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Article

Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis

by
Zhipeng He
1,†,
Xinju Wei
1,†,
Yaping Li
2,
Xinqi Deng
1 and
Zhihang Zhuo
1,*
1
College of Life Science, China West Normal University, Nanchong 637002, China
2
College of Intelligent Engineering, Henan Mechanical and Electrical Vocational College, Zhengzhou 451191, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2025, 15(11), 1224; https://doi.org/10.3390/agriculture15111224
Submission received: 15 April 2025 / Revised: 16 May 2025 / Accepted: 3 June 2025 / Published: 4 June 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

:
Aromia bungii Faldermann (Coleoptera, Cerambycidae) is one of the most serious stem-boring pests that infests Rosaceae fruit trees and ornamental trees. This study, based on occurrence data for this species, employed the MaxEnt model and meta-analysis method to predict the distribution range and centroid movement of A. bungii under the current and future climates in China. The study also analyzed the impact of environmental variables on its distribution. The meta-analysis results revealed that A. bungii has the highest distribution density within the altitude range of 0 to 300 m. The MaxEnt model identified six key environmental variables influencing the distribution of A. bungii, namely the minimum temperature of the coldest month (bio6), mean temperature of the wettest quarter (bio8), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), precipitation seasonality (coefficient of variation) (bio15), and altitude. Under the current climate conditions, the most suitable distribution range of A. bungii is located between 92.6–120.38° E and 16.17–44.46° N, with highly suitable areas predominantly found in the North China Plain, the Shandong Hills, the area around the Bohai Sea, and the middle–lower reaches of the Yangtze River, covering a total area of 41.43 × 104 km2. Scenarios related to the future climate indicate a shift in the suitable habitats of A. bungii towards higher latitudes, with the centroid of the potentially suitable area shifting towards the northeast. This study provides supporting information for the control and management of this pest.

1. Introduction

The adults of Aromia bungii (Faldermann, 1835) (Coleoptera: Cerambycidae) typically exhibit distinct morphological characteristics and behaviors that are crucial for their identification and management. The adults typically exhibit a reddish-brown ‘red-necked’ form on the prothorax, although there are very few individuals that manifest an entirely black ‘black-necked’ form [1]. This species is native to East Asia and is widely distributed in China, ranging from the southern regions of Fujian, Guangdong, and Guangxi to the northern areas of Liaoning and Inner Mongolia and the coastal regions to the east. Its distribution extends westward to Ningxia, Shaanxi, and Gansu and includes parts of Sichuan and Yunnan, stopping near the longitude of 102° [2]. Excluding high-altitude and severely cold regions such as Xinjiang and Tibet, this species is found throughout China. Internationally, it is primarily present in countries such as the Korean Peninsula, Vietnam, and Russia [3,4,5]. In recent years, there have been reports of A. bungii establishing itself as a pest in some European countries, including the United Kingdom, Italy, and Germany, and it has also been captured in the United States [4]. Aromia bungii is considered one of the most serious stem-boring pests that infests Rosaceae fruit trees and ornamental trees. It also poses a threat to various tree species, including Prunus yedoensis (Matsum.), Salix babylonica L., Prunus armeniaca L., Prunus salicina (Lindl), and Armeniaca mume (Siebold & Zucc.) [6]. Similarly to many longhorn beetles, the developing larvae of A. bungii primarily feed on nutrient-rich cambium, phloem, and outer sapwood, overwintering as larvae, sometimes even for several consecutive years [7]. The larvae create irregular galleries inside the tree [8], damaging the tree’s vascular tissue and hindering the transport of water and nutrients. Simultaneously, they emit woodchip-like frass through exit holes, with severe impacts on the growth and development of trees [3,9]. Mature larvae tunnel into the wood to form pupal chambers and emerge as adult beetles that are active day and night during the peak summer period. The adult beetles mainly damage the main trunks and branches of fruit trees, especially during the late fruiting period and the aging stage of stone fruit trees [10]. In severe cases, this infestation can lead to weakened tree vigor and eventual tree death. Due to the concealed nature of developing larvae within the host’s cortical tissue, effective control measures are challenging [11].
In recent years, with the increase in peach tree cultivation, the damage caused by A. bungii to fruit trees has been gradually intensifying [1]. If this pest continues to spread to other regions of China, it will pose a potential threat to local fruit trees and ornamentals. Previous studies have focused on various aspects, including the occurrence patterns of A. bungii [1], the mating behavior of adult beetles, host plant selectivity [12], the identification and analysis of antennal olfactory genes [13], ultrastructural features [14], and mitochondrial genome and phylogenetic analysis [15], as well as the impacts of pheromones and insecticides on A. bungii [16,17]. However, the potential distribution of this pest in China remains unknown. Global warming provides a potential opportunity for A. bungii to spread from low to high latitudes, bringing higher risks [2]. Therefore, determining the potential geographic distribution of A. bungii in China will contribute to the establishment of early monitoring and warning systems for its management.
Species distribution models (SDMs) primarily utilize known species distribution information and relevant environmental data to infer a species’ ecological requirements based on ecological niche theory and thus predict their climatically suitable areas [18]. MaxEnt is a method based on maximum entropy to determine the associations between target species occurrence data and environmental data [19]. It has a weak dependence on physiological factors and is widely used to predict the potential geographic distributions of various invasive species [20,21]. Moreover, MaxEnt has advantages such as intuitive modeling, high simulation accuracy, ease of operation, and low sample requirements, which is why it has been widely used in various fields [22,23]. When there are differences or inconsistencies in the results of multiple studies, the use of meta-analysis helps to derive quantitative results that approximate the true situation [24]. In recent years, the MaxEnt model has seen a continuous increase in its application to predict potentially suitable areas for species and has become one of the most influential tools globally for the forecasting of the potential geographic distributions of invasive pests [25]. It has been successfully employed in predicting the potential geographic distributions of species such as Monochamus saltuarius [26], Meloidogyne enterolobii [27], and Schrankia costaestrigalis [28] in China or globally. This study utilized the next-generation climate system model BCC-CSM2-MR, developed by the Beijing Climate Center (BCC) in China. This model is capable of reasonably reproducing the climate distribution characteristics, with a correlation coefficient of 0.86 between its simulation results and observations, indicating a high level of reliability [29].
In this study, the potential geographic distribution of A. bungii in China was determined through meta-analysis and the MaxEnt model. Utilizing occurrence data, we estimated the current and future potential distribution of A. bungii and analyzed the trends in the centroid shift in its potential geographic distribution. Subsequently, we discussed the relationships between this pest and host plants, as well as its potential threats to fruit trees and ornamental trees. The purpose of this study was to predict the distribution and spreading trends of A. bungii, providing supporting information for the prevention and control of this pest.

