Comparative spatial genetic structure of two rodent species in an agro-ecological landscape in southern Africa

Abstract

Determining the scale of genetic variation informs studies of dispersal, connectivity, and population dynamics particularly in heterogeneous landscapes. Mastomys natalensis and Mus minutoides are gen-eralist rodents that utilize multiple habitat types within the agro-ecological landscapes of southern African savannas. To study the comparative spatial genetic structure of these species we developed 9 new microsatellites for Mus and used 14 microsatellite loci previously developed for Mastomys, to genotype rodents sampled across an agro-ecological landscape (200 km2). Spatial genetic structure was measured using spatial autocorrelation and Moran’s Eigenvector Maps analysis. In both species, non-random genetic similarity was limited to only the smallest spatial scales (<600m), and at that scale, it was significantly greater in Mastomys than in Mus. Only a small proportion of the genetic signal across the landscape was due to spatial signal in Mastomys, and there was no spatial signal detected for Mus. The lack of spatial autocorrelation beyond the first six hundred meters for both species illustrated that they are capable of high rates of dispersal, while the observed patterns of genetic panmixia found for both species is the predicted genetic outcome for species with omnivorous habits and plastic habitat use. These findings have implications for both pest management and rodent-borne disease control.

This is a preview of subscription content, access via your institution.

References

  1. Anderson, CD., Epperson, B.K., Fortin, M.J., Holderegger, R., James, P.M., Rosenberg, M.S., Scribner, K.T., Spear, S., 2010. Considering spatial and temporal scale in landscape-genetic studies of gene flow. Mol. Ecol., 19, 3565–3575.

    Article  Google Scholar 

  2. Austin, J.D., Gore, JA, Greene, D.U., Gotteland, C., 2015. Conspicuous genetic structure belies recent dispersal in an endangered beach mouse (Peromyscus polionotus trissyllepsis). Conserv. Genet., 16, 915–928.

    Article  Google Scholar 

  3. Banks, S.C., Peakall, R., 2012. Genetic spatial autocorrelation can readily detect sex-biased dispersal. Mol. Ecol., 21, 2092–2105.

    Article  Google Scholar 

  4. Banks, S.C., Cary, G.J., Smith, A.L., Davies, I.D., Driscoll, D.A., Gill, A.M., Lindenmayer, D.B., Peakall, R., 2013. How does ecological disturbance influence genetic diversity? Trends Ecol. Evol., 28, 670–679.

    Article  Google Scholar 

  5. Barton, N.H., Depaulis, F., Etheridge, A.M., 2002. Neutral evolution in spatially continuous populations. Theor. Popul. Biol., 61, 31–48.

    Article  Google Scholar 

  6. Bowcock, A.M., Ruiz-Linares, A., Tomfohrde, J., Minch, E., Kidd, J.R., Cavalli-Sforza, L.L., 1994. High resolution of human evolutionary trees with polymorphic microsatellites. Nature, 368, 455–457.

    Article  CAS  Google Scholar 

  7. Brouat, C., Loiseau, A., Kane, M., Ba, K., Uplantier, J.M., 2007. Population genetic structure of two ecologically distinct multimammate rats: the commensal Mastomys natalensis and the wild Mastomys erythroleucus in southeastern Senegal. Mol. Ecol., 16, 2985–2997.

    Article  CAS  Google Scholar 

  8. Butet, A., Leroux, A.B.A., 2001. Effects of agriculture development on vole dynamics and conservation of Montagu’s harrer in western French wetlands. Biol. Conserv., 100, 289–295.

    Article  Google Scholar 

  9. Castiglia, R., Garagna, S., Merico, V., Oguge, N., Corti, M., 2006. Cytogenetics of a new cytotype of African Mus (subgenus Nannomys) minutoides (Rodentia, Muridae) from Kenya: C-and G-banding and distribution of (TTAGGG) n telomeric sequences. Chromosome Res., 14, 587–594.

    Article  CAS  Google Scholar 

  10. Chimimba, CT., Bennett, N., et al., 2005. Subfamily murinae. In: Skinner, J.D. (Ed.), The Mammals of the Southern African Subregion., 3rd edition. Cambridge University Press, Cambridge, pp. 127–162.

