TraitCapture and Phenomic Environmental Sensing Arrays: NextGen tools for scaling from seeds to traits to ecosystems.
Effectively addressing the environmental, land management and food production challenges of the 21st century requires exponential increases in our ability to understand and model ecosystem and agricultural processes. Lab approaches offer an opportunity to accurately model the genetic and environmental basis of yield and fitness traits. In the field, we can fit models to predict how genotype/environment interactions scale to ecosystems.
High-throughput phenomics in the lab can be used precisely measure thousands of plants under simulated climate conditions. Full-genome sequencing and genome-wide association studies (GWAS) are used to dissect how traits emerge as an interaction between genes and environment. This multilayer, high-dimensional data analysis is challenging and scaling such approaches to the field is very difficult.
To address this challenge, regional-scale monitoring projects such as NEON and new monitoring technologies (UAVs, mesh networks, etc.) combined with cloud-based computational resources allow us to monitor the environment at unprecedented resolutions. But our ability to collect data is rapidly outstripping our capacity to visualize and analyzing these data. The lack of data standards and open-source software are a major limiting factor in our ability to effectively make use of complex research data.
We present two projects focused on generating and managing “big data” for phenotypic analysis of plants in the lab and field.
TraitCapture is an open-source high-throughput phenotyping system combining multispectral lighting and environmental controls simulating regional or climate-shifted growth conditions with real-time phenotyping of 2,000 plants/month. Plant sequence data and phenotypes are co-analyzed with GWAS to identify heritable traits over time and across environments.
The Phenomic-Environmental-Sensing-Array (PESA) is a field-based system at the new National Arboretum in Canberra, Australia. PESA combines traditional and “NextGen” monitoring technologies including weather stations, microclimate mesh sensors, high-precision dendrometers, time-series UAV imaging, aerial and high-density ground-based LiDAR, phenocams and gigapixel resolution timelapse cameras. The site has biologically significant microclimate variation and trees will be subject to a multi-year watering trial. This phenomic and environmental data, combined with tree genetic data, will allow us to predict how environment and genetics shapes phenotype and explore how a maturing forest changes overall site microclimate.
Both projects have in common that they involve huge numbers of images, multiple data layers, and automated data processing, requiring development of robust data standards and novel visualization/analysis tools. To help bridge the gap from lab to field, all software tools are web-based, open-source and work with both lab and field datasets.