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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.

 

Abstract In recent decades, lakes have experienced unprecedented ice loss with widespread ramifications for winter ecological processes. The rapid loss of ice, resurgence of winter biology, and proliferation of remote sensing technologies, presents a unique opportunity to integrate disciplines to further understand the broad spatial and temporal patterns in ice loss and its consequences. Here, we summarize ice phenology records for 78 lakes in 12 countries across North America, Europe, and Asia to permit the inclusion and harmonization of in situ ice phenology observations in future interdisciplinary studies. These ice records represent some of the longest climate observations directly collected by people. We highlight the importance of applying the same definition of ice-on and ice-off within a lake across the time-series, regardless of how the ice is observed, to broaden our understanding of ice loss across vast spatial and temporal scales.