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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]

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]

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]

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]

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]

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]

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]

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]

Summary 1. For north temperate lakes, the well-studied empirical relationship between phosphorus (as measured by total phosphorus, TP), the most commonly limiting nutrient and algal biomass (as measured by chlorophyll a, CHL) has been found to vary across a wide range of landscape settings. Variation in the parameters of these TP-CHL regressions has been attributed to such lake variables as nitrogen/phosphorus ratios, organic carbon and alkalinity, all of which are strongly related to catchment characteristics (e.g. natural land cover and human land use). Although this suggests that landscape setting can help to explain much of the variation in ecoregional TP-CHL regression parameters, few studies have attempted to quantify relationships at an ecoregional spatial scale. 2. We tested the hypothesis that lake algal biomass and its predicted response to changes in phosphorus are related to both local-scale features (e.g. lake and catchment) and ecoregional-scale features, all of which affect the availability and transport of covarying solutes such as nitrogen, organic carbon and alkalinity. Specifically, we expected that land use and cover, acting at both local and ecoregional scales, would partially explain the spatial pattern in parameters of the TP-CHL regression. 3. We used a multilevel modelling framework and data from 2105 inland lakes spanning 35 ecoregions in six US states to test our hypothesis and identify specific local and ecoregional features that explain spatial heterogeneity in TP-CHL relationships. We include variables such as lake depth, natural land cover (for instance, wetland cover in the catchment of lakes and in the ecoregions) and human land use (for instance, agricultural land use in the catchment of lakes and in the ecoregions). 4. There was substantial heterogeneity in TP-CHL relationships across the 35 ecoregions. At the local scale, CHL was negatively and positively related to lake mean depth and percentage of wooded wetlands in the catchment, respectively. At the ecoregional scale, the slope parameter was positively related to the percentage of pasture in an ecoregion, indicating that CHL tends to respond more rapidly to changes in TP where there are high levels of agricultural pasture than where there is little. The intercept (i.e. the ecoregion-average CHL) was negatively related to the percentage of wooded wetlands in the ecoregion. 5. By explicitly accounting for the hierarchical nature of lake-landscape interactions, we quantified the effects of landscape characteristics on the response of CHL to TP at two spatial scales. We provide new insight into ecoregional drivers of the rate at which algal biomass responds to changes in nutrient concentrations. Our results also indicate that the direction and magnitude of the effects of certain land use and cover characteristics on lake nutrient dynamics may be scale dependent and thus likely to represent different underlying mechanisms regulating lake productivity. [ABSTRACT FROM AUTHOR]

Abstract: We examined the ability of lake and landscape features to predict a variety of macrophyte cover metrics using 54 north temperate lakes. We quantified submersed cover, emergent cover, floating leaf cover, Eurasian watermilfoil cover and total macrophyte cover. Measured lake features included lake physio-chemical and morphometric variables and landscape features included hydrologic, catchment and land use/cover variables. Univariate regression analyses demonstrated that these macrophyte cover metrics are predicted by a wide range of predictor variables, most commonly by: Secchi disk depth, maximum or mean depth, catchment morphometry, road density and the proportion of urban or agricultural land use/cover in the riparian zone or catchment (r 2 =0.06–0.46). Using a combination of lake and landscape features in multiple regressions, we were able to explain 29–55% of the variation in macrophyte cover metrics. Total macrophyte cover and submersed cover were related to Secchi disk depth and mean depth, whereas the remaining metrics were best predicted by including at least one land use/cover variable (road density, proportion local catchment agriculture land use/cover, proportion cumulative catchment urban land use/cover, or proportion riparian agriculture land use/cover). The two main conclusions from our research are: (1) that different macrophyte growth forms and species are predicted by a different suite of variables and thus should be examined separately, and (2) that anthropogenic landscape features may override patterns in natural landscape or local features and are important in predicting present-day macrophytes in lakes. [Copyright &y& Elsevier]

• The limitation of TP and TN on the phytoplankton was explored at a macroscale. • Bayesian network implicitly accounts for impact of spatial heterogeneous. • Generally, TP played a more important role than TN. • Both TP and TN are important for hypereutrophic lakes. • The results contribute to the ongoing debate of P vs. N limitation in inland lakes. The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes. [ABSTRACT FROM AUTHOR]

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 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]

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]

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]

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]

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]

We quantified relationships between local wetland cover in the riparian lake buffer and lake total phosphorus (TP) and water color (color) using multilevel mixed-effects models that also incorporate landscape features such as hydrogeomorphology and land use at broad regional scales to determine the following: (1) Within regions, are local wetland relationships with TP and color affected by interactions with local land use or hydrogeomorphic variables? (2) Across regions, are local wetland relationships with TP and color different? And if so, (3) Are differences in local wetland relationships with TP and color a result of cross-scale interactions? We answered these questions by analyzing TP, color, and multiscaled landscape data for 1790 north temperate lakes. Local wetland-TP and wetland-color relationships were not affected by local-scale interactions. However, these same relationships were different when compared across regions, and these differences were related to cross-scale interactions with regional landscape characteristics. For example, regional human land use affected local wetland-TP relationships such that in regions with high amounts of agriculture, local wetlands were associated with decreased lake TP. However, in regions with low amounts of agriculture, local wetlands were associated with increased lake TP. In contrast, regional hydrogeomorphic characteristics influenced local wetland-color relationships such that in regions with high groundwater contribution, the strength of local wetland relationships were weak. Regional landscape setting influences local wetland relationships with TP and color through cross-scale interactions, and lake TP and color are controlled by both local-scale wetland extent and regional-scale landscape variables. [ABSTRACT FROM AUTHOR]

