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Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022. Three directories can be downloaded: DataOBS : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). FigPaper : Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). MY_SRC_PISCES_NEMO_3.6 : Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)

The model LAVESI (Kruse et al. 2016) was updated (Kruse 2023) and forced with historical and future climate forcing for 3 simulation repeats. This data set uses the data set of Kruse (2023) and applies a threshold of 0.68 km m-2 to differentiate forested areas according to the 2018 field inventories (Shevtsova et al., 2021). In this data set the total forest cover was summed up and the percent of total available areas is presented for the three climate forcings RCP 2.6, 4.5 and 8.5 and each complemented with a hypothetical cooling scenario from year 2300 CE onwards. The data provided is from years 1800, 1860, 1900, 1990, 2000 and in 5-year steps until 3000 CE and presents the mean over the three repeats of the sum of AGB of the whole study region: extent: 640008.2, 649998.2, 7475006, 7494716 m (xmin, xmax, ymin, ymax). Format: csv, with headers 1-year, Year in CE, 2-average percent forests cover for the study region, 3-upper and 4-lower, is the minimum and maximum value of the three simulations, 5-RCP, is the RCP scenario, 6-Cooling, contains in case of the cooling scenario the string “Cooling”. {"references": ["Stefan Kruse, Mareike Wieczorek, Florian Jeltsch and Ulrike Herzschuh (2016) Treeline dynamics in Siberia under changing climates as inferred from an individual-based model for Larix. Ecological Modelling, 338, 101\u2013121. http://dx.doi.org/10.1016/j.ecolmodel.2016.08.003 Additional data and results are available at https://doi.pangaea.de/10.1594/PANGAEA.863584", "Stefan Kruse (2023). StefanKruse/LAVESI: LAVESI-WIND with landscape (v2.0). Zenodo. https://doi.org/10.5281/zenodo.7505539", "Shevtsova, Iuliia, Herzschuh, Ulrike, Heim, Birgit, & Kruse, Stefan. (2023). Simulated above ground biomass of forests (larch) aggregated over the vicinity of the Ilirney lake system region, Chukotka, Russia [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7505616", "Shevtsova I, Herzschuh U, Heim B, Schulte L, St\u00fcnzi S, Pestryakova LA, Zakharov ES, Kruse S: Recent above-ground biomass changes in central Chukotka (Russian Far East) using field sampling and Landsat satellite data. Biogeosciences, 18, 3343\u20133366, https://doi.org/10.5194/bg-18-3343-2021, 2021."]} This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Forest density estimates visually determined by describing the amount of present trees satellite imagery from Esri basemap (Esri) at an area of ~30x30 m qualitatively for 6515 stratified sampled locations at an equal number of locations based on elevation, aspect and slope angle. The density was categorized ranging from 1: single trees to 4: dense tree stands present, and contains a 0: no trees present. Format: ESRI shapefile, points; projection UTM58N; extent: 642085.1, 654775.1, 7462263, 7492833 m (xmin, xmax, ymin, ymax) This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852). {"references": ["Esri: \"High-resolution satellite and aerial imagery, typically within 3-5 years\" [basemap]. Scale Not Given. \"World Imagery\". https://www.arcgis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9, Accession: 15 December 2020."]}

The elevation was accessed for the area of interest in 90 m spatial resolution from the TanDEM-X 90 m digital elevation model (DEM) product (Krieger et al, 2013). Prior to spatial topographical parameters extraction, the DEM was resampled from the 90-m cell spacing to a 30-m resolution. The result was classified into 589 different possible combinations of elevation, slope angle, aspect. For the classification we used the possible combinations of elevation, slope, and aspect which were grouped into the following categories: Elevation: 0-400 m 400-450m 450-500m 500-600m 600-650m 650-700m 700-1000m 1000-1500m Slope: 0-2° 2-4° 4-6° 6-8° 8-10° 10-12° 12-16° 16-18° 18-20° 20-25° 25-50° Aspect: 0-45° 45-90° 90-135° 135-180° 180-225° 225-270° 270-315° 315-360° Format: Geotiff; projection UTM58N and 30x30 m tiles; extent: 642010.1, 654910.1, 7462218, 7492908 m (xmin, xmax, ymin, ymax) {"references": ["Krieger G, Zink M, Bachmann M, Br\u00e4utigam B, Schulze D, Martone M, Rizzoli P, Steinbrecher U, Antony JW, De Zan F, Hajnsek I, Papathanassiou K, Kugler F, Rodriguez Cassola M, Younis M, Baumgartner S, L\u00f3pez-Dekker P, Prats P, Moreira A: TanDEM-X: a radar interferometer with two formation-flying satellites. Acta Astronautica, 89, 83\u201398, https://doi.org/10.1016/j.actaastro.2013.03.008, 2013."]} This work has been supported by the German Federal Ministry of Education and Research (BMBF), which enabled the Russian-German research programme "Kohlenstoff im Permafrost KoPf" (grant no. 03F0764A), by the Initiative and Networking Fund of the Helmholtz Association and by the ERC consolidator grant Glacial Legacy of Ulrike Herzschuh (grant no. 772852).

