Reducing the cost of benthic sample processing by using sieve retention probability models

Abstract

Estimation of abundance or biomass of benthic invertebrates requires considerable effort to process samples. Consequently, it has been suggested to process only organisms retained by a relatively coarse meshed sieve and apply size-specific correction factors based on the probability that a sieve retains individual organisms. Benthic samples were collected from 10 sites in 2 regions and processed to validate an existing empirical model predicting sieve retention probabilities, to test whether periphyton biomass affects probability of retention, and to determine the optimal strategy that minimizes both cost and variability of estimates. The existing model predicting sieve retention probabilities corrected for organisms lost through sieves and mostly corrected for underestimation of biomass, but this model lead to overestimates of the frequency of the smallest organisms. Inclusion of algal biomass improved slightly the proportion of correct predictions (whether an organism is retained or not by a sieve) by 0.6% relative to the existing model (from 90.8% to 91.4%), and removed the bias. Density and biomass estimates obtained by only processing organisms retained by 1- or 2-mm sieves and applying correction factors derived from the predicted retention probabilities were accurate and only marginally less precise than estimates obtained by processing all organisms. The reduced precision of estimates from subsets of organisms could be compensated by increasing sample size and still lead to a reduction of 40–60% of the number of organisms processed. Even though the use of subsets introduces additional analytical variability, this variability is relatively small compared to the natural spatial variability among replicates.