The spatial distribution of salmon and steelhead redds and optimal sampling design

Redd surveys are used extensively to estimate spawner population size for Pacific salmon (Onchorynchus spp.). Because redds tend to be spatially aggregated, estimates of total redds based on subsamples of the potential spawning grounds can be uncertain unless the spatial structure is accounted for. Here we use known redd locations for three populations over several years to compare five different probability sampling designs through simulation. The coefficient of variation (CV) for estimates based on simple random sampling was high, with values well over 15% when sampling a third of the reaches. Moving to a spatially balanced sampling design (generalized random tessellation stratified; GRTS) produced improvements in two of the three watersheds (16%-22% reduction in CV). Estimates based on a stratified GRTS design and a GRTS design that included a census of all reaches close to the peak count had higher accuracy, with an approximate CV of one-half to one-third of GRTS alone. We show how these improvements are predicted by theory and under which conditions the different approaches are likely to perform well.