Document Type

Article

Journal/Book Title/Conference

Ecosphere

Volume

10

Issue

6

Publisher

Ecological Society of America

Publication Date

6-24-2019

Keywords

biofilm, gross primary production, salmonids, spatial patterns, spatial statistical network models, stream metabolism

First Page

1

Last Page

20

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Understanding the factors that drive spatial patterns in stream ecosystem processes and the distribution of aquatic biota is important to effective management of these systems and the conservation of biota at the network scale. In this study, we conducted field surveys throughout an extensive river network in NE Oregon that supports diminishing populations of wild salmonids. We collected data on physical habitat, nutrient concentrations, biofilm standing stocks, stream metabolism (gross primary production [GPP] and ecosystem respiration [ER]), and ESA‐listed juvenile salmonid density from approximately 50 sites across two sub‐basins. Our goals were to (1) to evaluate network patterns in these metrics, and (2) determine network‐scale linkages among these metrics, thus providing inference of processes driving observed patterns. Ambient nitrate‐N and phosphate‐P concentrations were low across both sub‐basins (<40 μg/L). Nitrate‐N decreased with watershed area in both sub‐basins, but phosphate‐P only decreased in one sub‐basin. These spatial patterns suggest co‐limitation in one sub‐basin but N limitation in the other; experimental results using nutrient diffusing substrates across both sub‐basins supported these predictions. Solar exposure, temperature, GPP, ER, and GPP:ER increased with watershed area, but biofilm Chl a and ash‐free dry mass (AFDM) did not. Spatial statistical network (SSN) models explained between 70% and 75% of the total variation in biofilm Chl a, AFDM, and GPP, but only 21% of the variation in ER. Temperature and nutrient concentrations were the most supported predictors of Chl aand AFDM standing stocks, but these variables explained little of the total variation compared to spatial autocorrelation. In contrast, solar exposure and temperature were the most supported variables explaining GPP, and these variables explained far more variation than autocorrelation. Solar exposure, temperature, and nutrient concentrations explained almost none of the variation in ER. Juvenile salmonids—a key management focus in these sub‐basins—were most abundant in cool stream sections where rates of GPP were low, suggesting temperature constraints on these species restrict their distribution to oligotrophic areas where energy production at the base of the food web may be limited.

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