Research Tools & Resources

An important part of the LAGOS research program and philosophy is the development and publication of various open science tools to facilitate research using LAGOS data products. Our intention is to provide other researchers with the data, code, and software necessary to reproduce and build upon our own research using LAGOS data and software (i.e., let’s not reinvent the wheel if we don’t have to). The tools linked below represent fully citable objects with individual DOIs. In some instances, we wrote data papers describing datasets, processing methods, and our experiences developing these data products. These are also linked below.

Broadly, this page describes: 1) LAGOS data papers; 2) R software to access LAGOS data; 3) LAGOS ArcGIS toolboxes; 3) Code and packages;  4) Data science methods papers; 5) Complete list of all LAGOS publications by year

LAGOS Data papers

Cheruvelil, K. S., Soranno, P. A., McCullough, I. M., Webster, K. E., Rodriguez, L. K., & Smith, N. J. (2021). LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. Limnology and Oceanography Letters. https://doi.org/10.1002/lol2.10203 

King, K. B. S., Wang, Q., Rodriguez, L. K., and Cheruvelil, K. S. in press. Lake networks and connectivity metrics for the conterminous U.S. (LAGOS-US NETWORKS v1). Limnology and Oceanography Letters. https://doi.org/10.1002/lol2.10204 

Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell, C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr, K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M. Collins, J.D. Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk, M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C. Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K. Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S. King, S.A. Kishbaugh, J.-F. Lapierre, B. Lathrop, J.A. Latimore, Y. Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B. Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson, A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry, K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K. Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L. Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P. Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E. Webster, J.D. White, M.K. Wilmes, S. Yuan. (2017). LAGOS-NE: A multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. lakes. GigaScience 6(12)   https://doi.org/10.1093/gigascience/gix101 

Soranno, P. A., Bissell, E. G., Cheruvelil, K. S., Christel, S. T., Collins, S. M., Fergus, C. E., Filstrup, C. T., Lapierre, J., Lottig, N. R., Oliver, S. K., Scott, C. E., Smith, N. J., Stopyak, S., Yuan, S., Bremigan, M. T., Downing, J. A., Gries, C., Henry, E. N., Skaff, N. K., Stanley, E. H., Stow, C. A., Tan, P., Wagner, T., & Webster, K. E. (2015). Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. GigaScience, 4(1), s13742-015. https://doi.org/10.1186/s13742-015-0067-4

R software to access LAGOS data

The LAGOSNE R package provides an R interface to download LAGOS-NE data from the EDI data repository, store the data locally, and perform a variety of filtering and subsetting operations for easier access to the many data tables that make up LAGOS-NE. This package is specific to the version of LAGOS-NE that is used; the most recent version is linked below.

Stachelek, J., Oliver, S., Masrour, F. (2019). LAGOSNE: Interface to the Lake Multi-scaled Geospatial and Temporal Database. R package version 2.0.2. https://cran.r-project.org/web/packages/LAGOSNE/index.html   

A similar R package LAGOSUS is currently in development. 

LAGOS ArcGIS toolboxes

We make publicly available all LAGOS geospatial data processing workflows whenever possible. Processing performed using ArcGIS tools uses the Python scripting language. Below are links to the LAGOS ArcGIS toolbox associated with LAGOS-NE on Github and the full documentation associated with it. The LAGOS-US ArcGIS toolbox represents an update to the LAGOS-NE ArcGIS toolbox and is currently in development. It will be made available as soon as the geospatial processing is completed and that data are made available for LAGOS-US.

LAGOS-NE: Python code to run in the ArcGIS environment to calculate the geospatial metrics used to create the LAGOS-NE GEO module. Available on Github here: https://github.com/cont-limno/LAGOS_GIS_Toolbox 

LAGOS-NE: Documentation for the toolbox: Nicole J. Smith, Patricia A. Soranno, and Scott Stopyak. 2014. Additional file 8: LAGOS GIS Toolbox Documentation. in Soranno et al. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse GigaScience 4:28. https://doi.org/10.1186/s13742-015-0067-4

Code and packages

Stachelek, J.. 2020. nhdR: tools for working with the national hydrography dataset. R package v.0.5.3. https://github.com/jsta/nhdR

Stachelek, J. and Goteti, G. 2020. dams: Dams in the United States from the National Inventory of Dams (NID). R package v.0.3.0. https://github.com/jsta/dams

Stachelek, J., 2019. gssurgo: Python toolbox enabling an open source gSSURGO workflow. R package v.1.0.0. https://github.com/jsta/gssurgo

