Using continuous surveys to evaluate precision and bias of inferences from design-based reach-scale sampling of stream habitat

Abstract

Accurately estimating stream characteristics is essential for managing and restoring populations and aquatic ecosystems. Reach-based sampling designs have been used extensively to collect fisheries related data; however, few studies have examined the effectiveness of reach-based sampling designs for stream habitat assessments. Here, we used continuous habitat surveys to census stream attributes in tributaries in the upper Lewis River, WA and better understand the potential bias and precision of reach-based designs. We used resampling analyses via bootstrapping to create simulated outcomes of different sampling designs including simple random with equal probability, simple random with unequal probability, and a generalized random tessellation stratified design (GRTS). We found precision of estimates of habitat attributes (large woody debris, residual pool depth, and grain size) increased with sampling intensity; however, the effort needed to achieve reasonable precision (CV = 0.20) varied across streams, attributes, and designs. Bias was relatively low, but also varied across streams and attributes. Our findings illustrate the challenges of using reach-based designs for stream habitat assessments and the need for novel approaches for broader data collection.