Biological and environmental data for a study on transferability of statistical and machine learning models using North Sea Macrozoobenthos

<p>General</p> <p>Data documented here are not the product of our research but was scraped from various sources and processed - so no genuine reupload. This collection is a contribution to reproduceable reseach. All datasets are given in "RData" binary format</p> <p> </p> <p>Data description</p> <p>majornorthseabenthos </p> <p>This is macrozoobenthos data as data frame scraped from the GBIF repository (gbif.org). Species are  Corbula gibba, Tellina fabula, Turritella communis, Euspira pulchella, Corystes cassive- launus, Upogebia deltaura, Lanice conchilega, Nephtys hombergii, Echinocardium cordatum, and Amphiura filiformis. data was postprocessed to have only single occurrence fon the approxinatel 1x1 km grid used for this study. Also, occurrences closer than 5 km  close to shore were removed - including occurrences on land.</p> <p> </p> <p>Predictors</p> <p>A SpatialPixelsDataFrame in EPSG 4326 with five layers: Median grain size in micrometers, mud content in percent (both MUDAB database), water depth in meters above MSL (Weatherall et al, 2015), modelled average bottom shear stress from waves in N/sqrm (The Wamdi Group, 1988) and climatologival average winter bottom water temperature in deg. C (Stips et al, 2004).</p> <p> </p> <p> </p> <p>References</p> <p>Stips A, Bolding K, Pohlmann T, Burchard H (2004) Simulating the temporal and spatial dy- namics of the North Sea using the new model GETM (general estuarine transport model). Ocean Dynamics 54(2):266–283</p> <p>The Wamdi Group (1988) The WAM model-a third generation ocean wave prediction model. Journal of Physical Oceanography 18(12):1775–1810</p> <p>Weatherall P, Marks K, Jakobsson M, Schmitt T, Tani S, Arndt JE, Rovere M, Chayes D, Ferrini V, Wigley R (2015) A new digital bathymetric model of the world’s oceans. Earth and Space Science 2(8):331–345</p> <p> </p>