Predicting the distribution of C3 (Festuca spp.) grass species using topographic variables and binary logistic regression model

References

• Achia TN, Wangombe A, Khadioli N. 2010. A logistic regression model to identify key determinants of poverty using demographic and health survey data. Eur J Soc Sci. 13:38–45.
   Google Scholar
• Adair EC, Burke IC. 2010. Plant phenology and life span influence soil pool dynamics: bromus tectorum invasion of perennial C3–C4 grass communities. Plant Soil. 335:255–269.10.1007/s11104-010-0413-3
   Web of Science ®Google Scholar
• Adelabu S, Dube T. 2014. Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland. Geocarto Int. 30:457–471.
   Web of Science ®Google Scholar
• Adjorlolo C. 2008. Estimating woody vegetation cover in an African Savanna using remote sensing and geostatistics. Pietermaritzburg: University of KwaZulu-Natal.
   Google Scholar
• Adjorlolo C. 2013. Remote sensing of the distribution and quality of subtropical C3 and C4 grasses. Pietermaritzburg: University of KwaZulu-Natal.
   Google Scholar
• Adjorlolo C, Mashiyi N, Mangara P, Mutanga O. 2016. Trends in seasonal composition of plant functional types: retrieving fraction of winter-green grass cover in the Drakensberg Mountains using long-term satellite observations (platform). 51st Annual Congress of the Grassland Society of Southern Africa, George.
   Google Scholar
• Agresti A, Kateri M. 2011. Categorical data analysis. Berlin Heidelberg: Springer.10.1007/978-3-642-04898-2
   Google Scholar
• Anderson RP, Lew D, Peterson AT. 2003. Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Model. 162:211–232.10.1016/S0304-3800(02)00349-6
   Web of Science ®Google Scholar
• Archer ER. 2004. Beyond the “climate versus grazing” impasse: using remote sensing to investigate the effects of grazing system choice on vegetation cover in the eastern Karoo. J Arid Environ. 57:381–408.10.1016/S0140-1963(03)00107-1
   Web of Science ®Google Scholar
• Baldwin RA. 2009. Use of maximum entropy modeling in wildlife research. Entropy. 11:854–866.10.3390/e11040854
   Web of Science ®Google Scholar
• Barbehenn RV, Chen Z, Karowe DN, Spickard A. 2004. C3 grasses have higher nutritional quality than C4 grasses under ambient and elevated atmospheric CO2. Global Change Biol. 10:1565–1575.10.1111/gcb.2004.10.issue-9
   Web of Science ®Google Scholar
• Bates D, Maechler M, Bolker B, Walker S. 2013. lme4: linear mixed-effects models using Eigen and S4. R package version 1.4.
   Google Scholar
• Bond WJ, Parr CL. 2010. Beyond the forest edge: ecology, diversity and conservation of the grassy biomes. Biol Conserv. 143:2395–2404.10.1016/j.biocon.2009.12.012
   Web of Science ®Google Scholar
• Boothway G, Martindale G, Gillings K. 2012. Fort Nottingham nature reserve: combined management plan version 1.0. Pietermaritzburg: Ezemvelo KwaZulu-Natal Wildlife.
   Google Scholar
• Botha JO, Boothway G, Keith M. 2012. An assessment of the rangelands of the Fort Nottingham Townlands. Pietermaritzburg: Department of Agriculture and Environmental Affairs Natural Resources Macro Planning Section.
