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    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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    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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    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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    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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    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.

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    Distribution of community bioturbation potential BPc (log-transformed values) resulting from natural neighbour interpolation. Bioturbation potential BPc is a metric to 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 was used (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.Natural neighbour interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas in order to interpolate a value (Sibson, 1981). Its basic properties are that it is local, using only a subset of samples that surround a query point, and that interpolated heights are guaranteed to be within the range of the samples used. All details are reported 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.Sibson, R., 1981. A brief description of natural neighbour interpolation. In: Barnett,V. (Ed.), Interpreting Multivariate Data. Wiley, New York, pp. 21–36.

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    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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    Different modes of the bioturbation process are displayed, particle mixing and non-local bioturbation, as measured and modelled from vertical distributions of the particle tracer chlorophyll-a in n=24 sediment cores (MUC, 10 cm diameter)

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    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).

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    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