2. Materials and Methods

2.1. Species Occurrence Data

In this study, occurrence data for A. bungii were primarily sourced from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, accessed on 5 October 2024), the National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/, accessed on 5 October 2024), and the National Animal Specimen Database (http://museum.ioz.ac.cn/index.html, accessed on 5 October 2024). The obtained data were then analyzed and processed, and geographic coordinates were determined using the Google Earth Pro software 7.3.5. To avoid spatial autocorrelation, the collected data were filtered to remove duplicate and erroneous coordinate records [30]. All obtained records were saved in ‘CSV’ format. In total, 120 occurrence records for A. bungii were ultimately acquired. Among them, 113 occurrence records in China were used for modeling, as depicted in Figure 1.

2.2. Environmental Variables and Data Processing

Nineteen bioclimatic variables and one topographic variable were collected as candidate variables for modeling. Relevant bioclimatic variables and altitude data were obtained from the WorldClim v2.1 dataset (https://www.worldclim.org/, accessed on 5 October 2024). The future climate data reflected three scenarios, namely SSP1-2.6 (scenario with low greenhouse gas emissions), SSP2-4.5 (scenario with medium greenhouse gas emissions), and SSP5-8.5 (scenario with high greenhouse gas emissions), from the new-generation climate system model BCC-CSM2-MR [31], developed by the Beijing Climate Center, to predict the distribution of A. bungii.
Compared to other species distribution models (SDMs), the MaxEnt model has many advantages, including broad applicability, simplicity in operation, low sample requirements, high accuracy, and stable results [32]. This model utilizes accurate geographic locations of species occurrence and relevant environmental variables to estimate the maximum entropy distribution of a species under specific environmental constraints in a given region. Therefore, the MaxEnt model is widely applied in fields such as ecology, conservation biology, evolutionary biology, and invasive species management [19,26]. A study indicates that the selection of environmental variables has a certain impact on the predictive results of ecological niche models. To avoid issues such as autocorrelation and multicollinearity among variables during the modeling process, it is necessary to screen the environmental variables first [33,34].
Earlier studies demonstrated that multicollinearity among bioclimatic variables can affect the simulation accuracy of MaxEnt [35]. Therefore, to avoid multicollinearity among the selected variables, Pearson correlation coefficients among bioclimatic factors were calculated using the SPSS 26 (Table 1). Variables with correlation coefficients greater than or equal to 0.8 were excluded, while those with correlation coefficients less than 0.8 were retained. The model was pre-built using the built-in cross-validation method, and then the final environmental variables used for modeling were determined by comprehensively considering the importance of the environmental factors provided by the model, the correlation coefficients, and the ecological significance. In the end, six environmental variables, namely the minimum temperature of the coldest month (bio6), mean temperature of the wettest quarter (bio8), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), precipitation seasonality (coefficient of variation) (bio15), and altitude, were retained (Table 2) to predict the potential geographic distribution of A. bungii.

2.3. Modeling Process and Statistical Analysis

The Aromia bungii occurrence data in ‘CSV’ format and the selected six environmental variables from Table 2 were imported into MaxEnt 3.4.4. The ‘Create Response Curves’ analysis tool was utilized to examine the relationships between the variables’ gradients and A. bungii’s presence. The percentage of random test points was set at 25%, with the remaining 75% used to train the model. The operation was conducted 10 times, and the maximum number of iterations was set to 10,000. Default settings were used for the feature classes and regularization multiplier. The relative contributions of each environmental variable to the model were determined using the contribution rate, permutation importance, and jackknife test [36]. To estimate the potential distribution of A. bungii in the future, model projections were obtained using the above three future climate scenarios using existing climate data.
The results from MaxEnt were processed using ArcGIS 10.8. Using the natural breakpoint method, the suitable habitats for A. bungii were reclassified into four potential habitat types: ‘high’ (p ≥ 0.66), ‘medium’ (0.33 ≤ p < 0.66), ‘low’ (0.1 ≤ p < 0.33), and ‘unsuitable’ (p < 0.1). The SDM toolbox 2.0 package in ArcGIS was utilized to calculate the centroid positions of suitable areas for A. bungii at different periods, revealing the migration direction of A. bungii’s distribution areas. Additionally, the distribution centroid was used to assess the migration of climatically suitable areas for A. bungii.

2.4. Model Evaluation

The area under the curve (AUC) was used to evaluate the accuracy of the model. AUC values range from 0.5 to 1, with higher values indicating higher model accuracy [37]. The specific evaluation criteria are as follows: 0.5–0.6 indicates poor performance; 0.6–0.7 suggests fair performance; 0.8–0.9 represents good results; and AUC > 0.9 indicates superior performance.

2.5. Meta-Analysis

2.5.1. Data Collection and Processing

The data processing of the collected distribution points of A. bungii was performed as follows: (1) the coordinate points were imported into Google Earth v23 to obtain elevation data for the distribution points; (2) the dataset was divided into a low-altitude group (0–1000 m) as the treatment group and a high-altitude group (1500–1600 m and above) as the control group. The required sample size, mean, and standard deviation were calculated, yielding a total of 41 data groups.

2.5.2. Data Analysis

In this study, we employed the rma.mv function from the R package metafor (R version 4.3) to conduct the meta-analyses. Initially, the relative risk (RR+) was calculated using a random-effects model, and the between-study variance (I2) was estimated via restricted maximum likelihood (REML). Moderator variables were introduced based on the magnitude of I2. Subsequently, a random-effects model was applied to estimate the overall mean effect size across all altitude treatment groups. Finally, comprehensive statistical tests were conducted, including analyses of the mean effect sizes, 95% confidence intervals (CI), Qt, and I2 [38].
For each analysis, we report the mean effect size along with the 95% confidence intervals (CI), Cochran’s Q statistic (Qt), and I2. The heterogeneity among studies was assessed using Qt, which follows a chi-squared distribution with k − 1 degrees of freedom.
We interpreted the effect sizes as follows: if the 95% CI included 0, the difference between the experimental and control groups was considered not statistically significant (p > 0.05); if the entire CI was greater than 0, the effect size of the experimental group was significantly greater than the control group (p < 0.05); and if the CI was entirely below 0, the experimental effect was significantly smaller than that of the control group (p < 0.05).

3. Results

3.1. Survival Response to Altitude

The significance of the overall effect in the analysis was determined by calculating the cumulative effect size. The calculation process involved two model selections: the fixed-effects model and the random-effects model. The random-effects model took into account not only the variation in the effect sizes within cases but also the variation between cases. The results, as shown in Figure 2a, indicated that the cumulated effect size calculated by the model was greater than 0 and fell within the confidence interval (0.2157–1.4973), suggesting that, as the altitude decreases, the distribution of A. bungii gradually increases in China. Across different altitude gradients, variations in the density of the A. bungii distribution were observed. As the altitude increases, the distribution of A. bungii decreases, with the highest density observed within the altitude range of 0–300 m (Figure 2b).

