B-E.00353.Pdf

© University of Hamburg 2018 All rights reserved Klaus Hess Publishers Göttingen & Windhoek <a href="/goto?url=http://www.k-hess-verlag.de" target="_blank">www.k-hess-verlag.de </a>ISBN: 978-3-933117-95-3 (Germany), 978-99916-57-43-1 (Namibia) Language editing: Will Simonson (Cambridge), and Proofreading Pal Translation of abstracts to Portuguese: Ana Filipa Guerra Silva Gomes da Piedade Page desing & layout: Marit Arnold, Klaus A. Hess, Ria Henning-Lohmann Cover photographs: front: Thunderstorm approaching a village on the Angolan Central Plateau (Rasmus Revermann) back: Fire in the miombo woodlands, Zambia (David Parduhn) Cover Design: Ria Henning-Lohmann ISSN 1613-9801 Printed in Germany Suggestion for citations: Volume: Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N. (eds.) (2018) Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions. Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek. Articles (example): Archer, E., Engelbrecht, F., Hänsler, A., Landman, W., Tadross, M. & Helmschrot, J. (2018) Seasonal prediction and regional climate projections for southern Africa. In: Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N.), pp. 14–21, Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek. Corrections brought to our attention will be published at the following location: <a href="/goto?url=http://www.biodiversity-plants.de/biodivers_ecol/biodivers_ecol.php" target="_blank">http://www.biodiversity-plants.de/biodivers_ecol/biodivers_ecol.php </a>Biodiversity & Ecology Journal of the Division Biodiversity, Evolution and Ecology of Plants, Institute for Plant Science and Microbiology, University of Hamburg Volume 6: Climate change and adaptive land management in southern Africa Assessments, changes, challenges, and solutions Edited by Rasmus Revermann1, Kristin M. Krewenka1, Ute Schmiedel1, Jane M. Olwoch2, Jörg Helmschrot2,3, Norbert Jürgens1 1 Institute for Plant Science and Microbiology, University of Hamburg 2 Southern African Science Service Centre for Climate Change and Adaptive Land Management 3 Department of Soil Science, Faculty of AgriSciences, Stellenbosch University Hamburg 2018 RPlease cite the article as follows: Tshireletso, K., Makhabu, S.W., Nsinamwa, M., Kgosiesele, E., Setlalekgomo, M.R., Seletlo, Z., Majaga, S. &Legwatagwata, B. (2018) Diversity patterns of woody vegetation of Kgatleng District, Botswana. In: Climate change and adaptive land management in