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Abstract While there is a diversity of approaches for modeling phytoplankton blooms, their accuracy in predicting the onset and manifestation of a bloom is still lagging behind what is needed to support effective management. We outline a framework that integrates trait theory and ecosystem modeling to improve bloom prediction. This framework builds on the concept that the phenology of blooms is determined by the dynamic interaction between the environment and traits within the phytoplankton community. Phytoplankton groups exhibit a collection of traits that govern the interplay of processes that ultimately control the phases of bloom initiation, maintenance, and collapse. An example of process‐trait mapping is used to demonstrate a more consistent approach to bloom model parameterization that allows better alignment with models and laboratory‐ and ecosystem‐scale datasets. Further approaches linking statistical‐mechanistic models to trait parameter databases are discussed as a way to help optimize models to better simulate bloom phenology and allow them to support a wider range of management needs. 

Abstract Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor's law of fluctuation scaling, which states that the variance of a population's size or density is proportional to a positive power of the mean size or density, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. Here, we use water clarity observations from 10,531 days of high‐frequency measurements in 35 globally distributed lakes, and lower‐frequency measurements over multiple decades from 6342 lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide the first documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor's law, and that model coefficients increase in a predictable yet non‐linear manner with decreasing observation frequency. This discovery effectively links high‐frequency sensor network observations with long‐term historical monitoring records, thereby affording new opportunities to understand and predict ecological dynamics on time scales from days to decades. 

China has made a concerted effort to successfully improve water quality of rivers, but lake water quality has not improved. Lakes require controls on both catchment external nutrient loads and in-lake internal loads, where nature-based solutions are coupled with engineered systems to achieve the United Nations Sustainable Development Goals (SDGs). 

Abstract. The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants. 

Abstract Climate change and other anthropogenic stressors have led to long-term changes in the thermal structure, including surface temperatures, deepwater temperatures, and vertical thermal gradients, in many lakes around the world. Though many studies highlight warming of surface water temperatures in lakes worldwide, less is known about long-term trends in full vertical thermal structure and deepwater temperatures, which have been changing less consistently in both direction and magnitude. Here, we present a globally-expansive data set of summertime in-situ vertical temperature profiles from 153 lakes, with one time series beginning as early as 1894. We also compiled lake geographic, morphometric, and water quality variables that can influence vertical thermal structure through a variety of potential mechanisms in these lakes. These long-term time series of vertical temperature profiles and corresponding lake characteristics serve as valuable data to help understand changes and drivers of lake thermal structure in a time of rapid global and ecological change. 

Abstract Bacteria play key roles in the function and diversity of aquatic systems, but aside from study of specific bloom systems, little is known about the diversity or biogeography of bacteria associated with harmful cyanobacterial blooms (cyanoHABs). CyanoHAB species are known to shape bacterial community composition and to rely on functions provided by the associated bacteria, leading to the hypothesized cyanoHAB interactome, a coevolved community of synergistic and interacting bacteria species, each necessary for the success of the others. Here, we surveyed the microbiome associated withMicrocystis aeruginosaduring blooms in 12 lakes spanning four continents as an initial test of the hypothesizedMicrocystisinteractome. We predicted that microbiome composition and functional potential would be similar across blooms globally. Our results, as revealed by 16S rRNA sequence similarity, indicate thatM. aeruginosais cosmopolitan in lakes across a 280° longitudinal and 90° latitudinal gradient. The microbiome communities were represented by a wide range of operational taxonomic units and relative abundances. Highly abundant taxa were more related and shared across most sites and did not vary with geographic distance, thus, likeMicrocystis, revealing no evidence for dispersal limitation. High phylogenetic relatedness, both within and across lakes, indicates that microbiome bacteria with similar functional potential were associated with all blooms. WhileMicrocystisand the microbiome bacteria shared many genes, whole‐community metagenomic analysis revealed a suite of biochemical pathways that could be considered complementary. Our results demonstrate a high degree of similarity across globalMicrocystisblooms, thereby providing initial support for the hypothesizedMicrocystisinteractome.