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The above described construction of diversity is a rather descriptive analysis. Neighbourhood 
relations are examined without considering the quality of these relations as functions. If all 
possible relations can be evaluated in a matrix, these qualitative aspects should be added into a 
model. In this first step, the simplification is assumed, that all borderlines are equivalent for 
their function within the ecosystem. Resulting values are compared with some indicator 
species. Correlation of the reclassified structural diversity and the territories of the lesser 
spotted woodpecker ( Dendrocopus minor ) show a strong correlation (Blaschke in prep ). 
Outcomes for other bird species such as the oriole ( Oriolus oriolus) underline the assumption, 
that strong correlation exist (further statistical analysis is required). Investigations on other 
taxa are going on with recent progress for some butterfly and dragonfly species. 
Spatial data as we can handle today are almost approximated to generalisations of the real 
world and they are full of uncertainty and inaccuracy. Depending on data models and scales a 
loss of information results. On the other hand Geographic Information Systems allow to 
process and generate data, which have been inaccessible or unpayable before. In this case 
study, it was tried to figure out, that a data layer structural diversity can be processed which 
highly correlates to the distribution of some indicator species. Although a lot of impediments 
have to be removed on the way towards an operational, easy to use model, the structural 
diversity approach can serve as a framework for practical nature conservation evaluation to 
save time and money on fieldwork, if the data exist. The main assumption behind, that the 
more heterogeneous and complex the physical environment becomes, the more complex the 
plants and animal communities supported and the higher the species diversity, could be applied 
to this case study. Concerning that most geographical constructs are implicitly uncertain, 
including spatial objects (meadow, hedgerow, wood) and their relationships (inside, across, 
near) the results give hope for further investigation. 
From modelling and monitoring habitat suitability to management 
The most straightforward way for the management of regional to local biodiversity and a 
sustainable use of the ecosystem is a multiple habitat modelling of all indicator species. The 
indicator concept combines species and community perspectives and involves monitoring 
species that are characteristic of a particular habitat, as a measure of the condition of an entire 
community. An example of such an indicator species for the alluvial flood plain of the Salzach 
is the yellow thrush ( Oriolus oriolus ), which is a typical bird for natural, plentifully structured 
riparian forests. The hypothesis is, maintaining an adequate habitat for an indicator can 
presumably preserve most other species dependent upon the same habitat. It is an alternative 
approach to the mapping of species richness. For one indicator, this concept seems to be 
critical, but there can be expected much of a well chosen species composition, a so called 
"assemblage". A crucial step is the overlay of the resulting habitat maps. It was worked out, 
that arithmetic operations average the extreme values of the different layers (Blaschke 1995). 
Formulas like 
Habitat Suitability = (layer A + layer B + layer C) / 3 
HSI = (layer A * layer B * layer C) 1/3 
are inadequate for small-scale analysis. The detailed investigation of an fluvial/alluvial 
ecosystem showed, that these arithmetic overlays assimilate "hot spots" of essential habitats for 
single indicator species. Fig. 2 shows very different habitat suitability's for three indicator 
species. The only effective method was a logical combination using a simple rule like: "take the