Assessing the geodiversity index and the relation with biotopes in the N.W. Rätikon mountains and S. Walgau, Austria.

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Assessing the geodiversity index and the relation with biotopes in

the N.W. Rätikon mountains and S. Walgau, Austria.

BS

C

T

HESIS

S. E. NIJDAM

SUPERVISOR: DHR. DR. A. C. SEIJMONSBERGEN University of Amsterdam (UvA) BSc Future Planet Studies – Major Earth Sciences

27 JANUARY 2019

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1)IMPRESSION OF THE SCE NERY OF VORARLBERG,AUSTRIA. RETRIEVED FROM:

HTTPS://WWW.TELEGRAPH.CO.UK/TRAVEL/DESTINATIONS/EUROPE/AUSTRIA/VORARL BERG/ARTICLES/VORARLBE RG-TRAVEL-GUIDE-/CREDIT:YURIY BRYKAYLO.

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Abstract

Assessing the geodiversity index is important to retrieve insight of the area and for making policy recommendations regarding geoconservation. The N.W. Rätikon Mountains and S. Walgau is an area located in Vorarlberg, Austria. This research is twofold, first a workflow is established to calculate the geodiversity index after which the relationship with biotopes is assessed. First, the geodiversity is calculated o n a fine scale (cell size 100x100m) with the use of ArcGIS Pro and made into a map. The geodiversity consist of the sub -indices hydrology, geology, geomorphology and topography. This research showed that the sub-indices with the highest correlation with the geodiversity index area geomorphology and topography. Secondly, the relationship with the geodiversity index and the five most occurring biotopes is assessed so more in depth knowledge is gained for bio conservation. Biotopes are defined as area with uniform environmental conditions providing a habitat for a specific assemblage of plants and animals. The biotope “Auen Quellwälder” is mainly present in areas with a low geodiversity index, because of the high influence of h ydrology. The biotope “Tobel-, Hang-, & Schluchtwälder” mainly occurs in areas with a high geodiversity index , because topography plays an important role. The workflow to calculate the geodiversity index presented in this research can be used in future res earch. Furthermore it can form the basis of making policy recommendations in order to help with bio conservation. The fine scaled approach, facilitates possible up -scaling in future research.

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1. Introduction

Sustainable development goal 15 “Life on land”, sets the target to ensure the conservation of mountain ecosystems, including their biodiversity, in order to enhance their capacity to provide benefits that are essential for sustainable development (United Nations., n.d.). The alpine mountains accommodates a large variety of ecosystems and are one of the most geologically divers areas in the world.

Enduring geophysical features such as topography, soil, rocks and water, form the stage on which nature’s play is enacted and can be used to prioritize sites for conservation (Beier et al., 2015). This combination between geophysical features and conservation is a relative new idea that is now becoming a significant aspect in bio conservation. In earlier days the conservation of nature was part of biologists, but since the 1990s the idea has risen that abiotic nature also influences biodiversity (Gray, 2008).

Besides the importance of researching the biotic diversity, abiotic diversity can be a good indirect indicator for biodiversity in an area. The abiotic diversity can be described by geodiversity. Geodiversity is defined by Gray (2004) as the natural range (diversity) of geological (rocks, minerals, fossils), geomorphological (landform, processes), and soil features. It includes their assemblages, relationships, properties, interpretations and systems and it can be used to describe the variety within abiotic nature.

Using the knowledge of geodiversity in an area, the connection can be made with biodiversity, since they are highly connected and influenced by each other. However, in this proposal the focus is aimed at investigating the relationship between geodiversity and biotopes instead of the relationship with biodiversity. This will be done by calculating the correlation between the geodiversity and biotopes. Biotopes are defined as areas of uniform environmental conditions that provide a living place for a specific assemblage of plants and animals (Rizwam & Athapattu, 2014). Biotopes are influenced by biotic and abiotic factors. Wondzell et al., (1996) state that biotic processes are limited by water and nitrogen, and the interactions between landforms, geomorphic processes, soils, and plant communities control the redistribution of these limiting factors. Therefore, it is important to not only include biotic factors, but also abiotic factors (geology, topography, hydrology, etc.) in research to biotopes.

The focus of this research is aimed at calculating the geodiversity index on a fine-scale in the research area located in Vorarlberg (fig. 1). Combining the fine-scaled approach with the local study area, it is possible to retrieve detailed results. When assessing the relationship between a geodiversity index and biotopes at this fine-scale, it is possible to give accurate policy recommendations for this area in the future. In addition, the realization came that biodiversity occurs at multiple spatial scales and levels of biological organization (Schwartz, 1999). Poiani et al. (2000) state that a greater emphasis to conserve this diversity must be placed at all appropriate levels and scales. Therefore, the fine-scaled approach is a good addition to the bio and geoconservation research.

The aim of this research is to calculate a fine-scaled geodiversity index of an area in Vorarlberg, Austria, and relate this to the existing biotopes. This results in the following main research question:

What is the correlation between biotopes and geodiversity in the NW. Rätikon mountains and S. Walgau (Vorarlberg, Austria)?

To answer this question, the following sub-questions will be addressed: - What is the geodiversity index (GI) of the research area?

- How is the geodiversity index related to biotopes in the research area?

This research provides a detailed methodology for calculating the geodiversity index on a fine-scale in a local area in Vorarlberg.

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1.1. Geodiversity concept

Although ecologists have long recognized geodiversity as a key driver of biodiversity and species distribution patterns (Lawler et al., 2015), conservation biologists were slow to consider using geodiversity to prioritize areas for biological conservation. Only since 1988 the conserving of geodiversity is seen as a surrogate for conserving biodiversity (Beier et al., 2015).

Stanley (2001) notes that geodiversity is the link between people, landscapes and culture; it is the variety of geological environments, phenomena and processes that make those landscapes, rocks, minerals, fossils and soils which provide the framework for life on Earth. This link between people, landscapes and culture is closely related to the idea of ecosystem services. These services include provisioning, regulating, habitat and cultural services (BISE, n.d.), which makes it possible to give economical values to the services an ecosystem provides. It is important to sustain these services, because it has a positive influence on human wellbeing. For this, it is relevant to research these ecosystems in order to give relevant policy recommendations for their conservation. Not only is this a way to protect ecosystems, but also to secure human wellbeing (Alcamo et al., 2003).

