Search

Background. Wildfires may cause serious injuries to the anatomical structure of trees that can lead to tree death or long-lasting injury recovery, limiting their growth and vitality for several years. Post-fire management involves a wide range of measures aimed at recovering and restoring burnt areas. Usually, the first step is "salvage logging", i.e., the removal of irremediably injured trees. The burn severity depends on several parameters and is variable within the burnt area. For this reason, in some areas, the death of apparently healthy individuals has often been observed even after several years. This study aims to assess delayed/latent mortality by analyzing glucose like a tracer in wood by using a blood glucometer and HPLC. Results. The glucose in the phloem, cambium, and last xylem rings was measured using a glucometer developed for measuring glucose in the blood. The adopted approach detected glucose concentrations that were recognizable for different functional levels of the trees. Conclusions. The glucometer was suitable to detect the glucose in wood and phloem in order to define the death or health of the disturbed and undisturbed trees post-fire. Further investigations are required to find new solutions for a rapid evaluation of the abiotic and biotic factors that influence tree functionality in the forest. This approach will be used to predict the probability of the death of the individuals injured, which would improve the efficiency and the economy of recovery operations. [ABSTRACT FROM AUTHOR]

Abstract One of the main impacts of climate change on agriculture production is the dramatic increase of saline (Na+) content in substrate, that will impair crop performance and productivity. Here we demonstrate how the application of smart technologies such as an in vivo sensor, termed bioristor, allows to continuously monitor in real-time the dynamic changes of ion concentration in the sap of Arundo donax L. (common name giant reed or giant cane), when exposed to a progressive salinity stress. Data collected in vivo by bioristor sensors inserted at two different heights into A. donax stems enabled us to detect the early phases of stress response upon increasing salinity. Indeed, the continuous time-series of data recorded by the bioristor returned a specific signal which correlated with Na+ content in leaves of Na-stressed plants, opening a new perspective for its application as a tool for in vivo plant phenotyping and selection of genotypes more suitable for the exploitation of saline soils.

The development of tools to quickly identify the fate of damaged trees after a stress event such as a wildfire is of great importance. In this context, an innovative approach to assess irreversible physiological damage in trees could help to support the planning of management decisions for disturbed sites to restore biodiversity, protect the environment and understand the adaptations of ecosystem functionality. The vitality of trees can be estimated by several physiological indicators, such as cambium activity and the amount of starch and soluble sugars, while the accumulation of ethanol in the cambial cells and phloem is considered an alarm sign of cell death. However, their determination requires time-consuming laboratory protocols, making the approach impractical in the field. Biosensors hold considerable promise for substantially advancing this field. The general objective of this review is to define a system for quantifying the plant vitality in forest areas exposed to fire. This review describes recent electrochemical biosensors that can detect plant molecules, focusing on biosensors for glucose, fructose, and ethanol as indicators of tree vitality. [ABSTRACT FROM AUTHOR]

The quantification of tree-related microhabitats (TreMs) and multi-taxon biodiversity is pivotal to the implementation of forest conservation policies, which are crucial under the current climate change scenarios. We assessed the capacity of Airborne Laser Scanning (ALS) data to quantify biodiversity indices related to both forest beetle and bird communities and TreMs, calculating the species richness and types of saproxylic and epixylic TreMs using the Shannon index. As biodiversity predictors, 240 ALS-derived metrics were calculated: 214 were point-cloud based, 14 were pixel-level from the canopy height model, and 12 were RGB spectral statistics. We used the random forests algorithm to predict species richness and the Shannon diversity index, using the field plot measures as dependent variables and the ALS-derived metrics as predictors for each taxon and TreMs type. The final models were used to produce wall-to-wall maps of biodiversity indices. The Shannon index produced the best performance for each group considered, with a mean difference of −6.7%. Likewise, the highest R2 was for the Shannon index (0.17, against 0.14 for richness). Our results confirm the importance of ALS data in assessing forest biodiversity indicators that are relevant for monitoring forest habitats. The proposed method supports the quantification and monitoring of the measures needed to implement better forest stands and multi-taxon biodiversity conservation. [ABSTRACT FROM AUTHOR]

