Katherine Emerson Webster

Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for a causal effect of climate or the nonlinear dynamics needed to demonstrate regime shifts. We modeled the causal effects of climate on annual lake chlorophyll (a measure of algal biomass) over 34 y for 24,452 lakes across broad ecoclimatic zones of the United States and evaluated the potential for regime shifts. We found that algal biomass was causally related to climate in 34% of lakes. In these cases, 71% exhibited abrupt but mostly temporary shifts as opposed to persistent changes, 13% had the potential for regime shifts. Climate was causally related to algal biomass in lakes experiencing all levels of human disturbance, but with different likelihood. Climate causality was most likely to be observed in lakes with minimal human disturbance and cooler summer temperatures that have increased over the 34 y studied. Climate causality was variable in lakes with low to moderate human disturbance, and least likely in lakes with high human disturbance, which may mask climate causality. Our results explain some of the previously observed heterogeneous climate responses of lake algal biomass globally and they can be used to predict future climate effects on lakes.

Broad-scale, long-term water quality (WQ) studies are critical for understanding increasing pressures on inland waters but remain rare due to cost. The LAGOS-US LANDSAT dataset provides 37-year remote sensing-derived WQ estimates for thousands of U.S. lakes > 4 ha (1984–2020). WQ estimates use machine-learning models with Landsat imagery and ground-truthed Water Quality Portal data (LAGOS-US LIMNO). The dataset includes: (a) 45.9 million whole-lake water reflectance (six bands and 15 band ratios); (b) 740,627 matchups from 13,756 lakes with in situ data for six WQ variables: chlorophyll, Secchi depth, true color, dissolved organic carbon, total suspended solids, or turbidity; and (c) predictions for each WQ variable across lake-time combinations with quality imagery. Two random forest models were fit for each variable: Holdout-data (75/25 spatially representative train-test split) and Full-data (trained on all data). Variance explained for the Full-data predictions ranged from 20.7% for TSS to 63.7% for Secchi depth. Imagery underwent cloud and pixel quality control, and workflow components were validated guiding future research.

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.

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.

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.

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