NOTAS Y MEMORIAS

For purposes of land use modelling, the classification of BVCs used to establish bioclimatic envelopes (Tables 3.1 and A2.1) was disaggregated (see: Section 4.3 for further details). This revised classification (see: Table 4.1 in Section 4.3) consists of a number of vegetation community types, which are of particular significance in terms of conservation, and others, which are regarded as less significant. Those communities identified as conservationally significant are defi ed as p io it BVCs PBVCs fo the pu poses of this esea h a d a e the particular focus of Chapters Five, Six and Seven. Those which are regarded as less significant in terms of conservation

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are defined as Non-P io it BVCs (NPBVCs). To distinguish between the two classification schemes used within this research, all subsequent references to the vegetation units defined in Table 3.1 use the term BVC; references to the vegetation units defined in Table 4.1 use the term P/NPBVC.

Figure 4.1 provides an overview of the adopted land use modelling methodology. The model was applied using various Idrisi Taiga Change Analysis modules (Eastman, 2009; Clark Labs, IDRISI Taiga, no date). The methodology can be broadly separated into two distinct, yet interrelated, parts:

Part one (Figure 4.1: right side) involves the formulation of future socio-economic scenario storylines for the study area based on a review of literature and key driver analysis. This ensures that the storylines are reasonable, meaningful, and contextually relevant. The scenarios are used:

1) as a way to identify specific P/NPBVC types likely to expand under a particular set of future socio-economic circumstances and; 2) to derive quantitative estimates of likely levels of future expansion for those P/NPBVCs.

Figure 4.1

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Part two (Figure 4.1: left side) involves the creation of spatially-explicit data for the study area expressing the suitability of land for specific P/NPBVC types in terms of certain significant biophysical and management related variables. This employed a knowledge-based approach (Aspinall, 1998). Specifically, P/NPBVC requirements in terms of these variables are gauged through literature review. This information is then used to delineate suitability indices, for each P/NPBVC in relation to each of the variables. When applied to spatially-explicit biophysical data for the study area, within ID‘ISI Taiga s Fuzz Set Me e ship Fu tio FSMF odule, this information facilitates the creation of spatial data expressing the suitability of land for each P/NPBVC type in terms of each of the variables (Figure 4.1: Suita ilit La e s A ). Spatial data expressing the overall suitability of land within the study area for each P/NPBVC type ( Suitability La e s B ) are created by combining Suitability Layers A for each P/NPBVC within ID‘ISI Taiga s Multi-C ite ia E aluatio (MCE) module. The overall suitability layers for the appropriate P/NPBVC types are then entered into ID‘ISI Taiga s Multiple O je ti e La d Allo atio MOLA module, along with the scenario specific information on levels of P/NPBVC expansion (see above), to produce the spatially explicit predictions of future P/NPBVC distributions.

Information from JNCC (2007), Lane (1999), Furniss & Lane (1999), Lane & Tait (1999), Fitter &

Peat (1994), Grime et al. (1988), and Tansley (1949) was used to gauge the key characteristics of the P/NPBVCs. P/NPBVC suitability was ultimately modelled in terms of five biophysical variables (soil pH, soil water, soil type, elevation and slope) and two management related variables (distance and cross-suitability). The two management variables were used to incorporate significant human management considerations more directly into the scenarios (Verburg et al., 2006). For both economic and environmental reasons, areas in closer proximity to existing extents of a particular P/NPBVC are likely to be more suitable for its expansion than those further away (Griffiths et al., 2011; Swetnam et al., 2010; Verburg et al., 2006; Busch, 2006; KanKaanpaa &

Carter, 2004). Cross-suitability considers the potential for converting areas of land from one P/NPBVC to another, based on the management actions required to facilitate conversion (Griffiths et al., 2011). Figure 4.2 illustrates the creation of all suitability layers for Blanket Bog.

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Figure 4.2: Creation of all suitability layers for Blanket Bog

Suitailit Laes A

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An important assumption of this research is that the potential occurrence of P/NPBVC types is largely determined by their tolerances or suitability in terms of the selected biophysical and management variables. However, whilst the suitability indices are vital in determining the suitability of land for particular P/NPBVCs, it is important to note that the socio-economic trends inherent within the scenarios are the major drivers of future land use change (i.e. the socio-economic characteristics determine the specific P/NPBVC types undergoing expansion as well as appropriate quantitative information on associated levels of expansion).

The final results of the land use modelling are maps depicting the distribution of P/NPBVC patches in the future, as influenced by changes in land use (Figures 4.4 b & c). Landscape metrics (see:

Chapter Six) and a Climate Stress measure (see: Chapter Five) are then applied to these results for the PBVCs (Table 4.1), so that their potential future vulnerability under the scenarios may be assessed.

This chapter primarily covers information relating to the first part of the methodology (Figure 4.1:

right side). Detailed information relating to the basic P/NPBVC categories used for the land use modelling is provided in Section 4.3 to provide context to subsequent sections regarding the evaluation and validation of the land use model (stages of evaluation and validation of the land use model are not shown in Figure 4.1). Though providing a vital underpinning to the modelling of land use change, substantial information relating to the investigation of key biophysical and management variables, the associated P/NPBVC tolerances, delineation of suitability indices and the GIS software employed are provided in Appendix 2.