Ecosystem services can be valued, so is geodiversity. Gray (2004) stated that over 30 values of Earth’s geodiversity can be recognized and classified into intrinsic, cultural, aesthetic, economic, functional and scientific values. He stated that viewing the Earth in terms of this diversity and the developing utilization of this diversity by human societies trough to the present day, enriches our appreciation of the values of the natural world and of our geoheritage. The geodiversity index exists of multiple sub-indices, which can quantify the diversity of abiotic factors in the area. For this research the geodiversity index includes geology, geomorphology, hydrology and topography, however depending on the subject and area, the sub-indices may vary. For example it may include soil diversity, tectonic diversity, solar radiation diversity, radiation diversity, etc. (Anderson et al., 2015; Seijmonsbergen et al., 2018)

1.2. Adaptation of geodiversity

Anderson et al. (2015) described eight case studies in which geodiversity was incorporated in nature conservation. All studies found that some geodiversity elements (especially soils, elevation and topography) had high correspondence with the distribution of dominant vegetation types. These studies demonstrated that the concept of geodiversity and of Digital Elevation Model (DEM) derived approaches for biodiversity conservation turned out to be successful. Seijmonsbergen et al. (2018) conducted a large scaled geodiversity research in the area of Vorarlberg in Austria. Two different workflows have been used for geodiversity mapping; 1) an index-based geodiversity method applied on a regional scale for the state of Vorarlberg in Austria, and 2) a combined local-scale expert-driven and GIS-supported method with a focus on geomorphological mapping implemented west on the village Au. The two methods proved to be a sound basis for geoconservation, because of the possibility to assign new areas for geoconservation.

The calculation of the geodiversity index was also implemented by Seijmonsbergen et al. (2018), where the role of time in geodiversity was assessed and the long-term effects of the geological evolution of seven Hawaiian hot-spot islands on geodiversity dynamics was explored. With using the geodiversity index, it could be concluded that there is a strong correlation between this index and the age of the islands. In addition, they found that geodiversity is strongly correlated to the range and standard deviation of slope and elevation (Seijmonsbergen et al., 2018).

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regional scale. This scale provides an overview of the differences throughout the study area, but makes it hard to examine the results in detailed from. Therefore, a fine-scaled approach is used to assess the geodiversity index, and study its relationship with biotopes in more detail. 1.3. Research Area

The study area of this research is located in the North-West Rätikon mountains and the Southern Walgau which is part of Vorarlberg, Austria (fig. 1). The area is described in the PhD dissertation of dhr. dr. A. C. Seijmonsbergen, which forms the basis of this research and was focused on the geomorphological evolution of the alpine area. A detailed geomorphological map was made, which will be used in this research (table 1).

The natural vegetation in the research area has been strongly affected by different types of land use: mainly intensive agriculture and in the higher areas of Vorarlberg by low- to mid- intensity agriculture, such as dairy farming combined with mowing and haymaking. Other land use types are forestry and alpine ski slopes at higher elevations (Seijmonsbergen et al., 2018). Even though there are still many threats, biodiversity conservation has been very successful in Vorarlberg (Seijmonsbergen et al., 2018). For this reason, it is relevant to research the area in more detail and improve the current conservation methods. It would be a good addition to the seven step conservation method of The Nature Conservancy (TNC) (Grovers et al., 2019). The steps consist of: step 1) identify conservation targets, 2) collect information and identify information gaps, 3) establish conservation goals, 4) assess existing conservation areas, 5) evaluate ability if conservation targets to persist, 6) assemble a portfolio of conservation areas and 7) identify priority conservation areas.

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1.4. Biotopes

Biotopes are defined as an area with uniform environmental conditions providing a habitat for a specific assemblage of plants and animals (Sukopp and Weiler, 1988). Forman (1995) modified the definition by including landscape ecological information. The concept of a biotope is scale-dependent and partly species-specific (Löfvenhaft et al., 2012). Therefore, a biotope can be seen as a variable-scale environmental unit of a landscape, characterized by specific conditions and populated by a characteristic biota (Qiu et al., 2010).

The focal point has been set to only the five most common biotopes in the study area (fig. 2), in order to make the research more feasible. These biotopes have the largest individual total area of the 31 biotopes that cover the area (Appendix C).

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In order to keep the research reproducible, the legend names of the biotopes found in the dataset have been used throughout this thesis. Table 1 shows the total area of each biotope, together with the English names.

Table 1. The biotope legend, English names and area in km2

Legend Biotope Area (km2₎

Hang-, Flach-, und Quellmoore

Slope-, flat-, and spring water- mires and bogs 2.55 km2 Magerwiesen (Trespe) Nutrient-poor meadows 3.14 km2 Tobel-, Hang-, und

Schluchtwälder

Ravine-, slope-, and valley forests

6.13 km2

Auen- und Quellwälder

Grassland and wet seepage forests 2.29 km2 Montan-Subalpine Nadelwälder Montane-Subalpine coniferous forest 3.83 km2

1.4.1. Hang-, Flach-, und Quellmoore

The biotope Hang-, Flach- and Quellmoore are defined as fen and mire environments that are present at slopes, flat areas and/or present near spring water. A “Moore” is a peat-forming plant community.

The mires were very common in the past, but due to human interaction these environments are now endangered and worthy of protection. One of the biggest examples in Vorarlberg is the Talmoore in the Rheintal and Walgau. In addition, “Flachmoore” are designated as minerogenic fans, which means they are influenced by groundwater (Broggi et al., 1991).

1.4.2. Magerwiesen

Magerwiesen are meadows that are common in valleys. These meadows are nutrient poor, but has a high species richness. It is a colourful environment with lots of flowers, insects and birds. The vegetation mostly exists of drought resistant species (i.e. the silver thistle, Hauhechel and meadow brome) (Broggi et al., 1991).

Figure 3. Flachmoor complex Gsieg, Lustenau, Vorarlberg (Broggi et al., 1991)

Figure 4. Magerwiese Unterwäldele, Mittelberg, Vorarlberg (Broggi et al., 1991)

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1.4.3. Tobel- Hang- und Schluchtwälder

Tobel- (ravine), Hang- (slope), and Schlucht- (canyon) forests are not intensely influenced by humans because these areas are difficult to enter due to high slopes and elevation differences. They have a high biological value, since they have hardly changed by humans.

