Pine Wilt Disease is one of the most destructive pests affecting coniferous forests. After being infected by the harmful Bursaphelenchus xylophilus nematode, most trees die within one year. The complex spreading pattern of the disease and the tedious hard labor process of diagnosis involving field wood sampling followed by laboratory analysis call for alternative methods to detect and manage the infected areas. Remote sensing comes naturally into play owing to the possibility of covering relatively large areas and the ability to discriminate healthy from sick trees based on spectral characteristics. This paper presents the development of machine learning classification algorithms for the detection of Pine Wilt Disease in Pinus pinaster, performed in the framework of the European Commission’s Horizon 2020 project “Operational Forest Monitoring using Copernicus and UAV Hyperspectral Data” (FOCUS) in two provinces of central Portugal. Five flight campaigns have been carried out in two consecutive years in order to capture a multitemporal variation of disease distribution. Classification algorithms based on a Random Forest approach were separately designed for the acquired very-high-resolution multispectral and hyperspectral data, respectively. Both algorithms achieved overall accuracies higher than 0.91 in test data. Furthermore, our study shows that the early detection of decaying trees is feasible, even before symptoms are visible in the field.

Moderate-resolution satellite imagery is essential to detect conifer tree decline on a regional scale and address the threat caused by pinewood nematode (PWN), (Bursaphelenchus xylophilus. This is a quarantine organism responsible for pine wilt disease (PWD), which has caused substantial ecological and economic losses in the maritime pine (Pinus pinaster) forests of Portugal. This study describes the first instance of a pre-operational algorithm applied to Sentinel-2 imagery to detect PWD-compatible decline in maritime pine. The Random Forest model relied on a pre-wilting and an in-season image, calibrated with data from a 24-month long field campaign enhanced with Worldview-3 data and the analysis of biological samples (hyperspectral reflectance, pigment quantification in needles, and PWN identification). Independent validation results attested to the good performance of the model with an overall accuracy of 95%, particularly when decline affects more than 30% of the 100 m² pixel of Sentinel-2. Spectral angle mapper applied to hyperspectral measurements suggested that PWN infection cannot be separated from other drivers of decline in the visible-near infrared domain. Our algorithm can be employed to detect regional decline trends and inform subsequent aerial and field surveys, to further investigate decline hotspots.

In Portugal, the Cova da Beira region is well-known for the production of Prunus spp. and is considered the main peach production area in the country. In the spring of 2021 and 2022, field surveys in peach and nectarine orchards showed symptoms of decline such as cankers, gummosis, dry branches, abortion of flowers, mummified fruits and the partial or total death of some plants. Brown rot is caused by three species of the genus Monilinia, M. fructigena, M. laxa and M. fructicola, the last is an OEPP/EPPO A2 quarantine organism on peach trees. Brown rot disease had previously been described in the Cova da Beira region, however, the recent high mortality and severity of symptoms raised doubts as to the species involved. Symptomatic plant material was collected from thirteen orchards and used for fungal isolation and molecular detection according to the OEPP/EPPO standard. M. fructicola was confirmed morphologically and molecularly in two orchards, and molecularly (duplex real-time PCR) detected in two others. Whole genome sequencing using Oxford Nanopore MinION was also carried out to confirm the identification. Pathogenicity tests were performed on peach, nectarine and sweet cherry fruit according to Koch’s postulates. Based on all the results obtained, we report the first detection of M. fructicola in P. persica in Portugal.

