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.

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

A changing climate and increasing human population necessitate understanding global freshwater availability. To enable assessment of lake water variability from local-to-global and monthly-to-decadal scales, we extended the Global Lake area, Climate, and Population (GLCP) dataset, which contains monthly lake surface area for 1.42 million lakes with paired basin-level climate and population data from 1995 through 2020. In comparison to the previous version of the GLCP, the extended version is monthly and includes information on lake ice cover as well as basin-level snow area, humidity, longwave and shortwave radiation, and cloud cover. The extended GLCP emphasizes FAIR data principles by expanding its scripting repository and maintaining unique HydroLAKES identifiers, which enables the GLCP to be joined with other HydroLAKES-derived products. Compared to the original version, the extended GLCP contains a richer suite of variables that enable disparate analyses of lake water trends at broad spatial and temporal scales.

Access to clean freshwater is a basic human right, yet freshwater is distributed unequally around the globe. Further, freshwater resources are sensitive to degradation by many environmental stressors, including climate change, inputs of nutrients and pollutants, alterations in land use, introduction of invasive species, and overfishing. Restoration and remediation methods are available for mitigating the degradation of lakes and rivers to an extent, but often at great expense and effort, highlighting the crucial importance of conservation and protection. Understanding, managing, protecting, and remediating freshwater all require extensive collaboration for limnology as a discipline to fulfill its potential. Inclusive, diverse, interdisciplinary collaborative teams are essential to integrating research disciplines, incorporating new technologies, and sparking creative solutions to safeguard our global freshwater resources and work toward a future of equitable access to clean freshwater worldwide.

Algal blooms appear to be increasing on benthic substrates of naturally nutrient-poor lakes worldwide, yet common drivers across these systems remain elusive. The phenomenon has been notable in high-elevation mountain lakes, which is enigmatic given their relative remoteness from human disturbance. We suggest that warming-induced changes in redox conditions that promote nutrient release from sediments warrant more attention. Warming associated with climate change reduces oxygen content and hastens microbial processes, enhancing release of nutrients which can be intercepted by the benthic algae before reaching the water column. Warming effects may be particularly noticeable in high-elevation lakes that hold less oxygen at saturation, are warming more rapidly than lowland lakes, and can receive relatively high solar radiation.

Climate change is reducing winter ice cover on lakes; yet, the full societal and environmental consequences of this ice loss are poorly understood. The socioeconomic implications of declining ice include diminished access to ice-based cultural activities, safety concerns in traversing ice, changes in fisheries, increases in shoreline erosion, and declines in water storage. Longer ice-free seasons allow more time and capacity for water to warm, threatening water quality and biodiversity. Food webs likely will reorganize, with constrained availability of ice-associated and cold-water niches, and ice loss will affect the nature, magnitude, and timing of greenhouse gas emissions. Examining these rapidly emerging changes will generate more-complete models of lake dynamics, and transdisciplinary collaborations will facilitate translation to effective management and sustainability.

The Bulletin of the Ecological Society of America is the official record of business of the Ecological Society of America, publishing non-refereed articles that cover ecological events, news and reports.

Ice phenology has shifted with anthropogenic warming such that many lakes are experiencing a shorter ice season. However, changes to ice quality — the ratio of black and white ice layers — remain little explored, despite relevance to lake physics, ecological function, human recreation and transportation. In this Review, we outline how ice quality is changing and discuss knock-on ecosystem service impacts. Although direct evidence is sparse, there are suggestions that ice quality is diminishing across the Northern Hemisphere, encompassing declining ice thickness, decreasing black ice and increasing white ice. These changes are projected to continue in the future, scaling with global temperature increases, and driving considerable impacts to related ecosystem services. Rising proportions of white ice will markedly reduce bearing strength, implying more dangerous conditions for transportation (limiting operational use of many winter roads) and recreation (increasing the risk of fatal spring-time drownings). Shifts from black to white ice conditions will further reduce the amount of light reaching the water column, minimizing primary production, and altering community composition to favour motile and mixotrophic species; these changes will affect higher trophic levels, including diminished food quantity for zooplankton and fish, with potential developmental consequences. Reliable and translatable in situ sampling methods to assess and predict spatiotemporal variations in ice quality are urgently needed. Lake ice has witnessed considerable changes in its phenology, but less is known about ice quality — the ratio of black ice to white ice. This Review assesses the changes in lake ice quality and its ecosystem services, noting diminished ice quality in observations and projections.

