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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]

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. [ABSTRACT FROM AUTHOR]

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]

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. [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]

Peer‐review and subject‐matter editing is the backbone of scientific publishing. However, early‐career researchers (ECRs) are given few opportunities to participate in the editorial process beyond reviewing articles. Thus, a disconnect exists: science needs high‐quality editorial talent to conduct, oversee and improve the publishing process, yet we dedicate few resources to building editorial talent nor giving ECRs formal opportunities to influence publishing from within. ECRs can contribute to the publishing landscape in unique ways given their insight into new and rapidly developing publishing trends (e.g. open science). Here, we describe a two‐way fellowship model that gives ECRs a "seat" at the editorial table of a field‐leading journal. We describe both the necessary framework and benefits that can stem from editorial fellowships for ECRs, editors, journals, societies, and the ​broader scientific community. [ABSTRACT FROM AUTHOR]

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8‐million‐km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non‐random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change. [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]

Ecological research increasingly relies on broad‐scale databases containing information collected by personnel from a variety of sources, including government agencies, universities, and citizen‐science programs. However, the contribution of citizen‐science programs to these databases is not well known. We analyzed one such database to quantify the contribution of citizen science to lake water‐quality data from seven US states. Citizen‐science programs not only provided over half of the observations for commonly sampled water‐quality measures (water clarity, nutrients, and algal biomass) from the past 31 years, but also contributed to the majority of long‐term monitoring (>15 years) for selected measures in lakes. While previous studies have demonstrated the usefulness of citizen science for research, management, policy, and public engagement, our study demonstrates that citizen science can also make valuable contributions to populating broad‐scale ecological databases. Strengthening partnerships between citizen‐science programs and monitoring agencies can help maintain and expand spatial and temporal data coverage during the "big data" era of ecology. [ABSTRACT FROM AUTHOR]

Editors are often described as gatekeepers of scientific publishing, as they are responsible for maintaining journal standards, deciding what is published, and ultimately guiding discourse. Scientists who are journal editors gain career benefits, yet these are rarely described to early career researchers, much less how to prepare for such a role. Additionally, disparities at the editorial level could impact which scientists receive benefits of filling these roles. To better characterize the demographics and professional experiences of current associate editors, while also highlighting the benefits and potential challenges to this position, we conducted a survey of associate editors for the Association for the Sciences of Limnology & Oceanography society journals. Our results highlight potential demographic disparities present in the editorial pool, including that non‐native English speaking editors assume the role after obtaining more experience serving as a peer reviewer than native English speaking editors. Our results also highlight several rewards along with challenging components of being an editor. We hope our results can inform early career researchers on steps they can take to prepare themselves for editorial work, as well as provide strategies for scientific societies to minimize editorial board disparities. [ABSTRACT FROM AUTHOR]

Given how important lakes are to people, it might seem safe to assume that careful thought has been put into the naming of lakes, and that lake names reflect the high societal value people place on lakes. We examined these assumptions by analyzing the official names in the U.S. Geographic Names Information System for the 479,950 lakes ≥ 1 ha in the conterminous U.S. We found that 83% of lakes were unnamed and most of these were small lakes with 80% of unnamed lakes being smaller than 4 ha. Based on the 83,115 named lakes, we found that lake names reflect peoples' everyday lives, that lakes can inspire creativity (although the most common lake name is "Mud"), that Native American and indigenous languages have played a role in lake naming, and that there are regional differences in lake names. Unfortunately, we also found that derogatory terms were part of some lake names. We advocate for thoughtful and inclusive official naming of the 400,000 unnamed lakes in the U.S., as well as renaming of the lakes with derogatory terms to help focus attention on the importance of lakes to local communities and nations. [ABSTRACT FROM AUTHOR]

Faced with limitations in data availability, funding, and time constraints, ecologists are often tasked with making predictions beyond the range of their data. In ecological studies, it is not always obvious when and where extrapolation occurs because of the multivariate nature of the data. Previous work on identifying extrapolation has focused on univariate response data, but these methods are not directly applicable to multivariate response data, which are common in ecological investigations. In this paper, we extend previous work that identified extrapolation by applying the predictive variance from the univariate setting to the multivariate case. We propose using the trace or determinant of the predictive variance matrix to obtain a scalar value measure that, when paired with a selected cutoff value, allows for delineation between prediction and extrapolation. We illustrate our approach through an analysis of jointly modeled lake nutrients and indicators of algal biomass and water clarity in over 7000 inland lakes from across the Northeast and Mid-west US. In addition, we outline novel exploratory approaches for identifying regions of covariate space where extrapolation is more likely to occur using classification and regression trees. The use of our Multivariate Predictive Variance (MVPV) measures and multiple cutoff values when exploring the validity of predictions made from multivariate statistical models can help guide ecological inferences. [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]

