Introducing and validating a high-throughput bud break sensor Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9k41zk49t

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  • Bud break is a key adaptive trait that can help us understand how plants respond to a changing climate from the molecular to landscape scale. Despite this, acquisition of bud break data is currently constrained by cost, scale, and a lack of information at the plant scale on the environmental stimuli that precede and cause bud dormancy to release. Additionally, to date, little effort has been devoted to phenotyping plants in nature owing to the challenge of controlling for environmental variation to isolate genomic effects on plant phenotype. Notwithstanding, natural selection operates on natural populations, and investigation of adaptive phenotypes in situ is warranted and can validate results from controlled laboratory experiments. In order to identify genomic effects on individual plant phenotypes in nature, environmental drivers must be concurrently measured and characterized. Here, we innovated and evaluated a sensor to meet these requirements for temperate woody plants. It was designed for use on a tree branch to measure the timing of bud break together with its key environmental drivers, temperature and day length. Specifically, we evaluated the sensor through independent corroboration with time-lapse photography and a suite of environmental sampling instruments. We also tested whether the presence of the device itself on a branch influenced the timing of bud break. Our results indicated the following: the bud break sensor's digital thermometer closely approximated a thermocouple touching plant tissue; the photoperiod detector measured ambient light with the same accuracy as time lapse photography; the phenology sensor accurately detected the timing of bud break; and the sensor itself did not influence bud phenology. Future use of the sensor can help galvanize a new field of study, landscape phenomics, or high-throughput phenotyping of organisms at the landscape level for integration with landscape genomics among other potential applications and disciplines.
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Last modified: 10/27/2017

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