Future Distribution Dynamics of the Forestry Pest Batocera horsfieldi and Its Implications for Forestry and Urban Green Spaces in China

Future Distribution Dynamics of the Forestry Pest Batocera horsfieldi and Its Implications for Forestry and Urban Green Spaces in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Future Distribution Dynamics of the Forestry Pest Batocera horsfieldi and Its Implications for Forestry and Urban Green Spaces in China zhipeng He, Danping Xu, xinqi deng, Xuanlin Li, Zhiwei Zhu, Wei Xinju, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9259817/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Batocera horsfieldi is a significant forestry pest with a devastating impact on China's forestry, urban landscaping, and green spaces. To investigate the potential distribution of B. horsfieldi across the country under climate change and its potential threat to forestry and urban landscaping, this study utilized the species distribution modeling platform Biomod2. By combining B. horsfieldi distribution data with bioclimatic and topographic variables, we predicted changes in suitable habitats and key climate factors and analyzed the shift in its centroid under the SSP2-4.5 scenario. The results showed that the main environmental factors influencing the distribution of B. horsfieldi include the minimum temperature of the coldest month (bio6), mean temperature of the coldest quarter (bio11), annual mean temperature (bio1), temperature seasonality (bio4), and mean temperature of the driest quarter (bio9). The current suitable habitats for B. horsfieldi are mainly concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal urban areas. In the future, highly and moderately suitable areas will expand to higher latitudes or elevations (except for a decrease of 467 km 2 in the 2070s under the Emca pathway), and the centroid of suitable habitats will continue to shift northward. Climate change may increase the risk of pest outbreaks, and these findings provide a scientific reference for early warning, monitoring, and control of B. horsfieldi in Xinjiang and northern China. Batocera horsfieldi Biomod2 climate change potential distribution Overlapping suitable areas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Batocera horsfieldi belongs to the Cerambycidae family of the Coleoptera order and is an important wood-boring pest in China's forests. It is commonly found in urban landscapes and plain greening areas, posing a significant threat (Diao Zhie and Ding Fubo, 2004 ). This pest is mainly distributed in countries such as China, Vietnam, Japan, India, and Myanmar (Yang et al., 2010), and primarily damages tree species including Salix babylonica , Fraxinus chinensis , Populus spp., Morus alba , and Juglans regia (Wei et al., 2024 ; Li Mengmeng and Wang Jiachang, 2022). B. horsfieldi typically completes one generation in 2–3 years, with larvae, pupae, or adults overwintering inside the tree trunk (Yu et al., 2007 ). The larvae initially feed under the bark and gradually burrow into the xylem as they grow. Overwintering starts in October, with feeding resuming in April of the following year. Mature larvae create pupal chambers at the top of their tunnels, where they pupate. Adults emerge in September, remaining in the pupal chamber to overwinter and begin to emerge from the tree trunk from mid- to late-May through June. After feeding on new branches for additional nourishment, mating and oviposition occur from June to July (Li et al., 2009 ; Liu et al., 2014 ). This cycle results in crisscrossing tunnels inside the tree trunk, which, when severe, can cause the entire tree to die or break in the wind, significantly impacting forestry development. Moreover, longhorn beetle larvae can be introduced to various parts of the world through transportation, trade, and tourism, leading to biological invasions and posing a threat to ecosystems. As human activities increasingly impact the environment, the global biodiversity crisis continues to worsen. The Earth's environment is rapidly changing, including global warming, land use changes, and biological invasions (A. et al., 2022; Blackburn et al., 2019 ; D et al., 2004 ; Tim et al., 2015 ). Environmental changes will lead to shifts in species' distribution ranges, making it crucial to predict future distribution changes under different scenario models for developing control strategies (Thomas, 2010 ). Specifically, global warming affects the population dynamics of forestry pests, leading to shifts in their distribution and the colonization of new habitats, posing potential threats to ecosystems and the economy (Camille and Gary, 2003 ). However, traditional research methods such as statistical observation struggle to predict their occurrence on a large scale. Therefore, simulating the potential distribution of Batocera horsfieldi under future climate scenarios is crucial. This not only helps understand its dispersal patterns but also provides key insights for developing effective control measures, thereby reducing its impact on municipal economies and green landscapes. In studying the impact of climate change on species' geographic distribution, ecological niche models for species distribution have become a research trend. These models can simulate the potential distribution of species based on actual geographic distribution data and relevant environmental variables (Guanghua et al., 2021 ). Species distribution models (SDMs) are diverse, and selecting a modeling tool that can accurately predict species invasion risks requires a systematic and scientific approach (Pradeep et al., 2022 ). Currently, the main species distribution models used include Generalized Linear Model (GLM), Generalized Boosted regression Models (GBM), Generalized Additive Model (GAM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and Maximum Entropy Model (MaxEnt) (Jihong et al., 2018 ). However, single models often produce unstable results with significant bias when predicting species' potential distribution (Yanfeng et al., 2022 ). In contrast, the Biomod ensemble model, by integrating multiple strong individual models, offers more stable and accurate predictions of species distribution (Yaqin et al., 2021 ; Luo et al., 2017). Biomod2 developed on the R platform, allows for extensive calculations using different types of models and conducts a comprehensive analysis of similarities, differences, and uncertainties across all results. The Biomod2 model can incorporate the aforementioned species distribution models (SRE, GLM, GAM, CTA, ANN, GBM, RF, FDA, MARS, MaxEnt) to predict and simulate the potential distribution probability of species (B., 2009; Guanghua et al., 2021 ; Bi et al., 2022 ). By weighting and integrating the predictive accuracy of 10 individual species distribution models, the ensemble species distribution model effectively addresses data redundancy and overfitting issues caused by habitat diversity, thereby improving model precision and enhancing the accuracy of future species distribution predictions (Zhang et al., 2024 ; Bi et al., 2022 ). Currently, predictions of B. horsfieldi distribution are primarily based on the Representative Concentration Pathways (RCPs) released in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Li et al., 2020 ). Wei et al. ( 2024 ) used CMIP6 data to predict the suitable habitat of B. horsfieldi in China under climate change (Xinju et al., 2023 ). However, these predictions relied solely on the MaxEnt model, and the predictive accuracy may be lower than that of the Biomod2 ensemble model (School Of BioSciences et al., 2020 ). Few studies have used ensemble models to predict the suitable distribution of B. horsfieldi . In this study, based on B. horsfieldi distribution data and environmental variables, we used the Biomod2 modeling platform, integrating 10 individual models to construct an ensemble model that predicts the potential suitable distribution of B. horsfieldi . The study aims to identify the optimal model and key environmental variables influencing the distribution of B. horsfieldi and analyze its response to climate change, providing a scientific basis for the precise control of B. horsfieldi in the future. 2. Materials and Methods 2.1 B. horsfieldi species distribution data Before conducting the niche modeling, it is essential to collect distribution data for B. horsfieldi . The geographic distribution data for B. horsfieldi primarily comes from relevant literature, the Global Biodiversity Information Facility (GBIF, https://www.gbif.org ), and the Teaching Specimen Resource Sharing Platform ( http://mnh.scu.edu.cn ). To ensure the accuracy of the model construction, duplicate and erroneous occurrence samples were removed, resulting in a final dataset of 219 distribution points. 