Context: Biodiversity conservation for terrestrial species often emphasizes land protection to help maintain connectivity among habitat patches. However, conservation of aquatic and semi-aquatic species is challenging because aquatic species (e.g., fish) move among lakes using aquatic connections (e.g., streams, wetlands), whereas semi-aquatic species (e.g., amphibians) use both aquatic connections and upland habitats. Objectives: We applied the patch-matrix model to create an aquatic and semi-aquatic connectivity framework for lakes. We applied our framework using lakes in Michigan, USA to examine (1) the relationship between aquatic and semi-aquatic connectivity for lakes and (2) the extent to which protected areas encompass aquatic and semi-aquatic connectivity among lakes. Methods: We used principal component analysis to calculate aquatic and semi-aquatic connectivity scores for lakes. We then examined relationships among aquatic and semi-aquatic connectivity scores and existing protected areas (strict and multi-use). Results: Fewer than 3% of lakes had high scores for either aquatic or semi-aquatic connectivity. Connectivity scores were generally higher in Michigan's Upper Peninsula, which is heavily forested with greater land protection. Although lake protection was overall low (16 and 32% of lake watersheds in Michigan were ≥ 10% protected under strict and multi-use protection, respectively), highly connected lakes were generally more protected than less connected lakes. Conclusions: We propose using our aquatic and semi-aquatic connectivity framework to (1) identify and prioritize lakes for conservation that are likely to have high biodiversity and conservation value and (2) generate testable hypotheses for studying the integrated terrestrial-aquatic landscape under global change. [ABSTRACT FROM AUTHOR]

Although spatial and temporal variation in ecological properties has been well‐studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation – spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Scientific research—especially high-impact research—is increasingly being performed in teams that are interdisciplinary and demographically diverse. Nevertheless, very little research has investigated how the climate on these diverse science teams affects data sharing or the experiences of their members. To address these gaps, we conducted a quantitative study of 266 scientists from 105 NSF-funded interdisciplinary environmental science teams. We examined how team climate mediates the associations between team diversity and three outcomes: satisfaction with the team, satisfaction with authorship practices, and perceptions of the frequency of data sharing. Using path analyses, we found that individuals from underrepresented groups perceived team climate more negatively, which was associated with lower satisfaction with the team and more negative perceptions of authorship practices and data sharing on the team. However, individuals on teams with more demographic diversity reported a more positive climate than those on teams with less demographic diversity. These results highlight the importance of team climate, the value of diverse teams for team climate, and barriers to the full inclusion and support of individuals from underrepresented groups in interdisciplinary science teams. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many problems facing society and the environment need ecologists to use increasingly larger volumes and heterogeneous types of data and approaches designed to harness such data—that is, data-intensive science. In the present article, we argue that data-intensive science will be most successful when used in combination with open science and team science. However, there are cultural barriers to adopting each of these types of science in ecology. We describe the benefits and cultural barriers that exist for each type of science and the powerful synergies realized by practicing team science and open science in conjunction with data-intensive science. Finally, we suggest that each type of science is made up of myriad practices that can be aligned along gradients from low to high level of adoption and advocate for incremental adoption of each type of science to meet the needs of the project and researchers. [ABSTRACT FROM AUTHOR]

Aim: We aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi‐scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them. Location and time period: We studied > 8,500 lakes in a 1.8 million km2 area of Northeast U.S.A. Data comprised 10‐year medians (2002–2011) for measured ecosystem properties, long‐term climate averages and recent land use/land cover variables. Major taxa studied: We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity. Methods: We quantified spatial structure in ecosystem properties and their geographical driver variables using distance‐based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties. Results: The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns. Main conclusion: Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi‐scaled environmental changes on ecosystem properties. [ABSTRACT FROM AUTHOR]