3.2. Model Performance and Accuracy Assessment

The accuracy of the MaxEnt model’s predictions was evaluated using the ROC curve method. The AUC value for the training dataset was 0.921, that for the testing dataset was 0.919, and the average AUC over ten replicates was 0.906 (Figure 3). According to the AUC value criteria, a value greater than 0.9 indicates good predictive performance, suggesting that the prediction results are highly accurate and reliable. This indicates that the model can be used to simulate the suitable habitats of A. bungii.

3.3. Selection of Key Environmental Factors

A total of six environmental variables were selected to predict the potential geographical distribution of A. bungii. The contributions and permutation importance of these six environmental variables are presented in Table 3. The altitude had the highest contribution rate to the predictive model, accounting for 43.6%. Following this, the minimum temperature of the coldest month (Bio06), precipitation seasonality (coefficient of variation) (Bio15), and precipitation of the wettest month (Bio13) contributed 25.7%, 13.3%, and 9.9%, respectively. The permutation importance of the altitude and minimum temperature of the coldest month (Bio06) was 33.1% and 43.5%, respectively, significantly higher than other environmental variables. Through the comparison of the environmental permutation importance, it was found that these variables played a crucial role in the model’s predictions.
Additionally, jackknife tests were conducted on the selected bioclimatic variables, as shown in Figure 4. The results indicate that, when using only one bioclimatic variable, the altitude has the most significant gain in model training, with a gain value close to 0.9. Following the altitude, the precipitation of the wettest month (Bio13), minimum temperature of the coldest month (Bio06), and mean temperature of the wettest quarter (Bio8) also exhibit substantial gains, with values ranging between 0.5 and 0.8. The gains for other environmental variables are relatively low, all being less than 0.4. This suggests that the altitude, Bio13, Bio06, and Bio8 are the key variables influencing the potential distribution of A. bungii, while the impacts of the remaining environmental variables are relatively minor. When considering the omission of individual variables, the most significant reduction in model gain is observed for the minimum temperature of the coldest month (Bio06), indicating that Bio6 provides more effective information than the other environmental variables. Following closely is the precipitation seasonality (coefficient of variation) (Bio15). Combining the contribution percentages and permutation importances of each environmental variable, it is evident that the altitude, precipitation of the wettest month (Bio13), minimum temperature of the coldest month (Bio06), and mean temperature of the wettest quarter (Bio8) are the key environmental factors influencing the distribution of A. bungii.

3.4. Potential Distribution of A. bungii in the Current Period in China

Using ArcGIS 10.8, a distribution prediction map for the current period for A. bungii was established, and the areas were classified into four levels: high-suitability, moderate-suitability, low-suitability, and unsuitable areas (Figure 5). Based on the simulation results regarding the six key environmental variables and the occurrence records of A. bungii, the climatically suitable areas for A. bungii are primarily located between 92.6–120.38° E and 16.17–44.46° N. A small number of suitable areas are also found in the northern part of the Tarim Basin in Xinjiang and along the eastern coastal zone of Jilin Province.
The statistics of the high-suitability areas for A. bungii are presented in Table 4. The results indicate that the high-suitability regions are mainly concentrated in Liaoning, Hebei, Beijing, Shanxi, Tianjin, Shaanxi, Shandong, Henan, Jiangsu, Anhui, and Hubei Provinces, accounting for 4.71%, 24.76%, 2.75%, 1.85%, 2.95%, 3.05%, 29.03%, 12.79%, 4.20%, 3.45%, and 8.14% of the total high-suitability areas in the country, respectively. The total area of the high-suitability regions is 41.43 × 104 km2, constituting 4.30% of the total high-suitability area nationwide. Additionally, it is noteworthy that the high-suitability area in Beijing (69.55%) is close to 70%, and Tianjin is entirely classified as a high-suitability area (101.85%).

3.5. Potential Distribution of A. bungii in Future Periods in China

For the scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5, the distribution prediction maps for A. bungii under favorable climatic conditions for the years 2041–2060 and 2081–2100 are shown in Figure 6. From Figure 5, it can be observed that the high-suitability areas for A. bungii are mainly concentrated in the North China Plain along the Bohai Sea, the hills of Shandong, and the middle and lower reaches of the Yangtze River, with a small portion in the Sichuan Basin and the northern part of Taiwan. In the 2050s, compared to the SSP1-2.6 and SSP2-4.5 scenarios, under the SSP5-8.5 scenario, the high-suitability area will gradually expand. There is a trend towards spreading to the south in the northeastern plain in the northern part, spreading to the Loess Plateau in the west, and gradually expanding in the middle and lower reaches of the Yangtze River in the south. In the 2090s (under the SSP2-4.5 scenario), the rate of expansion of the high-suitability area will slow compared to the 2050s (under the SSP5-8.5 scenario), but it still shows an increasing trend.
Under the scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5, an analysis of the suitable areas for A. bungii under future climate conditions was conducted (Table 5). In the 2050s, under the SSP1-2.6 scenario, the low-, medium-, and high-suitability areas for A. bungii will decrease by 1.65%, 3.39%, and 6.9%, respectively. Remarkably, under the SSP5-8.5 scenario, the low- and high-suitability areas of A. bungii will increase by 22.58% and 78.88%, respectively, while the moderately suitable area will decrease by 10.37%. The overall suitable area will increase by 19.12%. In the 2050s and 2090s, under the SSP1-2.6 scenario, the high-suitability areas of A. bungii show a decreasing trend, reducing by 6.90% and 8.98%, respectively. In the 2050s, compared to the current situation, the SSP2-4.5 and SSP5-8.5 scenarios show increases of 2.78% and 78.88%, respectively. In the 2090s, the high-suitability areas under SSP2-4.5 and SSP5-8.5 will increase by 20.11% and 7.77%, respectively, compared to the current situation. In the 2050s, the total area of climatically suitable areas under the SSP5-8.5 scenario will increase by 19.12% compared to the current situation, while the SSP1-2.6 and SSP2-4.5 scenarios show a decreasing trend. In the 2090s, the total area of climatically suitable areas under the SSP2-4.5 scenario will decrease compared to the current situation, while the SSP1-2.6 and SSP5-8.5 scenarios show an increasing trend.