   Google Scholar
• Boutton TW, Harrison AT, Smith BN. 1980. Distribution of biomass of species differing in photosynthetic pathway along an altitudinal transect in southeastern wyoming grassland. Oecologia. 45:287–298.10.1007/BF00540195
   PubMed Web of Science ®Google Scholar
• Breebaart L, O’Connor T, Hughes J, Morris C, Rushworth I. 2001. The effects of topo-edaphic variables and cultivation on the woody vegetation of Weenen Nature Reserve, KwaZulu-Natal. Afr J Range Forage Sci. 18:75–91.10.2989/10220110109485760
   Google Scholar
• Bremond L, Boom A, Favier C. 2012. Neotropical C3/C4 grass distributions–present, past and future. Global Change Biol. 18:2324–2334.10.1111/j.1365-2486.2012.02690.x
   Web of Science ®Google Scholar
• Burke IC, Reiners WA, Olson RK. 1989. Topographic control of vegetation in a mountain big sagebrush steppe. Vegetatio. 84:77–86.10.1007/BF00036508
   Google Scholar
• Cabrera AF. 1994. Logistic regression analysis in higher education: an applied perspective. In: Todd Engstrom R, editor. Higher education: handbook of theory and research. New York: Agathon Press; vol. 10. p. 225–256.
   Google Scholar
• Carbutt C, Tau M, Stephens A, Escott B. 2011. The conservation status of temperate grasslands in southern Africa. Grassroots. 11:17–23.
   Google Scholar
• Chamaillé-Jammes S, Bond WJ. 2010. Will global change improve grazing quality of grasslands? A call for a deeper understanding of the effects of shifts from C4 to C3 grasses for large herbivores. Oikos. 119:1857–1861.10.1111/more.2010.119.issue-12
   Web of Science ®Google Scholar
• Chazdon RL. 1978. Ecological aspects of the distribution of C4 grasses in selected habitats of Costa Rica. Biotropica. 1:265–269.10.2307/2387678
   Web of Science ®Google Scholar
• Clements GR, Rayan DM, Aziz SA, Kawanishi K, Traeholt C, Magintan D, Yazi MFA, Tingley R. 2012. Predicting the distribution of the Asian tapir in Peninsular Malaysia using maximum entropy modeling. Integr Zool. 7:400–406.10.1111/inz.2012.7.issue-4
   PubMed Web of Science ®Google Scholar
• Colgan M, Asner G, Levick S, Martin R, Chadwick O. 2012. Topo-edaphic controls over woody plant biomass in South African savannas. Biogeosciences. 9:1809–1821.10.5194/bg-9-1809-2012
   Web of Science ®Google Scholar
• Collatz GJ, Berry JA, Clark JS. 1998. Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: present, past, and future. Oecologia. 114:441–454.10.1007/s004420050468
   PubMed Web of Science ®Google Scholar
• Collett D. 1991. Modelling binary data. New York: CRC Press.10.1007/978-1-4899-4475-7
   Google Scholar
• Dai F, Lee C. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island. Hong Kong Geomorphol. 42:213–228.
   Google Scholar
• De’ath G, Fabricius KE. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology. 81:3178–3192.10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
   Web of Science ®Google Scholar
• Díaz S, Cabido M. 1997. Plant functional types and ecosystem function in relation to global change. J Veg Sci. 7:463–474.10.2307/3237198
   Web of Science ®Google Scholar
• Draper N, Smith H. 1981. Applied regression analysis. New York, NY: Wiley; 709pp.
   Google Scholar
• Edwards D. 1983. A broad-scale vegetation classification of vegetation for practical purposes. Bothalia. 14:705–712.
   Google Scholar
• Ehleringer JR. 2005. The influence of atmospheric CO2, temperature, and water on the abundance of C3/C4 taxa. In: A history of atmospheric CO2 and its effects on plants, animals, and ecosystems. New York: Springer; p. 214–231.
   Google Scholar
• Essery R, Pomeroy J. 2004. Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations for an Arctic Tundra basin. J Hydrometeorol. 5:735–744.10.1175/1525-7541(2004)005<0735:VATCOW>2.0.CO;2
   Web of Science ®Google Scholar
• Fagan WF, Lewis MA, Neubert MG, van den Driessche P. 2002. Invasion theory and biological control. Ecol Lett. 5:148–157.10.1046/j.1461-0248.2002.0_285.x
   Web of Science ®Google Scholar
• Fielding AH, Bell JF. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 24:38–49.10.1017/S0376892997000088
   Web of Science ®Google Scholar
• Foley WJ, McIlwee A, Lawler I, Aragones L, Woolnough AP, Berding N. 1998. Ecological applications of near infrared reflectance spectroscopy-a tool for rapid, cost-effective prediction of the composition of plant and animal tissues and aspects of animal performance. Oecologia. 116:293–305.10.1007/s004420050591
   PubMed Web of Science ®Google Scholar
• Foody GM, Dash J. 2007. Discriminating and mapping the C3 and C4 composition of grasslands in the northern Great Plains. Ecol Inf. 2:89–93.10.1016/j.ecoinf.2007.03.009
   Web of Science ®Google Scholar
• Frank TD. 1988. Mapping dominant vegetation communities in the Colorado Rocky mountain front range with Landsat Thematic Mapper and digital terrain data. Photogramm Eng Remote Sens. 54:172–173.
