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Benthos, Dichte

Beschreibung: Räumliche Verbreitung ausgewählter makrozoobenthischer Arten in der deutschen Bucht. Datenquelle: Daten aus Umweltverträglichkeitsstudien (UVS) im Rahmen von Genehmigungsverfahren des Bundesamtes für Seeschifffahrt und Hydrographie in der AWZ der Nordsee und Forschungsdaten des Alfred-Wegener-Instituts (AWI), Helmholtz-Zentrum für Polar- und Meeresforschung; Erfassungszeitraum: 1997 bis 2011, hauptsächlich Frühjahrs- und Herbstdaten (UVS-Daten), aber auch Sommer- und Winterdaten (AWI-Daten) Beprobungsstandards: Die Daten aus UVSn folgen dem Standarduntersuchungskonzept StUK 1-3 (BSH 2007); AWI-Daten dem ICES Standard (Rumohr 1999) Beprobungsgerät: hauptsächlich van-Veen-Greifer (0,1 qm, 30-95 kg je nach Sediment), wenige Stationen Kastengreifer (0,1 qm, 160 kg), für Nephrops norvegicus und Goneplax rhomboides Baumkurre und Dredge (1-3 m Breite) Probennahme: 1-3 Parallelproben pro Station, Siebung über 1 mm, Fixierung in Seewasser-gepuffertem Formalin, Daten aus Kurre/Dredge an Bord erfasst oder Unterproben eingefroren, Abundanz und Biomasse (g Nassgewicht) pro Art Datenauswertung: Fachinformationssystem für benthische Invertebraten; Prüfung der Qualität, Datenharmonisierung, Produkterstellung durch das AWI Produktbeschreibung: Grid: 5x5 qkm für Greiferdaten, 10x10 km² für Daten zu N. norvegicus und G. rhomboides aus Baumkurrendaten; Vorhandene auswählbare Parameter: Anzahl der Stationen, Minimum, Maximum, Mittelwert, Median und Standardabweichung der Dichte (m-2) je Art; Klassifizierungsmethode: Natürliche Unterbrechungen (Jenks-Caspall-Algorithmus); Die Produkte enthalten eine unterschiedliche Klassifizierung der Dichten je Art! Hinweis: Bitte beachten Sie die unterschiedlichen Wertebereiche! Rumohr, H. (1999). "Soft bottom macrofauna: Collection, treatment, and quality assurance of samples." ICES Techniques in Environmental Sciences, No. 27: 1-19. BSH (2007): Standard "Untersuchung der Auswirkungen von Offshore-Windenergieanlagen auf die Meeresumwelt (StUK 3)", Hamburg. Weitere Informationen finden Sie unter: https://gdi.bsh.de/de/data/Benthos-Density_Information_Benthos_Dichte_DE.pdf

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

Cerastoderma glaucum - 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

Total biomass

These data sets are based on approx. 1400 stations sampled in the German Baltic Sea by the Leibniz Institute for Baltic Sea Research (IOW) during the past 15 years (as part of the regular monitoring or within different research programmes). Benthic samples were taken with a 0.1 m² van Veen grab. Depending on sediment composition, grabs of different weights were used. As a standard three replicates of grab samples were taken at each station. Additionally a dredge haul (net mesh size 5 mm) was taken in order to obtain mobile or rare species. All samples were sieved through a 1 mm screen and animals were preserved in the field with 4% formaldehyde. For sorting in the laboratory, a stereomicroscope with 10–40 magnification was used, species were counted and weighted. Total ash free dry weight biomass was derived using random forests statistical analysis (Breiman, 2001) in R environment (Version 3.0.2, The R Foundation for Statistical Computing, 2013) and the package ‘random Forest’ (RF, Version 4.6–7, Liaw and Wiener, 2002). Total biomass shows AFDW biomass g per m².Environmental data used as predictors: Substrate (Tauber 2012), Depth (FEMA project), Salinity mean, temperature mean JJA, bottom velocity max (GETM, Klingbeil et al. 2013) Light penetration depth (mean over growth period), oxygen deficit zones (number of days / year smaller 2 ml / l) and detritus rate (mm / year) (ERGOM, Friedland et al. 2012).

Number of species

These data sets are based on approx. 1400 stations sampled in the German Baltic Sea by the Leibniz Institute for Baltic Sea Research (IOW) during the past 15 years (as part of the regular monitoring or within different research programmes). Benthic samples were taken with a 0.1 m² van Veen grab. Depending on sediment composition, grabs of different weights were used. As a standart three replicates of grab samples were taken at each station. Additionally a dredge haul (net mesh size 5 mm) was taken in order to obtain mobile or rare species. All samples were sieved through a 1 mm screen and animals were preserved in the field with 4% formaldehyde. For sorting in the laboratory, a stereomicroscope with 10–40 magnification was used, species were counted and weighted. Macrobenthic species richness was derived from stations based data by ordinary kriging of centered-point-data acquired via fishnet of 5 km x 5 km cell size. Macrobenthic species richness shows the number of species for 1 km grid.Environmental data used as predictors: Substrate (Tauber 2012), Depth (FEMA project), Salinity mean, temperature mean JJA, bottom velocity max (GETM, Klingbeil et al. 2013) Light penetration depth (mean over growth period), oxygen deficit zones (number of days / year smaller 2 ml / l) and detritus rate (mm / year) (ERGOM, Friedland et al. 2012).

Diastylis rathkei - 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

Scoloplos armiger - 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

Peringia ulvae - 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

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

Arctica islandica - 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

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