Contribution of the synergistic interaction between topography and climate variables to pine caterpillar (Dendrolimus spp.) outbreaks in Shandong Province, China

Bao, Yu; Han, Aru; Zhang, Jiquan; Liu, Xingpeng; Tong, Zhijun; Bao, Yuhai

doi:10.1016/j.agrformet.2022.109023

Cited by 8 publications

(3 citation statements)

References 67 publications

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“…According to CABI, online, D. punctatus outbreaks occur in over 1 million ha each year in China (Anhui, Jiangxi, Hubei, Hunan, Zhejing, Fujian, Guangdong and Guangxi provinces). According to Bao et al (2022), outbreaks result in substantial impacts on forest ecosystem structure, distribution and productivity. Outbreaks are also common in Vietnam (Billings, 1991).…”

Section: Impactsmentioning

confidence: 99%

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Pest categorisation of Dendrolimus punctatus

Bragard,

Baptista,

Chatzivassiliou

et al. 2024

EFS2

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The EFSA Panel on Plant Health performed a pest categorisation of Dendrolimus punctatus (Lepidoptera: Lasiocampidae), following a commodity risk assessment of bonsai Pinus parviflora grafted onto P. thunbergii from China, in which D. punctatus was identified as a pest of possible concern to the European Union (EU). D. punctatus, also known as the Masson pine caterpillar, is present in China, Taiwan, Vietnam, India and has recently spread to Japanese islands close to Taiwan. Larval feeding on the needles of Pinus elliottii, P. luchuensis, P. massoniana, P. merkusii and P. tabulaeformis causes important damage. D. punctatus larvae can also feed on P. armandii, P. echinata, P. latteri, P. parviflora, P. sylvestris var. mongolica, P. taeda, P. taiwanensis and P. thunbergii, but full development on these hosts is uncertain. The pest has three to five generations per year; winter is spent as larvae on branch tips, on tree trunks and in the soil. The females lay egg clusters on pine needles. Pupation occurs in cocoons attached to branches or needles. D. punctatus could enter the EU either as eggs, larvae or pupae in the foliage of plants for planting or cut branches, as larvae on wood with bark or as overwintering larvae in branches, crevices in the bark or in the litter of potted plants. However, Annex VI of 2019/2072 prohibits the introduction of D. punctatus hosts (Pinus spp.) from countries and areas where the pest occurs. There are climate zones where the pest occurs in Asia that also occur in the EU, though they are limited, which constitutes an uncertainty regarding establishment. The pest's main hosts are not grown in the EU. However, the fact that it attacks the North American Pinus echinata, P. elliottii and P. taeda in its Asian native area suggests a potential capacity to shift to pine species occurring in the EU territory. D. punctatus satisfies all the criteria that are within the remit of EFSA to assess for it to be regarded as a potential Union quarantine pest. Whether the Pinus commonly found in Europe could act as hosts is unknown but is fundamental, affecting the criteria of establishment and magnitude of impact.

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Section: Impactsmentioning

confidence: 99%

“…According to Bao et al. ( 2022 ), outbreaks result in substantial impacts on forest ecosystem structure, distribution and productivity. Outbreaks are also common in Vietnam (Billings, 1991 ).…”

Section: Pest Categorisationmentioning

confidence: 99%

Pest categorisation of Dendrolimus punctatus

Bragard,

Baptista,

Chatzivassiliou

et al. 2024

EFS2

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“…The study area is in the north temperate zone, with a continental monsoon climate and an annual average temperature of 12 • C. The terrain gradually decreases from north to south, and the south is adjacent to the Yellow Sea. There are 204 km 2 of pine forests in the study area, mainly consisting of red pine and black pine [40]. The study area and data acquisition are shown in Figure 2.…”

Section: Overview Of the Study Area And Data Collectionmentioning

confidence: 99%

Detection of Pine Wilt Disease Using Drone Remote Sensing Imagery and Improved YOLOv8 Algorithm: A Case Study in Weihai, China

Wang,

Cao,

et al. 2023

Forests

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Pine Wilt Disease (PWD) is a devastating global forest disease that spreads rapidly and causes severe ecological and economic losses. Drone remote sensing imaging technology is an effective way to detect PWD and control its spread. However, the existing algorithms for detecting PWD using drone images have low recognition accuracy, difficult image calibration, and slow detection speed. We propose a fast detection algorithm for PWD based on an improved YOLOv8 model. The model first adds a small object detection layer to the Neck module in the YOLOv8 base framework to improve the detection performance of small diseased pine trees and then inserts three attention mechanism modules on the backbone network to extend the sensory field of the network to enhance the extraction of image features of deep diseased pine trees. To evaluate the proposed algorithm framework, we collected and created a dataset in Weihai City, China, containing PWD middle-stage and late-stage infected tree samples. The experimental results show that the improved YOLOv8s-GAM model achieves 81%, 67.2%, and 76.4% optimal detection performance on mAP50, mAP50-95, and Mean evaluation metrics, which is 4.5%, 4.5%, and 2.7% higher than the original YOLOv8s model. Our proposed improved YOLOv8 model basically meets the needs of large-scale PWD epidemic detection and can provide strong technical support for forest protection personnel.

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