southern Africa – assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & Jürgens, N.), pp. 416-423, Biodiversity & Ecology, 6, Klaus Hess Publishers, Göttingen & Windhoek. doi:10.7809/b-e.00353 Diversity patterns of woody vegetation of Kgatleng District, Botswana Koketso Tshireletso1*, Shimane W. Makhabu2, Mackenzie Nsinamwa1, Ednah Kgosiesele3, Mpho R. Setlalekgomo2, Zibanani Seletlo1, Sipho Majaga1, and Boipuso Legwatagwata2. 1 Department of Animal Science and Production, Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana 2 Department of Basic Sciences, Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana 3 Department of Agricultural Engineering and Land Use Planning, Botswana University of Agriculture and Natural Resources, Private Bag 0027, Gaborone, Botswana * Corresponding author: [email protected] Abstract: Vegetation assessments have intensified around the globe to document plant diversity in the wake of climatic shifts and local environmental changes. Botswana also recognises the value of documenting plant diversity, and in this study we present the results of a vegetation survey of Kgatleng District. Between November 2013 and December 2016 we assessed tree species abundance on 77 plots sized 20 m × 50 m. Vegetation classification resulted in the identification of five woody plant communities: Terminalia sericea–Maytenus tenuispina, Dichrostachys cinerea–Combretum apiculatum, Combretum zeyheri–Vachellia tortilis, Senegalia erubescens–Senegalia mellifera, and Grewia flava–Rhigozium brevispinosum. Oneway analysis of variance indicated that species richness was not different among the five plant communities, nor was tree density. We furthermore provide a vegetation map of the district based on supervised classification of Landsat 8 imagery. Resumo: Estudos de vegetação intensificaram em todo o mundo, de modo a documentar a diversidade de plantas na sequência das alterações climáticas e de mudanças ambientais locais. O Botswana também reconhece o valor de documentar a diversidade de plantas e, neste estudo, apresentamos os resultados do levantamento da vegetação do Districto de Kgatleng. Entre Novembro de 2013 e Dezembro de 2016, avaliámos a abundância de espécies de árvores em 77 parcelas de 20 m × 50 m. A classificação da vegetação resultou na identificação de cinco comunidades de plantas lenhosas: T e rminalia sericea–Maytenus tenuispina, Dichrostachys cinerea–Combretum apiculatum, Combretum