   Web of Science ®Google Scholar
• Fu B, Liu S, Ma K, Zhu Y. 2004. Relationships between soil characteristics, topography and plant diversity in a heterogeneous deciduous broad-leaved forest near Beijing. Plant Soil. 261:47–54.10.1023/B:PLSO.0000035567.97093.48
   Web of Science ®Google Scholar
• Groom R, Harris S. 2010. Factors affecting the distribution patterns of zebra and wildebeest in a resource-stressed environment. Afr J Ecol. 48:159–168.10.1111/aje.2010.48.issue-1
   Web of Science ®Google Scholar
• Gu W, Swihart RK. 2004. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol Conserv. 116:195–203.10.1016/S0006-3207(03)00190-3
   Web of Science ®Google Scholar
• Guimarães RJ, Freitas CC, Dutra LV, Felgueiras CA, Moura AC, Amaral RS, Drummond SC, Scholte RG, Oliveira G, Carvalho OS. 2009. Spatial distribution of biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures. Acta Trop. 109:181–186.10.1016/j.actatropica.2008.10.012
   PubMed Web of Science ®Google Scholar
• Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecol Lett. 8:993–1009.10.1111/ele.2005.8.issue-9
   PubMed Web of Science ®Google Scholar
• Guisan A, Weiss SB, Weiss AD. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 143:107–122.10.1023/A:1009841519580
   Web of Science ®Google Scholar
• Helsel DR, Hirsch RM. 2002. Statistical methods in water resources. Reston, VA: US Geological Survey.
   Google Scholar
• Hirzel A, Guisan A. 2002. Which is the optimal sampling strategy for habitat suitability modelling. Ecol Model. 157:331–341.10.1016/S0304-3800(02)00203-X
   Web of Science ®Google Scholar
• Holmgren CA, Norris J, Betancourt JL. 2007. Inferences about winter temperatures and summer rains from the late Quaternary record of C4 perennial grasses and C3 desert shrubs in the northern Chihuahuan Desert. J Quat Sci. 22:141–161.10.1002/(ISSN)1099-1417
   Web of Science ®Google Scholar
• Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. 1997. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 16:965–980.10.1002/(ISSN)1097-0258
   PubMed Web of Science ®Google Scholar
• Hosmer D, Lemeshow S. 1989. Applied logistic regression: Wiley series in probability and mathematical statistics. Amherst, MA: Wiley.
   Google Scholar
• Hosmer D, Lemeshow S. 2000. Applied logistic regression, Wileys series in probability and statistics. New York, NY: Wiley.10.1002/0471722146
   Google Scholar
• Jafari M, Chahouki MZ, Tavili A, Azarnivand H, Amiri GZ. 2004. Effective environmental factors in the distribution of vegetation types in Poshtkouh rangelands of Yazd Province (Iran). J Arid Environ. 56:627–641.10.1016/S0140-1963(03)00077-6
   Web of Science ®Google Scholar
• Jaggard E. 2012. Soil Carbon dynamics following switchgrass establishment for bioenergy production in Southeastern Ontario. Kingston: Queen’s University.
