Code

Most of the code that our lab creates is done within the LAGOS research project. For all of the research products associated with LAGOS, please see here.

For additional information on code associated with LAGOS, please see here.

Example code products produced by members of our lab in collaboration with others:

LAGOS R package (available on cran)

The LAGOS 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. For more information on this and other documentation for LAGOS-NE, see here.

SpectralClustering4Regions

R code for a novel method to create ecoregions from geospatial data.

Citation for the R code:   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

Citation for the article that describes this novel method for delineating ecological regions:      Yuan, S., P.-N. Tan, K.S. Cheruvelil, S.M. Collins, and P.A. Soranno. 2015. Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics.  19-21 October 2015. Pg 1-10. doi:10.1109/DSAA.2015.7344878 PDF

Citation for the article in which the method is applied to an ecological research question:  Cheruvelil, K.S., S. Yuan, K.E. Webster, P.-N. Tan, J.-F. Lapierre, S.M. Collins, C.E. Fergus, C.E. Scott, E.N. Henry, P.A. Soranno, C.T. Filstrup, T. Wagner. 2017. Creating multi-themed ecological regions for macrosystems ecology: Testing a flexible, repeatable, and accessible clustering method. Ecology and Evolution 7: 3046–3058. doi: 10.1002/ece3.2884 Open access

LAGOS GIS Toolbox for ArcGIS 10.1

Python code to run in the ArcGIS environment to calculate the geospatial metrics used to create the LAGOS-GEO module.

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.

Citation for the article that provides additional detail for the data used in the GIS toolbox: Soranno, P.A., E.G. Bissell, K.S. Cheruvelil, S.T. Christel, S.M. Collins, C.E. Fergus, C.T. Filstrup, J.F. Lapierre, N.R. Lottig, S.K. Oliver, C.E. Scott, N.J. Smith, S. Stopyak, S. Yuan, M.T. Bremigan, J.A. Downing, C. Gries, E.N. Henry, N.K. Skaff, E.H. Stanley, C.A. Stow, P.-N. Tan, T. Wagner, K.E. Webster. 2015. Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science and data reuse. GigaScience 4:28 doi:10.1186/s13742-015-0067-4. Open access

Other code

  • Wagner, T. (2020, July 22). txw19/Joint_nutrient_criteria_using_QR: Creating joint nutrient criterial using quantile regression (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3956328
  • 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.
  • 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.
  • Stachelek, J., 2019. gssurgo: Python toolbox enabling an open source gSSURGO workflow. R package v.1.0.0
    https://github.com/jsta/gssurgo
  • Stachelek, J., 2020. nhdR: tools for working with the national hydrography dataset. R package v.0.5.3https://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