zeyheri– V a chellia tortilis, Senegalia erubescens–Senegalia mellifera, e Grewia fl ava–Rhigozium brevispinosum. A análise da variância a um factor indicou que nem a riqueza específica, nem a densidade de árvores, eram diferentes entre as cinco comunidades de plantas. Fornecemos ainda um mapa da vegetação do districto baseado na classificação supervisionada de imagens de Landsat 8. Introduction species composition (Moore & Attwell, tally friendly and sustainable methods 1999). of farming observing correct rangeland Bush encroachment threatens the eco- stocking rates have been advocated for in Botswana, like most southern African countries, comprises expansive areas of logical integrity of savanna ecosystems Botswana (Kgosikoma et al., 2012). savanna woodlands. Kgatleng District and is of concern in southernAfrica (Trol- Species such as Dichrostachys cinerea lies within the woodland and thorn bush lope, 1980; Skarpe 1990; Ward, 2005; can affect the development of other savanna ecosystems. ese ecosystems Britz & Ward, 2007) and Botswana (Rho- woody vegetation (Mudzengi et al., provide browse for domestic and wild de et al., 2006), as it reduces diversity of 2013), thus reducing species diversity animals, habitat cover for wild animals, vegetation. A number of causal drivers to and richness. A study by Mudzengi et al. wood products, and traditional medi- changes in vegetation composition have (2013) observed significant differences cines, among other uses. e diversity of been proposed, ranging from low rainfall between invaded and uninvaded sites ecosystems is integral to rural livelihoods, to support grass growth, overgrazing, re- in abundance, density, and richness of as they use veld products to sustain them- duced fire frequency favouring woody woody species. selves. As such, effective management of plant establishment (Kgosikoma et al., Environmental variables such as soil savannas requires proper knowledge and 2012) and climatic shifts (O’Connor and topography influence the occurdocumentation of vegetation types and et al., 2014). To that end, environmen- rence of plant species and as such govern <ul style="display: flex;"><li style="flex:1">416 </li><li style="flex:1">Cꢀꢁꢂꢃꢄꢅ ꢆꢇꢃꢈꢉꢅ ꢃꢈꢊ ꢃꢊꢃꢋꢄꢁꢌꢅ ꢀꢃꢈꢊ ꢂꢃꢈꢃꢉꢅꢂꢅꢈꢄ ꢁꢈ ꢍꢎꢏꢄꢇꢅꢐꢈ Aꢑꢐꢁꢆꢃ </li></ul>Figure 1: Location map for Kgatleng District, Botswana. Map on the bottom right shows the location of Kgatleng District in Botswana. <ul style="display: flex;"><li style="flex:1">species composition and the diversity of </li><li style="flex:1">Kgatleng District, with a land area of the results of the phytosociological clas- </li></ul>the vegetation (Miyamoto et al., 2003). 