Occurrence of species-indicators and zooplankton biomass in the Barents Sea in September-October 1997

Samples of zooplankton were collected in the Barents Sea during cruise 11 of R/V Akademik Sergey Vavilov in September-October 1997. Three different sampling methods were used: 30 l bottle, Judey net, and BR net. More than 40 species of zooplankton were revealed. The greatest species diversity occurred in zones of junction of waters of different origin. Within the 100 m upper water layer zooplankton biomass was rather high: aver. 32 g/m**2. The highest biomass was observed in the northeastern part of the region under study and over the shelf of the Russkaya Gavan' Bay. The lowest biomass occurred in the southern part and in the region of the Gusinaya Banka. The average autumn value of zooplankton biomass in the 100 m upper layer (321 mg/m**3) slightly exceeded the multiannual average for the summer period (200 mg/m**3)

Distribution models for mountain plant species: The value of elevation

The climatic conditions of mountain habitats are greatly influenced by topography. Large differences in microclimate occur with small changes in elevation, and this complex interaction is an important determinant of mountain plant distributions. In spite of this, elevation is not often considered as a relevant predictor in species distribution models (SDMs) for mountain plants. Here, we evaluated the importance of including elevation as a predictor in SDMs for mountain plant species. We generated two sets of SDMs for each of 73 plant species that occur in the Pacific Northwest of North America; one set of models included elevation as a predictor variable and the other set did not. AUC scores indicated that omitting elevation as a predictor resulted in a negligible reduction of model performance. However, further analysis revealed that the omission of elevation resulted in large over-predictions of species' niche breadths-this effect was most pronounced for species that occupy the highest elevations. In addition, the inclusion of elevation as a predictor constrained the effects of other predictors that superficially affected the outcome of the models generated without elevation. Our results demonstrate that the inclusion of elevation as a predictor variable improves the quality of SDMs for high-elevation plant species. Because of the negligible AUC score penalty for over-predicting niche breadth, our results support the notion that AUC scores alone should not be used as a measure of model quality. More generally, our results illustrate the importance of selecting biologically relevant predictor variables when constructing SDMs.

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.