Cheruvelil, K.S. 2016. R code-Creating multi-themed ecological regions for macroscale ecology: testing a flexible, repeatable, and accessible clustering method. https://github.com/cont-limno/SpectralClustering4Regions 

Data science methods papers

Liang, Z., Liu, Y., Xu, Y., & Wagner, T. (2021). Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems. Water Research, 117287. https://doi.org/10.1016/j.watres.2021.117287

North, J. S., Schliep, E. M., & Wikle, C. K. (2020). On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics. Environmetrics, e2665. https://doi.org/10.1002/env.2665

Schliep, E. M., Collins, S. M., Rojas-Salazar, S., Lottig, N. R., & Stanley, E. H. (2020). Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes. The Annals of Applied Statistics, 14(4), 1651-1675. https://doi.org/10.1214/20-AOAS1371 

Bartley, M. L., Hanks, E. M., Schliep, E. M., Soranno, P. A., & Wagner, T. (2019). Identifying and characterizing extrapolation in multivariate response data. PloS one, 14(12), e0225715. https://doi.org/10.1371/journal.pone.0225715 

Liu, B., Tan, P. N., & Zhou, J. (2019). Augmented multi-task learning by optimal transport. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 19-27). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.3

Wang, Q., Boudreau, C., Luo, Q., Tan, P. N., & Zhou, J. (2019). Deep multi-view information bottleneck. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 37-45). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.5

Liu, B., Tan, P. N., & Zhou, J. (2018). Enhancing predictive modeling of nested spatial data through group-level feature disaggregation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1784-1793). https://doi.org/10.1145/3219819.3220091

Wagner, T., & Schliep, E. M. (2018). Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales. Limnology and Oceanography, 63(6), 2372-2383. https://doi.org/10.1002/lno.10944

Wang, Q., Tan, P. N., & Zhou, J. (2018). Imputing structured missing values in spatial data with clustered adversarial matrix factorization. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1284-1289). IEEE. https://doi.org/10.1109/ICDM.2018.00173

Yuan, S., Tan, P. N., Cheruvelil, K. S., Fergus, C. E., Skaff, N. K., & Soranno, P. A. (2017). Hash-based feature learning for incomplete continuous-valued data. In Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 678-686). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974973.76 PDF 

Yuan, S., Tan, P. N., Cheruvelil, K. S., Collins, S. M., & Soranno, P. A. (2015). Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE. https://doi.org/10.1109/DSAA.2015.7344878

Levy, O., Ball, B. A., Bond-Lamberty, B., Cheruvelil, K. S., Finley, A. O., Lottig, N. R., Punyasena, S. W., Xiao, J., Zhou, J., Buckley, L. B., Filstrup, C. T., Keitt, T. H., Kellner, J. R., Knapp, A. K., Richardson, A. D., Tcheng, D., Toomey, M., Vargas, R., Voordeckers, J. W., Wagner, T., & Williams, J. W. (2014). Approaches to advance scientific understanding of macrosystems ecology. Frontiers in Ecology and the Environment, 12(1), 15-23. https://doi.org/10.1890/130019

Datasets and code associated with LAGOS publications 

Per our philosophy of open science, most LAGOS research publications have associated data/code published alongside manuscripts. The list below is non-exhaustive and we suggest looking up individual research publications to obtain links to associated data/code. 

Wang, Q. & King, K. (2020). Code for LAGOS-US NETWORKS v1.0 (Version v1.0.0) Zenodo. http://doi.org/10.5281/zenodo.4383172

Wagner, T. (2020, July 22). txw19/Joint_nutrient_criteria_using_QR: Creating joint nutrient criteria using quantile regression (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3956328

Poisson, A. C., and I. M. McCullough. 2019. Quantifying the contribution of citizen science to broad-scale ecological databases (github repository) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3236490.

McCullough, I. M. (2019). cont-limno/LivinOnTheEdge: Aquatic and semi-aquatic connectivity among lakes in relation to protected areas (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3463394.

Filstrup, C. T., King, K and I. M. McCullough. 2019. Macro-scale analysis of biodiversity- ecosystem functioning relationships in lakes (github repository) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3440182.

McCullough, I. M. and N. K. Skaff. 2019. No lake left behind: Lake protection in the continental US (github repository) (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3361751.