7960 km2, is relatively small and is un- sification, and (4) to determine the speA study by Dahlberg (2000) in north- der immense pressure from different cies richness of the tree communities. eastern Botswana observed that woody land uses. Like most communal parts of species richness was higher on red soil Botswana, the district is exposed to both than on white soil, but species richness arable farming and open-access com- Methods did not differ between ranching and communal livestock grazing. For sustainable munal grazing land. The study also found management of vegetation resources, Study area significantly (P < 0.01) more shrubs on documentation of plant communities and The study was conducted between Nocommunal land than on ranch land, prob- species richness in these areas is of piv- vember 2013 and December 2016. The ably because of less competition with otal importance. Meanwhile, literature is study area covered the Kgatleng District the herbaceous plants, resulting in shrub lacking as far as plant diversity and rich- of Botswana, located between latitude seedling establishment. In Dahlberg’s ness in these areas are concerned. Thus, 23.88°S–24.51°S and longitude 25.89°E– (2000) study conducted in the north- defining plant communities in these 26.82°E (Fig. 1). The district covers an east district of Botswana, Senegalia ni- systems in an era of climate-change ef- area of 7,960 km2 and is characterized by grescens, Combretum apiculatum, and fects on vegetation is critical to serve as vegetation dominated by V a chellia spp., Grewia spp. were observed to prefer red reference points for vegetation changes. Senegalia spp., and Grewia spp., among soil whereas Colophospermum mopane Therefore, the objectives of the study others. Annual rainfall for the district preferred white soil. Other environmen- were to (1) determine the abundance of ranges between 450 mm and 550 mm. tal variables such as elevation have also woody (trees and shrubs) species, (2) to The temperatures range between 6°C and been noted to affect species and commu- identify tree communities and their eco- 20°C in winter and 22°C and 30°C in sumnity diversity of vascular plants (Khan et logical drivers, (3) to produce a vegeta- mer (Bhalotra, 1987). Soils are predomi- <ul style="display: flex;"><li style="flex:1">al., 2011). </li><li style="flex:1">tion map based on Landsat imagery and nantly arenosols and luvisols (Moganane, </li></ul><ul style="display: flex;"><li style="flex:1">Bꢁꢎꢊꢁꢌꢅꢐꢍꢁꢄꢒ ꢓ Eꢆꢎꢀꢎꢉꢒ 6 2018 </li><li style="flex:1">417 </li></ul>Table 1. Abundance (individuals/ha) and indicator species for the woody plant communities identifed in Kgatleng District, Botswana. ꢁďƵŶĚĂŶĐĞꢀцꢀ^ꢂꢀ /ŶĚŝĐĂƚŽƌꢀ ;ŝŶĚŝǀŝĚƵĂůƐͬŚĂͿꢀ sĂůƵĞꢀ;/sͿꢀ <ul style="display: flex;"><li style="flex:1">&ĂŵŝůǇꢀ </li><li style="flex:1">^ƉĞĐŝĞƐꢀ </li><li style="flex:1">DĞĂŶꢀцꢀ^ꢃꢀ </li><li style="flex:1">ƉꢀΎꢀ </li></ul>hŶŝƚꢀϭꢀdĞƌŵŝŶĂůŝĂꢀƐĞƌŝĐĞĂʹDĂǇƚĞŶƵƐꢀƚĞŶƵŝƐƉŝŶĂꢀĐŽŵŵƵŶŝƚǇꢀ dĞƌŵŝŶĂůŝĂꢀƐĞƌŝĐĞĂꢀ ϵϲ͘ϭꢀцꢀϮϴ͘ϭϰꢀ ϯϰ͘ϲꢀцꢀϮϬ͘Ϭϭꢀ ϮϬ͘Ϭꢀцꢀϴ͘Ϭϭꢀ ϭϮ͘ϴꢀцꢀϳ͘ϰϰꢀ ϳϬ͘Ϯꢀ ϮϬ͘Ϯꢀ ϭϴ͘ϲꢀ ϳ͘ϰꢀ ϭϳ͘ϭꢀцꢀϰ͘Ϯϱꢀ Ϭ͘ϬϬϬϮꢀ ϭϭ͘ϳꢀцꢀϱ͘Ϯϲꢀ Ϭ͘ϬϳϯϮꢀ ϵ͘ϯꢀцꢀϰ͘ϳϯꢀ Ϭ͘ϬϱϬϲꢀ ϭϬ͘ϲꢀцꢀϱ͘ϭϮꢀ Ϭ͘ϳϬϱϭꢀ &RPEUHWDFHDHꢀ &HODVWUDFHDHꢀ &DHVDOSLQLDFHDHꢀ &HODVWUDFHDHꢀ DĂǇƚĞŶƵƐꢀƚĞŶƵŝƐƉŝŶĂꢀ ꢁĂƵŚŝŶŝĂꢀƉĞƚĞƌƐŝĂŶĂꢀ DĂǇƚĞŶƵƐƐĞŶĞŐĂůĞŶƐŝƐꢀ <ul style="display: flex;"><li style="flex:1">ꢀ</li><li style="flex:1">ꢀ</li></ul>hŶŝƚꢀϮꢀꢁŝĐŚƌŽƐƚĂĐŚǇƐꢀĐŝŶĞƌĞĂʹꢂŽŵďƌĞƚƵŵꢀĂƉŝĐƵůĂƚƵŵꢀĐŽŵŵƵŶŝƚǇꢀ ꢂŝĐŚƌŽƐƚĂĐŚǇƐꢀĐŝŶĞƌĞĂꢀ ꢃŽŵďƌĞƚƵŵꢀĂƉŝĐƵůĂƚƵŵ 'ƌĞǁŝĂꢀďŝĐŽůŽƌꢀ ^ĞŶĞŐĂůŝĂꢀŶŝŐƌĞƐĐĞŶƐꢀ ^ĐůĞƌŽĐĂƌǇꢀĂďŝƌƌĞĂꢀ ꢁŽƐĐŝĂĨŽĞƚŝĚĂꢀ ꢃƌŽƚŽŶꢀŐƌĂƚŝƐƐŝŵƵƐꢀ ꢃŽŵďƌĞƚƵŵŝŵďĞƌďĞꢀ ^ƉŝƌŽƐƚĂĐŚǇƐĂĨƌŝĐĂŶĂꢀ sĂŶŐƵĞƌŝĂŝŶĨĂƵƐƚĂꢀ yŝŵĞŶŝĂĂŵĞƌŝĐĂŶĂꢀ ϭϰϳ͘ϰꢀцꢀϮϮ͘ϵϱꢀ ϯϱ͘ϱꢀцꢀϴ͘ϭϭꢀ ϯϯ͘ϴꢀцꢀϲ͘ϵϲꢀ Ϯ͘Ϭꢀцꢀϭ͘Ϭϯꢀ Ϭ͘ϳꢀцꢀϬ͘ϯϱꢀ Ϭ͘ϯꢀцꢀϬ͘ϮϮꢀ ϴ͘ϱꢀцꢀϲ͘ϭϬꢀ Ϭ͘ϮꢀцꢀϬ͘Ϯϱꢀ Ϭ͘ϮꢀцꢀϬ͘Ϯϱꢀ Ϭ͘ϭꢀцꢀϬ͘ϭϮꢀ Ϭ͘ϱꢀцꢀϬ͘ϱϭꢀ ϱϳ͘ϭꢀ ϱϰ͘ϯꢀ ϱϭ͘ϵꢀ Ϯϴꢀ ϮϬꢀ ϭϮꢀ ϲ͘ϵꢀ ϰꢀ ϰꢀ ϰꢀ ϰꢀ ꢀϮϯ͘ϭꢀцꢀϯ͘ϵϰꢀ Ϭ͘ϬϬϬϮꢀ ϭϰ͘ϳꢀцꢀϰ͘ϲϲꢀ Ϭ͘ϬϬϬϮꢀ ϭϴ͘ϱꢀцꢀϱ͘ϱϮꢀ Ϭ͘ϬϬϬϲꢀ ϵ͘ϯꢀцꢀϱ͘Ϭϭꢀ Ϭ͘ϬϬϱϲꢀ ϳ͘ϲꢀцꢀϰ͘ϯϭꢀ Ϭ͘ϬϮϬϰꢀ 0LPRVDFHDHꢀ &RPEUHWDFHDHꢀ 7LOLDFHDHꢀ 0LPRVDFHDHꢀ $QDFDUGLDFHDHꢀ &DSSDUDFHDHꢀ (XSKRUELDFHDHꢀ &RPEUHWDFHDHꢀ (XSKRUELDFHDHꢀ 5XELDFHDHꢀ <ul style="display: flex;"><li style="flex:1">ϲ͘ϳꢀцꢀϰ͘ϬϮꢀ </li><li style="flex:1">Ϭ͘ϭϲϲꢀ </li></ul>ϴ͘ϵꢀцꢀϰ͘ϲϲꢀ Ϭ͘ϲϭϱϵꢀ ϲ͘ϱꢀцꢀϭ͘ϴϭꢀ ϲ͘ϱꢀцꢀϭ͘ϴϬꢀ ϲ͘ϱꢀцꢀϭ͘ϴϬꢀ ϭꢀ ϭꢀ ϭꢀ <ul style="display: flex;"><li style="flex:1">ϲ͘ϱꢀцꢀϭ͘ϴϭꢀ </li><li style="flex:1">ϭꢀ </li></ul>ꢀ2ODFDFHDHꢀ ꢀhŶŝƚꢀϯꢀꢂŽŵďƌĞƚƵŵꢀǌĞǇŚĞƌŝʹsĂĐŚĞůůŝĂꢀƚŽƌƚŝůŝƐꢀĐŽŵŵƵŶŝƚǇꢀ ꢃŽŵďƌĞƚƵŵꢀǌĞǇŚĞƌŝꢀ sĂĐŚĞůůŝĂꢀƚŽƌƚŝůŝƐꢀ 'ƌĞǁŝĂꢀƌĞƚŝŶĞƌǀŝƐꢀ ZŚƵƐꢀƚĞŶƵŝŶĞƌǀŝƐꢀ WĞůƚŽƉŚŽƌƵŵꢀ ĂĨƌŝĐĂŶƵŵꢀ ϱ͘ϯꢀцꢀϮ͘ϲϰꢀ ϰϬ͘ϲꢀцꢀϭϮ͘Ϯϱꢀ Ϯϴ͘ϵꢀцꢀϲ͘ϯϵꢀ Ϯ͘ϴꢀцꢀϭ͘Ϭϵꢀ Ϯϵ͘ϰꢀ Ϯϵꢀ Ϯϴ͘ϱꢀ ϭϵ͘ϱꢀ ϴ͘ϴꢀцꢀϰ͘ϳϰꢀ Ϭ͘ϬϬϮϴꢀ ϭϴ͘ϭꢀцꢀϱ͘ϯϯꢀ Ϭ͘Ϭϰϭϴꢀ ϭϳ͘ϰꢀцꢀϱ͘ϲϯꢀ Ϭ͘ϬϱϬϲꢀ ϭϬ͘ϯꢀцꢀϱ͘Ϭϱꢀ Ϭ͘ϬϱϯϮꢀ &RPEUHWDFHDHꢀ 0LPRVDFHDHꢀ 7LOLDFHDHꢀ ꢁŶĂĐĂƌĚŝĂĐĞĂĞꢀ <ul