948 resultados para Functional Model


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The smallest marine phytoplankton, collectively termed picophytoplankton, have been routinely enumerated by flow cytometry since the late 1980s, during cruises throughout most of the world ocean. We compiled a database of 40,946 data points, with separate abundance entries for Prochlorococcus, Synechococcus and picoeukaryotes. We use average conversion factors for each of the three groups to convert the abundance data to carbon biomass. After gridding with 1° spacing, the database covers 2.4% of the ocean surface area, with the best data coverage in the North Atlantic, the South Pacific and North Indian basins. The average picophytoplankton biomass is 12 ± 22 µg C L-1 or 1.9 g C m-2. We estimate a total global picophytoplankton biomass, excluding N2-fixers, of 0.53 - 0.74 Pg C (17 - 39 % Prochlorococcus, 12 - 15 % Synechococcus and 49 - 69 % picoeukaryotes). Future efforts in this area of research should focus on reporting calibrated cell size, and collecting data in undersampled regions.

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This study is a first effort to compile the largest possible body of data available from different plankton databases as well as from individual published or unpublished datasets regarding diatom distribution in the world ocean. The data obtained originate from time series studies as well as spatial studies. This effort is supported by the Marine Ecosystem Data (MAREDAT) project, which aims at building consistent data sets for the main PFTs (Plankton Functional Types) in order to help validate biogeochemical ocean models by using converted C biomass from abundance data. Diatom abundance data were obtained from various research programs with the associated geolocation and date of collection, as well as with a taxonomic information ranging from group down to species. Minimum, maximum and average cell size information were mined from the literature for each taxonomic entry, and all abundance data were subsequently converted to biovolume and C biomass using the same methodology.

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Microzooplankton database. Originally published in: Buitenhuis, Erik, Richard Rivkin, Sévrine Sailley, Corinne Le Quéré (2010) Biogeochemical fluxes through microzooplankton. Global Biogeochemical Cycles Vol. 24, GB4015, doi:10.1029/2009GB003601 This new version has had some mistakes corrected.

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High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics.

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Interannual environmental variability in Peru is dominated by the El Niño Southern Oscillation (ENSO). The most dramatic changes are associated with the warm El Niño (EN) phase (opposite the cold La Niña phase), which disrupts the normal coastal upwelling and affects the dynamics of many coastal marine and terrestrial resources. This study presents a trophic model for Sechura Bay, located at the northern extension of the Peruvian upwelling system, where ENSO-induced environmental variability is most extreme. Using an initial steady-state model for the year 1996, we explore the dynamics of the ecosystem through the year 2003 (including the strong EN of 1997/98 and the weaker EN of 2002/03). Based on support from literature, we force biomass of several non-trophically-mediated 'drivers' (e.g. Scallops, Benthic detritivores, Octopus, and Littoral fish) to observe whether the fit between historical and simulated changes (by the trophic model) is improved. The results indicate that the Sechura Bay Ecosystem is a relatively inefficient system from a community energetics point of view, likely due to the periodic perturbations of ENSO. A combination of high system productivity and low trophic level target species of invertebrates (i.e. scallops) and fish (i.e. anchoveta) results in high catches and an efficient fishery. The importance of environmental drivers is suggested, given the relatively small improvements in the fit of the simulation with the addition of trophic drivers on remaining functional groups' dynamics. An additional multivariate regression model is presented for the scallop Argopecten purpuratus, which demonstrates a significant correlation between both spawning stock size and riverine discharge-mediated mortality on catch levels. These results are discussed in the context of the appropriateness of trophodynamic modeling in relatively open systems, and how management strategies may be focused given the highly environmentally influenced marine resources of the region.

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We compiled a database of bacterial abundance of 39 766 data points. After gridding with 1° spacing, the database covers 1.3% of the ocean surface. There is data covering all ocean basins and depth except the Southern Hemisphere below 350 m or from April until June. The average bacterial biomass is 3.9 ± 3.6 µg l-1 with a 20-fold decrease between the surface and the deep sea. We estimate a total ocean inventory of about 1.3 - 1029 bacteria. Using an average of published open ocean measurements for the conversion from abundance to carbon biomass of 9.1 fg cell-1, we calculate a bacterial carbon inventory of about 1.2 Pg C. The main source of uncertainty in this inventory is the conversion factor from abundance to biomass.

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Macrozooplankton are an important link between higher and lower trophic levels in the oceans. They serve as the primary food for fish, reptiles, birds and mammals in some regions, and play a role in the export of carbon from the surface to the intermediate and deep ocean. Little, however, is known of their global distribution and biomass. Here we compiled a dataset of macrozooplankton abundance and biomass observations for the global ocean from a collection of four datasets. We harmonise the data to common units, calculate additional carbon biomass where possible, and bin the dataset in a global 1 x 1 degree grid. This dataset is part of a wider effort to provide a global picture of carbon biomass data for key plankton functional types, in particular to support the development of marine ecosystem models. Over 387 700 abundance data and 1330 carbon biomass data have been collected from pre-existing datasets. A further 34 938 abundance data were converted to carbon biomass data using species-specific length frequencies or using species-specific abundance to carbon biomass data. Depth-integrated values are used to calculate known epipelagic macrozooplankton biomass concentrations and global biomass. Global macrozooplankton biomass has a mean of 8.4 µg C l-1, median of 0.15 µg C l-1 and a standard deviation of 63.46 µg C l-1. The global annual average estimate of epipelagic macrozooplankton, based on the median value, is 0.02 Pg C. Biomass is highest in the tropics, decreasing in the sub-tropics and increasing slightly towards the poles. There are, however, limitations on the dataset; abundance observations have good coverage except in the South Pacific mid latitudes, but biomass observation coverage is only good at high latitudes. Biomass is restricted to data that is originally given in carbon or to data that can be converted from abundance to carbon. Carbon conversions from abundance are restricted in the most part by the lack of information on the size of the organism and/or the absence of taxonomic information. Distribution patterns of global macrozooplankton biomass and statistical information about biomass concentrations may be used to validate biogeochemical models and Plankton Functional Type models.

