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...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.展开更多
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 effect...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.展开更多
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 appro...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.展开更多
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...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.展开更多
[Objective] This study was aimed to review the controlling experience of pine wilt disease in the past 25 years, explore the theories and methods of controlling pine wilt disease, and improve the scientific level of c...[Objective] This study was aimed to review the controlling experience of pine wilt disease in the past 25 years, explore the theories and methods of controlling pine wilt disease, and improve the scientific level of controlling techniques and the protection capacity of healthy pine trees. [Method] Eleven items of effects were used to refine the theory of clearing dead pine trees affected by pine wilt disease, namely, ＂1 priority＂, ＂2 objections＂, ＂3 principles＂, ＂4 measures＂, and ＂5 manage- ments＂. On the basis of comprehensive control and complete removal of the infect- ed pine trees, a variety of comprehensive and efficient controlling methods were developed to carry out targeted chemical ecology trapping, bionic pesticide killing and releasing natural enemies of Sclerodermus guani, Dastarcus helophoroides. High ef- ficient emamectin benzoate immune injection was developed to inject the healthy pine trees for prevention, so as to extinguish the pine wilt disease. [Result] The pine wilt disease dropped from the peak of 3.5 million dead trees with an infecting area of 28 273 hectares in 1999 to 0.068 million with an area of 4 333 hectares in 2012 gradually, reducing by 98.06% in number and 84.84% in area, respectively. On the basis of removal, Dastarcus helophoroides was also released, which could make the number of dead pines decrease more significantly than the control, and af- ter releasing for 5 consecutive years, the dead pine trees dropped to 0.511 plant/hm2 in 2012, with a mortality rate of 0.022 7%, which achieved the control effect, reaching extremely significant level. ＂Forest land removal＋infected trees isolation＋natural enemy release＂ could extinguish the pine wilt disease. The test of isolating 24 heaps of infected pine trees showed that there were 9 heaps of pine trees extinguished the pine wilt disease, which controlled the occurrence of pine wilt disease for 100%, accounting for 37.5% of the total, in which the number of those isolated using iron netting and nylon net were 4 for each, accounting for 88.9%, and there was one heap using polypropylene net, accounting for 11.1%. The invention of em- amectin benzoate immune injection laid the foundation for extinguishing pine wilt disease. The follow checking of the effects of emamectin benzoate immune injection on pine wilt disease found that the number of dead trees caused by pine wilt dis- ease decreased significantly after injecting, and became very small in October of the next year, and the disease was completely extinguished in the third year. [Conclusionl Pine wilt disease could be controlled and extinguished with positive control by using ＂comprehensive cleaning＋industrialized removal＂, ＂comprehensive cleaning＋ natural enemy release＂, ＂comprehensive cleaning＋infected trees isolation＋natural ene- my release＂ and ＂comprehensive cleaning＋emamectin benzoate immune＂.展开更多
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 ons...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.展开更多
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 nematode...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.展开更多
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 ...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.展开更多
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 eq...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 <em>R</em><sub>1</sub> < 1, and in such a case, the endemic equilibrium does not exist. The disease uniformly persists only if <em>R</em><sub>2</sub> > 1.展开更多
A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times...A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.展开更多
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 populatio...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展开更多
The present paper investigates the dynamics of pine wilt disease with saturated incidence rate. The proposed model is stable both locally and globally. The local stability of the disease-free equilibrium is determined...The present paper investigates the dynamics of pine wilt disease with saturated incidence rate. The proposed model is stable both locally and globally. The local stability of the disease-free equilibrium is determined by the basic reproduction R0. The disease-free equilibrium is stable locally and globally whenever R0〈 1. If R0 〉 1, then the endemic state is stable both locally and globally. Further, a brief discussion with conclusion on the numerical results of the proposed model is presented.展开更多
