a single watershed, the Niger basin, rather than between watersheds. However, other localities displayed evidence of reduced gene flow between watershed boundaries. Across methods, the westernmost population exhibited lower estimates of N e as well as lower levels of genetic diversity compared to the other inferred populations. While we did not detect evidence for recent population bottlenecks, our analyses indicated historic population declines around 1,000–1,800 years ago.
We found that the underlying genetic structure of Varanus niloticus across Sahelian Africa reflects historic as well as present-day patterns of riverine drainages. The high degree of differentiation found for the westernmost population indicates the presence of a separate lineage, and should be taken into consideration when setting harvest limits. The historic population decline for two of the populations corresponds to a drastic expansion of an ancient human civilization in the region, suggesting that human exploitation of V. niloticus has a longer history than previously thought.
Overexploitation has been identified as the main source of decline in many threatened species worldwide . Direct effects of overharvesting are well-supported, including population declines, extirpation, and even extinction . However, indirect consequences of exploitation, such as changes in genetic diversity and population structure, must also be considered in order for species to be sustainably harvested . Overharvesting can alter genetic subdivisions and gene flow patterns, either leading to population isolation and genetic drift, or to increased gene flow resulting in genetic swamping and the loss of local adaptations . By targeting a specific age class or sex, harvesting can reduce the effective population size (N e ), and thus the genetic diversity, without affecting the overall population size . Even non-specific hunting can cause significant genetic and morphological changes in a population .
The Nile monitor (Varanus niloticus) is the second most heavily traded varanid species in both the leather and the pet industries [6,7]. Reaching lengths of over two meters, V. niloticus is also commonly used as a source of food in some regions . The distribution of this species encompasses most of sub-Saharan Africa and additionally extends northward along the Nile River into Egypt . Due to their semi-aquatic nature, the only consistent habitat requirement throughout their range is the presence of permanent bodies of water [9,10]. The diet of V. niloticus is entirely carnivorous, preying upon a large variety of organisms from snails and insects to small mammals .
While the distribution of Varanus niloticus is widespread across Africa, the major skin exporters are concentrated in the Sahelian region, specifically Mali, Cameroon, Chad, Nigeria, and Sudan [6,11,12]. Approximately 500,000 V. niloticus skins legally enter international trade annually to be used for shoes, wallets, belts, handbags, and watchstraps [6,8,11,12]. Accounting for damaged skins and undocumented trade, harvest estimates in the Sahelian region alone have reached two million in peak years . The pet trade adds additional pressure to V. niloticus populations. Between 1975 and 2005, live exports of V. niloticus constituted 22.9% of the global trade in varanids, totaling 309,759 individuals .
Although Varanus niloticus displays high fecundity (average of 20 eggs per clutch) and relatively early sexual maturation (around two years of age), intense exploitation could be affecting their populations [9,13]. Highly exploited populations in eastern Mali were shown to exhibit earlier sexual maturation, higher reproductive output, and shorter longevity than populations near Lake Chad with lower harvest rates [13,14]. A study by de Buffrénil and Hémery  additionally reported that V. niloticus individuals collected near Lake Chad after years of intense harvesting were significantly smaller than those collected in a similar location years earlier when harvesting was minimal. Furthermore, the combined effects of harvesting V. niloticus for food, leather, and the pet trade are likely leading to local extirpation of subpopulations . However, unless the genetic subdivisions within this species are identified, recognizing or preventing the loss of genetically unique populations will not be possible.
While Varanus niloticus is protected under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES, Appendix II), little is known about the population size and genetic structure of this species, which hinders effective management decisions . This study examines the genetic structure, effective population size, and dispersal patterns of V. niloticus populations throughout Sahelian Africa, where harvesting is heavily concentrated. Due to the semi-aquatic behavior and habitat requirements of this species , we expected the genetic partitioning within V. niloticus to correspond to river drainage basins throughout the region.
All tissue samples used in this study were collected and transported in compliance with the Convention on International Trade in Endangered Species of Fauna and Flora.
