Spatial sampling on streams: principles for inference on aquatic networks
Title: Spatial sampling on streams: principles for inference on aquatic networks
Category: Technical Report
Updated Date: 31.01.2017
Author(s)/Source(s): Nicholas A. Soma, Pascal Monestiez, Jay M. Ver Hoef, Dale L. Zimmerman, Erin E. Peterson
Publication Date: 2014-May-21
Focal Topic: Aquatic Habitat / Invertebrates / Insects, In-Stream Flow / Flow Regime, Hydrology
Location: United States
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.
been contributed to by US Government employees and their work is in the public domain in the USA.
sampling design, spatial statistics, stream networks