960 resultados para time-series forecasting


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This data set comprises a 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 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. Aboveground community biomass was 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 of the main 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 (i.e. the central 10 x 15 m). 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.

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Spectral absorption coefficients of total particulate matter ap (lambda) were determined using the in vitro filter technique. The present analysis deals with a set of 1166 spectra, determined in various oceanic (case 1) waters, with field chl a concentrations ([chl]) spanning 3 orders of magnitude (0.02-25 mg/m**3). As previously shown [Bricaud et al., 1995, doi:10.1029/95JC00463] for the absorption coefficients of living phytoplankton a phi (lamda), the ap (labda) coefficients also increase nonlinearly with [chl]. The relationships (power laws) that link ap (lambda) and a phi (lambda) to [chl] show striking similarities. Despite large fluctuations, the relative contribution of nonalgal particles to total absorption oscillates around an average value of 25-30% throughout the [chl] range. The spectral dependence of absorption by these nonalgal particles follows an exponential increase toward short wavelengths, with a weakly variable slope (0.011 ± 0.0025/nm). The empirical relationships linking ap (lambda) to ([chl]) can be used in bio-optical models. This parameterization based on in vitro measurements leads to a good agreement with a former modeling of the diffuse attenuation coefficient based on in situ measurements. This agreement is worth noting as independent methods and data sets are compared. It is stressed that for a given ([chl]), the ap (lambda) coefficients show large residual variability around the regression lines (for instance, by a factor of 3 at 440 nm). The consequences of such a variability, when predicting or interpreting the diffuse reflectance of the ocean, are examined, according to whether or not these variations in ap are associated with concomitant variations in particle scattering. In most situations the deviations in ap actually are not compensated by those in particle scattering, so that the amplitude of reflectance is affected by these variations.