Predicting Distributional Changes of Bactrocera diaphora (Hendel) (Diptera: Tephritidae) in China Using an Optimized MaxEnt Model

Predicting Distributional Changes of Bactrocera diaphora (Hendel) (Diptera: Tephritidae) in China Using an Optimized MaxEnt Model | 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 Predicting Distributional Changes of Bactrocera diaphora (Hendel) (Diptera: Tephritidae) in China Using an Optimized MaxEnt Model Ruijun Liu, Hengchuang Gao, Wensheng Wu, Ziyu Lin, Qianran Hong, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8832813/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Bactrocera ( Zeugodacus ) diaphora (Hendel) is a quarantine invasive pest that causes substantial damage to fruit and vegetable crops. To assess its potential range in China under climate change, we compiled 83 occurrence records and calibrated a MaxEnt model using an optimized feature combination (LQP) and a regularization multiplier (RM = 0.5). Model validation yielded a mean AUC > 0.96, indicating excellent performance. Using current climate data and four CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) for four future periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100), we projected current and future suitable habitats in China. Six key bioclimatic variables were retained after Pearson correlation analysis and jackknife testing: mean diurnal temperature range (bio2), temperature seasonality (bio4), maximum temperature of warmest month (bio5), precipitation seasonality (bio15), precipitation of warmest quarter (bio18), and precipitation of coldest quarter (bio19); bio18 and bio5 contributed most (32% and 28.8%, respectively). Under current climate conditions, total suitable habitat covered 25.73% of China (247.47×10⁴ km²), with highly suitable areas concentrated in eastern Sichuan, Chongqing, and the coastal areas of South China. Future projections showed divergent trends among SPPs: total suitable area expanded under SSP1-2.6 and SSP2-4.5 (notably in the 2050s and 2070s), declined then increased under SSP3-7.0, and contracted steadily under SSP5-8.5 (a 17.87% decrease by the 2090s). The current geographical centroid of suitability was located in Tongren, Guizhou (108.326°E, 28.399°N) and is projected to shift northward and eastward. These findings provide a quantitative foundation for targeted monitoring, early warning, and region-specific management of B. diaphora under future climate scenarios. invasive insect optimized MaxEnt climate change Potential distribution Pest risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Bactrocera (Zeugodacus) diaphora (Hendel) (Diptera: Tephritidae) is a quarantine pest that causes considerable damage on a variety of fruit and vegetable crops including citrus, cucumbers, luffa, and navel oranges etc. Damage results primarily from female oviposition: hatched larvae bore into host tissues and remain largely concealed within the fruit, which complicates detection and control. This cryptic habit also facilitates long-distance passive dispersal, primarily via eggs and larvae transported inside infested fruits and via pupae moved with packing materials or vehicles. With the intensification of international trade in fresh produce, B. diaphora has demonstrated high invasive potential and was listed as a quarantine invasive pest in China [ 1 ].First intercepted at Huizhou Port (Guangdong Province) in 2017, the species has since been detected in multiple provinces and regions, including Hainan, Yunnan, Sichuan, Chongqing, and Taiwan, indicating its capacity for rapid establishment and dispersal. Nevertheless, its overall distribution remains relatively limited due to ongoing official control measures. In Chongqing, outbreaks have mainly affected cucumbers, luffa, and navel oranges production [ 2 ]. Current management of B. diaphora in China relies on generalized strategies extrapolated from other Bactrocera species due to a lack of species-specific research. These strategies comprise: (1) monitoring and early warning via food-based lures [ 3 ]; (2) quarantine measures to prevent movement through trade [ 4 ]; (3) adult suppression using mass trapping and attract-and-kill methods [ 5 ]; (4) biological control, including SIT and natural enemies release [ 6 ]; and (5) chemical control via bait sprays and soil treatments [ 7 ]. However, increasing trap captures in related species, indicate that B. diaphora could similarly increase in abundance and expand its geographical range [ 8 ]. Given this invasive potential and the likehood of population growth, further research on risk assessment and correspondingly targeted control strategies is urgently needed. Biological invasions represent an escalating threat to China, where the number of invasive insect species continues to rise, resulting in annual economic losses estimated at 119.876 billion yuan [ 9 ]. This challenge is further compounded by global climate change, which is reshaping ecosystems and agricultural sustainability through rising temperatures and altered hydrological regimes [ 10 ]. Over recent decades, China has experienced pronounced warming and destabilized agro-ecosystems, heightening the country’s vulnerability to invasion risks [ 11 ]. Because insects are ectotherms, fluctuations in temperature and moisture directly influence their physiology and phenology, often facilitating range expansion, enhanced overwinter survival, and accelerated development [ 12 , 13 ]. While empirical research specifically targeting B. diaphora remains