If more UHR DOM data is the answer, what was the question?

The rapid expansion of FT-ICR-MS applications has redefined the chemical resolution at which we interrogate dissolved organic matter (DOM), yielding molecular inventories of unprecedented breadth. Yet the predictive capacity of freshwater carbon-cycling models has not scaled commensurately. This gap exposes a central methodological tension: we routinely generate exquisitely detailed molecular atlases without equivalently rigorous articulation of the ecological questions that necessitate such resolution. What we require are mechanistic roadmaps—explicit links between molecular features, microbial strategies, and biogeochemical fluxes. Within the Ecology of Molecules framework, I delineate three mechanistic pathways through which molecular information can (and must) translate to ecosystem-level prediction. (1) Molecular keystones: discrete compounds or compound classes exerting disproportionate influence on reaction kinetics, microbial community assembly, or coupled nutrient cycles—functional analogs to keystone taxa in community ecology. (2) Chemodiversity–function relationships: empirical tests of whether molecular diversity and compositional redundancy regulate process rates, stability domains, or resilience to perturbation, paralleling biodiversity–ecosystem function theory. (3) DOM–microbe interaction networks: environmentally contingent coupling between molecular composition and microbial metabolic strategies across lakes, rivers, and wetlands, revealing bidirectional feedbacks between chemical architecture and microbial trait distributions. Through comparative analyses spanning contrasting hydrologic, thermal, and land-use regimes, I show that hypothesis-driven molecular approaches—interrogating explicit mechanisms such as photochemical lability, microbial selectivity, or hydrologic sorting—consistently outperform unconstrained molecular surveys. I systematically evaluate when bulk or optical metrics achieve equivalent explanatory power, and when molecular-level resolution is indispensable for forecasting DOM reactivity, transformation pathways, and persistence under environmental change. To advance predictive freshwater C-cycle models, I propose decision criteria for strategically deploying ultra-high-resolution mass spectrometry: conditions under which molecular data resolve mechanistic uncertainty versus those in which they merely document analytical capability. Without well-posed hypotheses, comprehensive characterization becomes descriptive accumulation rather than ecological inference. I conclude with a set of actionable frameworks for designing DOM studies that operationalize molecular information into ecological prediction—and with provocation for the community: How do we shift from expanding molecular inventories to constraining carbon fate? Under what circumstances has molecular resolution genuinely reframed ecosystem understanding, and when has it obscured the mechanistic narrative we seek to explain?

Presentation Preference: Standard Oral (12 Minutes)

Primary Presenter: Erika Freeman, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) (erika.freem@gmail.com)

Authors:

Erika Freeman, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB)  (erika.freem@gmail.com)