Because the combination of rock formations, steepness, exposure, elevation and hydrology, these biotopes exists in multiple forms and combinations (Broggi et al., 1991).

1.4.4. Auen- und Quellwälder

“Auen” are meadows or floodplains with river accompanying vegetation, that are occasionally inundated with water during a flood. These areas are characterized by the presence of forests. The most significant feature of this biotope is that it is highly influenced by water.

One of the most common vegetation is lavender. The blue willow bush is very rare and only grows in these floodplains (Broggi et al., 1991).

1.4.5. Montan-subalpine Nadelwälder

Montan-Subalpine Nadelwälder translates to Montane-subalpine coniferous forest.

Coniferous forests cover over 80% of Vorarlberg forests. Because they cover such a large area, they are subdivided into different categories (for instance Lärchen-Zribenwald, Föhrenwald, etc) (Broggi et al., 1991). In this case, they are present in Montane-Subalpine areas.

Figure 5. Schluchtwälder, Eyenbach, Sulzberg, Vorarlberg (Broggi et al., 1991)

Figure 6. Auenwälder, Rheinspits, Gaissau, Vorarlberg – high water (Broggi et al., 1991)

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11 Figure 8. Workflow; pre-processing, analysis and deliverables & results

2. Methods

The methodology for this research is divided into three parts. The workflow consists of; 1) pre-processing, 2) analysis and 3) deliverables and results (fig. 8). This set up is used to make the research transparent, efficient and, moreover reproducible.

During the pre-processing phase, data is made available by dhr. dr. A. C. Seijmonsbergen (IBED, University of Amsterdam). The data will be set to the correct coordinate system and format and the grid size will be calculated. During the analysis phase, four steps are conducted; 1) calculate the DEM derivatives, 2) calculating the geodiversity index, 3) classification of the GI, and finally, 4) correlating the geodiversity index to the existing biotopes. After the analysis phase, the output is visualized into a geodiversity map, multiple correlation matrices and bar graphs. This is followed by the interpretations of the results and finally, conclusions are drawn.

2.1. Pre-processing

2.1.1 Data

The first step in the pre-processing routine is data acquisition. The following data is used: geology, geomorphology, hydrology and existing biotopes. Additionally, a detailed Digital Elevation Model (DEM) is used to obtain the elevation and to calculate the slope of the area. An overview of the metadata is shown in table 2.

1. Pre-processing • 1. Data acquisistion • 2. Data preparation • 3. Determining grid size 2. Analysis • 1. DEM derivatives • 2. Calculating Geodiverstiy Index • 3. Classification • 4. Calculate correlation 3. Deliverables & Results • 1. Visualisation of the geodiversity map • 2. Correlation matrices • 3. Graphs • 4. Interpretating the results

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12 Table 2: Metadata of used datasets

2.1.2. Data preparation

First, the different data is loaded into ArcGIS Pro. Secondly, they are set to the correct coordinate system; projected coordinate system: MGI Austria GK West and geographic coordinate system: GCS MGI. This is followed by transforming the polygon data into raster data using the Polygon-to-Raster tool in ArcGIS. The grid size of these rasters are based on Hengl (2006) and will be explained in section 5.1.3. Where necessary, the clip tool is used to clip the datasets to the extent of the study area (fig 1). All the maps of the used datasets can be found in Appendix F.

2.1.3. Determining grid size

In order to calculate the Geodiversity Index (GI) , a new grid is made. The method provided by Hengl (2006) is used to calculate an appropriate grid size. With this method, the coarsest, finest and recommended grid size can be obtained.

Finest grid size:

𝑃 ≥ 𝑆𝑁 ∗ 0.0001 (1)

Coarsest grid size:

𝑃 ≤ 𝑆𝑁 ∗ 0.0025 (2)

Recommended grid size:

𝑃 = 𝑆𝑁 ∗ 0.0005 (3)

where:

P = grid resolution and SN = scale number

Layer Description Data type Geographic

Coordinate system Scale/Cell size Publication date Source

LiDAR DEM Surface

elevation Raster MGI_Austria_ Transverse_M ercator 5x5 m 2015 Land Vorarlberg (2015) Geomorphology Geomorphology map

Vector GCS_MGI 1:10.000 1992 Seijmonsbergen (1992) Geology Tectonic overview Feature data GCS_MGI 1:50.000 2012 Geologische Bundesantstalt (2012)

Hydrology Rivers Polyline GCS_MGI 1:50.000 2015 Land Vorarlberg

(2015)

Hydrology Lakes Polygon GCS_MGI - 2015 Land Vorarlberg

(2015)

Biotopes Biotope data Polygon GCS_MGI - 2008 Vorarlberger

Biotopinventar (2008)

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The optimal grid size is based on these calculations and compared to the polygon size of the biotope data, so the data can be compared.

The scale number (SN) is based on the scale size of the geology map (1:50.000) which results in: finest = 5m, coarsest = 125m and recommended = 25m. Keeping in mind these results, the grid size of 100x100 is chosen for this research. This cell size is chosen because it is within the range of the coarsest and recommended and also maintains some of the variety within the different sub-indices. In addition, when performing tests with a smaller cell size, it turned out that there is no or few diversity in each cell which ultimately leads to a uniform geodiversity index.

With the Create-Fishnet tool in ArcGIS, this new grid with a cell size of 100 was made and will function as the basis of the analysis part.

2.2. Analysis

2.2.1. DEM derivatives

A detailed Digital Elevation Model, with a 5m cell size, is used to calculate the different DEM derivatives; elevation, slope and the wetness index of the area. The slope was calculated with the use of the Slope tool. It identifies the steepness at each cell of a raster surface (Esri, 2018).

2.2.2. Calculating Geodiversity Index

The first step in calculating the cell-based geodiversity index, is to determine the formula for the GI. This includes choosing the right sub-indices. In this research, the GI is based on formula 4.

