Estimates and comparisons of the effects of sampling variation using ‘national’ macroinvertebrate sampling protocols on the precision of metrics used to assess ecological status

Abstract

The Water Framework Directive (WFD) of the European Union requires all member countries to provide information on the level of confidence and precision of results in their river monitoring programmes to assess the ecological status class of river sites. As part of the European Union project STAR, the overall effects of sampling variation for a wide range of commonly used metrics and sampling methods were assessed. Replicate samples were taken in each of two seasons at 2-6 sites of varying ecological status class within each of 18 stream types spread over 12 countries, using both the STAR-AQEM method and a national sampling method or, where unavailable, the RIVPACS sampling protocol. The sampling precision of a combination of sampling method and metric was estimated by expressing the replicate sampling variance as a percentage P samp of the total variance in metric values with a stream type; low values of P samp indicate high precision. Most metrics had percentage sampling variances less than 20% for all or most stream types and methods. Most national methods including RIVPACS had sampling precisions at least as good as those for the STAR-AQEM method as used in their country at the same sites; the main exceptions were the national methods used in Latvia and Sweden. The national methods used in the Czech Republic, Denmark, France, Poland and the RIVPACS method used in the UK and Austria all had percentage sampling variances of less than 10% for the majority of metrics assessed. In contrast, none of the metrics had percentage sampling variances less than 10% when based on either the Italian (IBE) method, which used bank-side sorting, or the Latvian national method which identifies only a limited set of taxa. P samp was lowest on average for the two stream types sampled in the Czech Republic using either the PERLA national method or the STAR-AQEM method. Averaged over all stream types and methods, the three Saprobicbased metrics had the lowest average percentage sampling variances (3-6%) amongst the 26 metrics assessed. These estimates of sampling standard deviation can be used to help assess the uncertainty in single or multi-metric systems for estimating site ecological status using the general STAR Bioassessment Guidance Software (STARBUGS) developed within the STAR project.

Ecological status improvement over a decade along the Ligurian coast according to a macroalgae based index (CARLIT)

“…The total variance (σ 2 T ) and variance components associated with each factor (σ 2 x ) were estimated and then the percentage of variance explained by each factor (P samp ) was calculated, following [37]: where, …”

Monitoring the health of large rivers with macroinvertebrates: Do dominant taxa help or hinder the assessment?

“…Macroinvertebrate sampling and assessment protocols for wadeable streams and rivers have undergone a variety of modifications and innovations since 1970(e.g. Chutter, 1972Armitage et al, 1987;Resh and Jackson, 1993;Hilsenhoff, 1998;Barbour et al, 1999;Clarke et al, 2006;Chessman et al, 2007) whereas there has been little modernization of sampling and assessment protocols for large (!7th order), non-wadeable rivers. We believe that progress for macroinvertebrate monitoring in large rivers has been slowed by limited number of large river studies, and this reflects the relative scarcity of large rivers on the landscape (e.g.…”

Balance between the Reliability of Classification and Sampling Effort: A Multi-Approach for the Water Framework Directive (WFD) Ecological Status Applied to the Venice Lagoon (Italy)

“…From results of the estimation of confidence, it was possible to investigate where a reduction of the monitoring network effort could be allowed, avoiding excessively increasing the risk of misclassification. Recent studies proposed, for monitoring programs assessing status of WBs, to identify the optimal allocation of samples in time and space, through the quantification of the different uncertainty components affecting monitoring data [22,23]. As reported above, in this study, the main factors affecting the reliability of MaQI results were spatial variations, therefore changes focused only on the number and location of sampling stations.…”

“…Previous theoretical studies proposed mixed models to estimate uncertainties in monitoring data, considering numerous different sources of variation that could affect an indicator, from the uncertainty related to sampling and analysis, to spatial and temporal variations and their interactions. However, when real data were considered, if some sources of uncertainty were small, they were disregarded by analysis [20][21][22][23][24][25][26]. In the current study, temporal variations were not considered, as the application of the MaQI index foresees to merge both spring and autumnal data collected in the year of monitoring, avoiding contribution from intra-annual variations to uncertainty [9].…”