Sample collection and DNA extraction
Collection locality information and number of samples ( N ) for Varanus niloticus throughout Sahelian Africa
N (Included in Analyses)
21 (1996 Only)
5. Lake Lere
7. Lake Chad: Djaloua
8. Lake Chad: Garaerem
9. Lake Chad: Fodio
10. Lake Chad: Kambé
11. Lake Chad: Moussarom
12. Lake Chad: Fodiawalli
13. Lake Chad: Koutkou
14. Abba Liman
15. Lake Fitri
16. Am N’Guitey
Lake Chad: Banangore (Excluded)
North of Garoua (Excluded)
A 1 cm2 section of tissue was removed from each sample of dried muscle/ bone, sterilizing instruments between sampling with DNAAway TM to prevent cross contamination. Genomic DNA extraction was performed using the Qiagen DNeasy® Blood and Tissue Kit, following manufacturer’s protocol for tissue.
Microsatellite screening and genotyping
Using a subset of Varanus niloticus DNA extracts, we screened 29 microsatellite loci identified in congeners V. komodoensis, abbreviated “K” [15,16], V. salvator, abbreviated “VARSA” , and V. acanthurus, abbreviated “VA” . Of these loci, 11 amplified consistently and were polymorphic in V. niloticus: K7, K10, K11, K15, K22, K23, VARSA10, VARSA07, VA17, VA38, and VA74.
PCR reactions contained 10.0 ng DNA template, 0.4 μM fluorescently-labeled forward primer, 0.4 mM reverse primer, and 1X AmpliTaq Gold® 360 Master Mix in a final reaction volume of 13.75 μL. PCR amplifications were performed in Techne TC-5000 or Applied Biosystems 2720 thermocycler and conditions varied by locus (Additional file 1: Table S1). PCR products were visualized on a 1.5% agarose gel to confirm successful amplification. Fragment analysis was carried out using an ABI 3100 Genetic Analyzer with GeneScan™ 500 LIZ® size standard and allele sizes were called using GeneMarker®.
The microsatellite data were examined for null alleles and mis-scoring using MICRO-CHECKER . Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium were assessed with the web-based program GENEPOP v.4.2 . We calculated expected heterozygosity (H e ), observed heterozygosity (H o ), and number of alleles (N a ) with GenAlEx v.6.5  and allelic richness (A R ) and private allelic richness (PA R ) were calculated with HP-Rare 1.1  which uses a rarefaction method to correct for large differences in sample size.
We used multiple approaches to examine the genetic partitioning among Varanus niloticus populations in Sahelian Africa. We measured genetic differentiation (F ST ) among sample localities in ARLEQUIN 188.8.131.52  using 10,100 permutations to test for significance. Principal Coordinate Analysis (PCoA) based on the calculated pairwise F ST values was conducted in GENALEX v.6.5  and plotted in R v.3.1 [24,25] to visualize the relationships among localities.
Bayesian clustering analyses were performed using STRUCTURE v.2.3.3  and BAPS v.6.0 . STRUCTURE groups individuals into randomly mating populations by maximizing Hardy-Weinberg and linkage equilibrium . We used a correlated allele frequency model with admixture and did not incorporate sampling locality. Ten runs were carried out for each K value, from 1 to 10, with an initial burn-in of 106 iterations and an additional 107 iterations after the burn-in. The optimal number of clusters (K) was estimated by examining the Ln P(X|K)  as well as the ΔK  with the web-based program STRUCTURE HARVESTER . For each K value analyzed, the results were averaged across runs using CLUMPP v.1.1.2 . For comparison, we also performed a spatial clustering of individuals with BAPS v.6.0 , followed by an admixture analysis with 107 iterations, 100 reference individuals, and 1,000 iterations of reference individuals . BAPS directly infers the number of populations from the dataset without the need for examining multiple K values and additional ad-hoc analyses. Results from both analyses were visualized with DISTRUCT v.1.1 .
A hierarchical analysis of molecular variance (AMOVA) was carried out in ARLEQUIN v.184.108.40.206  to determine the significance of the genetic partitioning.
Effective population size and demographic changes
For the following set of analyses, we grouped samples into their inferred populations based on the results from the clustering methods. We implemented MSVAR v.1.3  for our dataset, which estimates effective population size (N e ) as well as assesses historical demographic changes by applying a Bayesian coalescent-based technique. Ten separate runs were conducted for each of the inferred populations using different starting points and 2.0x104 thinned updates with a thinning interval of 1.0x104. Following de Buffrénil and Rimblot-Baly , the generation time was set to 2 years. The initial 10% of the resulting data points were discarded to avoid starting configuration bias. The remaining data points were concatenated and then plotted in R v.3.1 [24,25] to interpret the results. We also compared the estimates of current N e with NeESTIMATOR v.2  using the linkage disequilibrium method for random mating .