𝐺𝐼 = 𝑇𝑑𝑖 + 𝐻𝑑𝑖 + 𝐺𝑚𝑑𝑖 + 𝐺𝑑𝑖 (4)

where:

GI = Geodiversity Index Tdi = Topographical diversity, Hdi = Hydrological diversity,

Gmdi = Geomorphological diversity and Gdi = Geological diversity.

Soils are not taken into account while calculating the GI because of practical and redundancy reasons. First, there is no detailed soil map available for the study area. When using the general soil map of Austria (Bundesanstalt fur Bodenwirtschaft, 1989), the results of the geodiversity index will be biased due to the low soil diversity. The second reason to exclude the soil data is that the combination of the geological and geomorphological will hold the same information as a soil map will do. Geology and geomorphology are highly correlated with soils (Gessler et al., 1995). When using the soil data, the information is included twice, so this will result in a biased geodiversity index.

The zonal statistics tool is applied to calculate the number of unique values (variety) per pre-defined grid cell of 100x100m. This derived the geomorphological and geological sub-indices (Gmdi and Gdi). The calculating of the Topographical diversity index and Hydrological diversity index has been done slightly different, and will be explained separately.

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𝑇𝑑𝑖 = 0.5 ∗ (𝑆𝑠𝑑 + 𝑆𝑟) + 0.5 ∗ (𝐸𝑠𝑑 + 𝐸𝑟) (5)

where:

Ssd = Standard deviation of slope diversity, Sr = Range of slope diversity,

Eds = Standard deviation of elevation diversity and Er = Range of elevation diversity.

The zonal statistics tool is used instead of the variety option, because of the wide range of slope and elevation. This prevents and overabundance of classes, so the Tdi does not have a bigger influence on the GI then the other sub-indices.

Formula 5 is based on Seijmonsbergen et al. (2018) where the authors used the standard deviation and range of slope and elevation. They found that slope and elevation are highly correlated, so in this research they both get a value of 0.5, so the Topographical diversity weights the same as the other sub-indices. The Hdi is also based on two input variables, so the variables are also multiplied by 0.5 for the same reason as for the Tdi. The individual values will be multiplied, so when the two values are summed they will count as one. This means that every individual input of the GI will have the same influence on the GI.

The hydrological diversity in the area, consists of the hydrology diversity and the topographical water index diversity. This is expressed in the following formula:

𝐻𝑑𝑖 = (0.5 ∗ 𝐻𝑦𝑑𝑖) + (0.5 ∗ 𝑇𝑊𝐼𝑑𝑖) (6)

where:

Hydi = Hydrology diversity and

TWIdi = Topographical wetness index diversity.

The hydrological diversity is calculated by first calculating the hydrology diversity. The same method as proposed by Seijmonsbergen et al. (2018) is applied; the scoring scheme is slightly different. Instead of summing the variation in a cell, the scoring scheme ranges from zero to two, where: no surface water (0), the presence of a lake(s) or river(s) (1), the presence of both lakes and rivers (2).

The Topographical Wetness Index (TWI) is calculated using a python script (Wolf & Fricker, 2013) which is used to make a toolbox in ArcMap 10.6.1 (appendix A). The DEM is used as the input, and the output is a dataset containing the Topographical Wetness index of the research area. Small sinks can occur in a LiDAR derived DEM, so the Python script fills in all those sinks (Wolf & Fricker, 2013). To use the data as an input variable for the GI formula, the

zonal statistics tool is used to calculate the mean within the previously determined 100x100

grid.

The outputs of the Tdi and the Hdi are reclassified with the natural breaks, also called “Jenks” classification into five classes. The natural breaks classes are based on natural groupings in the datasets. The class breaks are identified that best group similar values and that maximize the differences between classes. The features of the dataset are divided into classes whose boundaries are set automatically at the places where there are relatively big differences in the data values (ArcGIS Pro, 2018). The reclassification is done in order to keep the influence of the individual sub-indices at the same level. The Tdi is reclassified into 8 classes, which is

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the same amount of classes calculated for the Gmdi. Hebeler and Perves (2009) state that a relatively small number of topographic indices form the core of most descriptive use of DEMs within geomorphology. Since the Tdi and Gmdi are correlated (Hebeler & Perves, 2009), the same amount of classes is given. The Hdi is reclassified into 5 classes, which is the same as the output of the total GI.

Finally, the raster calculator tool is used to sum the values of the sub-indices according to formula 4.

2.2.3. Classification

When the geodiversity index is calculated, the classification is adjusted to five classes, ranging from 1 to 5 (Seijmonsbergen et al., 2018). Since Tdi and Gmdi exists of 8 classes, and Hdi and Gdi of five, the minimum amount of classes (5) is used to make the final classification of the geodiversity index. The classes are made using the Reclassify tool in ArcGIS Pro. For this purpose, the natural breaks classification is used. The classes were then named “very low”, “low”, “moderate”, “high” and “very high”.

2.2.4. Calculate correlation

To calculate how much of the variation in the Geodiversity Index is determined by the individual sub-indices, the Band Collection Statistics tool is used to calculate basic statistics (mean, variety, sum etc.) and to calculate variation and correlation matrices.

The Band Collection Statistics tool is also used to calculate the correlation between the Geodiversity Index and the five most common biotopes. For this last step, the geodiversity map was clipped to the extent of the five individual biotopes, otherwise the correlation between the geodiversity and the areas where no biotopes are present are also calculated, which will affect the end result.

2.3. Deliverables & Results

When the GI is calculated, it can be visualized in a map. In total 5 maps will be created, including the diversity maps of geomorphology, geology, topography and hydrology and the final Geodiversity map.

Correlation matrices of the geodiversity index and sub-indices and graphs of the distribution of the geodiversity index per biotope are created. Both the correlation matrices and graphs are included in the interpretation of the data and the results.

Finally, the derived results can be interpreted and possible policy recommendations can be made.

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3. Results

3.1. Geodiversity Index

The Geodiversity Index is a mathematical representation of the geodiversity in a research area. With the sub-indices geology, hydrology, geomorphology and topography, it provides good insight in this diversity. The results of this index that are calculated for the N.W. Rätikon Mountains and S. Walgau are shown in fig. 9.

