Respiration regimes in rivers: Partitioning source‐specific respiration from metabolism time series

Abstract

Respiration in streams is controlled by the timing, magnitude, and quality of organic matter (OM) inputs from internal primary production and external fluxes. Here, we estimated the contribution of different OM sources to seasonal, annual, and event-driven characteristics of whole-stream ecosystem respiration (ER) using an inverse modeling framework that accounts for possible time-lags between OM inputs and respiration. We modeled site-specific, dynamic OM stocks contributing to ER: autochthonous OM from gross primary production (GPP); allochthonous OM delivered during flow events; and seasonal pulses of leaf litter. OM stored in the sediment and dissolved organic matter (DOM) transported during baseflow were modeled as a stable stock contributing to baseline respiration. We applied this modeling framework to five streams with different catchment size, climate, and canopy cover, where multi-year time series of ER and environmental variables were available. Overall, the model explained between 53% and 74% of observed ER dynamics. Respiration of autochthonous OM tracked seasonal peaks in GPP in spring or summer. Increases in ER were often associated with high-flow events. Respiration associated with litter inputs was larger in smaller streams. Time lags between leaf inputs and respiration were longer than for other OM sources, likely due to lower biological reactivity. Model estimates of source-specific ER and OM stocks compared well with existing measures of OM stocks, inputs, and respiration or decomposition. Our modeling approach has the potential to expand the scale of comparative analyses of OM dynamics within and among freshwater ecosystems.

Patterns and Drivers of Dissolved Gas Concentrations and Fluxes Along a Low Gradient Stream

“…On every date, the potential supply of DOC is more than sufficient to support the entire flux of CO 2 to the atmosphere based on typical DOC uptake rates (Mineau et al., 2016, Figure S7 in Supporting Information ). Much of this water column DOC may not be labile, but large accumulations of POC in the streambed are also an important source of C to support heterotrophic metabolism as previously observed in New Hope Creek (Carter, 2021; C. A. S. Hall, 1972), and in other streams (Bertuzzo et al., 2022).…”

“…Much of this water column DOC may not be labile, but large accumulations of POC in the streambed are also an important source of C to support heterotrophic metabolism as previously observed in New Hope Creek (Carter, 2021;C. A. S. Hall, 1972), and in other streams (Bertuzzo et al, 2022).…”

Light and hydrologic connectivity drive dissolved oxygen synchrony in stream networks

“…Moreover, the influence of riparian evapotranspiration on streamflow grows as discharge decreases (Bond et al 2002; Cadol et al 2012), suggesting an increasing effect of riparian vegetation on hydrologic connectivity patterns over the growing season. These observations provide additional motivation to study terrestrial–aquatic connections within riparian corridors (Hynes 1975; Pinay et al 2018), including the synchrony of their productivity (Bernhardt et al 2018; Mejia et al 2019; Walter et al 2021) and respiration regimes (Bertuzzo et al 2022).…”

A Network‐Scale Modeling Framework for Stream Metabolism, Ecosystem Efficiency, and Their Response to Climate Change

“…To this end, a preliminary analysis could be carried out following the method proposed by Bertuzzo et al (2022), which allows partitioning the contribution of different OC sources to ER using DO time series. Regarding other environmental variables, the acquisition of data in any catchment is progressively facilitated by recent methodological advances which combine satellite information with modeling, see for instance Revel et al (2021) and Hossain et al (2022) for streamflow discharge, Savoy et al (2021) for light availability at streambed, and Bertuzzo et al (2022) for litterfall input derived from Leaf Area Index remote observation. Finally, crucial to the applicability of the presented model are long (annual or multiannual), high-frequency time series of DO, which are increasingly available thanks to the recent advent of cheap sensor technology (Bernhardt et al, 2018).…”

“…So it is advisable to first identify the dominant OC sources in order to guide the experimental sampling effort. To this end, a preliminary analysis could be carried out following the method proposed by Bertuzzo et al (2022), which allows partitioning the contribution of different OC sources to ER using DO time series. Regarding other environmental variables, the acquisition of data in any catchment is progressively facilitated by recent methodological advances which combine satellite information with modeling, see for instance Revel et al (2021) and Hossain et al (2022) for streamflow discharge, Savoy et al (2021) for light availability at streambed, and Bertuzzo et al (2022) for litterfall input derived from Leaf Area Index remote observation.…”