Using spatial interpolation to estimate stressor levels in unsampled streams.

Accurate estimates of stressor levels in unsampled streams would provide valuable information for managing these resources over large regions. Spatial interpolation of stream characteristics have rarely been attempted, partly because defining separation distances between distinct stream samples is not straightforward. That is, conventional Eulerian definitions of separation distance may not apply to stream networks where information flows along distinct paths. A two-stage model for estimating stressor levels in unsampled streams is presented. Mean characteristics within streams are predicted usign a generalized additive model and residual variation is estimated using a conventional application of spatial statistics. The model is developed and tested using stream survey data collected in the state of Maryland, USA. Model efficiency is compared for three stream variables (nitrate concentration, sulfate concentration, and epifaunal substrate score) known to be associated with biological impairments in streams. Accounting for spatial autocorrelation in the residual variation improved model R2 from 0.71 to 0.81 for nitrate, from 0.29 to 0.63 for sulfate, and from 0.21 to 0.31 for epifaunal substrate score.