Biomass of mesozooplankton in the western part of the Black Sea in 1992 during the Cooperative Marine Science Program for the Black Sea (CoMSBlack)

The "CoMSBlack92" dataset is based on samples collected in the summer of 1992 along the Bulgarian coast including coastal and open sea areas. The whole dataset is composed of 79 samples (28 stations) with data of zooplankton species composition, abundance and biomass. Sampling for zooplankton was performed from bottom up to the surface at standard depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 ?m. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Sampling volume was estimated by multiplying the mouth area with the wire length. The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972 ). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m**3.

Biomass of mesozooplankton in the western part of the Black Sea in summer 1991 during the NATO Programme Hydroblack

The "Hydroblack91" dataset is based on samples collected in the summer of 1991 and covers part of North-Western in front of Romanian coast and Western Black Sea (Bulgarian coasts) (between 43°30' - 42°10' N latitude and 28°40'- 31°45' E longitude). Mesozooplankton sampling was undertaken at 20 stations. The whole dataset is composed of 72 samples with data of zooplankton species composition, abundance and biomass. Samples were collected in discrete layers 0-10, 0-20, 0-50, 10-25, 25-50, 50-100 and from bottom up to the surface at depths depending on water column stratification and the thermocline depth. Zooplankton samples were collected with vertical closing Juday net,diameter - 36cm, mesh size 150 µm. Tows were performed from surface down to bottom meters depths in discrete layers. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. Mesozooplankton abundance: The collected materia was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 (based on species specific wet weight). Wet weight values were transformed to dry weight using the equation DW=0.16*WW as suggested by Vinogradov & Shushkina, 1987. Taxon-specific abundance: The collected material was analysed using the method of Domov (1959). Samples were brought to volume of 25-30 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 5 ml of sample was taken and poured in the counting chamber which is a rectangle form for taxomomic identification and count. Copepods and Cladoceras were identified and enumerated; the other mesozooplankters were identified and enumerated at higher taxonomic level (commonly named as mesozooplankton groups). Large (> 1 mm body length) and not abundant species were calculated in whole sample. Counting and measuring of organisms were made in the Dimov chamber under the stereomicroscope to the lowest taxon possible. Taxonomic identification was done at the Institute of Oceanology by Asen Konsulov using the relevant taxonomic literature (Mordukhay-Boltovskoy, F.D. (Ed.). 1968, 1969,1972). The biomass was estimated as wet weight by Petipa, 1959 ussing standard average weight of each species in mg/m3. WW were converted to DW by equation DW=0.16*WW (Vinogradov ME, Sushkina EA, 1987).

Abundance and biomass of macrobenthos in the Black Sea during Mare Nigrum cruise S-RO1

Dataset containing macrobenthos data for samples collected during April 2008 in the North-West Black Sea (between 44°46' - 43°45' N latitude and 30° 11' - 29°35' E longitude). Macrobenthos sampling was done in 4 stations using a 0.14 m**2 Van Veen grab. Washing of the sample through two sieves - 1 mm and 0.25 mm mesh size; the material retained by the two sieves was examined at the binocular microscope; all animals were extracted, using fine tweezers and the species or group of species were identified and counted (in order to determine the density of populations); the larger organisms were measured and weighed (structure and biomass); for smaller organisms, the average wet weights inscribed in standard tables were used to calculate the biomass. Taxonomic identification was done at the GeoEcoMar by A. Teaca and T. Begun using the relevant taxonomic literature (Key-book for the identification of the Black Sea and Sea of Azov Fauna, 1968 -1972, Kiev - in Russian, V 1-4; BACESCU, M.C., MÜLLER, G. I., GOMOIU, M.-T., 1971 and BACESCU, M.C., MÜLLER, G. I., GOMOIU, M.-T., 1971-Benthic ecological research to Black Sea. Comparative quantitative and qualitative analyse of pontic benthic fauna. Marine Ecology, 4, 1-357 (in Romanian).