3.6. Primary Climatic Variables Influencing Distribution of A. bungii

Using the MaxEnt 3.4.0, a jackknife analysis was performed on the key environmental variables. The analysis included scenarios with only one variable, without the variable, and with all variables, aiming to reflect the importance of environmental variables in the distribution of A. bungii (Figure 4). Probability–response curves were generated to illustrate the relationships between the probability of A. bungii occurrence and the environmental variables (Figure 7). The probability of exceeding the habitat suitability classification threshold (0.66) indicates the range suitable for the growth and survival of A. bungii. The trends for bio6, bio8, and bio13 are generally similar.
The existence probability of A. bungii in bio6 and bio8 increased slowly at −25.07 °C and 10 °C, respectively (Figure 7b,c), and this phenomenon also existed in bio13 at 0 mm (Figure 7d). The suitable ranges for bio6, bio8, and bio13 are −11.5 to −1.79 °C (peak at −9 °C), 23.57 to 28.44 °C (peak at 25.76 °C), and 147.02 to 273.86 mm (peak at 169.24 mm), respectively. The suitable range for Alt is 0 to 158.12 m (Figure 7a). Compared to other variables, bio14 (Figure 7e) rapidly increases from 0 mm to 2.57 mm, after which the probability of its presence does not change significantly. The suitable range for bio14 is 2.57 to 144.97 mm, with a peak of 3.26 mm. It is noteworthy that the bio15 variable shows a second increase in its trend (Figure 7f). Its suitable range is between 50.50 to 61.03 (peak at 54.36) and 118.73 to 160 (peak at 132.89), with the coefficient of variation starting to level off after 150.

3.7. Changes in Centroid of Potential Distribution

Regarding the current range of A. bungii, the centroid is located in Xinyang City, Henan Province (113°47′26″ E, 32°20′32″ N) (Figure 8). Under the SSP1-2.6 scenario, the predicted centroid of the suitable area is expected to move to Zhumadian City, Henan Province (113°55′52″ E, 32°43′47″ N) in the 2050s and further move to 113°56′8″ E, 32°31′38″ N in the 2090s. Overall, the distribution center is projected to shift in the northeast direction. In the SSP2-4.5 scenario, the centroid of the future suitable area is projected to shift northwestward in the 2050s (113°39′44″ E, 32°37′52″ N) and further northeastward in the 2090s (113°54′39″ E, 32°34′40″ N). However, under the SSP5-8.5 scenario, in the 2050s, it will shift westward to Xiangyang City, Hubei Province (111°31′22″ E, 32°23′9″ N), and, in the 2090s, it will move to Xinyang City, Henan Province (113°51′47″ E, 32°25′49″ N). Overall, in the future climate scenarios, the centroid movement of A. bungii is relatively small, indicating a slight northeastward shift in the climatically suitable area.

4. Discussion

4.1. Predictive Capabilities of the MaxEnt Model

In this study, we employed cross-validation experiments and Pearson correlation coefficient analysis to assess the contributions of each environmental variable to the species distribution data. Ultimately, we selected six key environmental variables with correlation coefficients less than 0.8 for model reconstruction, thereby enhancing the accuracy of the predictive results. According to the model evaluation, the AUC value of 0.906 indicates that the model has good predictive accuracy and can be used to simulate a suitable habitat for A. bungii. Understanding the future spreading patterns of A. bungii using this model can assist in the development of more effective control strategies, reducing its potential risks under future climate change. Therefore, our research results provide a beneficial framework for future prevention and control measures against the further spread of A. bungii in China.

4.2. Key Environmental Factors Influencing the Distribution of A. bungii

In this study, the MaxEnt model selected five bioclimatic variables and one topographic variable to predict the climatically suitable distribution of Aromia bungii. The model outputs, including overlap tests, permutation importance, and contribution rates, revealed that the minimum temperature of the coldest month (Bio6), mean temperature of the wettest quarter (Bio8), precipitation of the wettest month (Bio13), precipitation of the driest month (Bio14), precipitation seasonality (coefficient of variation) (Bio15), and altitude are the primary environmental factors shaping the potential distribution and regional variation of A. bungii.
As poikilothermic organisms, insects’ physiology is highly influenced by the environmental temperature, which in turn affects their development, survival, and geographic range [39,40]. A. bungii exhibits a relatively long generation cycle, typically lasting 2–4 years depending on the latitude and local climatic conditions [41]. For example, adult emergence peaks in mid-June in Yun County, Hubei Province, while, in Xingtai, Hebei Province, emergence is delayed until mid-July [9], underscoring the strong influence of the temperature on its phenology.
In addition to the temperature, precipitation and humidity also play important roles. Previous studies have demonstrated that rainfall can influence insect development and reproductive success [42], and humidity has been found to affect the population dynamics of A. bungii specifically [43]. Currently, A. bungii is primarily distributed in regions with a temperate monsoon climate, with some populations extending into subtropical monsoon zones. These climates are characterized by hot, humid summers with long photoperiods and relatively high temperatures, while the winters vary in severity. The temperate monsoon climate features an annual mean temperature not lower than 0 °C, cumulative annual temperatures ranging from 3200 °C to 4500 °C, and annual precipitation between 400 and 800 mm, with the coldest month’s mean temperature typically below 0 °C. Even in drier regions, where the precipitation falls below 250 mm annually, the species can persist, suggesting broad ecological tolerance.
Ongoing global warming and increasingly frequent warm winters may favor the overwintering success of A. bungii, potentially increasing population survival and leading to greater pest pressure during the growing season. Similar observations have been made for other forest and fruit tree pests [44]. Moreover, the meta-analysis conducted in this study indicated that lower altitudes are more conducive to the survival of A. bungii (Figure 2b), a trend that aligns with prior observations for other longhorn beetles [45].
In summary, the potential distribution of A. bungii is predominantly influenced by the temperature, precipitation, and elevation. These findings not only help to explain the current distribution pattern but also provide a foundation for the anticipation of future shifts in the beetle’s range under changing climatic conditions.