The geodiversity map (fig. 9) shows that the area is highly diverse, with exaptation of the lower situated areas which are located in the Northern part of the study area. This might seem contradictory when looking at the Hydrological Diversity (Appendix B4.). The Hdi is inverted in comparison with the GI, i.e. the areas with a high GI have a low Hdi and vice versa. This is also notable in the correlation matrix (table 3). The Hdi has a negative correlation of -0,23, and the other sub-indices have a positive correlation with the GI.

The correlation matrix of table 3 shows how much of the variation of the Geodiversity Index is influenced by the four sub-indices (hydrology, geology, geomorphology and topography).

Table 3. Correlation Matrix Geodiversity Index and Sub-Indices

Layer GI Hdi Gmdi Gdi E range E sd S range S sd Tdi

GI 1 Hdi -0,22722 1 Gmdi 0,76889 -0,17018 1 Gdi 0,42429 -0,08233 0,12139 1 E range 0,56519 -0,76229 0,2252 0,11679 1 E sd 0,55883 -0,75153 0,22191 0,11557 0,98297 1 S range 0,59334 -0,57026 0,37431 0,10687 0,61322 0,60827 1 S sd 0,56661 -0,42538 0,38066 0,10692 0,48411 0,49629 0,89119 1 Tdi 0,63763 -0,76605 0,2811 0,12147 0,95344 0,94168 0,76477 0,63435 1

The Tdi is split up in Er, Esd, Sr and Ssd. These factors have been added individually to the correlation matrix so their influence on the GI is also assessed. It can be noted that their influence is very similar compared to each other, ranging from 0,55883 to 0,59334. Adding these four factors together results in the Tdi, which has together with the geomorphological diversity index the highest correlation with the GI and therefore have the highest influence.

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17 Figure 9. Geodiversity Index Vorarlberg, Austria

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3.2. Geodiversity and Biotopes

To provide more insight in the distribution of the geodiversity index values along the five different biotopes, bar charts are created with the geodiversity index on the x-axis and on the y-axis the counts of this value in the area where that specific biotope is present. It can be noticed that almost all of the biotopes, where the most index values are between “low” and “high” (fig. 11), have similar distributions.

Two biotopes will be discussed in more detail, namely Auen Quellwälder and Tobel-, Hang & Schluchtwälder. A table with the complete statistic overview of all biotopes can be found in Appendix C.

The two biotopes that are different to Hang-, Flach-, and Quellmoore, Magerwiesen, and Montan-Subalpine Nadelwälder, are Auen Quellwälder where the distribution of the GI values are right skewed and Tobel-, Hang & Schluchtwälder where the distribution is left skewed (fig. 10 and fig. 11) and can be explained by the composition of the biotope and its preference to water (Broggi et al., 1991).

Figure 10. Distribution of the GI of Auen Quellwälder

The distribution of the GI values, which ranges from “very low” to “very high”, for the biotope Auen Quellwälder is highest for the GI value of 1. The count per GI value decreases when the GI goes up.

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19 Figure 11. Distribution of the GI of Tobel-, Hang-& Schluchtwälder

The distribution of the GI values for the biotope Tobel-, Hang- & Schluchtwälder is highest for the GI value “high”. This is the opposite compared to the Auen Quellwälder.

To clarify the graphs of figures 10 and 11, tables 4 and 5 show the percentages of the count per index value of the total counts.

Table 4. Percentages distribution GI Auen Quellwälder

GI Count Percentage Very low 44723 50.8% Low 28808 32.7% Moderate 7056 8.0% High 5890 6.7% Very High 1593 1.8%

Table 5. Percentages distribution GI Tobel-, Hang- & Schluchtwälder

GI Count Percentage Very low 5219 3.1% Low 24526 14.7% Moderate 30318 18.1% High 75534 45.2% Very high 31504 18.9%

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Tables 6 and 7 show the counts and percentages of the sub-indices.

Table 6. Counts and percentages of sub-indices of Auen Quellwälder

Index Value Hdi Count Gmdi Count Gdi Count Tdi Count

% Hdi % Gmdi % Gdi % Tdi

1 999 56212 71449 79247 1.1 63.7 81.0 89.8 2 2785 17446 16249 4707 3.2 19.8 18.4 5.3 3 66088 10191 531 3407 75.1 11.5 0.6 3.9 4 18144 3107 0 809 20.6 3.5 0.9 5 0 890 0 57 1.0 0.1 6 0 413 0 0 0.5 7 0 0 0 0 Total Count 88016 88259 88229 88227

Table 7. Counts and percentages of sub-indices of Tobel-, Hang- & Schluchtwälder

Index Value Hdi Count Gmdi Count Gdi Count Tdi Count % Hdi % Gmdi % Gdi % Tdi 1 29538 13102 102182 6858 17.7 7.8 61.1 4.1 2 44772 62526 57805 14132 26.8 37.4 34.6 8.5 3 68070 58557 7023 26578 40.7 35.0 4.2 15.9 4 23116 24372 286 33205 13.8 14.6 0.2 19.9 5 1605 6936 0 47414 1 4.1 28.4 6 0 1679 0 31089 1.0 18.6 7 0 125 0 7565 0.1 4.5 8 0 0 0 400 0.2 Total Count 167101 167297 167296 167241

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Figure 12. Distribution of the GI of Hang-Flach- & Quellmoore, Magerwiesen and Montan-Subaline Nadelwälder

The distributions of the Hang-, Flach- & Quellmoore, Magerwiesen and Montan-subalpine Nadelwälder (fig. 12) assemble a normal distribution. The lowest and highest values are represented less than the middle values.

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4. Discussion

4.1. Discussion of the Results

The first research question of this research is: What is the geodiversity index of an area in the

N.W. Rätikon Mountains and S. Walgau?

This question is answered in the form of the geodiversity map (fig. 9) and sub-indices diversity maps (Appendix B).The difference in diversity over the area can be explained by the correlation matrix of the sub-indices in relationship to the geodiversity index. What is most notable is that the lower elevation areas have a lower geodiversity index. This is due to the nonexistence of a high topographic diversity, few geomorphological units and not much variation in geology. However, when looking at the Hydrological diversity index map (Appendix B4) it can be seen that this area is highly hydrological diverse, because this area is rich in lakes and rivers. This part of the area is the lowest elevation point in the area which means all the rivers and water lead to this area. The TWI calculates the amount of water present in a cell, and this is highest in this part. In addition, through this valley runs the main river of the area. These aspects cause the Hdi to be higher in this region.

