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A variety of classification approaches are used to facilitate understanding, prediction, monitoring, and the management of lakes. However, broad‐scale applicability of current approaches is limited by either the need for in situ lake data, incompatibilities among approaches, or a lack of empirical testing of approaches based on ex situ data. We developed a new geographic classification approach for 476,697 lakes ≥ 1 ha in the conterminous U.S. based on lake archetypes representing end members along gradients of multiple geographic features. We identified seven lake archetypes with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. Individual lakes were assigned weights for each of the seven archetypes such that groups of lakes with similar combinations of archetype weights tended to cluster spatially (although not strictly contiguous) and to have similar limnological properties (e.g., concentrations of nutrients, chlorophyll a(Chl a), and dissolved organic carbon). Further, archetype lake classification improved commonly measured limnological relationships (e.g., between nutrients and Chl a) compared to a global model; a discrete archetype classification slightly outperformed an ecoregion classification; and considering lakes as continuous mixtures of archetypes in a more complex model further improved fit. Overall, archetype classification of US lakes as continuous mixtures of geographic features improved understanding and prediction of lake responses to limnological drivers and should help researchers and managers better characterize and forecast lake states and responses to environmental change.

ABSTRACTLake and reservoir (waterbody) depth is a critical characteristic that influences many important ecological processes. Unfortunately, depth measurements are labor-intensive to gather and are only available for a small fraction of waterbodies globally. Therefore, scientists have tried to predict depth from characteristics easily obtained for all waterbodies, such as surface area or the slope of the surrounding land. One approach for predicting waterbody depth simulates basins using a geometric cone model where the nearshore land slope and distance to the center of the waterbody are assumed to be representative proxies for in-lake slope and distance to the deepest point respectively. We tested these assumptions using bathymetry data from ∼5000 lakes and reservoirs to examine whether differences in waterbody type or shape influenced depth prediction error. We found that nearshore land slope was not representative of in-lake slope, and using it for prediction increases error substantially relative to models using true in-lake slope for all waterbody types and shapes. Predictions were biased toward overprediction in concave waterbodies (i.e., bowl-shaped; up to 18% of the study population) and reservoir waterbodies (up to 30% of the study population). Despite this systematic overprediction, model errors were fewer (in absolute and relative terms, irrespective of any specific slope covariate) for concave than convex waterbodies, suggesting the geometric cone model is an adequate representation of depth for these waterbodies. But because convex waterbodies are far more common (>72% of our study population), minimizing overall depth prediction error remains a challenge.

Depth is a fundamental property of lakes, essential for understanding and quantifying a range of biological, chemical, and physical processes of individual lakes as well as the collective functioning of the millions of lakes across the globe. Despite this importance, lake depth is rarely available for large numbers of lakes across the broad scales of regions and continents. We describe a new open‐access dataset, LAGOS‐US DEPTH, which contains 17,675 maximum depths and 6,137 mean depths for lakes in the conterminous United States. These data represent one of the largest compilations of lake depths in the world and are connected to other data products through the LAGOS‐US research platform. Here, we describe characteristics of lake maximum depth across the conterminous United States and identify gaps in data coverage that we encourage other researchers to fill by linking their own depth datasets for lakes within the LAGOS‐US spatial footprint. Extending this open‐access effort to include other regions and countries across the globe to build a more comprehensive and representative database of lake depth would better position scientists to quantify and articulate the critical roles that lakes play globally in biogeochemical cycling, maintaining biodiversity, and in maintaining the many ecosystem services that lakes provide.

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.

Lake water residence time and depth are known to be strong predictors of phosphorus (P) retention. However, there is substantial variation in P retention among lakes with the same depth and residence time. One potential explanatory factor for this variation is differences in freshwater connectivity of lakes (i.e., the type and amount of freshwater connections to a lake), which can influence watershed P trapping or the particulate load fraction of P delivered to lakes via stream connections. To examine the relationship between P retention and connectivity, we quantified several different measures of connectivity including those that reflect downstream transport of material (sediment, water, and nutrients) within lake‐stream networks (lake‐stream‐based metrics) as well as those that reflect transport of material from hillslope and riparian areas adjacent to watershed stream networks (stream‐based metrics). Because it is not always clear what spatial extent is appropriate for determining functional differences in connectivity among lakes, we compared connectivity metrics at two important spatial extents: the lake subwatershed extent and the lake watershed extent. We found that variation in P retention among lakes was more strongly associated with connectivity metrics measured at the broader lake watershed extent rather than metrics measured at the finer lake subwatershed extent. Our results suggest that both connectivity between lakes and streams as well as connectivity of lakes and their terrestrial watersheds influence P retention.

