Impact of Climate Change on Potential Distribution of Chinese White Pine Beetle Dendroctonus armandi in China

Abstract

Dendroctonus armandi (Coleoptera: Curculionidae: Scolytidae) is a bark beetle native to China and is the most destructive forest pest in the Pinus armandii woodlands of central China. Due to ongoing climate warming, D. armandi outbreaks have become more frequent and severe. Here, we used Maxent to model its current and future potential distribution in China. Minimum temperature of the coldest month and precipitation seasonality are the two major factors constraining the current distribution of D. armandi. Currently, the suitable area of D. armandi falls within the Qinling Mountains and Daba Mountains. The total suitable area is 15.83 × 104 km2. Under future climate scenarios, the total suitable area is projected to increase slightly, while remaining within the Qinling Mountains and Daba Mountains. Among the climate scenarios, the distribution expanded the most under the maximum greenhouse gas emission scenario (representative concentration pathway (RCP) 8.5). Under all assumptions, the highly suitable area is expected to increase over time; the increase will occur in southern Shaanxi, northwest Hubei, and northeast Sichuan Provinces. By the 2050s, the highly suitable area is projected to increase by 0.82 × 104 km2. By the 2050s, the suitable climatic niche for D. armandi will increase along the Qinling Mountains and Daba Mountains, posing a major challenge for forest managers. Our findings provide information that can be used to monitor D. armandi populations, host health, and the impact of climate change, shedding light on the effectiveness of management responses.

Climate Change Influences the Population Density and Suitable Area of Hippotiscus dorsalis (Hemiptera: Pentatomidae) in China

“…They, respectively, represent the total radiative forcing of 2100 as 2.6 W/m², 4.5 W/m², 6 W/m², and 8.5 W/m² higher than 1750 [ 38 ]. RCP2.6, RCP4.5, and RCP8.5 were usually chosen as the basic scenarios in some of the research [ 39 ]. RCP8.5 was regarded as an unlikely high-risk future scenario and was not selected in this study.…”

“…In order to prevent over-fitting, the correlations of 19 environmental variables were analyzed by ArcGIS and SPSS [ 39 , 40 ]. Using 97 valid distribution data and 19 environmental data, the specific values of 19 environmental variables of 97 distributed data were extracted via ArcGIS; the output was in TXT format and was converted to CSV format.…”

Prediction of Suitable Distribution of a Critically Endangered Plant Glyptostrobus pensilis

“…The maximum entropy (MaxEnt) [16] model is the most commonly used prediction model for the potential distribution area of species; it has a high prediction accuracy and good versatility [17]. This model is widely used to predict the distribution of invasive species [18], endangered animals and plants [19][20][21], natural disasters [22] and pests and diseases [23,24]. It can also be used for Wildfire Risk Assessment and Zoning [25].…”

Molecular Mechanism of Overcoming Host Resistance by the Target of Rapamycin Gene in Leptographium qinlingensis

“…Chinese white pine beetle, Dendroctonus armandi Tsai and Li (Coleoptera: Curculionidae: Scolytidae), is the most harmful bark beetle species in the natural forest ecosystems in Northwest China [ 15 , 16 ]. It has mainly invaded and endangered the healthy Pinus armandii Franch grown in the Qinling Mountains for more than 30 years, resulting in the rapid decline of the tree potential and resistance, consequently leading to the occurrence of other diseases and pests [ 17 , 18 ].…”

“…It has become a major obstacle to the sustainable development of the forest ecosystem and the construction of the ecological environment in the Qinling Mountains. Controlling the invasion harm and population reproduction of D. armandi in the forest ecosystem, especially the implementation of population density regulation, has become an urgent theoretical and practical problem to effectively control the occurrence and harm of D. armandi [ 16 , 19 , 20 ].…”