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The impact of global warming on transpiration and photosynthesis would compromise plant fitness, impacting on crop yields and ecosystem functioning. In this frame, we explored the performance of a set of Arabidopsis mutants carrying partial or total loss-of-function alleles of stomatal development genes and displaying distinct stomatal abundances. Using microscopy and non-invasive imaging techniques on this genotype collection, we examined anatomical leaf and stomatal traits, plant growth and development, and physiological performance at optimal (22°C) and supra-optimal (30°C) temperatures. All genotypes showed thermomorphogenetic responses but no signs of heat stress. Data analysis singled out an extremely low stomatal abundance mutant, spch-5 . At 22°C, spch-5 had lower transpiration and warmer leaves than the wild type. However, at 30°C, this mutant developed larger stomata and thinner leaves, paralleled by a notable cooling capacity, similar to that of the wild type. Despite their low stomatal density (SD), spch-5 plants grown at 30°C showed no photosynthesis or growth penalties. The behavior of spch-5 at supra-optimal temperature exemplifies how the effect of very low stomatal numbers can be counteracted by a combination of larger stomata and thinner leaves. Furthermore, it provides a novel strategy for coping with high growth temperatures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pérez-Bueno, Illescas-Miranda, Martín-Forero, de Marcos, Barón, Fenoll and Mena.)

A rapid diagnosis of black rot in brassicas, a devastating disease caused by Xanthomonas campestris pv. campestris (Xcc), would be desirable to avoid significant crop yield losses. The main aim of this work was to develop a method of detection of Xcc infection on broccoli leaves. Such method is based on the use of imaging sensors that capture information about the optical properties of leaves and provide data that can be implemented on machine learning algorithms capable of learning patterns. Based on this knowledge, the algorithms are able to classify plants into categories (healthy and infected). To ensure the robustness of the detection method upon future alterations in climate conditions, the response of broccoli plants to Xcc infection was analyzed under a range of growing environments, taking current climate conditions as reference. Two projections for years 2081-2100 were selected, according to the Assessment Report of Intergovernmental Panel on Climate Change. Thus, the response of broccoli plants to Xcc infection and climate conditions has been monitored using leaf temperature and five conventional vegetation indices (VIs) derived from hyperspectral reflectance. In addition, three novel VIs, named diseased broccoli indices (DBI 1 -DBI 3 ), were defined based on the spectral reflectance signature of broccoli leaves upon Xcc infection. Finally, the nine parameters were implemented on several classifying algorithms. The detection method offering the best performance of classification was a multilayer perceptron-based artificial neural network. This model identified infected plants with accuracies of 88.1, 76.9, and 83.3%, depending on the growing conditions. In this model, the three Vis described in this work proved to be very informative parameters for the disease detection. To our best knowledge, this is the first time that future climate conditions have been taken into account to develop a robust detection model using classifying algorithms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pineda, Pérez-Bueno and Barón.)

Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised., (Copyright © 2019 Pérez-Bueno, Pineda and Barón.)

The evolutionary processes underlying the high diversity and endemism in the Cerrado, the most extensive Neotropical savanna, remain unclear, including the factors promoting the presence and evolution of savanna enclaves in the Amazon forest. In this study, we investigated the effects of past climate changes on genetic diversity, dynamics of species range and the historical connections between the savanna enclaves and Cerrado core for Qualea grandiflora , a tree species widely distributed in the biome. Totally, 40 populations distributed in the Cerrado core and Amazon savannas were analyzed using chloroplast and nuclear DNA sequences. We used phylogeographic, coalescent and ecological niche modeling approaches. Genetic data revealed a phylogeographic structure shaped by Pleistocene climatic oscillations. An eastern-western split in the Cerrado core was observed. The central portion of the Cerrado core harbored most of the sampled diversity for cpDNA. Ecological niche models predicted the presence of a large historical refuge in this region and multiple small refuges in peripheral areas. Relaxed Random Walk (RRW) models indicated the ancestral population in the north-western border of the central portion of the Cerrado core and cyclical dynamics of colonization related to Pleistocene climatic oscillations. Central and western ancient connections between Cerrado core and Amazonian savannas were observed. No evidence of connections among the Amazonian savannas was detected. Our study highlights the importance of Pleistocene climatic oscillations for structuring the genetic diversity of Q. grandiflora and complex evolutionary history of ecotonal areas in the Cerrado. Our results do not support the recent replacement of a large area in the Amazon forest by savanna vegetation. The Amazonian savannas appear to be fragmented and isolated from each other, evolving independently a long ago.

The bacterium Dickeya dadantii is responsible of important economic losses in crop yield worldwide. In melon leaves, D. dadantii produced multiple necrotic spots surrounded by a chlorotic halo, followed by necrosis of the whole infiltrated area and chlorosis in the surrounding tissues. The extent of these symptoms, as well as the day of appearance, was dose-dependent. Several imaging techniques (variable chlorophyll fluorescence, multicolor fluorescence, and thermography) provided spatial and temporal information about alterations in the primary and secondary metabolism, as well as the stomatal activity in the infected leaves. Detection of diseased leaves was carried out by using machine learning on the numerical data provided by these imaging techniques. Mathematical algorithms based on data from infiltrated areas offered 96.5 to 99.1% accuracy when classifying them as mock vs. bacteria-infiltrated. These algorithms also showed a high performance of classification of whole leaves, providing accuracy values of up to 96%. Thus, the detection of disease on whole leaves by a model trained on infiltrated areas appears as a reliable method that could be scaled-up for use in plant breeding programs or precision agriculture.