McCullough, I. M. 2019. Fire in US lake watersheds (1500 m lake buffers) from 1984-2015 (github repository) [Data set]. Global Change Biology. Zenodo. http://doi.org/10.5281/zenodo.2654989

Stanley E. H., S. M. Collins, N. R. Lottig, S. K. Oliver, K. E. Webster, K. S. Cheruvelil, P. A. Soranno. 2019. LAGOS Lake nutrient, carbon and chlorophyll data to evaluate biases in lake water quality sampling practices in a 17-state region of the US. Environmental Data Initiative. https://doi.org/10.6073/pasta/ad2516f5a98df32f1a8cbd7e658c088f.

King, K. 2018. Lake, wetland, and stream biotic and abiotic properties from the National Aquatic Resource Surveys. Knowledge Network for Biocomplexity. https://doi.org/10.5063/F13J3B5D.

Collins S. M., I. M. McCullough, S. K. Oliver, N. K. Skaff. 2018. LAGOS-NE Annual, seasonal, and monthly climate data for lakes and watersheds in a 17-state region of the U.S. Environmental Data Initiative. https://doi.org/10.6073/pasta/4abe86a2c00dc9a628924aa149d7bf34.

Lapierre J., S. Collins, D. Seekell, K. Cheruvelil, P. Tan, N. Skaff, Z. Taranu, C. Fergus, P. Soranno. 2017. LAGOS-NE – Lake nutrient chemistry and geospatial data to measure spatial structure of ecosystem properties in a 17-state region of the U.S.. Environmental Data Initiative.https://doi.org/10.6073/pasta/4e479018aa3d23603736bb5849f9a200.

Cheruvelil, K.S., J. Lapierre, and D. Seekell. 2017a. Latitudinal gradients in phosphorus and vegetation biomass for Sweden and Denmark. DOI: 10.6084/m9.figshare.5614588.

Cheruvelil, K.S. J. Lapierre, and D. Seekell. 2017b. Institutions studying lake carbon cycling. DOI: 10.6084/m9.figshare.5612968.

Lottig, N. R., P. Tan, T. Wagner, K. Cheruvelil, P. Soranno, E. Stanley, C. Scott, C. Stow, and S. Yuan. 2017. LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States. Environmental Data Initiative. https://doi.org/10.6073/pasta/d356b860d14635df1b604bbfb7e2a1d2

Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. 2016. Data for and ecological regions from: Creating multi-themed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method. Long Term Ecological Research Network Data Repository. https://doi.org/10.6073/pasta/1532a9a0c023fa50a912021291c8fc1f

Collins, S.M.; Oliver, S.K.; Lapierre, J..; Stanley, E.H.; Jones, J.; Wagner, T.; Soranno, P.A. 2016. LAGOS – Lake nitrogen, phosphorus, stoichiometry, and geospatial data for a 17-state region of the U.S.. Environmental Data Initiative data repository. http://dx.doi.org/10.6073/pasta/3abb4a56e76a52a12a366a338fc07dd8

Oliver S., S. Collins, P. Soranno, T. Wagner, E. Stanley, J. Jones, C. Stow, N. Lottig. 2016. LAGOS-NE v.1.054.1 – Lake water quality time series and geophysical data from a 17-state region of the United States. Environmental Data Initiative. http://dx.doi.org/10.6073/pasta/bd1b6e49413f009950ddc08abf061c1c

Oliver S.K., Soranno P.A., Fergus E.C., Wagner T, Webster KE, Scott C.E., Winslow L.A., Downing J.A., Stanley E.H. 2016. LAGOS – Predicted and observed maximum depth values for lakes in a 17-state region of the U.S. Long Term Ecological Research Network. https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-ntl.320.4

Soranno, P.A., K.S. Cheruvelil, T. Wagner, K.E. Webster, M.T. Bremigan. 2015. Data from: Effects of land use on lake nutrients: The importance of scale, hydrologic connectivity, and region. Dryad Digital Repository. http://dx.doi.org/10.5061/dryad.58445

Rüegg, J., Gries, C., Bond-Lamberty, B., Bowen, G. J., Felzer, B. S., McIntyre, N. E., Soranno, P. A., Vanderbilt, K. L., & Weathers, K. C. (2014). Completing the data life cycle: using information management in macrosystems ecology research. Frontiers in Ecology and the Environment, 12(1), 24-30. https://doi.org/10.1890/120375

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Complete list of LAGOS Publications

2021

Cheruvelil, K. S., Soranno, P. A., McCullough, I. M., Webster, K. E., Rodriguez, L. K., & Smith, N. J. (2021). LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. Limnology and Oceanography Letters. https://doi.org/10.1002/lol2.10203 