In this article, I provide six simple steps for authors to make their data available and meet the most common data publishing requirements for both open‐access and non‐open–access journals. These steps are simple and can easily be integrated into authors' workflows when conducting research and writing research articles. The steps include: deciding authorship of the dataset vs. authorship of the research article; exporting the data needed for all article analyses into archivable (preferably plain text) files using simple table formats; writing the metadata; depositing the data and metadata in a data repository; writing a data availability statement in the article; and citing the dataset in the methods and literature cited sections. Authors who implement these guidelines will reap many benefits for their science while also following best practices for open, robust, and reproducible science.

Publication is the key means by which science is disseminated, with evaluation by journal editors and peer reviewers an important component of the scientific process. Peer reviews are, however, a typically occluded genre of documents not publicly available. Consequently, relatively little is known about peer reviews, including what makes them relevant to editors who are assessing submitted manuscripts. Here we aim to address this knowledge gap by answering the questions: (1) Does reviewer and editor (dis)agreement on the manuscript decision (i.e., accept, minor revision, major revision, reject) relate to how editors assess overall review quality? and, (2) What are the characteristics of review text that distinguish high quality reviews from lower quality reviews for editors? We analyzed 49 reviews of 26 manuscript submissions to Limnology and Oceanography: Letters. We found editor perception of review quality was based on review content rather than if there was agreement on the manuscript decision. Specifically, reviews judged by editors to be ‘highly relevant’ rather than ‘sufficient’ were typically: longer; included more comments related to study goals, analyses conducted, and resulting claims; and contained more descriptive terms related to the manuscript's importance. Reviewers who consider these factors may produce reviews most relevant to editor decisions.

Understanding the broad-scale response of lake CO2dynamics to global change is challenging because the relative importance of different controls of surface water CO2is not known across broad geographic extents. Using geostatistical analyses of 1080 lakes in the conterminous United States, we found that lake partial pressure of CO2(pCO2) was controlled by different chemical and biological factors related to inputs and losses of CO2along climate, topography, geomorphology, and land use gradients. Despite weak spatial patterns in pCO2across the study extent, there were strong regional patterns in the pCO2driver-response relationships, i.e., in pCO2“regulation.” Because relationships between lake CO2and its predictors varied spatially, global models performed poorly in explaining the variability in CO2for U.S. lakes. The geographically varying driver-response relationships of lake pCO2reflected major landscape gradients across the study extent and pointed to the importance of regional-scale variation in pCO2regulation. These results indicate a higher level of organization for these physically disconnected systems than previously thought and suggest that changes in climate and land use could induce shifts in the main pathways that determine the role of lakes as sources and sinks of atmospheric CO2. In this study we show that changes in climate and terrestrial landscapes could affect which are the main mechanisms responsible for the widespread emissions of CO2by lakes. Although mechanisms such as aquatic primary production, respiration by microorganisms, or terrestrial loadings of carbon have been studied extensively, their relative importance across broad geographic extents with different climate or land use remains unknown. Based on an analysis of 1080 lakes distributed across the continental U.S., we show that lake CO2dynamics depend on the climate and landscape context where these lakes are found, such as precipitation, elevation, percent agriculture, or wetlands in the lakes catchments. We observed a widespread effect of in-lake primary production, while the color of water, which has often been identified as one of the main controls of lake CO2in northern lakes, was important in only a small fraction of the lakes studied. Our results show that controls on lake CO2 dynamics vary geographically and that considering that variation will be important for creating accurate global carbon models. Key scaling relationships for lake CO2concentrations vary geographicallyThere are contiguous regions of similar scaling relationshipsRegional variation in lake CO2regulation reflects climate and land-use gradients

AbstractLake depth is an important characteristic for understanding many lake processes, yet it is unknown for the vast majority of lakes globally. Our objective was to develop a model that predicts lake depth using map-derived metrics of lake and terrestrial geomorphic features. Building on previous models that use local topography to predict lake depth, we hypothesized that regional differences in topography, lake shape, or sedimentation processes could lead to region-specific relationships between lake depth and the mapped features. We therefore used a mixed modeling approach that included region-specific model parameters. We built models using lake and map data from LAGOS, which includes 8164 lakes with maximum depth (Zmax) observations. The model was used to predict depth for all lakes ≥4 ha (n = 42 443) in the study extent. Lake surface area and maximum slope in a 100 m buffer were the best predictors of Zmax. Interactions between surface area and topography occurred at both the local and regional scale; surface area had a larger effect in steep terrain, so large lakes embedded in steep terrain were much deeper than those in flat terrain. Despite a large sample size and inclusion of regional variability, model performance (R2= 0.29, RMSE = 7.1 m) was similar to other published models. The relative error varied by region, however, highlighting the importance of taking a regional approach to lake depth modeling. Additionally, we provide the largest known collection of observed and predicted lake depth values in the United States.