Jelly biomass sinking speed reveals a fast carbon export mechanism

Sinking of gelatinous zooplankton biomass is an important component of the biological pump removing carbon from the upper ocean. The export efficiency, e.g., how much biomass reaches the ocean interior sequestering carbon, is poorly known because of the absence of reliable sinking speed data. We measured sinking rates of gelatinous particulate organic matter (jelly-POM) from different species of scyphozoans, ctenophores, thaliaceans, and pteropods, both in the field and in the laboratory in vertical columns filled with seawater using high-quality video. Using these data, we determined taxon-specific jelly-POM export efficiencies using equations that integrate biomass decay rate, seawater temperature, and sinking speed. Two depth scenarios in several environments were considered, with jelly-POM sinking from 200 and 600 m in temperate, tropical, and polar regions. Jelly-POM sank on average between 850 and 1500 m/d (salps: 800-1200 m/d; ctenophores: 1200-1500 m/d; scyphozoans: 1000-1100 m d; pyrosomes: 1300 m/d). High latitudes represent a fast-sinking and low-remineralization corridor, regardless of species. In tropical and temperate regions, significant decomposition takes place above 1500 m unless jelly-POM sinks below the permanent thermocline. Sinking jelly-POM sequesters carbon to the deep ocean faster than anticipated, and should be incorporated into biogeochemical and modeling studies to provide more realistic quantification of export via the biological carbon pump worldwide.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2010

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2011

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Abundance and biomass of macrobenthos in the western part of the Black Sea during Mare Nigrum cruise S-RO2

Dataset containing macrobenthos data for samples collected during September 2008 in the North-West Black Sea (between 44°46' - 43°45' N latitude and 30° 11' - 29°35' E longitude). Macrobenthos sampling was done in 4 stations using a 0.14 m**2 Van Veen grab. Washing of the sample through two sieves - 1 mm and 0.25 mm mesh size; the material retained by the two sieves was examined at the binocular microscope; all animals were extracted, using fine tweezers and the species or group of species were identified and counted (in order to determine the density of populations); the larger organisms were measured and weighed (structure and biomass); for smaller organisms, the average wet weights inscribed in standard tables were used to calculate the biomass. Taxonomic identification was done at the GeoEcoMar by A. Teaca and T. Begun using the relevant taxonomic literature ( "Key-book for the identification of the Black Sea and Sea of Azov Fauna, 1968 -1972, Kiev - in Russian, V 1-4; BACESCU, M.C., MÜLLER, G. I., GOMOIU, M.-T., 1971). BACESCU, M.C., MÜLLER, G. I., GOMOIU, M.-T., 1971-Benthic ecological research to Black Sea. Comparative quantitative and qualitative analyse of pontic benthic fauna. Marine Ecology, 4, 1-357 (in Romanian). Key-book for the identification of the Black Sea and Sea of Azov Fauna, 1968 -1972, Kiev, V. 1-4 (in Russian).

Spatially explicit estimates of length and biomass of Clupea harengus (Norwegian spring spawning herring) from acoustic and trawl surveys in the North-East Atlantic in May 2007

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Spatially explicit estimates of length and biomass of Micromesistius poutassou (Blue Whiting) from acoustic and trawl surveys in the North-East Atlantic in May 2012

Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

Plant species, biomass and environmental characteristics of relevés along the North America Arctic bioclimate gradient

Question: How do interactions between the physical environment and biotic properties of vegetation influence the formation of small patterned-ground features along the Arctic bioclimate gradient? Location: At 68° to 78°N: six locations along the Dalton Highway in arctic Alaska and three in Canada (Banks Island, Prince Patrick Island and Ellef Ringnes Island). Methods: We analysed floristic and structural vegetation, biomass and abiotic data (soil chemical and physical parameters, the n-factor [a soil thermal index] and spectral information [NDVI, LAI]) on 147 microhabitat releves of zonalpatterned-ground features. Using mapping, table analysis (JUICE) and ordination techniques (NMDS). Results: Table analysis using JUICE and the phi-coefficient to identify diagnostic species revealed clear groups of diagnostic plant taxa in four of the five zonal vegetation complexes. Plant communities and zonal complexes were generally well separated in the NMDS ordination. The Alaska and Canada communities were spatially separated in the ordination because of different glacial histories and location in separate floristic provinces, but there was no single controlling environmental gradient. Vegetation structure, particularly that of bryophytes and total biomass, strongly affected thermal properties of the soils. Patterned-ground complexes with the largest thermal differential between the patterned-ground features and the surrounding vegetation exhibited the clearest patterned-ground morphologies.

