Dispersal Patterns of Pine Wilt Disease in the Early Stage of Its Invasion in South Korea

Impact of stand- and landscape-level variables on pine wilt disease-caused tree mortality in pine forests

BACKGROUND

Pine wilt disease (PWD) outbreaks have affected extensive areas of South China's forests, but the factors explaining landscape patterns of pine mortality are poorly understood. The objective of this study was to determine the relative importance of stand structure, topography, landscape context, and beetle pressure in explaining PWD severity. During 2020-2021, we identified 66 plots based on mapped PWD infestation severity. We built PWD infestation maps for 2019-2021 through field surveys. Stand structure and topography were obtained from Forest Resources Management 'One Map' and elevation raster data. We then used 'One Map' and PWD infestation maps to determine landscape context and beetle pressure variables at different spatial scales. The relative importance of 12 explanatory variables was analyzed using multi-model inference.

RESULTS

In this study, we show that: (i) 1 km was the best spatial scale related to pine mortality, and (ii) models including landscape context and beetle pressure were much better at predicting pine mortality than models using only stand-level variables.

CONCLUSION

Landscape-level variables, particularly beetle pressure, were the most consistent predictors of subsequent pine mortality within susceptible stands. These results may help forest managers identify locations vulnerable to PWD and improve existing strategies for outbreak control. © 2023 Society of Chemical Industry.

Potentially Suitable Geographical Area for Monochamus alternatus under Current and Future Climatic Scenarios Based on Optimized MaxEnt Model

M. alternatus is considered to be an important and effective insect vector for the spread of the important international forest quarantine pest, Bursaphelenchus xylophilus. The precise determination of potential suitable areas of M. alternatus is essential to monitor, prevent, and control M. alternatus worldwide. According to the distribution points and climatic variables, the optimized MaxEnt model and ArcGIS were used to predict the current and future potentially suitable areas of M. alternatus worldwide. The optimized MaxEnt model parameters were set as feature combination (FC) = LQHP and β = 1.5, which were determined by the values of AUC_diff, OR₁₀, and ΔAICc. Bio2, Bio6, Bio10, Bio12, and Bio14 were the dominant bioclimatic variables affecting the distribution of M. alternatus. Under the current climate conditions, the potentially suitable habitats of M. alternatus were distributed across all continents except Antarctica, accounting for 4.17% of the Earth's total land area. Under future climate scenarios, the potentially suitable habitats of M. alternatus increased significantly, spreading to a global scale. The results of this study could provide a theoretical basis for the risk analysis of the global distribution and dispersal of M. alternatus as well as the precise monitoring and prevention of this beetle.

Expansion of non-native plant Flaveria bidentis (L.) Kuntze driven by a range of factors leading to patchy distribution patterns

Given the growing concern over the ecological impacts of non-native species, exploring these species' expansion edge and distribution patterns and their driving factors is important for developing suitable management measures. Flaveria bidentis (L.) Kuntze, a non-native plant that was introduced to China in the 1990s, has spread from southern Hebei Province, where it first took root, to the surrounding regions and has become one of the most notorious invasive weeds in northern China. Based on 15 years (2006-2021) of extensive field investigations, the spatial distribution of sampling and occurrence points were mapped in the recently expanded region of F. bidentis' population. Then, nearest neighbor analysis is used to characterize the spatial pattern differences between samplings and occurrences. An exponential decay function was used to elucidate the driving factors contributing to the presence and absence of F. bidentis. Our results demonstrated an effective random sampling setup, a heterogeneous spatial distribution of F. bidentis, and a multi-regional independent aggregation distribution pattern (p < .01). There were significant spatial correlations between the aggregation areas of plant occurrence points and the locations of roads and construction sand distribution centers. These findings suggest that human activities involving major roads and construction sand distribution centers were driving factors contributing to this long-distance dispersal and spatially discontinuous distribution patterns. The presence of these patchy distribution patterns has important implications for ongoing efforts to manage populations of non-native species.

