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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 Globally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade⁻¹, comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m⁻³decade⁻¹). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade⁻¹), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade⁻¹to + 0.65 °C decade⁻¹. The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences. 

Summary Global warming and eutrophication contribute to the worldwide increase in cyanobacterial blooms, and the level of cyanobacterial biomass is strongly associated with rises in methane emissions from surface lake waters. Hence, methane‐metabolizing microorganisms may be important for modulating carbon flow in cyanobacterial blooms. Here, we surveyed methanogenic and methanotrophic communities associated with floatingMicrocystisaggregates in 10 lakes spanning four continents, through sequencing of 16S rRNA and functional marker genes. Methanogenic archaea (mainlyMethanoregulaandMethanosaeta) were detectable in 5 of the 10 lakes and constituted the majority (~50%–90%) of the archaeal community in these lakes. Three of the 10 lakes contained relatively more abundant methanotrophs than the other seven lakes, with the methanotrophic generaMethyloparacoccus,Crenothrix, and an uncultured species related toMethylobacterdominating and nearly exclusively found in each of those three lakes. These three are among the five lakes in which methanogens were observed. Operational taxonomic unit (OTU) richness and abundance of methanotrophs were strongly positively correlated with those of methanogens, suggesting that their activities may be coupled. TheseMicrocystis‐aggregate‐associated methanotrophs may be responsible for a hitherto overlooked sink for methane in surface freshwaters, and their co‐occurrence with methanogens sheds light on the methane cycle in cyanobacterial aggregates.