Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.
Highlights
Emerging advances in unmanned aerial vehicles (UAVs) and miniaturization of sensors allow the acquisition of ultra-high-resolution data (>1000 points/m2) for phenotyping and studying biological processes at the individual tree level in ways never before imagined [1,2]
We focus on key structural traits associated with tree architecture and productivity estimated for two Eucalyptus species regularly used for ecological restoration of temperate woodland ecosystems in south-eastern Australia
Our study demonstrates the potential of UAV-light detection and ranging (LiDAR) for the quantification of traditional forest traits at the individual tree-level, as well as for the development of novel LiDAR-derived traits that can offer new insights into species differentiation and local adaptation
Summary
Emerging advances in unmanned aerial vehicles (UAVs) and miniaturization of sensors allow the acquisition of ultra-high-resolution data (>1000 points/m2) for phenotyping and studying biological processes at the individual tree level in ways never before imagined [1,2]. Phenotyping is the first step in unravelling the genetic control of traits of ecological and economical significance. This understanding is important as the genetic variation in traits among populations often reflects plant adaptation to environmental variation [3], and allows the prediction of population responses to selection, be it artificial (breeding) or natural (evolutionary response) [4]. In the case of quantitative genetics, such studies require phenotyping a large number of individuals to allow the accurate estimation of genetic parameters, such as provenance differences and heritability. While traditional phenotypic traits, such as tree height, can be quantified using remote sensing with greater speed and precision than on-ground measurements [5], dense UAV-derived point clouds allow the estimation of difficult-to-measure traits (e.g., above-ground biomass or crown architectural traits) and the development of novel crown shape and density traits
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