Global macrozoobenthos production and energy budget data base V150731, with link to database

I developed a new model for estimating annual production-to-biomass ratio P/B and production P of macrobenthic populations in marine and freshwater habitats. Self-learning artificial neural networks (ANN) were used to model the relationships between P/B and twenty easy-to-measure abiotic and biotic parameters in 1252 data sets of population production. Based on log-transformed data, the final predictive model estimates log(P/B) with reasonable accuracy and precision (r2 = 0.801; residual mean square RMS = 0.083). Body mass and water temperature contributed most to the explanatory power of the model. However, as with all least squares models using nonlinearly transformed data, back-transformation to natural scale introduces a bias in the model predictions, i.e., an underestimation of P/B (and P). When estimating production of assemblages of populations by adding up population estimates, accuracy decreases but precision increases with the number of populations in the assemblage.

Database of Ice-Rich Yedoma Permafrost (IRYP)

Vast portions of Arctic and sub-Arctic Siberia, Alaska and the Yukon Territory are covered by ice-rich silty to sandy deposits that are containing large ice wedges, resulting from syngenetic sedimentation and freezing. Accompanied by wedge-ice growth in polygonal landscapes, the sedimentation process was driven by cold continental climatic and environmental conditions in unglaciated regions during the late Pleistocene, inducing the accumulation of the unique Yedoma deposits up to >50 meters thick. Because of fast incorporation of organic material into syngenetic permafrost during its formation, Yedoma deposits include well-preserved organic matter. Ice-rich deposits like Yedoma are especially prone to degradation triggered by climate changes or human activity. When Yedoma deposits degrade, large amounts of sequestered organic carbon as well as other nutrients are released and become part of active biogeochemical cycling. This could be of global significance for future climate warming as increased permafrost thaw is likely to lead to a positive feedback through enhanced greenhouse gas fluxes. Therefore, a detailed assessment of the current Yedoma deposit coverage and its volume is of importance to estimate its potential response to future climate changes. We synthesized the map of the coverage and thickness estimation, which will provide critical data needed for further research. In particular, this preliminary Yedoma map is a great step forward to understand the spatial heterogeneity of Yedoma deposits and its regional coverage. There will be further applications in the context of reconstructing paleo-environmental dynamics and past ecosystems like the mammoth-steppe-tundra, or ground ice distribution including future thermokarst vulnerability. Moreover, the map will be a crucial improvement of the data basis needed to refine the present-day Yedoma permafrost organic carbon inventory, which is assumed to be between 83±12 (Strauss et al., 2013, doi:10.1002/2013GL058088) and 129±30 (Walter Anthony et al., 2014, doi:10.1038/nature13560) gigatonnes (Gt) of organic carbon in perennially-frozen archives. Hence, here we synthesize data on the circum-Arctic and sub-Arctic distribution and thickness of Yedoma for compiling a preliminary circum-polar Yedoma map. For compiling this map, we used (1) maps of the previous Yedoma coverage estimates, (2) included the digitized areas from Grosse et al. (2013) as well as extracted areas of potential Yedoma distribution from additional surface geological and Quaternary geological maps (1.: 1:500,000: Q-51-V,G; P-51-A,B; P-52-A,B; Q-52-V,G; P-52-V,G; Q-51-A,B; R-51-V,G; R-52-V,G; R-52-A,B; 2.: 1:1,000,000: P-50-51; P-52-53; P-58-59; Q-42-43; Q-44-45; Q-50-51; Q-52-53; Q-54-55; Q-56-57; Q-58-59; Q-60-1; R-(40)-42; R-43-(45); R-(45)-47; R-48-(50); R-51; R-53-(55); R-(55)-57; R-58-(60); S-44-46; S-47-49; S-50-52; S-53-55; 3.: 1:2,500,000: Quaternary map of the territory of Russian Federation, 4.: Alaska Permafrost Map). The digitalization was done using GIS techniques (ArcGIS) and vectorization of raster Images (Adobe Photoshop and Illustrator). Data on Yedoma thickness are obtained from boreholes and exposures reported in the scientific literature. The map and database are still preliminary and will have to undergo a technical and scientific vetting and review process. In their current form, we included a range of attributes for Yedoma area polygons based on lithological and stratigraphical information from the original source maps as well as a confidence level for our classification of an area as Yedoma (3 stages: confirmed, likely, or uncertain). In its current version, our database includes more than 365 boreholes and exposures and more than 2000 digitized Yedoma areas. We expect that the database will continue to grow. In this preliminary stage, we estimate the Northern Hemisphere Yedoma deposit area to cover approximately 625,000 km². We estimate that 53% of the total Yedoma area today is located in the tundra zone, 47% in the taiga zone. Separated from west to east, 29% of the Yedoma area is found in North America and 71 % in North Asia. The latter include 9% in West Siberia, 11% in Central Siberia, 44% in East Siberia and 7% in Far East Russia. Adding the recent maximum Yedoma region (including all Yedoma uplands, thermokarst lakes and basins, and river valleys) of 1.4 million km² (Strauss et al., 2013, doi:10.1002/2013GL058088) and postulating that Yedoma occupied up to 80% of the adjacent formerly exposed and now flooded Beringia shelves (1.9 million km², down to 125 m below modern sea level, between 105°E - 128°W and >68°N), we assume that the Last Glacial Maximum Yedoma region likely covered more than 3 million km² of Beringia. Acknowledgements: This project is part of the Action Group "The Yedoma Region: A Synthesis of Circum-Arctic Distribution and Thickness" (funded by the International Permafrost Association (IPA) to J. Strauss) and is embedded into the Permafrost Carbon Network (working group Yedoma Carbon Stocks). We acknowledge the support by the European Research Council (Starting Grant #338335), the German Federal Ministry of Education and Research (Grant 01DM12011 and "CarboPerm" (03G0836A)), the Initiative and Networking Fund of the Helmholtz Association (#ERC-0013) and the German Federal Environment Agency (UBA, project UFOPLAN FKZ 3712 41 106).

The CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters

The CoastColour project Round Robin (CCRR) project (http://www.coastcolour.org) funded by the European Space Agency (ESA) was designed to bring together a variety of reference datasets and to use these to test algorithms and assess their accuracy for retrieving water quality parameters. This information was then developed to help end-users of remote sensing products to select the most accurate algorithms for their coastal region. To facilitate this, an inter-comparison of the performance of algorithms for the retrieval of in-water properties over coastal waters was carried out. The comparison used three types of datasets on which ocean colour algorithms were tested. The description and comparison of the three datasets are the focus of this paper, and include the Medium Resolution Imaging Spectrometer (MERIS) Level 2 match-ups, in situ reflectance measurements and data generated by a radiative transfer model (HydroLight). The datasets mainly consisted of 6,484 marine reflectance associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: Total Suspended Matter (TSM) and Chlorophyll-a (CHL) concentrations, and the absorption of Coloured Dissolved Organic Matter (CDOM). Inherent optical properties were also provided in the simulated datasets (5,000 simulations) and from 3,054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three datasets are compared. Match-up and in situ sites where deviations occur are identified. The distribution of the three reflectance datasets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.

Availability of sequences of aquatic and terrestrial taxa in genetic databases (GenBank and BOLD - the Barcode of Life Database) in 2010, 2012 and 2016

Correct species identifications are of tremendous importance for invasion ecology, as mistakes could lead to misdirecting limited resources against harmless species or inaction against problematic ones. DNA barcoding is becoming a promising and reliable tool for species identifications, however the efficacy of such molecular taxonomy depends on gene region(s) that provide a unique sequence to differentiate among species and on availability of reference sequences in existing genetic databases. Here, we assembled a list of aquatic and terrestrial non-indigenous species (NIS) and checked two leading genetic databases for corresponding sequences of six genome regions used for DNA barcoding. The genetic databases were checked in 2010, 2012, and 2016. All four aquatic kingdoms (Animalia, Chromista, Plantae and Protozoa) were initially equally represented in the genetic databases, with 64, 65, 69, and 61% of NIS included, respectively. Sequences for terrestrial NIS were present at rates of 58 and 78% for Animalia and Plantae, respectively. Six years later, the number of sequences for aquatic NIS increased to 75, 75, 74, and 63% respectively, while those for terrestrial NIS increased to 74 and 88% respectively. Genetic databases are marginally better populated with sequences of terrestrial NIS of plants compared to aquatic NIS and terrestrial NIS of animals. The rate at which sequences are added to databases is not equal among taxa. Though some groups of NIS are not detectable at all based on available data - mostly aquatic ones - encouragingly, current availability of sequences of taxa with environmental and/or economic impact is relatively good and continues to increase with time.

