Soil nematode community under the non-native trees in the Botanic Garden of Petrozavodsk State University

The particularities of soil nematode communities of the rhizosphere of non-native trees were studied in the Botanic Garden of Petrozavodsk State University (Republic of Karelia). Taxonomic diversity, abundance, community structure and ecological indices derived from nematode fauna analysis were used as the evaluation parameters. Nematode fauna included 51 genera, 6 of them were plant parasitic. The dominant eco-trophic group in the nematode community structure of coniferous trees was bacterial feeders; fungal feeders in most cases were observed in the second numbers. The contribution of bacterial feeders was decreased and plant parasites were increased in eco-trophic structure of nematode communities of deciduous trees in compared with coniferous trees. Analysis of ecological indices showed that the state of soil nematode communities reflects complex, structured (stable) soil food web in the biocenoses with deciduous trees, and degraded (basal) food web – under coniferous trees

Zoological researches in Botanical Garden of PetrSU

In recent years, the Botanical Garden of Petrozavodsk State University has been paying great attention to zoological research. To date, there have been data on the nesting of some species of birds for 4 years, the study of the fauna of reservoirs, the studies of the necrophilous fauna, the registration of quarantine insects, the study of communities of soil nematodes are carried out annually

A global database of soil nematode abundance and functional group composition

As the most abundant animals on earth, nematodes are a dominant component of the soil community. They play critical roles in regulating biogeochemical cycles and vegetation dynamics within and across landscapes and are an indicator of soil biological activity. Here, we present a comprehensive global dataset of soil nematode abundance and functional group composition. This dataset includes 6,825 georeferenced soil samples from all continents and biomes. For geospatial mapping purposes these samples are aggregated into 1,933 unique 1-km pixels, each of which is linked to 73 global environmental covariate data layers. Altogether, this dataset can help to gain insight into the spatial distribution patterns of soil nematode abundance and community composition, and the environmental drivers shaping these patterns.Peer reviewe

Soil nematode abundance and functional group composition at a global scale

Soil organisms are a crucial part of the terrestrial biosphere. Despite their importance for ecosystem functioning, few quantitative, spatially explicit models of the active belowground community currently exist. In particular, nematodes are the most abundant animals on Earth, filling all trophic levels in the soil food web. Here we use 6,759 georeferenced samples to generate a mechanistic understanding of the patterns of the global abundance of nematodes in the soil and the composition of their functional groups. The resulting maps show that 4.4 ± 0.64 × 1020 nematodes (with a total biomass of approximately 0.3 gigatonnes) inhabit surface soils across the world, with higher abundances in sub-Arctic regions (38% of total) than in temperate (24%) or tropical (21%) regions. Regional variations in these global trends also provide insights into local patterns of soil fertility and functioning. These high-resolution models provide the first steps towards representing soil ecological processes in global biogeochemical models and will enable the prediction of elemental cycling under current and future climate scenario

A global database of soil nematode abundance and functional group composition

This study uses direct measurements of soil nematode abundance from 6,825 georeferenced locations around the world, covering all continents and all terrestrial biomes. We describe the data sources, methodology and data processing steps to transform the data into a version that can be used for, for example, geospatial modeling. To do so, the samples were aggregated to the 1-km2 pixel level, each pixel is linked to 73 global covariate layers. These include on soil physiochemical properties, and vegetation, climate, and topographic, anthropogenic, and spectral reflectance information