Simulating Pine Wilt Disease Dispersal With an Individual-Based Model Incorporating Individual Movement Patterns of Vector Beetles

Individual movements of the insect vector pine sawyer beetles were incorporated into an individual-based model (IBM) to elucidate the dispersal of pine wilt disease (PWD) and demonstrate the effects of control practices. The model results were compared with the spatial data of infested pine trees in the Gijang-gun area of Busan, Republic of Korea. Step functions with long- and middle-distance movements of individual beetles effectively established symptomatic and asymptomatic trees for the dispersal of PWD. Pair correlations and pairwise distances were suitable for evaluating PWD dispersal between model results and field data at short and long scales, respectively. The accordance between model and field data was observed in infestation rates at 0.08 and 0.09 and asymptomatic rates at 0.16-0.17 for disease dispersal. Eradication radii longer than 20 m would effectively control PWD dispersal for symptomatic transmission and 20-40 m for asymptomatic transmission. However, the longer eradication radii were more effective at controlling PWD. Therefore, to maximize control effects, a longer radius of at least 40 m is recommended for clear-cutting eradication. The IBM of individual movement patterns provided practical information on interlinking the levels of individuals and populations and could contribute to the monitoring and management of forest pests where individual movement is important for population dispersal.

Risk Prediction and Variable Analysis of Pine Wilt Disease by a Maximum Entropy Model

Pine wilt disease (PWD) has caused a huge damage to pine forests. PWD is mainly transmitted by jumping diffusion, affected by insect vectors and human activities. Since the results of climate change, pine wood nematode (PWN—Bursaphelenchus xylophilus) has begun invading the temperate zones and higher elevation area. In this situation, predicting the distribution of PWD is an important part of the prevention and control of the epidemic situation. The research established the Maxent model to conduct a multi-angle, fine-scale prediction on the risk distribution of PWD. We adjusted two parameters, regularization multiplier (RM) and feature combination (FC), to optimize the model. Influence factors were selected and divided into natural, landscape, and human variables, according to the physical characteristics and spread rules of PWD. The middle-suitability regions and high-suitability regions are distributed in a Y-shape, and divided the study area into three parts. The high-suitability areas are concentrated in the region with high temperature, low elevation, and intensive precipitation. Among the selected variables, natural factors still play the most important role in the distribution of the disease, and human factors and landscape factors are also worked well. The permutation importance of factors is different due to differences in climate and other conditions in different regions. The multi-angle, fine-scale model can help provide useful information for effective control and tactical management of PWD.

Predicting the Global Distribution of Solenopsis geminata (Hymenoptera: Formicidae) under Climate Change Using the MaxEnt Model

Simple Summary

Climate change influences the distribution of species. The tropical fire ant Solenopsis geminata (Hymenoptera: Formicidae) is a serious invasive species that damages the native ecosystem. In this study, we evaluated the current and future distribution of S. geminata under climate change using the ecological niche model. The model results showed that the favorable habitat area of S. geminata will expand to higher latitudes on a global scale due to future global warming. Some countries located in America and East Asia, such as Brazil, China, South Korea, the USA, and Uruguay, can be threatened by S. geminata due to climate change.

Abstract

The tropical fire ant Solenopsis geminata (Hymenoptera: Formicidae) is a serious invasive species that causes a decline in agricultural production, damages infrastructure, and harms human health. This study was aimed to develop a model using the maximum entropy (MaxEnt) algorithm to predict the current and future distribution of S. geminata on a global scale for effective monitoring and management. In total, 669 occurrence sites of S. geminata and six bioclimatic variables of current and future climate change scenarios for 2050 and 2100 were used for the modeling. The annual mean temperature, annual precipitation, and precipitation in the driest quarter were the key influential factors for determining the distribution of S. geminata. Although the potential global distribution area of S. geminata is predicted to decrease slightly under global warming, the distribution of favorable habitats is predicted to expand to high latitudes under climate scenarios. In addition, some countries in America and East Asia, such as Brazil, China, South Korea, the USA, and Uruguay, are predicted to be threatened by S. geminata invasion under future climate change. These findings can facilitate the proactive management of S. geminata through monitoring, surveillance, and quarantine measures.

