985 resultados para TOTAL ABOVEGROUND BIOMASS
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
The dynamic of early spring nanoprotozoa was investigated in three characteristic water masses of the Southern Ocean: the Marginal Ice Zone, the intermediate waters of the Antarctic Circumpolar Current and the Polar Frontal Zone. Biomass and feeding activities of nanoprotozoa were measured, as well as the biomass of their potential prey-bacteria and phototrophic flagellates-on the 6°W meridian in the Southern Ocean along three repetitive transects between 47 and 60° South from October to November 1992. On average, nanoprotozooplankton biomass accounted for 77% of the combined biomass of bacteria and phototrophic flagellates, and was dominated by dinoflagellates and flagellates smaller than 5 µm. As a general trend, low protozoan biomass of 2 mg C/m**3 was typical of the ice covered area, while significantly higher biomasses culminating at 15 mg C/m**3 were recorded at the Polar Front. Biomasses of bacteria and total phytoplankton were distributed accordingly, with larger values at the Polar Front. Phototrophic flagellates did not show any geographical trend. No seasonal trend could be identified in the Marginal Ice Zone and in the intermediate waters of the Antarctic Circumpolar Current. On the other hand, at the Polar Front region a three-fold increase was observed within a 2-month period for nanoprotozooplankton biomass. Such a biomass increase was also detected for bacterioplankton and total phytoplankton biomass. Half-saturation constants and maximum specific ingestion of nanoprotozoan taxons feeding on bacteria and phototrophic flagellates were determined using the technique of fluorescent labelled bacteria (FLB) and algae (FLA) over a large range of prey concentrations. Maximum ingestion rates ranged between 0.002 and 0.015/h for bactivorous nanoprotozoa and heterotrophic flagellates larger than 5 µm feeding on phototrophic flagellates. The markedly high maximum ingestion rates of 0.4/h characterising nanophytoplankton ingestion by dinoflagellates evidenced the strong ability of dinoflagellates for feeding on nanophytoplankton. Daily ingestion rates were calculated from nanoprotozoan grazing parameters and carbon biomass of prey and predators. This indicated that nanoprotozoa ingestion of daily bacterioplankton and phytoplankton production in early spring ranged from 32 to 40%.
Resumo:
Collections made with 150 l sampling bottles and BR 113/140 nets, as well as direct counts from the Mir submersible are used to analyze vertical distribution of total biomass of meso- and macroplankton and biomass distributions of their main component groups in the central oligotrophic regions of the North Pacific. Biomass of mesoplankton in the upper 200 m layer ranges from 3.1 to 8.6 g/m**2, but sometimes it increases up to as much as 98 g/m**2 in local population explosions of salps. Jellies predominate in macroplankton at depths of up to 2-3 km, contributing 97-98% of live weight and 30-70% of biomass as organic carbon. In importance they are followed by micronecton fishes (up to 40% of organic carbon). Contributions of other groups countable from the submersible were negligible. Distributions of species at particular stations are discussed.
Resumo:
Studies of picophytoplankton were carried out in the open Black Sea from February to April 1991 with concomitant blooming of diatoms. During this period cyanobacteria predominated in picoplankton averaging 98.8% of total picophytoplankton abundance and 95% of total picoplankton biomass. In February number of cells reached 1.5x10**9 per liter in the East Black Sea. Picoplankton biomass decreased during the observation period. From February to March biomass varied from 452 to 4918 mg/m**2 (av. 1632 mg/m**2), and from March through April from 4 to 656 mg/m**2 (av. 190 mg/m**2). Vertical distribution of picoplankton was determined by the upper margin of the main pycnocline. The major part of picoplankton biomass occurred in the mixed layer. With appearance of seasonal pycnoclines in the last days of March maximum biomass occurred under the upper mixed layer. No relationship was observed between Nitzschia delicatula bloom and picoplankton.
Resumo:
The Tibetan highlands host the largest alpine grassland ecosystems worldwide, bearing soils that store substantial stocks of carbon (C) that are very sensitive to land use changes. This study focuses on the cycling of photoassimilated C within a Kobresia pygmaea pasture, the dominating ecosystems on the Tibetan highlands. We investigated short-term effects of grazing cessation and the role of the characteristic Kobresia root turf on C fluxes and belowground C turnover. By combining eddy-covariance measurements with 13CO2 pulse labeling we applied a powerful new approach to measure absolute fluxes of assimilates within and between various pools of the plant-soil-atmosphere system. The roots and soil each store roughly 50% of the overall C in the system (76 Mg C/ha), with only a minor contribution from shoots, which is also expressed in the root:shoot ratio of 90. During June and July the pasture acted as a weak C sink with a strong uptake of approximately 2 g C/m**2/ in the first half of July. The root turf was the main compartment for the turnover of photoassimilates, with a subset of highly dynamic roots (mean residence time 20 days), and plays a key role for the C cycling and C storage in this ecosystem. The short-term grazing cessation only affected aboveground biomass but not ecosystem scale C exchange or assimilate allocation into roots and soil.
Resumo:
Background and Aims: Anthropogenic depletion of stratospheric ozone in Arctic latitudes has resulted in an increase of ultraviolet-B radiation (UV-B) reaching the biosphere. UV-B exposure is known to reduce aboveground biomass and plant height, to increase DNA damage and cause accumulation of UV-absorbing compounds in polar plants. However, many studies on Arctic mosses tended to be inconclusive. The importance of different water availability in influencing UV-B impacts on lower plants in the Arctic has been poorly explored and might partially explain the observed wide variation of responses, given the importance of water in controlling bryophyte physiology. This study aimed to assess the long-term responses of three common sub-Arctic bryophytes to enhanced UV-B radiation (+UV-B) and to elucidate the influence of water supply on those responses. Results: Responses were species specific: H. splendens responded most to +UV-B, with reduction in both annual growth (-22%) and sporophyte production (-44%), together with increased b-carotene, violaxanthin, total chlorophyll and NPQ, and decreased zeaxanthin and de-epoxidation of the xanthophyll cycle pool (DES). Barbilophozia lycopodioides responded less to +UV-B, showing increased b-carotene and sclerophylly and decreased UV-absorbing compounds. Polytrichum commune only showed small morphogenetic changes. No effect of UV-B on bryophyte cover was observed. Water availability had profound effects on bryophyte ecophysiology, and plants showed, in general, lower growth and ETR, together with a higher photoprotection in the drier site. Water availability also influenced bryophyte responses to +UV-B and, in particular, responses were less detectable in the drier site. Conclusions: Impacts of UV-B exposure on Arctic bryophytes were significant, in contrast to modest or absent UV-B effects measured in previous studies. The impacts were more easily detectable in species with high plasticity such as H. splendens and less obvious, or more subtle, under drier conditions. Species biology and water supply greatly influences the impact of UV-B on at least some Arctic bryophytes and could contribute to the wide variation of responses observed previously.