King, K. B. S., Wang, Q., Rodriguez, L. K., and Cheruvelil, K. S. in press. Lake networks and connectivity metrics for the conterminous U.S. (LAGOS-US NETWORKS v1). Limnology and Oceanography Letters. https://doi.org/10.1002/lol2.10204 

Liang, Z., Liu, Y., Xu, Y., & Wagner, T. (2021). Bayesian change point quantile regression approach to enhance the understanding of shifting phytoplankton-dimethyl sulfide relationships in aquatic ecosystems. Water Research, 117287. https://doi.org/10.1016/j.watres.2021.117287

Liang, Z., Xu, Y., Qiu, Q., Liu, Y., Lu, W., & Wagner, T. (2021). A framework to develop joint nutrient criteria for lake eutrophication management in eutrophic lakes. Journal of Hydrology, 594, 125883. https://doi.org/10.1016/j.jhydrol.2020.125883

2020

King, K.B.S., Bremigan, M.T., Infante, D., & Cheruvelil, K.S. (2020). Surface water connectivity affects lake and stream fish species richness and composition. Canadian Journal of Fisheries and Aquatic Sciences. https://doi.org/10.1139/cjfas-2020-0090

Liang, Z., Soranno, P. A., & Wagner, T. (2020). The role of phosphorus and nitrogen on chlorophyll a: Evidence from hundreds of lakes. Water Research, 185, 116236. https://doi.org/10.1016/j.watres.2020.116236

North, J. S., Schliep, E. M., & Wikle, C. K. (2020). On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi‐annual harmonics. Environmetrics, e2665. https://doi.org/10.1002/env.2665

Poisson, A. C., McCullough, I. M., Cheruvelil, K. S., Elliott, K. C., Latimore, J. A., & Soranno, P. A. (2020). Quantifying the contribution of citizen science to broad‐scale ecological databases. Frontiers in Ecology and the Environment, 18(1), 19-26. https://doi.org/10.1002/fee.2128

Schliep, E. M., Collins, S. M., Rojas-Salazar, S., Lottig, N. R., & Stanley, E. H. (2020). Data fusion model for speciated nitrogen to identify environmental drivers and improve estimation of nitrogen in lakes. The Annals of Applied Statistics, 14(4), 1651-1675. https://doi.org/10.1214/20-AOAS1371 

Soranno, P. A., Cheruvelil, K. S., Liu, B., Wang, Q., Tan, P. N., Zhou, J., King, K. B. S, McCullough, I. M., Stachelek, J., Bartley, M., Filstrup, C. T., Hanks. E. M., Lapierre, J., Lottig, N. R., Schliep, E. M., Wagner, T., & Webster, K. E. (2020). Ecological prediction at macroscales using big data: Does sampling design matter?. Ecological Applications, 30(6), e02123. https://doi.org/10.1002/eap.2123

Soranno, P. A., Webster, K. E., Smith, N. J., Díaz Vázquez, J., & Cheruvelil, K. S. (2020). What Is in a “Lake” Name? That Which We Call a Lake by Any Other Name. Limnology and Oceanography Bulletin, 29(1), 1-7. https://doi.org/10.1002/lob.10355

Stachelek, J., Weng, W., Carey, C. C., Kemanian, A. R., Cobourn, K. M., Wagner, T., Weathers, K. C., & Soranno, P. A. (2020). Granular measures of agricultural land use influence lake nitrogen and phosphorus differently at macroscales. Ecological Applications, 30(8), e02187. https://doi.org/10.1002/eap.2187

Wagner, T., Lottig, N. R., Bartley, M. L., Hanks, E. M., Schliep, E. M., Wikle, N. B., King, K. B. S., McCullough, I. M., Stachelek, J., Cheruvelil, K. S., Filstrup, C. T., Lapierre, J., Liu, B., Soranno, P. A., Tan, P., Wang, Q., Webster, K. E., & Zhou, J. (2020). Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data. Limnology and Oceanography Letters, 5(2), 228-235. https://doi.org/10.1002/lol2.10134

2019

Bartley, M. L., Hanks, E. M., Schliep, E. M., Soranno, P. A., & Wagner, T. (2019). Identifying and characterizing extrapolation in multivariate response data. PloS one, 14(12), e0225715. https://doi.org/10.1371/journal.pone.0225715 

Collins, S. M., Yuan, S., Tan, P. N., Oliver, S. K., Lapierre, J. F., Cheruvelil, K. S., Fergus, C. E., Skaff, N. K., Stachelek, J., Wagner, T., & Soranno, P. A. (2019). Winter precipitation and summer temperature predict lake water quality at macroscales. Water Resources Research, 55(4), 2708-2721. https://doi.org/10.1029/2018WR023088