To improve our understanding of lake landscape position, we compared four metrics based on different aspects of lake surface hydrologic connections: (1) "lake hydrology," which is a general measure of lake surface hydrologic position, (2) "lake order," which measures connections to streams by stream order, (3) "lake network number," which measures connections to other lakes, and (4) "lake network complexity," which measures the complexity of connections to other lakes (in a chain or branched). We sampled 71 lakes in northern Michigan, U.S.A. and measured lake landscape position and landscape characteristics around each lake (e.g., land use/cover and geology) to answer two questions: (1) which metric of landscape position explains the most variation in lake water chemistry/clarity? and (2) what landscape and physical features are also related to landscape position? All four landscape position metrics explained significant variation in some water chemistry/clarity variables. However, lake order, the metric based on stream order, consistently explained the most variation, ranging from 22% (dissolved organic carbon) to 53% (conductivity and calcium), with lake hydrology, the metric based on both streams and lakes, explaining similar amounts of variation to lake order, but less overall. Landscape position was also significantly related to both lake morphometry and the proportion of wetland types in buffer areas, which may help explain why landscape position is related to lake water chemistry and clarity variables. [ABSTRACT FROM AUTHOR]

1. Within a lake district of relatively homogeneous geomorphology, the responses of lakes to climate are influenced by the complexity of the hydrogeologic setting, position in the landscape, and lake-specific biological and physical features. We examined lake chemical responses to drought in surface water- and groundwater-dominated districts to address two general questions. (1) Are spatial patterns in chemical dynamics among lakes uniform and synchronous within a lake district, suggesting broad geomorphic controls; variable in a spatially explicit pattern, with synchrony related to landscape position, suggesting hydrologic flowpath controls; or spatially unstructured and asynchronous, suggesting overriding control by lake-specific factors? (2) Are lake responses to drought a simple function of precipitation quantity or are they dictated by more complex interactions among climate, unique lake features, and hydrologic setting? 2. Annual open-water means for epilimnetic concentrations of chloride, calcium, sulfate, ANC, DOC, total nitrogen, silica, total phosphorus, and chlorophyll a measured between 1982 and 1995 were assembled for lakes in the Red Lake and ELA districts of north-western Ontario, the Muskoka – Dorset district in south-central Ontario, and the Northern Highland district of Wisconsin. Within each district, we compared responses of lakes classified by landscape position into highland or lowland, depending on relative location within the local to regional hydrologic flow system. Synchrony, defined as a measure of the similarity in inter-annual dynamics among lakes within a district, was quantified as the Pearson product-moment correlation (r) between two lakes with observations paired by year. To determine if solute concentrations were directly related to interannual variations in precipitation quantity, we used regression analysis to fit district-wide slopes describing the relationship between each chemical variable and annual (June to May) and October to May (Oct–May) precipitation. 3. Among lakes in each of the three Ontario districts, the pattern of chemical response to interannual shifts in precipitation was spatially uniform. In these surface water- dominated districts, solute concentrations were generally a simple function of precipitation. Conservative solutes, like calcium and chloride, tended to be more synchronous and were negatively related to precipitation. Solutes such as silica, total phosphorus, and chlorophyll a, which are influenced by in-lake processes, were less synchronous and relationships with precipitation tended to be positive or absent. 4. In the groundwater-dominated Northern Highland lakes of Wisconsin, we observed spatial structure in drought response, with lowland lakes more synchronous than highland lakes. However, there was no evidence for a direct relationship between any solute and precipitation. Instead, increases in the concentration of the conservative ion calcium during drought were not followed by a symmetrical return to pre-drought conditions when precipitation returned to normal or above-average values. 5. For calcium, time lags in recovery from drought appeared related to hydrologic features in a complex way. In the highland Crystal Lake, calcium concentrations tracked lake stage inversely, with a return to pre-drought concentrations and lake stage five years after the drought. This pattern suggests strong evaporative controls. In contrast, after five years of normal precipitation, calcium in the lowland Sparkling Lake had not returned to pre-drought conditions despite a rebound in lake stage. This result suggests that calcium concentrations in lowland lakes were controlled more by regional groundwater flowpaths, which track climatic signals more slowly. 6. Temporal dynamics driven by climate were most similar among lakes in districts that have a relatively simple hydrology, such as ELA. Where hydrologic setting was more complex, as in the groundwater-dominated Northern Highland of Wisconsin, the expression of climate signals in lakes showed lags and spatial patterns related to landscape position. In general, we expect that landscape and lake-specific factors become increasingly important in lake districts with more heterogeneous hydrogeology, topography or land use. These strong chemical responses to climate need to be considered when interpreting the responses of lakes to other regional disturbances. [ABSTRACT FROM AUTHOR]

Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world. [ABSTRACT FROM AUTHOR]

Abstract: In regions with thousands of lakes, large scale regional macrophyte surveys are rarely done due to logistical difficulties and high costs. We examined whether remote sensing can be used for regional monitoring of macrophytes in inland lakes using a field study of 13 lakes in Michigan, USA (nine model development lakes and four model testing lakes). Our objectives were: (1) to determine if different levels of macrophyte cover, different growth forms or specific species could be detected using the Landsat-5 TM sensor, and (2) to determine if we could improve predictions of macrophyte abundance and distribution in lakes by including sediment type or measures of water clarity (Secchi disk transparency, chlorophyll a, phytoplankton biovolume, or water color) in our models. Using binomial and multinomial logistic regression models, we found statistically significant relationships between most macrophyte measures and Landsat-5 TM values in the nine model development lakes (percent concordant values: 58–97%). Additionally, we found significant correlations between three lake characteristics and the TM values within lake pelagic zones, despite the inability of these variables to improve model predictions. However, model validation using four lakes was generally low, suggesting caution in applying these models to other lakes. Although the initial model development results suggest that remote sensing is a potentially promising tool for regionally assessing macrophytes, more research is necessary to refine the models in order for them to be applied to unsampled lakes. [Copyright &y& Elsevier]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Ecologists increasingly recognize the need to understand how landscapes and food webs interact. Reservoir ecosystems are heavily subsidized by nutrients and detritus from surrounding watersheds, and often contain abundant populations of gizzard shad, an omnivorous fish that consumes plankton and detritus. Gizzard shad link terrestrial landscapes and pelagic reservoir food webs by consuming detritus, translocating nutrients from sediment detritus to the water column, and consuming zooplankton. The abundance of gizzard shad increases with watershed agriculturalization, most likely through a variety of mechanisms operating on larval and adult life stages. Gizzard shad have myriad effects on reservoirs, including impacts on nutrients, phytoplankton, zooplankton, and fish, and many of their effects vary with ecosystem productivity (i.e., watershed land use). Interactive feedbacks among watersheds, gizzard shad populations, and reservoir food webs operate to maintain dominance of gizzard shad in highly productive systems. Thus, effective stewardship of reservoir ecosystems must incorporate both watershed and food-web perspectives. [ABSTRACT FROM AUTHOR]

Using cross-sectional data for making ecological inference started as a practical means of pooling data to enable meaningful empirical model development. For example, limnologists routinely use sample averages from numerous individual lakes to examine patterns across lakes. The basic assumption behind the use of cross-lake data is often that responses within and across lakes are identical. As data from multiple study units across a wide spatiotemporal scale are increasingly accessible for researchers, an assessment of this assumption is now feasible. In this study, we demonstrate that this assumption is usually unjustified, due largely to a statistical phenomenon known as the Simpson's paradox. Through comparisons of a commonly used empirical model of the effect of nutrients on algal growth developed using several data sets, we discuss the cognitive importance of distinguishing factors affecting lake eutrophication operating at different spatial and temporal scales. Our study proposes the use of the Bayesian hierarchical modeling approach to properly structure the data analysis when data from multiple lakes are employed. • Simpson's paradox affects large data analysis due to confounding factors. • Hierarchical model is shown to be useful through analyzing large lake datasets. • Hierarchical model is also valuable for site/source-specific data analysis. • Bayesian interpretation of Stein's paradox solves Simpson's paradox problems. [ABSTRACT FROM AUTHOR]

We examined spatial patterns of trends in ice phenology and duration for 65 waterbodies across the Great Lakes region (Minnesota, Wisconsin, Michigan, Ontario, and New York) during a recent period of rapid climate warming (1975-2004). Average rates of change in freeze (3.3 d decade-1) and breakup (22.1 d decade-1) dates were 5.8 and 3.3 times more rapid, respectively, than historical rates (1846-1995) for Northern Hemisphere waterbodies. Average ice duration decreased by 5.3 d decade-1. Over the same time period, average fall through spring temperatures in this region increased by 0.7°C decade-1, while the average number of days with snow decreased by 5.0 d decade21, and the average snow depth on those days decreased by 1.7 cm decade-1. Breakup date and ice duration trends varied over the study area, with faster changes occurring in the southwest. Trends for each site were compared to static waterbody characteristics and meteorological variables and their trends. The trend toward later freeze date was stronger in large, low-elevation waterbodies; however, freeze date trends had no geographic patterns or relationships to meteorological variables. Variability in the strength of trends toward earlier breakup was partially explained by spatial differences in the rate of change in the number of days with snow cover, mean snow depth, air temperature (warmer locations showed stronger trends), and rate of change in air temperature. Differences in ice duration trends were explained best by a combination of elevation and the local rate of change in either temperature or the number of days with snow cover. [ABSTRACT FROM AUTHOR]