Determining the quality of taxonomic data

Stribling, James B.; Moulton, Stephen R.; Lester, Gary T.

doi:10.2307/1468357

Cited by 38 publications

(43 citation statements)

References 19 publications

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“…Slide-mounting specimens in these two groups is usually (though, not always) necessary to attain genus level nomenclature, and sometimes even tribal level for midges. Because taxonomy is a major potential source of error in any kind of biological monitoring data sets (Stribling et al, 2003(Stribling et al, , 2008aMilberg et al, 2008;Bortolus, 2008), it is critical to define taxonomic expectations and to treat all samples consistently, both by a single taxonomist and among multiple taxonomists. This, in part, requires specifying both hierarchical targets and counting rules.…”

Section: Potential Error Sources In Indicators 41 Field Samplingmentioning

confidence: 99%

“…Measurement quality objectives (MQO) are control points above (or below) which most observed values fall (Diamond et al, 2006;Flotemersch et al, 2006;Stribling et al, 2003Stribling et al, , 2008aHerbst & Silldorf, 2006), and are roughly analogous to the Shewhart (1939) Table 3. Error partitioning framework for biological assessments and biological assessment protocols for benthic macroinvertebrates.…”

Section: Measurement Quality Objectives (Mqo)mentioning

confidence: 99%

“…Defensibility would potentially be diminished. Values that are >MQO are not automatically taken to be unacceptable data points; rather, such values are targeted for closer scrutiny to determine possible reasons for exceedence and might indicate a need for corrective actions (Stribling et al 2003, Montana DEQ 2006. Simultaneously, they can be used to help quantify performance of the field teams in consistently applying the methods.…”

Section: Measurement Quality Objectives (Mqo)mentioning

confidence: 99%

“…where a is the number of agreements, and N is the total number of organisms in the larger of the two counts (Stribling et al, 2003(Stribling et al, , 2008a. Percent difference in enumeration (PDE) quantifies the consistency of specimen counts in samples, and is determined by calculating a comparison of results from two independent laboratories or taxonomists using the formula:…”

Section: Field Samplingmentioning

confidence: 99%

“…where n 1 is the number of organisms in a sample counted by the first laboratory, and n 2 , the second (Stribling et al 2003). Percent taxonomic completeness (PTC) describes the proportion of specimens in a sample that meet the target identification level (Stribling et al 2008) and is calculated as:…”

Section: Field Samplingmentioning

confidence: 99%

See 4 more Smart Citations

Partitioning Error Sources for Quality Control and Comparability Analysis in Biological Monitoring and Assessment

Stribling¹

2011

Modern Approaches to Quality Control

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Section: Potential Error Sources In Indicators 41 Field Samplingmentioning

confidence: 99%

Section: Measurement Quality Objectives (Mqo)mentioning

confidence: 99%

Section: Measurement Quality Objectives (Mqo)mentioning

confidence: 99%

Section: Field Samplingmentioning

confidence: 99%

Section: Field Samplingmentioning

confidence: 99%

See 3 more Smart Citations

Partitioning Error Sources for Quality Control and Comparability Analysis in Biological Monitoring and Assessment

Stribling¹

2011

Modern Approaches to Quality Control

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Field and laboratory performance characteristics of a new protocol for sampling riverine macroinvertebrate assemblages

Blocksom

Flotemersch

2008

River Research & Apps

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Measurement and estimation of performance characteristics (i.e. precision, bias, performance range, interferences and sensitivity) are often neglected in the development and use of biological sampling methods. However, knowledge of this information is critical in enabling potential users to assess data quality and make comparisons among different sampling methods. In this study, the performance characteristics were evaluated for both the field and laboratory components of a new large river macroinvertebrate bioassessment protocol (mLR-BP) for non-wadeable streams. We sampled 19 sites across two depth classes, collecting three replicate samples at each site and sorting three 300-organism subsamples from each sample. The replicate samples provided data for estimates of precision in the laboratory and field, and abiotic variables allowed for measurements of overall sensitivity. Precision and performance range differed between shallow and deep sites, particularly for the field component. As compared with precision measured in other studies of bioassessment methods, the field component of the mLR-BP performed similarly, particularly in shallow sites. Based on the measures of combined field and laboratory sensitivity, this protocol should be able to detect differences of approximately 20-25% in the metrics evaluated in this study, if used for bioassessment in similar types of rivers. With all sites and the field and laboratory components combined, metrics were most responsive to a gradient of urban land cover but also showed some relationship with agricultural land cover. However, metric responsiveness does not necessarily correlate with precision, and metric selection can influence the performance characteristics of the method. Overall, the sampling protocol shows great utility for bioassessment and monitoring of non-wadeable rivers, as well as for measuring the success of restoration efforts. In addition, the design of this study provides a template for estimating performance characteristics in other non-wadeable systems.

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An approach for determining bioassessment performance and comparability

Diamond

Stribling

Huff³

et al. 2011

Environ Monit Assess

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Many organizations in the USA collect aquatic bioassessment data using different sampling and analysis methods, most of which have unknown performance in terms of data quality produced. Thus, the comparability of bioassessments produced by different organizations is often unknown, ultimately affecting our ability to make comprehensive assessments on large spatial scales. We evaluated a pilot approach for determining bioassessment performance using macroinvertebrate data obtained from several states in the Southeastern USA. Performance measures evaluated included precision, sensitivity, and responsiveness to a human disturbance gradient, defined in terms of a land disturbance index value for each site, combined with a value for specific conductance, and instream habitat quality. A key finding of this study is the need to harmonize ecoregional reference conditions among states so as to yield more comparable and consistent bioassessment results. Our approach was also capable of identifying potential areas for refinement such as reevaluation of less precise, sensitive, or responsive metrics that may result in suboptimal index performance. Higher performing bioassessments can yield information beyond "impaired" versus "unimpaired" condition. Acknowledging the limitations of this pilot study, we would recommend that performance evaluations use at least 50 sites, 10 of which are ecoregional reference sites. Efforts should be made to obtain data from the entire human disturbance gradient in an ecoregion to improve statistical confidence in performance measures. Having too few sites will result in an under-representation of certain parts of the disturbance gradient (e.g., too few poor quality sites), which may bias sensitivity and responsiveness estimates.

show abstract

Determining the quality of taxonomic data

Cited by 38 publications

References 19 publications

Partitioning Error Sources for Quality Control and Comparability Analysis in Biological Monitoring and Assessment

Partitioning Error Sources for Quality Control and Comparability Analysis in Biological Monitoring and Assessment

Field and laboratory performance characteristics of a new protocol for sampling riverine macroinvertebrate assemblages

An approach for determining bioassessment performance and comparability

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