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.

Theory and Practice of Controlling and Extinguishing Pine Wilt Disease

[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＂.

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.

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.

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.

New insights into the life history of Monochamus saltuarius(Cerambycidae:Coleoptera)can enhance surveillance strategies for pine wilt disease

Pine wilt disease was first discovered in Dongtang town,Liaoning Province,China,in 2017.Monochamus saltuarius Gebler is a new vector of pinewood nematode and the only known vector in Liaoning Province.The biology of this pest has not been reported thus far;therefore,it is necessary to study its life history.During 2018 and 2019,we collected 138 and 491 adult M.saltuarius beetles,respectively,to analyze their eclosion from larva to adult stage.In mid-March,overwintering larvae began to feed(on xylem)and seek nutrition in preparation for pupation and eclosion.The adults began to appear in mid-April,and the population reached its peak in late May.The life span of the adults was 28-76 days.After approximately 1 week of supplemental nutrition(feeding on twigs),adults began to mate and lay eggs.The egg stage of M.saltuarius lasted 4-8 days.The larvae in Dongtang town have 4 instars and overwinter in tunnels as 3rd-4th instars.The 1st-instar stage lasted 3-9 days,the 2nd-instar stage lasted 11-23 days,the 3rd-instar stage lasted 30-130 days,and the 4th-instar stage lasted 44-180 days.The pupal stage lasted 7-12 days,and the life span of the adults was 28-76 days.In this study we systematically monitored the life history of M.saltuarius for the first time.Our objective was to lay a foundation for improving control of this pinewood nematode vector.

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.

Bayesian Network and Factor Analysis for Modeling Pine Wilt Disease Prevalence

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.

Establishment of Real-time Fluorescent Quantitative PCR for Pineapple mealybug wilt associated virus-3

[ Objective ] The paper aimed to establish a real-time fluorescent quantitative PCR （qPCR） detection method for Pineapple mealybug wilt associated vi- rus-3 （ PMWaV3 ）. [ Method] Specific TaqMan probe and primers were designed and synthesized according to the conserved sequence of coat protein（CP） gene of PMWaV-3, and the standard curve was established after optimizing the amplification condition of qPCR. [ Result] The results showed that the method was specific for the detection of PMWaV-3, and the sensitivity of the present method was about 10 times higher compared to general RT-PCR ; the variation coefficients of intra- assay and inter-assay were less than 1.73, respectively. [ Conclusion] The qPCR is an easy, fast and reliable method for quantitative detection of PMWaV-3.

Identification and fine mapping of qSW2 for leaf slow wilting in soybean

Drought is one of the abiotic stresses limiting the production of soybean(Glycine max).Elucidation of the genetic and molecular basis of the slow-wilting(SW)trait of this crop offers the prospect of its genetic improvement.A panel of 188 accessions and a set of recombinant inbred lines produced from a cross between cultivars Liaodou 14 and Liaodou 21 were used to identify quantitative-trait loci(QTL)associated with SW.Plants were genotyped by Specific-locus amplified fragment sequencing and seedling leaf wilting was assessed under three water-stress treatments.A genome-wide association study identified 26 SW-associated single-nucleotide polymorphisms(SNPs),including three located in a 248-kb linkage-disequilibrium(LD)block on chromosome 2.Linkage mapping revealed a major-effect QTL,qSW2,associated with all three treatments and adjacent to the LD block.Fine mapping in a BC_(2)F_(3) population derived from a backcross between Liaodou 21 and R26 confined qSW2 to a 60-kb interval.Gene expression and sequence variation analysis identified the gene Glyma.02 g218100,encoding an auxin transcription factor,as a candidate gene for qSW2.Our results will contribute significantly to improving drought-resistant soybean cultivars by providing genetic information and resources.

Occurrence and Effects of Pineapple Mealybug Wilt Disease in Central Uganda

Pineapple mealybug wilt disease （PMWD） is one of the latest outbreaks of diseases attacking pineapple in Uganda. However, its occurrence and effects have not been documented and quantified, yet the disease poses a serious threat to the pineapple industry. Therefore, the objective of this study was to assess the occurrence and effects of PMWD on pineapple in central Uganda. Semi-structured questionnaire was used to solicit information from 82 respondents consisting of farmers, opinion leaders, key informants, political and technical leadership during May 2011. PMWD was observed in all the fields surveyed but with varying incidences and severities. In addition, PMWD was more common during the dry seasons than the rainy seasons where higher incidences were associated with high mealybug populations. PMWD manifested as a syndrome characterized by yellowing of leaves, stunting, wilting and rotting of roots. The effects of PMWD were variable but yield reductions and low plant populations were widely reported. Although, the occurrence of PMWD was reported to the different level of authority in the districts, very little was done to curb its spread.