Database on the molluscan fauna from the Mediterranean Reef Ecosystems (CorMolDB - Part I)

In the Mediterranean Sea, infralittoral and circalittoral rocky bottoms (from 15 to 120 m) are characterized by a biogenic habitat, named "coralligenous", formed by the concretion of calcareous organisms, mainly algal thalli, and- to a lesser extent- by animal skeletons. This complex habitat is inhabited by a rich fauna that belongs to different taxonomic groups. Sponges, bryozoans, cnidarians and ascidians are the most common sessile organisms that inhabit the area while crustacean and molluscs are the common mobile organisms. Little information on the diversity of the molluscs that thrive in the coralligenous habitat is known while this information is highly important for biodiversity management purposes. After thoroughly studying the available and accessible published literature, a database for the molluscs of the coralligenous habitat has been designed and implemented for the collection and management of this information. From its index compilation more than 511 species of molluscs have been recorded so far from the coralligenous formations, the majority of which belongs to the class Gastropoda (357 sp.) followed by the Bivalvia (137 sp.), Polyplacophora (14 sp.), Cephalopoda (2 sp.) and Scaphopoda (1 sp.). Among these, the gastropod Luria lurida (Linnaeus, 1758) and Charonia lampas (Linnaeus, 1758), the endemic bivalve Pinna nobilis Linnaeus, 1758 and the endolithic bivalve Lithophaga lithophaga (Linnaeus, 1758), are protected by international conventions.

List of size fractionated eukaryotic plankton community samples and associated metadata (Database W1)

The present data set provides an Excel file in a zip archive. The file lists 334 samples of size fractionated eukaryotic plankton community with a suite of associated metadata (Database W1). Note that if most samples represented the piconano- (0.8-5 µm, 73 samples), nano- (5-20 µm, 74 samples), micro- (20-180 µm, 70 samples), and meso- (180-2000 µm, 76 samples) planktonic size fractions, some represented different organismal size-fractions: 0.2-3 µm (1 sample), 0.8-20 µm (6 samples), 0.8 µm - infinity (33 samples), and 3-20 µm (1 sample). The table contains the following fields: a unique sample sequence identifier; the sampling station identifier; the Tara Oceans sample identifier (TARA_xxxxxxxxxx); an INDSC accession number allowing to retrieve raw sequence data for the major nucleotide databases (short read archives at EBI, NCBI or DDBJ); the depth of sampling (Subsurface - SUR or Deep Chlorophyll Maximum - DCM); the targeted size range; the sequences template (either DNA or WGA/DNA if DNA extracted from the filters was Whole Genome Amplified); the latitude of the sampling event (decimal degrees); the longitude of the sampling event (decimal degrees); the time and date of the sampling event; the device used to collect the sample; the logsheet event corresponding to the sampling event ; the volume of water sampled (liters). Then follows information on the cleaning bioinformatics pipeline shown on Figure W2 of the supplementary litterature publication: the number of merged pairs present in the raw sequence file; the number of those sequences matching both primers; the number of sequences after quality-check filtering; the number of sequences after chimera removal; and finally the number of sequences after selecting only barcodes present in at least three copies in total and in at least two samples. Finally, are given for each sequence sample: the number of distinct sequences (metabarcodes); the number of OTUs; the average number of barcode per OTU; the Shannon diversity index based on barcodes for each sample (URL of W4 dataset in PANGAEA); and the Shannon diversity index based on each OTU (URL of W5 dataset in PANGAEA).

