Pine wilt disease: a short review of worldwide research

This article summarizes the results of the research papers presented at the International Symposium on pine wilt disease （IUFRO Working Party Meeting 4.04.03） held in July 2009, at Nanjing, China. The general topics covered were on pine wilt disease （PWD）, its causal organism, the pinewood nematode （PWN） Bursaphelenchus xylophilus, plus other PWN-associated microorganisms that play a significant role in PWD such as bacteria （e.g. Pseudomonasfluorescens）. Most of the papers that are reviewed are based on work on PWD-PWN in East Asia and Russia. Specific topics covered include： 1） the fundamental conceptions of PWD development, 2） pathogenicity, 3） host-parasite relationships including the histopathology of diseased conifers and the role of toxins from bacteria-nematode ecto-symbionts, 4） PWN life cycle and transmission, 5） B. xylophilus dissemination models, 6） associations （with other nematodes）, 7） diagnostics, 8） quarantine and control of the PWN and 9） biocontrol of the PWN.

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

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.

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease(PWD)has recently caused substantial pine tree losses in Republic of Korea.PWD is considered a severe problem due to the importance of pine trees to Korean people,so this problem must be handled appropriately.Previously,we examined the history of PWD and found that it had already spread to some regions of Republic of Korea;these became our study area.Early detection of PWD is required.We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD.Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees.To differentiate healthy pine trees from those with PWD,we produced a land cover(LC)map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods,i.e.,artificial neural network(ANN)and support vector machine(SVM).Furthermore,compared the accuracy of two types of Global Positioning System(GPS)data,collected using drone and hand-held devices,for identifying the locations of trees with PWD.We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD.In Anbi,the SVM had an overall accuracy of 94.13%,which is 6.7%higher than the overall accuracy of the ANN,which was 87.43%.We obtained similar results in Wonchang,for which the accuracy of the SVM and ANN was 86.59%and 79.33%,respectively.In terms of the GPS data,we used two type of hand-held GPS device.GPS device 1 is corrected by referring to the benchmarks sited on both locations,while the GPS device 2 is uncorrected device which used the default setting of the GPS only.The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang.However,in Anbi,we obtained better results from GPS device 2 than from GPS device 1.In Anbi,the error in the data from GPS device 1 was 7.08 m,while that of the GPS device 2 data was 0.14 m.In conclusion,both classifiers can distinguish between healthy trees and those with PWD based on LC data.LC data can also be used for other types of classification.There were some differences between the hand-held and drone GPS datasets from both areas.

Surveillance of pine wilt disease by high resolution satellite

Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China.This destructive disease has the characteristics of bring wide-spread,fast onset,and long incubation time.Most importantly,in China,the fatality rate in pines is as high as 100%.The key to reducing this mortality is how to quickly find the infected trees.We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool.This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite.The recognition accuracy of the test data set was 99.4%,and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees.It can provide strong technical support for the prevention and control of pine wilt disease.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease(PWD)is currently one of the main causes of large-scale forest destruction.To control the spread of PWD,it is essential to detect affected pine trees quickly.This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD.We used an unmanned aerial vehicle(UAV)platform equipped with an RGB digital camera to obtain high spatial resolution images,and multiscale segmentation was applied to delineate the tree crown,coupling the use of object-oriented classification to classify trees discolored by PWD.Then,the optimal segmentation scale was implemented using the estimation of scale parameter(ESP2)plug-in.The feature space of the segmentation results was optimized,and appropriate features were selected for classification.The results showed that the optimal scale,shape,and compactness values of the tree crown segmentation algorithm were 56,0.5,and 0.8,respectively.The producer’s accuracy(PA),user’s accuracy(UA),and F1 score were 0.722,0.605,and 0.658,respectively.There were no significant classification errors in the final classification results,and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation.The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing.This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Early Monitoring of Pine Wilt Disease in Pinus massioniana based on Hyperspectral Data

We selected healthy Pinus massioniana for pine wood nematode inoculation experiments to get the spectral reflectance of healthy and infected Pinus mas- sioniana in different infection stages via a ground spectrometer （ wavelength in 350 - 2 500 nm）, and analyzed the changes in chlorophyll content at various periods. The original spectral reflectance of healthy and infected P. massoniana was significantly different in the middle and late infection stages, and the reflection peak and absorption valley in visible light region and near infrared region gradually weakened and even disappeared to a straight line. There was significant correlation rela- tionship between chlorophyll content of infected plants and spectral reflectance at the wavelength of 1 405 nm, and the quantitative inversion model of chlorophyll content was correspondingly established as follows： Car = - 1.74（X1~ ）2 ＋ 4. 72X1,~ - 0. 76. Through first-order derivative spectra at the wavelength of 593 nm, combined with quantitative inversion of the corresponding chlorophyll content, we can discriminate whether P. massoniana is infected by pine lt disease or not, especially in the early stages before disease features are visible to the naked eyes it has a good quantitative monitoring effect.

Pulse Roguing Strategy in a Pine Wilt Disease Epidemic Model with General Nonlinear Incidence Rate

In this study, we investigate a pine wilt transmission model with general nonlinear incidence rates and time-varying pulse roguing. Using the stroboscopic map and comparison theorem, we proved that the disease-free equilibrium is global attractive determined by the basic reproduction number R1 < 1, and in such a case, the endemic equilibrium does not exist. The disease uniformly persists only if R2 > 1.

