Spatial sampling on streams: principles for inference on aquatic networks

Abstract

For ecological and environmental data, prior inquiries into spatial sampling designs have considered two-dimensional domains and have shown that design optimality depends on the characteristics of the target spatial domain and intended inference. The structure and water-driven continuity of streams prompted the development of spatial autocovariance models for stream networks. The unique properties of stream networks, and their spatial processes, warrant evaluation of sampling design characteristics in comparison with their two-dimensional counterparts. Common inference scenarios in stream networks include spatial prediction, estimation of fixed effects parameters, and estimation of autocovariance parameters, with prediction and fixed effects estimation most commonly coupled with autocovariance parameter estimation. We consider these inference scenarios under a suite of network characteristics and stream-network spatial processes. Our results demonstrate, for parameter estimation and prediction, the importance of collecting samples from specific network locations. Additionally, our results mirror aspects from the prior two-dimensional sampling design inquiries, namely, the importance of collecting some samples within clusters when autocovariance parameter estimation is required. These results can be applied to help refine sample site selection for future studies and further showcase that understanding the characteristics of the targeted spatial domain is essential for sampling design planning. Published 2014. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Spatial and Temporal Variation of Water Temperature Regimes on the Snoqualmie River Network

“…Som et al . () found that a spatially balanced survey design, similar to the one used here, provides reasonably accurate parameter estimates for the predictors and the tail‐up covariance structure. Our leave‐one‐out cross‐validation analysis also indicates reasonable model fits and predictive abilities (Table ).…”

Using watershed characteristics to inform cost-effective stream temperature monitoring

“…Each of our 19 streams flows directly into a single lake-river network independently of each other. This simple network is similar to coastal streams that drain directly into the ocean but differ from more complex stream networks that are spatially correlated due to convergence downstream (Peterson et al 2013;Som et al 2014). Therefore, temperature for a downstream site is a function of upstream tributaries in addition to watershed characteristics.…”

“…This directional autocorrelation could be useful for informing the selection of cost-effective sites because downstream sites integrate upstream temperatures. Som et al (2014) provide suggestions for the most effective sampling designs for monitoring stream networks. This framework could be easily modified to integrate the autocorrelation between connected sites and which may provide additional cost savings due to higher correlations Fig.…”

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

“…Along with increasingly available spatial data coverages and a push for more spatially explicit modeling in watersheds, more and more observed response data from a broad range of monitoring programs are also becoming available. The response data can come from existing monitoring programs (Peterson et al ., ; Isaak et al ., ) or studies specifically designed for SSN modeling (Frieden et al ., ; Som et al ., ). Because of the spatial dependencies among monitoring sites inherently necessary for SSN modeling, the sampling design of such studies influences the statistical analysis approaches that can be adopted (McDonnell et al ., ) and possibly the validity of the inferences made from their results.…”