LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes

Abstract

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.

Genome evolution and host‐microbiome shifts correspond with intraspecific niche divergence within harmful algal bloom‐forming Microcystis aeruginosa

“…Lakes were selected to span a large nutrient gradient, from oligotrophic to hypereutrophic, as determined by total phosphorus concentration (TP, a widely used index of lake productivity). Our TP concentrations spanned over an order of magnitude from ~8 to 200 µg/L, which encompasses the range of TP documented in over 82% of lakes in the northeastern United States (Soranno et al, ). While we also measured lake and , neither nitrogen measure corresponded with TP (correlations not significant, both p > .10; see Table for TP, and measures per lake).…”

Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes

“…We used the LAGOS‐NE database, including data modules for geographical data (LAGOS GEO , version 1.03; LAGOS LOCUS , version 1.01) and lake ecosystem data (LAGOS LIMNO , version 1.054.1) (Soranno et al, ). We used a subset of the LAGOS‐NE database that includes any lake that has at least one record for the ecosystem variables we considered ( n = 8,744).…”

“…We used an empirical approach that takes advantage of emerging large integrated databases of ecological systems at broad scales. We use LAGOS‐NE (LAke multi‐scaled GeOSpatial and temporal), a large, multi‐scaled and multi‐themed database of> 8,000 lakes in a sub‐continental spatial extent (Soranno et al, ). Our specific objectives were as follows: (a) to quantify the spatial structure in ecosystem properties and their geographical drivers; (b) to examine whether the strength of the relationship between ecosystem and geographical variables is related to similarity in spatial structure; and (c) to compare these empirical findings with analyses of simulated data to assess the generality of our results.…”

Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales

“…Instead, researchers study the different nutrient‐productivity variables individually by developing univariate nutrient regressions, where, for example, independent TP, total nitrogen (TN), and CHL regressions are developed and modeled as a function of one or more landscape predictors and then compared (Jones et al ; Carle et al ; Chen et al ; Soranno et al ). Including additional nutrient‐productivity variables as predictors into these models can be problematic because there is often missing nutrient‐productivity data (Soranno et al ), and these nutrient‐productivity variables will likely be confounded with each other and with other landscape predictors. The missing nutrient‐productivity data can be problematic because—although accommodating missing data during analysis is a statistical issue—commonly used statistical software programs for fitting regression models require values of all predictor variables for each lake, either observed or imputed.…”