Stoichiometric imbalances complicate prediction of phytoplankton biomass in U.S. lakes: implications for nutrient criteria

Consistent stoichiometric long-term relationships between nutrients and chlorophyll-a across shallow lakes

Aquatic ecosystems are threatened by eutrophication from nutrient pollution. In lakes, eutrophication causes a plethora of deleterious effects, such as harmful algal blooms, fish kills and increased methane emissions. However, lake-specific responses to nutrient changes are highly variable, complicating eutrophication management. These lake-specific responses could result from short-term stochastic drivers overshadowing lake-independent, long-term relationships between phytoplankton and nutrients. Here, we show that strong stoichiometric long-term relationships exist between nutrients and chlorophyll a (Chla) for 5-year simple moving averages (SMA, median R² = 0.87) along a gradient of total nitrogen to total phosphorus (TN:TP) ratios. These stoichiometric relationships are consistent across 159 shallow lakes (defined as average depth < 6 m) from a cross-continental, open-access database. We calculate 5-year SMA residuals to assess short-term variability and find substantial short-term Chla variation which is weakly related to nutrient concentrations (median R² = 0.12). With shallow lakes representing 89% of the world's lakes, the identified stoichiometric long-term relationships can globally improve quantitative nutrient management in both lakes and their catchments through a nutrient-ratio-based strategy.

Describing and simulating phytoplankton of a small and shallow reservoir using decision trees and rule-based models

Phytoplankton represents one of the most important biological components of primary production, trophic interactions, and circulation of organic matter in lakes and reservoirs. To contribute to the understanding of eutrophication processes and ecological status of the small, shallow Butoniga reservoir, a machine learning tool for induction of models in form of decision trees and rule-based models was applied on a dataset comprising physical, chemical, and biological variables measured at four stations. Two types of models were successfully elaborated, i.e., (1) model describing phytoplankton Phylum, which describes and connects phytoplankton Phylum with phytoplankton abundance and biomass, and (2) model simulating phytoplankton biomass according to environmental variables which could be used in management purposes. Such models and their presentation contribute to a better understanding of the Butoniga reservoir ecosystem functioning.

Elucidating phytoplankton limiting factors in lakes and reservoirs of the Chinese Eastern Plains ecoregion

Knowledge of phytoplankton limiting factors is essential for cost-efficient lake eutrophication management. Herein, we propose a statistical framework to explore site-specific phytoplankton limiting factors and their dependence on water depth (WD) in 54 lakes in the Chinese Eastern Plains ecoregion. First, the maximal chlorophyll a (Chla) response to total N (TN) or P (TP), representing a region-specific "standard" model where phytoplankton were primarily N- or P-limited, was quantified using a 95% quantile regression. Second, site-specific limiting factors were identified using analogical residual analysis. N- or P-limitation was inferred if Fraction_TN (i.e. fraction of Chla observed and predicted by the "standard" model for a given TN) > 0.95 or Fraction_TP >0.95; if both Fraction_TN and Fraction_TP <0.95 in a specific environmental condition (e.g. high non-algal turbidity), light limitation was suggested. As a result, 5%, 7%, 4%, 36%, 16%, 2%, and 30% of the sampling sites were limited by N, P, N+P, light availability, rapid flushing, abundant macrophytes, and unmeasured factors, respectively. Bloom control suggestions in the short run are proposed considering these actual limiting factors. Furthermore, the maximal Fraction_TN or Fraction_TP response to WD was explored, reflecting the effect of WD on Fraction_TN (or Fraction_TP) without significant confounders. The results indicated that phytoplankton in the studied freshwaters would be potentially light-limited, N-limited, N+P-co-limited, or P-limited depending on WD (<1.8, 1.8-2.1, 2.1-5.2, or >5.2 m, respectively), because N will gradually become surplus with increasing WD, while at very shallow depths, strong sediment re-suspension induces light limitation. This finding implies that long-term nutrient management strategies in the studied freshwaters that have WDs of 0-2.1, 2.1-5.2, and >5.2 m can entail control of N, N+P, and P, respectively. This study provides essential information for formulating context-dependent bloom control for lakes in our study area and serves as a valuable reference for developing a cost-efficient eutrophication management framework for other regions.