Featured Data

The Lake Trophic State - US (LTS-US) Dataset

October 17, 2023

Michael Meyer

Citation

Meyer, M.F., S.N. Topp, T.V. King, R. Ladwig, R.M. Pilla, H.A. Dugan, J.R. Eggleston, S.E. Hampton, D.M. Leech, I.A. Oleksy, J.C. Ross, M.R. Ross, R.I. Woolway, X. Yang, M.R. Brousil, K.C. Fickas, J.C. Padowski, A.I. Pollard, J. Ren, and J.A. Zwart. 2023. National-scale, remotely sensed lake trophic state (LTS-US) 1984-2020 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/212a3172ac36e8dc6e1862f9c2522fa4.

Description

This data package is an excellent example of using merging remote sensing, in situ, national-scale sampling campaigns, and discipline-specific expertise to expand the spatial and temporal scope of freshwater science.

Recently, the LTS-US has been highlighted as a U.S. Geological Survey Open Science Success Story, an initiative stemming from the White House's Office of Science and Technology's declaration of 2023 as the Year of Open Science. In particular, the LTS-US dataset's highly detailed metadata and scripts in tandem with a completely reproducible compute environment earned the LTS-US dataset accolades among USGS data librarian and curation specialists.

The LTS-US dataset brings together over 35 years of Landsat surface reflectance data with the U.S. Environmental Protection Agency's National Lake Assessment's data to both construct and apply models that discriminate between blue (i.e., oligotrophic), green (i.e., eutrophic/mixotrophic), and brown (i.e., dystrophic) lakes. By classifying lakes based on Nutrient-Color Paradigm (NCP) groupings, the LTS-US Dataset presents a powerful tool for assessing both autochthonous and allochthonous components of aquatic ecosystems, which can lend further information into an ecosystem's physical, chemical, and biological processes.

Distribution of Sulfate-S concentrations.
Figure 1: Nutrient-Color Paradigm (NCP) scheme for classifying oligotrophic, eutrophic, dystrophic, and mixotrophic lakes. Schematic is adapted from Williamson et al. (1999) and Webster et al. (2008), and characteristic lake descriptions broadly stem from results presented in Leech et al. (2018) and Oleksy et al. (2020) Within each NCP-quadrant, there are a suite of physical, chemical, and biological characteristics that distinguish each type of lake: colored Dissolved Organic Matter (cDOM), primary production, cyanobacterial abundance, and higher order production. Lower cDOM concentrations (blue water drops) are characteristic in oligotrophic and eutrophic lakes. When cDOM is low, light can transmit through the water column more deeply, allowing for primary producers to undergo photosynthesis and zooplankton to consume primary producers (oligotrophic). When nutrients, such as phosphorus, are at higher concentrations and cDOM is low (eutrophic), primary production, cyanobacterial abundance, and higher order production can all increase, resulting in increased biomass. When cDOM concentrations are high (brown water drop) and nutrient concentrations are low (dystrophic), the increased light attenuation can result in decreased primary production, which can reciprocally cause decreased higher order production. Lastly, when nutrient and cDOM concentrations are both high (mixotrophic), primary production, cyanobacterial abundance, and higher order production can exceed values observed when solely cDOM or nutrient concentrations alone are higher. Phytoplankton and filled-in zooplankton cartoons were downloaded from the University of Maryland Center for Environmental Science Integration and Application Network (https://ian.umces.edu/media-library/). Phytoplankton were designed by Tracey Saxby of the Integration and Application Network, Dieter Tracey of the Water and Rivers Commission, Kim Kraeer and Lucy Van Essen-Fishman of the Integration and Application Network. Transparent crustacean zooplankton and rotifer cartoons were drawn by Stephanie E. Hampton.

Beyond the LTS-US's contribution to freshwater science, the LTS-US employs a range of data science and open science tools to support scalability and reproducibility as software evolves and new data become available. 1. The LTS-US dataset is constructed using the targets` Workflow Management Software, a tool that enables future users to automatically re-run the data processing pipeline. 2. The LTS-US dataset is produced in a ensemble-inspired framework, where each time the pipeline is run, predictions of a given lakes's trophic state are generated using 3 modeling techniques: a gradient boosted regression, logistic regression, and miltilayer perceptron model. 3. The LTS-US compute environment is containerized within a Docker Image that is available from the Environmental Data Initiative. Depending on future users' experience working with Docker, the container is available as an already rendered image or in an uncompiled format.

Together, the LTS-US dataset is the largest, reproducible, and machine-readable compilation of lake trophic state, by containing annual predictions for over 55,000 lakes from 1984 through 2020.

We welcome any future questions on using and reproducing the LTS-US dataset. Questions can be directed to Michael Meyer (mfmeyer@usgs.gov).

References

  • Leech, D. M., A. I. Pollard, S. G. Labou, and S. E. Hampton. 2018. Fewer blue lakes and more murky lakes across the continental U.S.: Implications for planktonic food webs. Limnology and Oceanography 63: 2661–2680. doi:10.1002/lno.10967

  • Meyer, M. F., S. N. Topp, T. V. King, and others. 2023. National-scale, remotely sensed lake trophic state (LTS-US) 1984-2020. Environmental Data Initiative. doi:10.6073/pasta/212a3172ac36e8dc6e1862f9c2522fa4

  • Webster, K. E., P. A. Soranno, K. S. Cheruvelil, and others. 2008. An empirical evaluation of the nutrient-color paradigm for lakes. Limnology and Oceanography 53: 1137–1148. doi:10.4319/lo.2008.53.3.1137

  • Williamson, C. E., D. P. Morris, M. L. Pace, and O. G. Olson. 1999. Dissolved organic carbon and nutrients as regulators of lake ecosystems: Resurrection of a more integrated paradigm. Limnology and Oceanography 44: 795–803. doi:10.4319/lo.1999.44.3_part_2.0795

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