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Forest health is an attractive concept in forestry research, which environmental, social, and political interests have shaped. Assessing forest health is crucial, but finding a single definition of the concept is complex. It is determined by the aim of the forest study, different areas of knowledge, scales of work, technology, methodologies, the historical moment, or the source of funding, among others. With almost a century of scientific evidence, the aim is to identify and contextualise temporal changes in the relevance of this key concept. Trends are analysed through the construction of three main descriptors (state variables, drivers, and methods) and the main conceptual subdomains (themes). This review reveals the significant geographical bias driven by diverse needs and interests. Methodologies have evolved from traditional inventories to the use of advanced tools such as remote sensing or ecophysiology, improving forest characterisation at both global and individual scales. Research has evolved from unicausality towards holistic and multidisciplinary approaches, influencing research to date and future scenarios. We identified key knowledge gaps in the scientific literature, particularly the concepts of ecosystem services, Essential Biodiversity Variables (EBVs) and the concept of "one health". These findings highlight the need for a research assessment, future directions, and scenarios for forest health research. [ABSTRACT FROM AUTHOR]

Currently, climate change requires the quantification of carbon stored in forest biomass. Synthetic aperture radar (SAR) data offers a significant advantage over other remote detection measurement methods in providing structural and biomass-related information about ecosystems. This study aimed to develop non-parametric Random Forest regression models to assess the changes in the aboveground forest biomass (AGB), basal area (G), and tree density (N) of Mediterranean pine forests by integrating ALOS-PALSAR, Sentinel 1, and Landsat 8 data. Variables selected from the Random Forest models were related to NDVI and optical textural variables. For 2015, the biomass models with the highest performance integrated ALS-ALOS2-Sentinel 1-Landsat 8 data (R2 = 0.59) by following the model using ALS data (R2 = 0.56), and ALOS2-Sentinel 1-Landsat 8 (R2 = 0.50). The validation set showed that R2 values vary from 0.55 (ALOS2-Sentinel 1-Landsat 8) to 0.60 (ALS-ALOS2-Sentinel 1-Landsat 8 model) with RMSE below 20 Mg ha−1. It is noteworthy that the individual Sentinel 1 (R2 = 0.49). and Landsat 8 (R2 = 0.47) models yielded equivalent results. For 2020, the AGB model ALOS2-Sentinel 1-Landsat 8 had a performance of R2 = 0.55 (validation R2 = 0.70) and a RMSE of 9.93 Mg ha−1. For the 2015 forest structural variables, Random Forest models, including ALOS PAL-SAR 2-Sentinel 1 Landsat 8 explained between 30% and 55% of the total variance, and for the 2020 models, they explained between 25% and 55%. Maps of the forests' structural variables were generated for 2015 and 2020 to assess the changes during this period using the ALOS PALSAR 2-Sentinel 1-Landsat 8 model. Aboveground biomass (AGB), diameter at breast height (dbh), and dominant height (Ho) maps were consistent throughout the entire study area. However, the Random Forest models underestimated higher biomass levels (>100 Mg ha−1) and overestimated moderate biomass levels (30–45 Mg ha−1). The AGB change map showed values ranging from gains of 43.3 Mg ha−1 to losses of −68.8 Mg ha−1 during the study period. The integration of open-access satellite optical and SAR data can significantly enhance AGB estimates to achieve consistent and long-term monitoring of forest carbon dynamics. [ABSTRACT FROM AUTHOR]

Abstract Phytophthora cinnamomi Rands. is an important root rot pathogen widely distributed in the north hemisphere, with a large host range. Among others diseases, it is known to be a principal factor in the decline of holm oak and cork oak, the most important tree species in the “dehesa” ecosystem of south-western Spain. Previously, the focus of studies on P. cinnamomi and holm oak have been on molecular tools for identification, functional responses of the host, together with other physiological and morphological host variables. However, a microscopic index to describe the degree of infection and colonization in the plant tissues has not yet been developed. A colonization or infection index would be a useful tool for studies that examine differences between individuals subjected to different treatments or to individuals belonging to different breeding accessions, together with their specific responses to the pathogen. This work presents a methodology based on the capture and digital treatment of microscopic images, using simple and accessible software, together with a range of variables that quantify the infection and colonization process.

Phytophthora root rot is considered one of the main factors associated with holm oak (Quercus ilex L.) mortality. The effectiveness and accuracy of soilborne pathogen and management could be influenced by soil spatial heterogeneity. This factor is of special relevance in many afforestation of southwestern Spain, which were carried out without phytosanitary control of the nursery seedlings. We selected a study area located in a 15 year-old afforestation of Q. ilex, known to be infested by Phytophthora cinnamomi Rands. Soil samples (ntotal = 132) were taken systematically from a grid under 4 trees, and analysed to quantify 12 variables, the colony forming units (cfu) of P. cinnamomi plus 11 physical and chemical soil properties. The combined analysis of all variables was performed with linear mixed models (GLMM), and the spatial patterns of cfu were characterised using an aggregation index (Ia) and a clustering index (ν) by SADIE. Cfu values ranged from 0 to 211 cfu g−1, and the GLMM built with the variables silt, P, K and soil moisture explained the cfu distribution to the greatest extent. The spatial analysis showed that 9 of the 12 variables presented spatial aggregation (Ia > 1), and the clustering of local patches (νi ≥ 1.5) for organic matter, silt, and Ca. The spatial patterns of the P. cinnamomi cfu under planted holm oak trees are related to edaphic variables and canopy cover. Small-scale spatial analysis of microsite variability can predict which areas surrounding trees can influence lower oomycetes cfu availability. [ABSTRACT FROM AUTHOR]

The sustainability of "dehesas" is threatened by the Holm oak decline. It is thought that the effects of root rot on plant physiology vary depending on external stress factors. Plant growth and biomass allocation are useful tools to characterize differences in the response to drought and infection. The study of physiological responses together with growth patterns will clarify how and to what extent root rot is able to damage the plant. A fully factorial experiment, including drought and Phytophtora cinnamomi Rands infection as factors, was carried out with Quercus ilex L. seedlings. Photosynthesis, biomass allocation and root traits were assessed. Photosynthetic variables responded differently to drought and infection over time. The root mass fraction showed a significant reduction due to infection. P. cinnamomi root rot altered the growth patterns. Plants could not recover from the physiological effects of infection only when the root rot coincided with water stress. Without additional stressors, the strategy of our seedlings in the face of root rot was to reduce the biomass increment and reallocate resources. Underlying mechanisms involved in plant-pathogen interactions should be considered in the study of holm oak decline, beyond the consideration of water stress as the primary cause of tree mortality. [ABSTRACT FROM AUTHOR]