18 Apr 2024

Standardized NEON Organismal Data for Biodiversity Research

Abstract: Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is limited by a lack of standardized, high-quality, empirical data spanning large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the US and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be accessible and easily integrated into investigators’ workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format available through the ecocomDP R package; provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.

30 Sep 2022

Automated Ground-Penetrating-Radar Post-Processing Software in R Programming

Abstract: Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI soft-ware to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.