scholarly journals Optimization of factors affecting the rooting of pine wilt disease resistant Masson pine (Pinus massoniana) stem cuttings

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0251937
Author(s):  
Ting Pan ◽  
Xue-lian Chen ◽  
Yan-ping Hao ◽  
Chun-wu Jiang ◽  
Song Wang ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Pinus Massoniana ◽  
Wilt Disease ◽  
Stem Cuttings ◽  
Simple Method ◽  
Masson Pine ◽  
Factors Affecting ◽  
Pine Wilt ◽  
Pine Trees ◽  
Root Quality

Pine wilt disease (PWD) is a devastating disease affecting trees belonging to the genus Pinus. To control the spread of PWD in the Masson pine forest in China, PWD resistant Masson pine clones have been selected by the Anhui Academy of Forestry. However, because Masson pine is a difficult-to-root species, producing seedlings is challenging, especially from trees older than 5 years of age, which impedes the application of PWD resistant clones. In this study, we investigated the factors affecting rooting of PWD resistant clones and established a cheap, reliable, and simple method that promotes rooting. We tested the effects of three management methods, four substrates, two cutting materials, two cutting treatments, and three collection times on the rooting of cuttings obtained from 9-year-old PWD resistant clones. Rooting was observed only in stem cuttings treated with the full-light automatic spray management method. Additionally, stem cuttings showed a significantly higher rooting rate and root quality than needles cuttings. Compared with other substrates, stem cuttings planted in perlite produced the longest adventitious root and the highest total root length and lateral root number. Moreover, stem cuttings of PWD resistant clones collected in May showed a significantly higher rooting rate and root quality than those collected in June and July. Moreover, stem cuttings prepared with a horizontal cut while retaining the needles showed significantly higher rooting rate and root quality than those prepared with a diagonal cut while partly removing the needles. This study promotes the reproduction of seedlings of PWD-resistant Masson pine clones which helps control the spread of PWD, meanwhile, provides a technical reference for the propagation of mature pine trees via cuttings.

scholarly journals Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

scholarly journals The Microbiome of Endophytic, Wood Colonizing Bacteria from Pine Trees as Affected by Pine Wilt Disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Diogo Neves Proença ◽  
Romeu Francisco ◽  
Susanne Kublik ◽  
Anne Schöler ◽  
Gisle Vestergaard ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Pine Trees

scholarly journals Regeneration of Different Plant Functional Types in a Masson Pine Forest Following Pine Wilt Disease

PLoS ONE ◽  
2012 ◽  
Vol 7 (5) ◽  
pp. e36432 ◽  
Author(s):  
Guang Hu ◽  
Xuehong Xu ◽  
Yuling Wang ◽  
Gao Lu ◽  
Kenneth J. Feeley ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Forest ◽  
Plant Functional Types ◽  
Masson Pine ◽  
Pine Wilt ◽  
Functional Types ◽  
Masson Pine Forest

Volatile compounds in pine stands suffering from pine wilt disease: qualitative and quantitative evaluation

Nematology ◽  
2006 ◽  
Vol 8 (6) ◽  
pp. 869-879 ◽  
Author(s):  
Kazuyoshi Futai ◽  
Natsumi Kanzaki ◽  
Yuko Takeuchi
Keyword(s):  
Volatile Compounds ◽  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Economic Damage ◽  
Qualitative And Quantitative ◽  
Pine Wilt ◽  
Pine Trees ◽  
Quantitative Properties ◽  
Pine Stands

AbstractPine wilt disease causes ecological and economic damage in Japanese pine forests in spite of intensive effort to protect them from the pine wood nematode, Bursaphelenchus xylophilus. Pine trees infected with B. xylophilus emit a characteristic bouquet of volatile compounds bioactive to the vector beetle of the nematode, Monochamus alternatus, and potentially affecting symptom development inside the trees. To investigate the qualitative and quantitative properties of volatile compounds in the field, we profiled the volatile emissions in two Japanese black pine stands, one naturally suffering from pine wilt disease and the other artificially inoculated with B. xylophilus. In both pine stands, the emission of some terpenoids from the infected trees such as (−)-α-pinene, began to increase in summer, overlapping the oviposition season of the vector beetle, but peaked in the summer and autumn. These data suggest that the beetles may not necessarily depend on the tremendous quantity of volatiles alone when they search for suitable trees on which to oviposit.

Effects of spring-killed pine trees on the epidemics of pine wilt disease

2012 ◽  
Vol 47 (4) ◽  
pp. 311-318 ◽  
Author(s):  
Katsumi Togashi ◽  
Katsunori Nakamura ◽  
Shota Jikumaru
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Pine Trees

scholarly journals A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

2021 ◽  
Vol 14 (1) ◽  
pp. 150
Author(s):  
Jie You ◽  
Ruirui Zhang ◽  
Joonwhoan Lee
Keyword(s):  
Real World ◽  
Early Stage ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Image Resolution ◽  
Test Time ◽  
Detection Model ◽  
Pine Wilt ◽  
Pine Trees ◽  
Uav Images

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.