2.2 Selection of environmental variables This study selected two environmental factors, topography and climate, for model construction to more accurately simulate species distribution. The climate data for the current period (1970–2000) and the future ( 2021 –2100) are sourced from the World Climate Database ( https://www.worldclim.org/ ). The three topographic variables, elevation, slope, and aspect, are sourced from the Resource and Environmental Science Data Center, Chinese Academy of Sciences ( https://www.resdc.cn/ ). Future climate factors were selected from the four shared socioeconomic pathways (SSPs) with medium resolution from the National Climate Center (Beijing) in the Sixth Coupled Model Intercomparison Project (CMIP6) (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Data were chosen from the SSP2-4.5 climate scenario for the three time periods of the 2050s, 2070s, and 2090s. Using ArcGIS 10.8 software, we extracted 19 bioclimatic data points for B. horsfieldi at each known occurrence site. To avoid overfitting due to high correlations among environmental variables, correlation analysis (Pearson) was conducted using R software. Additionally, a heatmap of the correlation among Bio1-19 environmental variables (Supplementary Figure S1 ) and principal component analysis (Supplementary Figure S2 ) was utilized to remove environmental variables with a correlation coefficient exceeding 0.8, resulting in a final selection of 11 variables (Table 1 ). Table 1 Environmental variables affecting the distribution of B. horsfieldi . Code Environmental variables Unit Bio1 Annual mean temperature ℃ Bio3 Isothermality % Bio4 Temperature seasonality ℃ Bio5 Max temperature of warmest month ℃ Bio6 Min temperature of coldest month ℃ Bio9 Mean temperature of driest quarter ℃ Bio11 Mean temperature of coldest quarter ℃ Bio17 Precipitation of driest quarter mm slope slope ° aspect aspect - elev elevation m 2.3 Construction and evaluation of the suitable area of the combined model In this study, the Biomod2 package in R was used to model the suitable distribution of B. horsfieldi . Compared to single models, the ensemble model offers more reliable predictions. Biomod2 is an integrated platform for species distribution modeling (SDM) that includes 10 individual models and utilizes various modeling methods to analyze the relationship between species and their environment. It is widely used for predicting species distribution and has demonstrated good predictive performance in previous studies (Ruut et al., 2018 ). During the construction of the ensemble model, all models except for MaxEnt used default parameters, with 75% of the distribution data allocated for model training and 25% for model validation. Additionally, 1,000 pseudo-absences were randomly generated, and the process was repeated 10 times, resulting in a total of 180 simulated model outcomes. The fitting accuracy of the ensemble model was evaluated using the True Skill Statistics (TSS) and Kappa coefficient (2006; L, 2012 ). The values of Kappa and TSS range from 0 to 1, with values closer to 1 indicating more reliable predictions. Specifically, a value of 0 to 0.4 indicates model failure; 0.4 to 0.55 indicates average model performance; 0.5 to 0.7 indicates good model performance; 0.7 to 0.85 indicates very good model performance; and 0.85 to 1 indicates excellent model performance. 2.4 Species suitable area and change prediction processing The results of the ensemble model from Biomod2 were loaded into ArcGIS and exported as raster data. Using the natural breaks method, the predicted results for B. horsfieldi were classified into four categories: unsuitable area (p < 0.2), low suitable area (0.2 ≤ p < 0.4), medium suitable area (0.4 ≤ p < 0.6), and high suitable area (p ≥ 0.6) (Wei et al. 2024 ). The SDM_Toolbox_v2.5 plugin in ArcGIS 10.8 was used to calculate the changes in the spatial patterns of B. horsfieldi and the shifts in the centroids of suitable habitats. 3. Results 3.1 Model accuracy assessment Based on 219 occurrence data points and environmental variables, the potential distribution probability and habitat suitability of B. horsfieldi were simulated. The simulation accuracy of the 10 individual models and the ensemble model was evaluated using TSS and Kappa coefficients (Fig. 1 ). The results indicated that the Random Forest (RF) model performed best, with Kappa and TSS values of 0.987 and 0.995, respectively. This was followed by the GBM and GAM models, which had Kappa and TSS values of 0.803 and 0.872 and 0.764 and 0.877, respectively. The SRE model performed the worst, failing to pass the model accuracy test. The performances of the GLM, MARS, ANN, FDA, CTA, and MaxEnt models fell between those mentioned above. Individual models with TSS values greater than 0.7 were combined for subsequent modeling. This study employed the relative majority voting method (EMca) and the weighted probability method (EMwm) for further modeling. 3.2 Environmental factors influencing the distribution of B. horsfieldi By constructing an ensemble model from individual models with TSS values greater than 0.7, the relative importance and contribution rates of each environmental variable factor were obtained and ranked in descending order by percentage, as shown in Table 2 . Based on the contribution rates of each environmental variable, key variables influencing the distribution of B. horsfieldi were identified. These 11 environmental variable factors are bio6 (27.21%), bio11 (16.73%), bio1 (15.40%), bio4 (9.98%), bio9 (9.68%), elev (5.40%), bio5 (5.16%), bio17 (4.83%), bio3 (3.56%), slope (1.45%), and aspect (0.62%). Thus, the main environmental factors affecting the distribution of B. horsfieldi are bio6, bio11, bio1, bio4, and bio9. Table 2 Contribution values and contribution rates of each environmental variable. Code Environmental variables Contribution value Contribution rate bio6 Min temperature of coldest month 0.55 27.21% bio11 Mean temperature of coldest quarter 0.34 16.73% bio1 Annual mean temperature 0.31 15.40% bio4 Temperature seasonality 0.20 9.98% bio9 Mean temperature of driest quarter 0.19 9.68% elev elevation 0.11 5.40% bio5 Max temperature of warmest month 0.10 5.16% bio17 Precipitation of driest quarter 0.10 4.83% bio3 Isothermality 0.07 3.56% slope slope 0.03 1.45% aspect aspect 0.01 0.62% According to Fig. 2 , it can be observed that when the survival probability exceeds 0.4 (corresponding to the environmental conditions of medium or high suitability), it is more favorable for the growth of B. horsfieldi . The results in the figure indicate a noticeable trend in the occurrence probability of B. horsfieldi after 0.4. Among the factors, the minimum temperature of the coldest month varies most significantly between − 10°C and 10°C, while the mean temperature of the driest quarter varies significantly between 0°C and 10°C(Fig. 2 ). 3.3 Potential Geographic Distribution of Suitable Habitats for B. horsfieldi in the Current Period The results of the ensemble models established using EMca and EMwm are shown in Fig. 3 . Both ensemble models indicate that B. horsfieldi is mainly distributed in subtropical monsoon climates and temperate monsoon climates in China. As seen in the figure, the area of high suitability in the EMca model is significantly broader compared to that in the EMwm model. B. horsfieldi is primarily distributed in the southwestern, central, eastern, and northern regions of China, with a small presence in the northwest. The high-suitability areas are mainly located in the Sichuan Basin, Chongqing, north of the Qinling Mountains, the middle and lower Yangtze River Plain, the southeastern hills, the North China Plain, the Shandong Hills, and along the coastal regions of Jiangsu and Zhejiang. There are also differences between the ensemble models of EMca and EMwm: in the EMca model, the medium suitability areas in the southeastern direction of Yunnan and Tibet are noticeably fewer than in the EMwm model, while in the EMwm model, the suitable areas for B. horsfieldi expand into higher latitude regions. 3.4 Distribution of Suitable Habitats for B. horsfieldi under Future Scenarios. This study selected the SSP2-4.5 scenario to investigate the suitable distribution range of B. horsfieldi in the 2050s, 2070s, and 2090s (Fig. 4 ). Compared to the potential distribution under current climate conditions, the area of suitable habitats nationwide shows an increasing trend under future climate scenarios. These suitable distribution areas are primarily concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, the Bohai Rim, and along the coasts of Jiangsu and Zhejiang. Additionally, a small portion of suitable habitats is located in the southwestern direction of the Tarim Basin and near the Tarim River. From Table 3 , it can be seen that the area of suitable habitats for B. horsfieldi has significantly increased in both the EMca and EMwm pathways compared to the current period, except for the 2070s, where the area in the EMca pathway shows a decreasing trend (a reduction of -1.04%). The other periods all indicate an increasing trend. Notably, in the 2090s, both methods (EMca and EMwm) show an increase in the suitable area for B. horsfieldi , with increases of 22.36% and 37.48%, respectively. Particularly under the EMwm method, the percentage change in area compared to the current period is close to 40% in the 2090s. Table 3 Changes in the Area of Suitable Habitats for B. horsfieldi under Current and Future Climate Scenarios Using Different Methods. SSP2-4.5 Scenarios area variation (km 2 ) Ratio of change EMca EMwm EMca EMwm current 44806 59158 / / 2050s 49833 63770 11.22% 7.80% 2070s 44339 68268 -1.04% 15.40% 2090s 54826 81333 22.36% 37.48% 3.5 Overlap of Suitable Habitats for B. horsfieldi under Future Scenarios From Fig. 5 and Table 4 , it can be observed that, whether under the EMwm or EMca pathway, the area of suitable habitats for B. horsfieldi shows an increasing trend in future climate scenarios. Overall, the distribution area of B. horsfieldi is rising, with a noticeable expansion in coastal regions. In the 2050s, both the EMwm and EMca pathways reveal a similar trend in the suitable habitats, with relatively minor losses and gains in the future suitable areas. In the 2070s and 2090s, the area of unsuitable habitats in the middle and lower Yangtze River Plain and the Tarim Basin in Xinjiang is increasing, with previously unsuitable areas gradually transforming into suitable habitats and expanding toward higher latitudes. Under the EMca scenario in the 2070s, the area lost for B. horsfieldi is 21,450 km 2 , with a reduction area of 21,043 km 2 . Notably, under the EMwm scenario in the 2090s, the area gained for B. horsfieldi is the highest, at 46,695 km 2 . Additionally, in all three time periods under the EMwm scenario, there is a slight increase in suitable habitats in Yunnan. Table 4 Changes in the Area of B. horsfieldi from Current to Future. Type lost (km 2 ) Gain (km 2 ) 2050 Emca 10904 15931 EMwm 9080 13692 2070 EMca 21450 21043 EMwm 15613 24723 2090 EMca 19245 25561 EMwm 12463 46695 Overall, compared to the current period, the future potential distribution of suitable habitats for B. horsfieldi is mainly shifting northward. Specifically, the suitable habitats in Shaanxi, Shanxi, Hebei, Beijing, and Tianjin will expand northward, and suitable areas will also emerge in the Tarim Basin in Xinjiang. Under future climate conditions, almost the entire regions of Shandong, Tianjin, Anhui, Jiangsu, Hubei, and Chongqing will have distributions. 