To test for evidence of recent population declines, we used BOTTLENECK v.1.2.02  which tests for deviations from expected heterozygosity under the infinite allele model (IAM), stepwise mutation model (SMM), and two-phase model (TPM) with 70% SMM. The statistical analyses implemented by the program (sign test, standardized differences test, and Wilcoxon sign rank test) were used to test for significant excess in heterozygosity.
Gene flow and dispersal
To assess the degree of gene flow among the inferred populations, we used BAYESASS v.1.3  with 107 iterations, a burn-in of 106 and sampling every 2,000 iterations.
Lastly, we examined whether differences in gene flow among localities were correlated with river distance and environmental distance. Pairwise river distances between localities were measured in ArcGIS 10.1 . We performed a Mantel test using pairwise F ST /(1-F ST ) calculations, following Rousset , across all 16 localities using IBDWS v.3.23 .To assess the correlation between genetic distance and environmental heterogeneity, we performed a Partial Mantel test with IBDWS and controlled for the influence of river distance. Environmental variables included BIOCLIM layers for annual precipitation, temperature seasonality, and elevation (www.worldclim.org/bioclim), and pairwise environmental distances were calculated as the pixel value difference between each locality.
Of the 434 tissue samples originally obtained, 318 individuals from 16 localities successfully amplified at seven or more loci and were included in further analyses. Examination of the data with MICRO-CHECKER  indicated no evidence of null alleles. Two loci (K23 and VARSA10) were found to significantly depart from Hardy-Weinberg equilibrium (Additional file 2: Table S2); however, because this result was not consistent across sampling locations, both loci were retained in further analyses. These two loci also exhibited evidence of linkage disequilibrium at one sample locality (site 7), but because no other sample sites showed evidence of non-random associations, the loci were not removed.
Pairwise F ST values among Varanus niloticus sample localities
The clustering analyses showed a hierarchical pattern of structuring across Varanus niloticus sample localities throughout Sahelian Africa. Following the method of Evanno et al. , the largest ΔK occurred at K = 2, followed by a secondary peak at K = 4 (Additional file 3: Figure S1). Additionally, the plot of Ln P(X|K) did not begin to plateau until K = 4. We interpreted these results to mean that while the largest genetic break occurred at K = 2, the optimal number of genetic clusters was four. This decision was bolstered by the BAPS result which also produced four clusters.
Hierarchical AMOVA results for Varanus niloticus populations across Sahelian Africa
Source of Variation
Sum of Squares
Percentage of Variation
Between West vs. East Inner Delta
Among Populations within Groups
Among Genetic Clusters
Among Populations within Clusters
Among Populations within Watersheds
Effective population size and demographic changes
For the following analyses, localities were grouped into the following populations based on genetic cluster assignment: western population = Flabougou (site 1) and Niono (site 2), central population = Mopti (site 3) and Niamey (site 4), Lake Lere population = Lake Lere (site 5), and Lake Chad population = Lake Chad watershed (sites 6–16). Only one population, Lake Chad, showed evidence of a recent population bottleneck under the infinite allele model and the standardized differences test (P = 0.04234); however other mutation models and statistical tests did not produce significant results (Additional file 4: Table S3).
Summary of genetic diversity measures and N e for the four inferred populations of Varanus niloticus
Estimated Current N e
Estimated Ancestral N e (95% CI)
LD (95% CI)
MSVAR (95% CI)
Gene flow and dispersal among populations
Bayesian assessment of migration rates among the four inferred Varanus niloticus populations
0.990 (SD 0.010)
0.679 (SD 0.012)
0.303 (SD 0.018)
0.277 (SD 0.032)
0.690 (SD 0.022)
0.998 (SD 0.002)
Isolation by river distance and environmental heterogeneity across Varanus niloticus collection localities
P ( r < = 0)
River distance (km)
Controlling for river distance:
Annual Precipitation (−km)
Temperature Seasonality (−km)
Although managed as a single taxonomic unit, the results of this study clearly show that Varanus niloticus exhibits a substantial degree of genetic partitioning throughout Sahelian Africa. Our results indicate considerably restricted gene flow between the westernmost population and the rest of the region, despite relatively short geographic distances (approximately 175 km between the western and central populations). The level of intraspecific differentiation found among V. niloticus populations throughout the Sahel is comparable to proposed distinct evolutionary lineages in other reptile species [42,43]. Mitochondrial sequence data from all populations matched voucher museum specimens of V. niloticus from respective localities (Dowell, unpublished dissertation), alleviating concerns of possible misidentification. Additionally, all sample localities are outside of the published distribution of the ornate monitor, V. ornatus, a morphologically similar species restricted to the forested regions of western and central Africa [44,45]. For comparison, sequences were also generated from V. ornatus museum material (Dowell, unpublished dissertation), further increasing our confidence in the sample identification. Future studies are necessary to determine whether other ecological, behavioral, or chromosomal factors are contributing to the reproductive isolation of the western population.
