Landscape fragmentation in Europe

Publication Created 17 Aug 2011 Published 05 Sep 2011
EEA Report No 2/2011
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Joint EEA-FOEN report

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Landscape fragmentation 2009 per 1 km² grid - data files Landscape fragmentation 2009 per 1 km² grid - data files Fragmentation data in CSV and GeoTIFF format
Illustration of the statistical analysis using multiple linear regression Illustration of the statistical analysis using multiple linear regression This simple example uses the data of the NUTS-X regions from Belgium (FG-B2). The effective mesh density (seff) is shown as the response variable as a function of two predictor variables: population density (PD, between 64 and 600 people per km2) and gross domestic product per capita (GDPc, between 20 500 and 37 000 euros PPs). The gridded plane shows the predicted values for the effective mesh density for each combination of PD and GDPc. The differences between the observed values of seff (shown as small squares) and the predicted values are shown as perpendicular lines and are called residuals. In this example, the predicted level of fragmentation increases with higher population densities and with higher gross domestic product per capita, and the variation in population density has a higher influence than the variation in GDPc.
Overlay of the Natura 2000 network with fragmentation geometry FG-A2 'Major and medium anthropogenic fragmentation', showing Spain and Portugal as an example Overlay of the Natura 2000 network with fragmentation geometry FG-A2 'Major and medium anthropogenic fragmentation', showing Spain and Portugal as an example -
Overlay of the wildlife corridor network of trans-regional importance in Switzerland with the Swiss fragmentation geometry FG4 'Land areas below 2 100 m' Overlay of the wildlife corridor network of trans-regional importance in Switzerland with the Swiss fragmentation geometry FG4 'Land areas below 2 100 m' The trans-regional corridor network in Switzerland for terrestrial fauna includes the wildlife corridors and the trans-regional movement axes. Red, yellow and green colours indicate the sizes of the remaining patches.
Examples of the use of effective mesh density in monitoring systems of sustainable development, biodiversity, and landscape quality Examples of the use of effective mesh density in monitoring systems of sustainable development, biodiversity, and landscape quality -
Illustration of the level of landscape fragmentation measured by effective mesh size and represented as regular gridroot transformation for seff. Illustration of the level of landscape fragmentation measured by effective mesh size and represented as regular gridroot transformation for seff. Map shows a regular grid at a different cell size for each countrie according to its value of fragmentation for FG-B2
Absolute differences between the observed and the predicted values of seff using the six global models for groups A to F Absolute differences between the observed and the predicted values of seff using the six global models for groups A to F Map shows the differences between the level of fragmentation for FG-B2 calculated and the level of fragmentation predicted by 6-group-European model in the 28 countries investigated
Predicted and observed values of effective mesh density according to the six predictive models for the six groups of regions studied in Europe Predicted and observed values of effective mesh density according to the six predictive models for the six groups of regions studied in Europe Effective mesh density values by NUTS-X region for Fragmentation Geometry FG-B2 in 2009. The NUTS-X regions represented by points above the diagonal line are more fragmented than predicted and those represented by by points below the diagonal line are less fragmented than predicted. All models shown include all available variables in each group and use a square root transformation for seff.
Six groups of regions identified and used for separate analysis Six groups of regions identified and used for separate analysis -
Predicted and observed values of effective mesh density according to the pan‑European predictive model Predicted and observed values of effective mesh density according to the pan‑European predictive model Effective mesh density values by NUTS-X region for Fragmentation Geometry FG-B2 in 2009. The NUTS-X regions represented by points above the diagonal line are more fragmented than predicted (e.g., FR107 Val de Marne) and those represented by by points below the diagonal line are less fragmented than predicted (e.g., CH04 Zurich).
Absolute differences between the observed and the predicted values of seff according to the pan-European model Absolute differences between the observed and the predicted values of seff according to the pan-European model Map shows the differences between the level of fragmentation for FG-B2 calculated and the level of fragmentation predicted by the pan-European model in the 28 countries investigated
