UAV thermal image detects genetic trait differences among populations and genotypes of Fremont cottonwood (Populus fremontii, Salicaceae)

Abstract:

Unmanned aerial vehicle (UAV)-based sensors have been successfully used to distinguish vegetation cover types and species. UAV sensors now need to be tested in detecting genetic trait differences within single species to evaluate what species, populations, and genotypes will survive in projected climate change scenarios, because many plants are becoming increasingly maladapted to their environments due to changing climate and environmental conditions. We evaluate UAV-based high resolution thermal images for differentiating populations and genotypes in Fremont cottonwood (Populus fremontii S. Wats.), a foundation tree species that supports high levels of biodiversity and associated processes in riparian ecosystems. Specifically, we compare UAV thermal image-derived tree canopy temperatures among 16 different populations and ten replicated genotypes within two of the populations of Fremont cottonwood trees sourced from a broad environmental gradient and growing together in a common garden in central Arizona, USA. The UAV image-derived tree canopy temperatures ranged 30-42 ºC resulting in a high overall accuracy of 85% in tree canopy classification. Our results indicate that the UAV thermal image-derived mean tree canopy temperatures were significantly different among most of the 16 populations (p <0.001). Within a warm-adapted Sonoran Desert population and a cooler High Plateau population, the UAV thermal image-derived tree canopy temperatures were also significantly different among many genotypes (p<0.001). Furthermore, the UAV thermal image-derived tree canopy temperatures were significantly correlated with tree canopy cover (R²=0.73; p-value<0.001) and varied with locations across the garden. Our findings have important implications for characterizing intraspecific genetic diversity in long-lived forest trees like Fremont cottonwood and inferences for understanding ecosystem processes and guiding restoration efforts. We suggest that UAV thermal images can be used to rapidly scale laboratory- and plot-based genetics research up to the landscape level. Ecological restoration efforts informed by projected climate scenarios can benefit from the UAV-based genetics findings to identify future climate-adapted populations and genotypes for potential propagation sources.