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Predicting the Distribution of Fynbos and Succulent Karoo Biome Boundaries and Plant Communities Using Generalised Linear Models

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Predicting the Distribution of Fynbos and Succulent Karoo Biome Boundaries and Plant Communities Using Generalised Linear Models S. Mc. J. Bot. 1999, 65( I): 89- 96 89 Predicting the distribution offynbos and succulent karoo biome boundaries and plant communities using generalised linear models and geographic information systems R.G. Lechmere-Oertel and R.M . Cowling* Institute for Plant Conservation, Department of Botany, University of Cape Town, Rondebosch, 7701 Republic of South Africa Received 28 Alay 1998: revised 18 NOl'ember /998 Vegetation survey data from Matjiesrivier Nature Reserve, in the eastern Cederberg Mountains of the fyn bos biome, were used to model the distribution of fynbos and succulent karoe biomes and plant communities. Predictions were based on generalised linear models (GLMs) with the presence or absence of a biame or community as the response variable. Geology, altitude, rad iation and landtype, which were derived from remotely-sensed sources (maps, aerial photographs and digital terrain models), were used as explanatory va riables in the GLMs The boundary between fynbos and succulent karoo could be predicted accurately based on geology and altitude alone. Fynbos occurred on sandstone above 800m; succulent karoo occurred on sandstone below 800m, or on shale at any altitude. Community distributions were less accurately predicted, but could be used to generate a geographic information system (GIS) map similar to a vegetation map derived from ground survey. These models can be used to map vegetation over large areas in the fynbos/succulent karoo ecotone with relatively little effort or cost. Keywords: Biome boundary, fynbos, GIS, growth forms, modelling, succulent karoo. *To whom correspondence should be addressed. E·mail: rmC@botzoo .uct .ac.za Introduction Cape. He concluded that topo-climatic factors and geology were Static and non -predictive vegetation maps are problematic for a the primary determinant of this boundary. Cowling el al. (1997) number of reasons. Firstly, vegetation is dynamic: composition have reviewed the literature on the fy nbos/succulent karoo and physiognomy change over varying scales in response to boundary, and concluded that moist ure availability, rather than environmental change, biotic interactions and alterations in di s· geology, is the primary determi nant. However, despite the turbance regimes. Secondly, such maps cann ot be used to predict importance of biome boundary determinants for predicti ng the changes in vegetation boundaries, either in response to impacts of global change on biome distributions, no attempt has short-term fluctuations (e.g. drought effects or changes in the fire been made to model the tyn bo S/succu lent karoo boundary in regime) (Austin 1991; van der Rijt el al. 1996), or lo nger-term terms of easil y measurable explanatory variables. Thi s study changes induced by, for exampl e, climate change (N ielson attempted to model the di stribution of communities and th e fyn­ 1993). Thi rd ly, ' snapshot' maps cannot be ex trapolated to large bos/sllccu lent karoo bound ary in the study area using several eas­ areas of unsurveyed terrai n (Franklin 1995). This increases the ily measurable environmental variables. We asked three time, and hence the cost, of vegetation mapping, since each new questions: area must be re-mapped from scratch (Margules & Austin 199 1). I. Can the distribution and environmental determinants of th e On the other hand, predictive vegetati on maps, especially major communities be predicted accurately? when derived from models based on easily measurable environ­ 2. Can th e geographical positi on and environmental determinants mental vari ables, have many advantages (Frankl in 1995). They of the boundary between fynbos and succul ent karoo be pre­ can be extrapolated over large areas of remote terrain, thereby di cted accurately? reducing th e survey effort for checking predictions, and can be 3. Can the models derived from the first two questions be used in used for monitoring and predicting vegetation change in a GIS to produce realistic maps of th e vegetation of th e study response to global change. Generalised linear models (G LMs) area? have com mon ly been used to derive predicti ons on the environ· mental contro ls of species' (Margules el 01. 