Contrary to our expectations, the largest genetic break occurs within a single drainage basin, near the Inner Niger Delta in Mali, an area characterized by floodplains and reticulating river networks. The Inner Niger Delta represents a suture zone for other Sahelian species, separating western and eastern lineages [46,47]. These congruent biogeographic patterns across species indicate that historic events likely shaped the evolutionary history of the Sahelian faunal community . McIntosh  provides evidence that the Niger River stopped flowing during Pleistocene glacial periods and that the redistribution of sand caused large dunes to form in the region of the Inner Delta. When the Niger River began to flow again, these sand dunes acted as a dam, forming the large Paleo-lake Debo during the late Pleistocene and early Holocene [49,50]. The drying of the Niger River during glacial periods likely acted as a barrier to Varanus niloticus dispersal, and populations isolated in riverine refugia differentiated from one another.
Other evidence suggests that historic drainage patterns could have played a large role in shaping the present-day genetic patterns within Varanus niloticus. Individuals within the Senegal and Niger watersheds were assigned to the same genetic cluster. This low degree of genetic differentiation could be a reflection of the historic connection between the Senegal and Niger drainage basins, which occurred until the late Pleistocene , and likely facilitated past migration. Presently, the frequent flooding of these riverine systems has possibly allowed for the continuing exchange of migrants. On the other hand, the relatively low yet significant pairwise F ST value between these localities suggests that gene flow might be slightly reduced, possibly due to the currently separate watersheds.
The genetic structure of the central, Lake Lere, and Lake Chad populations supports our hypothesis of reduced gene flow across present-day watershed boundaries. Our analysis of migration rates indicated a high degree of gene flow between the central and Lake Lere populations, located in the western and eastern drainages of the Niger basin, respectively. Although Lake Lere is in close proximity to locations within the Lake Chad watershed (shortest distance = 209 km), our analyses indicated reduced gene flow between these populations, evidenced by significant F ST values and the spatial clustering analysis with BAPS. In contrast to BAPS, the STRUCTURE results showed a higher level of admixture and hierarchical partitioning among these populations; however, at higher K values (not shown) the distinctiveness of the Lake Lere population became more evident. The STRUCTURE analysis also showed additional sub-structuring among Varanus niloticus localities within the Lake Chad watershed. Sample sites along tributaries displayed differing proportions of ancestry than localities within Lake Chad itself. This could reflect source-sink dynamics of individuals dispersing downstream along tributaries and intermixing in Lake Chad, where the rivers converge.
Across methods, the western population showed evidence of having a small effective population size (N e ) and lower genetic diversity compared to other Varanus niloticus populations in the region. This could be a result of the limited gene flow that we detected. Additionally, the prolonged intensive harvesting of V. niloticus in Mali  could be leading to reduced population sizes and lower genetic diversity in the region.
The decline in Varanus niloticus populations that occurred around 1,000–1,800 years BP corresponds to the drastic expansion and organization of human civilizations in West Africa . Specifically, the ancient city of Jenné-jeno inhabiting the Inner Niger Delta region, expanded rapidly to cover an area of 330 km2 in 800–900 AD before eventually declining . This ancient city was a major hub along the trans-Saharan trade route . Archaeological excavations at the Jenné-jeno site recovered a high proportion of aquatic reptiles, including varanids, and this bias was mainly attributed to exploitation by fisherman . Therefore, the negative effects of humans on V. niloticus populations in Sahelian Africa likely have a longer history than previously thought.
Our findings have important implications for the management of Sahelian Varanus niloticus populations. Although listed on Appendix II of CITES, managing international trade of V. niloticus without population size and genetic structure information could be detrimental to the species. The four genetic groups that we identified in Sahelian Africa represent separate demographic units and possibly a distinct lineage based on private allelic richness and differing allele frequencies. These genetic groups provide an objective and useful delineation for managing populations of V. niloticus throughout Mali, Niger, and Chad. In the future, a genetic assessment of V. niloticus should be expanded to the full range of this species to enable more thorough monitoring of international trade and the ability to source harvested individuals from other locations.