Filstrup, C. T., King, K. B. S., & McCullough, I. M. (2019). Evenness effects mask richness effects on ecosystem functioning at macro‐scales in lakes. Ecology Letters, 22(12), 2120-2129. https://doi.org/10.1111/ele.13407

King, K., Cheruvelil, K. S., & Pollard, A. (2019). Drivers and spatial structure of abiotic and biotic properties of lakes, wetlands, and streams at the national scale. Ecological Applications, 29(7), e01957. https://doi.org/10.1002/eap.1957

Liu, B., Tan, P. N., & Zhou, J. (2019). Augmented multi-task learning by optimal transport. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 19-27). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.3

McCullough, I. M., Cheruvelil, K. S., Collins, S. M., & Soranno, P. A. (2019). Geographic patterns of the climate sensitivity of lakes. Ecological Applications, 29(2), e01836. https://doi.org/10.1002/eap.1836

McCullough, I. M., Cheruvelil, K. S., Lapierre, J. F., Lottig, N. R., Moritz, M. A., Stachelek, J., & Soranno, P. A. (2019). Do lakes feel the burn? Ecological consequences of increasing exposure of lakes to fire in the continental United States. Global Change Biology, 25(9), 2841-2854. https://doi.org/10.1111/gcb.14732

McCullough, I. M., King, K. B., Stachelek, J., Diaz, J., Soranno, P. A., & Cheruvelil, K. S. (2019). Applying the patch-matrix model to lakes: a connectivity-based conservation framework. Landscape Ecology, 34(11), 2703-2718. https://doi.org/10.1007/s10980-019-00915-7 

McCullough, I. M., Skaff, N. K., Soranno, P. A., & Cheruvelil, K. S. (2019). No lake left behind: How well do US protected areas meet lake conservation targets?. Limnology and Oceanography Letters, 4(6), 183-192. https://doi.org/10.1002/lol2.10123

Qian, S. S., Stow, C. A., Nojavan, F., Stachelek, J., Cha, Y., Alameddine, I., & Soranno, P. (2019). The implications of Simpson’s paradox for cross-scale inference among lakes. Water Research, 163, 114855. https://doi.org/10.1016/j.watres.2019.114855

Soranno, P. A. (2019). Six simple steps to share your data when publishing research articles. Limnology and Oceanography Bulletin, 28(2), 41-44. https://doi.org/10.1002/lob.10303

Soranno, P. A., Wagner, T., Collins, S. M., Lapierre, J. F., Lottig, N. R., & Oliver, S. K. (2019). Spatial and temporal variation of ecosystem properties at macroscales. Ecology Letters, 22(10), 1587-1598. https://doi.org/10.1111/ele.13346

Stanley, E. H., Collins, S. M., Lottig, N. R., Oliver, S. K., Webster, K. E., Cheruvelil, K. S., & Soranno, P. A. (2019). Biases in lake water quality sampling and implications for macroscale research. Limnology and Oceanography, 64(4), 1572-1585. https://doi.org/10.1002/lno.11136

Stanley, E. H., Rojas‐Salazar, S., Lottig, N. R., Schliep, E. M., Filstrup, C. T., & Collins, S. M. (2019). Comparison of total nitrogen data from direct and Kjeldahl‐based approaches in integrated data sets. Limnology and Oceanography: Methods, 17(12), 639-649. https://doi.org/10.1002/lom3.10338

Stachelek, J., & Soranno, P. A. (2019). Does freshwater connectivity influence phosphorus retention in lakes?. Limnology and Oceanography, 64(4), 1586-1599. https://doi.org/10.1002/lno.11137

Wang, Q., Boudreau, C., Luo, Q., Tan, P. N., & Zhou, J. (2019). Deep multi-view information bottleneck. In Proceedings of the 2019 SIAM International Conference on Data Mining (pp. 37-45). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611975673.5

2018

Cheruvelil, K. S., & Soranno, P. A. (2018). Data-intensive ecological research is catalyzed by open science and team science. BioScience, 68(10), 813-822. https://doi.org/10.1093/biosci/biy097

Lapierre, J. F., Collins, S. M., Seekell, D. A., Spence Cheruvelil, K., Tan, P. N., Skaff, N. K., Taranu, Z. E., Fergus, C. E., & Soranno, P. A. (2018). Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes. Global Ecology and Biogeography, 27(10), 1251-1263. https://doi.org/10.1111/geb.12781

