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

Abstract

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.

Habitat Suitability of Pine Wilt Disease in Northeast China under Climate Change Scenario

“…This method has been widely used in the prediction and forecasting of pest risk analysis (PRA) [44,45], predicting the hazards caused by exotic species and their trends, and can be used for pest epidemic monitoring, analysis, and control [46]. Currently, there are many applications of the MaxEnt model for predicting the habitat suitability of PWD, such as MaxEnt, which has been used in multi-angle and fine-scale studies to predict the risk of PWD continuing to spread in China [25]. The MaxEnt model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China [26]; MaxEnt was used to model PWD-damaged forest distributions during the period 1982 to 2020 and environmental factors, including annual meteorological and human activity factors [27], using the MaxEnt model to assess the potential distribution of PWD in China based on actual distribution data and current and future climate information [47].…”

“…Among these, the MaxEnt model has already been widely used in the dispersal and distribution study of different plants, insects, and fungi [24] and, therefore, is applicable to our study. For example, MaxEnt has been used in multi-angle and fine-scale studies to predict the risk of PWD's continued spread in China [25]. The MaxEnt model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China [26]; MaxEnt was used to model PWDdamaged forest distributions during the period 1982 to 2020, and environmental factors included annual meteorological and human activity factors [27].…”

Monitoring of Discolored Trees Caused by Pine Wilt Disease Based on Unsupervised Learning with Decision Fusion Using UAV Images

“…Furthermore, k-means and Fuzzy C-means (FCM) are two classical clustering algorithms that are simple and fast to run [29], in contrast to (1) Based on the vegetation index m(ExG-ExR), followed by adaptive local threshold segmentation using the maximum between-class variance [26] algorithm and morphological processing to correct the results to obtain the initial result G index . (2) The extraction result G Lab_k-means was obtained by iterative calculations and morphological processing based on the k-means algorithm in the Lab color space. (3) According to the FCM algorithm in Lab color space, the extraction result G Lab_FCM was calculated iteratively, and the result was corrected by morphological processing.…”

“…Pine wilt disease (PWD) is a kind of devastating forest pest disease with strong pathogenicity and rapid spread [1,2]. After being infected by PWD, the needles turn red, and most trees die within one year.…”

Predicting the Potential Distribution of Pine Wilt Disease in China under Climate Change

“…To assess if, in the future, the distribution of PWD may be affected by climate variables, there has been much research regarding the distribution of PWD under climate change. For example, the risk of PWD was predicted by MaxEnt based on a multi-angle and fine-scale study [27]; MaxEnt was used to model the PWD-damaged forest distributions during the period 1982 to 2020 and environmental factors included annual meteorological and human activity factors [26], and three host plants in China were chosen for MaxEnt modeling to simulate the impact of climatic change on PWD [20]. However, unlike earlier studies that employed the MaxEnt model to forecast the suitability of PWD, the current study examined the impact of scenario changes in greenhouse gas emissions under socioeconomic changes and policy interventions on the geographic distribution of species in addition to taking into account the relationship between carbon dioxide concentration and climate [28].…”