   Google Scholar
• James GM. 2002. Generalized linear models with functional predictors. J R Stat Soc Series B Stat Methodol. 64:411–432.10.1111/rssb.2002.64.issue-3
   Web of Science ®Google Scholar
• Kerr JT, Ostrovsky M. 2003. From space to species: ecological applications for remote sensing. Trends Ecol Evol. 18:299–305.10.1016/S0169-5347(03)00071-5
   Web of Science ®Google Scholar
• Killick DJB. 1990. A field guide to the flora of the Natal Drakensberg. Johannesburg: Jonathan Ball and Ad Donker; p. 200, -illus., col. illus., map. ISBN 947464352.
   Google Scholar
• King G. 2002. An explanation of the 1: 500 000 General Hydrogeological Map: Durban 2928. Durban: Directorate Geohydrology, Department of Water Affairs and Forestry.
   Google Scholar
• Kleinbaum DG, Klein M. 2010. Introduction to logistic regression. In: Todd Engstrom R, editor. Logistic regression. New York: Springer; p. 437–486.10.1007/978-1-4419-1742-3
   Google Scholar
• Kotze DC, O’connor TG. 2000. Vegetation variation within and among palustrine wetlands along an altitudinal gradient in KwaZulu-Natal. Plant Ecol. 146:77–96.10.1023/A:1009812300843
   Web of Science ®Google Scholar
• Kremelberg D. 2010. Practical statistics: a quick and easy guide to IBM^® SPSS^® statistics, STATA, and other statistical software. New York: Sage.
   Google Scholar
• Kubien D, Sage R. 2004. Dynamic photo-inhibition and carbon gain in a C4 and a C3 grass native to high latitudes. Plant Cell Environ. 27:1424–1435.10.1111/pce.2004.27.issue-11
   Web of Science ®Google Scholar
• Lamelas M, Marinoni O, Hoppe A, de la Riva J. 2008. Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain). Environ Geol. 54:963–977.10.1007/s00254-007-0895-3
   Web of Science ®Google Scholar
• Landis JR, Koch GG. 1977. The measurement of observer agreement for categorical data. Biometrics. 33:159–174.10.2307/2529310
   PubMed Web of Science ®Google Scholar
• Langley JA, Megonigal JP. 2010. Ecosystem response to elevated CO2 levels limited by nitrogen-induced plant species shift. Nature. 466:96–99.10.1038/nature09176
   PubMed Web of Science ®Google Scholar
• Lemeshow S, Hosmer DW. 1982. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol. 115:92–106.
   PubMed Web of Science ®Google Scholar
• Li J, Wang G, Liu X, Han J, Liu M, Liu X. 2009. Variations in carbon isotope ratios of C3 plants and distribution of C4 plants along an altitudinal transect on the eastern slope of Mount Gongga. Sci China Series D Earth Sci. 52:1714–1723.10.1007/s11430-009-0170-4
   Web of Science ®Google Scholar
• Lobo JM, Jiménez-Valverde A, Hortal J. 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography. 33:103–114.10.1111/eco.2010.33.issue-1
   Web of Science ®Google Scholar
• Low AB, Rebelo AG. 1998. Vegetation of South Africa, Lesotho and Swaziland: a companion to the vegetation map of South Africa, Lesotho and Swaziland. Pretoria: Department of Environmental Affairs & Tourism.
   Google Scholar
• Malahlela OE, Cho MA, Mutanga O. 2015. Mapping the occurrence of Chromolaena odorata (L.) in subtropical forest gaps using environmental and remote sensing data. Biol Invasions. 17:1–16.
   Web of Science ®Google Scholar
• Manel S, Dias J-M, Ormerod SJ. 1999. Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird. Ecol Model. 120:337–347.10.1016/S0304-3800(99)00113-1
   Web of Science ®Google Scholar
• Mansour K, Mutanga O, Everson T. 2012. Remote sensing based indicators of vegetation species for assessing rangeland degradation: opportunities and challenges. Afr J Agric Res. 7:3261–3270.
