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Abstract The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges. 

Abstract Growth of macroscale limnological research has been accompanied by an increase in secondary datasets compiled from multiple sources. We examined patterns of data availability in LAGOS‐NE, a dataset derived from 87 sources, to identify biases in availability of lake water quality data and to consider how such biases might affect perceived patterns at a subcontinental scale. Of eight common water quality parameters, variables indicative of trophic state (Secchi, chlorophyll, and total P) were most abundant in terms of total observations, lakes sampled, and long‐term records, whereas carbon variables (true color and dissolved organic carbon) were scarcest. Most data were collected during summer from larger (≥ 20 ha) lakes over 1–3 yr. Approximately 80% of data for each variable is derived from ~ 20% of sampled lakes. Long‐term (≥ 20 yr) records were rare and spatially clustered. Data availability is linked to major management challenges (eutrophication and acid rain), citizen science, and a few programs that quantify C and N variables. Resampling exercises suggested that correcting for the surface area sampling bias did not substantially change statistical distributions of the eight variables. Further, estimating a lake's long‐term median Secchi, chlorophyll, and total P using average record lengths had high uncertainty, but modest increases in sample size to > 5 yr yielded estimates with manageable error. Although the specific nature of sampling biases may vary among regions, we expect that they are widespread. Thus, large integrated datasets can and should be used to identify tendencies in how lakes are studied and to address these biases as part broad‐scale limnological investigations. 

Abstract Wildfires are becoming larger and more frequent across much of the United States due to anthropogenic climate change. No studies, however, have assessed fire prevalence in lake watersheds at broad spatial and temporal scales, and thus it is unknown whether wildfires threaten lakes and reservoirs (hereafter, lakes) of the United States. We show that fire activity has increased in lake watersheds across the continental United States from 1984 to 2015, particularly since 2005. Lakes have experienced the greatest fire activity in the western United States, Southern Great Plains, and Florida. Despite over 30 years of increasing fire exposure, fire effects on fresh waters have not been well studied; previous research has generally focused on streams, and most of the limited lake‐fire research has been conducted in boreal landscapes. We therefore propose a conceptual model of how fire may influence the physical, chemical, and biological properties of lake ecosystems by synthesizing the best available science from terrestrial, aquatic, fire, and landscape ecology. This model also highlights emerging research priorities and provides a starting point to help land and lake managers anticipate potential effects of fire on ecosystem services provided by fresh waters and their watersheds. 

Abstract Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll aon observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.