This study highlights the utility of genetic methods for informing management practices and conservation decisions. Many CITES-listed species are currently managed without empirical data and could greatly benefit from species- and population-level assessments of genetic structure and diversity . Monitor lizards, in particular, are all protected under CITES; however, information on population size and vulnerability status is lacking for a majority of varanid species . Genetic tools can provide an effective means to detect genetic diversity loss, estimate effective population size, and identify independent population segments within harvested species [3,55].
While biodiversity data is limited throughout the Sahel, recent studies in this region have uncovered a high degree of endemism and species richness . Similarly, genetic assessments have revealed cryptic diversity and genetic structuring in many Sahelian taxa [46,47,57-59]. With increasing aridity predicted for this region in future years  and continual destruction of habitat [8,56], genetic isolation and fragmentation of Sahelian species will likely increase. Therefore, understanding and protecting the complex patterns of biodiversity in the Sahel should be of great importance.
We would like to extend our gratitude to the Herpetology Department of the American Museum of Natural History (AMNH), especially David Kizirian, for facilitating the transportation of tissue samples. We would also like to thank Tim Vines and Robert Kraus at Axios Reviews for their editorial assistance as well as the three anonymous reviewers who provided valuable suggestions. Funding for this study was provided by the Clare Boothe Luce Foundation and by Fordham University.
- Baillie J, Hilton-Taylor C, Stuart SN. 2004 IUCN red list of threatened species: a global species assessment. Cambridge, UK: IUCN; 2004.Google Scholar
- Burney DA, Flannery TF. Fifty millennia of catastrophic extinctions after human contact. Trends Ecol Evol. 2005;20(7):395–401.PubMedView ArticleGoogle Scholar
- Allendorf FW, England PR, Luikart G, Ritchie PA, Ryman N. Genetic effects of harvest on wild animal populations. Trends Ecol Evol. 2008;23(6):327–37.PubMedView ArticleGoogle Scholar
- Laikre L, Ryman N. Effects on intraspecific biodiversity from harvesting and enhancing natural populations. Ambio. 1996;25(8):504–9.Google Scholar
- Law R. Fisheries-induced evolution: present status and future directions. Mar Ecol Prog Ser. 2007;335:271–7.View ArticleGoogle Scholar
- Jenkins M, Broad S. International Trade in Reptile Skins: A Review and Analysis of the Main Consumer Markets, 1983–91: Traffic International. 1994.Google Scholar
- Pernetta AP. Monitoring the trade: using the CITES database to examine the global trade in live monitor lizards (Varanus spp.). Biawak. 2009;3(2):37–45.Google Scholar
- de Buffrénil V. Monitor Hunting. CITES/ C&M International Magazine. 1995;4:6–20.Google Scholar
- Lenz S. Varanus niloticus. In: Pianka ER, King D, King RA, editors. Varanoid lizards of the world. Bloomington, Indiana: Indiana University Press; 2004. p. 133–8.Google Scholar
- Bayless M. The distribution of African monitor lizards (Sauria: Varanidae). Afr J Ecol. 1997;35(4):374–7.View ArticleGoogle Scholar
- de Buffrénil V, Castanet J. Age estimation by skeletochronology in the Nile monitor (Varanus niloticus), a highly exploited species. J Herpetol. 2000;34(3):414–24.View ArticleGoogle Scholar
- De Lisle HF. The natural history of monitor lizards: Krieger Malabar. 1996.Google Scholar
- de Buffrénil V, Rimblot-Baly F. Female reproductive output in exploited Nile monitor lizard (Varanus niloticus L.) populations in Sahelian Africa. Can J Zool. 1999;77(10):1530–9.View ArticleGoogle Scholar
- de Buffrénil V, Hémery G. Variation in longevity, growth, and morphology in exploited Nile monitors (Varanus niloticus) from Sahelian Africa. J Herpetol. 2002;36(3):419–26.View ArticleGoogle Scholar
- Ciofi C, Bruford M. Isolation and characterization of microsatellite loci in the Komodo dragon Varanus komodoensis. Mol Ecol. 1998;7(1):134–6.PubMedGoogle Scholar
