128 resultados para Minkowski Sum of Sets
Resumo:
The present dataset includes results of analysis of 227 zooplankton samples taken in and off the Sevastopol Bay in the Black Sea in 1976, 1979-1980, 1989-1990, 1995-1996 and 2002-2003. Exact coordinates for stations 1, 4, 5 and 6 are unknown and were calculated using Google-earth program. Data on Ctenophora Mnemiopsis leidyi and Beroe ovata are not included. Juday net: Vertical tows of a Juday net, with mouth area 0.1 m**2, mesh size 150µm. Tows were performed at layers. Towing speed: about 0.5 m/s. Samples were preserved by a 4% formaldehyde sea water buffered solution. Sampling volume was estimated by multiplying the mouth area with the wire length. The collected material was analysed using the method of portions (Yashnov, 1939). Samples were brought to volume of 50 - 100 ml depending upon zooplankton density and mixed intensively until all organisms were distributed randomly in the sample volume. After that 1 ml of sample was taken by calibrated Stempel-pipette. This operation was produced twice. If divergence between two examined subsamples was more than 30% one more subsample was examined. Large (> 1 mm body length) and not abundant species were calculated in 1/2, 1/4, 1/8, 1/16 or 1/32 part of sample. Counting and measuring of organisms were made in the Bogorov chamber under the stereomicroscope to the lowest taxon possible. Number of organisms per sample was calculated as simple average of two subsamples meanings multiplied on subsample volume. Total abundance of mesozooplankton was calculated as sum of taxon-specific abundances and total abundance of Copepods was calculated as sum of copepods taxon-specific abundances.
Resumo:
The Danubs 2001 dataset contains zooplankton data collected in March, June, September and October 2001 in 11 station allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken at 11 stations where samples were collected using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.
Resumo:
The "SESAME_IT4_ZooAbundance_0-50-100m_SZN" dataset contains data of mesozooplankton species composition and abundance (ind./m**3) from samples collected in the Western Mediterranean in the early spring of 2008 (20 March-5 April) during the SESAME-WP2 cruise IT4. Samples were collected by vertical tows with a closing WP2 net (56 cm diameter, 200 µm mesh size) in the following depth layers: 100-200 m, 50-100 m, 0-50 m. Sampling was always performed in light hours. A flowmeter was applied to the mouth of the net, however, due to its malfunctioning, the volume of filtered seawater was calculated by multiplying the the area by the height of the sampled layer from winch readings. After collection, each sample was split in two halves (1/2) after careful mixing with graduated beakers. Half sample was immediately fixed and preserved in a formaldehyde-seawater solution (4% final concentration) for species composition and abundance. The other half sample was kept fresh for biomass measurements (data already submitted to SESAME database in different files). Here, only the zooplankton abundance of samples in the upper layers 0-50 m and 50-100 m are presented. The abundance data of the samples in the layer 50-100 m will be submitted later in a separate file. The volume of filtered seawater was estimated by multiplying the the area by the height of the sampled layer from winch readings. Identification and counts of specimens were performed on aliquots (1/20-1/5) of the fixed sample or on the total sample (half of the original sample) by using a graduate large-bore pipette. Copepods were identified to the species level and separated into females, males and juveniles (copepodites). All other taxa were identified at the species level when possible, or at higher taxonomic levels. Taxonomic identification was done according to the most relevant and updated taxonomic literature. Total mesozooplankton abundance was computed as sum of all specific abundances determined as explained above.
Resumo:
The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.
Resumo:
The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.
Resumo:
The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. North Atlantic albacore tuna is exploited all year round by longline and in summer and autumn by surface fisheries and fishery statistics compiled by the International Commission for the Conservation of Atlantic Tunas (ICCAT). Catch and effort with geographical coordinates at monthly spatial resolution of 1° or 5° squares were extracted for this species with a careful definition of fisheries and data screening. In total, thirteen fisheries were defined for the period 1956-2010, with fishing gears longline, troll, mid-water trawl and bait fishing. However, the spatialized catch effort data available in ICCAT database represent a fraction of the entire total catch. Length frequencies of catch were also extracted according to the definition of fisheries above for the period 1956-2010 with a quarterly temporal resolution and spatial resolutions varying from 1°x 1° to 10°x 20°. The resolution used to measure the fish also varies with size-bins of 1, 2 or 5 cm (Fork Length). The screening of data allowed detecting inconsistencies with a relatively large number of samples larger than 150 cm while all studies on the growth of albacore suggest that fish rarely grow up over 130 cm. Therefore, a threshold value of 130 cm has been arbitrarily fixed and all length frequency data above this value removed from the original data set.
Resumo:
The SHELF 1998 dataset contains zooplankton data collected in May, July and September 19978 allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.
Resumo:
The Est Constanta 1979 dataset contains zooplankton data collected monthly from January 1979 to december 1979 allong a 5 station transect in front of the city Constanta (44°10'N, 28°41.5'E - EC1; 44°10'N, 28°47'E - EC2; 44°10'N, 28°54'E - EC3; 44°10'N, 29°08'E - EC4; 44°10'N, 29°22'E - EC5). Zooplankton sampling was undertaken at 5 stations where samples were collected using a Juday closing net in the 0-10, 10-25, 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.
Resumo:
The SHELF 1999 dataset contains zooplankton data collected in April, June and September 1999 allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.
Resumo:
This data set contains measurements of dissolved nitrogen (total dissolved nitrogen: TDN, dissolved organic nitrogen: DON, dissolved ammonium: NH4+, and dissolved nitrate: NO3-) in samples of soil water collected from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). 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). Plots were maintained by bi-annual weeding and mowing. In April 2002 glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-) and ammonium (NH4+) concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+). In 5% of the samples, TDN was equal to or smaller than mineral N. In these cases, DON was assumed to be zero.
Resumo:
This data set contains measurements of dissolved nitrogen (total dissolved nitrogen: TDN, dissolved organic nitrogen: DON, dissolved ammonium: NH4+, and dissolved nitrate: NO3-) in samples of soil water collected from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). 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). Plots were maintained by bi-annual weeding and mowing. In April 2002 glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-) and ammonium (NH4+) concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+). In 5% of the samples, TDN was equal to or smaller than mineral N. In these cases, DON was assumed to be zero.
Resumo:
The software PanGet is a special tool for the download of multiple data sets from PANGAEA. It uses the PANGAEA data set ID which is unique and part of the DOI. In a first step a list of ID's of those data sets to be downloaded must be created. There are two choices to define this individual collection of sets. Based on the ID list, the tool will download the data sets. Failed downloads are written to the file *_failed.txt. The functionality of PanGet is also part of the program Pan2Applic (choose File > Download PANGAEA datasets...) and PanTool2 (choose Basic tools > Download PANGAEA datasets...).
Resumo:
As an estimate of plant-available N, this data set contains measurements of inorganic nitrogen (NO3-N and NH4-N, the sum of which is termed mineral N or Nmin) determined by extraction with 1 M KCl solution of soil samples from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). 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). Plots were maintained by bi-annual weeding and mowing. Soil sampling and analysis: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m and 0.15 to 0.3 m of the mineral soil from each of the experimental plots in March, June, and October 2003. Samples of the soil cores per plot were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, Skalar, Breda, Netherlands).