Quantifying the Extent of and Factors Associated with the Temporal Variability of Physical Stream Habitat in Headwater Streams in the Interior Columbia River Basin

Abstract

AbstractThe quality and quantity of stream habitat can have profound impacts on the distribution and abundance of aquatic species. Stream networks, however, are dynamic in their response to natural‐ and human‐induced disturbance regimes, which results in spatially explicit patterns of temporal variability. Quantifying spatial patterns in habitat (temporal) variability across different sites and identifying those factors associated with different levels of variability are important steps for stream habitat assessments. We evaluated the temporal variability in stream habitat over a 9‐year period for 47headwater streams of the interior Columbia River basin. We used repeat‐measures analyses to calculate temporal variability as root mean square error for six habitat attributes at each site. Multiple linear regression analyses with root mean square error as the response were then used to quantify which landscape, climate, and disturbance attributes were associated with different levels of temporal variability among habitat attributes. Our results indicated a considerable range of temporal variability in physical stream attributes across sites and an almost fourfold difference in the overall variability at sites. Landscape factors affecting stream power, land management activities, and recent fire regimes were all factors associated with the different levels of temporal variability across sites; surprisingly, we found little association with the different climatic attributes considered herein. The observed differences in temporal variability across sites suggest that a “one‐size‐fits‐all” approach to monitoring stream habitat in response to restoration and management activities may be misleading, particularly in terms of sampling intensity, required resources, and statistical power; thus, in situ measures of temporal variability may be required for accurate assessments of statistical power.