style="display: flex;"><li style="flex:1">ϭ͘ϵꢀцꢀϬ͘ϳϲꢀ </li><li style="flex:1">ϭϳ͘ϳꢀ </li><li style="flex:1">ϵ͘ϵꢀцꢀϰ͘ϵϴꢀ Ϭ͘ϬϳϰϮꢀ </li></ul>&DHVDOSLQLDFHDHꢀ KĐŚŶĂꢀƉƵůĐŚƌĂꢀ ϮϬ͘ϳꢀцꢀϭϭ͘ϰϬꢀ ϭϲ͘ϮꢀцꢀϭϬ͘ϰϮꢀ Ϭ͘ϮꢀцꢀϬ͘Ϯϱꢀ ϯ͘ϭꢀцꢀϭ͘ϳϱꢀ ϲ͘ϰꢀцꢀϲ͘Ϯϯꢀ ϭϮ͘ϰꢀ ϭϬ͘ϴꢀ ϴ͘ϯꢀ ϴ͘ϭꢀ ϱ͘ϴꢀ ꢀϭϮ͘ϵꢀцꢀϲ͘ϭϵꢀ Ϭ͘ϰϯϭϭꢀ ϴ͘Ϭꢀцꢀϰ͘ϯϲꢀ Ϭ͘Ϯϰϳϴꢀ ϲ͘ϱꢀцꢀϭ͘ϴϭꢀ Ϭ͘ϯϭϰϵꢀ ϴ͘ϳꢀцꢀϰ͘ϱϯꢀ Ϭ͘ϰϯϰϱꢀ 2FKQDFHDHꢀ (EHQDFHDHꢀ &DHVDOSLQLDFHDHꢀ 0LPRVDFHDHꢀ 0LPRVDFHDHꢀ ꢂŝŽƐƉǇƌŽƐꢀůǇĐŝŽŝĚĞƐꢀ ꢁƵƌŬĞĂꢀĂĨƌŝĐĂŶĂꢀ sĂĐŚĞůůŝĂꢀĞƌŝŽůŽďĂꢀ sĂĐŚĞůůŝĂꢀŬĂƌƌŽŽꢀ ϲ͘ϱꢀцꢀϯ͘ϰϰꢀ Ϭ͘ϰϮϭϵꢀ ꢀhŶŝƚꢀϰꢀ^ĞŶĞŐĂůŝĂꢀĞƌƵďĞƐĐĞŶƐʹ^ĞŶĞŐĂůŝĂꢀŵĞůůŝĨĞƌĂꢀĐŽŵŵƵŶŝƚǇꢀ ^ĞŶĞŐĂůŝĂꢀĞƌƵďĞƐĐĞŶƐꢀ ^ĞŶĞŐĂůŝĂꢀŵĞůůŝĨĞƌĂꢀ ꢁŽƐĐŝĂꢀĂůďŝƚƌƵŶĐĂꢀ ꢃŽŵďƌĞƚƵŵꢀŚĞƌĞƌŽĞŶƐĞꢀ ꢄƵĐůĞĂꢀƵŶĚƵůĂƚĂꢀ ϯϲ͘ϭꢀцꢀϵ͘ϰϮꢀ ϯϭ͘ϰꢀцꢀϭϮ͘ϭϳꢀ ϭϬ͘ϭꢀцꢀϮ͘Ϭϭꢀ Ϯ͘ϮꢀцꢀϬ͘ϴϯꢀ ϭϴ͘ϵꢀцꢀϱ͘ϳϴꢀ Ϯ͘ϰꢀцꢀϭ͘ϭϱꢀ Ϭ͘ϯꢀцꢀϬ͘Ϯϴꢀ <ul style="display: flex;"><li style="flex:1">ϱϱꢀ </li><li style="flex:1">ϭϳ͘ϵꢀцꢀϱ͘ϬϮꢀ Ϭ͘ϬϬϬϮꢀ </li></ul>ϭϯ͘ϭꢀцꢀϰ͘ϳϰꢀ Ϭ͘ϬϬϭϰꢀ ϭϳ͘ϭꢀцꢀϰ͘ϲϵꢀ Ϭ͘ϯϭϭϳꢀ 0LPRVDFHDHꢀ 0LPRVDFHDHꢀ &DSSDUDFHDHꢀ &RPEUHWDFHDHꢀ (EHQDFHDHꢀ 5KDPQDFHDHꢀ 0LPRVDFHDHꢀ ϯϳ͘ϭꢀ ϭϴ͘ϱꢀ ϭϮ͘ϰꢀ ϭϬ͘ϴꢀ ϴ͘ϭꢀ <ul style="display: flex;"><li style="flex:1">ϭϬ͘Ϯꢀцꢀϰ͘ϴϬꢀ </li><li style="flex:1">Ϭ͘Ϯϰϳꢀ </li></ul>ϭϯ͘ϵꢀцꢀϱ͘ϰϭꢀ Ϭ͘ϲϴϰϳꢀ ϵ͘Ϭꢀцꢀϰ͘ϰϵꢀ Ϭ͘ϰϲϬϵꢀ ϳ͘ϰꢀцꢀϯ͘Ϯϱꢀ Ϭ͘ϱϵϵϵꢀ ŝǌŝƉŚƵƐꢀŵƵĐƌŽŶĂƚĂꢀ sĂĐŚĞůůŝĂꢀŶŝůŽƚŝĐĂꢀ ϲ͘ϵꢀ <ul style="display: flex;"><li style="flex:1">ꢀ</li><li style="flex:1">ꢀ</li><li style="flex:1">ꢀ</li></ul>hŶŝƚꢀϱꢀ'ƌĞǁŝĂꢀĨůĂǀĂʹZŚŝŐŽǌƵŵꢀďƌĞǀŝƐƉŝŶŽƐƵŵꢀĐŽŵŵƵŶŝƚǇꢀ 'ƌĞǁŝĂꢀĨůĂǀĂꢀ ZŚŝŐŽǌƵŵꢀ <ul style="display: flex;"><li style="flex:1">ϭϬϯ͘ϳꢀцꢀϭϰ͘ϱϰꢀ </li><li style="flex:1">ϱϲ͘Ϯꢀ </li></ul>ϯϯ͘ϱꢀ Ϯϯ͘Ϭꢀцꢀϯ͘ϵϮꢀ Ϭ͘ϬϬϬϮꢀ ϭϰ͘Ϯꢀцꢀϱ͘ϳϮꢀ Ϭ͘Ϭϭꢀ 7LOLDFHDHꢀ Ϯϰ͘ϴꢀцꢀϴ͘ϴϳꢀ %LJQRQLDFHDHꢀ ďƌĞǀŝƐƉŝŶŽƐƵŵꢀ ꢃŽŵŵŝƉŚŽƌĂꢀĂĨƌŝĐĂŶĂꢀ 'ƌĞǁŝĂꢀĨůĂǀĞƐĐĞŶƐꢀ ^ĞŶĞŐĂůŝĂꢀĨůĞĐŬŝŝꢀ DĂǇƚĞŶƵƐꢀďƵǆŝĨŽůŝĂꢀ yŝŵĞŶŝĂꢀĐĂĨĨƌĂꢀ ϰϯ͘Ϯꢀцꢀϴ͘ϴϲꢀ ϳ͘ϲꢀцꢀϯ͘Ϭϵꢀ ϴ͘ϭꢀцꢀϯ͘ϵϲꢀ ϭ͘ϱꢀцꢀϭ͘ϭϱꢀ Ϭ͘ϮꢀцꢀϬ͘ϭϴꢀ ϯϯ͘ϯꢀ Ϯϰ͘ϱꢀ ϭϲ͘ϱꢀ ϲ͘ϱꢀ Ϯϭ͘ϵꢀцꢀϱ͘ϵϮꢀ Ϭ͘Ϭϰϴϲꢀ ϭϮ͘ϵꢀцꢀϱ͘ϴϳꢀ Ϭ͘ϬϱϬϰꢀ ϭϬ͘ϯꢀцꢀϱ͘ϮϬꢀ Ϭ͘ϭϮϱϮꢀ ϲ͘ϲꢀцꢀϯ͘ϯϴꢀ Ϭ͘ϰϲϱϳꢀ ϲ͘Ϯꢀцꢀϯ͘Ϯϲꢀ Ϭ͘ϲϴϲϯꢀ %XUVHUDFHDHꢀ 7LOLDFHDHꢀ 0LPRVDFHDHꢀ &HODVWUDFHDHꢀ 2ODFDFHDHꢀ ϰ͘ϳꢀ <ul style="display: flex;"><li style="flex:1">418 </li><li style="flex:1">Cꢀꢁꢂꢃꢄꢅ ꢆꢇꢃꢈꢉꢅ ꢃꢈꢊ ꢃꢊꢃꢋꢄꢁꢌꢅ ꢀꢃꢈꢊ ꢂꢃꢈꢃꢉꢅꢂꢅꢈꢄ ꢁꢈ ꢍꢎꢏꢄꢇꢅꢐꢈ Aꢑꢐꢁꢆꢃ </li></ul>Figure 2: Cluster analysis showing the five plant communities identified in Kgatleng District, Botswana. Terminalia sericea–Maytenus tenuispina (1), Dichrostachys cinerea–Combretum apiculatum (2), Combretum zeyheri–Vachellia tortilis (3), Senegalia erubescens–Senegalia mellifera (4) and Grewia fl ava–Rhigozium brevispinosum (5). 