Modelling the key drivers of an aerial Phytophthora foliar disease epidemic, from the needles to the whole plant

Remote sensing-based detection of brown spot needle blight: a comprehensive review, and future directions

Pine forests are increasingly threatened by needle diseases, including Brown Spot Needle Blight (BSNB), caused by Lecanosticta acicola. BSNB leads to needle loss, reduced growth, significant tree mortality, and disruptions in global timber production. Due to its severity, L. acicola is designated as a quarantine pathogen in several countries, requiring effective early detection and control of its spread. Remote sensing (RS) technologies provide scalable and efficient solutions for broad-scale disease surveillance. This study systematically reviews RS-based methods for detecting BSNB symptoms, assessing current research trends and potential applications. A comprehensive bibliometric analysis using the Web of Science database indicated that direct RS applications for BSNB remain scarce. However, studies on other needle diseases demonstrated the effectiveness of multisource RS techniques for symptom detection, spatial mapping, and severity assessment. Advancements in machine learning (ML) and deep learning (DL) have further improved RS capabilities for automated disease classification and predictive modeling in forest health monitoring. Climate-driven factors, such as temperature and precipitation, regulate the distribution and severity of emerging pathogens. Geospatial analyses and species distribution modeling (SDM) have been successfully applied to predict BSNB pathogen's range expansion under changing climatic conditions. Integrating these models with RS-based monitoring enhances early detection and risk assessment. However, despite these advancements, direct RS applications for BSNB detection remain limited. This review identifies key knowledge gaps and highlights the need for further research to optimize RS-based methodologies, refine predictive models, and develop early warning systems for improved forest management.

Phytophthora pluvialis maintenance, spore production and detached needle assays

Phytophthora pluvialis is an oomycete that primarily infects Pinus radiata and Pseudotsuga menziesii causing the destructive foliar disease red needle cast (RNC). Recent observations show that P. pluvialis can also infect western hemlock inducing resinous cankers. High-throughput and reproducible infection assays are integral to find key information on tree health and oomycete pathogenicity. In this protocol, we describe the propagation and spore induction of P. pluvialis, followed by detached needle assays for verification and quantification of virulence of P. pluvialis in P. radiata needles. These needle assays can be employed for high-throughput screening of tree needles with diverse genetic backgrounds. In downstream analysis, Quantitative PCR (qPCR) was utilized to assess relative gene expression, as exemplified by candidate RxLR effector protein PpR01. Additional techniques like RNA sequencing, metabolomics, and proteomics can be combined with needle assays and can offer comprehensive insights into P. pluvialis infection mechanisms.

Quantitative Detection and Monitoring of Colletotrichum siamense in Rubber Trees Using Real-Time PCR

Colletotrichum siamense is one of the most important pathogens of rubber trees in Asia. The proper detection and quantification of C. siamense populations in rubber trees are of importance for monitoring the epidemics of the disease. In this study, we developed an internal transcribed spacer-based real-time PCR method to efficiently detect C. siamense infecting rubber trees, which reliably detected as little as 100 fg of genomic DNA, 100 copies of target DNA, and 20 conidia. The real-time PCR protocol recognized all C. siamense isolates collected from three provinces in China, whereas no amplification was observed with the rubber tree and its other pathogens. Detection and quantification of C. siamense were performed in artificially and naturally infected rubber leaves. We could still detect C. siamense in plant mixes, of which only 0.0001% of the tissue was infected. An accumulation of C. siamense DNA was observed during the whole infection process at all three leaf phenological stages, suggesting that the real-time PCR method can be used to monitor C. siamense development in rubber trees. Finally, the method allowed the detection of C. siamense in naturally infected and symptomless leaves of rubber trees in the fields. Compared with earlier detection methods, the real-time PCR method is more specific and more sensitive, and it will be of great use for studies aiming to gain a better understanding of the epidemiology of Colletotrichum leaf disease, as well as the prediction of disease risk and proposals to control it.

Molecular detection of Phytophthora pluvialis, the causal agent of red needle cast in Pinus radiata

BACKGROUND

Phytophthora pluvialis was first described in 2013 and is the causal agent of red needle cast (RNC) in Pinus radiata as well as infection in Douglas fir (Pseudotsuga menziesii). A species-specific PCR is necessary for detection of this pathogen and diagnosis of RNC.

OBJECTIVE

To design and validate a species-specific molecular assay for P. pluvialis using isolates from infected pine needles.

METHODS

Species-specific PCR primers were generated from the ras-related GTP-binding protein 1 gene (ypt1) gene sequence, concentrating on DNA regions unique to P. pluvialis, and real-time and quantitative polymerase chain reaction (qPCR) were used to detect P. pluvialis from both artificially inoculated and naturally infected samples.

RESULTS

The species-specific PCR assay was generated following P. pluvialis DNA sequence analysis. In vitro tests of the specificity of the probe-based, quantitative, polymerase chain reaction (qPCR) assay showed that no amplification was observed with other Phytophthora species including other closely-related clade 3 species, or with fungal species associated with pine or with pine DNA. The limit of detection of the qPCR assay was 2 pg/μl. When the qPCR assay was used to detect P. pluvialis in artificially-inoculated and naturally infected P. radiata needles, a PCR product was detected in all inoculated samples; the mean concentration ranges of P. pluvialis DNA in the inoculated and naturally infected samples tested were 5.9-124.5 pg/μl and 8.1-340.2 pg/μl, respectively. The assays described herein were used with serological diagnostic strips, providing the ability to identify to species level.

CONCLUSIONS

The assay described herein detects P. pluvialis with high specificity and sensitivity from a range of DNA samples, including those extracted from infected plant material and serological diagnostic strips. The ability to detect and identify P. pluvialis, from infected tissues directly, provides value and practicality to diagnostics, biosecurity and research.