Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca)

Weak local adaptation to drought in seedlings of a widespread conifer

Tree seedlings from populations native to drier regions are often assumed to be more drought tolerant than those from wetter provenances. However, intraspecific variation in drought tolerance has not been well-characterized despite being critical for developing climate change mitigation and adaptation strategies, and for predicting the effects of drought on forests. We used a large-scale common garden drought-to-death experiment to assess range-wide variation in drought tolerance, measured by decline of photosynthetic efficiency, growth, and plastic responses to extreme summer drought in seedlings of 73 natural populations of the two main varieties of Douglas-fir (Pseudotsuga menziesii var. menziesii and var. glauca). Local adaptation to drought was weak in var. glauca and nearly absent in menziesii. Var. glauca showed higher tolerance to drought but slower growth than var. menziesii. Clinal variation in drought tolerance and growth species-wide was mainly associated with temperature rather than precipitation. A higher degree of plasticity for growth was observed in var. menziesii in response to extreme drought. Genetic variation for drought tolerance in seedlings within varieties is maintained primarily within populations. Selective breeding within populations may facilitate adaptation to drought more than assisted gene flow.

Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.