Trophic state (TS) characterizes a waterbody’s biological productivity and depends on its morphometry, physics, chemistry, biology, climate, and history. However, multiple TS operational definitions have emerged to meet use-specific classification needs. These differing operational definitions can create inconsistent understanding, can lead to miscommunication, and can result in siloed management strategies for TS. For example, some regulatory agencies use TS to signify ecological integrity as opposed to biological productivity, where TS classification may trigger intervention efforts. These inconsistencies may be compounded when interdisciplinary projects employ varied TS frameworks. To emphasize the consequences of using multiple TS classification schemes, we present three scenarios for which an improved understanding of the TS concept could advance limnological research, management efforts, and interdisciplinary collaboration. As the field of limnology continues to expand, we highlight the importance of re-evaluating even the most fundamental limnological concepts, such as TS, to ensure congruence with evolving, cutting-edge science.

Pressing environmental research questions demand the integration of increasingly diverse and large‐scale ecological datasets as well as complex analytical methods, which require specialized tools and resources. Computational training for ecological and evolutionary sciences has become more abundant and accessible over the past decade, but tool development has outpaced the availability of specialized training. Most training for scripted analyses focuses on individual analysis steps in one script rather than creating a scripted pipeline, where modular functions comprise an ecosystem of interdependent steps. Although current computational training creates an excellent starting place, linear styles of scripting can risk becoming labor‐ and time‐intensive and less reproducible by often requiring manual execution. Pipelines, however, can be easily automated or tracked by software to increase efficiency and reduce potential errors. Ecology and evolution would benefit from techniques that reduce these risks by managing analytical pipelines in a modular, readily parallelizable format with clear documentation of dependencies. Workflow management software (WMS) can aid in the reproducibility, intelligibility and computational efficiency of complex pipelines. To date, WMS adoption in ecology and evolutionary research has been slow. We discuss the benefits and challenges of implementing WMS and illustrate its use through a case study with the targets r package to further highlight WMS benefits through workflow automation, dependency tracking and improved clarity for reviewers. Although WMS requires familiarity with function‐oriented programming and careful planning for more advanced applications and pipeline sharing, investment in training will enable access to the benefits of WMS and impart transferable computing skills that can facilitate ecological and evolutionary data science at large scales.

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.

Mountain lakes experience interannual variability in spring snowpack and ice cover that can lead to differences in physical, chemical, and biological properties in the succeeding summer. Lake studies that capture extreme years of snow and ice would be useful to understand and anticipate effects of climate change, but such data are rare for remote mountain lakes. Monitoring of lakes in Olympic, North Cascades, and Mount Rainier National Parks from 2007 to 2018 allowed us to examine limnological differences along interannual and elevation‐driven climate gradients that included unusually high (2011–2012) and 100‐yr record low (2015) snowpack years. Years with lower spring snowpack had earlier ice‐out. Across lakes, our analysis suggested an average of 0.075°C lake warming per day of lost ice duration (0.525°C per week), giving rise to other ecosystem changes linked to temperature such as lower dissolved oxygen, higher total dissolved N, higher chlorophyll, and higher abundance of cladoceran zooplankton. Conversely, in years with higher snowpack and a shorter ice‐free season, lakes were colder and clearer (1 m deeper Secchi depth for every 1 m May snow water equivalent), with more dilute ions as well as lower algal biomass and zooplankton abundance. These results add to evidence that changes in snowpack or ice‐out dates alter mountain lake ecology through multiple processes associated with hydrology, terrestrial‐aquatic connection, water temperature, productivity, ion composition, and plankton communities.

Sewage released from lakeside development can introduce nutrients and micropollutants that can restructure aquatic ecosystems. Lake Baikal, the world's most ancient, biodiverse, and voluminous freshwater lake, has been experiencing localized sewage pollution from lakeside settlements. Nearby increasing filamentous algal abundance suggests benthic communities are responding to localized pollution. We surveyed 40-km of Lake Baikal's southwestern shoreline from 19 to 23 August 2015 for sewage indicators, including pharmaceuticals, personal care products, and microplastics, with colocated periphyton, macroinvertebrate, stable isotope, and fatty acid samplings. The data are structured in a tidy format (a tabular arrangement familiar to limnologists) to encourage reuse. Unique identifiers corresponding to sampling locations are retained throughout all data files to facilitate interoperability among the dataset's 150+ variables. For Lake Baikal studies, these data can support continued monitoring and research efforts. For global studies of lakes, these data can help characterize sewage prevalence and ecological consequences of anthropogenic disturbance across spatial scales.