The high diversity in the upper elevation parts are mainly caused by the high Tdi, Gdi and Gmdi on these parts. One cell of 100x100 meter consists of different geological units, a high difference in Sr, Ssd, Er and Esd, and a large diversity in geomorphology. Since the Tdi and Gmdi have the highest correlation with the GI, this will lead to a high diversity in this part of the research area.

In table 3 it is shown that both the Er and Esd, and also the Sr and Ssd, have similar correlation values with the total GI. In the methods this is one of the assumptions that is made, and that is why these values are multiplied by 0.5 and summed to form 1.

In addition, the Tdi and Gmdi have more classes (8) compared to the Hdi and Gdi (5). These differences were set because of the assumption they would have a higher diversity in and also higher influence on the area (table 3).

The second part of this research was focused on the relationship of the geodiversity index with biotopes in the research area. The correlation between the GI and biotopes could not be calculated with the use of ArcGIS, because of the difference in data types. Where the GI and biotope data are both categorical data, the GI is ordinal data and the biotopes are nominal data. In ArcGIS it is not possible to calculate statistics for these two data types. However, graphs are made to show the distribution of the geodiversity index per biotope. First, figure 10 shows the distribution of Auen Quellwälder. What is most notable about this graph is that this biotope is most present in areas with a low geodiversity index. The same can be seen when looking at the percentages of values “very low” and “low” of the GI in table 4. The presence of Auen Quellwälder at a low geodiversity index is due to the fact that this biotope is highly influenced by water, since the vegetation present in these biotopes only occurs in wet areas (Broggi et al., 1991). As stated before, the Hdi has a negative correlation with the GI, which means when there is a low GI, it is most likely that there is a high Hdi. This is due to the difference in slope and elevation. In the parts of high elevation and high slope, the runoff of water is also high, therefore the TWI is low. At a flat terrain, the water is able to For Auen Quellwälder, the percentages for a Hdi of 3 is 75.1% (table 6). This corresponds with the high influence of water on this biotope. The same can be stated for the biotope “Tobel-, Hang- & Schluchtwälder”. This biotope is most present in areas with a high GI (45.2% of GI 4). This is influenced by the high preference of steep slopes and large elevation differences (Tdi).

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4.2. Recommendations Future Research

This research focusses on a fine-scaled research of the Geodiversity Index in the study area located in the N.W. Rätikon Mountains and the S. Walgau. The geodiversity index has been calculated for different study areas in multiple researches (Anderson et al., 2015; Seijmonsbergen et al., 2018; Seijmonsbergen et al., 2018) however, the fine-scaled methodology provides a new angle of approach in researching this geodiversity on a local scale. Therefore, this research provides new methods of implementing the fine-scaled approach. Since little research has been done, it was not possible to calculate the correlation with the geodiversity and biotopes. However, it provides a workflow for calculating the geodiversity index that can be used in future researches and can be elaborated on. This means, some parts of the research can be changed in these future researches and will be listed and elucidated in the following section.

First, the classification methods that is used, the “Jenks” method, influences the final result, since the values are put in a class and this can differ when using other classification methods (e.g. manual interval, defined interval, equal interval, quantile, geometrical interval and standard deviation). For this research this method has been chosen, because the most common values will be in the middle class and the rarer values will be classified more towards the lower and upper classes. This will give more insight in the “outliers” of the data and these are the most interesting in assessing the geodiversity index in this research. When choosing for a different classification method, the class boundaries will be set at different places, and therefore leading to a different GI. To clarify, when using equal interval classification, all classes contain the same amount of values in each class and this will cause the upper and lower GI value to be higher in quantity.

Second, the research question: “How is the geodiversity index related with biotopes in the research area?” could not be answered with statistically ground results. Correlation values could not be calculated with the use of ArcGIS. This is due to the fact that the two datasets contain different values (ordinal and nominal), so no correlation could be calculated. With the use of Excel, graphs were made to show the distribution of the geodiversity index per biotope. However, this is not a statistical test, and therefore does not provide a significant answer to the question. A possible method to solve this problem is to load the data in MATLAB (2018b) and treat the data as categorical data. Only the data of the areas where biotopes are present should be loaded into MATLAB. The GI data should be clipped to the extent of the individual or all the biotope data. When not doing this, the results are biased, because most of the research area is not covered with biotopes that are researched.

Thirdly, in this research only the largest biotopes in the area are used to research the relationship with the geodiversity index. When using a script in e.g. MATLAB, all the biotopes in the area can be included in the research and will provide a more complete overview of the relationship in this research area.

Moreover, when using data that has to be processed multiple times during the research, there might be an issue with the accuracy of the final data. In every step in the process, minor errors can result in larger errors in the final product; in this case the GI. This problem is set aside, since it is not the focus of this research, which is to provide a workflow to calculate the geodiversity index in this area.

Finally, when using the workflow that is provided by this research, it would be possible to asses potential biotopes in the area. These are biotopes that are not yet present, but could be by changing biotic and abiotic factors. For instance, when an area is drained by humans, biotopes influenced by water are unable to settle in that area. If the drainage stops, it might be

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possible for these biotopes to grow. When these possible biotopes are mapped and researched, good detailed policy recommendations could be made for biotope conservation. For instance, this local scaled research could help complete the seven step method for bio conservation made by The Nature Conservancy (Grovers et al., 2019).

5. Conclusion

Geodiversity has long been recognized by ecologist, however it has been only recently acknowledged as a concept for bio conservation. It can play an important role in geo and bio conservation and in creating possible policy recommendations.

This research provides a workflow consisting of three steps: 1) Pre-processing, 2) Analysis and 3) Results and deliverables. Because of the fine-scaled approach, the results are detailed and useful for future research and potential policy recommendations.

