Even if frequently shown to be very powerful, landscape-level approaches are sub-jected to several inaccuracies arising from the scale chosen for the particular analysis, the inclusion of biased base data and inaccuracies provoked by generalisations needed to perform the analyses. Gotelliet al. (2009) suggest that a large number of potential sources of error are linked with each data layer included in the analysis.
7.3.1 Bias due to generalisations, data resolutions and spatial scale
The representation of 'real world' conditions and patterns in 'computer space' is al-ways subject to necessary generalisations (i. e. delimitation and discretisation pro-cesses) that help to pool the vast amount of available information. It is important that the magnitude of generalisation applied to the data should match the processes under consideration (Lang & Blaschke, 2007). The ATKIS-DLM data used in the present thesis represent a good example for generalisations: The DLM defines hard
boundaries along more or less homogeneous landscape elements that in most cases can not fully reflect the real conditions, as the boundaries between many land cover types (especially natural habitats) are usually more continuous. Because the latter cannot be represented cartographically and furthermore would provoke problems in data analysis, the hard boundaries are accepted as the best approximation of reality (Green& Hartley, 2000).
Generalisations affect both the spatial and the thematic resolution of the data. The latter describes the way in which the geo-spatial data are categorised10. The spatial resolution of an analysis, in turn, is described by the extent of the study area as well as the grain size11. It was shown to affect all levels of data, i. e. species, land cover and environmental data.
In this context, several studies have shown that patterns of species rich-ness/diversity change with the scale of observation or analysis (Palmer & White, 1994; He et al., 2002; Kallimanis et al., 2008) and therefore "areas of high species richness at one scale may appear of low species richness at the other scale" (Kalli -manis et al., 2008, p. 148). This particular behaviour is attributable to the fact that species richness is non-additive when aggregated across different spatial scales (He et al., 2002).
Most studies conducted at the landscape scale make use of land cover data. Usually, landscape metrics in this context are used to quantify the composition and configura-tion of a landscape unit. As these indices are assumed to be strongly scale-dependent, a lot of attention has focussed on the behaviour of the metrics across changing the-matic and spatial resolutions. Within these studies, the thethe-matic resolution was found to largely affect the outcome of most landscape metrics (Bailey et al., 2007; Buyan -tuyev& Wu, 2007). Related to this, the usefulness of the indices in describing land-scape characteristics at varying thematic resolutions was found to largely depend on resolution: Whilst some indices are more useful at a coarser thematic resolution oth-ers are more suitable at finer scales (Baileyet al., 2007). Similarly, only few landscape metrics have been identified to behave consistently across different spatial resolutions (Turner et al., 1989; Saura & Martinez-Millán, 2001; Wu, 2004). In this context, Turneret al. (1989) identified an increase in grain size to be responsible for the loss of rare and small-scale land cover types in the landscape. Schindleret al. (2013) stud-ied the performance of landscape metrics as indicators of species richness of various taxonomic groups across multiple scales. They found the effects of landscape struc-ture expressed in terms of landscape metrics on species richness to strongly depend on the spatial scale of the analysis.
Finally, spatial scale also plays a role in the analysis of environmental conditions.
According to Siefert et al. (2012) both the extent and grain size may influence the amount of variance observed in a given environmental factor. Due to the spatial
au-10cp. subsection 2.2.2 for the categorisation of DLM data applied in the present study
11i. e. the size of the smallest unit of observation, (Palmer& White, 1994; Turner, 2005)
tocorrelation observed in most environmental variables, their variance is supposed to increase when the extent of the study area is enlarged (Wiens, 1989; Siefert et al., 2012). However, the size of this effect largely varies between different environmen-tal variables. Siefert et al. (2012) distinguish coarse- and fine-grained variables. The former include e. g. climate data which vary across broader scales, the latter are repre-sented by e. g. edaphic variables that may show a large variation even within a small area.
The aforementioned examples clearly illustrate the importance of scale. However, the knowledge of this scale-dependence of landscape-level approaches can only rarely be included in research as the spatial extent and grain of an analysis are usually determined by the availability of the base data.
7.3.2 Bias due to data format
"Yes, raster is faster, but raster is vaster, and vector just seems more corrector."
C. Dana TomlinXXXXX For environmental assessments using GIS, both vector and raster data are commonly used (Wade et al., 2003). Usually, vector data are referred to as allowing high levels of cartographic accuracy12(Wyatt & Ralphs, 2003). In contrast, raster data are sup-posed to be less precise than vector data as it is difficult to "represent objects with a sufficient level of cartographic precision using raster methods, since the resolution of the raster grid needs to be prohibitively high to capture an equivalent level of detail"
(Wyatt& Ralphs, 2003, p. 49). Nevertheless, raster data are often used in all kinds of studies because in many cases they represent the only data source available and are faster to process (Lang & Blaschke, 2007). For the present thesis, several data sources were available in raster format (e. g. DEM and climate data, see Table 2.1).
Furthermore, some vector data (DLM) had to be converted to raster format as the software used for the subsequent analysis (Fragstats 3.3, McGarigal & Cushman, 2002) was limited to this type of data. In transforming the data, an additional bias is introduced because a balance between resolution and file size has to be found: If pixel size is too large, the data set will suffer from cartographic imprecision, whilst a very high resolution will generate a file too large for computation (Wyatt& Ralphs, 2003). In the present thesis the resolution of the transformed data was set to a pixel size of 25 m2. This value was chosen because of hardware limitations (cp. subsection 2.2.5). However, for the analyses conducted in context of this thesis this resolution should be sufficient to capture the overall characteristics of the landscape.
However, the pixel size of a raster not only affects the precision of the data set but can also directly influence the calculation of landscape metrics (McGarigal &
Cushman, 2002; Neel et al., 2004). Indices that include the complexity of patches are particularly affected because of the step-wise structure of the patch margins that
12However, this attribute still depends on the accuracy of data acquisition.
results in the overestimation of patch complexity and edge length (Leitãoet al., 2006).
In the present study this is especially true for theTotal Edgeas well as theShape Index utilised in chapter 6. The latter suffers from one further limitation: Because of the raster format the simplest shape is set to be a square even though in reality circular patches have the smallest perimeter-area relationship and thus experience fewer edge effects.
7.3.3 Bias in species records
In species record data the main problem is the reliability of these data (Haeupler, 2000; Honnayet al., 2003; Diekmannet al., 2008). Frequently, data on species occur-rences are incomplete because some areas are better inventoried than others (Barth -lottet al., 1996). This problem apparently may provoke misleading results. Thus, it is highly important to check data on species records prior to subsequent analyses.
The database utilised in the present thesis was carefully checked for its quality by the responsible authority (Schacherer, 2001), so that the bias related to this particular source of error should be of minor concern.