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Abstract. Water quality in lakes is an emergent property of complex biotic and abiotic processes that differ across spatial and temporal scales. Water quality is also a determinant of ecosystem services that lakes provide and is thus of great interest to ecologists. Machine learning and other computer science techniques are increasingly being used to predict water quality dynamics as well as to gain a greater understanding of water quality patterns and controls. To benefit the sciences of both ecology and computer science, we have created a benchmark dataset of lake water quality time series and vertical profiles. LakeBeD-US contains over 500 million unique observations of lake water quality collected by multiple long-term monitoring programs across 17 water quality variables from 21 lakes in the United States. There are two published versions of LakeBeD-US: the “Ecology Edition” published in the Environmental Data Initiative repository (https://doi.org/10.6073/pasta/c56a204a65483790f6277de4896d7140, McAfee et al., 2024) and the “Computer Science Edition” published in the Hugging Face repository (https://doi.org/10.57967/hf/3771, Pradhan et al., 2024). Each edition is formatted in a manner conducive to inquiries and analyses specific to each domain. For ecologists, LakeBeD-US: Ecology Edition provides an opportunity to study the spatial and temporal dynamics of several lakes with varying water quality, ecosystem, and landscape characteristics. For computer scientists, LakeBeD-US: Computer Science Edition acts as a benchmark dataset that enables the advancement of machine learning for water quality prediction. 

Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for complex feature interaction selection problems. MCES allows models within every population to evolve adaptively, selecting relevant feature interactions for different lake types and tasks. Evaluated on diverse lakes in the Midwestern USA, MCES not only consistently produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lake types, embodying the innovative concept of “AI from nature, for nature”. 

Abstract The dynamics of water and solutes were investigated in two northern bog ponds using sensor networks and discrete water samples. Embedded sensors monitored water level (S), precipitation (P), evaporation (E), water temperature (T) and specific conductivity (SC) in the peatlands and encircled ponds at 30 min time intervals from 2009 to 2015. Pond water chemistry was monitored seasonally from 2000 to 2020. Daily hydrographs and water budgets indicated that both bogs are ombrotrophic systems, perched above the local water table. Although the predominant flowpath for liquid water was precipitation → pond → peatland → underlying glacial deposits, evaporation accounted for 70% to 90% of water losses. High dissolved organic matter (DOM) in the ponds resulted from transient reversals of flowpath and from molecular diffusion across the peatland/pond interface (a tea bag effect). DOM of peatland origin dominated pond water chemistry, regulating the concentration of important metals, major nutrients and the acid–base status of both bog ponds. Elevated concentrations of Fe, Hg and MeHg in the ponds reflected ligand binding by DOM. The formation of DOM‐Fe‐PO₄complexes likely accounted for >3‐fold higher P concentration relative to nearby clearwater lakes. Linear regression of dissolved organic carbon (DOC) against the anion deficit indicated that DOM contributed up to 6.6 mEq of strong acid per gramme carbon in pond waters. Winter maxima in the seasonal cycles of DOC, Ca, Mg, N, P, Hg and MeHg in both bog ponds were attributable, in large part, to salting out during ice formation. We conclude that multiple methods are needed to understand the dynamics of water and solutes in bog ecosystems. 

Abstract Species invasions can disrupt aquatic ecosystems by re‐wiring food webs. A trophic cascade triggered by the invasion of the predatory zooplankter spiny water flea (Bythotrephes cederströmii) resulted in increased phytoplankton due to decreased zooplankton grazing. Here, we show that increased phytoplankton biomass led to an increase in lake anoxia. The temporal and spatial extent of anoxia experienced a step change increase coincident with the invasion, and anoxic factor increased by 11 d. Post‐invasion, anoxia established more quickly following spring stratification, driven by an increase in phytoplankton biomass. A shift in spring phytoplankton phenology encompassed both abundance and community composition. Diatoms (Bacillaryophyta) drove the increase in spring phytoplankton biomass, but not all phytoplankton community members increased, shifting the community composition. We infer that increased phytoplankton biomass increased labile organic matter and drove hypolimnetic oxygen consumption. These results demonstrate how a species invasion can shift lake phenology and biogeochemistry. 

The data are associated with the following manuscript: Hanson, P. C., Ladwig, R., Buelo, C., Albright, E. A., Delany, A. D., & Carey, C. (2023). Legacy phosphorus and ecosystem memory control future water quality in a eutrophic lake. Lake water and ice observational data and lake bathymetry are from the North Temperate Lakes Long Term Ecological Research program. Brief abstract of the work: To investigate how water quality in Lake Mendota might respond to nutrient pollution reduction, we used computer models to simulate the elimination of phosphorus inputs from the catchment and track water quality change. The data herein are used to drive and calibrate the model. In addition, model code and simulation output are included as "other entities." 

Abstract. Hypolimnetic oxygen depletion during summer stratification in lakes can lead to hypoxic and anoxic conditions. Hypolimnetic anoxia is a water quality issue with many consequences, including reduced habitat for cold-water fish species, reduced quality of drinking water, and increased nutrient and organic carbon (OC) release from sediments. Both allochthonous and autochthonous OC loads contribute to oxygen depletion by providing substrate for microbial respiration; however, their relative contributions to oxygen depletion across diverse lake systems remain uncertain. Lake characteristics, such as trophic state, hydrology, and morphometry, are also influential in carbon-cycling processes and may impact oxygen depletion dynamics. To investigate the effects of carbon cycling on hypolimnetic oxygen depletion, we used a two-layer process-based lake model to simulate daily metabolism dynamics for six Wisconsin lakes over 20 years (1995–2014). Physical processes and internal metabolic processes were included in the model and were used to predict dissolved oxygen (DO), particulate OC (POC), and dissolved OC (DOC). In our study of oligotrophic, mesotrophic, and eutrophic lakes, we found autochthony to be far more important than allochthony to hypolimnetic oxygen depletion. Autochthonous POC respiration in the water column contributed the most towards hypolimnetic oxygen depletion in the eutrophic study lakes. POC water column respiration and sediment respiration had similar contributions in the mesotrophic and oligotrophic study lakes. Differences in terms of source of respiration are discussed with consideration of lake productivity and the processing and fates of organic carbon loads. 

This repository includes the setup and output from the analysis ran on Lake Mendota to explore the trophic cascade caused by invasion of spiny water flea in 2010. Scripts to run the model are located under /src, and the processed results for the discussion of the paper are located under /data_processed.</p>