3.6 Shift of Centroids of Suitable Habitats for B. horsfieldi under Future Climate Conditions Figure 6 shows the centroid of the total suitable habitat for B. horsfieldi under the SSP2-4.5 climate scenario. Under current climate conditions, the centroid is located in Xiangyang City, Hubei Province (112.29 E, 31.70 N). By the 2050s, the centroid migrates 112.66 km northeast to Nanyang City, Henan Province (112.95 E, 32.55 N); in the 2070s, it shifts 145.59 km north to Pingdingshan City, Henan Province (113.03 E, 33.91 N); and by the 2090s, it moves 139.09 km northwest to Luoyang City, Henan Province (111.61 E, 34.17 N). Compared to the current centroid location, the centroids for the 2050s, 2070s, and 2090s all shift northward, with the centroid of suitable habitats continuously moving northward over time. 4. Discussion The study indicates that there are differences in the prediction processes and parameter algorithms among the 10 individual models, which may lead to uncertainties in their predictions (Guo et al., 2021 ; Yuan et al., 2023 ). The ensemble model minimizes the bias caused by single modeling methods through the weighting of evaluated individual models (Zhao et al., 2020 ; Guo et al., 2021 ). The ensemble model provides unique advantages in model selection methods, helping to mitigate issues related to variability in current statistical approaches to ecological predictions (Tanaka et al., 2020 ). This study utilized the ensemble model built on the Biomod2 platform to predict the potential habitat distribution of B. horsfieldi , effectively reducing the uncertainty and bias associated with individual models. To prevent overfitting, we employed Pearson correlation analysis to eliminate strongly correlated environmental factors, ultimately selecting 8 climate variables and 3 topographic variables for modeling. We combined the three best-performing individual models using two methods: the relative majority voting method (EMca) and the weighted probability method (EMwm). These methods achieved the highest model accuracy, better reflecting the response relationship between the species and its environment. This indicates that the predictions from the ensemble model are more accurate than those from individual models, further enhancing the reliability of the results (School Of BioSciences et al., 2020 ). The model simulation results show that the main environmental variables affecting the distribution of B. horsfieldi , include: the minimum temperature of the coldest month (bio6, 27.21%), mean temperature of the coldest quarter (bio11, 16.73%), annual mean temperature (bio1, 15.40%), temperature seasonality (bio4, 9.98%), mean temperature of the driest quarter (bio9, 9.68%), elevation (elev, 5.40%), the max temperature of the warmest month (bio5, 5.16%), precipitation of the driest quarter (bio17, 4.83%), isothermality (bio3, 3.56%), slope (1.45%), and aspect (0.62%). This indicates that the distribution of B. horsfieldi is constrained by various environmental conditions, including both biotic and abiotic factors. With global climate warming and the intensification of future climate change, rising temperatures are likely to increase the frequency and intensity of forest pest occurrences(Mukhopadhyay, 2009 ; Gao Junkai, 2005 ). As ectothermic animals, insects are highly dependent on environmental temperatures to regulate their physiological functions and are extremely sensitive to temperature changes. Under extreme temperature conditions, both the survival and reproduction of insects can be suppressed (Daniel et al., 2020 ; Hafker et al., 2024 ). In particular, under extremely cold conditions, mortality rates among insects may increase, which significantly impacts overwintering life stages or newly hatched larvae in early spring (MacQuarrie et al., 2019 ). Additionally, when environmental humidity is too high or too low, it can hinder insect development and even cause a sharp decline in their population. The interaction of multiple climatic factors also affects the survival and reproduction of forestry pests in various ways (Zhou et al., 2024 ). With the ongoing warming of the Earth's climate, there has been an increase in extreme weather events and a rise in average annual temperatures, making winter the fastest-warming season (E and L, 2021). Rising temperatures may cause some pests to emerge earlier in the spring and delay overwintering in the fall, leading to earlier adult emergence, extended occurrence periods, and an increase in population size (Wang et al., 2011 ). Research shows that overwintering adults of B. horsfieldi typically begin to emerge in late April each year, with peak emergence occurring from mid to late June and ending in early August (Diao Zhie and Ding Fubo, 2004 ; Wang et al., 2004). The expansion of B. horsfieldi 's suitable habitat under future climate scenarios may be related to this. Additionally, the distribution of B. horsfieldi is not only highly correlated with climatic factors, but the distribution of host plants also plays a significant role (Xinju et al., 2023 ). The study points out that during the nutrient supplementation phase, adult B. horsfieldi can select from various host plants (Zhang et al., 2016 ) and exhibit different behavioral variations, closely related to their strong host switching and metabolic adaptability. These factors are crucial for the differentiation and spread of B. horsfieldi populations (Wu et al., 2021 ), and they may also contribute to the increase in the potential suitable distribution of B. horsfieldi in the future. The model results predict that the currently suitable distribution areas for B. horsfieldi are mainly concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal cities. This indicates that these regions are climatically suitable for the survival of B. horsfieldi under current climate conditions. However, in the future, these areas will face severe heat stress (Zhang et al., 2017 ). Overall, there is a trend of increasing suitable habitats, particularly evident in Xinjiang, Shandong, Hebei, Tianjin, Beijing, Gansu, Shaanxi, and Shanxi. At the same time, some medium-suitability areas are gradually evolving into high-suitability areas. These regions primarily fall within subtropical monsoon climates and temperate monsoon climates, which share similar climate characteristics. Due to the close relationship between elevation and climate, it is especially important to assess the ecological impacts of current and future climate change in high-latitude regions. Research indicates that elevation gradients can influence insect species composition, community structure, and patterns of diversity (Lijuan et al., 2023 ), and insect distributions may also shift toward higher latitudes and elevations (MUSOLIN, 2007 ), which is consistent with our findings. Although high-suitability areas are expanding northward, the area of existing high-suitability regions is still slowly decreasing, suggesting that B. horsfieldi may possess a certain degree of temperature adaptability. For example, Eogystia hippophaecolus is projected to move northwest in the future (Xue et al., 2018 ), and the overall centroid of suitable habitats for Bactrocera tsuneonis will gradually shift west and north (Mao et al., 2024 ). In different decades, the geographical latitude boundaries of B. horsfieldi have moved further north. Under the influence of global warming, the numbers of high, medium, and low suitability areas are all increasing. This indicates that, against the backdrop of global warming, more regions will become suitable for the survival of B. horsfieldi , thereby intensifying the challenges for control and management. There are many factors influencing the distribution of B. horsfieldi , and besides climate, other environmental factors also play significant roles. Biotic interactions, such as variables related to hosts, natural enemies, and vegetation conditions, are critical in determining insect distribution (H et al., 2008 ). Temperature and precipitation not only affect species abundance and distribution but also impact the reproduction, survival, dispersal, and population dynamics of pests, while also influencing the physiology of host plants, thereby indirectly affecting the pests (Sandra et al., 2021 ). Moreover, in addition to abiotic factors, the overexploitation of biological resources, climate change, human activities, environmental pollution, the biological characteristics of the species themselves, and biological invasions (Wei et al., 2014 ) may also be related to the distribution of B. horsfieldi . Notably, as global warming continues, areas that were previously unsuitable are gradually becoming suitable habitats, suggesting that B. horsfieldi may appear in regions with higher elevations, higher latitudes, or more extreme temperature and humidity conditions, indicating a certain invasion potential. Therefore, predicting the potential distribution of this species under future climate scenarios is of great significance for formulating pest control policies in forestry. 