Appendix A. Microfocus X-ray Computed Tomography cross-sectional images of Joergensenium arcticum Ikenoue, Dumitrica and Bjørklund n. sp.

Radiolarians in the Arctic Ocean have been studied lately in both plankton and sediment trap samples in the Chukchi Sea area. These studies have shed light on new radiolarian taxa, especially within the order Entactinaria, including two new species of Joergensenium, Joergensenium arcticum from the western Arctic Ocean, so far restricted to the Pacific Winter Water in the Chukchi Sea, and Joergensenium clevei hitherto found in the northern part of the Norwegian Sea south of the Fram Strait. The taxonomic position of the order Entactinaria is discussed and the genus Joergensenium has been emended. We have also observed in detail the internal structure of J. arcticum using Microfocus X-ray Computed Tomography and have utilized three-dimensional imaging for the first time in a species description.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling, links to files in GeoTIFF format

Topographic variation, the spatial variation in elevation and terrain features, underpins a myriad of patterns and processes in geography and ecology and is key to understanding the variation of life on the planet. The characterization of this variation is scale-dependent, i.e. it varies with the distance over which features are assessed and with the spatial grain (grid cell resolution) of analysis. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale basic research and analytical applications, however to date, such technique is unavailable. Here we used the digital elevation model products of global 250 m GMTED and near-global 90 m SRTM to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile and tangential curvature, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches (median, average, minimum, maximum, standard deviation, percent cover, count, majority, Shannon Index, entropy, uniformity). While a global cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at http://www.earthenv.org and can serve as a basis for standardized hydrological, environmental and biodiversity modeling at a global extent.