1987~ Austi n et al. Materials and Methods 1990; see review in Franklin 1995) and community (Austin el at. Study area 1983, 1984; Valverde & Montana 1996) di st ributi ons. Maps are usually created in a geographical information system (G IS). Our Matji esrivier Nature Reserve (MNR) (32°25'S. 19°17'E) straddles interest in this paper is the distribution of plant communit ies the boundary between fynbos and succulent karoo bi omes in the associated with the transition between the fy nbos and sllcculent Western Cape, South Africa. MNR experiences a mediterra­ nean-type climate and has a gradient of decreasing an nual rai nfall karoo biomes in the eastern Cederberg Mountains. This region from th t! west (300 mm) to the east ( J 00 mm) (Lechmere-Oertel includes the Matjiesrivier Nature Reserve (th e study area) and a 1998). The geology of MNR comprises three groups in the Cape large area of inhospitable terrai n that is being incorporated into Supergroup. The shale and silty sandstone of the Bokkeveld Group the conservati on system as 'conservancies'. Th erefore, there is a lie between the sand stone and quartzite of the Table Mountain and need to derive a vegetation map for conservati on planning in th e Wittebe rg Grou ps. The conformity between the rocks of these diffe r· area, specifically to assess the extent to whi ch different plant enl geo logic groups is abrupt. giving rise to steep edaphic gradien ts. communities are represented in the system. These climatic and edaphic gradients are reflected in a transition We were also interested in assessing the determinants of the from fynbos into succu lent karoo over distances of a few meters to a fy nbos/succul en t karoo boundary in the region. Euston-Brown f\!w kilometers. Overall, the vegetation of the MNR is floristically ( 1995) used GLMs to pred ict th e distribution of families and and structurally complex. since it compri ses elements fro m two di s­ growth forms diagnostic fo r fynbos and thicket biome vegetation tinct and typically diverse vegetation types: mountain fynbos and in the Kouga-Baviaanskloof mountain complex in the Eastern lowland succulent karoo (Low & Rebelo 1996) (Table t). 90 S. Mr. J. Bot. 1999. 65( I ) Table 1 The major vegetation types in the Matjiesrivier categories (Table 2). After preliminary modelling, gravel plains were grouped with rocky slopes to reduce model instability. Nature Reserve (MNR) and their relationship to biomes and broad-scale vegetation types Model development Vegdatinn typc in MNR (ex momc and vcgcttltion t) pe (sensu The data used for model derivation "ere not distributed normally Lcc hnere~Ocrtel 1998) Low & Rebel o 1996) and included both categorical and continuous variables. Generalised AstcmCC()llS Fynbos Matrix Fynbos biome linear models (GLMs) extend the regression framework to three situ~ ations \vhere ordinary regression would not be appropriate. These RestiOld Sandy fynbos MOllntain Fynbos arc: (I) where the data are not normally distributed; (2) \vhere the data need to he transformed (using a link function) bcfor!; a li near Shale SUccllknt Ktlroo Succulent Karon nimHe model can be titted: and (3) \vhere the data comprise both categori~ Sandy Succuknt KtlnJO Lowland Succulent Karon cal and continuous variables (Genstat 5 Committee 1987: Crawley 1993: Trexler & Travis 1993). There are three main issues that need SucCtlkny Karon Matrix to be considered when using GLMs to predict the distribution of communities: These arc: (1) the selection of an appropriate statistical model; (2) the selection ofsuitahle variables to be used as predictors: Vegetation data and (3) the critical evaluation of the titted regression model for out­ Data comprise 125 lOin x 10 m sites samplLd for species and liers and intluential observations (N icholls 1991). gnl\\"th form composition and subsequently classified into commun i ~ We modelled each of the biomes and major communities Cfable tics using standard multivariate methods (st.:t.: Lechmere~Oerlel 1998 1) identified in the MNR, except for azonal types (Dwarf Bedrock fo r details). The sites were positioned through MNR using an Shrub land and Kloof Thickt:t: see Lechmere-Oertel 1998). sepa­ approach similar to that employcd by gradst.:ct sampling (Gillison & ratdy agains.t the explanatol}' environmental variables. The response Brewer 1985: Austin & Heyligers 1(91). variable indicated the presence ( I) or abs!;ncc (0) of the relevant attribute (community or biome) in a site. Therefore. in each of the Explanatory variables models, the distribution of the response variable was assumed to be We chose environmental explanatory variables thtlt \vere rt.:adily binomial; therefore, a 10git link function \vas used (McCullagh & available from remotdy ~sens ed or computcr~generated data sets, and Nc1der 1989). The sites used in these analyses were assumed to be that rdlected current hypotheses on the determinants boundaries independent observations of the vegetation. be tween fynhos and adjacent biomes ~ principal ly soil moisture sta~ Significant explanatory variables were selected for the models tus and soil nutrient status (Cow·ling et a/. 1997). Rainfall. evapora­ using a forward step-wise process (GENSTAT 5 Committee 1987, tion and soil texture (amongst other hlctors) control moisture Crmvley 1987). The process of model building was iterative. A ll the availability whereas soi l nutrient status depends largely on geologi­ explanatory variables were added to the model singly on the tirsl cal parent material. While it is possible to derive cl imatic data from pass. The variable that accounted for the highest significant change accessible dawbases, most soil factors n:quire intensive sampling. in deviance was added to the model. All the remaining variables l-l owever. in the fynbos biomt.:.
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