4.3. Future Distribution Changes for A. bungii

According to the model predictions, the current suitable distribution range for A. bungii is between 92.6–120.38° E and 16.17–44.46° N. The highly suitable areas are mainly concentrated in the North China Plain, the Shandong Hills, along the Bohai Sea, and in the middle and lower reaches of the Yangtze River Plain (Figure 4). As shown in Table 5, it can be seen that climate change has a significant impact on the geographical distribution of A. bungii, with notable differences between different periods and climate scenarios, which may be related to the temperature and precipitation variations in different emission scenarios [46]. According to Figure 5, there are some differences in the predicted potential suitable areas for A. bungii under different periods and climate scenarios, but the overall trend is consistent, with the expansion of highly suitable areas and fluctuations in moderately and less suitable areas. In future climate conditions, the centroid of suitable areas gradually shifts towards the northeast over time. Overall, the trend of centroid migration is mainly observed along latitudinal lines, with less apparent trends based on altitude. Additionally, under the SSP5-8.5 scenario, the highly and moderately suitable distribution areas of A. bungii are expanded to include Xinjiang, Jiangsu, and Zhejiang. This suggests that, with the projected increase in future temperatures and precipitation, the potential suitable distribution range of A. bungii may expand. Therefore, local forestry departments should enhance their monitoring efforts to prevent the invasion of A. bungii in these areas. The study’s results also indicate that there are hardly any suitable distribution areas for A. bungii in the Qinghai–Tibet region and Northwest China, suggesting that the distribution of A. bungii is influenced by the altitude. However, under future climate conditions, A. bungii may extend its range to these areas, potentially associated with the global warming trend.
Insects are not only influenced by climatic factors but also by host plants. Research has shown that A. bungii larvae frequently change hosts during their developmental stages, while adult females typically choose to reside and mate on host plants [12]. This indicates that host plants can attract and influence the behavior of A. bungii. As depicted in Figure 9, the main distribution of A. bungii’s host plants is concentrated in the North China Plain, the middle and lower reaches of the Yangtze River, and the Sichuan Basin, aligning with the predicted suitable areas. Additionally, the figure shows that the actual distribution range of A. bungii’s host plants is larger than that of A. bungii itself, which could be a contributing factor to its future spread. Therefore, forestry-related authorities outside the suitable regions should pay full attention to this situation and strengthen their monitoring and control measures for A. bungii as needed.

4.4. Limitations of This Study

While this study identified six key environmental variables influencing the potential distribution of A. bungii, these variables may not comprehensively represent all ecological, climatic, and anthropogenic factors affecting its actual distribution. Beyond the selected climatic and topographic predictors, additional abiotic (e.g., soil type, solar radiation), biotic (e.g., host plant availability, natural enemies), and human-mediated factors (e.g., timber transport, urban development) could significantly influence the species’ range and establishment [47]. Although we have provided response curves for the dominant predictors, secondary variables such as the slope and aspect were not included in detail due to data limitations. Moreover, insect distribution is the result of complex interactions among multiple factors, including the climate, vegetation, host species availability, and biological interactions [31]. These complexities are only partially captured by the environmental layers used in this model. A further limitation is that the MaxEnt model relies on presence-only occurrence data and assumes that these records sufficiently reflect the environmental niche of the species. However, this assumption may not hold if the current range is restricted by dispersal barriers, a recent invasion history, or insufficient sampling. Consequently, the climatically suitable areas predicted by the model may overestimate the actual occupied range [48]. Additionally, our projections are based on a single general circulation model (BCC-CSM2-MR) under three SSP scenarios. While this model has been widely used in ecological forecasting, different GCMs or ensemble modeling approaches could yield varying predictions. Future work could explore the model’s robustness across multiple GCMs and emission scenarios [37]. Furthermore, our model does not incorporate potential human-assisted spread—an important factor given A. bungii’s known association with the wood trade—and does not explicitly include host plant distributions, which may constrain colonization even within climatically suitable regions. Taken together, these limitations suggest that the model outputs should be interpreted with caution. While they provide valuable insights into potential distribution patterns, they are best used as part of a broader risk assessment framework that includes biological, ecological, and socioeconomic factors [49].

5. Conclusions

This study, for the first time, utilized the MaxEnt model and meta-analysis to simulate the potential distribution of A. bungii based on the current climate conditions and future climate changes. The results indicate that the primary environmental factors influencing its distribution include bio6, bio8, bio13, bio14, bio15, and the altitude. Under the current climate conditions, the suitable distribution range of A. bungii is between 92.6–120.38° E and 16.17–44.46° N. The highly suitable regions are mainly located in the North China Plain, the Shandong Hills, the region along the Bohai Sea, and the middle and lower reaches of the Yangtze River, covering a total area of 41.43 × 104 km2. Under future climate change scenarios, the high- and low-suitability areas for A. bungii show an increasing trend, while the moderately suitable areas mostly exhibit a decreasing trend. The overall trend indicates a shift towards higher latitudes. The predicted centroid of the potential suitable area has generally shifted northeast, indicating that Northern China may face a risk of invasion in the future, providing supportive information for the control and management of this pest.

