This article is composed of three independent commentaries about the state of ICON principles (Goldman et al. 2021) in the AGU Biogeosciences section and discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: Global collaboration, technology transfer and application (Section 2), Community engagement, citizen science, education, and stakeholder involvement (Section 3), and Field, experimental, remote sensing, and real-time data research and application (Section 4). We discuss needs and strategies for implementing ICON and outline short- and long-term goals. The inclusion of global data and international community engagement are key to tackle grand challenges in biogeosciences. Although recent technological advances and growing open-access information across the world have enabled global collaborations to some extent, several barriers ranging from technical to organizational to cultural have remained in advancing interoperability and tangible scientific progress in biogeosciences. Overcoming these hurdles is necessary to address pressing large-scale research questions and applications in the biogeosciences, where ICON principles are essential. Here, we list several opportunities for ICON, including coordinated experimentation and field observations across global sites, that are ripe for implementation in biogeosciences as a means to scientific advancements and social progress.

Warmer and drier climate has contributed to increased occurrence of large, high severity wildfires in the Pacific Northwest, drawing concerns for water quality and ecosystem recovery. While nutrient fluxes generally increase post-fire, the composition of organic matter (OM) transported to streams immediately following a fire is poorly constrained, yet can play an integral role in downstream water quality and biogeochemistry. Here, we quantified spatiotemporal patterns of dissolved OM (DOM) chemistry for five streams burned by wildfires in Oregon, USA in 2020. We sampled over a 24-hour storm event one month after the fire, revealing variable temporal behavior in DOM dynamics. DOM chemistry was directly related with burn severity spatially. Specifically, nitrogen and aromatic character of DOM increased in streams burned at greater severity. Our results suggest a spatial overprinting of DOM dynamics immediately following fire activity and highlight a key gap in our knowledge of post-fire DOM transport to streams.

Priming leads to the significant changes in the decomposition rate of organic matter (OM) in natural ecosystems induced by minimal treatments. A fundamental understanding of priming effects is critical to accurately predict biogeochemical dynamics and carbon/nitrogen OM cycles in natural ecosystems. However, we poorly understand how the priming effect is mechanistically induced and what factors govern the process among microbial activities and environmental constraints. Here, we propose a generalizable theory to collectively explain diverse patterns of priming effects via the cybernetic approach that accounts for regulation as key features of microbial growth. The cybernetic model treats microorganisms as dynamic systems that optimally regulate metabolic functions with respect to environmental conditions to safeguard their survival. Motivated by priming phenomenon observed in the hyporheic corridor of a riverine ecosystem, we formulated our model to investigate how the addition of exogenous labile OM primes the microbial respiration of polymeric OM. Our model accounts for interspecies interactions between various assortments of microbial groups with distinct metabolic traits to enable prediction of both increase (positive priming) and decrease (negative priming) of OM turnover using the same model structure. Our modeling framework reveals that: (1) the priming effects are manifestations of microbial regulatory response to diverse environmental conditions, and (2) priming magnitude and direction are highly dependent on the polymeric OM richness and the extent of treatment with labile OM. Beyond elucidating qualitative understanding of the phenomenon, our model also suggests that interspecies interactions between microbial groups with distinct metabolic traits (i.e., population turnover, sensitivity to labile OM, and efficiency in degrading polymeric OM) potentially drive the priming effects. By integrating contextual knowledge and a generalizable theory, our holistic modeling framework is effective for investigation and prediction of biogeochemical dynamics of natural ecosystems across diverse biological and environmental settings.

River corridors, the spatial domains around rivers in which river water interacts with surrounding sediment and rock, are important components of watersheds. They comprise extremely complex ecosystems: heterogeneous at all spatial scales with strong temporal dynamics, coupled biological, geochemical, and hydrologic processes, and ubiquitous human impacts. We present several ways that our project, focused around the 75 km Hanford Reach of the Columbia River but with multiple connections to other systems, is addressing this challenge. These include 1) deployment of intensive, automated sensor networks supplemented by data from the Hanford Environmental Information System (HEIS) for hyporheic zone monitoring 2) data assimilation of these and other data into models using joint hydrologic and geophysical inversion, 3) integrating MASS2 model outputs and bathymetry data using machine learning to classify hydromorphologic features, 4) a community-based effort to develop broad understanding of organic carbon biogeochemistry and microbiomes in diverse river systems, and 5) use of multi-‘omics data to develop new biogeochemical reaction networks. These underpin the incorporation of process understanding and diverse data into high-resolution mechanistic models, and employment of those models to develop reduced-order models that can be applied at large scales while retaining the effects of local features and processes. In so doing we are contributing to reduction of uncertainties associated with major Earth system biogeochemical fluxes, thus improving predictions of environmental and human impacts on water quality and riverine ecosystems and supporting environmentally responsible management of linked energy-water systems.