Demographic and leaf attribute data for beargrass (Xerophyllum tenax Melanthiaceae) from Mount Hood National Forest, Oregon

This data publication contains plant demographic and leaf quality data for beargrass (Xerophyllum tenax Melanthiaceae) from three different wildfire sites on the Mount Hood National Forest in Oregon. Data include life history information for a set of approximately 1000 plants that were tracked over the course of three years. Each plant was first tagged with a metal tag and then plant growth, survival, flowering, vegetative reproduction and seed capsule production were measured for each plant near the end of each growing season in 2015, 2016 and 2017. Soil moisture was measured twice per season in both 2016 and 2017. Canopy openness was measured once, either in 2016 or 2017 with hemispheric lens at the plant level. A harvest experiment was performed in 2016 on a random subset of 180 plants evenly distributed across sites and plot types. Leaf qualities of three harvested leaves per harvested plant were measured and recorded. Measurements included length, width in three places along the leaf, color, and leaf mass per area (LMA).
This project aims to identify the management and environmental conditions which best support the long-term persistence of fire-adapted plants. These conditions may also be those that reduce fire risk. The project focuses on beargrass, an understory forest plant used for basket making and other purposes by American Indians in the western United States that is thought to be declining due to fire suppression and commercial harvest. Additional goals for the project included identifying ecological conditions that produce the highest leaf quality for basketry and gathering recommendations of tribal members for the perpetuation of basket making traditions. For the data shared here, plants at three wildfire sites were measured over three years to understand how light, water, fire severity and harvest influence population growth and leaf quality for weaving. Data are well-suited for analysis with Integral Projection Models, as well as other approaches.