Abundance and biovolume data of the community of larger phytoplankton from the mesocosm experiment conducted in the Canary Islands in autumn 2019. Depth-integrated (0-2.5m) water samples were taken in 2-days intervals over the course of 33 days and autotrophic taxa assessed to the lowest taxonomic level possible using Utermöhl microscopy. Only taxa larger than approx. >5 µm could be considered with this method. Biovolume was calculated based on geometrical measurements (dominant taxa) or the literature (rare taxa). Carbon biomass estimates were purposefully not provided, as the standard literature conversion factors from biovolume to carbon biomass did not apply to many of our samples, likely due to low carbon density within cells. Predominantly mixotrophic or heterotrophic taxa are not provided in this dataset. The upwelling treatment started on day 6. Methodological details in Goldenberg et al. (doi:10.3389/fmars.2022.1015188).

Abstract The discovery of stimuli-responsive high affinity host–guest pairs with potential applications under biologically relevant conditions is a challenging goal. This work reports a high-affini...

The tropical Angolan upwelling system is a highly productive ecosystem with a distinct seasonal cycle in surface temperature and primary production. The lowest sea surface temperature, strongest cross-shore temperature gradient, and maximum productivity occur in austral winter when seasonally prevailing upwelling favorable winds are weakest. A multi cruise dataset of microstructure profiles collected between 2013 and 2022 in the tropical Angolan upwelling system was used to analyze the importance of mixing for cooling of the mixed layer. The data were collected during six cruises on board of the R/V Meteor. The results show that cooling due to turbulent heat fluxes at the base of the mixed layer is an important cooling term. This turbulent cooling, that is strongest in shallow shelf regions, is capable of explaining the observed negative cross-shore temperature gradient.

We developed habitat suitability models for 14 vulnerable and foundation cold-water coral (CWC) taxa of the Azores (NE Atlantic) using GAM and MAXENT models. The modelled taxa are: Acanthogorgia spp., Callogorgia verticillata, Coralliidae spp., Dentomuricea aff. meteor, Desmophyllum pertusum, Errina dabneyi, Leiopathes cf. expansa, Madrepora oculata, Narella bellissima, Narella versluysi, Paracalyptrophora josephinae, Paragorgia johnsoni, Solenosmilia variabilis and Viminella flagellum. Models were built using a model grid having a cell size of a 1.13 x 1.11 km (i.e. about 0.01° in the UTM zone 26N projection). This resolution was considered a good compromise between the original resolution of occurrence and environmental data and our capacity to resolve suitable and unsuitable areas within the same geomorphological feature using model predictions. Study area and model background were limited to depths shallower than 2000 m where most of the sampling events took place. Predictors variables included bathymetric position indexes (5 km and 20 km radii), slope, particulate organic carbon flux, seawater chemistry (principal component of dissolved near-seafloor nutrient concentration and calcite/aragonite saturation levels) and near seafloor values of current speed, oxygen saturation and temperature. Presence records were obtained from two different sources: species annotations from underwater imagery (76%) and longline and handline bycatch records (24 %). The published data include: 1. Binary GAM and Maxent habitat suitability predictions. A bootstrap process (n = 100) evaluated the local confidence of model predictions. Each bootstrap iteration sampled occurrence data with replacement, fitted HSMs models and produced binary suitability maps based on sensitivity‐specificity sum maximization thresholds. Depending on the number of times individual raster cells were predicted as suitable they were classified as: low [1-30%), medium [30-70%) or high [70-100%] confidence suitable cells. This process was repeated independently for GAM and Maxent models. In raster layers: (3) identifies high-confidence suitable cells, (2) medium-confidence suitable cells, (1) low-confidence suitable cells and NAs unsuitable cells. 2. Local fuzzy matching of GAM and Maxent habitat suitability predictions. The level of similarity between the spatial distribution of GAM and Maxent binary predictions (low, medium and high confidence suitable cells) at a local (i.e. cell) level was measured considering two membership functions: category similarity, which assumed that some categories were more similar than others; distance decay, which defined the fuzzy similarity of two cells as (i) identical if they matched perfectly, (ii) linearly decreasing with distance if the matching category was found within a 2-cell radius (~2 km) or (iii) totally different when no matching category was found within a 2-cell radius. After combining the two membership functions similarity scores ranged from 0 (totally different) to 1 (identical). Values of similarity greater than 0.5 indicate raster cells that are more similar than different. 3. Combined habitat suitability maps. Suitable raster cells of combined habitat suitability maps were classified as follows: (i) high confidence suitable cell (3 in raster layers), raster cell predicted as suitable with high-confidence by both GAM and Maxent models; (ii) medium confidence suitable cell (2 in raster layers), raster cell predicted as suitable with medium or high confidence by GAM, Maxent or both and with a local fuzzy similarity greater than 0.5; (iii) low confidence suitable cell (1 in raster layers), any other cell predicted as suitable by GAM and/or Maxent. 4. Cold water coral richness based on habitat suitability predictions. The .tif file shows the number of taxa predicted as suitable for each raster cell. Note that only high confidence suitable cells of combined habitat suitability maps are considered.