Spatial heterogeneities of human-mediated dispersal vectors accelerate the range expansion of invaders with source-destination-mediated dispersal

Rapid range expansions of invasive species are a major threat to ecosystems. Understanding how invasive species increase their habitat ranges and how environmental factors, including intensity of human activities, influence dispersal processes is an important issue in invasion biology, especially for invasive species management. We have investigated how spatially heterogeneous factors influence range expansion of an invasive species by focusing on long-distance dispersal, which is frequently assisted by human activities. We have developed models varying two underlying processes of a dispersal event. These events are described by source and destination functions that determine spatial variations in dispersal frequency and the probability of being a dispersal destination. Using these models, we investigated how spatially heterogeneous long-distance dispersal influences range expansion. We found that: (1) spatial variations in the destination function slow down late population dynamics, (2) spatial variations in the source function increase the stochasticity of early population dynamics, and (3) the speed of early population dynamics changes when both the source and the destination functions are spatially heterogeneous and positively correlated. These results suggest an importance of spatial heterogeneity factors in controlling long-distance dispersal when predicting the future spread of invasive species.

A Machine Learning Approach to Detecting Pine Wilt Disease Using Airborne Spectral Imagery

Pine Wilt Disease is one of the most destructive pests affecting coniferous forests. After being infected by the harmful Bursaphelenchus xylophilus nematode, most trees die within one year. The complex spreading pattern of the disease and the tedious hard labor process of diagnosis involving field wood sampling followed by laboratory analysis call for alternative methods to detect and manage the infected areas. Remote sensing comes naturally into play owing to the possibility of covering relatively large areas and the ability to discriminate healthy from sick trees based on spectral characteristics. This paper presents the development of machine learning classification algorithms for the detection of Pine Wilt Disease in Pinus pinaster, performed in the framework of the European Commission’s Horizon 2020 project “Operational Forest Monitoring using Copernicus and UAV Hyperspectral Data” (FOCUS) in two provinces of central Portugal. Five flight campaigns have been carried out in two consecutive years in order to capture a multitemporal variation of disease distribution. Classification algorithms based on a Random Forest approach were separately designed for the acquired very-high-resolution multispectral and hyperspectral data, respectively. Both algorithms achieved overall accuracies higher than 0.91 in test data. Furthermore, our study shows that the early detection of decaying trees is feasible, even before symptoms are visible in the field.

A Spatiotemporal Analysis and Dispersal Patterns of the Potato Cyst Nematode Globodera pallida in Idaho

The potato cyst nematode Globodera pallida is a globally regulated potato pest. It was detected for the first time in the United States in the state of Idaho in 2006, and as of February 2019, the infestation is limited to 1,326 hectares. G. pallida is a specialized obligate sedentary endoparasite that can survive in the soil for up to 30 years in the absence of its potato host. In highly infested fields, the nematode can reduce tuber yields up to 80% and is spread mainly through the movement of soil, tubers, or farm equipment. The objectives of this study were to describe the spatiotemporal pattern of G. pallida in infested fields and model its dispersal patterns in southeastern Idaho. We used geostatistical tools and simulation models for precise mapping and to describe the relationships between G. pallida incidence and the spatial configurations. We found that the nematode is spatially clustered and prevalent around edges of fields, and its dispersal pattern followed the direction of cultivation. We found that the absence of potato in an infested field significantly reduced the number of cysts sampled each year subsequent to the initial delimitation sampling in 2007. Phytosanitary measures prohibiting the growth of potato contributed to stopping nematode reproduction, and the use of chemical fumigants and biofumigant cover crops contributed to a significant reduction in egg viability. We observed a process of a nonlinear decline in the prevalence of cysts as the distance separation from the primary infestation focus increased. A power law model was used to fit G. pallida dispersal capabilities. This study contributed to describing G. pallida infestation for Idaho. The goal of this study is to provide information on the spatial pattern and landscape ecology of G. pallida in Idaho for policy makers, industry, and researchers as well as facilitate common understandings on the challenges and opportunities for controlling this pest in Idaho.