5. Conclusions This study utilized the ensemble model built on the Biomod2 platform to predict and analyze the potentially suitable distribution areas of B. horsfieldi . The results indicate that under the SSP2-4.5 climate scenario, the currently suitable habitats for B. horsfieldi are primarily concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal cities. The environmental factors affecting its distribution mainly include bio6, bio11, bio1, bio4, bio9, bio5, bio17, bio3, slope, elevation, and aspect. As climate warming progresses, the suitable range for B. horsfieldi is expanding toward higher latitudes or elevations, with the centroid of suitable habitats continually shifting northward. Consequently, some regions may face an invasion risk in the future. This study provides important references for developing effective control and management strategies. Declarations Author contribution All authors contributed to the study conception and design. Conceptualization, Zhihang Zhuo; methodology, Zhipeng He; software, Danping Xu and Xinju Wei; formal analysis, Xuanlin Liand Zhihang Zhuo; investigation, Xinqi Deng; data curation, Zhiwei Zhu; writing-original draft preparation, Zhipeng He and Xinju Wei; writing-review and editing, Danping Xu and Zhihang Zhuo; supervision, Zhihang Zhuo. All authors read and approved the final manuscript.. Funding Declaration This work was supported by the Sichuan Province Science and Technology Support Program (2022NSFSCO986), and China West Normal University Support Program (20A007, 20E051, 21E040, and 22kA011) and National Natural Science Foundation of China (No. 32560352). Data availability statement The data supporting the results are available in a public repository at: GBIF.org (1 April 2026) GBIF Occurrence Download https://doi.org/10.15468/dl.9km399. Conflict of interest statement The authors declare no conflict of interest. References Assessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS). Journal of Applied Ecology. 43, 1223–1232. A., L. P., et al., 2022. Impacts of pastures and forestry plantations on herpetofauna: A global meta-analysis. Journal of Applied Ecology. 59, 3038–3048. B., T. W. L. B., 2009. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography. 32, 369–373. Bi Yaqiong, et al., 2022. 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Yaqin, F., et al., 2021. Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae. Science of the Total Environment. 756. Yu Jinxiu, et al., 2007. Occurrence and Pollution-Free Control of Batocera horsfieldi on Poplar. Hunan Forestry Science & Technology, 30–31. Yuan Yulin, et al., 2023. Changes in Suitable Habitat Distribution of Spodoptera frugiperda in Yunnan Province Based on Biomod2 Ensemble Model. Journal of Southern Agriculture. 54, 3571–3580. Zhang Dongyong, et al., 2016. Two Plant Volatiles with Trapping Effect on Batocera horsfieldi in Forest. Chinese Journal of Applied Entomology. 53, 856–863. Zhang, Y., et al., 2024. Impacts of climate changes on the potential habitat suitability of Grus japonensis on migration routes. Ecological Indicators. 166, 112462–112462. Zhang, Z., et al., 2017. Future extreme temperature and its impact on rice yield in China. International Journal of Climatology. 37, 4814–4827. Zhao, Z., et al., 2020. Potential distribution of Notopterygium incisum Ting ex H. T. Chang and its predicted responses to climate change based on a comprehensive habitat suitability model. Ecol Evol. 10, 3004–3016. Zhou, H., et al., 2024. Epidemiological model including spatial connection features improves prediction of the spread of pine wilt disease. Ecological Indicators. 163, 112103-. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigureS1.tif SupplementaryFigureS2.tif Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9259817","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620612374,"identity":"118ad5be-fc0a-4d39-b353-c6ded9bf2baf","order_by":0,"name":"zhipeng He","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"zhipeng","middleName":"","lastName":"He","suffix":""},{"id":620612378,"identity":"d493be1b-a7ce-4668-89d4-d6ebc11aa263","order_by":1,"name":"Danping Xu","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Danping","middleName":"","lastName":"Xu","suffix":""},{"id":620612384,"identity":"fba02dbf-4225-4ecb-889e-7a9630fae98f","order_by":2,"name":"xinqi deng","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"xinqi","middleName":"","lastName":"deng","suffix":""},{"id":620612388,"identity":"0a79ba51-c8c2-4617-8aa9-0967829d918a","order_by":3,"name":"Xuanlin Li","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xuanlin","middleName":"","lastName":"Li","suffix":""},{"id":620612391,"identity":"4c729e2d-12b0-422b-9881-5649aec43dab","order_by":4,"name":"Zhiwei Zhu","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Zhu","suffix":""},{"id":620612398,"identity":"eb176c5b-1fb8-4687-900b-5dbca28d1721","order_by":5,"name":"Wei Xinju","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xinju","suffix":""},{"id":620612404,"identity":"e7d11fab-666d-44b7-a8bd-6700c3b8d8f5","order_by":6,"name":"Zhihang Zhuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCSBmbJBIAFEPKkAizMwNRGthNjgD1sJIlBYGkBY2CbAWBgJa5Gf3GD78ucMij392+7WKAxWHo/nbgVp+VGzDqYVxzhljA8kzEsUSd86U3ThwJi13xmHGBsaeM7dxamGWyDGTMGyTSGy4kZN2+2ObTW4DUAszYxtuLWwgLYlALfOBWgoOtknkziekhQekBagyccON9GMMB4G2bCCkRUIirdiwEahl440cZgmQXzYCtRzE5xf5GckbH/5sq0ucdyP94QdgiOXOO3/44IMfFbi1ILvRAM48QIx6IGB/QKTCUTAKRsEoGGkAAC7BX4i1mj3qAAAAAElFTkSuQmCC","orcid":"","institution":"China West Normal University","correspondingAuthor":true,"prefix":"","firstName":"Zhihang","middleName":"","lastName":"Zhuo","suffix":""}],"badges":[],"createdAt":"2026-03-29 15:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9259817/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9259817/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106782667,"identity":"4fef7d5c-da6a-403b-b2e5-4909b8a0076a","added_by":"auto","created_at":"2026-04-13 11:58:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139585,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Kappa and TSS among the 10 individual models. (A: ROC-Kappa index; B: ROC-TSS index)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/ea249adabdd6784e8e72e960.png"},{"id":106782805,"identity":"7c43ed90-54de-4ea2-81b2-7d3747ef9bdb","added_by":"auto","created_at":"2026-04-13 11:58:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105454,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of important environmental variables for \u003cem\u003eB. horsfieldi.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/51f9d69a32efc83730127b9f.png"},{"id":106782679,"identity":"42ba7a4c-757c-4e59-a27a-f5f64bf4633a","added_by":"auto","created_at":"2026-04-13 11:58:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262505,"visible":true,"origin":"","legend":"\u003cp\u003eThe suitable distribution areas of \u003cem\u003eB. horsfieldi\u003c/em\u003e under current climatic conditions. A: Relative Majority Voting Method (EMca); B: Weighted Probability Method (EMwm).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/0ac181e215710ce9b2db6375.png"},{"id":106782671,"identity":"9c81a1fa-a186-4c46-8fb3-ec4cfe11e124","added_by":"auto","created_at":"2026-04-13 11:58:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":415147,"visible":true,"origin":"","legend":"\u003cp\u003eSuitable Distribution Areas for \u003cem\u003eB. horsfieldi\u003c/em\u003e under Future Climate Scenarios. (sodalite blue: unsuitable able; Yogo blue: poorly suitable area; lapis lazuli: moderately suitable area; peony pink: highly suitable area)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/08ab4c03ec695adaf9e42c83.png"},{"id":106782660,"identity":"a858e113-741d-4e2e-80e1-023829657fea","added_by":"auto","created_at":"2026-04-13 11:58:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":410443,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e under current to future climate change. (Red: lost, present but not in the future; blue: gain, not present now but in the future; chrysoprase: pres, present in both current and future; yellow-green: absent in both current and future.)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/12167835e4a8cec7ef85f34b.png"},{"id":106782803,"identity":"042ec414-1198-46f0-977c-832d7acbb33b","added_by":"auto","created_at":"2026-04-13 11:58:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":152753,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the Centroid of the Potential Distribution Area of \u003cem\u003eB. horsfieldi\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/6132bb1a33d63fcf0aed5743.png"},{"id":108996498,"identity":"e6e44d47-bc5f-4e3a-b38f-e970021d60b1","added_by":"auto","created_at":"2026-05-11 14:14:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1848848,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/7d65be62-6078-4f2d-bcdd-002ca0af06d1.pdf"},{"id":106782664,"identity":"679e66bd-1266-45f6-91e3-91c31705c491","added_by":"auto","created_at":"2026-04-13 11:58:19","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8403592,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/041833de0360882032e34fe4.tif"},{"id":106782804,"identity":"dc79564c-66bb-43e8-ac09-5caf1d60db26","added_by":"auto","created_at":"2026-04-13 11:58:35","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7671536,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9259817/v1/3e3aefcea1bade99e3ca2d63.