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Grubinger, Samuel Joseph

University of British Columbia

Forestry presents an opportunity for climate change mitigation, with the management of forest genetic resources playing a crucial role in ensuring forest adaptation to changing climates. Tree breeding programs and seed transfer policies rely on common-garden field trials to quantify genetic variation for traits related to growth, health, and adaptation. There is a need for (1) more efficient methods of measuring cumulative growth and (2) new traits quantifying unobserved patterns of adaptation. Airborne laser scanning (ALS), digital aerial photogrammetry (DAP), and recent advances in drone, sensor, and software technology allow for these data to be produced accurately, affordably, at fine spatial resolutions, and repeatedly. Multispectral imagery provides a window into physiology through vegetation indices relating to pigments, foliar structure, and phenology. This dissertation assesses structural and spectral traits from drone remote sensing for phenotyping of common-garden trials of conifers at stand, crown, and foliage levels. First, I applied ALS to characterize genetic differences in stand structure in coastal Douglas-fir (Pseudotsuga menziesii var. menziesii), across a gradient of site productivity. I validated height and leaf area metrics and demonstrated their use for predicting genetic gain for stem volume. Second, moving to the individual-tree scale, I used spectral traits from multispectral imagery and structural traits from DAP to analyze patterns of local adaptation to temperature, moisture, and elevational in interior spruce (Picea engelmannii, Picea glauca, and their hybrids), across a climate gradient. I validated remotely sensed proxies of height and diameter for seed sources and found stronger evidence of local adaptation to climate for spectral traits than for structural traits under certain conditions. Third, to examine potentially adaptive differences in foliar physiology, I analyzed foliar spectral trait dynamics over time at a single site in late winter, mid summer, and late summer. I revealed signals of adaptation to heat, cold, snow, growing season, and aridity, and showed that seasonal spectral dynamics can be used to distinguish populations by geoclimatic origin and hybrid ancestry. This dissertation generated novel drone remote sensing methodologies for phenotyping common-garden trials, revealed new dimensions of genetic variation through spectral reflectance, and contributed to functional knowledge of forest genetics.

Text

2024-07-16

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88646

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/