In vitro propagation of Prunus sp. using semi-solid media allows clonal multiplication but requires frequent subcultures and higher labor and material costs. In contrast, temporary immersion bioreactors (TIBs) promoted faster shoot elongation, higher biomass accumulation, and improved overall shoot quality, while reducing handling time and the cost per regenerated plant. This work aimed to establish in vitro selected rootstocks of Prunus avium and Gisela 6 ( Prunus cerasus x Prunus canescens ) and compare the multiplication and rooting processes of shoots obtained in different culture conditions, in semi-solid media and SETIS™ bioreactors a system that alternates brief liquid immersion with aeration to enhance growth and shoot quality. Process efficiency and shoot growth parameters were evaluated, including in vitro establishment percentages, shoot length, number of shoots, and Relative Growth Rate (RGR). Physiological and biochemical analysis were also conducted, such as photosynthetic pigments content, total flavonoids, malondialdehyde (MDA), and proline levels. The in vitro percentage establishment was 56% for P. avium and 62% for Gisela 6, consistent with seasonal conditions favoring lower contamination. While no statistical differences were observed in the number of shoots between systems or genotypes, shoot elongation was significantly higher in TIB, particularly for Gisela 6. The RGR index confirmed the superior performance of TIB in promoting shoot proliferation, and rooting capacity was also improved, with 57% rooting in Gisela 6 and 44% in P. avium from TIB compared to 16% and 10% in semi-solid medium, respectively. Biochemical analyses revealed that P. avium propagated in TIB showed reduced pigment levels and signs of stress likely related to hyperhydricity, whereas Gisela 6 exhibited greater stability across both systems. Overall, the results demonstrate that TIB is a more efficient alternative to semi-solid media for the micropropagation of sweet cherry rootstocks, enhancing shoot quality, growth, and ex vitro rooting, although genotype-specific responses highlight the need for further optimization.

In vitro propagation of Prunus sp. using semi-solid media allows clonal multiplication but requires frequent subcultures and higher labor and material costs. In contrast, temporary immersion bioreactors (TIBs) promoted faster shoot elongation, higher biomass accumulation, and improved overall shoot quality, while reducing handling time and the cost per regenerated plant. This work aimed to establish in vitro selected rootstocks of Prunus avium and Gisela 6 ( Prunus cerasus x Prunus canescens ) and compare the multiplication and rooting processes of shoots obtained in different culture conditions, in semi-solid media and SETIS ^TM bioreactors a system that alternates brief liquid immersion with aeration to enhance growth and shoot quality. Process efficiency and shoot growth parameters were evaluated, including in vitro establishment percentages, shoot length, number of shoots, and Relative Growth Rate (RGR). Physiological and biochemical analysis were also conducted, such as photosynthetic pigments content, total flavonoids, malondialdehyde (MDA), and proline levels. The in vitro percentage establishment was 56% for P. avium and 62% for Gisela 6, consistent with seasonal conditions favoring lower contamination. While no statistical differences were observed in the number of shoots between systems or genotypes, shoot elongation was significantly higher in TIB, particularly for Gisela 6. The RGR index confirmed the superior performance of TIB in promoting shoot proliferation, and rooting capacity was also improved, with 57% rooting in Gisela 6 and 44% in P. avium from TIB compared to 16% and 10% in semi-solid medium, respectively. Biochemical analyses revealed that P. avium propagated in TIB showed reduced pigment levels and signs of stress likely related to hyperhydricity, whereas Gisela 6 exhibited greater stability across both systems. Overall, the results demonstrate that TIB is a more efficient alternative to semi-solid media for the micropropagation of sweet cherry rootstocks, enhancing shoot quality, growth, and ex vitro rooting, although genotype-specific responses highlight the need for further optimization.

Fungal community dynamics play a fundamental role in plant health, influencing resilience, disease progression, and ecosystem stability. Monilinia fructicola, a quarantine pathogen affecting Prunus persica, is hypothesized to drive fungal dysbiosis by altering microbial diversity, ecological interactions, and functional roles. In this study, the impact on fungal community composition, trophic dynamics, and network structure was investigated using internal transcribed spacer metabarcoding based on high-throughput sequencing of flowers and branches from infected and noninfected P. persica trees. The results showed a pathogen-induced shift, most pronounced in flowers, in which M. fructicola increased richness (Chao1), diversity (Shannon), and dominance (Simpson). In wood samples, increased dominance suggested pathogen-associated ecological filtering. Network analysis demonstrated that M. fructicola disrupts fungal connectivity, leading to fragmentation of microbial interactions, reduced network complexity, and increased modularity, indicative of weakened ecological cohesion. Shifts in fungal primary lifestyles further suggested that M. fructicola accelerates tissue decomposition and induces an imbalance in functional guilds, potentially facilitating secondary infections. Taxonomic restructuring was marked by the decline of dominant genera and the emergence of stress-adapted or pathogen-associated taxa. The isolation of Epicoccum nigrum, a known biocontrol agent, highlights its potential as a nature-based solution for disease management. Overall, these findings identify M. fructicola as a driver of fungal dysbiosis in P. persica, with cascading effects on microbial function, network resilience, and plant health. Understanding these pathogen-induced shifts is essential to inform sustainable microbiome-based approaches to disease management.