easyFulcrum: An R package to process and analyze ecological sampling data generated using the Fulcrum mobile...

Area of Science:

• Ecology
• Bioinformatics
• Data Science

Background:

• Large-scale ecological sampling of microscopic organisms is challenging and expensive.
• Traditional methods involve manual data collection and laboratory processing, prone to errors.
• Molecular barcoding aids species-level identification but requires integrated data management.

Purpose of the Study:

• To develop a streamlined workflow for collecting and analyzing ecological field and laboratory data.
• To introduce the easyFulcrum R package for processing and visualizing ecological sampling data.
• To facilitate large-scale ecological sampling of microscopic organisms by integrating field and lab data.

Main Methods:

• Utilized the Fulcrum mobile application for geospatial data collection, including substrate photographs and environmental data.
• Developed the easyFulcrum R package to clean, process, and visualize data exported from Fulcrum.
• Linked field sampling data with laboratory-based organism isolation and molecular identification data.

Main Results:

• The Fulcrum platform and easyFulcrum R package reduce transcription errors associated with manual data entry.
• Standardized functions in easyFulcrum enable efficient cleaning, processing, and visualization of ecological data.
• Disparate datasets, including environmental and molecular data, can be effectively joined for comprehensive analysis.

Conclusions:

• The combination of Fulcrum and easyFulcrum offers a robust solution for large-scale ecological sampling of microscopic organisms.
• This integrated approach enhances data accuracy, efficiency, and analytical capabilities in ecological research.
• The easyFulcrum package is adaptable for various organisms beyond the initial application with wild nematodes.