Liu, B., Tan, P. N., & Zhou, J. (2018). Enhancing predictive modeling of nested spatial data through group-level feature disaggregation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1784-1793). https://doi.org/10.1145/3219819.3220091

Lottig, N. R., Tan, P. N., Wagner, T., Cheruvelil, K. S., Soranno, P. A., Stanley, E. H., … & Yuan, S. (2017). Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere, 8(12), e02024. https://doi.org/10.1002/ecs2.2024

Oliver, S. K., Fergus, C. E., Skaff, N. K., Wagner, T., Tan, P. N., Cheruvelil, K. S., & Soranno, P. A. (2018). Strategies for effective collaborative manuscript development in interdisciplinary science teams. Ecosphere, 9(4), e02206. https://doi.org/10.1002/ecs2.2206

Seekell, D. A., Lapierre, J. F., & Cheruvelil, K. S. (2018). A geography of lake carbon cycling. Limnology and Oceanography Letters, 3(3), 49-56. https://doi.org/10.1002/lol2.10078

Stow, C. A., Webster, K. E., Wagner, T., Lottig, N., Soranno, P. A., & Cha, Y. (2018). Small values in big data: The continuing need for appropriate metadata. Ecological Informatics, 45, 26-30. https://doi.org/10.1016/j.ecoinf.2018.03.002

Wagner, T., & Schliep, E. M. (2018). Combining nutrient, productivity, and landscape‐based regressions improves predictions of lake nutrients and provides insight into nutrient coupling at macroscales. Limnology and Oceanography, 63(6), 2372-2383. https://doi.org/10.1002/lno.10944

Wang, Q., Tan, P. N., & Zhou, J. (2018). Imputing structured missing values in spatial data with clustered adversarial matrix factorization. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1284-1289). IEEE. https://doi.org/10.1109/ICDM.2018.00173

2017

Cheruvelil, K. S., Yuan, S., Webster, K. E., Tan, P. N., Lapierre, J. F., Collins, S. M., Fergus, C. E., Scott, C. E., Henry, E. N., Soranno, P. A., Filstrup, C. T., & Wagner, T. (2017). Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method. Ecology and Evolution, 7(9), 3046-3058. https://doi.org/10.1002/ece3.2884

Collins, S. M., Oliver, S. K., Lapierre, J. F., Stanley, E. H., Jones, J. R., Wagner, T., & Soranno, P. A. (2017). Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub‐continental scales. Ecological Applications, 27(5), 1529-1540. https://doi.org/10.1002/eap.1545 PDF

Elliott, K. C., Settles, I. H., Montgomery, G. M., Brassel, S. T., Cheruvelil, K. S., & Soranno, P. A. (2017). Honorary authorship practices in environmental science teams: structural and cultural factors and solutions. Accountability in Research, 24(2), 80-98. https://doi.org/10.1080/08989621.2016.1251320

Fergus, C. E., Lapierre, J. F., Oliver, S. K., Skaff, N. K., Cheruvelil, K. S., Webster, K., … & Soranno, P. (2017). The freshwater landscape: lake, wetland, and stream abundance and connectivity at macroscales. Ecosphere, 8(8), e01911. https://doi.org/10.1002/ecs2.1911

Filstrup, C. T., Wagner, T., Oliver, S. K., Stow, C. A., Webster, K. E., Stanley, E. H., & Downing, J. A. (2018). Evidence for regional nitrogen stress on chlorophyll a in lakes across large landscape and climate gradients. Limnology and Oceanography, 63(S1), S324-S339. https://doi.org/10.1002/lno.10742

Lapierre, J. F., Seekell, D. A., Filstrup, C. T., Collins, S. M., Emi Fergus, C., Soranno, P. A., & Cheruvelil, K. S. (2017). Continental‐scale variation in controls of summer CO2 in United States lakes. Journal of Geophysical Research: Biogeosciences, 122(4), 875-885. https://doi.org/10.1002/2016JG003525 PDF

Oliver, S. K., Collins, S. M., Soranno, P. A., Wagner, T., Stanley, E. H., Jones, J. R., Stow, C. A., & Lottig, N. R. (2017). Unexpected stasis in a changing world: Lake nutrient and chlorophyll trends since 1990. Global Change Biology, 23(12), 5455-5467. https://doi.org/10.1111/gcb.13810

Resnik, D. B., Elliott, K. C., Soranno, P. A., & Smith, E. M. (2017). Data-intensive science and research integrity. Accountability in Research, 24(6), 344-358. https://doi.org/10.1080/08989621.2017.1327813

