Terradactyl: R Package for rangeland core methods data extraction and calculation

TitleTerradactyl: R Package for rangeland core methods data extraction and calculation
Publication TypeConference Paper
Year of Publication2019
AuthorsMcCord S, Stauffer N
Conference Name72nd Society for Range Management International Meeting
Date Published02/2019
PublisherSociety for Range Management Meeting Abstracts
Conference LocationMinneapolis, Minnesota
ARIS Log Number361638

Grassland, Shrubland, and Savanna Ecosystems are widely adopted by federal land management agencies, such as the Bureau of Land Management (BLM) and Natural Resources Conservation Service (NRCS), research institutions, and other land management organizations. Within the BLM and NRCS these data are stored in large databases and are generally shared with collaborators and partners via file geodatabase or text file. While a limited suite of indicator calculations is available with these raw data, custom calculations can be complicated given the differences in data structure. Here we present an R package, called terradactyl, which handles data formats from the BLM TerrAdat database, the NRCS Natural Resources Inventory, and the Database for Inventory Monitoring and Assessment. This package provides several groups of functions. The first are a set of tidying functions, which gather data from BLM and NRCS data files into a common long data format to improve calculation efficiency. The second set of functions provide a grammar of rangeland indicator descriptions within which specific classes of indicators (e.g., cover) can be flexibly calculated based on user's categorical inputs. The third set of functions provide outputs specifically formatted for the inputs into modelling applications such as AERO, RHEM, and APEX or specified data archiving formats, such as the BLM’s Terrestrial Indicators table. This R package is used by both the BLM and the NRCS to compute calculations from core methods data and is a powerful tool for integrating core methods data from different datasets.