Differences in Near Isohydric and Anisohydric Behavior of Contrasting Poplar Hybrids (I-101 (Populus alba L.) × 84K (Populus alba L. × Populus glandulosa Uyeki)) under Drought-Rehydration Treatments

Increasing atmospheric dryness reduces boreal forest tree growth

Rising atmospheric vapour pressure deficit (VPD) associated with climate change affects boreal forest growth via stomatal closure and soil dryness. However, the relationship between VPD and forest growth depends on the climatic context. Here we assess Canadian boreal forest responses to VPD changes from 1951-2018 using a well-replicated tree-growth increment network with approximately 5,000 species-site combinations. Of the 3,559 successful growth models, we observed a relationship between growth and concurrent summer VPD in one-third of the species-site combinations, and between growth and prior summer VPD in almost half of those combinations. The relationship between previous year VPD and current year growth was almost exclusively negative, while current year VPD also tended to reduce growth. Tree species, age, annual temperature, and soil moisture primarily determined tree VPD responses. Younger trees and species like white spruce and Douglas fir exhibited higher VPD sensitivity, as did areas with high annual temperature and low soil moisture. Since 1951, summer VPD increases in Canada have paralleled tree growth decreases, particularly in spruce species. Accelerating atmospheric dryness in the decades ahead will impair carbon storage and societal-economic services.

Limited plasticity of anatomical and hydraulic traits in aspen trees under elevated CO2 and seasonal drought

The timing of abiotic stress elicitors on wood formation largely affects xylem traits that determine xylem efficiency and vulnerability. Nonetheless, seasonal variability of elevated CO2 (eCO2) effects on tree functioning under drought remains largely unknown. To address this knowledge gap, 1-year-old aspen (Populus tremula L.) trees were grown under ambient (±445 ppm) and elevated (±700 ppm) CO2 and exposed to an early (spring/summer 2019) or late (summer/autumn 2018) season drought event. Stomatal conductance and stem shrinkage were monitored in vivo as xylem water potential decreased. Additional trees were harvested for characterization of wood anatomical traits and to determine vulnerability and desorption curves via bench dehydration. The abundance of narrow vessels decreased under eCO2 only during the early season. At this time, xylem vulnerability to embolism formation and hydraulic capacitance during severe drought increased under eCO2. Contrastingly, stomatal closure was delayed during the late season, while hydraulic vulnerability and capacitance remained unaffected under eCO2. Independently of the CO2 treatment, elastic, and inelastic water pools depleted simultaneously after 50% of complete stomatal closure. Our results suggest that the effect of eCO2 on drought physiology and wood traits are small and variable during the growing season and question a sequential capacitive water release from elastic and inelastic pools as drought proceeds.

Automatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance

The CO₂ and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.

An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model

Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.