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Long-term variability in bioassessments: a twenty-year study from two northern California streams.

Mazor RD, Purcell AH, Resh VH - Environ Manage (2009)

Bottom Line: Variability among years was high for most metrics (coefficients of variation, CVs ranging from 16% to 246% in spring) but lower for indices (CVs of 22-26% for the IBI and 21-32% for O/E scores in spring), which resulted in inconsistent assessments of biological condition.Climatic variables did not show consistent trends across all metrics, although several were related to the El Niño Southern Oscillation Index at some sites.In addition, these approaches may help managers anticipate alterations in reference streams caused by global climate change and high climatic variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Science, Policy, & Management, University of California, Berkeley, CA 94720-3114, USA. raphaelm@sccwrp.org

ABSTRACT
Long-term variability of bioassessments has not been well evaluated. We analyzed a 20-year data set (1984-2003) from four sites in two northern California streams to examine the variability of bioassessment indices (two multivariate RIVPACS-type O/E scores and one multimetric index of biotic integrity, IBI), as well as eight metrics. All sites were sampled in spring; one site was also sampled in summer. Variability among years was high for most metrics (coefficients of variation, CVs ranging from 16% to 246% in spring) but lower for indices (CVs of 22-26% for the IBI and 21-32% for O/E scores in spring), which resulted in inconsistent assessments of biological condition. Variance components analysis showed that the time component explained variability in all metrics and indices, ranging from 5% to 35% of total variance explained. The site component was large (i.e., >40%) for some metrics (e.g., EPT richness), but nearly absent from others (e.g., Diptera richness). Seasonal analysis at one site showed that variability among seasons was small for some metrics or indices (e.g., Coleoptera richness), but large for others (e.g., EPT richness, O/E scores). Climatic variables did not show consistent trends across all metrics, although several were related to the El Niño Southern Oscillation Index at some sites. Bioassessments should incorporate temporal variability during index calibration or include climatic variability as predictive variables to improve accuracy and precision. In addition, these approaches may help managers anticipate alterations in reference streams caused by global climate change and high climatic variability.

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Variance components for all metrics and indices. a Spatial variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to site. Gray portions of the bars represent the interaction between site and year. Only spring samples were used to calculate these variance components. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk; b Seasonal variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to season. Gray portions of the bars represent the interaction between season and year. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk. Only samples from site 1P were used to calculate these variance components
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Fig7: Variance components for all metrics and indices. a Spatial variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to site. Gray portions of the bars represent the interaction between site and year. Only spring samples were used to calculate these variance components. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk; b Seasonal variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to season. Gray portions of the bars represent the interaction between season and year. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk. Only samples from site 1P were used to calculate these variance components

Mentions: Variance components analysis of samples collected in spring showed different patterns for annual and spatial components of variability. For example, the annual component explained a portion of variability for all metrics and indices, ranging from a low of 5% of total variance explained (for Diptera richness) to a high of 35% (for % intolerant). In contrast, the spatial component of variability differed strongly among the metrics and indices, and did not always explain a portion of the variability. For example, the spatial component of the two O/E scores, % non-insect taxa, and EPT richness were all over 40%, indicating that these metrics and indices were strongly influenced by the characteristics of the site. However, other metrics had very small spatial components (e.g., % non-insect taxa, Coleoptera richness), or none at all (i.e., Diptera richness), indicating that the location had a minimal influence on these metrics independent from time (Fig. 7a). The spatial and temporal component explained the majority of the variance for all metrics and indices, except for Diptera richness and % non-gastropod scrapers.Fig. 7


Long-term variability in bioassessments: a twenty-year study from two northern California streams.

Mazor RD, Purcell AH, Resh VH - Environ Manage (2009)

Variance components for all metrics and indices. a Spatial variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to site. Gray portions of the bars represent the interaction between site and year. Only spring samples were used to calculate these variance components. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk; b Seasonal variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to season. Gray portions of the bars represent the interaction between season and year. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk. Only samples from site 1P were used to calculate these variance components
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2691804&req=5

Fig7: Variance components for all metrics and indices. a Spatial variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to site. Gray portions of the bars represent the interaction between site and year. Only spring samples were used to calculate these variance components. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk; b Seasonal variance components analysis. White portions of the bars represent the component of variability attributable to year. Black portions of the bars represent the component of variability attributable to season. Gray portions of the bars represent the interaction between season and year. Residual variance is indicated by the difference between 100% and the total height of the bars. Residual variance was not estimated for metrics marked with an asterisk. Only samples from site 1P were used to calculate these variance components
Mentions: Variance components analysis of samples collected in spring showed different patterns for annual and spatial components of variability. For example, the annual component explained a portion of variability for all metrics and indices, ranging from a low of 5% of total variance explained (for Diptera richness) to a high of 35% (for % intolerant). In contrast, the spatial component of variability differed strongly among the metrics and indices, and did not always explain a portion of the variability. For example, the spatial component of the two O/E scores, % non-insect taxa, and EPT richness were all over 40%, indicating that these metrics and indices were strongly influenced by the characteristics of the site. However, other metrics had very small spatial components (e.g., % non-insect taxa, Coleoptera richness), or none at all (i.e., Diptera richness), indicating that the location had a minimal influence on these metrics independent from time (Fig. 7a). The spatial and temporal component explained the majority of the variance for all metrics and indices, except for Diptera richness and % non-gastropod scrapers.Fig. 7

Bottom Line: Variability among years was high for most metrics (coefficients of variation, CVs ranging from 16% to 246% in spring) but lower for indices (CVs of 22-26% for the IBI and 21-32% for O/E scores in spring), which resulted in inconsistent assessments of biological condition.Climatic variables did not show consistent trends across all metrics, although several were related to the El Niño Southern Oscillation Index at some sites.In addition, these approaches may help managers anticipate alterations in reference streams caused by global climate change and high climatic variability.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Science, Policy, & Management, University of California, Berkeley, CA 94720-3114, USA. raphaelm@sccwrp.org

ABSTRACT
Long-term variability of bioassessments has not been well evaluated. We analyzed a 20-year data set (1984-2003) from four sites in two northern California streams to examine the variability of bioassessment indices (two multivariate RIVPACS-type O/E scores and one multimetric index of biotic integrity, IBI), as well as eight metrics. All sites were sampled in spring; one site was also sampled in summer. Variability among years was high for most metrics (coefficients of variation, CVs ranging from 16% to 246% in spring) but lower for indices (CVs of 22-26% for the IBI and 21-32% for O/E scores in spring), which resulted in inconsistent assessments of biological condition. Variance components analysis showed that the time component explained variability in all metrics and indices, ranging from 5% to 35% of total variance explained. The site component was large (i.e., >40%) for some metrics (e.g., EPT richness), but nearly absent from others (e.g., Diptera richness). Seasonal analysis at one site showed that variability among seasons was small for some metrics or indices (e.g., Coleoptera richness), but large for others (e.g., EPT richness, O/E scores). Climatic variables did not show consistent trends across all metrics, although several were related to the El Niño Southern Oscillation Index at some sites. Bioassessments should incorporate temporal variability during index calibration or include climatic variability as predictive variables to improve accuracy and precision. In addition, these approaches may help managers anticipate alterations in reference streams caused by global climate change and high climatic variability.

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