• aquatic habitat;
  • streambed sediment;
  • monitoring;
  • pebble counts;
  • visual classification;
  • sampling precision

Abstract:  Streamlined sampling procedures must be used to achieve a sufficient sample size with limited resources in studies undertaken to evaluate habitat status and potential management-related habitat degradation at a regional scale. At the same time, these sampling procedures must achieve sufficient precision to answer science and policy-relevant questions with an acceptable and statistically quantifiable level of uncertainty. In this paper, we examine precision and sources of error in streambed substrate characterization using data from the Environmental Monitoring and Assessment Program (EMAP) of the U.S. Environmental Protection Agency, which uses a modified “pebble count” method in which particle sizes are visually estimated rather than measured. While the coarse (2ϕ) size classes used in EMAP have little effect on the precision of estimated geometric mean (Dgm) or median (D50) particle diameter, variable classification bias among observers can contribute as much as 0.3ϕ, or about 15-20%, to the root-mean-square error (RMSE) of Dgm or D50 estimates. Dgm and D50 estimates based on EMAP data are nearly equal when fine sediments (<2 mm) are excluded, but otherwise can differ by up to a factor of 2 or more, with Dgm < D50 for gravel-bed streams. The RMSE of reach-scale particle size estimates based on visually classified particle count data from EMAP surveys, including variability associated with reoccupying unmarked sample reaches during revisits, is up to five to seven times higher than that reported for traditional measured pebble counts by multiple observers at a plot scale. Nonetheless, a variance partitioning analysis shows that the ratio of among site to revisit variance for several EMAP substrate metrics exceeds 8 for many potential regions of interest, suggesting that the data have adequate precision to be useful in regional assessments of channel morphology, habitat quality, or ecological condition.