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Future Distribution Dynamics of the Forestry Pest Batocera horsfieldi and Its Implications for Forestry and Urban Green Spaces in China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eBatocera horsfieldi\u003c/em\u003e belongs to the Cerambycidae family of the Coleoptera order and is an important wood-boring pest in China's forests. It is commonly found in urban landscapes and plain greening areas, posing a significant threat (Diao Zhie and Ding Fubo, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This pest is mainly distributed in countries such as China, Vietnam, Japan, India, and Myanmar (Yang et al., 2010), and primarily damages tree species including \u003cem\u003eSalix babylonica\u003c/em\u003e, \u003cem\u003eFraxinus chinensis\u003c/em\u003e, \u003cem\u003ePopulus\u003c/em\u003e spp., \u003cem\u003eMorus alba\u003c/em\u003e, and \u003cem\u003eJuglans regia\u003c/em\u003e (Wei et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li Mengmeng and Wang Jiachang, 2022). \u003cem\u003eB. horsfieldi\u003c/em\u003e typically completes one generation in 2\u0026ndash;3 years, with larvae, pupae, or adults overwintering inside the tree trunk (Yu et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The larvae initially feed under the bark and gradually burrow into the xylem as they grow. Overwintering starts in October, with feeding resuming in April of the following year. Mature larvae create pupal chambers at the top of their tunnels, where they pupate. Adults emerge in September, remaining in the pupal chamber to overwinter and begin to emerge from the tree trunk from mid- to late-May through June. After feeding on new branches for additional nourishment, mating and oviposition occur from June to July (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This cycle results in crisscrossing tunnels inside the tree trunk, which, when severe, can cause the entire tree to die or break in the wind, significantly impacting forestry development. Moreover, longhorn beetle larvae can be introduced to various parts of the world through transportation, trade, and tourism, leading to biological invasions and posing a threat to ecosystems.\u003c/p\u003e \u003cp\u003eAs human activities increasingly impact the environment, the global biodiversity crisis continues to worsen. The Earth's environment is rapidly changing, including global warming, land use changes, and biological invasions (A. et al., 2022; Blackburn et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; D et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tim et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Environmental changes will lead to shifts in species' distribution ranges, making it crucial to predict future distribution changes under different scenario models for developing control strategies (Thomas, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Specifically, global warming affects the population dynamics of forestry pests, leading to shifts in their distribution and the colonization of new habitats, posing potential threats to ecosystems and the economy (Camille and Gary, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, traditional research methods such as statistical observation struggle to predict their occurrence on a large scale. Therefore, simulating the potential distribution of Batocera horsfieldi under future climate scenarios is crucial. This not only helps understand its dispersal patterns but also provides key insights for developing effective control measures, thereby reducing its impact on municipal economies and green landscapes.\u003c/p\u003e \u003cp\u003eIn studying the impact of climate change on species' geographic distribution, ecological niche models for species distribution have become a research trend. These models can simulate the potential distribution of species based on actual geographic distribution data and relevant environmental variables (Guanghua et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Species distribution models (SDMs) are diverse, and selecting a modeling tool that can accurately predict species invasion risks requires a systematic and scientific approach (Pradeep et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Currently, the main species distribution models used include Generalized Linear Model (GLM), Generalized Boosted regression Models (GBM), Generalized Additive Model (GAM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Surface Range Envelope (SRE), Flexible Discriminant Analysis (FDA), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), and Maximum Entropy Model (MaxEnt) (Jihong et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, single models often produce unstable results with significant bias when predicting species' potential distribution (Yanfeng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, the Biomod ensemble model, by integrating multiple strong individual models, offers more stable and accurate predictions of species distribution (Yaqin et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al., 2017). Biomod2 developed on the R platform, allows for extensive calculations using different types of models and conducts a comprehensive analysis of similarities, differences, and uncertainties across all results. The Biomod2 model can incorporate the aforementioned species distribution models (SRE, GLM, GAM, CTA, ANN, GBM, RF, FDA, MARS, MaxEnt) to predict and simulate the potential distribution probability of species (B., 2009; Guanghua et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By weighting and integrating the predictive accuracy of 10 individual species distribution models, the ensemble species distribution model effectively addresses data redundancy and overfitting issues caused by habitat diversity, thereby improving model precision and enhancing the accuracy of future species distribution predictions (Zhang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, predictions of \u003cem\u003eB. horsfieldi\u003c/em\u003e distribution are primarily based on the Representative Concentration Pathways (RCPs) released in Phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Wei et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used CMIP6 data to predict the suitable habitat of \u003cem\u003eB. horsfieldi\u003c/em\u003e in China under climate change (Xinju et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these predictions relied solely on the MaxEnt model, and the predictive accuracy may be lower than that of the Biomod2 ensemble model (School Of BioSciences et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Few studies have used ensemble models to predict the suitable distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e. In this study, based on \u003cem\u003eB. horsfieldi\u003c/em\u003e distribution data and environmental variables, we used the Biomod2 modeling platform, integrating 10 individual models to construct an ensemble model that predicts the potential suitable distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e. The study aims to identify the optimal model and key environmental variables influencing the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e and analyze its response to climate change, providing a scientific basis for the precise control of \u003cem\u003eB. horsfieldi\u003c/em\u003e in the future.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eB. horsfieldi\u003c/em\u003e species distribution data\u003c/h2\u003e \u003cp\u003eBefore conducting the niche modeling, it is essential to collect distribution data for \u003cem\u003eB. horsfieldi\u003c/em\u003e. The geographic distribution data for \u003cem\u003eB. horsfieldi\u003c/em\u003e primarily comes from relevant literature, the Global Biodiversity Information Facility (GBIF, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org\u003c/span\u003e\u003cspan address=\"https://www.gbif.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Teaching Specimen Resource Sharing Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mnh.scu.edu.cn\u003c/span\u003e\u003cspan address=\"http://mnh.scu.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To ensure the accuracy of the model construction, duplicate and erroneous occurrence samples were removed, resulting in a final dataset of 219 distribution points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection of environmental variables\u003c/h2\u003e \u003cp\u003eThis study selected two environmental factors, topography and climate, for model construction to more accurately simulate species distribution. The climate data for the current period (1970\u0026ndash;2000) and the future (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u0026ndash;2100) are sourced from the World Climate Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The three topographic variables, elevation, slope, and aspect, are sourced from the Resource and Environmental Science Data Center, Chinese Academy of Sciences (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.resdc.cn/\u003c/span\u003e\u003cspan address=\"https://www.resdc.