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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.

 

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.

 

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 thetargets rpackage 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.

 

There is an increasing appreciation that food–energy–water (FEW) nexus problems are approaching criticality in both the developing and developed world. As researchers and managers attempt to address these complex resource management issues, the concept of the FEW nexus has generated a rapidly growing footprint in global sustainability discourse. However, this momentum in the FEW nexus space could be better guided if researchers could more clearly identify what is and is not a FEW problem. Without this conceptual clarity, it can be difficult to defend the position that FEW innovations will produce desired outcomes and avoid unintended consequences. Here we examine the growing FEW nexus scholarship to critically evaluate what features are necessary to define a FEW nexus. This analysis suggests that the FEW nexus differs from sector-focused natural resource or sustainability problems in both complexity and stakes. It also motivates two new foci for research: the identification of low-dimension indexes of FEW system status and approaches for identifying boundaries of specific FEW nexuses. 

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 (~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.

 

Given climatic uncertainty and human population growth, tracking the world's freshwater availability is essential. Fortunately, data necessary to identify surface water patterns are abundant. Wrangling these data into an analytically friendly format, however, can be difficult for researchers not experienced in data manipulation and high‐performance computing. To increase data accessibility, we developed the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP offers annually aggregated surface area, temperature, precipitation, and human population estimates for over 1.42 million lakes globally between 1995 and 2015. Our dataset is peer‐reviewed and publicly available in a tabular format, enabling researchers with a range of skill levels to effectively work with the data. All aggregation procedures were performed within Google Earth Engine and R, empowering future users to replicate and modify scripts. Three case studies are presented to highlight concrete applications of the GLCP with emphasis on natural resource management at local, regional, and national scales.