LB 36-306 - Drone-based predictions of big sagebrush demographic performance across an elevation gradient

Climate change is threatening rangeland ecosystems, including increasing frequency of extreme weather, wildfire, and drought. Identifying which native plants are likely to be resilient to these ongoing changes is crucial for developing climate-smart restoration plans. Sagebrush is a keystone species across western rangeland, supporting populations of native plant and animal species with structure and forage. Genetic variation underlies the wide home range of sagebrush, including resilience to varying abiotic conditions. To understand how sagebrush populations will respond to climate change, linking demographic response to weather events with genetic variation is necessary. We are applying high-resolution remotely sensed data to map sagebrush genotypes and demographic responses to drought conditions. Using unoccupied aerial systems (UAVs) we collected multispectral and RGB imagery that enabled canopy segmentation and image classification through machine learning algorithms. We applied these spatial data to quantify flower production for individual plants across our 240-acre study site in Castle Rocks State Park, Idaho. Our study includes two flowering periods in 2021, following an unprecedented heat wave early in the growing season, and 2022. Individual plants represent all three-sagebrush subspecies: Wyoming Big Sagebrush (Artemisia tri. wyomingensis), Mountain Sagebrush (Artemisia tri. vaseyana), and Basin Big Sagebrush (Artemisia tri. tridentata). We found that multispectral imagery enabled prediction of flower stalk production, with R2 >65%. Our results demonstrate significant differences in flower production between the three subspecies along an elevational gradient, including year-to-year differences that point to potential gene by environment interactions. Our work demonstrates the potential for UAS data collection to quantify how individual plants respond to weather events across landscape-scale environmental gradients, including an algorithm that can predict flower production. Our goal is to apply these results to enable land managers to identify locally adapted sagebrush genotypes that will thrive in future climate regimes.