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Abiotic and biotic factors influencing in situ trace metal levels in macroinvertebrates in freshwater ecosystems
Author(s) -
van Hattum Bert,
Timmermans Klaas R.,
Govers Harrie A.
Publication year - 1991
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620100217
Subject(s) - abiotic component , trace metal , trophic level , ecology , invertebrate , environmental chemistry , benthic zone , biotic component , trace element , ecosystem , aquatic ecosystem , environmental science , biology , chemistry , metal , organic chemistry
We examined the potential of a multiple regression model, including both biotic and abiotic variables, to predict trace metal levels (Cd, Pb, Cu and Zn) in benthic invertebrate communities from various aquatic habitats in The Netherlands. Variables included in the regression model and their relative contribution to the predictive power appeared to be highly element‐specific. Sediment‐ and water‐related abiotic variables were especially important for the prediction of Cd, Pb and, to a lesser extent, Zn. For the essential trace metals, the biotic variables such as the presence of hemocyanin (in the case of Cu) and trophic level (Zn) appeared to have a dominant influence. Species‐body weight showed a negative log‐linear relationship with trace metal levels of Pb and, to a lesser extent, Cu and Cd. For all metals, significant coefficients of multiple correlation were observed. The variance in invertebrate trace metal levels explained by the model ranged from 25% for Zn to 37% for Cd and Cu. For all trace metals, the introduction of biotic variables resulted in a significant increase in predictive power ( p < 0.02), compared to the use of only abiotic predictors. Marked differences in predictive power of the model were observed among various species or taxonomic categories. For several species remarkably significant relationships were revealed, indicating the potential usefulness of the organisms for biological monitoring and further modeling studies.