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Coccolithophores are calcifying marine phytoplankton of the class Prymnesiophyceae. They are considered to play an import role in the global carbon cycle through the production and export of organic carbon and calcite. We have compiled observations of global coccolithophore abundance from several existing databases as well as individual contributions of published and unpublished datasets. We estimate carbon biomass using standardised conversion methods and provide estimates of uncertainty associated with these values. The database contains 58 384 individual observations at various taxonomic levels. This corresponds to 12 391 observations of total coccolithophore abundance and biomass. The data span a time period of 1929-2008, with observations from all ocean basins and all seasons, and at depths ranging from the surface to 500 m. Highest biomass values are reported in the North Atlantic, with a maximum of 501.7 ?gCl-1. Lower values are reported for the Pacific (maximum of 79.4 ?gCl-1) and Indian Ocean (up to 178.3 ?gCl-1). Coccolithophores are reported across all latitudes in the Northern Hemisphere, from the Equator to 89degN, although biomass values fall below 3 ?gCl-1 north of 70degN. In the Southern Hemisphere, biomass values fall rapidly south of 50degS, with only a single non-zero observation south of 60degS. Biomass values show a clear seasonal cycle in the Northern Hemisphere, reaching a maximum in the summer months (June-July). In the Southern Hemisphere the seasonal cycle is less evident, possibly due to a greater proportion of low-latitude data.

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The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1).

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Planktic foraminifera are heterotrophic mesozooplankton of global marine abundance. The position of planktic foraminifers in the marine food web is different compared to other protozoans and ranges above the base of heterotrophic consumers. Being secondary producers with an omnivorous diet, which ranges from algae to small metazoans, planktic foraminifers are not limited to a single food source, and are assumed to occur at a balanced abundance displaying the overall marine biological productivity at a regional scale. We have calculated the assemblage carbon biomass from data on standing stocks between the sea surface and 2500 m water depth, based on 754 protein-biomass data of 21 planktic foraminifer species and morphotypes, produced with a newly developed method to analyze the protein biomass of single planktic foraminifer specimens. Samples include symbiont bearing and symbiont barren species, characteristic of surface and deep-water habitats. Conversion factors between individual protein-biomass and assemblage-biomass are calculated for test sizes between 72 and 845 µm (minimum diameter). The calculated assemblage biomass data presented here include 1057 sites and water depth intervals. Although the regional coverage of database is limited to the North Atlantic, Arabian Sea, Red Sea, and Caribbean, our data include a wide range of oligotrophic to eutrophic waters covering six orders of magnitude of assemblage biomass. A first order estimate of the global planktic foraminifer biomass from average standing stocks (>125 µm) ranges at 8.5-32.7 Tg C yr-1 (i.e. 0.008-0.033 Gt C yr-1), and might be more than three time as high including the entire fauna including neanic and juvenile individuals adding up to 25-100 Tg C yr-1. However, this is a first estimate of regional planktic-foraminifer assemblage-biomass (PFAB) extrapolated to the global scale, and future estimates based on larger data-sets might considerably deviate from the one presented here. This paper is supported by, and a contribution to the Marine Ecosystem Data project (MAREDAT).

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In Montiel Olea and Strzalecki (2014), authors have axiomatically developed an algorithm to infer the parameters of beta-delta model of cognitive bias (present and future biases). While this is extremely useful, it allows the implied beta to become very large when the response is impatient in the future choices relative to present choices, i.e., when there is a strong future bias. I modify the model to further exponentiate the functional form to get more reasonable beta values.

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Adaptive systems use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control architecture can be used to change different elements of the controller at four different levels: parameters of the control model, the control model itself, the functional organization of the agent and the functional components of the agent. The complexity of such a space of potential configurations is daunting. The only viable alternative for the agent ?in practical, economical, evolutionary terms? is the reduction of the dimensionality of the configuration space. This reduction is achieved both by functionalisation —or, to be more precise, by interface minimization— and by patterning, i.e. the selection among a predefined set of organisational configurations. This last analysis let us state the central problem of how autonomy emerges from the integration of the cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. In this paper we will show a general model of how the emotional biological systems operate following this theoretical analysis and how this model is also of applicability to a wide spectrum of artificial systems.

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Adaptive agents use feedback as a key strategy to cope with un- certainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent’s space of potential configurations is daunting. The only viable alternative for space- and time-constrained agents —in practical, economical, evolutionary terms— is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by func- tionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems1.