Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Hao, Zhuoqing; Huang, Jixia; Zhou, Yunfei; Fang, Guofei

doi:10.3390/f12060731

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…In this study, pine mortality decreased with elevation in the model using stand‐level variables only. On the one hand, the result was consistent with previous studies showing that elevation affects pine mortality due to temperature 49,80,81,88–90 . On the other hand, pine forests with high elevation were located in the core area.…”

Section: Discussionsupporting

confidence: 91%

“…On the one hand, the result was consistent with previous studies showing that elevation affects pine mortality due to temperature. 49,80,81,[88][89][90] On the other hand, pine forests with high elevation were located in the core area. The local forestry department has undertaken strict management measures to protect these pine forests.…”

Section: Discussionmentioning

confidence: 99%

“…The factors driving the recent PWD outbreak are complex and deserve further study. In addition, many studies have shown the spread of PWD at the administrative region or country level, [46][47][48][49] but PWD dispersal at a local scale is poorly understood.…”

Section: Introductionmentioning

confidence: 99%

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

Zhan

Ren

et al. 2023

Pest Management Science

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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.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

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

Zhan

Ren

et al. 2023

Pest Management Science

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“…The spatiotemporal pattern of pine wilt disease in the Yangtze River Basin has been analysed based on statistical data obtained from the State Forestry and Grassland Administration of China covering the years 2000 to 2018 (Hao et al 2021). Pine wilt in this area showed an "increase-decrease-increase" trend with a massive increase in 2018.…”

Section: Discussionmentioning

confidence: 99%

A Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2021

Jeger¹,

Fielder²,

Beale³

et al. 2022

Preprint

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A synoptic review of plant disease epidemics and outbreaks was made using two complementary approaches. The first approach involved reviewing scientific literature published in 2021, in which quantitative data related to new plant disease epidemics or outbreaks had been obtained via surveys or similar methodologies. The second approach involved retrieving new pest presence records added to the CABI Distribution Database in 2021. The literature review for the first approach had two stages. Stage 1 aimed to identify publications on plant diseases caused by pathogen taxonomic groups and led to retrieval of 99 core articles describing studies in 62 categories (pathogen species or species complexes) across more than 40 host species in 6 continents. In Stage 2, the core articles were augmented with further articles providing more context and information for the pathogen species identified in Stage 1. When both sets of articles were combined, the pathogen species with more than 5 articles were: Bursephalenchus xylophilus, Candidatus Liberibacter asiaticus, cassava mosaic viruses, citrus tristeza virus, Erwinia amylovora, Fusarium spp. complexes, Fusarium oxysporum f.sp cubense, Magnaporthe oryzae, maize lethal necrosis co-infecting viruses, Meloidogyne spp. complexes, Pseudomonas syringae pvs, Puccinia striiformis f.sp tritici, Xylella fastidiosa, and Zymoseptoria tritici. The automated search of the CABI Distribution Database led to 617 new distribution records from 283 plant pathogens in 2021 and was followed by manual review of all pathogens with more than 4 new records, to identify confirmed first reports in a new location. A total of 15 pathogens was identified: apple hammerhead viroid, apple rubbery wood viruses, Aphelenchoides besseyi, Biscogniauxia mediterranea, Ca. Liberibacter asiaticus, citrus tristeza virus, Colletotrichum siamense, cucurbit chlorotic yellows virus, Erwinia rhapontici, Erysiphe corylacearum, Fusarium oxysporum f.sp. cubense Tropical Race 4, Globodera rostochiensis, Nothophoma quercina, potato spindle tuber viroid, and tomato brown rugose fruit virus. Although 3 very important pathogens – Ca. Liberibacter asiaticus, citrus tristeza virus and Fusarium oxysporum f.sp cubense – were represented in the results of both approaches, in general the two approaches revealed distinct sets of plant disease outbreaks and new records, with little overlap in the results.

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“…PWD-damaged forests were discovered in 718 county-level administrative divisions in 17 provinces of China by the end of December 2020, with ~19.5 million dead trees and ~1.81 million hectares infected [9]. This widespread PWD outbreak in China caused major economic losses and ecological consequences for pine forests [5,10,11].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China

Wei

Peng

Liu

et al. 2022

Forests

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Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.

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Spatiotemporal Pattern of Pine Wilt Disease in the Yangtze River Basin

Cited by 8 publications

References 35 publications

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

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

A Synoptic Review of Plant Disease Epidemics and Outbreaks Published in 2021

Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China

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