As scientific teams in academia have become increasingly large, interdisciplinary, and diverse, more attention has been paid to honorary authorship (i.e., giving authorship to those not making a significant contribution). Our study examined whether honorary authorship occurs because of the desire to include all or many team members. Interviews with project principal investigators (n = 6) and early-career project members (n = 6) from 6 interdisciplinary environmental science research teams revealed that principal investigators frequently employed inclusion-motivated honorary authorship but that this practice had some negative impacts on early-career team members with less power and status, thereby undermining true inclusion of those from underrepresented groups. We believe our findings are of import not only for environmental scientists, but also for scholars who are interested in issues of authorship decision-making regardless of disciplinary affiliation. [ABSTRACT FROM AUTHOR]

Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well-developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual--empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi-disciplinary tool that captures diverse processes that operate at multiple scales, including land-management decision-making, hydrologic-solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross-disciplinary dialogue to move CNHS lake-catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision-making and policy. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

The United States (U.S.) has faced major environmental changes in recent decades, including agricultural intensification and urban expansion, as well as changes in atmospheric deposition and climate-all of which may influence eutrophication of freshwaters. However, it is unclear whether or how water quality in lakes across diverse ecological settings has responded to environmental change. We quantified water quality trends in 2913 lakes using nutrient and chlorophyll (Chl) observations from the Lake Multi-Scaled Geospatial and Temporal Database of the Northeast U.S. ( LAGOS- NE), a collection of preexisting lake data mostly from state agencies. LAGOS- NE was used to quantify whether lake water quality has changed from 1990 to 2013, and whether lake-specific or regional geophysical factors were related to the observed changes. We modeled change through time using hierarchical linear models for total nitrogen ( TN), total phosphorus ( TP), stoichiometry ( TN: TP), and Chl. Both the slopes (percent change per year) and intercepts (value in 1990) were allowed to vary by lake and region. Across all lakes, TN declined at a rate of 1.1% year−1, while TP, TN: TP, and Chl did not change. A minority (7%-16%) of individual lakes had changing nutrients, stoichiometry, or Chl. Of those lakes that changed, we found differences in the geospatial variables that were most related to the observed change in the response variables. For example, TN and TN: TP trends were related to region-level drivers associated with atmospheric deposition of N; TP trends were related to both lake and region-level drivers associated with climate and land use; and Chl trends were found in regions with high air temperature at the beginning of the study period. We conclude that despite large environmental change and management efforts over recent decades, water quality of lakes in the Midwest and Northeast U.S. has not overwhelmingly degraded or improved. [ABSTRACT FROM AUTHOR]

Understanding the factors that affect water quality and the ecological services provided by freshwater ecosystems is an urgent global environmental issue. Predicting how water quality will respond to global changes not only requires water quality data, but also information about the ecological context of individual water bodies across broad spatial extents. Because lake water quality is usually sampled in limited geographic regions, often for limited time periods, assessing the environmental controls of water quality requires compilation of many data sets across broad regions and across time into an integrated database. LAGOS-NE accomplishes this goal for lakes in the northeastern-most 17 US states. LAGOS-NE contains data for 51 101 lakes and reservoirs larger than 4 ha in 17 lake-rich US states. The database includes 3 data modules for: lake location and physical characteristics for all lakes; ecological context (i.e., the land use, geologic, climatic, and hydrologic setting of lakes) for all lakes; and in situ measurements of lake water quality for a subset of the lakes from the past 3 decades for approximately 2600-12 000 lakes depending on the variable. The database contains approximately 150 000 measures of total phosphorus, 200 000 measures of chlorophyll, and 900 000 measures of Secchi depth. The water quality data were compiled from 87 lake water quality data sets from federal, state, tribal, and non-profit agencies, university researchers, and citizen scientists. This database is one of the largest and most comprehensive databases of its type because it includes both in situ measurements and ecological context data. Because ecological context can be used to study a variety of other questions about lakes, streams, and wetlands, this database can also be used as the foundation for other studies of freshwaters at broad spatial and ecological scales. [ABSTRACT FROM AUTHOR]

In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics. [ABSTRACT FROM PUBLISHER]