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Future climate factors were selected from the four shared socioeconomic pathways (SSPs) with medium resolution from the National Climate Center (Beijing) in the Sixth Coupled Model Intercomparison Project (CMIP6) (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Data were chosen from the SSP2-4.5 climate scenario for the three time periods of the 2050s, 2070s, and 2090s. Using ArcGIS 10.8 software, we extracted 19 bioclimatic data points for \u003cem\u003eB. horsfieldi\u003c/em\u003e at each known occurrence site. To avoid overfitting due to high correlations among environmental variables, correlation analysis (Pearson) was conducted using R software. Additionally, a heatmap of the correlation among Bio1-19 environmental variables (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and principal component analysis (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) was utilized to remove environmental variables with a correlation coefficient exceeding 0.8, resulting in a final selection of 11 variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnvironmental variables affecting the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual mean temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsothermality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature seasonality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax temperature of warmest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin temperature of coldest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of coldest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e℃\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBio17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eelevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction and evaluation of the suitable area of the combined model\u003c/h2\u003e \u003cp\u003eIn this study, the Biomod2 package in \u003cem\u003eR\u003c/em\u003e was used to model the suitable distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e. Compared to single models, the ensemble model offers more reliable predictions. Biomod2 is an integrated platform for species distribution modeling (SDM) that includes 10 individual models and utilizes various modeling methods to analyze the relationship between species and their environment. It is widely used for predicting species distribution and has demonstrated good predictive performance in previous studies (Ruut et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). During the construction of the ensemble model, all models except for MaxEnt used default parameters, with 75% of the distribution data allocated for model training and 25% for model validation. Additionally, 1,000 pseudo-absences were randomly generated, and the process was repeated 10 times, resulting in a total of 180 simulated model outcomes.\u003c/p\u003e \u003cp\u003eThe fitting accuracy of the ensemble model was evaluated using the True Skill Statistics (TSS) and Kappa coefficient (2006; L, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The values of Kappa and TSS range from 0 to 1, with values closer to 1 indicating more reliable predictions. Specifically, a value of 0 to 0.4 indicates model failure; 0.4 to 0.55 indicates average model performance; 0.5 to 0.7 indicates good model performance; 0.7 to 0.85 indicates very good model performance; and 0.85 to 1 indicates excellent model performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Species suitable area and change prediction processing\u003c/h2\u003e \u003cp\u003eThe results of the ensemble model from Biomod2 were loaded into ArcGIS and exported as raster data. Using the natural breaks method, the predicted results for \u003cem\u003eB. horsfieldi\u003c/em\u003e were classified into four categories: unsuitable area (p\u0026thinsp;\u0026lt;\u0026thinsp;0.2), low suitable area (0.2\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.4), medium suitable area (0.4\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.6), and high suitable area (p\u0026thinsp;\u0026ge;\u0026thinsp;0.6) (Wei et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The SDM_Toolbox_v2.5 plugin in ArcGIS 10.8 was used to calculate the changes in the spatial patterns of \u003cem\u003eB. horsfieldi\u003c/em\u003e and the shifts in the centroids of suitable habitats.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model accuracy assessment\u003c/h2\u003e \u003cp\u003eBased on 219 occurrence data points and environmental variables, the potential distribution probability and habitat suitability of \u003cem\u003eB. horsfieldi\u003c/em\u003e were simulated. The simulation accuracy of the 10 individual models and the ensemble model was evaluated using TSS and Kappa coefficients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results indicated that the Random Forest (RF) model performed best, with Kappa and TSS values of 0.987 and 0.995, respectively. This was followed by the GBM and GAM models, which had Kappa and TSS values of 0.803 and 0.872 and 0.764 and 0.877, respectively. The SRE model performed the worst, failing to pass the model accuracy test. The performances of the GLM, MARS, ANN, FDA, CTA, and MaxEnt models fell between those mentioned above. Individual models with TSS values greater than 0.7 were combined for subsequent modeling. This study employed the relative majority voting method (EMca) and the weighted probability method (EMwm) for further modeling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Environmental factors influencing the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eBy constructing an ensemble model from individual models with TSS values greater than 0.7, the relative importance and contribution rates of each environmental variable factor were obtained and ranked in descending order by percentage, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on the contribution rates of each environmental variable, key variables influencing the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e were identified. These 11 environmental variable factors are bio6 (27.21%), bio11 (16.73%), bio1 (15.40%), bio4 (9.98%), bio9 (9.68%), elev (5.40%), bio5 (5.16%), bio17 (4.83%), bio3 (3.56%), slope (1.45%), and aspect (0.62%). Thus, the main environmental factors affecting the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e are bio6, bio11, bio1, bio4, and bio9.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution values and contribution rates of each environmental variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContribution value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContribution rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin temperature of coldest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of coldest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual mean temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature seasonality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.68%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eelevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax temperature of warmest month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of driest quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsothermality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eslope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003easpect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be observed that when the survival probability exceeds 0.4 (corresponding to the environmental conditions of medium or high suitability), it is more favorable for the growth of \u003cem\u003eB. horsfieldi\u003c/em\u003e. The results in the figure indicate a noticeable trend in the occurrence probability of \u003cem\u003eB. horsfieldi\u003c/em\u003e after 0.4. Among the factors, the minimum temperature of the coldest month varies most significantly between \u0026minus;\u0026thinsp;10\u0026deg;C and 10\u0026deg;C, while the mean temperature of the driest quarter varies significantly between 0\u0026deg;C and 10\u0026deg;C(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Potential Geographic Distribution of Suitable Habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e in the Current Period\u003c/h2\u003e \u003cp\u003eThe results of the ensemble models established using EMca and EMwm are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Both ensemble models indicate that \u003cem\u003eB. horsfieldi\u003c/em\u003e is mainly distributed in subtropical monsoon climates and temperate monsoon climates in China. As seen in the figure, the area of high suitability in the EMca model is significantly broader compared to that in the EMwm model. \u003cem\u003eB. horsfieldi\u003c/em\u003e is primarily distributed in the southwestern, central, eastern, and northern regions of China, with a small presence in the northwest. The high-suitability areas are mainly located in the Sichuan Basin, Chongqing, north of the Qinling Mountains, the middle and lower Yangtze River Plain, the southeastern hills, the North China Plain, the Shandong Hills, and along the coastal regions of Jiangsu and Zhejiang. There are also differences between the ensemble models of EMca and EMwm: in the EMca model, the medium suitability areas in the southeastern direction of Yunnan and Tibet are noticeably fewer than in the EMwm model, while in the EMwm model, the suitable areas for \u003cem\u003eB. horsfieldi\u003c/em\u003e expand into higher latitude regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Distribution of Suitable Habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e under Future Scenarios.\u003c/h2\u003e \u003cp\u003eThis study selected the SSP2-4.5 scenario to investigate the suitable distribution range of \u003cem\u003eB. horsfieldi\u003c/em\u003e in the 2050s, 2070s, and 2090s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared to the potential distribution under current climate conditions, the area of suitable habitats nationwide shows an increasing trend under future climate scenarios. These suitable distribution areas are primarily concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, the Bohai Rim, and along the coasts of Jiangsu and Zhejiang. Additionally, a small portion of suitable habitats is located in the southwestern direction of the Tarim Basin and near the Tarim River. From Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, it can be seen that the area of suitable habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e has significantly increased in both the EMca and EMwm pathways compared to the current period, except for the 2070s, where the area in the EMca pathway shows a decreasing trend (a reduction of -1.04%). The other periods all indicate an increasing trend. Notably, in the 2090s, both methods (EMca and EMwm) show an increase in the suitable area for \u003cem\u003eB. horsfieldi\u003c/em\u003e, with increases of 22.36% and 37.48%, respectively. Particularly under the EMwm method, the percentage change in area compared to the current period is close to 40% in the 2090s.