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

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.

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An ectomycorrhizal fungus, Cenococcum geophilum, in a coastal pine forest has a high tolerance for an insecticide used to control pine wilt disease

Landscape and Ecological Engineering ◽

10.1007/s11355-021-00455-w ◽

2021 ◽

Author(s):

Hirofumi Nakashima ◽

Yosuke Matsuda ◽

Naoki Hijii

Keyword(s):

Pine Wilt Disease ◽

Ectomycorrhizal Fungus ◽

Wilt Disease ◽

Pine Forest ◽

High Tolerance ◽

Cenococcum Geophilum ◽

Pine Wilt

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Relationship between Pine Wilt Disease Outbreaks and Climatic Variables in the Three Gorges Reservoir Region

Forests ◽

10.3390/f10090816 ◽

2019 ◽

Vol 10 (9) ◽

pp. 816 ◽

Cited By ~ 1

Author(s):

Ruihe Gao ◽

Zhuang Wang ◽

Haixiang Wang ◽

Yanping Hao ◽

Juan Shi

Keyword(s):

Three Gorges Reservoir ◽

Pine Wilt Disease ◽

Wilt Disease ◽

Climatic Variables ◽

Three Gorges ◽

Masson Pine ◽

The Three Gorges Reservoir ◽

The Three Gorges ◽

Pine Wilt ◽

Three Gorges Reservoir Region

Outbreaks of pine wilt disease (PWD, caused by the pinewood nematode Bursaphelenchus xylophilus), have caused mass mortality of the genus Pinus in Eurasia. Climate change may greatly influence the distribution and population dynamics of longhorn beetles of the genus Monochamus (the main vector of B. xylophilus), the survival and development of B. xylophilus, and the resistance of pines. The aim of this study was to investigate the effect of climatic variables associated with extensive PWD outbreaks in Masson pine (Pinus massoniana Lamb.) forest across the eastern part of the Three Gorges Reservoir region. Since its discovery in 2006, the most serious PWD outbreak occurred from 2014 to 2018; the most striking characteristic of this outbreak is the consistent increase in Masson pine mortality and extent of the affected areas. Moreover, 28 out of 46 PWD biological relevant climatic variables were selected and used for redundancy analysis. The ordination biplots reflect the complicated quantitative relationship between the PWD epidemic variables and the biologically relevant climatic variables of temperature, precipitation, relative humidity, and wind speed. The results will be useful for understanding the role climatic variables play in PWD outbreaks, for predicting the spread and pattern of PWD outbreaks, and for the advance preparation of management strategies with the purpose of preventing future PWD outbreaks.

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Effects of cavitation on the development of pine wilt disease caused by Bursaphelenchus xylophilus.

Japanese Journal of Phytopathology ◽

10.3186/jjphytopath.54.606 ◽

1988 ◽

Vol 54 (5) ◽

pp. 606-615 ◽

Cited By ~ 34

Author(s):

Keiko KURODA ◽

Toshihiro YAMADA ◽

Kazuhiko MINEO ◽

Hirotada TAMURA

Keyword(s):

Pine Wilt Disease ◽

Bursaphelenchus Xylophilus ◽

Wilt Disease ◽

Pine Wilt

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

Forest Ecosystems ◽

10.1186/s40663-021-00328-6 ◽

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.

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