1990). The eastern part of the district falls point along the transects, a 50 m × 20 m sation by maximum. The data were then in the hardveld whereas the north-western quadrat, the standard plot size agreed upon subjected to hierarchical cluster analysis part falls partly in the sandveld ecological for SASSCAL vegetation monitoring and (β linkage, β = –0.25, Sorensen distance) <ul style="display: flex;"><li style="flex:1">zone (Nsinamwa et al., 2005). </li><li style="flex:1">mapping activities, was established. In (McCune et al., 2002) based on 39 species </li></ul>each vegetation plot, all woody (tree and distributed in the 77 plots. Indicator speshrub) species rooted in the rectangle were cies analysis (Dufrene & Legendre, 1997) Vegetation and soil sampling Vegetation sampling involved the use of counted and recorded by species. Fur- was used to define meaningful vegetation transects cutting across different vegeta- thermore, the percent cover of each spe- communities. Indicator values (IVS) were tion types using a geographical position- cies was estimated visually as described assessed for statistical significance using ing system (GPS) receiver. Before field in Bonham (1989). A total of 77 plots the Monte Carlo technique. Sorensen diswork, satellite imagery from Google Earth were sampled (Fig. 1). Abundance data tance measure was used to examine difwas used to identify different vegetation for woody species was then expressed as ferences between vegetation communities types by contrasting colours along sys- individuals/ha. Nomenclature for scien- using a multi-response permutation protematically placed transects running in a tific names follows van Wyk & van Wyk cedure (McCune et al., 2002). Vegetation south-north direction through the entire (1997) and Kyalangalilwa et al. (2013). communities were plotted in ordination length of the district. At each sampling At each sampling plot, soil samples were space using non-metric multidimensional point a survey pin was thrown over the also collected at a depth of 10–15 cm in scaling (NMDS). And the environmental shoulder, and where it landed it formed the middle of each plot. Soil samples were variables (sand particles [%], clay partithe centre point of a vegetation plot. Sam- then analysed for particle size, soil pH, cles [%], silt [%], pH, EC, inclination, and pling points were established along tran- and electrical conductivity (EC) following exposition) were fitted post hoc. All of sects at distances ranging from 5 km to standard laboratory procedures. 