Using Community Science to Reveal the Global Chemogeography of River Metabolomes

Abstract

River corridor metabolomes reflect organic matter (OM) processing that drives aquatic biogeochemical cycles. Recent work highlights the power of ultrahigh-resolution mass spectrometry for understanding metabolome composition and river corridor metabolism. However, there have been no studies on the global chemogeography of surface water and sediment metabolomes using ultrahigh-resolution techniques. Here, we describe a community science effort from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to characterize global metabolomes in surface water and sediment that span multiple stream orders and biomes. We describe the distribution of key aspects of metabolomes including elemental groups, chemical classes, indices, and inferred biochemical transformations. We show that metabolomes significantly differ across surface water and sediment and that surface water metabolomes are more rich and variable. We also use inferred biochemical transformations to identify core metabolic processes shared among surface water and sediment. Finally, we observe significant spatial variation in sediment metabolites between rivers in the eastern and western portions of the contiguous United States. Our work not only provides a basis for understanding global patterns in river corridor biogeochemical cycles but also demonstrates that community science endeavors can enable global research projects that are unfeasible with traditional research models.

Organic Matter Transformations are Disconnected Between Surface Water and the Hyporheic Zone

“…In comparison, sediment OM may reflect processes occurring principally within the sampled volume. That is, a larger diversity of transformations may accumulate as surface water OM integrates processes and sources from across the stream network, which is conceptually consistent with previous work using the same data that found higher molecular richness in surface water than in sediment OM (Garayburu-Caruso et al, 2020b). This interpretation sets up the emergent (i.e., post-hoc) hypothesis that the number of transformations may increase with catchment area.…”

“…The samples used for data generation were collected and processed in 2019 as part of the WHONDRS consortium (Stegen and Goldman, 2018), and the data were retrieved from publicly available data packages (Toyoda et al, 2020;. Full details on sample and metadata collection are provided in Garayburu-Caruso et al (2020b); some additional sample data are used here that were not used in Garayburu-Caruso et al (2020b), but all methods are consistent. In short, at each site (Fig.…”

“…FTICR-MS analyses were performed at the Environmental Molecular Science Laboratory (EMSL) in Richland, WA using a 12 Tesla (12T) Bruker SolariX FTICR mass spectrometer (Bruker, SolariX, Billerica, MA, USA) in negative ionization mode. FTICR-MS spectra were processed to assign molecular formulae as described in Garayburu-Caruso et al (2020b). FTICR-MS data were used as presence-absence due to peak intensities providing unreliable estimates of absolute or relative concentrations, which is a limitation inherent to FTICR-MS analysis.…”

Inferring the Contribution of Microbial Taxa and Organic Matter Molecular Formulas to Ecological Assembly

“…significantly contribute to divergence across the entire dataset (Figure 4C). This might suggest that these more complex compounds may play variable roles depending on the situation; they could be a hallmark of differential nutrient limitation (i.e., the functional needs of the meta-assemblage are highly variable; Graham et al, 2018;Garayburu-Caruso et al, 2020;Danczak et al, 2021) or highlight divergent degradation potential (e.g., common metabolites might be processed through different pathways).…”

“…), N-containing homologous series (e.g., C18H15NO10, C19H17NO10), and some P-containing homologous series (e.g., C17H25O9P, C18H27O9P) often contribute to convergence across the entire dataset. CHO-only molecular formulas are the most frequently observed types of organic matter from sedimentary and aquatic sources (Garayburu-Caruso et al, 2020) suggesting that these types of compounds could drive functional similarities across organic matter assemblages. As with those compounds which contribute to divergence, the contribution to convergence by the N-containing and P-containing formulas may also point to conserved functional needs or pathways (i.e., N/P-limitation drives selection for N/P-containing formulas, or ongoing metabolisms continually cycling these formulas).…”