Author Contributions

Conceptualization, Z.Z.; methodology, Y.L. and X.W.; software, Z.H.; formal analysis, Y.L. and Z.Z.; investigation, X.W.; data curation, X.D.; writing—original draft preparation, Y.L.; writing—review and editing, X.W. and Z.Z.; supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Sichuan Province Science and Technology Support Program (2022NSFSCO986) and the China West Normal University Support Program (20A007, 20E051, 21E040, and 22kA011).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data supporting the results are available in a public repository at GBIF.org (17 January 2024). GBIF occurrence download: https://doi.org/10.15468/dl.byft54; A. bungii occurrence data: https://doi.org/10.6084/m9.figshare.25047044.v1; sources of host plant data: https://doi.org/10.6084/m9.figshare.25047071.v1.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ma, A.; Yang, X.; Liu, W.; Lu, Z.; Li, J.; Ran, H. The occurrence and green control technology of Aromia bungii Faldermann adults. China Plant Prot. 2023, 43, 54–59. [Google Scholar]
  2. Zhang, L.; Wang, P.; Xie, G.; Wang, W. Spatial distribution pattern of Aromia bungii within china and its potential distribution under climate change and human activity. Ecol. Evol. 2024, 14, e70520. [Google Scholar] [CrossRef] [PubMed]
  3. Bai, R.; Wang, Y.; Ma, Z.; Jia, Y.; Lv, D. Advance in research of Aromia bungii Faldermann. For. Pest Dis. 2017, 36, 5–9. [Google Scholar]
  4. Gong, Q.; Huang, A.; Tang, Y.; Xiong, J. Summary of IPM of Aromia bungii Faldermann. Biol. Disaster Sci. 2013, 36, 430–433. [Google Scholar]
  5. Han, M.; Ma, A.; Lu, Z.; Liu, W.; Li, J.; Ran, H. Research Progress of Occurrence, Damage and Control Technology of Aromia bungii Faldermann. J. Hebei Agric. Sci. 2019, 23, 47–50. [Google Scholar]
  6. Zhang, Y.; Wang, R.; Yu, Y.; Luo, Y. Damage and population dynamics of wood-boring Cerambycid beetles in Rosaceous fruit trees. Chin. J. Appl. Entomol. 2017, 54, 500–505. [Google Scholar]
  7. Xu, T.; Yasui, H.; Teale, S.A.; Fujiwara-Tsujii, N.; Wickham, J.D.; Fukaya, M.; Hansen, L.; Kiriyama, S.; Hao, D.; Nakano, A.; et al. Identification of a male-produced sex-aggregation pheromone for a highly invasive cerambycid beetle, Aromia bungii. Sci. Rep. 2017, 7, 7330. [Google Scholar] [CrossRef]
  8. Qu, Z.; Huang, Y.; Zhao, H.; Chen, Z.; Pan, T.; Hao, C.; Gao, S. Study on the Control of Aromia bungii Fald by the Combined Release of Adult and Egg of Dastarcus helophoroides (Fairmaire). J. Shandong Agric. Univ. (Nat. Sci. Ed.) 2022, 53, 863–867. [Google Scholar]
  9. Han, M. Research on Occurrence, Damage and Control Technology of the Aromia bungii Faldermann. Master’s Thesis, Hebei Agricultural University, Baoding, China, 2019. [Google Scholar]
  10. Yamamoto, Y.; Kaneko, S. Seasonal Prevalence of the Invasive Longhorn Beetle Aromia bungii in Osaka Prefecture, Japan. Insects 2022, 13, 222. [Google Scholar] [CrossRef]
  11. Wang, J. Study on Host Search Mechanism of Two Natural Enemies of Aromia gungii Using Kairomone and Their Joint Bio-control Effect. Master’s Thesis, Hebei University, Baoding, China, 2022. [Google Scholar]
  12. Chen, J.; Yan, S.; Cao, D.; Jing, J.; Zhang, K.; Jian, K.; Ma, H.; Wei, J. Mating behavior of Aromia bungii (Coleoptera: Cerambycidae) adults and their perching and oviposition preference on different host plants. Acta Entomol. Sin. 2020, 63, 851–860. [Google Scholar]
  13. Wu, Z. Identification and Expression Profile of Chemosensory Genes in Aromia bungii (Faldermann) Antennal Transcriptome. Master’s Thesis, Anhui Agricultural University, Hefei, China, 2022. [Google Scholar]
  14. Zhao, W.; Xie, G.; Wang, W. Ultrastructure of sensilla on the antennae, mouthparts and tarsi of Aromia bungii (Cerambycidae: Cerambycinae). J. Environ. Entomol. 2023, 46, 1–22. [Google Scholar]
  15. Li, R.; Song, X.; Du, Y. Mitochondrial genome of Aromia bungii (Coleoptera: Chrysomeloidea: Cerambycidae) and phylogenetic analysis. Mitochondrial DNA Part B 2021, 6, 71–72. [Google Scholar] [CrossRef]
  16. Sunamura, E.; Tamura, S.; Taki, H.; Buczkowski, G.; Shoda-Kagaya, E. Effects of neonicotinoid insecticide trunk injections on non-target arboreal ants, potential biological control agents for invasive longhorn beetle Aromia bungii on cherry trees. Appl. Entomol. Zoolog. 2023, 58, 401–407. [Google Scholar] [CrossRef]
  17. Zou, Y.; Hansen, L.; Xu, T.; Teale, S.A.; Hao, D.; Millar, J.G. Optimizing pheromone-based lures for the invasive red-necked longhorn beetle, Aromia bungii. J. Pest Sci. 2019, 92, 1217–1225. [Google Scholar] [CrossRef]
  18. Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]
  19. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  20. Byeon, D.; Jung, S.; Lee, W. Review of CLIMEX and MaxEnt for studying species distribution in South Korea. J. Asia-Pac. Biodivers. 2018, 11, 325–333. [Google Scholar] [CrossRef]
  21. Kumar, S.; Neven, L.G.; Zhu, H.; Zhang, R. Assessing the Global Risk of Establishment of Cydia pomonella (Lepidoptera: Tortricidae) using CLIMEX and MaxEnt Niche Models. J. Econ. Entomol. 2015, 108, 1708–1719. [Google Scholar] [CrossRef]
  22. Fuller, D.O.; Ahumada, M.L.; Quiñones, M.L.; Herrera, S.; Beier, J.C. Near-present and future distribution of Anopheles albimanus in Mesoamerica and the Caribbean Basin modeled with climate and topographic data. Int. J. Health Geogr. 2012, 11, 13. [Google Scholar] [CrossRef]
  23. Wang, X.; Duan, Y.; Jin, L.; Wang, C.; Peng, M.; Li, Y.; Wang, X.; Ma, Y. Prediction of historical, present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China. Acta Ecol. Sin. 2023, 43, 6590–6604. [Google Scholar]
  24. Lehrer, E.J.; Wang, M.; Sun, Y.; Zaorsky, N.G. An Introduction to Meta-Analysis. Int. J. Radiat. Oncol. Biol. Phys. 2023, 115, 564–571. [Google Scholar] [CrossRef]
  25. Santana, P.A.; Kumar, L.; Silva, R.S.; Picanço, M.C. Global geographic distribution of Tuta absoluta as affected by climate change. J. Pest Sci. 2019, 92, 1373–1385. [Google Scholar] [CrossRef]
  26. Gao, R.; Liu, L.; Li, R.; Fan, S.; Dong, J.; Zhao, L. Predicting potential distributions of Monochamus saltuarius, a novel insect vector of pine wilt disease in China. Front. For. Glob. Change 2023, 6, 1243996. [Google Scholar] [CrossRef]
  27. Pan, S.; Peng, D.; Li, Y.; Chen, Z.; Zhai, Y.; Liu, C.; Hong, B. Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling. J. Integr. Agric. 2023, 22, 2138–2150. [Google Scholar] [CrossRef]
  28. Xian, X.; Zhao, H.; Guo, J.; Zhang, G.; Liu, H.; Liu, W.; Fanghao, W. Estimation of the potential geographical distribution of a new potato pest (Schrankia costaestrigalis) in China under climate change. J. Integr. Agric. 2023, 22, 2441–2455. [Google Scholar] [CrossRef]
  29. Wu, T.; Lu, Y.; Fang, Y.; Xin, X.; Li, L.; Li, W.; Jie, W.; Zhang, J.; Liu, Y.; Zhang, L.; et al. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 2019, 12, 1573–1600. [Google Scholar] [CrossRef]
