Published August 22, 2024 | Version v1
Dataset Open

Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem Code and Data

Authors/Creators

  • 1. ROR icon United States Department of Agriculture

Description

The data here is summary data compiled from all years of the project that lead to the publication Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem with the Journal of Applied Ecology and code to analyze these data. Our study was focused on the Northern Great Basin ecosystem. We conducted surveys at 48 sites over the course of five years (2016-2020). All were located on public lands managed by either the Bureau of Land Management, Idaho Department of Lands, or Oregon State Lands Department. We looked at the influence of management, biotic, abiotic and weather variables predicting seedling establishment success, 45 predictor variables in all. Machine learning techniques were used to select most important predictor variables to be used in future work predicting good seedling establishment windows.  

Files

Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem - DATA.csv

Files (172.5 kB)

Additional details

Dates

Accepted
2024-06
by the Journal of Applied Ecology

Software

Programming language
R