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Life in a Contaminated Environment: How Soil Nematodes Can Indicate Long-Term Heavy-Metal Pollution

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Journal J Nematol
Date 2022 Dec 2
PMID 36457367
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Abstract

We investigated the genera, trophic groups, and functional guilds of soil nematodes at five alluvial meadows along the Litavka River in the Czech Republic to assess their usefulness as indicators of heavy metal pollution in soils. The Litavka River flows around the waste-sedimentation pond of a smelter in the city of Příbram in the Central Bohemian Region. Lead, zinc, and arsenic are the main pollutants in the soils in the vicinity of the smelter. The alluvial meadows closest to the pond and mine waste were the most heavily polluted sites, and contamination decreased downstream along the river with increasing distance from the sources of pollution. The nematode communities were sensitive to pollution, with the most contaminated sites having considerably fewer nematode individuals, fewer genera, and a less diverse and more degraded food web with less nematode biomass. Arsenic, lead, and zinc contents were significantly negatively correlated with the numbers of bacterivores, predators, omnivores, plant parasites, and fungivores, which were significantly less abundant at highly polluted sites. This correlation suggests that nematode groups with higher c-p values, and those with c-p 1 and 2 designations, can be useful indicators of high heavy-metal contamination in areas polluted for a long time. In contrast, the abundance of c-p 3 plant parasitic nematodes was positively correlated with copper, nickel, and zinc contents and with soil-moisture content in the alluvial meadows. Maturity index (MI) and MI2-5 were the most sensitive indices of the degree of disturbance of the soil ecosystem, with enrichment index, structure index, and basal index indicating the altered decomposition channels and diminished structure of the food web.

Citing Articles

Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach.

Zhu Y, Liu B, Jin G, Wu Z, Wang D Toxics. 2024; 12(3).

PMID: 38535962 PMC: 10976007. DOI: 10.3390/toxics12030229.

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