Predicting when the grass is greener: new phenological forecasts for invasive annual grasses
Two major hurdles in the effective control of invasive grasses are: (1) Anticipating the timing of key stages that are susceptible to management actions, and (2) accurately mapping where treatments are most needed over vast landscapes in real time. To address these hurdles, researchers created range-wide phenology forecasts for two problematic invasive annual grasses: cheatgrass and red brome.
Invasive annual grasses – a nuisance for western ecosystems
Non-native, annual grass species like cheatgrass (Bromus tectorum) and red brome (Bromus rubens) were introduced to the United States in the late 1800s and have since invaded and critically transformed ecosystems like the sagebrush steppe and southwestern deserts. Where present, both grasses increase wildfire risk, reduce native wildlife habitat, and alter soil properties like erosion potential, nutrient availability, and water retention.
Their ability to invade so effectively is directly related to their phenology; both cheatgrass and red brome grow faster and flower earlier in the Spring than neighboring native plants. In disturbed habitats, like a burned grassland, these growth patterns give invasive grasses an advantage over native species, allowing them to dominate a habitat after disturbance.
Background photo: cheatgrass invasion of a post-fire landscape.
Effective management needs precise timing
Invasive plant managers can exploit the phenological differences between native and invasive grasses by concentrating management actions like grazing or herbicide application on the window of time in early Spring when invasive grasses are growing and native grasses are not. However, it can be difficult to determine exactly when cheatgrass and red brome will begin different phases of their annual life cycle, given the myriad environmental variables, such as precipitation, elevation, and temperature, that can potentially affect plant growth.
Predicting the phenology of invasive annual grasses
Accurate predictions of the timing of phenological phases require computational models that can account for multiple, complex environmental variables across vast landscapes. Model outputs must also be tested for accuracy by comparing their predictions to real-world data.
In this study, researchers tested 18 different mechanistic models against multiple types of real-world data – including timelapse photography, herbarium records, iNaturalist and volunteer observations, and experimental data – to determine which model best predicted phenological timing of cheatgrass and red brome. In all, they found that models incorporating day length, temperature and fine-scale topographic information predicted timing of growth and flowering of both invasive grasses within eight days of observations, and were the most accurate models of those tested.
Background: Timelapse photography of cheatgrass growing on a hillside in the Cameron Pass area of Colorado. To measure the timing of cheatgrass' annual life cycle, researchers set up a trail camera to take pictures at the same time every day from May 20–July 13, 2021. Photos show the progression of cheatgrass' life cycle, from green-up to senescence.
Management uses for phenology forecast models
The phenology forecast models developed in this study can be adapted to:
- Time management activities, such as intensive short-duration grazing, to reduce undesirable invasive grasses
- Promote forage production and biodiversity in grasslands
- Forecast when and where invasive annual grasses have dried out and fire danger may be greatest
- Aid remote sensing specialists in selecting imagery that captures distinctive phenological phases to accurately map invasive grasses
Related
Predicting the phenology of invasive grasses under a changing climate to inform mapping and management
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Predicting the phenology of invasive grasses under a changing climate to inform mapping and management
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