This article, titled "Editorial: Combined abiotic interactions in woody plants," discusses the impact of combined abiotic stressors, such as drought and high temperatures, on the physiology, growth, and survival of trees. The simultaneous occurrence of these stressors can exacerbate their individual effects, leading to challenges for plants and potentially reducing ecosystem productivity. The article also explores the effects of light availability and heavy metal stress on tree growth and physiology. Understanding these interactions is crucial in the context of climate change, which is increasing the frequency and severity of droughts and heatwaves globally. The article suggests that further research is needed to study multi-stress interactions and develop effective adaptation strategies for climate change. [Extracted from the article]

Trees have been used for phytoremediation and as biomonitors of air pollution. However, the mechanisms by which trees mitigate nanoparticle pollution in the environment are still unclear. We investigated whether two important tree species, European beech (Fagus sylvatica L.) and Scots pine (Pinus sylvestris L.), are able to take up and transport differently charged gold nanoparticles (Au-NPs) into their stem by comparing leaf-to-root and root-to-leaf pathways. Au-NPs were taken up by roots and leaves, and a small fraction was transported to the stem in both species. Au-NPs were transported from leaves to roots but not vice versa. Leaf Au uptake was higher in beech than in pine, probably because of the higher stomatal density and wood characteristics of beech. Confocal (3D) analysis confirmed the presence of Au-NPs in trichomes and leaf blade, about 20–30 μm below the leaf surface in beech. Most Au-NPs likely penetrated into the stomatal openings through diffusion of Au-NPs as suggested by the 3D XRF scanning analysis. However, trichomes were probably involved in the uptake and internal immobilization of NPs, besides their ability to retain them on the leaf surface. The surface charge of Au-NPs may have played a role in their adhesion and uptake, but not in their transport to different tree compartments. Stomatal conductance did not influence the uptake of Au-NPs. This is the first study that shows nanoparticle uptake and transport in beech and pine, contributing to a better understanding of the interactions of NPs with different tree species. [ABSTRACT FROM AUTHOR]

The study of afforestation is crucial to monitor land transformations and represents a central topic in sustainable development procedures, in terms of climate change, ecosystem services monitoring, and planning policies activities. Although surveying afforestation is important, the assessment of the growing forests is difficult, since land cover has different durations depending on the species. In this context, remote sensing can be a valid instrument to evaluate the afforestation process. Nevertheless, while a vast literature on forest disturbance exists, only a few studies focus on afforestation and almost none directly exploits remote sensing data. This study aims to automatically classify non-forest, afforestation, and forest areas using remote sensing data. To this purpose, we constructed a reference dataset of 61 polygons that suffered a change from non-forest to forest in the period 1988-2020. The reference data were constructed with the Land Use Inventory of Italy and through photointerpretation of orthophotos (1988-2012, spatial resolution 50 × 50 cm) and very high-resolution images (2012-2020, spatial resolution 30 × 30 cm). Using Landsat Best Available Pixel composites time-series (1984-2020) we calculated 52 temporal predictors: four temporal metrics (median, standard deviation, Pearson's correlation coefficient R, and slope) calculated for 13 different bands (the six Landsat spectral bands, three Spectral Vegetation Indices, and four Tasseled Cap Indices). To verify the possibility of distinguishing afforestation from non-forest and forest, given the differences between them can be minimal, we tested four different models aiming at classifying the following categories: (i) non-forest/afforestation, (ii) afforestation/forest, (iii) non-forest/forest and (iv) non-forest/afforestation/forest. Temporal predictors were used with random forest which was calibrated using random search, validated using k-fold Cross-Validation Overall Accuracy (OAcv), and further using out-ofbag independent data (OAoob). Results illustrate that the distinction of afforestation/forest reaches the largest OAcv (87%), followed by non-forest/forest (83%), non-forest/afforestation (75%) and non-forest/afforestation/forest (72%). The different OA values confirm that the difference in photosynthetic activity between forest and afforestation can be analysed through remote sensing to distinguish them. Although remote sensing data are currently not exploited to monitor afforestation areas our results suggest it may be a valid support for country-level monitoring and reporting. [ABSTRACT FROM AUTHOR]