Nature without barriers Natura2000 sites as Green Infrastructure in the Austrian-Hungarian transborder region Fertö-Hansag-Neusiedlersee Thomas Wrbka Michael Kuttner Univ. Vienna - Department of Biodiversity & Botany International Workshop on Remote Sensing and GIS for Monitoring of Habitat Quality, Vienna, 24 25 September 2014 1
Study Aims Assessment of Ecological Functionality of (agri)cultural landscapes, with special emphasis on Central European transboundary regions Identification of Green Infrastructure and investigating the particular role of protected sites (eg. Natura2000) >> operational rapid assessment methods >> proxy indicators for biodiversity in absence of fullcoverage data on species-distribution and habitat-quality
Scientific concepts: pattern & process paradigm semi-natural landscape increasing human influence Landscape structure captures frozen energycascades and matterflows ( landscape ecology ) The excessive use of fossil energy creates simple geometry in agricultural landscapes ( fractal geometry ) Landscape elements, landscape types, regions, etc. are part of a hierarchical system ( hierarchy theory ) intensive agriculture
Scientific concepts: landscape structure vs. naturalness Relationship between land use intensity / hemerobiotic state and boundary complexity (Moser et al 2002): Boundary complexity (SUM-NSCP) 5000 R 2 = 0.51 4000 3000 2000 1000 alpine pasture 0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Land use intensity (Avg-Hemeroby) conserved landscape structure but intensive land use Negative corelation between boundary complexity and degree of naturalness can be assessed Cropland and vineyards: rectangular, low NPSC Natural inland salt marshes: irregular, high NPSC Scatterdiagramm of land use intensity (Hemerobiotic state) and landscape complexity (NSCP = Number of Shape Characterstic Points) ; r²=0,51, p<0,01
Study Area: Neusiedlersee / Fertö-Hansag Central Europe Transborder regions Variety of landforms Different landscape character types High significance for nature conservation
different ecological context! Elevated terrace dominated by cropland Sand & salt lake country Mires, Alder carrs & other wetlands
Environmental stratification: main landforms
Protected islands surrounded by cropland
Stratified randomly selected landscape samples
Methodological framework & workflow
Landcover maps derived from orthofotointerpretation and fieldmapping
Aggregation of LC-types to functional elements
Ecological functionality 1. Calculation of landscape metrics for each landscape element 2. Rule-based assessment of indices >>> ecological functionality
landscape level: Calculation of landscape metrics» ex.: area-weighted mean Shape Index increases with shape complexity LID LCT PA NP LPI LSI SHAPE_AM TCA (ha) CONTAG CONNECTDIVISION PR SHDI e4830n2759 1 1 293 12.4522 15.8121 2.8882 24.622 67.8757 11.2216 0.9535 21 1.8398 e4834n2764 1 1 255 4.3917 16.7466 2.5196 11.6559 63.2776 5.6561 0.9831 25 2.2173 e4835n2756 1 2 112 12.7187 10.5592 2.0953 97.6894 90.6284 11.4913 0.9367 13 0.4116 e4837n2757 1 2 252 13.3291 14.5501 2.3317 29.3513 77.8041 7.186 0.9605 23 1.2702 e4846n2751 2 2 67 19.6341 7.1937 1.9229 133.5865 80.7201 24.1206 0.8937 15 0.9893 e4846n2790 3 1 193 9.7616 14.0674 2.5046 48.1347 82.0791 7.6401 0.9669 17 0.9119 e4849n2789 3 1 236 8.3996 20.6926 3.4004 24.2118 78.2346 5.0198 0.9729 17 1.0974 e4850n2785 3 1 303 7.5396 20.7874 2.8818 32.2623 73.0078 5.6163 0.9727 21 1.4913 e4812n2768 3 2 280 5.5726 18.9621 2.4361 24.5824 76.1336 4.7227 0.9839 22 1.336 e4847n2788 3 2 217 33.5369 12.4937 2.4475 100.5851 77.493 4.8819 0.875 17 1.1814 e4843n2784 5 1 162 15.3729 12.2439 2.2465 66.7613 81.0874 7.5204 0.9474 16 0.9508 e4844n2783 5 1 132 25.2054 11.4531 2.4957 86.4574 85.1107 10.874 0.8861 18 0.7706 e4837n2783 5 2 129 15.4035 11.375 2.3819 90.4842 85.8416 11.4177 0.937 15 0.6849 e4844n2779 5 2 139 9.6811 12.8551 2.1764 45.5292 81.3767 8.2345 0.9642 16 0.9284 e4846n2779 5 2 126 12.0447 10.815 1.9928 63.216 72.8613 10.8018 0.9559 15 1.383 e4819n2761 7 1 1094 3.9249 34.9893 2.8167 1.4605 70.0324 2.355 0.9911 27 1.7305 e4816n2759 7 2 213 15.6077 12.1553 2.1874 55.5761 69.5512 12.242 0.9472 21 1.7509 e4817n2779 8 1 57 17.4419 11.9214 3.796 35.5489 79.0156 14.23 0.9207 6 0.6737 e4808n2771 8 2 134 73.9683 6.9662 2.6955 144.3175 82.3238 13.0319 0.4498 18 0.9658
