Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach

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dc.contributor.advisorBronner, Gary Nen_ZA
dc.contributor.advisorBennett, Nigel Cen_ZA
dc.contributor.advisorBloomer, Pauletteen_ZA
dc.contributor.advisorRobertson, M Pen_ZA
dc.contributor.authorRampartab, Chanelen_ZA
dc.date.accessioned2016-07-28T12:22:15Z
dc.date.available2016-07-28T12:22:15Z
dc.date.issued2016en_ZA
dc.description.abstractGolden moles are subterranean mammals endemic to sub-Saharan Africa and threatened by anthropogenic habitat loss. At present, little is known about the biology, taxonomy, distribution and severity of threats faced by many of these taxa. In an attempt to raise awareness of these elusive grassland flagship taxa, the Endangered Wildlife Trust's Threatened Grassland Species Programme (EWT-TGSP) identified the need for more information on the distributions and conservation status of four poorly-known golden mole taxa (Amblysomus hottentotus longiceps, A. h. meesteri, A. robustus, A. septentrionalis) that are endemic to the Grassland Biome, and which may be heavily impacted by anthropogenic habitat alteration in the Highveld regions of Mpumalanga Province. This study employed species distribution modelling to predict the distributional ranges of these taxa, and involved four main processes: (i) creating initial models trained on sparse museum data records; (ii) ground-truthing field surveys during austral spring/summer to gather additional specimens at additional localities; (iii) genetic analyses (using cytochrome-b) to determine the species identities of the newly-acquired specimens, as these taxa are morphologically indistinguishable; and (iv) refining the models and determining the conservation status of these Highveld golden moles. Initial species distribution models were developed using occurrence records for 38 specimens, based on interpolated data for 19 bioclimatic variables, continuous altitude data, as well as categorical spatial data for landtypes, WWF ecoregions and vegetation types. These initial models helped to effectively focus survey efforts within a vast study area, with surveying during the austral spring-summer of 2013-4 resulting in the acquisition of 25 specimens from across Mpumalanga, nine individuals of which (A. h. meesteri n = 2; A. septentrionalis n = 5; unknown n = 2) were captured in five new quarter-degree-squares (QDSs) where no previous golden moles have been recorded. Additionally, observed activity was also recorded in nine new QDSs (see Appendix 3), showing that the model refinement methods used (variable selection, auto-correlation, non-repeated versus cross-validated models, jackknife of variable importance and localities, independent data testing) were effective in locating golden mole populations. By using genetically-identified historical golden mole records, predictive distribution models were calibrated in maximum entropy (MaxEnt) software to focus ground-truthing efforts.en_ZA
dc.identifier.apacitationRampartab, C. (2016). <i>Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Biological Sciences. Retrieved from http://hdl.handle.net/11427/20967en_ZA
dc.identifier.chicagocitationRampartab, Chanel. <i>"Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Biological Sciences, 2016. http://hdl.handle.net/11427/20967en_ZA
dc.identifier.citationRampartab, C. 2016. Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Rampartab, Chanel AB - Golden moles are subterranean mammals endemic to sub-Saharan Africa and threatened by anthropogenic habitat loss. At present, little is known about the biology, taxonomy, distribution and severity of threats faced by many of these taxa. In an attempt to raise awareness of these elusive grassland flagship taxa, the Endangered Wildlife Trust's Threatened Grassland Species Programme (EWT-TGSP) identified the need for more information on the distributions and conservation status of four poorly-known golden mole taxa (Amblysomus hottentotus longiceps, A. h. meesteri, A. robustus, A. septentrionalis) that are endemic to the Grassland Biome, and which may be heavily impacted by anthropogenic habitat alteration in the Highveld regions of Mpumalanga Province. This study employed species distribution modelling to predict the distributional ranges of these taxa, and involved four main processes: (i) creating initial models trained on sparse museum data records; (ii) ground-truthing field surveys during austral spring/summer to gather additional specimens at additional localities; (iii) genetic analyses (using cytochrome-b) to determine the species identities of the newly-acquired specimens, as these taxa are morphologically indistinguishable; and (iv) refining the models and determining the conservation status of these Highveld golden moles. Initial species distribution models were developed using occurrence records for 38 specimens, based on interpolated data for 19 bioclimatic variables, continuous altitude data, as well as categorical spatial data for landtypes, WWF ecoregions and vegetation types. These initial models helped to effectively focus survey efforts within a vast study area, with surveying during the austral spring-summer of 2013-4 resulting in the acquisition of 25 specimens from across Mpumalanga, nine individuals of which (A. h. meesteri n = 2; A. septentrionalis n = 5; unknown n = 2) were captured in five new quarter-degree-squares (QDSs) where no previous golden moles have been recorded. Additionally, observed activity was also recorded in nine new QDSs (see Appendix 3), showing that the model refinement methods used (variable selection, auto-correlation, non-repeated versus cross-validated models, jackknife of variable importance and localities, independent data testing) were effective in locating golden mole populations. By using genetically-identified historical golden mole records, predictive distribution models were calibrated in maximum entropy (MaxEnt) software to focus ground-truthing efforts. DA - 2016 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2016 T1 - Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach TI - Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach UR - http://hdl.handle.net/11427/20967 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20967
dc.identifier.vancouvercitationRampartab C. Facilitating golden mole conservation in South African highland grasslands : a predictive modelling approach. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Biological Sciences, 2016 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20967en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Biological Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherConservation Biologyen_ZA
dc.titleFacilitating golden mole conservation in South African highland grasslands : a predictive modelling approachen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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