   Google Scholar
• Mansour K, Mutanga O, Everson T. 2013. Spectral discrimination of increaser species as an indicator of rangeland degradation using field spectrometry. J Spatial Sci. 58:101–117.10.1080/14498596.2012.759090
   Web of Science ®Google Scholar
• Margules CR, Pressey RL. 2000. Systematic conservation planning. Nature. 405:243–253.10.1038/35012251
   PubMed Web of Science ®Google Scholar
• McGarigal K, Landguth E, Stafford S. 2013. Multivariate statistics for wildlife and ecology research. New York: Springer Science & Business Media.
   Google Scholar
• Miller J, Franklin J, Aspinall R. 2007. Incorporating spatial dependence in predictive vegetation models. Ecol Model. 202:225–242.10.1016/j.ecolmodel.2006.12.012
   Web of Science ®Google Scholar
• Morris C, Tainton N. 1993. The effect of defoliation and competition on the regrowth of Themeda triandra and Aristida junciformis subsp. junciformis. Afr J Range Forage Sci. 10:124–128.10.1080/10220119.1993.9638338
   Google Scholar
• Morris C, Tainton N, Hardy M. 1992. Plant species dynamics in the Southern Tall Grassveld under grazing, resting and fire. J Grassland Soc Southern Afr. 9:90–95.10.1080/02566702.1992.9648305
   Google Scholar
• Morris C, Taintoi N, Boleme S. 1993. Classification of the eastern alpine vegetation of Lesotho. Afr J Range Forage Sci. 10:47–53.10.1080/10220119.1993.9638321
   Google Scholar
• Mucina L, Rutherford MC. 2006. The vegetation of South Africa. Lesotho: South African National Biodiversity Institute.
   Google Scholar
• Mutanga O, Skidmore AK, Prins H. 2004. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens Environ. 89:393–408.10.1016/j.rse.2003.11.001
   Web of Science ®Google Scholar
• Niu S, Liu W, Wan S. 2008. Different growth responses of C3 and C4 grasses to seasonal water and nitrogen regimes and competition in a pot experiment. J Exp Bot. 59:1431–1439.10.1093/jxb/ern051
   PubMed Web of Science ®Google Scholar
• Norby RJ, Luo Y. 2004. Evaluating ecosystem responses to rising atmospheric CO2 and global warming in a multi-factor world. New Phytol. 162:281–293.10.1111/nph.2004.162.issue-2
   Web of Science ®Google Scholar
• O’Mara FP. 2012. The role of grasslands in food security and climate change. Ann Bot, mcs209. 209:1–8.
   Google Scholar
• Ojoyi M, Mutanga O, Odindi J, Abdel-Rahman EM. 2016. Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc Mountains of Tanzania. Geocarto Int. 31:1–21.10.1080/10106049.2015.1041557
   Web of Science ®Google Scholar
• Parker KC. 1991. Topography, substrate, and vegetation patterns in the Northern Sonoran desert. J Biogeogr. 18:151–163.10.2307/2845289
   Web of Science ®Google Scholar
• Pau S, Still CJ. 2014. Phenology and productivity of C3 and C4 Grasslands in Hawaii. PLoS One. 9:e107396.10.1371/journal.pone.0107396
   PubMed Web of Science ®Google Scholar
• Pearce J, Ferrier S. 2000. Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model. 133:225–245.10.1016/S0304-3800(00)00322-7
   Web of Science ®Google Scholar
• Pearcy R, Ehleringer J. 1984. Comparative ecophysiology of C3 and C4 plants. Plant Cell Environ. 7:1–13.10.1111/pce.1984.7.issue-1
   Web of Science ®Google Scholar
• Philpott SM, Lin BB, Jha S, Brines SJ. 2008. A multi-scale assessment of hurricane impacts on agricultural landscapes based on land use and topographic features. Agric Ecosyst Environ. 128:12–20.10.1016/j.agee.2008.04.016
   Web of Science ®Google Scholar
• Prasad N, Semwal M, Roy P. 2015. Remote sensing and GIS for biodiversity conservation. In: Recent advances in Lichenology. New York: Springer; p. 151–179.10.1007/978-81-322-2181-4
   Google Scholar
• Raich JW, Tufekciogul A. 2000. Vegetation and soil respiration: correlations and controls. Biogeochemistry. 48:71–90.10.1023/A:1006112000616
   Web of Science ®Google Scholar
• Rossiter D, Loza A. 2012. Analyzing land cover change with logistic regression in R. Enschede: Faculty of Geo-Information Science & Earth Observation (ITC), University of Twente.