Example: ENN (Euclidian nearest neighbor) Output values from Fragstats: Transformed metrics: Transformed & normalised: 100 value for negative relation: Arithmetic mean:
Assessment rules for landscape metrics Index has positive (+) or negative (-) influence on ecological functionality
Structural Functionality In Different Landscapes 1
Structural Functionality In Different Landscapes 2 >>> better structural functionality in landscapes with higher share of forests & wetlands
Protected vs.unprotected sites: Example Lake Basin >>> landscape samples with a high share of protected areas have a higher structural functionality
Methodological framework & workflow (ctd.)
GUIDOS and MSPA Freeware program, written by Peter Vogt at the JRC Based on binary raster maps it allows for several spatial analysis, generating a multithematic output In our case, we only performed the MSPA (Morphological Spatial Pattern Analysis) so far:
MSPA: Analysis of ecological networks Specialists are using +/- less disturbed and semi(natural) parts of a landscape as home ranges like forests, natural grasslands, hedges, old fallow land, orchards Edge species appear on the boundaries and transitions between several types of open cultural land categories (e.g. field or grassland margins) as well as on the edges of woodlands and open land.
Allocation of travelling costs Average travelling costs were allocated for each single Sample site within the Investigation area To ensure equal conditions for the allocation, a standardized sampling method was undertaken in every sample site:» 1. The centroid of the largest core area was selected as the starting point.» 2. A buffer zone, encompassing a 1km radius was set, originating from the Core area centroid» 3. At the circular line of the buffer -points were set in 15 degree intervals and 7 of them were randomly chosen as end points The actual travelling costs were calculated in ArcView using PATHMATRIX (Ray N. (2005)), a tool to compute effective geographic distances among samples based on least cost path algorithms. After that, the resulting values were transformed and and the mean value for each sample site was calculated
Habitat Suitability of Green Infrastructure and Cost Surface mapping (1): * Mean ratio of Core areas and Connectivity; Agricultural dominated matrix; Average Dispersal Costs *
Functionality Of Ecological Infrastructure Based On Cost Surface
Functionality & Travelling Costs Log. Regression corr. r²=0.729 Quad. Regression corr. r²=0.872 Log. Regression corr. r²=0.677 overall functionality per sample site is strongly depending on areal share of (highly-functional) GI- elements travelling costs of the DSG is also strongly depending on areal share and Functionality of GI-elements
Correlation ES Provision / Structural Functionality In addition: Structural functionality is positively corelated with the provision of main ecosystem services, like habitat and provisioning function, regulation etc.
Protected vs. Unprotected Areas as Ecological Infrastructure? landscape samples with a high share of protected areas have more and larger core areas & corridors >>> protected areas can be seen as important elements of ecological infrastructure
Thank You for Your Attention! References Kuttner, M., Hainz-Renetzeder, C., Hermann, A., Wrbka, Th. (2013): Borders without barriers Structural functionality and green infrastructure in the Austrian Hungarian transboundary region of Lake Neusiedl. Ecological Indicators 31; 59-72. Hermann, A., Kuttner, M., Hainz- Renetzeder, C., Konkoly-Gyuró, É, Tirászi, Á, Brandenburg, C., Allex, B., Ziener, K., Wrbka, T. (2014): Assessment framework for landscape services in European cultural landscapes: An Austrian Hungarian case study. Ecological Indicators 37; 229-240.