Soranno, P.A., L.C. Bacon, M. Beauchene, K.E. Bednar, E.G. Bissell, C.K. Boudreau, M.G. Boyer, M.T. Bremigan, S.R. Carpenter, J.W. Carr, K.S. Cheruvelil, S.T. Christel, M. Claucherty, S.M. Collins, J.D. Conroy, J.A. Downing, J. Dukett, C.E. Fergus, C.T. Filstrup, C. Funk, M.J. Gonzalez, L.T. Green, C. Gries, J.D. Halfman, S.K. Hamilton, P.C. Hanson, E.N. Henry, E.M. Herron, C. Hockings, J.R. Jackson, K. Jacobson-Hedin, L.L. Janus, W.W. Jones, J.R. Jones, C.M. Keson, K.B.S. King, S.A. Kishbaugh, J.-F. Lapierre, B. Lathrop, J.A. Latimore, Y. Lee, N.R. Lottig, J.A. Lynch, L.J. Matthews, W.H. McDowell, K.E.B. Moore, B.P. Neff, S.J. Nelson, S.K. Oliver, M.L. Pace, D.C. Pierson, A.C. Poisson, A.I. Pollard, D.M. Post, P.O. Reyes, D.O. Rosenberry, K.M. Roy, L.G. Rudstam, O. Sarnelle, N.J. Schuldt, C.E. Scott, N.K. Skaff, N.J. Smith, N.R. Spinelli, J.J. Stachelek, E.H. Stanley, J.L. Stoddard, S.B. Stopyak, C.A. Stow, J.M. Tallant, P.-N. Tan, A.P. Thorpe, M.J. Vanni, T. Wagner, G. Watkins, K.C. Weathers, K.E. Webster, J.D. White, M.K. Wilmes, S. Yuan. (2017). LAGOS-NE: A multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of U.S. lakes. GigaScience 6(12)   https://doi.org/10.1093/gigascience/gix101 

Yuan, S., Tan, P. N., Cheruvelil, K. S., Fergus, C. E., Skaff, N. K., & Soranno, P. A. (2017). Hash-based feature learning for incomplete continuous-valued data. In Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 678-686). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974973.76 PDF 

2016

Elliott, K. C., Cheruvelil, K. S., Montgomery, G. M., & Soranno, P. A. (2016). Conceptions of good science in our data-rich world. BioScience, 66(10), 880-889. https://doi.org/10.1093/biosci/biw115

Fergus, C. E., Finley, A. O., Soranno, P. A., & Wagner, T. (2016). Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales. PLoS One, 11(10), e0164592. https://doi.org/10.1371/journal.pone.0164592

Oliver, S. K., Soranno, P. A., Fergus, C. E., Wagner, T., Winslow, L. A., Scott, C. E., Webster, K. E., Downing, J. A., & Stanley, E. H. (2016). Prediction of lake depth across a 17-state region in the United States. Inland Waters, 6(3), 314-324. https://doi.org/10.1080/IW-6.3.957 PDF

Skaff, N. K., & Cheruvelil, K. S. (2016). Fine-scale wetland features mediate vector and climate-dependent macroscale patterns in human West Nile virus incidence. Landscape ecology, 31(7), 1615-1628. https://doi.org/10.1007/s10980-016-0346-1

Wagner, T., Fergus, C. E., Stow, C. A., Cheruvelil, K. S., & Soranno, P. A. (2016). The statistical power to detect cross‐scale interactions at macroscales. Ecosphere, 7(7), e01417. https://doi.org/10.1002/ecs2.1417

2015

Soranno, P. A., Bissell, E. G., Cheruvelil, K. S., Christel, S. T., Collins, S. M., Fergus, C. E., Filstrup, C. T., Lapierre, J., Lottig, N. R., Oliver, S. K., Scott, C. E., Smith, N. J., Stopyak, S., Yuan, S., Bremigan, M. T., Downing, J. A., Gries, C., Henry, E. N., Skaff, N. K., Stanley, E. H., Stow, C. A., Tan, P., Wagner, T., & Webster, K. E. (2015). Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. GigaScience, 4(1), s13742-015. https://doi.org/10.1186/s13742-015-0067-4

Soranno, P. A., Cheruvelil, K. S., Elliott, K. C., & Montgomery, G. M. (2015). It’s good to share: Why environmental scientists’ ethics are out of date. BioScience, 65(1), 69-73. https://doi.org/10.1093/biosci/biu169

Yuan, S., Tan, P. N., Cheruvelil, K. S., Collins, S. M., & Soranno, P. A. (2015). Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE. https://doi.org/10.1109/DSAA.2015.7344878

