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Global change may introduce fundamental alterations in phytoplankton biomass and community structure that can alter the productivity of northern lakes. In this study, we utilized Swedish and Finnish monitoring data from lakes that are spatially (135 lakes) and temporally (1995-2019, 110 lakes) extensive to assess how phytoplankton biomass (PB) of dominant phytoplankton groups related to changes in water temperature, pH and key nutrients [total phosphorus (TP), total nitrogen (TN), total organic carbon (TOC), iron (Fe)] along spatial (Fennoscandia) and temporal (25 years) gradients. Using a machine learning approach, we found that TP was the most important determinant of total PB and biomass of a specific species of Raphidophyceae - Gonyostomum semen - and Cyanobacteria (both typically with adverse impacts on food-webs and water quality) in spatial analyses, while Fe and pH were second in importance for G. semen and TN and pH were second and third in importance for Cyanobacteria. However, in temporal analyses, decreasing Fe and increasing pH and TOC were associated with a decrease in G. semen and an increase in Cyanobacteria. In addition, in many lakes increasing TOC seemed to have generated browning to an extent that significantly reduced PB. The identified discrepancy between the spatial and temporal results suggests that substitutions of data for space-for-time may not be adequate to characterize long-term effects of global change on phytoplankton. Further, we found that total PB exhibited contrasting temporal trends (increasing in northern- and decreasing in southern Fennoscandia), with the decline in total PB being more pronounced than the increase. Among phytoplankton, G. semen biomass showed the strongest decline, while cyanobacterial biomass showed the strongest increase over 25 years. Our findings suggest that progressing browning and changes in Fe and pH promote significant temporal changes in PB and shifts in phytoplankton community structures in northern lakes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Estimating constituent loads from discrete water quality samples coupled with stream discharge measurements is critical for management of freshwater resources. Nutrient loads calculated based on discharge-concentration relationships form the basis of government nutrient load targets and scientific studies of the response of receiving waters to external loads. In this study, a new model is developed using random forests and applied to estimate concentrations and loads of total phosphorus, dissolved phosphorus, total nitrogen, and chloride, using data from 17 tributaries to Lake Champlain monitored from 1992 to 2021. I benchmark this model against one of the most widespread models currently used to estimate nutrient loads, Weighted Regressions on Time, Discharge, and Season (WRTDS). The random forest model outperformed both the base WRTDS model and an extension of the WRTDS model using Kalman filtering in the great majority of cases, likely due to the inclusion of rate-of-change in discharge and antecedent discharge over different leading windows as predictors, and to the flexibility of the random forest to model predictor-response relationships. The random forest also had useful visualization capabilities which provided important process insights. WRTDS remains a useful model for many applications, but this study represents a promising new approach for load estimation which can be applied easily to existing datasets, and which is easy to customize for different applications., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)