  30. Xu, D.; Zhuo, Z.; Wang, R.; Ye, M.; Pu, B. Modeling the distribution of Zanthoxylum armatum in China with MaxEnt modeling. Glob. Ecol. Conserv. 2019, 19, e00691. [Google Scholar] [CrossRef]
  31. Wei, X.; Xu, D.; Liu, Q.; Wu, Y.; Zhuo, Z. Predicting the potential distribution range of Batocera horsfieldi under CMIP6 climate change using the MaxEnt model. J. Econ. Entomol. 2024, 117, 187–198. [Google Scholar] [CrossRef]
  32. Li, Y.; Li, M.; Li, C.; Liu, Z. Optimized Maxent Model Predictions of Climate Change Impacts on the Suitable Distribution of Cunninghamia lanceolata in China. Forests 2020, 11, 302. [Google Scholar] [CrossRef]
  33. Chen, X.; Lei, Y.; Zhang, X.; Jia, H. Effects of Sample Sizes on Accuracy and Stability of Maximum Entropy Model in Predicting Species Distribution. Sci. Silvae Sin. 2012, 48, 53–59. [Google Scholar]
  34. Wang, R.; Li, Q.; Feng, C.; Shi, Z. Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling. Acta Ecol. Sin. 2017, 37, 8556–8566. [Google Scholar]
  35. Radosavljevic, A.; Anderson, R.P. Making better MAXENT models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 2014, 41, 629–643. [Google Scholar] [CrossRef]
  36. Phillips, S.J.; Anderson, R.P.; Dudik, M.; Schapire, R.E.; Blair, M.E. Opening the black box: An open-source release of Maxent. Ecography 2017, 40, 887–893. [Google Scholar] [CrossRef]
  37. Zhao, G.; Cui, X.; Sun, J.; Li, T.; Wang, Q.; Ye, X.; Fan, B. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models. Ecol. Indic. 2021, 132, 108256. [Google Scholar] [CrossRef]
  38. Zhang, H.; Xu, D.; Deng, X.; Liu, Z.; He, Z.; Wu, J.; Zhuo, Z. Impact of temperature variation on the biological traits and lifecycle of spodoptera exigua (lepidoptera: Noctuidae): A meta-analysis approach. Insects 2025, 16, 155. [Google Scholar] [CrossRef]
  39. Bale, J.S.; Masters, G.J.; Hodkinson, I.D.; Awmack, C.; Bezemer, T.M.; Brown, V.K.; Butterfield, J.; Buse, A.; Coulson, J.C.; Farrar, J.; et al. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Change Biol. 2002, 8, 1–16. [Google Scholar] [CrossRef]
  40. Kocmánková, E.; Trnka, M.; Juroch, J.; Dubrovský, M.; Semerádová, D.; Možný, M.; Žalud, Z. Impact of climate change on the occurrence and activity of harmful organisms. Plant Prot. Sci. 2009, 45, S48–S52. [Google Scholar] [CrossRef]
  41. Cao, D.; Liu, J.; Zhao, Z.; Yan, X.; Wang, W.; Wei, J. Chemical Compounds Emitted from Mentha spicata Repel Aromia bungii Females. Insects 2022, 13, 244. [Google Scholar] [CrossRef]
  42. Wang, J.; Wang, F.; Wang, R.; Zhang, J.; Zhao, X.; Yang, H.; Yang, W.; Yang, C.; Wang, Z.; Li, A. Modeling the effects of bioclimatic characteristics and distribution on the occurrence of Cyrtotrachelus buqueti in the Sichuan Basin. Glob. Ecol. Conserv. 2019, 17, e00540. [Google Scholar] [CrossRef]
  43. Qiu, X.; Xu, Y.; Chu, S.; Song, Y. Effect of Dastarcus helophoroides control on Aromia bungii and its relationship with meteorological factors. South China Fruits 2019, 48, 107–110. [Google Scholar]
  44. Hlávková, D.; Doležal, P. Cambioxylophagous pests of scots pine: Ecological physiology of european populations—A review. Front. For. Glob. Change 2022, 5, 864651. [Google Scholar] [CrossRef]
  45. Qi, J.; Nan, J.; Zhao, X.; Liang, C.; Fan, J.; He, H. Genetic structure of monochamus alternatus (hope) in qinling-daba mountains and expansion trend: Implications for pest prevention and management. Ecol. Evol. 2024, 14, e70373. [Google Scholar] [CrossRef] [PubMed]
  46. Xu, D.; Zhuo, Z.; Li, X.; Wang, R. Distribution and invasion risk assessment of Oryctes rhinoceros (L.) in China under changing climate. J. Appl. Entomol. 2022, 146, 385–395. [Google Scholar] [CrossRef]
  47. Qin, S.; Yan, Y.; Ouyang, S. Prediction of the potential distribution of Chimonanthus praecox in China based on MAXENT model and Arc GIS. Ecol. Sci. 2020, 39, 49–56. [Google Scholar]
  48. Li, G.; Liu, C.; Liu, Y.; Yang, J.; Zhang, X.; Guo, K. Advances in theoretical issues of species distribution models. Acta Ecol. Sin. 2013, 33, 4827–4835. [Google Scholar]
  49. Karmezi, M.; Bataka, A.; Papachristos, D.; Avtzis, D.N. Nematodes in the pine forests of northern and central greece. Insects 2022, 13, 194. [Google Scholar] [CrossRef]
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Figure 1. Occurrence records of A. bungii in China (green triangles: Aromia bungii; elevation increases from red to blue).
Figure 1. Occurrence records of A. bungii in China (green triangles: Aromia bungii; elevation increases from red to blue).
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Figure 2. Response of A. bungii to altitude. (a) represents the cumulative effect size (the blue line represents a cumulative effect size of 0.8565); (b) represents the relationship between the altitude and the survival effect values of A. bungii.
Figure 2. Response of A. bungii to altitude. (a) represents the cumulative effect size (the blue line represents a cumulative effect size of 0.8565); (b) represents the relationship between the altitude and the survival effect values of A. bungii.
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Figure 3. Receiver operating characteristic (ROC) curve generated by the MaxEnt model.
Figure 3. Receiver operating characteristic (ROC) curve generated by the MaxEnt model.
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Figure 4. Jackknife test of variable importance in the MaxEnt model for the distribution of A. bungii (light sea green, without a variable; blue, with only a single variable; red, with all variables).
Figure 4. Jackknife test of variable importance in the MaxEnt model for the distribution of A. bungii (light sea green, without a variable; blue, with only a single variable; red, with all variables).
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Figure 5. Under the current climatic conditions, the suitable distribution areas for A. bungii were predicted in different regions of China. The occurrence probability of A. bungii in this map is represented by color blocks. The red color indicates a highly suitable area with a probability greater than 0.66; orange represents a moderately suitable area with a probability between 0.33 and 0.66; yellow indicates a less suitable area with a probability between 0.1 and 0.33; and white represents an unsuitable area.
Figure 5. Under the current climatic conditions, the suitable distribution areas for A. bungii were predicted in different regions of China. The occurrence probability of A. bungii in this map is represented by color blocks. The red color indicates a highly suitable area with a probability greater than 0.66; orange represents a moderately suitable area with a probability between 0.33 and 0.66; yellow indicates a less suitable area with a probability between 0.1 and 0.33; and white represents an unsuitable area.