- Ciofi C, Tzika AC, Natali C, Watts PC, Sulandari S, Zein MS, et al. Development of a multiplex PCR assay for fine‐scale population genetic analysis of the Komodo monitor Varanus komodoensis based on 18 polymorphic microsatellite loci. Mol Ecol Resour. 2011;11(3):550–6.PubMedView ArticleGoogle Scholar
- Fu M, Yu D, Peng J, Wang Y, Gao S, Wang L, et al. Isolation and characterization of novel microsatellite markers in Water monitor (Varanus salvator). Conserv Genet Resour. 2011;3(4):777–9.View ArticleGoogle Scholar
- Fitch AJ, Goodman AE, Donnellan SC. Isolation and characterisation of microsatellite markers for the Australian monitor lizard, Varanus acanthurus (Squamata: Varanidae) and their utility in other selected varanid species. Mol Ecol Notes. 2005;5(3):521–3.View ArticleGoogle Scholar
- Van Oosterhout C, Hutchinson WF, Wills DP, Shipley P. MICRO‐CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4(3):535–8.View ArticleGoogle Scholar
- Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J Hered. 1995;86(3):248–9.Google Scholar
- Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics. 2012;28(19):2537–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Kalinowski ST. hp‐rare 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol Ecol Notes. 2005;5(1):187–9.View ArticleGoogle Scholar
- Excoffier L, Lischer HE. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10(3):564–7.PubMedView ArticleGoogle Scholar
- R Core Team. R: A Language and Environment for Statistical Computing: R Foundation for Statistical Computing. 2014.Google Scholar
- Wickham H. ggplot2: Elegant graphics for data analysis. New York: Springer; 2009.View ArticleGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59.PubMed CentralPubMedGoogle Scholar
- Corander J, Sirén J, Arjas E. Bayesian spatial modeling of genetic population structure. Comput Stat. 2008;23(1):111–29.View ArticleGoogle Scholar
- Pritchard JK, Wen W. Documentation for STRUCTURE software: version 2. 2003.Google Scholar
- Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8):2611–20.PubMedView ArticleGoogle Scholar
- Earl DA. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour. 2012;4(2):359–61.View ArticleGoogle Scholar
- Jakobsson M, Rosenberg NA. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007;23(14):1801–6.PubMedView ArticleGoogle Scholar
- Corander J, Marttinen P, Siren J, Tang J. Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinformatics. 2008;9:539.PubMed CentralPubMedView ArticleGoogle Scholar
- Rosenberg NA. DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004;4(1):137–8.View ArticleGoogle Scholar
- Storz JF, Beaumont MA. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution. 2002;56(1):154–66.PubMedView ArticleGoogle Scholar
- Do C, Waples RS, Peel D, Macbeth G, Tillett BJ, Ovenden JR. NeEstimator v2: re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol Ecol Resour. 2014;14(1):209–14.PubMedView ArticleGoogle Scholar
- Waples RS, Do C. LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol Ecol Resour. 2008;8(4):753–6.PubMedView ArticleGoogle Scholar
- Piry S, Luikart G, Cornuet J-M. BOTTLENECK: a program for detecting recent effective population size reductions from allele data frequencies. France: Montpellier; 1999.Google Scholar
- Wilson GA, Rannala B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics. 2003;163(3):1177–91.PubMed CentralPubMedGoogle Scholar
- ESRI (Environmental Systems Resource Institute). ArcMap 10.1. California: Redlands; 2012.Google Scholar
- Rousset F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics. 1997;145(4):1219–28.PubMed CentralPubMedGoogle Scholar
- Jensen JL, Bohonak AJ, Kelley ST. Isolation by distance, web service. BMC Genet. 2005;6(1):13.PubMed CentralPubMedView ArticleGoogle Scholar
- Hekkala ER, Amato G, DeSalle R, Blum MJ. Molecular assessment of population differentiation and individual assignment potential of Nile crocodile (Crocodylus niloticus) populations. Conserv Genet. 2010;11(4):1435–43.View ArticleGoogle Scholar