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in the Area of Suitable Habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e under Current and Future Climate Scenarios Using Different Methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSSP2-4.5 Scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003earea variation (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRatio of change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMca\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEMwm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEMca\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEMwm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2070s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Overlap of Suitable Habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e under Future Scenarios\u003c/h2\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be observed that, whether under the EMwm or EMca pathway, the area of suitable habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e shows an increasing trend in future climate scenarios. Overall, the distribution area of \u003cem\u003eB. horsfieldi\u003c/em\u003e is rising, with a noticeable expansion in coastal regions. In the 2050s, both the EMwm and EMca pathways reveal a similar trend in the suitable habitats, with relatively minor losses and gains in the future suitable areas. In the 2070s and 2090s, the area of unsuitable habitats in the middle and lower Yangtze River Plain and the Tarim Basin in Xinjiang is increasing, with previously unsuitable areas gradually transforming into suitable habitats and expanding toward higher latitudes. Under the EMca scenario in the 2070s, the area lost for \u003cem\u003eB. horsfieldi\u003c/em\u003e is 21,450 km\u003csup\u003e2\u003c/sup\u003e, with a reduction area of 21,043 km\u003csup\u003e2\u003c/sup\u003e. Notably, under the EMwm scenario in the 2090s, the area gained for \u003cem\u003eB. horsfieldi\u003c/em\u003e is the highest, at 46,695 km\u003csup\u003e2\u003c/sup\u003e. Additionally, in all three time periods under the EMwm scenario, there is a slight increase in suitable habitats in Yunnan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChanges in the Area of \u003cem\u003eB. horsfieldi\u003c/em\u003e from Current to Future.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elost (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGain (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMwm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMwm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMca\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEMwm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, compared to the current period, the future potential distribution of suitable habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e is mainly shifting northward. Specifically, the suitable habitats in Shaanxi, Shanxi, Hebei, Beijing, and Tianjin will expand northward, and suitable areas will also emerge in the Tarim Basin in Xinjiang. Under future climate conditions, almost the entire regions of Shandong, Tianjin, Anhui, Jiangsu, Hubei, and Chongqing will have distributions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Shift of Centroids of Suitable Habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e under Future Climate Conditions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the centroid of the total suitable habitat for \u003cem\u003eB. horsfieldi\u003c/em\u003e under the SSP2-4.5 climate scenario. Under current climate conditions, the centroid is located in Xiangyang City, Hubei Province (112.29 E, 31.70 N). By the 2050s, the centroid migrates 112.66 km northeast to Nanyang City, Henan Province (112.95 E, 32.55 N); in the 2070s, it shifts 145.59 km north to Pingdingshan City, Henan Province (113.03 E, 33.91 N); and by the 2090s, it moves 139.09 km northwest to Luoyang City, Henan Province (111.61 E, 34.17 N). Compared to the current centroid location, the centroids for the 2050s, 2070s, and 2090s all shift northward, with the centroid of suitable habitats continuously moving northward over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study indicates that there are differences in the prediction processes and parameter algorithms among the 10 individual models, which may lead to uncertainties in their predictions (Guo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ensemble model minimizes the bias caused by single modeling methods through the weighting of evaluated individual models (Zhao et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ensemble model provides unique advantages in model selection methods, helping to mitigate issues related to variability in current statistical approaches to ecological predictions (Tanaka et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study utilized the ensemble model built on the Biomod2 platform to predict the potential habitat distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e, effectively reducing the uncertainty and bias associated with individual models. To prevent overfitting, we employed Pearson correlation analysis to eliminate strongly correlated environmental factors, ultimately selecting 8 climate variables and 3 topographic variables for modeling. We combined the three best-performing individual models using two methods: the relative majority voting method (EMca) and the weighted probability method (EMwm). These methods achieved the highest model accuracy, better reflecting the response relationship between the species and its environment. This indicates that the predictions from the ensemble model are more accurate than those from individual models, further enhancing the reliability of the results (School Of BioSciences et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model simulation results show that the main environmental variables affecting the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e, include: the minimum temperature of the coldest month (bio6, 27.21%), mean temperature of the coldest quarter (bio11, 16.73%), annual mean temperature (bio1, 15.40%), temperature seasonality (bio4, 9.98%), mean temperature of the driest quarter (bio9, 9.68%), elevation (elev, 5.40%), the max temperature of the warmest month (bio5, 5.16%), precipitation of the driest quarter (bio17, 4.83%), isothermality (bio3, 3.56%), slope (1.45%), and aspect (0.62%). This indicates that the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e is constrained by various environmental conditions, including both biotic and abiotic factors. With global climate warming and the intensification of future climate change, rising temperatures are likely to increase the frequency and intensity of forest pest occurrences(Mukhopadhyay, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gao Junkai, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). As ectothermic animals, insects are highly dependent on environmental temperatures to regulate their physiological functions and are extremely sensitive to temperature changes. Under extreme temperature conditions, both the survival and reproduction of insects can be suppressed (Daniel et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hafker et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In particular, under extremely cold conditions, mortality rates among insects may increase, which significantly impacts overwintering life stages or newly hatched larvae in early spring (MacQuarrie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, when environmental humidity is too high or too low, it can hinder insect development and even cause a sharp decline in their population. The interaction of multiple climatic factors also affects the survival and reproduction of forestry pests in various ways (Zhou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With the ongoing warming of the Earth's climate, there has been an increase in extreme weather events and a rise in average annual temperatures, making winter the fastest-warming season (E and L, 2021). Rising temperatures may cause some pests to emerge earlier in the spring and delay overwintering in the fall, leading to earlier adult emergence, extended occurrence periods, and an increase in population size (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Research shows that overwintering adults of \u003cem\u003eB. horsfieldi\u003c/em\u003e typically begin to emerge in late April each year, with peak emergence occurring from mid to late June and ending in early August (Diao Zhie and Ding Fubo, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang et al., 2004). The expansion of \u003cem\u003eB. horsfieldi\u003c/em\u003e's suitable habitat under future climate scenarios may be related to this. Additionally, the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e is not only highly correlated with climatic factors, but the distribution of host plants also plays a significant role (Xinju et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The study points out that during the nutrient supplementation phase, adult \u003cem\u003eB. horsfieldi\u003c/em\u003e can select from various host plants (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and exhibit different behavioral variations, closely related to their strong host switching and metabolic adaptability. These factors are crucial for the differentiation and spread of \u003cem\u003eB. horsfieldi\u003c/em\u003e populations (Wu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and they may also contribute to the increase in the potential suitable distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e in the future.