Chitosan oligosaccharides(COSs)are the main degradation products from chitosan or chitin and have been reported to induce resistance to diseases in herbaceous plants like cucumber and Arabidopsis.Concomitantly,pine wi...Chitosan oligosaccharides(COSs)are the main degradation products from chitosan or chitin and have been reported to induce resistance to diseases in herbaceous plants like cucumber and Arabidopsis.Concomitantly,pine wilt disease(PWD)is a devastating disease of conifer tree species.Here,we hypothesized that COSs induce plant resistance gene(PRG)expression in the woody plant Masson pine,Pinus massoniana.COSs were inoculated into P.massoniana seedlings and the BGISEQ-500 platform was used to generate transcriptomes from COSs-treated P.massoniana and control seedlings.A total of 501 differentially expressed genes(DEGs)were identified by comparing the treatment and control groups.A total of 251(50.1%)DEGs were up-regulated in the treatment relative to the control seedlings and 250(49.9%)were down-regulated.Inoculation of COSs induced the expression of 31 PRGs in P.massoniana seedlings and the relative expression levels of six of the PRGs were verified by RT-qPCR.This is the first study to demonstrate that COS induces the expression of PRGs in a tree species.These results provide important insights into the function of COSs and further the prospects of developing a COS-based immune inducer for controlling PWD.展开更多
基金supportedby a Key Program of the National Natural Science Foundation of China (Grant No. 30430580)the State Forestry Administration of China (Grant No.20070430)a review is done in frames of the project 10-04-01644-a of the Russian Foundation for Basic Research
文摘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.
基金funded by the National Key Research&Development Program of China(2018YFD0600200)Beijing’s Science and Technology Planning Project(Z191100008519004)Major emergency science and technology projects of National Forestry and Grassland Administration(ZD202001–05).
文摘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.
基金This research was supported by a grant from the National Research Foundation of Korea,provided by the Korean government(2017R1A2B4003258).
文摘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.
基金supported by the National Natural Science Foundation of China(No.31870620)the National Technology Extension Fund of Forestry([2019]06)the Fundamental Research Funds for the Central Universities(No.PTYX202107)。
文摘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.
基金Supported by the Scientific Research Project of National Level of YANG Zhongqi of Chinese Academy of Forestry(2012AA101503)~~
文摘[Objective] This study was aimed to review the controlling experience of pine wilt disease in the past 25 years, explore the theories and methods of controlling pine wilt disease, and improve the scientific level of controlling techniques and the protection capacity of healthy pine trees. [Method] Eleven items of effects were used to refine the theory of clearing dead pine trees affected by pine wilt disease, namely, ＂1 priority＂, ＂2 objections＂, ＂3 principles＂, ＂4 measures＂, and ＂5 manage- ments＂. On the basis of comprehensive control and complete removal of the infect- ed pine trees, a variety of comprehensive and efficient controlling methods were developed to carry out targeted chemical ecology trapping, bionic pesticide killing and releasing natural enemies of Sclerodermus guani, Dastarcus helophoroides. High ef- ficient emamectin benzoate immune injection was developed to inject the healthy pine trees for prevention, so as to extinguish the pine wilt disease. [Result] The pine wilt disease dropped from the peak of 3.5 million dead trees with an infecting area of 28 273 hectares in 1999 to 0.068 million with an area of 4 333 hectares in 2012 gradually, reducing by 98.06% in number and 84.84% in area, respectively. On the basis of removal, Dastarcus helophoroides was also released, which could make the number of dead pines decrease more significantly than the control, and af- ter releasing for 5 consecutive years, the dead pine trees dropped to 0.511 plant/hm2 in 2012, with a mortality rate of 0.022 7%, which achieved the control effect, reaching extremely significant level. ＂Forest land removal＋infected trees isolation＋natural enemy release＂ could extinguish the pine wilt disease. The test of isolating 24 heaps of infected pine trees showed that there were 9 heaps of pine trees extinguished the pine wilt disease, which controlled the occurrence of pine wilt disease for 100%, accounting for 37.5% of the total, in which the number of those isolated using iron netting and nylon net were 4 for each, accounting for 88.9%, and there was one heap using polypropylene net, accounting for 11.1%. The invention of em- amectin benzoate immune injection laid the foundation for extinguishing pine wilt disease. The follow checking of the effects of emamectin benzoate immune injection on pine wilt disease found that the number of dead trees caused by pine wilt dis- ease decreased significantly after injecting, and became very small in October of the next year, and the disease was completely extinguished in the third year. [Conclusionl Pine wilt disease could be controlled and extinguished with positive control by using ＂comprehensive cleaning＋industrialized removal＂, ＂comprehensive cleaning＋ natural enemy release＂, ＂comprehensive cleaning＋infected trees isolation＋natural ene- my release＂ and ＂comprehensive cleaning＋emamectin benzoate immune＂.