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Figure 6. Predicted distribution maps of potentially suitable areas for A. bungii in China under different climate change scenarios. Different color blocks indicate the probability of A. bungii occurrence. Red represents the high-suitability area with a probability greater than 0.66, orange represents the moderate-suitability area with a probability between 0.33 and 0.66, yellow represents the low-suitability area with a probability between 0.1 and 0.33, and white represents the unsuitable area.
Figure 6. Predicted distribution maps of potentially suitable areas for A. bungii in China under different climate change scenarios. Different color blocks indicate the probability of A. bungii occurrence. Red represents the high-suitability area with a probability greater than 0.66, orange represents the moderate-suitability area with a probability between 0.33 and 0.66, yellow represents the low-suitability area with a probability between 0.1 and 0.33, and white represents the unsuitable area.
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Figure 7. Response curves for the probability of A. bungii’s presence according to environmental variables. The curves are depicted as mean values (red line) and standard deviations (SD, blue buffer). (a) Alt. (b) Bio6. (c) Bio8. (d) Bio13. (e) Bio14. (f) bio15. The red curve represents the average of ten replicates; blue margins represent the standard deviation.
Figure 7. Response curves for the probability of A. bungii’s presence according to environmental variables. The curves are depicted as mean values (red line) and standard deviations (SD, blue buffer). (a) Alt. (b) Bio6. (c) Bio8. (d) Bio13. (e) Bio14. (f) bio15. The red curve represents the average of ten replicates; blue margins represent the standard deviation.
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Figure 8. Changes in the centroid of the potential distribution for A. bungii in China. The black color represents the current centroid; the red color represents the future centroid under the SSP1-2.6 scenario (2050s and 2090s); the green color represents the future centroid under the SSP2-4.5 scenario (2050s and 2090s); and the blue color represents the future centroid under the SSP5-8.5 scenario (2050s and 2090s).
Figure 8. Changes in the centroid of the potential distribution for A. bungii in China. The black color represents the current centroid; the red color represents the future centroid under the SSP1-2.6 scenario (2050s and 2090s); the green color represents the future centroid under the SSP2-4.5 scenario (2050s and 2090s); and the blue color represents the future centroid under the SSP5-8.5 scenario (2050s and 2090s).
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Figure 9. A comparison of the actual distribution between A. bungii and host plants. Black triangles represent A. bungii.
Figure 9. A comparison of the actual distribution between A. bungii and host plants. Black triangles represent A. bungii.
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Table 1. Pearson correlation coefficients among environmental variables.
Table 1. Pearson correlation coefficients among environmental variables.
bio6bio8bio13bio14
bio8−0.046
bio130.402 **0.659 **
bio140.694 **0.253 **0.559 **
bio15−0.686 **0.570 **0.229 *−0.492 **
** Significantly correlated at the 0.01 level (two-tailed). * Significantly correlated at the 0.05 level (two-tailed).
Table 2. Environmental variables retained in the modeling process.
Table 2. Environmental variables retained in the modeling process.
VariableEnvironmental VariableUnit
Bio6Minimum Temperature of the Coldest Month°C
Bio8Mean Temperature of the Wettest Quarter°C
Bio13Precipitation of the Wettest Monthmm
Bio14Precipitation of the Driest Monthmm
Bio15Precipitation Seasonality (Coefficient of Variation)unitless
AltAltitudem
Table 3. Percentage contributions and permutation importance of environmental variables affecting the geographical distribution of A. bungii.
Table 3. Percentage contributions and permutation importance of environmental variables affecting the geographical distribution of A. bungii.
VariablePercent Contribution (%)Permutation Importance (%)
Alt43.633.1
Bio625.743.5
Bio1513.35
Bio139.93.8
Bio146.914.4
Bio80.70.2
Table 4. Analysis of highly suitable areas for A. bungii.
Table 4. Analysis of highly suitable areas for A. bungii.
ProvinceHighly Suitable Area (104 km2)Total (104 km2) *Percentage of Highly Suitable Area in Province (%)Percentage of Highly Suitable Area in China (%)
Liaoning1.9514.8013.174.71
Hebei10.2618.8854.3224.76
Beijing1.141.6469.552.75
Shanxi0.7715.674.901.85
Tianjin1.221.20101.852.95
Shaanxi1.2615.678.073.05
Shandong12.4015.8078.4729.93
Henan5.3016.7031.7212.79
Jiangsu1.7410.7216.234.20
Anhui1.4314.0110.213.45
Sichuan0.0148.600.030.03
Hubei3.3718.5918.158.14
Shanghai0.230.6336.930.56
Zhejiang0.0710.600.620.16
Hunan0.0821.180.380.19
Fujian0.0112.400.100.03
Taiwan0.193.605.160.45
China41.43//4.30
* Total area of the corresponding province.
Table 5. Comparison of the suitable areas for A. bungii under the current and future climatic conditions.
Table 5. Comparison of the suitable areas for A. bungii under the current and future climatic conditions.
Decade and
Scenario		Predicted Area (104 km2)Comparison with Current Distribution (%)
Poorly Suitable AreaModerately Suitable AreaHighly Suitable AreaTotal Suitable HabitatPoorly Suitable AreaModerately Suitable AreaHighly Suitable AreaTotal Suitable Habitat
Current174.72104.5341.43320.68
2050s, SSP1-2.6171.83100.9938.57311.39−1.65−3.39−6.90−2.90
2090s, SSP1-2.6179.06105.2237.71321.992.480.66−8.980.41
2050s, SSP2-4.5173.05103.4442.58319.07−0.96−1.042.78−0.50
2090s, SSP2-4.5168.13100.7049.76318.59−3.77−3.6620.11−0.65
2050s, SSP5-8.5214.1893.6974.11381.9822.58−10.3778.8819.12
2090s, SSP5-8.5174.06103.1144.65321.82−0.38−1.367.770.36
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He, Z.; Wei, X.; Li, Y.; Deng, X.; Zhuo, Z. Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis. Agriculture 2025, 15, 1224. https://doi.org/10.3390/agriculture15111224

AMA Style

He Z, Wei X, Li Y, Deng X, Zhuo Z. Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis. Agriculture. 2025; 15(11):1224. https://doi.org/10.3390/agriculture15111224

Chicago/Turabian Style

He, Zhipeng, Xinju Wei, Yaping Li, Xinqi Deng, and Zhihang Zhuo. 2025. "Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis" Agriculture 15, no. 11: 1224. https://doi.org/10.3390/agriculture15111224

APA Style

He, Z., Wei, X., Li, Y., Deng, X., & Zhuo, Z. (2025). Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis. Agriculture, 15(11), 1224. https://doi.org/10.3390/agriculture15111224

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