Pine wilt disease in Yunnan, China： Evidence of non-local pine sawyer Monochamus alternatus （Coleoptera： Cerambycidae） populations revealed by mitochondrial DNA

Monochamus alternatus （Hope） specimens were collected from nine geographical populations in China, where the pinewood nematode Bursaphelenchus xylophilus （Steiner et Buhrer） was present. There were seven populations in southwestern China in Yunnan Province （Ruili, Wanding, Lianghe, Pu＇er, Huaning, Stone Forest and Yongsheng）, one in central China in Hubei Province （Wuhan）, and one in eastern China in Zhejiang Province （Hangzhou）. Twenty-two polymorphic sites were recognized and 18 haplotypes were established by analyzing a 565 bp gene fragment of mitochondrial cytochrome oxidase subunit II （CO II）. Kimura two-parameter distances demonstrated that M. alternatus populations in Ruili, Wanding and Lianghe （in southwestern Yunnan） differed from the other four Yunnan populations but were similar to the Zhejiang population. No close relationship was found between the M. alternatus populations in Yunnan and Hubei. Phylogenetic reconstruction established a neighbor-joining （N J） tree, which divided haplotypes of southwestern Yunnan and the rest of Yunnan into different clades with considerable bootstrapping values. Analysis of molecular variance and spatial analysis of molecular variance also suggested significant genetic differentiation between M. alternatus populations in southwestern Yunnan and the rest of Yunnan. Our research suggests that non-local populations of M. alternates, possibly from eastern China, have become established in southwestern Yunnan. Key words mitochondrial DNA, non-local vector, pine wilt disease

Examination of Pine Wilt Epidemic Model through Efficient Algorithm

Pine wilt is a dramatic disease that kills infected trees within a few weeks to a few months.The cause is the pathogen Pinewood Nematode.Most plant-parasitic nematodes are attached to plant roots,but pinewood nematodes are found in the tops of trees.Nematodes kill the tree by feeding the cells around the resin ducts.The modeling of a pine wilt disease is based on six compartments,including three for plants(susceptible trees,exposed trees,and infected trees)and the other for the beetles(susceptible beetles,exposed beetles,and infected beetles).The deterministic modeling,along with subpopulations,is based on Law of mass action.The stability of the model along with equilibria is studied rigorously.The authentication of analytical results is examined through well-known computer methods like Non-standard finite difference(NSFD)and the model’s feasible properties(positivity,boundedness,and dynamical consistency).In the end,comparison analysis shows the effectiveness of the NSFD algorithm.

Investigation of beetle species that carry the pine wood nematode,Bursaphelenchus xylophilus(Steiner and Buhrer)Nickle,in China

In order to found new carriers of pine wood nematode(PWN),Bursaphelenchus xylophilus,beetles were collected from pine wilt disease-affected areas in six provinces in China.A total of 8830 beetles of 29 species was collected and examined to determine whether they were PWN carriers.Eight species were identified as carriers.Results included the first worldwide report of Monochamus uigromaculatus,Semanotus siuoauster,and Uraecha angusta being carriers of PWN,and the first report from China of A rhopalus rusticus carrying PWN.Monochamus alternatus was commonly collected in all six provinces and was the dominant species in four inland affected areas and A.rusticus was dominant in two coastal affected areas.The species varied between different neighboring regions in the same province.The distribution of the same species varied considerably over different regions.

Patterns of biomass,carbon,and nitrogen storage distribution dynamics after the invasion of pine forests by Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae) in the three Gorges Reservoir Region

Masson pine stands infected by Pine wilt disease(PWD) in the Three Gorges Reservoir Region of central China were surveyed to quantify the immediate responses and subsequent trajectories of biomass,carbon(C),and nitrogen(N) in stand-level major ecosystem compartments.The biomasses of above-and belowground tree components,as well as of the understory,forest floor,and mineral soil(0–40 cm),were determined within each stand.C and N storage were also estimated for each ecosystem compartment.Overstory biomass decreased steadily with the extent of PWD infection.Understory biomass ranged from1.97 to 4.16 Mg ha,and the observed value for forest floor biomass was 12.89–22.59 Mg ha.The highest mean C and N concentrations were found in the stem bark and needles of Masson pine,respectively,while the lowest were found in the semi-to fully decomposed layer of the forest floor and stem wood of Masson pine,respectively.The C and N storage of aboveground trees,tree roots,and the aboveground ecosystem decreased with the extent ofPWD infection.However,the C and N contents of the understory,forest floor,and total mineral soil initially declined after PWD infection before recovering over the following several years.Those result concluded that the biomass,C,and N storage of different forest ecosystem compartments have experienced certain variations following the PWD epidemic.This is vital to understand the shifts in stand-level C and N allocation in PWD-damaged forest stands,as well as for predicting the responses of regional and global C and N cycling.

Review of Pathogen Identification and Diagnosis Technology of Pinewood Nematode Disease

This article briefly introduced the disease incidence and disservice situation in China. The disease is regarded as cancer in pines for its higher spread and death speed and the difficulty in prevention. Strict quarantine is the main measure on countrol of the disease, for there are many difficulties in the prevention. The most important part of the disease study is the early diagnosis and detection.

Dispersal patterns of exotic forest pests in South Korea

Invasive species have potentially devastating effects on ecological communi- ties and ecosystems. To understand the invasion process of exotic forest pests in South Korea, we reviewed four major species of exotic forest pests： the pine needle gall midge （Thecodiplosis japonensis）, pine wilt disease caused by the pine wood nematode （Bur- saphelenchus xylophilus）, the fall webworm （Hyphantria cunea） and the black pine bast scale （Matsucoccus thunbergianae）. We consider their biology, ecology, invasion history, dispersal patterns and related traits, and management as exotic species. Among these species, the dispersal process of fall webworm was linear, showing a constant range expan- sion as a function of time, whereas the other three species showed biphasic patterns, rapidly increasing dispersal speed after slow dispersal at the early invasion stage. Moreover, human activities accelerated their expansion, suggesting that prevention of the artificial movement of damaged trees would be useful to slow expansion of exotic species. We believe that this information would be useful to establish management strategies for invasion species.