2014

Cheruvelil, K. S., Soranno, P. A., Weathers, K. C., Hanson, P. C., Goring, S. J., Filstrup, C. T., & Read, E. K. (2014). Creating and maintaining high‐performing collaborative research teams: the importance of diversity and interpersonal skills. Frontiers in Ecology and the Environment, 12(1), 31-38. https://doi.org/10.1890/130001

Filstrup, C. T., Wagner, T., Soranno, P. A., Stanley, E. H., Stow, C. A., Webster, K. E., & Downing, J. A. (2014). Regional variability among nonlinear chlorophyll—phosphorus relationships in lakes. Limnology and Oceanography, 59(5), 1691-1703. https://doi.org/10.4319/lo.2014.59.5.1691 PDF

Goring, S. J., Weathers, K. C., Dodds, W. K., Soranno, P. A., Sweet, L. C., Cheruvelil, K. S., Kominoski, J. S., Rüegg, J., Thorn, A. M., & Utz, R. M. (2014). Improving the culture of interdisciplinary collaboration in ecology by expanding measures of success. Frontiers in Ecology and the Environment, 12(1), 39-47. https://doi.org/10.1890/120370

Heffernan, J. B., Soranno, P. A., Angilletta Jr, M. J., Buckley, L. B., Gruner, D. S., Keitt, T. H., Kellner, J. R., Kominoski, J. S, Rocha, A. V., Xiao, J., Harms, T. K., Goring, S. J., Koenig, L. E., McDowell, W. H., Powell, H., Richardson, A. D., Stow, C. A., Vargas, R., & Weathers, K. C. (2014). Macrosystems ecology: understanding ecological patterns and processes at continental scales. Frontiers in Ecology and the Environment, 12(1), 5-14. https://doi.org/10.1890/130017

Levy, O., Ball, B. A., Bond-Lamberty, B., Cheruvelil, K. S., Finley, A. O., Lottig, N. R., Punyasena, S. W., Xiao, J., Zhou, J., Buckley, L. B., Filstrup, C. T., Keitt, T. H., Kellner, J. R., Knapp, A. K., Richardson, A. D., Tcheng, D., Toomey, M., Vargas, R., Voordeckers, J. W., Wagner, T., & Williams, J. W. (2014). Approaches to advance scientific understanding of macrosystems ecology. Frontiers in Ecology and the Environment, 12(1), 15-23. https://doi.org/10.1890/130019

Lottig, N. R., Wagner, T., Norton Henry, E., Spence Cheruvelil, K., Webster, K. E., Downing, J. A., & Stow, C. A. (2014). Long-term citizen-collected data reveal geographical patterns and temporal trends in lake water clarity. PloS One, 9(4), e95769. https://doi.org/10.1371/journal.pone.0095769

Rüegg, J., Gries, C., Bond-Lamberty, B., Bowen, G. J., Felzer, B. S., McIntyre, N. E., Soranno, P. A., Vanderbilt, K. L., & Weathers, K. C. (2014). Completing the data life cycle: using information management in macrosystems ecology research. Frontiers in Ecology and the Environment, 12(1), 24-30. https://doi.org/10.1890/120375

Soranno, P. A., Cheruvelil, K. S., Bissell, E. G., Bremigan, M. T., Downing, J. A., Fergus, C. E., Filstrup, C. T., Henry, E. N., Lottig, N. R., Stanley, E. H., Stow, C. A., Tan, P., Wagner, T., & Webster, K. E. (2014). Cross‐scale interactions: Quantifying multi‐scaled cause–effect relationships in macrosystems. Frontiers in Ecology and the Environment, 12(1), 65-73. https://doi.org/10.1890/120366

Soranno, P. A., & Schimel, D. S. (2014). Macrosystems ecology: big data, big ecology. Frontiers in Ecology and the Environment, 12(1) 3-3. https://doi.org/10.1890/1540-9295-12.1.3

2013

Cheruvelil, K. S., Soranno, P. A., Webster, K. E., & Bremigan, M. T. (2013). Multi‐scaled drivers of ecosystem state: Quantifying the importance of the regional spatial scale. Ecological Applications, 23(7), 1603-1618. https://doi.org/10.1890/12-1872.1

Winslow, L. A., Read, J. S., Hanson, P. C., & Stanley, E. H. (2014). Lake shoreline in the contiguous United States: quantity, distribution and sensitivity to observation resolution. Freshwater Biology, 59(2), 213-223. https://doi.org/10.1111/fwb.12258 PDF