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Landschaftsschutzgebiete Landkreis Lüchow-Dannenberg

Räumlichen Abgrenzung von Landschaftsschutzgebieten (LSG) im Landkreis Lüchow- Danneberg gemäß zugrunde liegender Verordnung.

Bodengroßlandschaften der Bundesrepublik Deutschland 1:250.000

Kleinmaßstäbige Bodenübersichtskarten werden in der Regel aus groß- oder mittelmaßstäbigen Bodenkarten durch räumliche Generalisierung und inhaltliche Aggregierung abgeleitet. Um dabei auch überregional ein vergleichbares Vorgehen zu sichern, ist eine einheitliche pedoregionale Gliederung erforderlich. Der vorliegende Datensatz auf dem Aggregierungsniveau der Bodengroßlandschaften umfasst die 36 wichtigsten bodenkundlich relevanten lithogenetischen Einheiten in Deutschland und basiert im Wesentlichen auf der von Bund und Ländern gemeinsam herausgegebenen Bodenübersichtskarte 1:200.000 (BÜK200) und deren Bodengroßlandschaftsgliederung. Zusätzlich enthaltenen sind die Böden größerer Siedlungs-, Industrie- und Gewerbegebiete (als BGL 13.1) und die Böden der Bergbaufolgelandschaften (als BGL 13.2). Nähere Informationen zu den Bodenregionen und Bodengroßlandschaften der Bundesrepublik Deutschland sind der Bodenkundlichen Kartieranleitung (KA5), Kapitel 6.5 ab Seite 335 zu entnehmen. Die digitale Kartengrafik basiert auf den topographischen Grundlagen des Digitalen Landschaftsmodells 1:250.000 (DLM250) aus dem Jahr 2014 des Bundesamtes für Kartographie und Geodäsie (BKG).

Limecola balthica - biomass (AFDW)

Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025

Bioturbation potential - modelled

Distribution of community bioturbation potential BPc (log-transformed values) resulting from random forest modeling using BPc as response variable. Bioturbation potential BPc is a metric to quantitatively estimate bioturbation intensity from benthic quantitative data suggested by Solan et al. (2004). Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m². For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used. Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025Solan, M., Cardinale, B.J., Downing, A.L., Engelhardt, K.A.M., Ruesink, J.L., Srivastava,D.S., 2004. Extinction and ecosystem function in the marine benthos. Science306, 1177–1180.Liaw, A., Wiener, M., 2002. Classification and regression by randomForest. R. News2, 18–22.Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.

Mya arenaria - biomass (AFDW).tif

Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025

Hediste diversicolor - biomass (AFDW)

Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025

Astarte borealis - biomass (AFDW)

Distribution of biomass (ash free dry weight in g/m²) for 10 key species modeled with random forests method.Macrozoobenthic data from 1191 sampling stations located in the German part of the Baltic Sea were analyzed (data sources: Leibniz Institute for Baltic Sea Research). Samples have been collected from 1999 to 2015. Sample data were averaged per stations and standardized to the area of 1 m².For modeling R package “Random Forest” (RF, Version 4.6–7, Liaw and Wiener, 2002), based on random forests statistical analysis (Breiman, 2001) is used.Predictors and modeling algorithm as described in Gogina, M., Morys, C., Forster, S., Gräwe, U., Friedland, R., Zettler, M.L. 2017. Towards benthic ecosystem functioning maps: Quantifying bioturbation potential in the German part of the Baltic Sea. Ecological Indicators 73: 574-588. doi.org/10.1016/j.ecolind.2016.10.025

Water-Sediment flux - oxygen - seasonal mean value (summer 1990 - 2000)

Modeled 3D biogeochemical sediment fluxes in the western Baltic Sea from 1990 to 2000 using MOM ERGOM SED. A new 3d sediment model ERGOM SED has been developed in the project SECOS for the western Baltic Sea. The model is documented in Radtke et al. (2019, doi: 10.5194/gmd-12-275-2019). It is an extension of the marine biogeochemical model ERGOM and is coupled to the physical model MOM. Simulation results of an eleven-year period from 1990 to 2000 covering the western Baltic Sea are provided for download. The model grid had a spatial resolution of 3 n.m. and covered the whole Baltic Sea (boundary conditions to North Sea in the Skagerrak). Detailed sediment data were compiled and a detailed validation was performed in the SECOS project for the western Baltic Sea. Hence, only model results of that region are provided. The sediment model was spun up for 240 years on a lower resolution and then run for forty years - whereas full model output was saved only for the last eleven years.

Water quality - nutrients

Nutrients (nitrogen, phosphorus, chlorophyll) in the german Baltic Sea with the measured values for the present and the orientation values (class limit good ecological status).The orientation value indicates the Median from 2001 till 2012.

Biomass - point

Seventeen major communities with characteristic species according to hierarchal clustering (based on point data aggregated to 5 km grid cells) and Indicator species analysis based on biomass data. Crosses indicate sampled cells with no benthic infauna recorded (mainly Polish dataset). Full coverage map of biomass-based communities distribution predicted with Random Forest.

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