Although the impact of Orobanche cumana Wallr. on sunflower ( Helianthus annuus L.) becomes evident with emergence of broomrape shoots aboveground, infection occurs early after sowing, the host physiology being altered during underground parasite stages. Genetic resistance is the most effective control method and one of the main goals of sunflower breeding programmes. Blue-green fluorescence (BGF) and thermal imaging allow non-destructive monitoring of plant diseases, since they are sensitive to physiological disorders in plants. We analyzed the BGF emission by leaves of healthy sunflower plantlets, and we implemented BGF and thermal imaging in the detection of the infection by O. cumana during underground parasite development. Increases in BGF emission were observed in leaf pairs of healthy sunflowers during their development. Lower BGF was consistently detected in parasitized plants throughout leaf expansion and low pigment concentration was detected at final time, supporting the interpretation of a decrease in secondary metabolites upon infection. Parasite-induced stomatal closure and transpiration reduction were suggested by warmer leaves of inoculated sunflowers throughout the experiment. BGF imaging and thermography could be implemented for fast screening of sunflower breeding material. Both techniques are valuable approaches to assess the processes by which O. cumana alters physiology (secondary metabolism and photosynthesis) of sunflower.

The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii . Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini- D. dadantii , which classify new samples as "healthy" or "infected." The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

Broomrape, caused by the root holoparasite Orobanche cumana, is the main biotic constraint to sunflower oil production worldwide. By the time broomrape emerges, most of the metabolic imbalance has been produced by O. cumana to sunflower plants. UV-induced multicolor fluorescence imaging (MCFI) provides information on the fluorescence emitted by chlorophyll (Chl) a of plants in the spectral bands with peaks near 680 nm (red, F680) and 740 nm (far-red, F740). In this work MCFI was extensively applied to sunflowers, either healthy or parasitized plants, for the first time. The distribution of red and far-red fluorescence was analyzed in healthy sunflower grown in pots under greenhouse conditions. Fluorescence patterns were analyzed across the leaf surface and throughout the plant by comparing the first four leaf pairs (LPs) between the second and fifth week of growth. Similar fluorescence patterns, with a delay of 3 or 4 days between them, were obtained for LPs of healthy sunflower, showing that red and far-red fluorescence varied with the developmental stage of the leaf. The use of F680 and F740 as indicators of sunflower infection by O. cumana during underground development stages of the parasite was also evaluated under similar experimental conditions. Early increases in F680 and F740 as well as decreases in F680/F740 were detected upon infection by O. cumana. Significant differences between inoculated and control plants depended on the LP that was considered at any time. Measurements of Chl contents and final total Chl content supported the results of MCFI, but they were less sensitive in differentiating healthy from inoculated plants. Sunflower infection was confirmed by the presence of broomrape nodules in the roots at the end of the experiment. The potential of MCFI in the red and far-red region for an early detection of O. cumana infection in sunflower was revealed. This technique might have a particular interest for early phenotyping in sunflower breeding programs. To our knowledge, this is the first work where the effect of a parasitic plant in its host is analyzed by means of fluorescence imaging in the red and far-red spectral regions.

The necrotrophic bacteria Dickeya dadantii is the causal agent of soft-rot disease in a broad range of hosts. The model plant Nicotiana benthamiana, commonly used as experimental host for a very broad range of plant pathogens, is susceptible to infection by D. dadantii. The inoculation with D. dadantii at high dose seems to overcome the plant defense capacity, inducing maceration and death of the tissue, although restricted to the infiltrated area. By contrast, the output of the defense response to low dose inoculation is inhibition of maceration and limitation in the growth, or even eradication, of bacteria. Responses of tissue invaded by bacteria (neighboring the infiltrated areas after 2-3 days post-inoculation) included: (i) inhibition of photosynthesis in terms of photosystem II efficiency; (ii) activation of energy dissipation as non-photochemical quenching in photosystem II, which is related to the activation of plant defense mechanisms; and (iii) accumulation of secondary metabolites in cell walls of the epidermis (lignins) and the apoplast of the mesophyll (phytoalexins). Infiltrated tissues showed an increase in the content of the main hormones regulating stress responses, including abscisic acid, jasmonic acid, and salicylic acid. We propose a mechanism involving the three hormones by which N. benthamiana could activate an efficient defense response against D. dadantii.

Loss of function of the positive stomata development regulators SPCH or MUTE in Arabidopsis thaliana renders stomataless plants; spch-3 and mute-3 mutants are extreme dwarfs, but produce cotyledons and tiny leaves, providing a system to interrogate plant life in the absence of stomata. To this end, we compared their cotyledon transcriptomes with that of wild-type plants. K-means clustering of differentially expressed genes generated four clusters: clusters 1 and 2 grouped genes commonly regulated in the mutants, while clusters 3 and 4 contained genes distinctively regulated in mute-3. Classification in functional categories and metabolic pathways of genes in clusters 1 and 2 suggested that both mutants had depressed secondary, nitrogen and sulfur metabolisms, while only a few photosynthesis-related genes were down-regulated. In situ quenching analysis of chlorophyll fluorescence revealed limited inhibition of photosynthesis. This and other fluorescence measurements matched the mutant transcriptomic features. Differential transcriptomes of both mutants were enriched in growth-related genes, including known stomata development regulators, which paralleled their epidermal phenotypes. Analysis of cluster 3 was not informative for developmental aspects of mute-3. Cluster 4 comprised genes differentially up-regulated in mute-3, 35% of which were direct targets for SPCH and may relate to the unique cell types of mute-3. A screen of T-DNA insertion lines in genes differentially expressed in the mutants identified a gene putatively involved in stomata development. A collection of lines for conditional overexpression of transcription factors differentially expressed in the mutants rendered distinct epidermal phenotypes, suggesting that these proteins may be novel stomatal development regulators. Thus, our transcriptome analysis represents a useful source of new genes for the study of stomata development and for characterizing physiology and growth in the absence of stomata.