Phenology forecasting models for detection and management of invasive annual grasses

Authors: PREVÉY, JANET - Us Geological Survey; PEARSE, IAN - Us Geological Survey; Blumenthal, Dana; HOWELL, A - Us Geological Survey; Kray, Julie; REED, SASHA - Us Geological Survey; STEPHENSON, MITCHELL - University Of Nebraska; JARNEVICH, C - Us Geological Survey

Submitted to: Ecosphere
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/2/2024
Publication Date: 10/10/2024
Citation: Prevéy, J.S., Pearse, I.S., Blumenthal, D.M., Howell, A.J., Kray, J.A., Reed, S.C., Stephenson, M.B., Jarnevich, C.S. 2024. Phenology forecasting models for detection and management of invasive annual grasses. Ecosphere. 15(10). Article e70023. https://doi.org/10.1002/ecs2.70023.
DOI: https://doi.org/10.1002/ecs2.70023

Interpretive Summary: Invasive annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity. Management strategies, such as grazing, can be designed to take adavantage of invasive annual grass phenology, but anticipating the timing of key phenological stages over vast landscapes is difficult. To address this challenge, we created range-wide phenology forecasts cheatgrass and red brome. Using 18 mechanistic phenology models and observations from monitoring experiments, volunteer science, herbarium records, timelapse camera observations we predicted the dates of annual grass flowering and senescence. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photo-thermal sum models that were then adjusted for variation in temperature across topographical gradients. Phenology forecasts based on these models can help managers to schedule management such as grazing, predict the timing of fire risk after annual grasses dry out, and select remote sensing imagery to accurately map invasive grass populations.

Technical Abstract: Non-native annual grasses can dramatically alter fire frequency and reduce forage quality and biodiversity in the ecosystems they invade. Effective management techniques are needed to reduce these undesirable invasive species and maintain ecosystem services. Well-timed management strategies, such as grazing, that are applied when invasive grasses are active prior to native plants can control invasive species spread and reduce their impact; however, anticipating the timing of key phenological stages that are susceptible to management over vast landscapes is difficult, as the phenology of these species can vary greatly over time and space. To address this challenge, we created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass (Bromus tectorum) and red brome (Bromus rubens). We tested a suite of 18 mechanistic phenology models using observations from monitoring experiments, volunteer science, herbarium records, timelapse camera observations, and downscaled gridded climate data to identify the models that best predicted the dates of flowering and senescence of the two invasive grass species. We found that the timing of flowering and senescence of cheatgrass and red brome were best predicted by photo-thermal sum models that were then adjusted for topography using gridded continuous heat-insolation load index values. Phenology forecasts based on these models can help managers make decisions about when to schedule management such as grazing to reduce undesirable invasive grasses and promote forage production, quality, and biodiversity in grasslands; to predict the timing of greatest fire risk after annual grasses dry out; and to select remote sensing imagery to accurately map invasive grasses across topographic and latitudinal gradients.