(Table A1) Annual biomass of plants and animals recorded on Bylot Island between 1993-2009

Determining the manner in which food webs will respond to environmental changes is difficult because the relative importance of top-down vs. bottom-up forces in controlling ecosystems is still debated. This is especially true in the Arctic tundra where, despite relatively simple food webs, it is still unclear which forces dominate in this ecosystem. Our primary goal was to assess the extent to which a tundra food web was dominated by plant-herbivore or predator--rey interactions. Based on a 17-year (1993-2009) study of terrestrial wildlife on Bylot Island, Nunavut, Canada, we developed trophic mass balance models to address this question. Snow Geese were the dominant herbivores in this ecosystem, followed by two sympatric lemming species (brown and collared lemmings). Arctic foxes, weasels, and several species of birds of prey were the dominant predators. Results of our trophic models encompassing 19 functional groups showed that <10% of the annual primary production was consumed by herbivores in most years despite the presence of a large Snow Goose colony, but that 20-100% of the annual herbivore production was consumed by predators. The impact of herbivores on vegetation has also weakened over time, probably due to an increase in primary production. The impact of predators was highest on lemmings, intermediate on passerines, and lowest on geese and shorebirds, but it varied with lemming abundance. Predation of collared lemmings exceeded production in most years and may explain why this species remained at low density. In contrast, the predation rate on brown lemmings varied with prey density and may have contributed to the high-amplitude, periodic fluctuations in the abundance of this species. Our analysis provided little evidence that herbivores are limited by primary production on Bylot Island. In contrast, we measured strong predator-prey interactions, which supports the hypothesis that this food web is primarily controlled by top-down forces. The presence of allochthonous resources subsidizing top predators and the absence of large herbivores may partly explain the predominant role of predation in this low-productivity ecosystem.

Collection of aboveground community and species-specific plant biomass from the Jena Experiment (time series since 2002).

This data set comprises time series of aboveground community plant biomass (Sown plant community, Weed plant community, Dead plant material, and Unidentified plant material; all measured in biomass as dry weight) and species-specific biomass from the sown species of several experiments at the field site of a large grassland biodiversity experiment (the Jena Experiment; see further details below). Aboveground community biomass was normally harvested twice a year just prior to mowing (during peak standing biomass twice a year, generally in May and August; in 2002 only once in September) on all experimental plots in the Jena Experiment. This was done by clipping the vegetation at 3 cm above ground in up to four rectangles of 0.2 x 0.5 m per large plot. The location of these rectangles was assigned by random selection of new coordinates every year within the core area of the plots. The positions of the rectangles within plots were identical for all plots. The harvested biomass was sorted into categories: individual species for the sown plant species, weed plant species (species not sown at the particular plot), detached dead plant material (i.e., dead plant material in the data file), and remaining plant material that could not be assigned to any category (i.e., unidentified plant material in the data file). All biomass was dried to constant weight (70°C, >= 48 h) and weighed. Sown plant community biomass was calculated as the sum of the biomass of the individual sown species. The data for individual samples and the mean over samples for the biomass measures on the community level are given. Overall, analyses of the community biomass data have identified species richness as well as functional group composition as important drivers of a positive biodiversity-productivity relationship. The following series of datasets are contained in this collection: 1. Plant biomass form the Main Experiment: In the Main Experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). 2. Plant biomass from the Dominance Experiment: In the Dominance Experiment, 206 grassland plots of 3.5 x 3.5 m were established from a pool of 9 species that can be dominant in semi-natural grassland communities of the study region. In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 3, 4, 6, and 9 species). 3. Plant biomass from the monoculture plots: In the monoculture plots the sown plant community contains only a single species per plot and this species is a different one for each plot. Which species has been sown in which plot is stated in the plot information table for monocultures (see further details below). The monoculture plots of 3.5 x 3.5 m were established for all of the 60 plant species of the Jena Experiment species pool with two replicates per species like the other experiments in May 2002. All plots were maintained by bi-annual weeding and mowing.