Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Chlorophyll a in lakes and streams of the United States (2005-2022)

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005-2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey's National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Light and temperature controls of aquatic plant photosynthesis downstream of a hydropower plant and the effect of plant removal

Many rivers worldwide are regulated, and the altered hydrology can lead to mass development of aquatic plants. Plant invasions are often seen as a nuisance for human activities leading to costly remedial actions with uncertain implications for aquatic biodiversity and ecosystem functioning. Mechanical harvesting is often used to remove aquatic plants and knowledge of plant growth rate could improve management decisions. Here, we used a simple light-temperature theoretical model to make a priori prediction of aquatic plant photosynthesis. These predictions were assessed through an open-channel diel change in O2 mass balance approach. A Michaelis-Menten type model was fitted to observed gross primary production (GPP) standardised at 10 °C using a temperature dependence from thermodynamic theory of enzyme kinetics. The model explained 87 % of the variability in GPP of a submerged aquatic plant (Juncus bulbosus L.) throughout an annual cycle in the River Otra, Norway. The annual net plant production was about 2.4 (1.0-3.8) times the standing biomass of J. bulbosus. This suggests a high continuous mass loss due to hydraulic stress and natural mechanical breakage of stems, as the biomass of J. bulbosus remained relatively constant throughout the year. J. bulbosus was predicted to be resilient to mechanical harvesting with photosynthetic capacity recovered within two years following 50-85 % plant removal. The predicted recovery was confirmed through a field experiment where 72 % of J. bulbosus biomass was mechanically removed. We emphasise the value of using a theoretical approach, like metabolic theory, over statistical models where a posteriori results are not always easy to interpret. Finally, the ability to predict ecosystem resilience of aquatic photosynthesis in response to varying management scenarios offers a valuable tool for estimating aquatic ecosystem services, such as carbon regulation. This tool can benefit the EU Biodiversity Strategy and UN Sustainable Development Goals.

Macroscale controls determine the recovery of river ecosystem productivity following flood disturbances

River ecosystem function depends on flow regimes that are increasingly modified by changes in climate, land use, water extraction, and flow regulation. Given the wide range of variation in flow regime modifications and autotrophic communities in rivers, it has been challenging to predict which rivers will be more resilient to flow disturbances. To better understand how river productivity is disturbed by and recovers from high-flow disturbance events, we used a continental-scale dataset of daily gross primary production time series from 143 rivers to estimate growth of autotrophic biomass and ecologically relevant flow disturbance thresholds using a modified population model. We compared biomass recovery rates across hydroclimatic gradients and catchment characteristics to evaluate macroscale controls on ecosystem recovery. Estimated biomass accrual (i.e., recovery) was fastest in wider rivers with less regulated flow regimes and more frequent instances of biomass removal during high flows. Although disturbance flow thresholds routinely fell below the estimated bankfull flood (i.e., the 2-y flood), a direct comparison of disturbance flows estimated by our biomass model and a geomorphic model revealed that biomass disturbance thresholds were usually greater than bed disturbance thresholds. We suggest that primary producers in rivers vary widely in their capacity to recover following flow disturbances, and multiple, interacting macroscale factors control productivity recovery rates, although river width had the strongest overall effect. Biomass disturbance flow thresholds varied as a function of geomorphology, highlighting the need for data such as bed slope and grain size to predict how river ecosystems will respond to changing flow regimes.