In the research area, which is located in the N.W. Rätikon Mountains and S. Walgau, the geodiversity index (GI) has been calculated with a cell size of 100x100m. The geodiversity index includes the diversity of hydrology, geology, geomorphology and topography. With the use of ArcGIS Pro, these sub-indices are calculated and summed to form the GI. Five classes are made using the Jenks classification method, ranging: “very low”, “low”, “moderate”, “high” and “very high”. The geodiversity is highly divers in the research area. In the Northern area, the GI is low and in the South it ranges from “very low” to “very high”. The Topographical diversity index correlates the most with the GI, and the Hydrological Diversity index the least. Finally, the geodiversity index is compared with the presence of the five most occurring biotopes in the research area. Biotopes are areas of uniform environmental conditions that provide a living place for a specific assemblage of plants and animals (Rizwam & Athapattu, 2014). The five biggest biotopes have been assessed: “Auen Quellwälder”, “Tobel-, Hang- & Schluchtwälder”, Hang-, Flach- & Quellmoore”, “Magerwiesen (Trespe)” and “Montan-Subalpine Nadelwälder”. The biotope Auen Quellwälder is most common in the areas with a low GI. This is due to the fact that it is highly related with a high Hdi. Because there is a negative correlation between GI and Hdi, the GI of this biotope is low. For biotope Tobel-, Hang- & Schluchtwälder it is vice versa. It is most common in areas with a high GI, because of the high influence of Tdi. The other three biotopes show no clear relationship with the GI, since they cover areas with GI values of mainly “low”, “moderate” and “high”.

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6. References

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University Press, Cambridge p.632

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Lawler, J.J., Ackerly, D.D., Albano, C.M., Anderson, M.G., Dobrowski, S.Z., Gill, J.L., Heller, N.E., Pressey, R.L., Sanderson, E.W., Weiss, S.B. (2015). The theory behind, and challenges of, conserving nature’s stage in a time of rapid change. Conservation Biology, 29, 618–629.

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7. Appendices

Appendix A – Python Script for calculating Topographic Wetness Index

"""

TWI-filled-plus0.1constant.py Topographic Wetness Index 2013-11-12

Jeffrey Wolf (EEB); G. Andrew Fricker (GEOG) UCLA

This script was written to be used as a tool in ArcGIS. This python script can be imported to create a TWI tool.

Inputs are the workspace and the input DEM, output is the TWI layer We chose to fill all sinks due to some small sinks in a lidar derived DEM.

We also add a small constant to the denominator to avoid dividing by zero. The original script was based off the arcpy script written by Prasad Pathak. http://arcscripts.esri.com/details.asp?dbid=16750

This revised script converts the terrain slope in degrees to radians

This script also uses the default settings for the flow accumulation raster however different methods to calculate flow accumulation can dramatically change the results of the TWI

""" import

arcpy, math

if __name__ == '__main__':

arcpy.CheckOutExtension("Spatial")

# Define workspace and set input and output files

arcpy.env.workspace = arcpy.GetParameterAsText(0)

inDEM =

arcpy.GetParameterAsText(1) outTWI

= arcpy.GetParameterAsText(2)

# Intermediates

arcpy.AddMessage("Filling DEM.\n")

DEM_filled = arcpy.sa.Fill(inDEM)

arcpy.AddMessage("Creating flow direction.\n")

outFlowDirection = arcpy.sa.FlowDirection(DEM_filled, "FORCE")

arcpy.AddMessage("Creating flow accumulation.\n")

#outFlowAccumulation = arcpy.sa.FlowAccumulation(outFlowDirection, "", "FLOAT")

+ 1 outFlowAccumulation = arcpy.sa.FlowAccumulation(outFlowDirection, "",

"INTEGER") + 1

arcpy.AddMessage("Creating

slope.\n") slope =

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arcpy.AddMessage("Converting slope in degrees to slope in radians")

# 2Pi radians = 360 degrees # Pi radians = 180 degrees

# conversion: Pi radians/180 degress

slope_radians = slope * math.pi/180.0

# Output

arcpy.AddMessage("Creating TWI\n")

TWI = arcpy.sa.Ln(outFlowAccumulation / (arcpy.sa.Tan(slope_radians)+.01))

TWI.save(outTWI)

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Appendix B – Diversity Maps of Sub-Indices

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30 Appendix B2. Geomorphological Diversity Map

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31 Appendix B3. Topographic Diversity Map

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32 Appendix B4. Hydrological Diversity Map

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Appendix C – Biotope and Geodiversity Statistics