   Google Scholar
• Rundel PW. 1980. The ecological distribution of C4 and C3 grasses in the Hawaiian Islands. Oecologia. 45:354–359.10.1007/BF00540205
   PubMed Web of Science ®Google Scholar
• Sakai AK, Allendorf FW, Holt JS, Lodge DM, Molofsky J, With KA, Baughman S, Cabin RJ, Cohen JE, Ellstrand NC. 2001. The population biology of invasive species. Ann Rev Ecol Syst. 32:305–332.10.1146/annurev.ecolsys.32.081501.114037
   Google Scholar
• Scholes RJ, Walker BH. 2004. An African savanna: synthesis of the Nylsvley study. Cambridge: Cambridge University Press.
   Google Scholar
• Scott L. 2002. Grassland development under glacial and interglacial conditions in Southern Africa: review of pollen, phytolith and isotope evidence. Palaeogeogr Palaeoclimatol Palaeoecol. 177:47–57.10.1016/S0031-0182(01)00351-0
   Web of Science ®Google Scholar
• Scott J, Heglund P, Morrison M, Haufler J, Raphael M, Wall W, Samson F. 2002. Predicting species occurrences: issues of scale and accuracy High Educ Rev. 10:225–256.
   Google Scholar
• Shaw CS, Escott B. 2011. KwaZulu–Natal vegetation type description document for vegetation map 2011. kznveg05v2_1_11_wll. shp. 2(1).
   Google Scholar
• Shoko C, Mutanga O, Dube T. 2016. Progress in the remote sensing of C3 and C4 grass species aboveground biomass over time and space. ISPRS J Photogramm Remote Sens. 120:13–24.10.1016/j.isprsjprs.2016.08.001
   Web of Science ®Google Scholar
• Stears K, Shrader AM. 2015. Increases in food availability can tempt oribi antelope into taking greater risks at both large and small spatial scales. Anim Behav. 108:155–164.10.1016/j.anbehav.2015.07.012
   Web of Science ®Google Scholar
• Stock W, Chuba D, Verboom G. 2004. Distribution of South African C3 and C4 species of cyperaceae in relation to climate and phylogeny. Austral Ecol. 29:313–319.10.1111/aec.2004.29.issue-3
   Web of Science ®Google Scholar
• Suárez-Seoane S, Osborne PE, Alonso JC. 2002. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species–habitat responses using generalized additive models. J Appl Ecol. 39:755–771.10.1046/j.1365-2664.2002.00751.x
   Web of Science ®Google Scholar
• Sumarga E. 2011. A comparison of logistic regression, geostatistics and Maxent for distribution modeling of a forest endemic; a pilot study on lobel’s maple at Mt. Pizzalto, Italy [Dissertation]. Enschede: University of Twente.
   Google Scholar
• Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science. 240:1285–1293.10.1126/science.3287615
   PubMed Web of Science ®Google Scholar
• Takaoka S, Swanson FJ. 2008. Change in extent of meadows and shrub fields in the central western cascade range, oregon. Prof Geogr. 60:527–540.10.1080/00330120802212099
   Web of Science ®Google Scholar
• Tedder MJ, Kirkman KP, Morris CD, Trollope WS, Bonyongo MC. 2013. Classification and mapping of the composition and structure of dry woodland and savanna in the eastern Okavango Delta. Koedoe. 55:1–8.
   Web of Science ®Google Scholar
• Terry E, M E, Jco DT. 2008. Report on Fort Nottingham vegetation assessment. Pietermaritzburg: University of KwaZulu-Natal.
