Location: Livestock and Range Research LaboratoryTitle: Fuel properties of effective greenstrips in simulated cheatgrass fires
Submitted to: Environmental Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/29/2022
Publication Date: 5/16/2022
Citation: McGranahan, D.A., Wonkka, C.L. 2022. Fuel properties of effective greenstrips in simulated cheatgrass fires. Environmental Management. 70:319-328. https://doi.org/10.1007/s00267-022-01659-y.
Interpretive Summary: Non-native annual grasses such as cheatgrass often create a major wildfire problem when they invade ecosystems like sagebrush steppe, which has adapted to long fire return intervals. In recent decades, managers have sought to establish long, linear stands of less-flammable vegetation, called greenstrips, in an effort to stop or slow fire spread across wide areas invaded by cheatgrass. But the most frequently-used species are also non-native, and there is very little research to support the selection of effective native plants for greenstrips. Landscape-level research on the issue is difficult, so we used computer simulations to compare plant traits. We created a set of fuel models to reflect a variety of plant traits related to fire spread, including total biomass, proportion live tissue, moisture content of live tissue, and leaf:stem ratio. We then used a landscape fire simulator to determine which combination of traits made for a greenstrip that stopped or slowed fire spread. Overall, low fuel loads were most effective at stopping fire spread. At the species level, those with low leaf:stem ratios and high live:dead fuel ratios to be most effective.
Technical Abstract: Invasive annual grasses alter fire regime in steppe ecosystems, and subsequent trends toward larger, more frequent wildfires impacts iconic biodiversity. A common solution is to disrupt novel, continuous swaths of annual grasses with greenstrips--linear, human-maintained stands of less-flammable vegetation. But selecting effective native species is challenged by the fact that identifying the optimal combination of plant traits that interrupt wildfire spread is logistically difficult. We employed fire behavior simulation modeling to determine plant traits with high potential to slow fire spread in annual Bromus-dominated fuelbeds. We found species with low leaf:stem ratios and high live:dead fuel ratios to be most effective. Our approach helps isolate fuelbed characteristics that slow fire spread, providing a geographically-agnostic framework to scale plant traits to greenstrip effectiveness. This framework helps managers assess potential native species for greenstrips without needing logistically-difficult experimental assessments to determine how a species might affect fire behavior.