Search

Although understanding nutrient limitation of primary productivity in lakes is among the oldest research priorities in limnology, there have been few broad‐scale studies of the characteristics of phosphorus (P)‐, nitrogen (N)‐, and co‐limited lakes and their environmental context. By analyzing 3342 US lakes with concurrent P, N, and chlorophyll a (Chl a) samples, we showed that US lakes are predominantly co‐limited (43%) or P‐limited (41%). Majorities of lakes were P‐limited in the Northeast, Upper Midwest, and Southeast, and co‐limitation was most prevalent in the interior and western United States. N‐limitation (16%) was more prevalent than P‐limitation in the Great Basin and Central Plains. Nutrient limitation was related to lake, watershed, and regional variables, including Chl a concentration, watershed soil, and wet nitrate deposition. N and P concentrations interactively affected nutrient–chlorophyll relationships, which differed by nutrient limitation. Our study demonstrates the value of considering P, N, and environmental context in nutrient limitation and nutrient–chlorophyll relationships. [ABSTRACT FROM AUTHOR]

This article explores the concept of "lay summaries" in scientific research articles, which aim to make research findings more accessible to a wider audience. However, the article argues that these summaries often still contain complex language and are difficult for nonspecialists to understand. The article provides recommendations for authors, publishers, and editors on how to improve the accessibility of these summaries, focusing on reducing jargon and improving readability. The authors suggest using shorter sentences, simpler language, and active voice, and recommend that publishers and editors provide clearer instructions and examples. The goal is to increase the visibility and impact of research by making summaries more easily understood by a broader range of people. [Extracted from the article]

The LAGOS‐US RESERVOIR data module classifies all 137,465 lakes ≥ 4 ha in the conterminous U.S. into three categories using a machine learning predictive model based on visual interpretation of lake outlines and a lake shape classification rule. Natural Lakes (NLs) are defined as naturally formed, lacking large, flow‐altering structures; Reservoir Class A's (RSVR_A) are defined as lakes likely human‐made or human‐altered by a large water control structure; and Reservoir Class B's (RSVR_Bs) are lakes likely human‐made but are not connected to streams and have a shape rare in NLs. We trained machine learning models on 12,162 manually classified lakes to predict assignment as an NL or RSVR, then further classified RSVRs based on NHD Fcodes, isolation, and angularity. Our classification indicates that > 46% of lakes ≥ 4 ha in the conterminous U.S. are reservoir lakes. These data can be easily combined with other LAGOS‐US modules and U.S. national databases for the broad‐scale study of reservoir lakes and NLs. [ABSTRACT FROM AUTHOR]

Macroscale studies of aquatic ecosystems are needed to address many contemporary broad‐scale problems related to global change, particularly to inform national‐scale environmental policies. In this data paper, we fill two important gaps in data availability for lakes at the scale of the conterminous U.S., the lack of: (1) high‐resolution geographic representations of lakes and their watersheds and (2) an open data platform to connect disparate U.S. national‐scale lake datasets. We describe the LAGOS‐US LOCUS v1.0 data module that includes a detailed User Guide, which is part of the LAGOS‐US extensible research‐ready open data platform. This platform can be used to study the 479,950 lakes and reservoirs larger than or equal to 1 ha across the entire conterminous U.S. at multiple scales of space and time. The LOCUS module includes spatially‐explicit information on lakes and their watersheds, including location information, physical characteristics, and identifiers of other important national‐scale datasets. [ABSTRACT FROM AUTHOR]

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 a on 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. [ABSTRACT FROM AUTHOR]

The Convention on Biological Diversity (https://www.cbd.int/) recommends globally protecting ≥ 17% of ecologically representative freshwaters by 2020 to sustain critical ecosystem services and rapidly declining freshwater biodiversity. We examined whether current conservation efforts meet this target for lakes (≥ 1 ha) across the continental U.S. and Environmental Protection Agency ecoregions using the U.S. Protected Areas Database. How one defines lake protection matters: 17.8% of lakes fell within multiuse or strictly protected areas, but only 7.5% of lakes had ≥ 80% of their catchments in strictly protected areas. Protected lakes occurred disproportionately in the lake‐poor western U.S. and most lake‐rich regions fell short of the 17% target. Lakes connected to streams and other lakes were disproportionately protected relative to headwater and isolated lakes, which are important for ecosystem services and biodiversity. Meeting conservation targets requires protecting thousands of additional U.S. lakes and catchments across more diverse ecological settings and explicitly considering connectivity. [ABSTRACT FROM AUTHOR]