Michael Frederick Meyer

The effects of winter environmental conditions on lake ecology can be highly variable. Ice and overlying snow isolate the water below, impeding exchange of materials among water, atmosphere, and the watershed. Even with these limitations, biological communities under the ice can persist, where cold‐tolerant taxa can be especially abundant. Because many under‐ice taxa tend to have a suite of unsaturated lipids that enable them to maintain metabolic function during cold conditions, under‐ice biological communities may contain highly nutritious resources for higher level consumers relative to warmer, open‐water conditions. To examine the effects of ice presence on under‐ice biological productivity and nutritional quality, we aggregated data on under‐ice physical, chemical, and biological variables from several existing databases. These data are among the most comprehensive, publicly available environmental and phytoplankton data that can be used to compare under‐ice and open‐water conditions at the community level. To understand how phytoplankton compositional shifts under ice can precipitate shifts in total algal nutritional content available to consumers relative to open‐water conditions, we merged characteristic phytoplankton fatty acid profiles with phytoplankton community composition data. Our results suggest that even though photosynthetically active radiation under ice is dramatically reduced by snow cover and opaque ice conditions, algal communities can be highly productive. Furthermore, these under‐ice communities tend to be dominated by cold‐water taxa, which are associated with elevated essential polyunsaturated fatty acids and reduced saturated fatty acids. Saturated fatty acids tend to be most associated with phytoplankton communities in the summer when cyanobacteria are prevalent, while winter communities tend to be most associated with high polyunsaturated fatty acids. As ice duration and quality continue to shift with increasing temperatures worldwide, phytoplankton assemblages are likely to exhibit altered nutritional profiles that have direct and indirect effects on food webs.

Temperature is a master variable in aquatic ecosystems. Despite its importance for ecosystem function, our capacity to measure temperature is often limited to focal systems with intensive in situ measurements or larger systems with readily accessible data from disparate data streams. These limitations are consequential for holistically understanding aquatic ecosystem status and change, especially considering that most lakes globally do not have consistent in situ data. To empower researchers with macroscale surface temperature observations, we present the first remotely sensed lake temperature dataset that includes measurements from Landsat 8 made every ~ 16 d for 137,394 lakes of at least 4 ha in surface area throughout the contiguous United States from 2013 through 2025. By aggregating data in a cloud computing framework, the dataset is amenable for continual update, thereby allowing end users to track local‐to‐continental scale changes in lake water temperature over time.

Zooplankton transfer substantial energy in aquatic food webs and are used as indicators of environmental change. Syntheses of zooplankton community dynamics globally require datasets that span a wide range of environmental gradients; however, these datasets are limited due to methodological differences across programs, taxonomic inconsistencies, and a lack of standardized metadata. To reconcile these challenges, we created the Zooplankton International Geospatial (ZIG) dataset, which includes original zooplankton, water physical and chemical variables, and lake morphometric data from 311 inland lakes and reservoirs. ZIG includes waterbodies ranging in size from 0.005 to 82,100 km ² and spanning broad latitudinal (−47.26 to 64.90) and longitudinal ranges (−165.04 to 176.53). Temporal coverage for individual waterbodies ranges between 1 and 60 yr with sampling frequency ranging from annually to weekly. With its extensive coverage and content, we consider ZIG to be a cornerstone for future investigations of global scale lake biodiversity change.

Satellite‐based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real‐time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic‐focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis‐ready, aquatic reflectance dataset for inland water derived from Sentinel‐2 for the conterminous United States.

For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem’s biological productivity. Because measuring productivity can be challenging within an ecosystem and across landscapes, multiple TS classification schemes, each relying on a variety of proxies for productivity, have emerged to meet use-specific needs. Most commonly, chlorophyll a, phosphorus, and Secchi depth are used to discriminate TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to discriminate TS based on autotrophic and heterotrophic production. Both classification schemes aim to characterize an ecosystem’s function broadly, but the relative emphasis on heterotrophic and autotrophic processes masks nuances in how an ecosystem’s function is understood. Moreover, differing classification schemes can create inconsistent understanding and can lead to narrowed interpretation of ecosystem integrity. For example, the U.S. Clean Water Act focuses exclusively on threats to autotrophic water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non-algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations, which can then be used to contextualize, compare, and project ecosystems across scales. However, to emphasize the consequences of using multiple TS classification schemes, we present three scenarios for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re-evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.

For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science.

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real‐time in situ monitoring, remote sensing, and modeling. The recent progress in low‐power in situ sensing and data telemetry allows continuous tracing of under‐ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process‐based or statistical data‐driven), with the recent technological advancements and integration of machine learning and hybrid process‐based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less‐studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross‐pollination of intensive and extensive modes of study to broaden knowledge of the winter period.