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The effect of fixed‐count subsampling on macroinvertebrate biomonitoring in small streams
Author(s) -
Doberstein Craig P.,
Karr James R.,
Conquest Loveday L.
Publication year - 2000
Publication title -
freshwater biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 156
eISSN - 1365-2427
pISSN - 0046-5070
DOI - 10.1046/j.1365-2427.2000.00575.x
Subject(s) - index of biological integrity , replicate , metric (unit) , statistics , benthic zone , streams , biomonitoring , variance (accounting) , sample size determination , sample (material) , invertebrate , ecology , water quality , environmental science , mathematics , biology , computer science , computer network , operations management , business , accounting , chemistry , chromatography , economics
Summary1 When rigorous standards of collecting and analysing data are maintained, biological monitoring adds valuable information to water resource assessments. Decisions, from study design and field methods to laboratory procedures and data analysis, affect assessment quality. Subsampling ‐ a laboratory procedure in which researchers count and identify a random subset of field samples ‐ is widespread yet controversial. What are the consequences of subsampling? 2 To explore this question, random subsamples were computer generated for subsample sizes ranging from 100 to 1000 individuals as compared with the results of counting whole samples. The study was done on benthic invertebrate samples collected from five Puget Sound lowland streams near Seattle, WA, USA. For each replicate subsample, values for 10 biological attributes (e.g. total number of taxa) and for the 10‐metric benthic index of biological integrity (B‐IBI) were computed. 3 Variance of each metric and B‐IBI for each subsample size was compared with variance associated with fully counted samples generated using the bootstrap algorithm. From the measures of variance, we computed the maximum number of distinguishable classes of stream condition as a function of sample size for each metric and for B‐IBI. 4 Subsampling significantly decreased the maximum number of distinguishable stream classes for B‐IBI, from 8.2 for fully counted samples to 2.8 classes for 100‐organism subsamples. For subsamples containing 100–300 individuals, discriminatory power was low enough to mislead water resource decision makers.