Relating spatial patterns of stream metabolism to distributions of juveniles salmonids at the river network scale

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 lg/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 a and 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.

Individual‐based simulations suggest mixed impacts of warmer temperatures and a nonnative predator on Chinook salmon

“…Second, we assumed that food availability varied throughout accessible habitat, increasing with stream order and decreasing with distance from the base of the network. We reasoned that this representation of larger and lower elevation streams as more productive was plausible given the temperature dependence of prey production (Brown et al 2004, Patrick et al 2019) and given that temperature, nutrient input, and primary production are often higher in lower regions of watersheds (Wipfli and Baxter 2010, Petty et al 2014, Huntsman et al 2016, Kaylor et al 2019).…”

Progress Towards a Comprehensive Approach for Habitat Restoration in the Columbia Basin: Case Study in the Grande Ronde River

“…Although some progress has been made in identifying diversity indicators and collecting the requisite data to quantify them, limited funding and uncertainty in measuring diversity has hindered progress. Recent research in the Grande Ronde basin has placed a greater emphasis on biological diversity by evaluating stream metabolism (Kaylor et al 2019a), food webs of the entire fish assemblage and macroinvertebrate community (Kaylor et al 2020), and novel prey availability indices (Sullivan and White 2017). A focused, collaborative effort among basin partners and funding agencies will be needed to reach consensus on high priority indicators and standardized methods for computing them.…”

“…Additionally, basin‐wide bathymetric, thermal profile, and hydrologic data were combined with remotely sensed data to assess limiting habitat factors and prioritize restoration actions (USBOR 2012, 2014). Researchers have discovered mechanisms driving spatial patterns in ecosystem processes by evaluating stream metabolism and juvenile salmon abundance at the river network scale (Kaylor et al 2019a) and assessing salmonid and overall food web responses to carcass additions (Kaylor et al 2019b; Kaylor et al 2020). In summer of 2020, CRITFC, GRMW, and BPA collaborated on obtaining remotely sensed, topobathymetric LiDAR data to assess river conditions across 76,188 acres of the Grande Ronde River and nearby Wallowa River watershed, including mainstem riparian and floodplains, but also larger fish‐bearing tributaries.…”

Fisheries Volume 46 Number 5 May 2021

“…Additionally, basin-wide bathymetric, thermal profi le, and hydrologic data were combined with remotely sensed data to assess limiting habitat factors and prioritize restoration actions (USBOR 2012(USBOR , 2014. Researchers have discovered mechanisms driving spatial patterns in ecosystem processes by evaluating stream metabolism and juvenile salmon abundance at the river network scale (Kaylor et al 2019a ) and assessing salmonid and overall food web responses to carcass additions (Kaylor et al 2019b ;Kaylor et al 2020 ). In summer of 2020, CRITFC, GRMW, and BPA collaborated on obtaining remotely sensed, topobathymetric LiDAR data to assess river conditions across 76,188 acres of the Grande Ronde River and nearby Wallowa River watershed, including mainstem riparian and fl oodplains, but also larger fi sh-bearing tributaries.…”

“…Although some progress has been made in identifying diversity indicators and collecting the requisite data to quantify them, limited funding and uncertainty in measuring diversity has hindered progress. Recent research in the Grande Ronde basin has placed a greater emphasis on biological diversity by evaluating stream metabolism (Kaylor et al 2019a ), food webs of the entire fi sh assemblage and macroinvertebrate community (Kaylor et al 2020 ), and novel prey availability indices (Sullivan and White 2017 ). A focused, collaborative effort among basin partners and funding agencies will be needed to reach consensus on high priority indicators and standardized methods for computing them.…”