Identifying the genetic basis of traits underlying climate adaptation remains a key goal for predicting species responses to climate change, enabling the elucidation of gene targets for future climate-resilient crops. Here, we measured 14 leaf and stomatal traits under control (well-watered) and drought conditions, subsampling a diversity collection of over 1,300 Populus trichocarpa genotypes, a potential biofuel feedstock crop. Stomatal traits were correlated with the climate of origin for genotypes, such that those originating from environments subject to water deficit tended to have smaller stomata, but with higher density. Stomatal traits were also correlated with leaf morphology, with larger leaves having larger stomata and lower stomatal density mirrored in correlations to climate of origin. The direction of plastic responses - reduced stomatal size under drought - mirrors the correlations seen among genotypes with respect to the aridity of environmental origin. Genome-Wide Association Studies (GWAS) identified loci underlying trait diversity, including candidates contributing to stomatal size. We used climate of origin to predict stomatal size in genotypes with unknown trait values and found that these predicted phenotypes confirmed empirically measured allele effects. Finally, we found evidence that future climates may select for alleles contributing to decreased stomatal size, with the strength of selection depending on the availability of moisture. These findings reveal adaptive variation in stomatal and physiological traits along with underlying genetic loci, with implications for future selection and breeding - providing insights into the responses to future climate change. Highlight Research on Populus trichocarpa reveals adaptation of physiological and stomatal traits linked to drought tolerance, with genotypes from arid regions exhibiting smaller stomata, offering insights for climate change adaptation and sustainable biofuel production. ### Competing Interest Statement The authors have declared no competing interest. * CID : Carbon Isotope Discrimination CO2 : Carbon dioxide SLA : Specific leaf area (Leaf surface area [mm2]/ dry mass [mg]) δ13C : ratio of the two stable isotopes of carbon 13C and 12C, a proxy for water use efficiency GWAS : Genome-wide association study NDVI : Normalized difference vegetation index PRI : Photochemical Reflectance Index RGR : Relative Growth Rate PWC : Proportional Water Content SAF : sustainable aviation fuel WUE : Water-use efficiency

Abstract California has large and diverse biomass resources and provides a pertinent example of how biomass use is changing and needs to change, in the face of climate mitigation policies. As in other areas of the world, California needs to optimize its use of biomass and waste to meet environmental and socioeconomic objectives. We used a systematic review to assess biomass use pathways in California and the associated impacts on climate and air quality. Biomass uses included the production of renewable fuels, electricity, biochar, compost, and other marketable products. For those biomass use pathways recently developed, information is available on the effects—usually beneficial—on greenhouse gas (GHG) emissions, and there is some, but less, published information on the effects on criteria pollutants. Our review identifies 34 biomass use pathways with beneficial impacts on either GHG or pollutant emissions, or both—the “good.” These included combustion of forest biomass for power and conversion of livestock‐associated biomass to biogas by anaerobic digestion. The review identified 13 biomass use pathways with adverse impacts on GHG emissions, criteria pollutant emissions, or both—the “bad.” Wildfires are an example of one out of eight pathways which were found to be bad for both climate and air quality, while only two biomass use pathways reduced GHG emissions relative to an identified counterfactual but had adverse air quality impacts. Issues of high interest for the “future” included land management to reduce fire risk, future policies for the dairy industries, and full life‐cycle analysis of biomass production and use.

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.