Approaches for advancing scientific understanding of macrosystems

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological patterns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require validation, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them

Approaches to advance scientific understanding of macrosystems ecology

The emergence of macrosystems ecology (MSE), which focuses on regional- to continental-scale ecological pat- terns and processes, builds upon a history of long-term and broad-scale studies in ecology. Scientists face the difficulty of integrating the many elements that make up macrosystems, which consist of hierarchical processes at interacting spatial and temporal scales. Researchers must also identify the most relevant scales and variables to be considered, the required data resources, and the appropriate study design to provide the proper inferences. The large volumes of multi-thematic data often associated with macrosystem studies typically require valida- tion, standardization, and assimilation. Finally, analytical approaches need to describe how cross-scale and hierarchical dynamics and interactions relate to macroscale phenomena. Here, we elaborate on some key methodological challenges of MSE research and discuss existing and novel approaches to meet them

Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse

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: LAGOS[subscript]GEO , 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 LAGOS[subscript]LIMNO , 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.Keywords: LAGOS, Integrated database, Data harmonization, Database Ecoinformatics, Macrosystems ecology, Landscape limnology, Water qualityKeywords: LAGOS, Integrated database, Ecoinformatics, Data harmonization, Water quality, Data sharing, Landscape limnology, Macrosystems ecology, Database documentation, Data reus

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Data from: Multi-scaled drivers of ecosystem state: quantifying the importance of the regional spatial scale

The regional spatial scale is a vital linkage for the informed extrapolation of results from local to continental scales to address broad-scale environmental problems. Among-region variation in ecosystem state is commonly accounted for by using a regionalization framework, such as an ecoregion classification. Rarely have alternative regionalization frameworks been tested for variables measuring ecosystem state, nor have the underlying relationships with the variables that are used to define them been assessed. In this study, we asked two questions: (1) How much among-region variation is there for ecosystems and does it differ by regionalization framework? (2) What are the likely causes of this among-region variation? We present a case study using a large data set of lake water chemistry, uni- and multi-scaled hydrogeomorphic and anthropogenic driver variables that likely influence lake chemistry at the subcontinental scale, and seven existing regionalization frameworks. We used multilevel models to quantify and explain within- and among-region variation in lake water chemistry. Our models account for local driver variables of ecosystem variation within regions, differences in regional mean ecosystem state (i.e., random intercepts in multilevel models), and differences in relationships between local drivers and ecosystem state by region (i.e., random slopes in multilevel models). Using one of the best performing regionalization frameworks (Ecological Drainage Units), we found that for lake phosphorus and alkalinity: (1) a majority of all the variation in water chemistry among the studied lakes occurred among regions, (2) very few regional-scale landscape driver variables were required to explain among-region variation in lake water chemistry, (3) a much higher proportion of the total variation among lakes was explained at the regional scale than at the local scale, and (4) some relationships between local-scale driver variables and lake water chemistry varied by region. Our results demonstrate the importance of considering the regional spatial scale for broad-scale research and ecosystem management and conservation. Our quantitative approach can be easily applied to other response variables, ecosystem types, geographic areas, and spatial extents to inform ecosystem responses to global environmental stressors

Cheruvelil EPA-NLAPP 6-state lake-landscape database

Lake data were compiled for six U.S. states: Maine (N=~593), New Hampshire (N=~651), Ohio (N=~55), Iowa (N=~117), Michigan (N=~557), and Wisconsin (N=~347). Lake water chemistry were sampled from the epilimnion. Data were collected from databases maintained by state agencies responsible for monitoring lakes under the Federal Clean Water Act, which requires standard procedures and quality assurance and quality control protocols. Lakes with surface area ≥ 1 ha and maximum depth ≥ 2 m were included in the dataset. Each lake was assigned a unique identifier. We collected data on LAKES (chemistry, clarity and depth); NATURAL LANDSCAPE FEATURES (catchment area, lake elevation, and features obtained from GIS (hydrology, runoff, precip, baseflow); and HUMAN IMPACT FEATURES (Land use/cover (NLCD) in 500m buffer around lakes, roads in 500m buffer, human census data within the smallest unit available (county subdivision). We also quantified land cover, land use, groundwater hydrology, and several other geographic variables within the Ecological Drainage Unit (EDU) region. Lake data came from the following state agency sources: Maine Department of Environmental Protection, Maine Department of Inland Fisheries and Wildlife’s Lake Survey table (1/21/03); New Hampshire Department of Environmental Services; Michigan Department of Environmental Quality; Wisconsin Department of Natural Resources; Ohio Environmental Protection Agency; and Iowa State University Limnology Laboratory (Joint Iowa DNR / ISU Project). The purpose of the dataset was to investigate lake and landscape controls on lake water chemistry across broad geographic regions

LAGOS‐US RESERVOIR: A database classifying conterminous U.S. lakes 4 ha and larger as natural lakes or reservoir lakes

Abstract 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

Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success

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, particularly early career researchers, because academic culture tends to value only some research outputs, such as primary-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 measures 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

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Abstract Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll 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