    Google Scholar 

  11. Davis, S., Calvet, E., 2005. Fluctuating rodent populations and risk to humans from rodent-borne zoonoses. Vector-Borne Zoonotic Dis., 5, 305–314.

    Article  CAS  Google Scholar 

  12. De Graaff, G., 1981. The Rodents of Southern Africa. Butterworth & Co, Durban.

    Google Scholar 

  13. Delany, M.J., 1986. Ecology of small rodents in Africa. Mamm. Rev., 16, 1–41.

    Article  Google Scholar 

  14. Dray, S., Legendre, P., Peres-Neto, P.R., 2006. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol. Model., 196, 483–493.

    Article  Google Scholar 

  15. Estes-Zumpf, W.A., Rachlow, Jl., Waits, L.P., Warheit, K.I., 2010. Dispersal, gene flow, and population genetic structure in the pygmy rabbit (Brachylagus idahoensis). J. Mammal., 91, 208–219.

    Article  Google Scholar 

  16. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: as simulation study. Mol. Ecol., 14, 2611–2620.

    Article  CAS  Google Scholar 

  17. Excoffier, L., Lischer, H.E., 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour., 10, 564–567.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Faircloth, B.C., 2008. MSATCOMMANDER: detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour., 8, 92–94.

    Article  CAS  Google Scholar 

  19. Galan, M., van Hooft, W.F., Legrand, D., Berthier, K., Loiseau, A., Granjon, L., Cosson, J.F., 2004. A multiplex panel of microsatellite markers for widespread sub-Saharan rodents of the genus Mastomys. Mol. Ecol. Notes, 4, 321–323.

    Article  CAS  Google Scholar 

  20. Galpern, P., Peres-Neto, P.R., Manseau, M., 2014. MEMGENE: spatial pattern detection in genetic distance data. Methods Ecol. Evol., 5, 1116–1120.

    Article  Google Scholar 

  21. Gratz, N.G., 1997. The burden of rodent-borne diseases in Africa South of the Sahara. Belg. J. Zool., 127, 71–84.

    Google Scholar 

  22. Hurst, Z.M., McCleery, R.A., Collier, B.A., Fletcher Jr., R.J., Silvy, N.J., Taylor, P.J., Monadjem, A., 2013. Dynamic edge effects in small mammal communities across a conservation-agricultural interface in Swaziland. PLoS One 8, e74520.

  23. Hurst, Z.M., McCleery, R.A., Collier, B.A., Silvy, N.J., Taylor, P.J., Monadjem, A., 2014. Linking changes in small mammal communities to ecosystem functions in an agricultural landscape. Mammal. Biol., 79, 17–23.

    Article  Google Scholar 

  24. Jacob, J., 2008. Response of small rodents to manipulations of vegetation height in agro-ecosystems. Integr. Zool., 3, 3–10.

    Article  Google Scholar 

  25. Kierepka, E.M., Anderson, S.J., Swihart, R.K., Rhodes, O.E.Jr., 2016. Evaluating the influence of life-history characteristics on genetic structure: a comparison of small mammals inhabiting complex agricultural landscapes. Ecol. Evol., 6, 6376–6396.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lalis, A., Leblois, R., LeCompte, E., Denys, C., ter Meulen, J., Wirth, T., 2012. The impact of human conflict on the genetics of Mastomys natalensis and Lassa virus in West Africa. PLoS One 7, e37068.

  27. Le Galliard, J.-F., Remy, A., Ims, R.A., Lambin, X., 2011. Patterns and processes of dispersal behavior in arvicoline rodents. Mol. Ecol., 21, 505–523.

    Article  Google Scholar 

  28. Leirs, H., Verheyen, W., 1994. Population Ecology of Mastomys natalensis (Smith, 1834). Implications for Rodent Control in Africa. Agricultural Edition Nr35. Belgian Administration for Development Cooperation, Brussels.

    Google Scholar 

  29. Leirs, H., Verhagen, R., Verheyen, W., 1993. Productivity of different generations in a population of Mastomys natalensis rats in Tanzania. Oikos, 68, 53–60.