Landscape fragmentation per capita (as the ratio of effective mesh density to Landscape fragmentation per capita (as the ratio of effective mesh density to Map shows the level of fragmentation per capita for FG-B2 in the 28 countries investigated
Bar diagram of effective mesh density values per NUTS-X region for FG-B2 in 2009 Bar diagram of effective mesh density values per NUTS-X region for FG-B2 in 2009 Effective mesh density values by NUTS-X region for Fragmentation Geometry FG-B2 in 2009. Fragmentation geometry has been created from input data (TeleAtlas roads/rails, CLC urban classes, mountain areas / mountain ridges based on Nordregio and WorldClim data and rivers/lakes based on Catchment Characterisation and Modelling (CCM) v.2 database and CLC database) and landscape fragmentation metrics (Jaeger 2000) has been calculated.
Landscape fragmentation in NUTS-X regions in 2009 Landscape fragmentation in NUTS-X regions in 2009 Map shows the level of fragmentation for FG-B2 in the 581 NUTS-X regions investigated
Bar diagram of effective mesh density values per country for FG-B2 in 2009 Bar diagram of effective mesh density values per country for FG-B2 in 2009 Effective mesh density values by country for Fragmentation Geometry FG-B2 in 2009. Fragmentation geometry has been created from input data (TeleAtlas roads/rails, CLC urban classes, mountain areas / mountain ridges based on Nordregio and WorldClim data and rivers/lakes based on Catchment Characterisation and Modelling (CCM) v.2 database and CLC database) and landscape fragmentation metrics (Jaeger 2000) has been calculated.
Landscape fragmentation per 1 km² grid in 2009 Landscape fragmentation per 1 km² grid in 2009 Map shows the patterns of fragmentation in the 29 countries investigated based on a grid of cells size of 1 km2
Landscape fragmentation per 1 km² grid in the region around the Alps in 2009 Landscape fragmentation per 1 km² grid in the region around the Alps in 2009 -
Landscape fragmentation per 1 km² grid in the Channel region in 2009 Landscape fragmentation per 1 km² grid in the Channel region in 2009 Map shows the patterns of fragmentation in the Channel region investigated based on a grid of cells size of 1 km2
Landscape fragmentation per country in 2009 Landscape fragmentation per country in 2009 Map shows the level of fragmentation for FG-B2 in the 29 countries investigated
Landscape fragmentation per country for all three fragmentation geometries Landscape fragmentation per country for all three fragmentation geometries Map shows the level of fragmentation in the 29 countries investigated
Illustration of fragmentation geometry FG-B2 using an example from Switzerland and adjacent countries Illustration of fragmentation geometry FG-B2 using an example from Switzerland and adjacent countries The barriers are shown in black (built-up areas, roads, railways), and the colours indicate the sizes of the remaining patches in the landscape. Lake Constance and Lake Geneva are visible at the top and on the left of the map, respectively. In FG-B2, the lakes and mountains were considered as barriers, but were not included in the reporting units.
Example illustrating the relationship between effective mesh size and effective Example illustrating the relationship between effective mesh size and effective In this hypothetical example, the trend remains constant. A linear rise in effective mesh density (right) corresponds to a 1/x curve in the graph of the effective mesh size (left). A slower increase in fragmentation results in a flatter curve for effective mesh size, and a more rapid increase produces a steeper curve. It is therefore easier to read trends off the graph of effective mesh density (right).
Illustration of the behaviour of five landscape metrics in the phases of shrinkage and attrition of the remaining parcels of open landscape due to the growth of an urban area Illustration of the behaviour of five landscape metrics in the phases of shrinkage and attrition of the remaining parcels of open landscape due to the growth of an urban area First row: change of the landscape over time (black lines = highways, black area = residential or commercial area; size of the landscape: 4 km × 4 km = 16 km2). Only the effective mesh size behaves in a suitable way (bottom diagram). APS and n both exhibit a jump in their values (even though the process in the landscape is continuous); DTL and nUDA100 do not respond to the increase in fragmentation. (meff = effective mesh size, n = number of patches, APS = average patch size, nUDA100 = number of large undissected low-traffic areas > 100 km2, DTL = density of transportation lines).
Four ecological impacts of roads on animal populations and the time lag for their cumulative effect Four ecological impacts of roads on animal populations and the time lag for their cumulative effect -
Effect of road network density on the abundance of brown hare in Canton Aargau, Switzerland Effect of road network density on the abundance of brown hare in Canton Aargau, Switzerland -

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