   Google Scholar
• Tieszen LL, Senyimba MM, Imbamba SK, Troughton JH. 1979. The distribution of C3 and C4 grasses and carbon isotope discrimination along an altitudinal and moisture gradient in Kenya. Oecologia. 37:337–350.10.1007/BF00347910
   PubMed Web of Science ®Google Scholar
• Tømmervik H, Johansen B, Riseth J, Karlsen S, Solberg B, Høgda K. 2009. Above ground biomass changes in the mountain birch forests and mountain heaths of Finnmarksvidda, northern Norway, in the period 1957–2006. Forest Ecol Manag. 257:244–257.10.1016/j.foreco.2008.08.038
   Web of Science ®Google Scholar
• Trenholm L, Schlossberg M, Lee G, Parks W, Geer S. 2000. An evaluation of multi-spectral responses on selected turfgrass species. Int J Remote Sens. 21:709–721.10.1080/014311600210524
   Web of Science ®Google Scholar
• Van de Water PK, Leavitt SW, Betancourt JL. 2002. Leaf δ13C variability with elevation, slope aspect, and precipitation in the Southwest United States. Oecologia. 132:332–343.10.1007/s00442-002-0973-x
   PubMed Web of Science ®Google Scholar
• Van Gils H, Mwanangi M, Rugege D. 2006. Invasion of an alien shrub across four land management regimes, west of St Lucia, South Africa: research in action. South Afr J Sci. 102:9–12.
   Web of Science ®Google Scholar
• Van Wyk E, Van Oudtshoorn F. 1999. Guide to grasses of Southern Africa. Pretoria: Briza.
   Google Scholar
• Verbyla DL, Litvaitis JA. 1989. Resampling methods for evaluating classification accuracy of wildlife habitat models. Environ Manage. 13:783–787.10.1007/BF01868317
   Web of Science ®Google Scholar
• Vitousek PM, Field CB, Matson PA. 1990. Variation in foliar δ13C in Hawaiian Metrosideros polymorpha: a case of internal resistance? Oecologia. 84:362–370.10.1007/BF00329760
   PubMed Web of Science ®Google Scholar
• Vogel JC, Fuls A, Ellis RP. 1978. Geographical distribution of Kranz grasses in South Africa. South Afr J Sci. 70:258–265.
   Google Scholar
• Wand SJ, Midgley G, Jones MH, Curtis PS. 1999. Responses of wild C4 and C3 grass (Poaceae) species to elevated atmospheric CO2 concentration: a meta-analytic test of current theories and perceptions. Global Change Biol. 5:723–741.10.1046/j.1365-2486.1999.00265.x
   Web of Science ®Google Scholar
• Webster R, Oliver MA. 2007. Geostatistics for environmental scientists. West Sussex: Wiley.10.1002/9780470517277
   Google Scholar
• With KA. 2002. The landscape ecology of invasive spread. Conserv Biol. 16:1192–1203.10.1046/j.1523-1739.2002.01064.x
   Web of Science ®Google Scholar
• Wohlfahrt G, Anderson-Dunn M, Bahn M, Balzarolo M, Berninger F, Campbell C, Carrara A, Cescatti A, Christensen T, Dore S. 2008. Biotic, abiotic, and management controls on the net ecosystem CO2 exchange of European mountain grassland ecosystems. Ecosystems. 11:1338–1351.10.1007/s10021-008-9196-2
   Web of Science ®Google Scholar
• Xia J, Liu S, Liang S, Chen Y, Xu W, Yuan W. 2014. Spatio-temporal patterns and climate variables controlling of biomass carbon stock of global grassland ecosystems from 1982 to 2006. Remote Sens. 6:1783–1802.10.3390/rs6031783
   Web of Science ®Google Scholar
• Xie Y, Sha Z, Yu M. 2008. Remote sensing imagery in vegetation mapping: a review. J Plant Ecol. 1:9–23.10.1093/jpe/rtm005
   Web of Science ®Google Scholar
• Young HJ, Young TP. 1983. Local distribution of C3 and C4 grasses in sites of overlap on Mount Kenya. Oecologia. 58:373–377.10.1007/BF00385238
   PubMed Web of Science ®Google Scholar