OREGON STATE UNIVERSITY

Spatial autocorrelation

On the Estimation and Application of Spatial and Temporal Autocorrelation in Headwater Streams
Som, N.
Sep-02-2009

This collection of three manuscripts serves to improve methods for collecting, interpreting, and utilizing autocorrelated data from headwater stream networks. Two chapters of this work relied on a unique and comprehensive set of data which constitutes a complete census of habitat unit fish counts from 40 randomly selected headwater basins in western Oregon. The first objective of this work was to evaluate how different sampling designs captured spatial autocorrelation, given the samples were drawn from a population of spatially autocorrelated observations. The second objective was to investigate spatial autocorrelation model range parameters as measures of patch sizes. The third objective was to refine the analysis of temporally autocorrelated hydrology data from paired watershed studies. These  are used to evaluate forest harvesting effects on stream biota and hydrology (i.e. fish, amphibians, insects, stream flow, and sediment yield).

DISCIPLINE: Hydrology & Water Quality    STUDY: Alsea, Hinkle Creek, Trask    TYPE: Theses    TAGS: autocorrelated data, headwater stream networks, Spatial autocorrelation, patch size, paired watershed studies
Sampling Headwater Stream Networks for Spatial Autocorrelation Detection and Autocovariance Parameter Estimation
Som, N., L.M. Ganio, R.E. Gresswell, D. Hockman-Wert
Dec-15-2010

Spatial autocorrelation is common in data collected for ecological studies, and the use of statistical models for spatial autocorrelation has evolved. Initially, these models were used to improve linear model parameter estimation uncertainty, but more recently ecologists have considered spatial autocorrelation as a valuable tool for describing ecological patterns. The structure and water-driven continuity of stream-networks makes these landscapes unique, and has prompted development of new models for describing spatial autocorrelation within these networks. We evaluate the spatial autocorrelation detection and parameter estimation of four sampling protocols applied to complete censuses of coastal cutthroat trout (Oncorhynchus clarkii clarkii) habitat unit fish counts. We consider two cluster- and two non cluster-based sampling protocols. Spatially distributed clusters were the most apt to contain spatial autocorrelation. Spatial autocorrelation detection was also associated with headwater basin attributes. Differences among sampling protocols in regards to autocorrelation parameter estimation were less distinct.

DISCIPLINE: Fisheries    STUDY: Hinkle Creek    TYPE: Journal Articles    TAGS: Spatial autocorrelation, parameter estimation, cluster-based sampling protocols
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