Drone-Based Monitoring of Reproductive Potential in a Foundational Shrub Species

Restoration and conservation of native plant populations will benefit from identifying individual plants with high reproductive success. While high-fecundity plants are ideal for seed sourcing, locating these plants across heterogeneous landscapes presents a logistical challenge. This challenge is especially significant for big sagebrush (Artemisia tridentata), a foundational species that is the focus of large-scale seed collection for restoration efforts in western rangelands. We evaluated whether cost-effective RGB imagery from unoccupied aerial vehicles (UAVs) could map flower stalk production in big sagebrush plants. Models were trained using three years of data from four sites spanning an elevational gradient that included all three big sagebrush subspecies: A. t. wyomingensis, A. t. vaseyana, and A. t. tridentata. Our model predicted flower stalk production from UAV imagery with a Mean Absolute Error (MAE) of [~]100 stalks, which is relatively low given that some plants produced more than 700 stalks. A hurdle model that explicitly accounted for excess zeroes outperformed simpler negative binomial models, suggesting that reproductive failure is distinct from flower stalk production in reproductive plants. Structural metrics, including height differences between June and September, canopy height, and edge-to-area ratio of plant crowns, had stronger effects in our model for counts of flower stalk production than spectral data. Model performance was consistent across environmentally heterogeneous sites but declined when applied to years excluded from training, indicating that year-specific training data may be necessary for interannual predictions. These results demonstrate that UAVs can monitor reproductive potential in wild plants and help identify high-fecundity individuals for seed collection. Our work underscores the need for future research that can improve predictions of flower production, including integrating multispectral data and increasing model reliability across years to support climate-resilient restoration strategies.