10 km apart depending on the differences these statistical analyses were performed in PCORD 6 (McCune et al., 2002). in the vegetation and land use (e.g., fenced Data analysis fields). The homogeneity or heterogeneity Species richness was calculated as the mean number of species occurring in the plots of each community. To test whether species richness differs among communi- of the vegetation determined the spacing Vegetation classification and sta- of the transects, and distances between tistical analyses transects were increased when the vegeta- Cover data (%) of species for all 77 vegeta- ties, the data were analysed with one-way tion was homogenous. At each sampling tion plots were standardised using relativi- analysis of variance using SPSS 16.0. <ul style="display: flex;"><li style="flex:1">Bꢁꢎꢊꢁꢌꢅꢐꢍꢁꢄꢒ ꢓ Eꢆꢎꢀꢎꢉꢒ 6 2018 </li><li style="flex:1">419 </li></ul>sifier. Five vegetation communities were mapped using ENVI 5.1, and the classification results were imported into ArcMap 10.3 for finalization of the map. 106 9DFQLO &RPPXQLWLHV 7HUVHUꢁꢄꢁ0D\WHQ 'LFFLQꢁꢄꢁ&RPDSL &RP]H\ꢁꢄꢁ9HFWRU 6HQHUXꢁꢄꢁ6HQPHO *UHZIODꢁꢄꢁ5KLEUH 6SLDIU Results &RPKHU 9DFHUL Diversity and abundance of woody species Atotalof15families,23genera,and39species were observed in the district (Tab. 1). Fabaceae was the most species-rich family in the study area, and there were more species of the subfamily Mimosaceae than of the subfamily Caesalpiniaceae. The Combretaceae family had 2 genera with a total of 5 species. Only 1 species was observed for each of Bignoniaceae, Burseraceae, Ochnaceae, Rhamnaceae, and Rubiaceae families in the district. 0D\VHQ 6HQPHO =L]PXF *UHIOV (XFXQG <ul style="display: flex;"><li style="flex:1">*UHIOD </li><li style="flex:1">&URJUD </li></ul>%RVIRH 9DFNDU 0D\WHQ 7HUVHU %XUDIU 5KLEUH ꢀ6$1' ꢀ&/$< 5KXWHV ꢀ6,/W (OHFWURQ S+ 2FKSXO %RVDOE &RPDIU 'LFFLQ ([SRVLWL ,QFOLQDW <ul style="display: flex;"><li style="flex:1">9DFWRU </li><li style="flex:1">'LRO\F </li></ul>0D\J\P %DXSHW *UHELF 0D\ER[ 6HQHUX 6HQQLJ *UHUHW 3HODIU The most abundant species in their respective communities were Dichrostachys cinerea with 147 individuals/ha followed by Grewia fl ava (103.7 ± 14.54 indiv./ha) and T e rminalia sericea (96 ± 28.1 indiv./ ha). Overall, abundance of the genera V a chellia and Senegalia was 50.4 and 77.6

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