Reactions of Oil Palm (Elaeis guineensis Jacq) Progenies to Fusarium Wilt Disease Caused by Fusarium oxysporum f.sp. Elaeidis under Natural Infection

The oil palm (Elaeisguineensis Jacq) is used worldwide in commercial agriculture for the production of palm oil, palm kernel oil and palm wine. It produces more oil per plant than any other oil-producing crop in the world. Production is constrained by several factors among which pests/diseases are of utmost importance. Vascular wilt (VW) caused by Fusarium oxysporum is the most devastating disease infecting this crop. Its soil-borne ecology has made the use of fungicides to manage this disease too expensive and inpragmatic. There is need for concerted research in the breeding and selection of wilt-tolerant progenies as an essential step in the management of Fusarium wilt disease. The study aims to assess the incidence and severity of vascular wilt among tested oil palm progenies, to evaluate the reduction in yield caused by the disease in the susceptible progenies and to identify the wilt-tolerant, high-yielding progenies. The study was carried out at Pamol Plantations Limited (PPL) in Ndian Estate (Ndian Division), in the Southwest Region of Cameroon. Three field trials were evaluated for tolerance/susceptibility to Fusarium wilt. Each trial consisted of 15 oil palm progenies replicated 4 times. Each progeny had 25 oil palm stands in each replicate. Hence, a total of 1500 oil palm stands were assessed. The experimental design was a randomized complete block (RCB) with trial codes: Trial 2001/1, planted in 2001;Trial 2001/2, planted in 2002;Trial 2001/3, planted in 2003. Each trail had an area of 12 ha, with a plant density of 143 palms·ha−1. Wilt incidence, severity, index, and yield were evaluated on 45 progenies from the 3 trails after identifying Fusarium oxysporum from oil palm plant part. Data was subjected to analysis of variance, Fischer’s least significant difference test (LSD) for mean separation. Identification of Fusarium was based on descriptive analysis. Incidence of VW in the 3 trials ranged from 1% - 39%. Also, 45% of infected plants were from progeny 676 while 1% was from progenies 689, 693, 694 and 710. Disease severity was from 0.9 in progeny 686 to 4.55 in 676. Wilt index ranged from 131 for progeny 694 and 710 to 495 for progenies 705. Out of the 45 progenies evaluated, 27 were tolerant (1 < 100) and 18 susceptible (1 ≥ 100). Within the tolerant progenies, 4 were significant (1 < 20) while 5 out of 18 were significantly susceptible (1 ≥ 185). Mean yield reduction of the susceptible progenies was 34.8% while in the tolerant progenies, it increased by 9.5% when compared to their controls. Progenies 702, 703 and 709 are recommended for planting based on the level of tolerance to Fusarium wilt disease and yield.

Cloning of sft-4 and its influence on vitality and virulence of pine wood nematode,Bursaphelenchus xylophilus

In our previous screening of the transcriptome of the causal agent of the devastating pine wilt disease,pine wood nematode(PWN,Bursaphelenchus xylophilus),after treatment with the nematicide fomepizole,Surfeit locus gene sft-4,which encodes a regulatory factor,was found to be downregulated.In situ hybridization results showed that the sft-4 was continuously expressed from egg to adult and was especially high in the reproductive system.Here in a study of the effect of RNA interference(RNAi)of sft-4 and recombinant SFT-4 on PWN activity,treatment with sft-4 dsRNA inhibited feeding,reproduction,oviposition and egg hatching of PWN with the greatest inhibition on reproduction and oviposition,whereas recombinant SFT-4 had the opposite effect.In addition,RNAi of sft-4 changed the female–male ratio and lifespan of PWN.In bioassays of PWNs,with RNAi of sft-4 on seedlings and 2-year-old Pinus thunbergii trees,none of the treated plants developed symp-toms during the monitoring period,indicating that virulence of PWNs was either significantly weakened.These results indicate that the influence of sft-4 on PWN pathogenicity may be mainly through regulating reproductive function of PWN and its lifespan.

Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data

Over the past decade,the presence of mistletoe(Viscum album ssp.austriacum)in Scots pine stands has increased in many European countries.Understanding the factors that influence the occurrence of mistletoe in stands is key to making appropriate forest management decisions to limit damage and prevent the spread of mistletoe in the future.Therefore,the main objective of this study was to determine the probability of mistletoe occurrence in Scots pine stands in relation to stand-related endogenous factors such as age,top height,and stand density,as well as topographic and edaphic factors.We used unmanned aerial vehicle(UAV)imagery from 2,247 stands to detect mistletoe in Scots pine stands,while majority stand and site characteristics were calculated from airborne laser scanning(ALS)data.Information on stand age and site type from the State Forest database were also used.We found that mistletoe infestation in Scots pine stands is influenced by stand and site characteristics.We documented that the densest,tallest,and oldest stands were more susceptible to mistletoe infestation.Site type and specific microsite conditions associated with topography were also important factors driving mistletoe occurrence.In addition,climatic water balance was a significant factor in increasing the probability of mistletoe occurrence,which is important in the context of predicted temperature increases associated with climate change.Our results are important for better understanding patterns of mistletoe infestation and ecosystem functioning under climate change.In an era of climate change and technological development,the use of remote sensing methods to determine the risk of mistletoe infestation can be a very useful tool for managing forest ecosystems to maintain forest sustainability and prevent forest disturbance.

Aging Mongolian pine plantations face high risks of drought-induced growth decline:evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas.An important species for afforestation in dry environments of northern China,Mongolian pine(Pinus sylvestris var.mongolica Litv.)has recently exhibited growth decline and dieback on many sites,particularly pronounced in old-growth plantations.However,changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood.In this study,tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young(9-13 years)and aging(35-52 years)plantations of Mongolian pine in a water-limited area of northern China.A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations.In addition,all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation.Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations,as reflected by their lower sapwood-and leaf-specific hydraulic conductivities.Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age.The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

Expression analysis of the R2R3-MYB gene family in upland cotton and functional study of GhMYB3D5 in regulating Verticillium wilt resistance

Improving plant resistance to Verticillium wilt(VW),which causes massive losses in Gossypium hirsutum,is a global challenge.Crop plants need to efficiently allocate their limited energy resources to maintain a balance between growth and defense.However,few transcriptional regulators specifically respond to Verticillium dahliae and the underlying mechanism has not been identified in cotton.In this study,we found that the that expression of most R2R3-MYB members in cotton is significantly changed by V.dahliae infection relative to the other MYB types.One novel R2R3-MYB transcription factor(TF)that specifically responds to V.dahliae,GhMYB3D5,was identified.GhMYB3D5 was not expressed in 15 cotton tissues under normal conditions,but it was dramatically induced by V.dahliae stress.We functionally characterized its positive role and underlying mechanism in VW resistance.Upon V.dahliae infection,the up-regulated GhMYB3D5 bound to the GhADH1 promoter and activated GhADH1expression.In addition,GhMYB3D5 physically interacted with GhADH1 and further enhanced the transcriptional activation of GhADH1.Consequently,the transcriptional regulatory module GhMYB3D5-GhADH1 then promoted lignin accumulation by improving the transcriptional levels of genes related to lignin biosynthesis(GhPAL,GhC4H,Gh4CL,and GhPOD/GhLAC)in cotton,thereby enhancing cotton VW resistance.Our results demonstrated that the GhMYB3D5 promotes defense-induced lignin accumulation,which can be regarded as an effective way to orchestrate plant immunity and growth.

Ecoregional height-diameter models for Scots pine in Turkiye

Ecoregion-based height-diameter models were developed in the present study for Scots pine(Pinus sylves-tris L.)stands in Turkiye and included several ecological factors derived from a pre-existing ecoregional classification system.The data were obtained from 2831 sample trees in 292 sample plots.Ten generalized height–diameter models were developed,and the best model(HD10)was selected according to statistical criteria.Then,nonlinear mixed-effects modeling was applied to the best model.The R2 for the generalized height‒diameter model(Richards function)modified by Sharma and Parton is 0.951,and the final model included number of trees,dominant height,and diameter at breast height,with a random parameter associated with each ecoregion attached to the inverse of the mean basal area.The full model predictions using the nonlinear mixed-effects model and the reduced model(HD10)predictions were compared using the nonlinear sum of extra squares test,which revealed significant differences between ecore-gions;ecoregion-based height–diameter models were thus found to be suitable to use.In addition,using these models in appropriate ecoregions was very important for achieving reliable predictions with low prediction errors.