Sewage released from lakeside development can reshape ecological communities. Nearshore periphyton can rapidly assimilate sewage-associated nutrients, leading to increases of filamentous algal abundance, thus altering both food abundance and quality for grazers. In Lake Baikal, a large, ultra-oligotrophic, remote lake in Siberia, filamentous algal abundance has increased near lakeside developments, and localized sewage input is the suspected cause. These shifts are of particular interest in Lake Baikal, where endemic littoral biodiversity is high, lakeside settlements are mostly small, tourism is relatively high (similar to 1.2 million visitors annually), and settlements are separated by large tracts of undisturbed shoreline, enabling investigation of heterogeneity and gradients of disturbance. We surveyed sites along 40 km of Baikal's southwestern shore for sewage indicators-pharmaceuticals and personal care products (PPCPs) and microplastics-as well as periphyton and macroinvertebrate abundance and indicators of food web structure (stable isotopes and fatty acids). Summed PPCP concentrations were spatially related to lakeside development. As predicted, lakeside development was associated with more filamentous algae and lower abundance of sewage-sensitive mollusks. Periphyton and macroinvertebrate stable isotopes and essential fatty acids suggested that food web structure otherwise remained similar across sites; yet, the invariance of amphipod fatty acid composition, relative to periphyton, suggested that grazers adjust behavior or metabolism to compensate for different periphyton assemblages. Our results demonstrate that even low levels of human disturbance can result in spatial heterogeneity of nearshore ecological responses, with potential for changing trophic interactions that propagate through the food web.

Grazing by microzooplankton has been shown to significantly impact freshwater cyanobacteria blooms; however, the contribution of rotifers to the overall effect of microzooplankton grazing is not well understood. We conducted monthly microzooplankton community grazing (dilution) experiments June–October 2019, concurrent with incubations of field-collected rotifers feeding upon the natural assemblage of microplankton prey < 75 µm in Vancouver Lake (Washington State, USA), a lake annually affected by cyanobacteria blooms. Our results showed that just days after a large bloom, the microzooplankton community grazing impact on phytoplankton biomass was exceptionally high (> 1000% d−1), yet the impact by rotifers was low (< 1% d−1). As the bloom diminished in September and October, the grazing impact of rotifers increased dramatically, specifically consuming substantial dinoflagellate (≤ 574%) and ciliate (≤ 382%) biomass daily. Analysis of rotifers in Vancouver Lake during these months showed the presence of large, carnivorous Asplanchna spp., which indicates multi-trophic grazing dynamics within the rotifer assemblage. We conclude that non-rotifer micro-grazers (i.e., ciliates) were likely responsible for the initial dissipation of cyanobacteria just after the bloom peak, while rotifers primarily removed micro-grazers later in autumn. This study highlights the trophic roles of micro-grazers in controlling harmful cyanobacteria blooms and quantifies the specific grazing contributions of rotifers.

Abstract At broad spatial scales, primary productivity in lakes is known to increase in concert with nutrients, and variables that may disrupt or modify the tight coupling of nutrients and algae are of increasing interest, particularly for those shifting with climate change. Storms may disrupt algae–nutrient relationships, but the expected effects differ between winter and summer seasons, particularly for seasonally ice‐covered lakes. In winter, storms can dramatically change the under‐ice light environment, creating light limitation that disrupts algae–nutrient relationships. Further, storms can bring both snow that blocks light and also wind that blows snow off of ice. In open water conditions, storms may promote turbulence and external nutrient loading. Here, we test the hypotheses that winter and summer storms differentially affect algae–nutrient relationships across 84 seasonally ice‐covered lakes included in the Ecology Under Lake Ice dataset. While nutrients explained most of the variation in chlorophyll across these lakes, we found that secondary drivers differed between seasons. Under‐ice chlorophyll was higher under a variety of precipitation and wind conditions that tend to promote snow‐free clear ice, highlighting the importance of light as a limiting factor for algal growth during winter. In summer, higher water temperatures and storms corresponded with higher chlorophyll. Our study suggests that examining ice‐covered lakes in a gradient from the perennial ice cover of the poles to the intermittent ice cover of lower latitudes would yield key information on the shifts in light and nutrient limitation that control algal biomass.

Predicting how food webs will respond to global environmental change is difficult because of the complex interplay between the abiotic forcing and biotic interactions. Mechanistic models of species interactions in seasonal environments can help understand the effects of global change in different ecosystems. Seasonally ice-covered lakes are warming faster than many other ecosystems and undergoing pronounced food web changes, making the need to forecast those changes especially urgent. Using a seasonally forced food web model with a generalist zooplankton grazer and competing cold-adapted winter and warm-adapted summer phytoplankton, we show that with declining ice cover, the food web moves through different dynamic regimes, from annual to biennial cycles, with decreasing and then disappearing winter phytoplankton blooms and a shift of maximum biomass to summer season. Interestingly, when predator-prey interactions were not included, a declining ice cover did not cause regime shifts, suggesting that both are needed for regime transitions. A cluster analysis of long-term data from Lake Baikal, Siberia, supports the model results, revealing a change from regularly occurring winter blooms of endemic diatoms to less frequent winter bloom years with decreasing ice cover. Together, the results show that even gradual environmental change, such as declining ice cover duration, may cause discontinuous or abrupt transitions between dynamic regimes in food webs.