Controls on Lake Pelagic Primary Productivity: Formalizing the Nutrient‐Color Paradigm

Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. In lakes, pelagic gross primary productivity (GPP) is strongly controlled by inputs of nutrients and dissolved organic matter. Although past studies have developed process models of this nutrient‐color paradigm (NCP), broad empirical tests of these models are scarce. We used data from 58 globally distributed, mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. The model includes three state variables–dissolved phosphorus, terrestrial dissolved organic carbon (DOC), and phytoplankton biomass–and generates realistic predictions for equilibrium rates of pelagic GPP. We calibrated our model using a Bayesian data assimilation technique on a subset of lakes where DOC and total phosphorus (TP) loads were known. We then asked how well the calibrated model performed with a larger set of lakes. Revised parameter estimates from the updated model aligned well with existing literature values. Observed GPP varied nonlinearly with both inflow DOC and TP concentrations in a manner consistent with increasing light limitation as DOC inputs increased and decreasing nutrient limitation as TP inputs increased. Furthermore, across these diverse lake ecosystems, model predictions of GPP were highly correlated with observed values derived from high‐frequency sensor data. The GPP predictions using the updated parameters improved upon previous estimates, expanding the utility of a process model with simplified assumptions for water column mixing. Our analysis provides a model structure that may be broadly useful for understanding current and future patterns in lake primary production. Plain Language Summary Understanding the controls on lake productivity is essential for predicting the response of lake ecosystems to global change. Recent advances in mathematical models have provided a conceptual framework for modeling lake pelagic productivity, but these models need to be tested and refined. In this study, we used data from 58 lakes around the world to develop and improve a mathematical model of the nutrient‐color paradigm. We found that the updated model had better predictive power than previous models and accurately predicted primary production, mixed layer depth, and concentrations of nutrients in a diverse set of lakes. This improved model has the potential to be a valuable