Monitoring, Assessment and Management of Forest Insect Pests and Diseases

Forest pests are one of the most important factors disturbing forest ecosystems, by impacting forestry economy, ecosystem services, biodiversity, and sustainable ecosystem management. Monitoring the occurrence of forest pests offers clues to understand their impacts on the forest ecosystem and develop a sustainable ecosystem management strategy. This special issue is designed to create a better understanding of the changes and impacts of forest pests according to forest changes, caused by natural or anthropogenic causes. There are 13 papers published in this special issue, covering several issues concerning forest pests. Two of the papers reviewed the changes in forest pests in Korea or Poland. The remaining twelve papers covered issues concerning the monitoring, assessment, and management of forest pests. Through this special issue, we expect to contribute towards the improvement of our knowledge of the structures and processes in forest ecosystems relating to forest pests and fundamental information for the effective management of forest pests.

Changes in Major Insect Pests of Pine Forests in Korea Over the Last 50 Years

Understanding the occurrence patterns of forest pests is fundamental for effective forest management from both economic and ecological perspectives. Here, we review the history of the occurrence patterns and causes of outbreaks and declines of pests in Korean pine forests over the last 50 years. During this period, the major pests of pine forests in Korea have shifted from pine caterpillar (Dendrolimus spectabilis Butler) to the pine needle gall midge (PNGM, Thecodiplosis japonensis (Uchida and Inouye)) and finally to pine wilt disease (PWD) caused by the pine wood nematode (Bursaphelenchus xylophilus (Steiner and Buhrer) Nickle). Outbreaks of pine caterpillar, a native species in Korea, have been recorded as far back as 900 years, and it was the most relevant forest pest in Korea until the 1970s. The decline of its importance has been attributed to reforestation and higher levels of subsequent natural enemy activity. The PNGM is an invasive species, first discovered in Korea in 1929, that became widely distributed by 1992 and the major forest pest in the 1980s and 1990s. A suite of parasitic wasps attacking the PNGM contributed at least partially to the decline of PNGM densities. Following the decline of the PNGM, damage from PWD has increased since 2003. These shifts in major forest pests might be related to changes in forest composition and interactions among forest pests. Therefore, a new management strategy for controlling forest pests is required to mitigate the decline of pine forests in Korea.

Occurrence Prediction of the Citrus Flatid Planthopper (Metcalfa pruinosa (Say, 1830)) in South Korea Using a Random Forest Model

Invasive species cause a severe impact on existing ecosystems. The citrus flatid planthopper (CFP; Metcalfa pruinosa (Say, 1830)) is an invasive species in many countries. Predicting potential occurrence areas of the species related to environmental conditions is important for effective forest ecosystem management. In this study, we evaluated the occurrence patterns of the CFP and predicted its potential occurrence areas in South Korea using a random forest model for a hazard rating of forests considering meteorological and landscape variables. We obtained the occurrence data of the CFP in South Korea from literature and government documents and extracted seven environmental variables (altitude, slope, distance to road (geographical), annual mean temperature, minimum temperature in January, maximum temperature in July, and annual precipitation (meteorological)) and the proportion of land cover types across seven categories (urban, agriculture, forest, grassland, wetland, barren, and water) at each occurrence site from digital maps using a Geographic Information System. The CFP occurrence areas were mostly located at low altitudes, near roads and urbanized areas. Our prediction model also supported these results. The CFP has a high potential to be distributed over the whole of South Korea, excluding high mountainous areas. Finally, factors related to human activities, such as roads and urbanization, strongly influence the occurrence and dispersal of the CFP. Therefore, we propose that these factors should be considered carefully in monitoring and surveillance programs for the CFP and other invasive species.