Total V9 rDNA information organized at the metabarcode level (Database W4)

The present data set provides a tab separated text file compressed in a zip archive. The file includes metadata for each TaraOceans V9 rDNA metabarcode including the following fields: md5sum = unique identifier; lineage = taxonomic path associated to the metabarcode; pid = % identity to the closest reference barcode from V9_PR2; sequence = nucleotide sequence of the metabarcode; refs = identity of the best hit reference sequence(s); TARA_xxx = number of occurrences of this barcode in each of the 334 samples; totab = total abundance of the barcode ; cid = identifier of the OTU to which the barcode belongs; and taxogroup = high-taxonomic level assignation of this barcode. The file also includes three categories of functional annotations: (1) Chloroplast: yes, presence of permanent chloroplast; no, absence of permanent chloroplast ; NA, undetermined. (2) Symbiont (small partner): parasite, the species is a parasite; commensal, the species is a commensal; mutualist, the species is a mutualist symbiont, most often a microalgal taxon involved in photosymbiosis; no the species is not involved in a symbiosis as small partner; NA, undetermined. (3) Symbiont (host): photo, the host species relies on a mutualistic microalgal photosymbiont to survive (obligatory photosymbiosis); photo_falc, same as photo, but facultative relationship; photo_klep, the host species maintains chloroplasts from microalgal prey(s) to survive; photo_klep_falc, same as photo_klep, but facultative; Nfix, the host species must interact with a mutualistic symbiont providing N2 fixation to survive; Nfix_falc, same as Nfix, but facultative; no, the species is not involved in any mutualistic symbioses; NA, undetermined. For example, the collodarian/Brandtodinium symbiosis is annotated: Chloroplast, "no"; Symbiont (small), "no"; Symbiont (host), "photo", for the collodarian host; and: Chloroplast, "yes"; Symbiont (small), "mutualist"; Symbiont (host), "no", for the dinoflagellate microalgal endosymbiont.chloroplast = "yes", "no" or "NA"; symbiont.small = "parasite", "commensal", "mutualist", "no" or "NA"; symbiont.host = "photo", "photo_falc", "photo_klep", "Nfix", no or NA; benef = "Nfix", "no" or "NA"; trophism = Metazoa , heterotroph , NA , photosymbiosis , phototroph according to the previous fields.

Database of GPS data collected by surveys on seismic and volcanic areas of Sicily (Italy) from 1994 to 2013

This is a 20-year long database of GPS data collected by geodetic surveys carried out over the seismically and volcanically active eastern Sicily, for a total of more than 6300 measurements. Data have been convertedi nto the international ASCII compressed RINEX standard in order to be imported and processed by any GPS analysis software. Database is provided with an explorer software for navigating into the dataset by spatial (GIS) and temporal queries.

A BRDF-BPDF database for the analysis of Earth targets reflectances

A database of representative BRDF and BPDF derived from the POLDER measurements. From the huge amount of data acquired by the spaceborne instrument over a period of 7 years, we selected a set of targets with high quality observations. The selection aimed at a large number of observations, free of cloud or aerosol contamination, acquired in diverse observation geometry with a focus on the backscatter direction that shows the specific Hot-Spot signature. The targets are sorted according to the 16-classes IGBP land cover classification system and the target selection aims at a spatial representativeness within the class. The database thus provides a set of high quality BRDF and BPDF samples that can be used to assess the typical variability of natural surface reflectances or to evaluate models.

Database of the GLAMAP project

A uniform chronology for foraminifera-based sea surface temperature records has been established in more than 120 sediment cores obtained from the equatorial and eastern Atlantic up to the Arctic Ocean. The chronostratigraphy of the last 30,000 years is mainly based on published d18O records and 14C ages from accelerator mass spectrometry, converted into calendar-year ages. The high-precision age control provides the database necessary for the uniform reconstruction of the climate interval of the Last Glacial Maximum within the GLAMAP-2000 project.