基金supported by the National Science and Technology Major Project of China’s High Resolution Earth Observation System(21-Y30B02-9001-19/22)the Heilongjiang Provincial Natural Science Foundation of China(YQ2020C018)。
文摘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.
文摘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.
基金Supported by Special Fund for Scientific Research(Forestry)in the Public Interest(201304208)National Natural Science Foundation of China(31100414,31470579)+1 种基金General Program of Natural Science Research in Colleges and Universities in Jiangsu Province(11KJB220001)Advantage Discipline Construction Project of Colleges and Universities in Jiangsu Province
文摘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.
文摘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 <em>R</em><sub>1</sub> < 1, and in such a case, the endemic equilibrium does not exist. The disease uniformly persists only if <em>R</em><sub>2</sub> > 1.
文摘A Bayesian network (BN) model was developed to predict susceptibility to PWD(Pine Wilt Disease). The distribution of PWD was identified using QuickBird and unmanned aerial vehicle (UAV) images taken at different times. Seven factors that influence the distribution of PWD were extracted from the QuickBird images and were used as the independent variables. The results showed that the BN model predicted PWD with high accuracy. In a sensitivity analysis, elevation (EL), the normal differential vegetation index (NDVI), the distance to settlements (DS) and the distance to roads (DR) were strongly associated with PWD prevalence, and slope (SL) exhibited the weakest association with PWD prevalence. The study showed that BN is an effective tool for modeling PWD prevalence and quantifying the impact of various factors.
文摘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
文摘The present paper investigates the dynamics of pine wilt disease with saturated incidence rate. The proposed model is stable both locally and globally. The local stability of the disease-free equilibrium is determined by the basic reproduction R0. The disease-free equilibrium is stable locally and globally whenever R0〈 1. If R0 〉 1, then the endemic state is stable both locally and globally. Further, a brief discussion with conclusion on the numerical results of the proposed model is presented.
文摘Chitosan oligosaccharides(COSs)are the main degradation products from chitosan or chitin and have been reported to induce resistance to diseases in herbaceous plants like cucumber and Arabidopsis.Concomitantly,pine wilt disease(PWD)is a devastating disease of conifer tree species.Here,we hypothesized that COSs induce plant resistance gene(PRG)expression in the woody plant Masson pine,Pinus massoniana.COSs were inoculated into P.massoniana seedlings and the BGISEQ-500 platform was used to generate transcriptomes from COSs-treated P.massoniana and control seedlings.A total of 501 differentially expressed genes(DEGs)were identified by comparing the treatment and control groups.A total of 251(50.1%)DEGs were up-regulated in the treatment relative to the control seedlings and 250(49.9%)were down-regulated.Inoculation of COSs induced the expression of 31 PRGs in P.massoniana seedlings and the relative expression levels of six of the PRGs were verified by RT-qPCR.This is the first study to demonstrate that COS induces the expression of PRGs in a tree species.These results provide important insights into the function of COSs and further the prospects of developing a COS-based immune inducer for controlling PWD.
