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MERC evaluates taxonomic bias (zooplankton, phytoplankton, bacteria) by comparing whole-sample
identifications completed by independent taxonomists. To calculate taxonomic bias, MERC generally
randomly selects 10 percent of the samples for recounts and re-identification by a second qualified
taxonomist.
Final counts for samples are dependent on the taxonomist. Comparison of counts is quantified by
calculation of Relative Percent Difference in Enumeration (RPDE), using the formula:
(|x
1
– x
2
|)
RPDE = x
1
+ x
2
x 100%
2
where x
1
= number of organisms in a sample counted by the first taxonomist
x
2
= the recount by the second taxonomist
Individual samples exceeding 5% are re-examined.
The measure for taxonomic bias is Percent Taxonomic Disagreement (PTD), which is calculated as:
PTD = 1- (comp
pos
) x 100
N
where comp
pos
= the number of agreements and
N
= the total number of individuals in the larger of the
two counts.
The lower the PTD, the more similar are taxonomic results. Individual samples exceeding 15% are
examined for taxonomic areas of substantial disagreement, and the reasons for disagreement investigated.
Where re-identification by an independent, outside taxonomist is not practical, percent similarity is
calculated. Percent similarity is a measure of similarity between two samples. Values range from 0% for
samples with no species in common, to 100% for samples which are identical. It is calculated as follows:
PSC =
1 − (0.5
!
−
!
!!!!
x 100%
where:
a
and
b
are, for a given species, the relative proportions of the total samples A and B, respectively,
which that species represents. The MQO for percent similarity of taxonomic identification is ≥85%. If
the MQO is not met, the reasons for the discrepancies between analysts are determined, and the batch of
samples with discrepancies may be recounted.
Representativeness
Representativeness, as defined by the American Society for Quality and published in the American
National Standards Institute (ANSI) document, ANSI/ASQC E4-1994,
Specifications and Guidelines for
Quality Systems for Environmental Data Collection and Environmental
Technology Programs
(ANSI/ASQC, 1994) is: "The measure of the degree to which data accurately and precisely represent a
characteristic of a population, parameter variations at a sampling point, a process condition, or an
environmental condition."
Developing a clear understanding of the "population" that is the subject of the test is the key to assessing
representativeness. The characteristics of the population include the subject's identity or class (e.g., the
particular property that needs to be measured), the spatial distribution of the property, and in some cases,
the temporal characteristics of the property. This definition of representativeness encompasses issues at