The relationship between chlorophyll a(Chl a) and total phosphorus (TP) is a fundamental relationship in lakes that reflects multiple aspects of ecosystem function and is also used in the regulation and management of inland waters. The exact form of this relationship has substantial implications on its meaning and its use. We assembled a spatially extensive data set to examine whether nonlinear models are a better fit for Chl a—TP relationships than traditional log‐linear models, whether there were regional differences in the form of the relationships, and, if so, which regional factors were related to these differences. We analyzed a data set from 2105 temperate lakes across 35 ecoregions by fitting and comparing two different nonlinear models and one log‐linear model. The two nonlinear models fit the data better than the log‐linear model. In addition, the parameters for the best‐fitting model varied among regions: the maximum and lower Chl aasymptotes were positively and negatively related to percent regional pasture land use, respectively, and the rate at which chlorophyll increased with TP was negatively related to percent regional wetland cover. Lakes in regions with more pasture fields had higher maximum chlorophyll concentrations at high TP concentrations but lower minimum chlorophyll concentrations at low TP concentrations. Lakes in regions with less wetland cover showed a steeper Chl a—TP relationship than wetland‐rich regions. Interpretation of Chl a—TP relationships depends on regional differences, and theory and management based on a monolithic relationship may be inaccurate.

Student discussions are a common teaching approach in graduate and advanced undergraduate courses because of their benefits to student learning, and to future professional development for natural resources professionals. However, traditional student-led discussions often are ineffective at meeting course and learning objectives and suffer from many common pitfalls, such as dominance by a few vocal students. I present the “student facilitator approach” to student-led discussions that changes the roles of all students in the discussion classroom, particularly the leader, and provides students clear guidelines about preparing for and participating in classroom discussions. A key feature of the approach is the replacement of the student discussion leader (who has control over both discussion content and process) with a student facilitator (who has control over only the discussion process). Using this approach, I found that students responded very positively, that it tended to encourage student participation, and that it created an environment where students took more ownership of the discussion.

ABSTRACT: Although limnologists have long been interested in regional patterns in lake attributes, only recently have they considered lakes connected and organized across the landscape, rather than as spatially independent entities. Here we explore the spatial organization of lake districts through the concept of landscape position, a concept that considers lakes longitudinally along gradients of geomorphology and hydrology. We analyzed long-term chemical and biological data from nine lake chains (lakes in a series connected through surface or groundwater flow) from seven lake districts of diverse hydrologic and geomorphic settings across North America. Spatial patterns in lake variables driven by landscape position were surprisingly common across lake districts and across a wide range of variables. On the other hand, temporal patterns of lake variables, quantified using synchrony, the degree to which pairs of lakes exhibit similar dynamics through time, related to landscape position only for lake chains with lake water residence times that spanned a wide range and were generally long (close to or greater than 1 year). Highest synchrony of lakes within a lake chain occurred when lakes had short water residence times. Our results from both the spatial and temporal analyses suggest that certain features of the landscape position concept are robust enough to span a wide range of seemingly disparate lake types. The strong spatial patterns observed in this analysis, and some unexplained patterns, suggest the need to further study these scales and to continue to view lake ecosystems spatially, longitudinally, and broadly across the landscape.

The ability of nutrient enrichment bioassays versus physiological indicators to detect ecosystem changes in nutrient cycling were compared. Field data spanned four years in three lakes, two of which were subjected to fish manipulations in year two. The manipulations changed phytoplankton and zooplankton assemblages and nutrient cycling. Phosphorus and nitrogen enrichments (phosphorus enrichment bioassay (PEB), nitrogen enrichment bioassay, nitrogen plus phosphorus enrichment bioassay, specific alkaline phosphatase activity, ammonium enhancement response, and total nutrients (total phosphorus, total nitrogen) were measured before and after the manipulations. Although physiological indicators were often more sensitive, nutrient enrichments and physiological indicators both led to the same conclusions regarding changes in nutrient cycling. Total nutrients did not respond to manipulation. Physiological indicators measure the extant algal assemblage, are rapidly assayed, can be run more frequently than nutrient enrichments, and lead to the same conclusions as nutrient enrichment bioassays.