- Ciofi C, Beaumontf MA, Swingland IR, Bruford MW. Genetic divergence and units for conservation in the Komodo dragon Varanus komodoensis. Proc R Soc Lond Ser B Biol Sci. 1999;266(1435):2269–74.View ArticleGoogle Scholar
- Böhme W, Ziegler T. A taxonomic review of the Varanus (Polydaedalus) niloticus (Linnaeus, 1766) species complex. Herpetol J. 1997;7(4):155–62.Google Scholar
- Böhme W, Ziegler T. Varanus ornatus. In: Pianka ER, editor. Varanoid lizards of the world. Bloomington, Indiana: Indiana University Press; 2004. p. 139–42.Google Scholar
- Bryja J, Granjon L, Dobigny G, Patzenhauerova H, Konecny A, Duplantier JM, et al. Plio-Pleistocene history of West African Sudanian savanna and the phylogeography of the Praomys daltoni complex (Rodentia): the environment/geography/genetic interplay. Mol Ecol. 2010;19(21):4783–99.PubMedView ArticleGoogle Scholar
- Brouat C, Tatard C, Bâ K, Cosson J-F, Dobigny G, Fichet-Calvet E, et al. Phylogeography of the Guinea multimammate mouse (Mastomys erythroleucus): a case study for Sahelian species in West Africa. J Biogeogr. 2009;36(12):2237–50.View ArticleGoogle Scholar
- Avise JC. Phylogeography: The history and formation of species. Cambridge, MA: Harvard University Press; 2000.Google Scholar
- McIntosh RJ. Floodplain geomorphology and human occupation of the upper inland delta of the Niger. Geogr J. 1983;149(2):182–201.View ArticleGoogle Scholar
- Drake NA, Blench RM, Armitage SJ, Bristow CS, White KH. Ancient watercourses and biogeography of the Sahara explain the peopling of the desert. Proc Natl Acad Sci. 2011;108(2):458–62.PubMed CentralPubMedView ArticleGoogle Scholar
- McIntosh SK, McIntosh RJ. West African Prehistory: Archaeological studies in recent decades have illuminated the prehistory of this vast region, revealing unexpected complexity in its development from 10,000 BC to AD 1000. Am Sci. 1981;69(6):602–13.Google Scholar
- McIntosh SK. Excavatons at Jenné-Jeno, Hambarketolo, and Kaniana (Inland Niger Delta, Mali), the 1981 Season, vol. 20. Los Angeles, California: University of California Press; 1995.Google Scholar
- Smith MJ, Benítez-Díaz H, Clemente-Muñoz MÁ, Donaldson J, Hutton JM, Noel McGough H, et al. Assessing the impacts of international trade on CITES-listed species: Current practices and opportunities for scientific research. Biol Conserv. 2011;144(1):82–91.View ArticleGoogle Scholar
- Koch A, Ziegler T, Böhme W, Arida E, Auliya M. Pressing problems: distribution, threats, and conservation status of the monitor lizards (Varanidae: Varanus ssp.) of Southeast Asia and the Indo-Australian Archipelago. Herpetol Conserv Biol. 2013;8:1–62.Google Scholar
- Schwartz MK, Luikart G, Waples RS. Genetic monitoring as a promising tool for conservation and management. Trends Ecol Evol. 2007;22(1):25–33.PubMedView ArticleGoogle Scholar
- Brito JC, Godinho R, Martínez‐Freiría F, Pleguezuelos JM, Rebelo H, Santos X, et al. Unravelling biodiversity, evolution and threats to conservation in the Sahara‐Sahel. Biol Rev. 2014;89(1):215–31.PubMedView ArticleGoogle Scholar
- Dobigny G, Tatard C, Gauthier P, Ba K, Duplantier J-M, Granjon L, et al. Mitochondrial and Nuclear Genes-Based Phylogeography of Arvicanthis niloticus (Murinae) and Sub-Saharan Open Habitats Pleistocene History. PLoS One. 2013;8(11):e77815.PubMed CentralPubMedView ArticleGoogle Scholar
- Froufe E, Gonçalves DV, Brito JC, Harris DJ. Nuclear and mitochondrial markers reveal the existence of several geographically concordant lineages within a Sahelian gecko species, Ptyodactylus ragazzii. Amphibia-Reptilia. 2013;34:85–93.View ArticleGoogle Scholar
- Gonçalves DV, Brito JC, Crochet P-A, Geniez P, Padial JM, Harris DJ. Phylogeny of North African Agama lizards (Reptilia: Agamidae) and the role of the Sahara desert in vertebrate speciation. Mol Phylogenet Evol. 2012;64(3):582–91.PubMedView ArticleGoogle Scholar
- De Wit M, Stankiewicz J. Changes in surface water supply across Africa with predicted climate change. Science. 2006;311(5769):1917–21.PubMedView ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.