O BJE CT ID ag g_ LR Ty p CO U N T A RE A M IN M A X RA N G E M EA N ST D SU M V A RIE TY M A JO RIT Y M IN O RIT Y M ED IA N 1 H an g-, F la ch - u nd Q ue llmo or e 93549 2338725 1 5 4 2. 88 45 1 1. 06 58 96 269843 5 2 1 3 2 M ag er w ie se n (T re sp e) 120168 3004200 1 5 4 2. 70 51 21 1. 03 05 56 325069 5 2 5 2 3 M ag er w ie se n (K omp le x) 16837 420925 1 5 4 2. 49 37 93 0. 82 00 51 41988 5 2 5 2 4 Pf ei fe ng ra s-Str eu w ie se n 35022 875550 1 5 4 2. 25 06 42 0. 90 99 39 78822 5 2 5 2 5 To be l-, H an u nd S ch lu ch tw äl de r 167077 4176925 1 5 4 3. 61 99 72 1. 04 57 28 604814 5 4 1 4 6 H oc hs ta ud en - u nd H oc hg ra sf lu re n 8598 214950 1 5 4 2. 90 47 45 1. 15 32 38 24975 5 2 1 3 7 M ag er w ei de n 12491 312275 1 5 4 2. 05 78 02 1. 09 21 17 25704 5 1 5 2 8 ar te nr ei ch e Fe tt w ie se n (G ol dh af er ) 9242 231050 1 5 4 2. 62 81 11 1. 09 73 41 24289 5 2 5 2 9 U fe rg eh öl zs äu me 642 16050 1 5 4 2. 81 30 84 1. 44 98 74 1806 5 3 4 3 10 Bä ch e un d Fl üs se 19019 475475 1 5 4 3. 49 92 9 0. 97 41 58 66553 5 4 1 4 11 Fe ld ge hö lz e, H ec ke n, G eb üs ch e 911 22775 1 4 3 1. 95 17 01 1. 21 90 74 1778 4 1 3 1 12 V or - u nd Ju ng w äl de r 16414 410350 1 5 4 2. 79 26 77 1. 03 45 99 45839 5 2 5 3 13 A ue u nd Q ue llw äl de r 88000 2200000 1 5 4 1. 75 82 73 0. 97 71 83 154728 5 1 5 1 14 Rö hr ic hte 3672 91800 1 5 4 3. 05 58 28 0. 97 26 03 11221 5 3 1 3 15 Fe tt w ei de n 1943 48575 1 4 3 1. 25 42 46 0. 57 58 94 2437 4 1 4 1 16 mo nta n-su ba lp in e N ad el w äl de r 153095 3827375 1 5 4 3. 01 49 25 1. 06 14 64 461570 5 4 5 3 17 an th ro po ge ne S ti llg ew äs se r 4691 117275 2 4 2 3. 48 19 87 0. 71 72 43 16334 3 4 2 4 18 M ag er w ie se n (G la tt ha fe r) 2219 55475 1 5 4 3. 73 36 64 1. 12 48 34 8285 5 4 1 4 19 ku ltu rl an ds ch af tl ic he B io top ko mp le xe 8791 219775 1 4 3 2. 08 94 1 0. 91 01 19 18368 4 2 4 2 20 Fo rs te u nd S ch lä ge 863 21575 1 2 1 1. 04 40 32 0. 20 51 67 901 2 1 2 1 21 G ro ßs eg ge nr ie de r 217 5425 3 5 2 3. 83 87 1 0. 85 15 5 833 3 3 4 4 22 Bü rs tl in gs ra se n 23357 583925 1 5 4 3. 72 13 68 0. 96 77 04 86920 5 4 1 4 23 su ba lp in -a lp in e Bi otop ko mp le xe 46922 1173050 1 5 4 3. 09 01 07 0. 92 31 48 144994 5 4 5 3 24 La ub -W ei de -W äl de r 3744 93600 1 5 4 3. 02 99 15 0. 89 88 14 11344 5 3 1 3 25 Se en u nd W ei he r 1325 33125 1 5 4 3. 09 58 49 0. 92 30 09 4102 5 3 1 3 26 H oc hmo or e 403 10075 1 4 3 2. 25 55 83 0. 88 92 45 909 4 2 4 2 27 Be rg w al db io top e 14947 373675 2 5 3 4. 16 73 91 0. 69 23 68 62290 4 4 2 4 28 Q ue lle n un d Q ue llf lu re n 151 3775 2 5 3 3. 36 42 38 1. 14 21 11 508 3 4 5 4 29 G rü nl an d fe uc hte r b is n as se r S ta nd or te 473 11825 1 3 2 2. 30 65 54 0. 82 57 86 1091 3 3 2 3 30 w är me lie be nd e La ub w äl de r 1861 46525 1 5 4 2. 95 32 51 0. 86 88 7 5496 5 2 5 3 31 Ü be rg an gs - u nd Zw is ch en mo or e 36 900 2 2 0 2 0 72 1 2 2 2

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Appendix D – Biotope Tables

Appendix D1. Biotope percentages

Table 1. Percentages distribution GI Hang-, Flach- & Quellmoore

GI Count % Total 1 4634 4.9 2 38906 41.5 3 19158 20.5 4 24531 26.2 5 6418 6.9

Table 2. Percentages distribution GI Magerwiesen (Trespe)

GI Count % Total 1 11196 9.6 2 49145 42.3 3 28059 24.2 4 27773 23.9 5 4142 3.6

Table 3. Percentages distribution GI Montan Subalpine Nadelwälder

GI Count % Total 1 10579 6.9 2 44513 29.1 3 38926 25.4 4 50210 32.8 5 8873 5.8

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Appendix D2. Counts and Percentages of Sub-Indices per Biotope Table 1. Counts and percentages of sub-indices of Hang-, Flach- & Quellmoore

Index Value Hdi Count Gmdi Count Gdi Count Tdi Count % Hdi % Gmdi % Gdi % Tdi 1 602 9099 57546 15446 0.6 9.7 61.4 16.50 2 19437 25383 33033 43506 20.8 27.1 35.3 46.47 3 40200 31165 3105 31110 42.9 33.3 30.3 33.23 4 25916 17898 0 3524 27.7 19.1 3.76 5 7492 7375 0 24 8.0 7.9 0.02 6 0 2520 0 0 2.7 7 0 244 0 0 0.3 Total Count 93647 93684 93684 93610

Table 2. Counts and percentages of sub-indices of Magerwiesen (Trespe)

Index Value Hdi Count Gmdi Count Gdi Count Tdi Count % Hdi % Gmdi % Gdi % Tdi 1 784 15204 83147 2691 0.6 12.52 68.4 2.21 2 41882 36827 34505 42728 34.8 30.32 28.4 35.15 3 66959 39723 3764 67265 55.7 32.70 3.1 55.34 4 10267 21298 57 7578 8.5 17.53 0.1 6.23 5 423 7839 0 809 0.4 6.45 0.67 6 0 544 0 426 0.45 0.35 7 0 38 0 54 0.03 0.04 Total Count 120315 121473 121473 121551

Table 3. Counts and percentages of sub-indices of Montan-Subalpine Nadelwälder

Index Value Hdi Count Gmdi Count Gdi Count Tdi Count % Hdi % Gmdi % Gdi % Tdi 1 51853 18776 97277 47 33.9 12.3 63.5 0.03 2 71411 69146 52643 14662 46.6 45.1 34.4 9.58 3 26027 44035 3249 37296 17.0 28.7 2.1 24.36 4 3580 16939 0 39830 2.3 11.1 26.01 5 230 3918 0 25209 0.2 2.6 16.46 6 0 355 0 23417 0.2 15.29 7 0 0 0 12655 8.26 Total Count 153101 153169 153169 153116

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Appendix E – Graphs Distribution Biotopes

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Appendix F – Maps of sub-indices

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42 Appendix F2. Geology Map of Vorarlberg

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44 Appendix F3. Elevation Map of Vorarlberg

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45 Appendix F4. Slope Map of Vorarlberg

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46 Appendix F5. Geomorphology Map of Vorarlberg

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Assessing the geodiversity index and the relation with biotopes in the N.W. Rätikon mountains and S. Walgau, Austria.