    Article  Google Scholar 

  30. Leirs, H., Verheyen, W., Verhagen, R., 1996a. Spatial patterns in Matomys natalensis in Tanzania (Rodentia: Muridae). Mammalia, 60, 545–555.

    Article  Google Scholar 

  31. Leirs, H., Verhagen, R., Verheyen, W., Mwanjabe, P., Mbise, T., 1996b. Forecasting rodent outbreaks in Africa: an ecological basis for Mastomys control in Tanzania. J. Appl. Ecol., 33, 937–943.

    Article  Google Scholar 

  32. Loiseau, A., Konecny, A., Galan, M., Bryja, J., Cosson, J.F., Brouat, C., 2007. New polymorphic microsatellite loci for rodents of the genus Mastomys using PCR multiplexing, and cross-species amplification in Myomys and Praomys. Mol. Ecol. Notes, 7, 684–687.

    Article  CAS  Google Scholar 

  33. Long, A.K., Bailey, K., Greene, D.U., Tye, C., Parr, C., Lepage, H.K., Gielow, K.H., Monadjem, A., McCleery, RA., 2013. Multi-scale habitat selection of Mus minutoides in the Lowveld of Swaziland. Afr. J. Ecol., 51, 493–500.

    Article  Google Scholar 

  34. Malécot, G., 1967. Identical loci and relationship. In: Lucien, M., Neyman, J. (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 4. University of California Press, Berkeley, pp. 317–332.

    Google Scholar 

  35. McArdle, B.H., Anderson, M.J., 2001. Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology, 82, 290–297.

    Article  Google Scholar 

  36. McCleery, R., Monadjem, A., Baiser, B., Fletcher Jr., R., Vickers, K., Kruger, L., 2018. Animal diversity declines with broad-scale homogenization of canopy cover in African savannas. Biol. Conserv., 226, 54–62.

    Article  Google Scholar 

  37. Meester, J.A.J., Lloyd, C.N.D., Rowe-Rowe, D.T., 1979. A note on the ecological role of Praomys natalensis. S. Afr. J. Sci., 75, 183–184.

    Google Scholar 

  38. Monadjem, A., 1997. Habitat preferences and biomass of small mammals in Swaziland. Afr. J. Ecol., 35, 64–72.

    Article  Google Scholar 

  39. Monadjem, A., 1999. Population dynamics of Mus minutoides and Steatomys pratensis(Muridae: Rodentia) in a sub-tropical grassland in Swaziland. Afr. J. Ecol., 37, 202–210.

    Article  Google Scholar 

  40. Monadjem, A., Perrin, M., 2003. Population fluctuations and community structure of small mammals in a Swaziland grassland over a three-year period. Afr. Zool., 38, 127–137.

    Article  Google Scholar 

  41. Monadjem, A., Mahlaba, TA, Dlamini, N., Eiseb, S.J., Belmain, S.R., Mulungu, L.S., Massawe, A.W., Makundi, R.H., Mohr, K., Taylor, P.J., 2011. Impact of crop cycle on movement patterns of pest rodent species between fields and houses in Africa. Wildl. Res., 38, 603–609.

    Article  Google Scholar 

  42. Monadjem, A., Taylor, P.J., Denys, C., Cotterill, F.P.D., 2015. Rodents of Sub-Saharan Africa, a Biogeographic and Taxonomic Synthesis. De Gruyter, Berlin.

    Google Scholar 

  43. Mwanjabe, P.S., Sirima, F.B., Lusingu, J., 2002. Crop losses due to outbreaks of Mastomys natalensis (Smith, 1834) Muridae, Rodentia, in the Lindi region of Tanzania. Int. Biodeterior. Biodegrad., 49, 133–137.

    Article  Google Scholar 

  44. Peakall, R., Smouse, P.E., Huff, D.R., 1995. Evolutionary implications of allozyme and RAPD variation in diploid populations of dioecious buffalograss Buchloë dactyloides. Mol. Ecol., 4, 135–147.

