Search

UAV-based photogrammetry can provide detailed, accurate, and consistent structural models of open-grown trees across different species and growth forms. Environmental and ambient light conditions impact the results. Our empirical knowledge of the complicated and highly dynamic three-dimensional structure of trees is limited. Drone-based photogrammetry offers a powerful and effective tool for capturing, analyzing, and monitoring adult trees, particularly individuals growing outside of the closed canopy forest (subsequently termed “open-grown”) in both natural and built environments. Here, we test the accuracy and consistency of high-resolution models of individual open-grown trees of three structurally distinct species. To validate model accuracy, we compared model estimates with direct measurements of branch diameter and internode length. We also examined the consistency of models of the same individual given different ambient light conditions by comparing corresponding measurements from two models of each tree. We use readily available equipment and software, so our protocol can be rapidly adopted by professional and citizen scientists alike. We found that the models captured diameter and interior internode length well (r2 = 0.87 and 0.98, respectively). Difference between measurements from different models of the same tree was less than 3 cm for diameters and interior internode lengths of most size classes. Thin distal branches were not captured well: measurements of terminal internodes in most size classes were underestimated by 50–60 cm. Models from overcast days were more accurate than models from sunny days (p = 0.0056), and a high-contrast background helps capture thin branches. Our approach constructs accurate and consistent three-dimensional models of individual open-grown trees that can provide the foundation for a wide range of physiological, behavioral, and growth studies.

Phenotypic plasticity can mask population genetic differentiation, reducing the predictability of trait-environment relationships. In short-lived plants, reproductive traits may be more genetically determined due to their direct impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with global field observations for the short-lived Plantago lanceolata, we 1) disentangled the genetic and plastic responses of functional traits to a set of environmental drivers and 2) assessed the utility of trait-environment relationshisps inferred from observational data for predicting genetic differentiation. Reproductive traits showed distinct genetic differentiation that was highly predictable from observational data, but only when correcting traits for differences in their (labile) biomass component. Vegetative traits showed higher plasticity and contrasting genetic and plastic responses, leading to unpredictable trait patterns. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related with fitness.

Genetic differentiation and phenotypic plasticity jointly shape intraspecific trait variation, but their roles differ among traits. In short- lived plants, reproductive traits may be more genetically determined due to their impact on fitness, whereas vegetative traits may show higher plasticity to buffer short- term perturbations. Combining a multi- treatment greenhouse experiment with observational field data throughout the range of a widespread short- lived herb, Plantago lanceolata, we (1) disentangled genetic and plastic responses of functional traits to a set of environmental drivers and (2) assessed how genetic differentiation and plasticity shape observational trait– environment relationships. Reproductive traits showed distinct genetic differentiation that largely determined observational patterns, but only when correcting traits for differences in biomass. Vegetative traits showed higher plasticity and opposite genetic and plastic responses, masking the genetic component underlying field- observed trait variation. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related to fitness.