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SPATIOTEMPORAL VARIABILITY OF TEMPERATE LAKE MACROINVERTEBRATE COMMUNITIES: DETECTION OF IMPACT
Author(s) -
Johnson Richard K.
Publication year - 1998
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(1998)008[0061:svotlm]2.0.co;2
Subject(s) - temperate climate , ecology , invertebrate , geography , environmental science , biology
Field assessments of environmental impacts often are confounded by attempts to isolate effects of interest (perturbation‐induced impacts) from noise introduced by natural spatial and temporal variability. Detection of impact often is constrained by variability of the data, the number of independent samples, the magnitude of impact to be detected, and statistical assumptions. Analysis of the spatial and temporal variability of macroinvertebrate indicator metrics of 16 Swedish lakes, situated in the boreo‐nemoral ecoregion, revealed that standardized effect sizes (i.e., effect sizes expressed in standard deviation units) and estimates of statistical power varied markedly among habitats and with choice of indicator metric. In general, indicator metrics relying on measures of the number of taxa (taxon richness, diversity, and ASPT, average score per taxon), and pollution‐specific metrics relying on taxon tolerance to pollution (acidification index) had higher standardized effect size and greater statistical power (primarily due to lower variability) than did measures of macroinvertebrate density and biomass. Indicator metrics for macroinvertebrate communities of sublittoral habitats often revealed greater standardized effect sizes and statistical power estimates than did metrics for profundal habitats, indicating that sublittoral habitats may provide more robust estimates of acidification stress. The greatest standardized effect occurred for the pollution‐specific acidification index of littoral habitats. Selecting indicator metrics for field assessment of impact should be carefully done, and in particular, more focus should be placed on evaluating the robustness of indicator metrics by analyzing indicator metric variance, expected effect size, and statistical power.