Inland water bodies and their surface hydrologic connections are active components in the landscape, influencing multiple ecological processes that can propagate to broad-scale phenomena such as regional nutrient and carbon cycles and metapopulation dynamics. However, while lake, wetland, and stream abundance has been estimated at regional and global extents, less attention has been paid to freshwater connectivity attributes among aquatic systems at macroscales. Thus, regional to continental patterns of freshwater abundance and connectivity are poorly understood. We measured lake, wetland, and stream abundance and surface connectivity attributes (i.e., landscape position within stream networks) at a subcontinental extent in the Midwest and Northeast United States to characterize macroscale spatial patterns of the freshwater landscape (i.e., abundance and connectivity attributes of lakes, wetlands, and streams). We found that lake and wetland abundance exhibited opposite spatial patterns from stream density that generally followed glaciation extent boundaries--lake and wetland abundance was high north of the glaciation boundary, whereas stream density was high south of the glaciation boundary. Freshwater connectivity attributes exhibited distinct spatial patterns as defined by our integrated freshwater clusters and revealed a layer of complexity not captured by abundance measures. Patterns of freshwater abundance and connectivity in the study extent were associated primarily with glaciation and secondarily with hydrogeomorphic (e.g., surficial geology and topography), climate (e.g., runoff), and land-use (e.g., agriculture) variables, providing insight into potential drivers of freshwater composition and distribution. The connectivity spatial patterns observed suggest that relying solely on freshwater abundance measures in macroscale analyses omits unique information on the structural attributes of freshwater systems that can be critical to key ecological processes. Adopting an integrated freshwater landscape framework to study and manage freshwaters is essential as freshwater systems face broad-scale disturbances that may alter hydrologic connections and subsequently may impact ecosystem processes and services. [ABSTRACT FROM AUTHOR]

Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry. Our results suggest ecological context mediates controls on lake nutrients and stoichiometry. Predicting stoichiometry was generally more difficult than predicting nutrient concentrations, but human activity may decouple N and P, leading to better prediction of N:P stoichiometry in regions with high anthropogenic activity. [ABSTRACT FROM AUTHOR]

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question-How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km2); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation-approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications. [ABSTRACT FROM AUTHOR]

Overinclusive authorship practices such as honorary or guest authorship have been widely reported, and they appear to be exacerbated by the rise of large interdisciplinary collaborations that make authorship decisions particularly complex. Although many studies have reported on the frequency of honorary authorship and potential solutions to it, few have probed how the underlying dynamics of large interdisciplinary teams contribute to the problem. This article reports on a qualitative study of the authorship standards and practices of six National Science Foundation-funded interdisciplinary environmental science teams. Using interviews of the lead principal investigator and an early-career member on each team, our study explores the nature of honorary authorship practices as well as some of the motivating factors that may contribute to these practices. These factors include both structural elements (policies and procedures) and cultural elements (values and norms) that cross organizational boundaries. Therefore, we provide recommendations that address the intersection of these factors and that can be applied at multiple organizational levels. [ABSTRACT FROM PUBLISHER]

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals. [ABSTRACT FROM AUTHOR]

Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin’s research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms. [ABSTRACT FROM AUTHOR]

Macroscale studies of ecological phenomena are increasingly common because stressors such as climate and land-use change operate at large spatial and temporal scales. Cross-scale interactions (CSIs), where ecological processes operating at one spatial or temporal scale interact with processes operating at another scale, have been documented in a variety of ecosystems and contribute to complex system dynamics. However, studies investigating CSIs are often dependent on compiling multiple data sets from different sources to create multithematic, multiscaled data sets, which results in structurally complex, and sometimes incomplete data sets. The statistical power to detect CSIs needs to be evaluated because of their importance and the challenge of quantifying CSIs using data sets with complex structures and missing observations. We studied this problem using a spatially hierarchical model that measures CSIs between regional agriculture and its effects on the relationship between lake nutrients and lake productivity. We used an existing large multithematic, multiscaled database, LAke multiscaled GeOSpatial, and temporal database (LAGOS), to parameterize the power analysis simulations. We found that the power to detect CSIs was more strongly related to the number of regions in the study rather than the number of lakes nested within each region. CSI power analyses will not only help ecologists design large-scale studies aimed at detecting CSIs, but will also focus attention on CSI effect sizes and the degree to which they are ecologically relevant and detectable with large data sets. [ABSTRACT FROM AUTHOR]