Differences in arthropod communities between island and inland Masson pine forests infested by pine wilt disease in Zhejiang Province

2008 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Hai-wei Wu ◽  
You-qing Luo ◽  
Juan Shi ◽  
Xiao-su Yan ◽  
Wei-ping Chen ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Zhejiang Province ◽  
Pine Forests ◽  
Arthropod Communities ◽  
Masson Pine ◽  
Pine Wilt

Endogenous bacterial community structure of Pinus massoniana with differing resistance to pine wilt disease

Nematology ◽  
2021 ◽  
pp. 1-17
Author(s):  
Wei Lu ◽  
Xiao-Jia Zhao ◽  
Jia-Jin Tan
Keyword(s):  
Disease Resistance ◽  
Bacterial Community ◽  
Endophytic Bacteria ◽  
Pine Wilt Disease ◽  
Illumina Miseq ◽  
Pinus Massoniana ◽  
Wilt Disease ◽  
Pine Wilt ◽  
Disease Resistant ◽  
Endophytic Bacterial Community

Summary Pine wilt disease (PWD) is a devastating pine disease caused by Bursaphelenchus xylophilus and its main host in China is Pinus massoniana. The relationship between endophytic bacteria and disease resistance in P. massoniana remains unclear. In this paper, the leaves, roots, stems and treetops of different disease-resistant P. massoniana were studied as the research objective and Illumina MiSeq sequencing was used to analyse whether there were significant differences in the composition and diversity of endophytic bacterial communities between different disease-resistant P. massoniana. The results showed that at the genus level there were no obvious differences in the composition of the endophytic bacterial community of different disease-resistant P. massoniana in the leaves, but there were obvious differences in the roots, stems and treetops. The richness and diversity of endophytic bacteria in P. massoniana had no significant impact on its disease resistance, whilst the structure of endophytic bacterial community in stems and treetops may be related to its disease resistance.

scholarly journals The specialization of Esteya vermicola hyphae in infection to Bursaphelenchus xylophilus and its colonization of pine tree

2020 ◽  
Author(s):  
Hai-Hua Wang ◽  
Can Yin ◽  
Jie Gao ◽  
Ran Tao ◽  
Piao-Piao Dai ◽  
...  
Keyword(s):  
Pine Wilt Disease ◽  
Bursaphelenchus Xylophilus ◽  
Wilt Disease ◽  
Fungal Colonization ◽  
Pinewood Nematode ◽  
Pine Tree ◽  
Pine Wilt ◽  
Pine Trees ◽  
Colonization Patterns ◽  
Esteya Vermicola

AbstractPine wilt disease (PWD) caused by the nematode Bursaphelenchus xylophilus is a serious problem on pines, and there is currently no effective control strategy for this disease. Although the endoparasitic fungus Esteya vermicola showed great effectiveness in controlling pine wilt disease, the colonization patterns of the host pine tree xylem by this fungus are unknown. To investigate the colonization patterns of pine xylem by this fungus, the species Pinus koraiensis grown in a greenhouse was used as an experimental host tree. The fungal colonization of healthy and wilting pine trees by E. vermicola was quantified using PCR with a TaqMan probe, and a green fluorescence protein (GFP) transformant was used for visualization. The results reported a specific infection approach used by E. vermicola to infect B. xylophilus and specialized fungal parasitic cells in PWN infection. In addition, the inoculated blastospores of E. vermicola germinated and grew inside of healthy pine xylem, although the growth rate was slow. Moreover, E. vermicola extended into the pine xylem following spray inoculation of wounded pine seedling stems, and a significant increase in fungal quantity was observed in response to B. xylophilus invasion. An accelerated extension of E. vermicola colonization was shown in PWN-infected wilting pine trees, due to the immigration of fungal-infected PWNs. Our results provide helpful knowledge about the extension rate of this fungus in healthy and wilting PWN-susceptible pine trees in the biological control of PWD and will contribute to the development of a management method for PWD control in the field.Author summaryPine wilt disease, caused by Bursaphelenchus xylophilus, has infected most pine forests in Asian and European forests and led to enormous losses of forest ecosystem and economy. Esteya vermicola is a bio-control fungus against pinewood nematode, showed excellent control efficient to pine wilt disease in both of greenhouse experiments and field tests. Although this bio-control agent was well known for the management of pine wilt disease, the infection mechanism of fungal infection and colonization of host pine tree are less understand. Here, we use GFP-tagged mutant to investigate the fungal infection to pinewood nematode; additionally, the temporal and spatial dynamics of E. vermicola colonize to pine tree were determined by the TaqMan real-time PCR quantification, as well as the response to pinewood nematode invasion. We found a specific infection approach used by E. vermicola to infect B. xylophilus and specialized fungal parasitic cells in PWN infection. In addition, the fungal germination and extension inside of pine tree xylem after inoculation were revealed. In addition, the quantity of E. vermicola increased as response to pinewood nematode invasion was reported. Our study provides two novel technologies for the visualization and detection of E. vermicola for the future investigations of fungal colonization and its parasitism against pinewood nematode, and the mechanisms of the bio-control process.

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