    Article  CAS  Google Scholar 

  45. Peakall, R., Ruibal, M., Lindenmayer, D.B., 2003. Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution, 57, 1182–1195.

    Article  Google Scholar 

  46. Petkova, D., Novembre, J., Stephens, M., 2016. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet., 48, 94–100.

    Article  CAS  Google Scholar 

  47. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics, 155, 945–959.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Richarson, J.L., Burak, M.K., Hernandez, C., Shirvell, J.M., Mariani, C., Carvalho-Pereira, T.S.A., Pertile, A.C., Panti-May, JA, Pedra, G.G., Serrano, S., Taylor, J., Carvalho, M., Rodrigues, G., Costa, F., Childs, J.E., Ko, A.I., Caccone, A., 2017. Using fine-scale spatial genetics of Norway rats to improve control efforts and reduce leptospirosis risk in urban slum environments. Evol. Appl., 10, 323–337.

    Article  Google Scholar 

  49. Rozen, S., Skaletsky, H., 2000. Primer3 on the WWW for general users and for biologist programmers. In: Misener, S., Krawetz, S.A. (Eds.), Bioinformatics Methods and Protocols. Humana Press, Totowa, NJ, pp. 365–386.

    Google Scholar 

  50. Russo, I.R.M., Sole, C.L., Barbato, M., von Bramann, U., Bruford, M.W., 2018. Landscape determinants of fine scale genetic structure of a small rodent in a heterogenous landscape (Hluhluwe-iMfolozi Park, South Africa). Sci. Rep. 6, 29168.

    Article  CAS  Google Scholar 

  51. Shirk, A.J., Wallin, D.O., Cushman, S.A., Rice, CG., Warheit, K.I., 2010. Inferring landscape effects on gene flow: a new model selection framework. Mol. Ecol., 19, 3603–3619.

    Article  CAS  Google Scholar 

  52. Smouse, P.E., Peakall, R., 1999. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity, 82, 561–573.

    Article  Google Scholar 

  53. Smouse, P.E., Peakall, R., Gonzales, E., 2008. A heterogeneity test for fine-scale genetic structure. Mol. Ecol., 17, 3389–3400.

    Article  Google Scholar 

  54. Stenseth, N.C., 1977. Onthe importance of spatio-temporal heterogeneity forthe population dynamics of rodents: towards a theoretical foundation of rodent control. Oikos, 29, 545–552.

    Article  Google Scholar 

  55. Swihart, R.K., Gehring, T.M., Kolozsvary, M.B., Nupp, T.E., 2003. Responses of ‘resistant’ vertebrates to habitat loss and fragmentation: the importance of niche breadth and range boundaries. Divers. Distrib., 9, 1–18.

    Article  Google Scholar 

  56. Van Hooft, P., Cosson, J.F., Vibe-Petersen, S., Leirs, H., 2008. Dispersal in Mastomys natalensis mice: use of fine scale genetic analyses for pest management. Hereditas, 145, 262–273.

    Article  Google Scholar 

  57. Van Oosterhout, C.V., Hutchinson, W.F., Mills, D.P.M., Shipley, P., 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Resour., 4, 535–538.

    Article  CAS  Google Scholar 

  58. Vekemans, X., Hardy, O.J., 2004. New insights from fine-scale spatial genetic structure analysis in plant populations. Mol. Ecol., 13, 921–935.

    Article  CAS  Google Scholar 

  59. Waples, R.S., 2015. Testing for Hardy-Weinberg proportions: Have we lost the plot? J. Hered., 106, 1–19.

    Article  Google Scholar 

  60. Wright, S.W., 1969. Evolution and the Genetics of Populations. University of Chicago Press, Chicago.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to James D. Austin.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bonzi, V.R., Carneiro, C.M., Wisely, S.M. et al. Comparative spatial genetic structure of two rodent species in an agro-ecological landscape in southern Africa. Mamm Biol 97, 64–71 (2019). https://doi.org/10.1016/j.mambio.2019.05.001

Download citation

Keywords

  • Agro-ecology
  • Comparative genetic structure
  • Heterogeneity
  • Neutral genetic structure
  • Spatial auto-correlation
  • Rodent