\u003c/p\u003e \u003cp\u003eThe model results predict that the currently suitable distribution areas for \u003cem\u003eB. horsfieldi\u003c/em\u003e are mainly concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal cities. This indicates that these regions are climatically suitable for the survival of \u003cem\u003eB. horsfieldi\u003c/em\u003e under current climate conditions. However, in the future, these areas will face severe heat stress (Zhang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Overall, there is a trend of increasing suitable habitats, particularly evident in Xinjiang, Shandong, Hebei, Tianjin, Beijing, Gansu, Shaanxi, and Shanxi. At the same time, some medium-suitability areas are gradually evolving into high-suitability areas. These regions primarily fall within subtropical monsoon climates and temperate monsoon climates, which share similar climate characteristics. Due to the close relationship between elevation and climate, it is especially important to assess the ecological impacts of current and future climate change in high-latitude regions. Research indicates that elevation gradients can influence insect species composition, community structure, and patterns of diversity (Lijuan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and insect distributions may also shift toward higher latitudes and elevations (MUSOLIN, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which is consistent with our findings. Although high-suitability areas are expanding northward, the area of existing high-suitability regions is still slowly decreasing, suggesting that \u003cem\u003eB. horsfieldi\u003c/em\u003e may possess a certain degree of temperature adaptability. For example, \u003cem\u003eEogystia hippophaecolus\u003c/em\u003e is projected to move northwest in the future (Xue et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the overall centroid of suitable habitats for \u003cem\u003eBactrocera tsuneonis\u003c/em\u003e will gradually shift west and north (Mao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In different decades, the geographical latitude boundaries of \u003cem\u003eB. horsfieldi\u003c/em\u003e have moved further north. Under the influence of global warming, the numbers of high, medium, and low suitability areas are all increasing. This indicates that, against the backdrop of global warming, more regions will become suitable for the survival of \u003cem\u003eB. horsfieldi\u003c/em\u003e, thereby intensifying the challenges for control and management.\u003c/p\u003e \u003cp\u003eThere are many factors influencing the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e, and besides climate, other environmental factors also play significant roles. Biotic interactions, such as variables related to hosts, natural enemies, and vegetation conditions, are critical in determining insect distribution (H et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Temperature and precipitation not only affect species abundance and distribution but also impact the reproduction, survival, dispersal, and population dynamics of pests, while also influencing the physiology of host plants, thereby indirectly affecting the pests (Sandra et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, in addition to abiotic factors, the overexploitation of biological resources, climate change, human activities, environmental pollution, the biological characteristics of the species themselves, and biological invasions (Wei et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) may also be related to the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e. Notably, as global warming continues, areas that were previously unsuitable are gradually becoming suitable habitats, suggesting that \u003cem\u003eB. horsfieldi\u003c/em\u003e may appear in regions with higher elevations, higher latitudes, or more extreme temperature and humidity conditions, indicating a certain invasion potential. Therefore, predicting the potential distribution of this species under future climate scenarios is of great significance for formulating pest control policies in forestry.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study utilized the ensemble model built on the Biomod2 platform to predict and analyze the potentially suitable distribution areas of \u003cem\u003eB. horsfieldi\u003c/em\u003e. The results indicate that under the SSP2-4.5 climate scenario, the currently suitable habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e are primarily concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal cities. The environmental factors affecting its distribution mainly include bio6, bio11, bio1, bio4, bio9, bio5, bio17, bio3, slope, elevation, and aspect. As climate warming progresses, the suitable range for \u003cem\u003eB. horsfieldi\u003c/em\u003e is expanding toward higher latitudes or elevations, with the centroid of suitable habitats continually shifting northward. Consequently, some regions may face an invasion risk in the future. This study provides important references for developing effective control and management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization, Zhihang Zhuo; methodology, Zhipeng He; software, Danping Xu and Xinju Wei; formal analysis, Xuanlin Liand Zhihang Zhuo; investigation, Xinqi Deng; data curation, Zhiwei Zhu; writing-original draft preparation, Zhipeng He and Xinju Wei; writing-review and editing, Danping Xu and Zhihang Zhuo; supervision, Zhihang Zhuo. All authors read and approved the final manuscript..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Sichuan Province Science and Technology Support Program (2022NSFSCO986), and China West Normal University Support Program (20A007, 20E051, 21E040, and 22kA011) and National Natural Science Foundation of China (No. 32560352).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the results are available in a public repository at: GBIF.org (1 April 2026) GBIF Occurrence Download https://doi.org/10.15468/dl.9km399.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAssessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS). 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Ecological Indicators. 163, 112103-.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Batocera horsfieldi, Biomod2, climate change, potential distribution, Overlapping suitable areas","lastPublishedDoi":"10.21203/rs.3.rs-9259817/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9259817/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eBatocera horsfieldi\u003c/em\u003e is a significant forestry pest with a devastating impact on China's forestry, urban landscaping, and green spaces. To investigate the potential distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e across the country under climate change and its potential threat to forestry and urban landscaping, this study utilized the species distribution modeling platform Biomod2. By combining \u003cem\u003eB. horsfieldi\u003c/em\u003e distribution data with bioclimatic and topographic variables, we predicted changes in suitable habitats and key climate factors and analyzed the shift in its centroid under the SSP2-4.5 scenario. The results showed that the main environmental factors influencing the distribution of \u003cem\u003eB. horsfieldi\u003c/em\u003e include the minimum temperature of the coldest month (bio6), mean temperature of the coldest quarter (bio11), annual mean temperature (bio1), temperature seasonality (bio4), and mean temperature of the driest quarter (bio9). The current suitable habitats for \u003cem\u003eB. horsfieldi\u003c/em\u003e are mainly concentrated in the Sichuan Basin, the middle and lower Yangtze River Plain, the North China Plain, and coastal urban areas. In the future, highly and moderately suitable areas will expand to higher latitudes or elevations (except for a decrease of 467 km\u003csup\u003e2\u003c/sup\u003e in the 2070s under the Emca pathway), and the centroid of suitable habitats will continue to shift northward. Climate change may increase the risk of pest outbreaks, and these findings provide a scientific reference for early warning, monitoring, and control of \u003cem\u003eB. horsfieldi\u003c/em\u003e in Xinjiang and northern China.\u003c/p\u003e","manuscriptTitle":"Future Distribution Dynamics of the Forestry Pest Batocera horsfieldi and Its Implications for Forestry and Urban Green Spaces in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 11:57:07","doi":"10.21203/rs.3.rs-9259817/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ce02126-6889-4c7e-aab9-69ce2099023d","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-11T13:58:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T12:08:06+00:00","index":35,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T14:21:13+00:00","index":34,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T01:59:57+00:00","index":33,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T20:00:54+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T14:12:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 11:57:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9259817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9259817","identity":"rs-9259817","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}