Catchment land uses, particularly agriculture and urban uses, have long been recognized as major drivers of nutrient concentrations in surface waters. However, few simple models have been developed that relate the amount of catchment land use to downstream freshwater nutrients. Nor are existing models applicable to large numbers of freshwaters across broad spatial extents such as regions or continents. This research aims to increase model performance by exploring three factors that affect the relationship between land use and downstream nutrients in freshwater: the spatial extent for measuring land use, hydrologic connectivity, and the regional differences in both the amount of nutrients and effects of land use on them. We quantified the effects of these three factors that relate land use to lake total phosphorus (TP) and total nitrogen (TN) in 346 north temperate lakes in 7 regions in Michigan, USA. We used a linear mixed modeling framework to examine the importance of spatial extent, lake hydrologic class, and region on models with individual lake nutrients as the response variable, and individual land use types as the predictor variables. Our modeling approach was chosen to avoid problems of multi-collinearity among predictor variables and a lack of independence of lakes within regions, both of which are common problems in broad-scale analyses of freshwaters. We found that all three factors influence land use-lake nutrient relationships. The strongest evidence was for the effect of lake hydrologic connectivity, followed by region, and finally, the spatial extent of land use measurements. Incorporating these three factors into relatively simple models of land use effects on lake nutrients should help to improve predictions and understanding of land use-lake nutrient interactions at broad scales. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor gives the information about being selected as the founding editor in chief for American Society of Limnology and Oceanography ASLO's new journal, Limnology and Oceanography Letters.

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km²). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration. [ABSTRACT FROM AUTHOR]

Although there have been many recent calls for increased data sharing, the majority of environmental scientists do not make their individual data sets publicly available in online repositories. Current data-sharing conversations are focused on overcoming the technological challenges associated with data sharing and the lack of rewards and incentives for individuals to share data. We argue that the most important conversation has yet to take place: There has not been a strong ethical impetus for sharing data within the current culture, behaviors, and practices of environmental scientists. In this article, we describe a critical shift that is happening in both society and the environmental science community that makes data sharing not just good but ethically obligatory. This is a shift toward the ethical value of promoting inclusivity within and beyond science. An essential element of a truly inclusionary and democratic approach to science is to share data through publicly accessible data sets. [ABSTRACT FROM PUBLISHER]

Agent-based models (ABMs) have been widely used to study socioecological systems. They are useful for studying such systems because of their ability to incorporate micro-level behaviors among interacting agents, and to understand emergent phenomena due to these interactions. However, ABMs are inherently stochastic and require proper handling of uncertainty. We propose a simulation framework based on quantitative uncertainty and sensitivity analyses to build parsimonious ABMs that serve two purposes: exploration of the outcome space to simulate low-probability but high-consequence events that may have significant policy implications, and explanation of model behavior to describe the system with higher accuracy. The proposed framework is applied to the problem of modeling farmland conservation resulting in land use change. We employ output variance decomposition based on quasi-random sampling of the input space and perform three computational experiments. First, we perform uncertainty analysis to improve model legitimacy, where the distribution of results informs us about the expected value that can be validated against independent data, and provides information on the variance around this mean as well as the extreme results. In our last two computational experiments, we employ sensitivity analysis to produce two simpler versions of the ABM. First, input space is reduced only to inputs that produced the variance of the initial ABM, resulting in a model with output distribution similar to the initial model. Second, we refine the value of the most influential input, producing a model that maintains the mean of the output of initial ABM but with less spread. These simplifications can be used to 1) efficiently explore model outcomes, including outliers that may be important considerations in the design of robust policies, and 2) conduct explanatory analysis that exposes the smallest number of inputs influencing the steady state of the modeled system. [ABSTRACT FROM AUTHOR]

Ecologists are increasingly discovering that ecological processes are made up of components that are multi-scaled in space and time. Some of the most complex of these processes are cross-scale interactions (CSIs), which occur when components interact across scales. When undetected, such interactions may cause errors in extrap-olation from one region to another. CSIs, particularly those that include a regional scaled component, have not been systematically investigated or even reported because of the challenges of acquiring data at sufficiently broad spatial extents. We present an approach for quantifying CSIs and apply it to a case study investigating one such interaction, between local and regional scaled land-use drivers of lake phosphorus. Ultimately, our approach for investigating CSIs can serve as a basis for efforts to understand a wide variety of multi-scaled prob-lems such as climate change, land-use/land-cover change, and invasive species. [ABSTRACT FROM AUTHOR]

