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Figure D source code 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
Located in Data and maps Maps and graphs
Figure 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
Located in Data and maps Maps and graphs
Figure 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.
Located in Data and maps Maps and graphs
Figure Effect of road network density on the abundance of brown hare in Canton Aargau, Switzerland
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Located in Data and maps Maps and graphs
Figure 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).
Located in Data and maps Maps and graphs
Figure Examples of the use of effective mesh density in monitoring systems of sustainable development, biodiversity, and landscape quality
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Located in Data and maps Maps and graphs
Figure Sybase Advantage Database Server Four ecological impacts of roads on animal populations and the time lag for their cumulative effect
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Located in Data and maps Maps and graphs
Figure 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).
Located in Data and maps Maps and graphs
Figure 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
Located in Data and maps Maps and graphs
Figure application/vnd.symbian.install 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.
Located in Data and maps Maps and graphs
European Environment Agency (EEA)
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Phone: +45 3336 7100