Stream ecosystem processes, such as metabolism, are dynamically impacted by flow intensity.Therefore, without integrating ecosystem processes with water quality, we miss opportunities to develop frameworks to understand metabolic responses to changing flow. Flow simultaneously affects the material transport and biological opportunities for material transformation. Combining the strengths of ecohydrology and stream ecology to understand how flow variation alters ecosystem processes, we analyzed more than 5 years of water quality and stream metabolism data. We created segmented process-discharge (P-Q) relationships to examine how metabolism rates vary across discharge and compared them to concentration-discharge (C-Q) relationships to explore the dynamic effects of discharge on processes and physicochemical parameters. Within the segmented P-Q relationships, we found the behavior of ecosystem respiration (ER), gross primary production (GPP), and net ecosystem production (NEP) to be different at high and low flows with varying degrees of statistical significance, demonstrating the potential for divergent metabolic responses across changing flows. GPP declined with increasing discharge. The rate of ER declined with discharge initially but then became unchanging at higher flows. NEP reflected the divergent trends between ER and GPP, as the relationship of NEP to Q was flat at lower discharge and declined at higher flows. Interrelated physicochemical parameters and ecosystem processes, such as pH and NEP, had mirrored responses to discharge. Coupling analyses of flow, water quality, and metabolism offers a more complete picture of interrelated ecosystem processes, allowing for a better understanding of ecosystem response to the physical and chemical changes that occur across flows. Plain Language SummaryUnderstanding how stream microorganisms respond to changes in water flow is needed to improve the health of aquatic ecosystems. While high discharge is frequent when nutrients and pollutants are swept downstream, we lack information about how biology and water quality change during high flow. To examine water quality across flows, concentration-discharge relationships are often quantified. However, by only looking at how water quality changes as a function of flow, we miss a key piece of information: Life within the stream. Life within a stream influences and is influenced by water quality and flow. Here, we used 5 years of chemistry and flow data to calculate stream metabolism: The processes of photosynthesis and respiration by the life within a stream. To assess how water quality changes across flow, we (1) identified how stream metabolism changes across flow via process-discharge analyses and (2) compared concentration-discharge and process-discharge trends to discover how they influenced one another. We found that stream metabolism exhibited different responses across flows. Moreover, multiple concentration-discharge relationships had mirrored responses to associated process-discharge relationships. This study ultim...

No abstract

Abstract. Streams are ecosystems organized by disturbance. One of the most frequent and variable disturbances in running waters is elevated flow. Yet, we still have few estimates of how ecosystem processes, such as stream metabolism (gross primary production and ecosystem respiration; GPP and ER), respond to high flow events. Furthermore, we lack a predictive framework for understanding controls on within-site metabolic responses to flow disturbances. Using 5 years of high-frequency dissolved oxygen data from an urban- and agricultural-influenced stream, we estimated daily GPP and ER and analyzed metabolic changes across 15 isolated high flow events. Metabolism was variable from day to day, even during lower flows; median and ranges for GPP and ER over the full measurement period were 3.7 (minimum, maximum = 0.0, 17.3) and −9.6 (−2.2, −20.5) g O2 m−2 d−1. We calculated metabolic resistance as the magnitude of departure (MGPP, MER) from the mean daily metabolism during antecedent lower flows (lower values of M represent higher resistance) and estimated resilience as the time until GPP and ER returned to the prior range of ambient equilibrium. We evaluated correlations between metabolic resistance and resilience with characteristics of each high flow event, antecedent conditions, and time since last flow disturbance. ER was more resistant and resilient than GPP. Median MGPP and MER were 0.38 and −0.09, respectively. GPP was typically suppressed following flow disturbances, regardless of disturbance intensity. The magnitude of departure from baseflow ER during isolated storms increased with disturbance intensity. Additionally, GPP was less resilient and took longer to recover (0 to >9 d, mean = 2.5) than ER (0 to 6 d, mean = 1.1). Prior flow disturbances set the stage for how metabolism responds to later high flow events: the percent change in discharge during the most recent high flow event was significantly correlated with M of both GPP and ER, as well as the recovery intervals for GPP. Given the flashy nature of streams draining human-altered landscapes and the variable consequences of flow for GPP and ER, testing how ecosystem processes respond to flow disturbances is essential to an integrative understanding of ecosystem function.

Abstract. Streams are ecosystems organized by disturbance. One of the most frequent and variable disturbances in running waters is elevated flow. Yet, we still have few estimates of how ecosystem processes, such as stream metabolism (gross primary production and ecosystem respiration; GPP and ER), respond to high flow events. Furthermore, we lack a predictive frame- work for understanding controls on within-site metabolic responses to flow disturbances. Using five years of high-frequency dissolved oxygen data from an urban- and agriculturally-influenced stream, we estimated daily GPP and ER and analyzed metabolic changes across 15 isolated high flow events. Metabolism was variable from day to day, even during lower flows. Thus, we calculated metabolic resistance as the magnitude of departure from the dynamic equilibrium during antecedent lower flows and quantified resilience from the days until GPP and ER returned to the range of antecedent dynamic equilibrium. We evaluated correlations between metabolic resistance and resilience with characteristics of each high flow event, antecedent conditions, and time since last flow disturbance. ER was more resistant and resilient than GPP. GPP was typically suppressed following flow disturbances, regardless of disturbance intensity. In contrast, the ER magnitude of departure increased with disturbance intensity. Additionally, GPP was less resilient and took longer to recover (0 to > 9 days, mean = 2.2) than ER (0 to 2 days, mean = 0.6). Given the flashy nature of streams draining human-altered landscapes and the variable consequences of flow for GPP and ER, testing how ecosystem processes respond to flow disturbances is essential to an integrative understanding of ecosystem function.