Interdisciplinary collaboration is essential to understand ecological systems at scales critical to human decision making. Current reward structures are problematic for scientists engaged in interdisciplinary research, particu-larly early career researchers, because academic culture tends to value only some research outputs, such as pri-mary-authored publications. Here, we present a framework for the costs and benefits of collaboration, with a focus on early career stages, and show how the implementation of novel measures of success can help defray the costs of collaboration. Success measures at team and individual levels include research outputs other than publications, including educational outcomes, dataset creation, outreach products (eg blogs or social media), and the application of scientific results to policy or management activities. Promotion and adoption of new measures of success will require concerted effort by both collaborators and their institutions. Expanded mea-sures should better reflect and reward the important work of both disciplinary and interdisciplinary teams at all career stages, and help sustain and stimulate a collaborative culture within ecology. [ABSTRACT FROM AUTHOR]

Collaborative research teams are a necessary and desirable component of most scientific endeavors. Effective col-laborative teams exhibit important research outcomes, far beyond what could be accomplished by individuals working independently. These teams are made up of researchers who are committed to a common purpose, approach, and performance goals for which they hold themselves mutually accountable. We call such collabora-tions "high-performing collaborative research teams". Here, we share lessons learned from our collective experi-ence working with a wide range of collaborative teams and structure those lessons within a framework developed from literature in business, education, and a relatively new discipline, "science of team science". We propose that high-performing collaborative research teams are created and maintained when team diversity (broadly defined) is effectively fostered and interpersonal skills are taught and practiced. Finally, we provide some strategies to fos-ter team functioning and make recommendations for improving the collaborative culture in ecology. [ABSTRACT FROM AUTHOR]

An important goal of macrosystems ecology (MSE) research is to advance understanding of ecological systems at both fine and broad temporal and spatial scales. Our premise in this paper is that MSE projects require inte-grated information management at their inception. Such efforts will lead to improved communication and sharing of knowledge among diverse project participants, better science outcomes, and more transparent and accessible (ie "open") science. We encourage researchers to "complete the data life cycle" by publishing well-documented datasets, thereby facilitating re-use of the data to answer new and different questions from the ones conceived by those involved in the original projects. The practice of documenting and submitting datasets to data repositories that are publicly accessible ensures that research results and data are available to and use-able by other researchers, thus fostering open science. However, ecologists are often unfamiliar with the require-ments and information management tools for effectively preserving data and receive little institutional or pro-fessional incentive to do so. Here, we provide recommendations for achieving these ends and give examples from current MSE projects to demonstrate why information management is critical for ensuring that scientific results can be reproduced and that data can be shard for future use. [ABSTRACT FROM AUTHOR]

Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisci-plines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents. [ABSTRACT FROM AUTHOR]

Efforts toward systematic conservation planning for freshwaters have progressed less than similar efforts in terrestrial and marine environments. Although there are important differences, many of the tools that have been used in terrestrial and marine systems can also be used for freshwater conservation planning. In this paper, we used return on investment (ROI) analysis to identify optimal riparian land conservation portfolios under different objectives. Our overarching research question is: how do different strategies for evaluating ROI objectives influence the resulting conservation portfolio and the outcome of interest, lake water quality? We examined agricultural riparian land investments to improve water quality in 55 inland lakes in southwestern Michigan. We simulated investments and compared the ROIs for the following strategies: (1) economic; (2) ecological; and (3) environmental policy. Furthermore, as a reference point, we also compared the resulting three conservation portfolios to that derived by simulating riparian land enrollment under the Conservation Reserve Program. We found that (1) investments in freshwater resources through the conservation of riparian land were more effective than the conservation of randomly selected parcels of similar land area in the catchment; (2) the costs and benefits of riparian land conservation varied considerably among lakes; (3) the choice of investment objectives, and therefore ROI strategies, resulted in very different conservation portfolios; (4) the simulated enrollment process of the Conservation Reserve Program fared poorly in terms of improving lake water quality as compared to the three strategies. [ABSTRACT FROM AUTHOR]