Insights into the quantification and reporting of model-related uncertainty across different disciplines.

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research.

Shine a light: Under-ice light and its ecological implications in a changing Arctic Ocean.

The Arctic marine ecosystem is shaped by the seasonality of the solar cycle, spanning from 24-h light at the sea surface in summer to 24-h darkness in winter. The amount of light available for under-ice ecosystems is the result of different physical and biological processes that affect its path through atmosphere, snow, sea ice and water. In this article, we review the present state of knowledge of the abiotic (clouds, sea ice, snow, suspended matter) and biotic (sea ice algae and phytoplankton) controls on the underwater light field. We focus on how the available light affects the seasonal cycle of primary production (sympagic and pelagic) and discuss the sensitivity of ecosystems to changes in the light field based on model simulations. Lastly, we discuss predicted future changes in under-ice light as a consequence of climate change and their potential ecological implications, with the aim of providing a guide for future research.

A model-based projection of historical state of a coastal ecosystem: Relevance of phytoplankton stoichiometry.

We employed a coupled physical-biogeochemical modelling framework for the reconstruction of the historic (H), pre-industrial state of a coastal system, the German Bight (southeastern North Sea), and we investigated its differences with the recent, control (C) state of the system. According to our findings: i) average winter concentrations of dissolved inorganic nitrogen and phosphorus (DIN and DIP) concentrations at the surface are ∼70-90% and ∼50-70% lower in the H state than in the C state within the nearshore waters, and differences gradually diminish towards off-shore waters; ii) differences in average growing season chlorophyll a (Chl) concentrations at the surface between the two states are mostly less than 50%; iii) in the off-shore areas, Chl concentrations in the deeper layers are affected less than in the surface layers; iv) reductions in phytoplankton carbon (C) biomass under the H state are weaker than those in Chl, due to the generally lower Chl:C ratios; v) in some areas the differences in growth rates between the two states are negligible, due to the compensation by lower light limitation under the H state, which in turn explains the lower Chl:C ratios; vi) zooplankton biomass, and hence the grazing pressure on phytoplankton is lower under the H state. This trophic decoupling is caused by the low nutritional quality (i.e., low N:C and P:C) of phytoplankton. These results call for increased attention to the relevance of the acclimation capacity and stoichiometric flexibility of phytoplankton for the prediction of their response to environmental change.