limited, its tropical origin and ectothermic biology suggest high sensitivity to thermal shifts and significant potential for population outbreaks under warming scenarios. Consequently, proactive, science-based monitoring and risk assessments are essential to anticipate and mitigate the heightened threats posed by B. diaphora and other emerging pests [ 14 ]. Species Distribution Models (SDMs) provide a quantitative framework to predict potentially suitable habitats by relating occurrence records to environmental variables (e.g., climate, hosts, natural enemies, altitude, and human activities). Among available algorithms such as BIOCLIM, GLM, GARP, ENFA, and MaxEnt, the MaxEnt algorithm, which is based on the principle of maximum entropy, has proven particularly effective for presence only data and limited sample sizes and is widely used to project invasive pest habitat under climate change [ 15 – 20 ]. MaxEnt has consistently demonstrated strong performance in identifying climate-driven distribution patterns and has been validated in studies of species such as Grapholita dimorpha (Komai) (Lepidoptera: Tortricidae), Anthonomus eugenii (Cano) (Coleoptera: Curculionidae), Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae), Tuta absoluta (Meyrick) (Lepidoptera: Pyralidae), and Ophelimus maskelli (Ashmead) (Hymenoptera: Ichneumonidae) [ 21 – 25 ]. These studies have provided essential information for pest risk analysis and have supported the development of scientifically informed, spatially tailored control strategie [ 26 ]. Previous suitability studies for B. diaphora have indicated strong thermal and winter-cold constraints but often suffer from methodological limitations, such as coarse-resolution climate data or default MaxEnt settings that can overlook microclimatic heterogeneity and promote model overfitting [ 27 ]. To address these issues, the present study compiles comprehensive occurrence records and high- resolution climate data and uses the ENMeval package in R (v.4.5.0) to optimize MaxEnt feature combinations (FC) and regularization multipliers (RM) based on Akaike Information Criterion (AICc), thereby improving ecological realism and reducing overfitting [ 28 , 29 ]. Accordingly, this study aims to (1) map the current and future potential distribution of B. diaphora across China under multiple climate-change scenarios, and (2) identify the key bioclimatic drivers of its habitat suitability. The results are intended to provide quantitative guidance for surveillance, early warning and regionally targeted management of this quarantine pest. 2. Materials and Methods 2.1. Collection and Processing of distribution data of B. diaphora We compiled presence-only occurrence data for B. diaphora from multiple online biodiversity repositories and the primary literature. Specifically, records were downloaded from GBIF ( http://www.gbif.org/ ; accessed on 19 June 2025), Bold Systems v4 ( http://www.boldsystems.org/ ; accessed on 20 January 2025), iNaturalist ( https://www.inaturalist.org/ ; accessed on 20 June 2025), CABI ( https://plantwiseplusknowledgebank.org/ ; accessed on 20 June 2025), iDigBio ( https://www.idigbio.org/ ; accessed on 20 January 2025), and the National Catalogue of Administrative Regions for the Distribution of Quarantine Pests of Agricultural Plants ( https://www.moa.gov.cn/nybgb/ ; accessed on 20 June 2025). In parallel, we conducted a structured literature search in Web of Science and CNKI using the standardized taxonomic name Bactrocera diaphora and geographic filters. Duplicate entries, records with missing coordinates, and records located in marine areas were removed, yielding 112 raw occurrence records (Fig. 1 ). All records were merged and de-duplicated, and geographic coordinates were validated. We excluded records with missing or obviously erroneous coordinates (e.g., zero) and records with ambiguous locality descriptions, those providing only coarse administrative levels (e.g., "China" or "Yunnan Province") without specific site details (e.g., city or town level), which precluded accurate georeferencing. To reduce spatial sampling bias and to match the resolution of the bioclimatic layers, we applied grid-based spatial thinning in R using the raster and sp packages: occurrences were downsampled so that no more than one presence fell within each ~ 5 km × 5 km grid cell (2.5 arc-minute resolution) of the WorldClim version 2.1 climate surfaces. After these quality-control and thinning procedures, the final dataset comprised 83 georeferenced presences, with the complete set of filtered occurrence and their spatial distribution provided in Supplementary Figure S1 .Locations were pinpointed using Google Earth Pro. 2.2. Collection and Screening of Bioclimatic Variables in B. diaphora We obtained the 19 standard bioclimatic variables (Bio1-Bio19) from the WorldClim version 2.1 database ( https://www.worldclim.org/ ) at 2.5 arc-minutes resolution, referenced to the 1970–2000 baseline. These high-resolution, long-term averages are widely used as standard predictors in species distribution modeling, and we used them as the present-day bioclimatic predictors [ 30 ]. Future climate projections were derived from the BCC-CSM2-MR general circulation model (CMIP6) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) for four time slices (2021–2040, 2041–2060, 2061–2080, 2081–2100). The SSPs span low to very-high greenhouse-gas forcing by 2100 (≈ 26, 45, 70, and 85 W·m − 2 , respectively) [ 31 , 32 ]. All climate rasters (current and future) were aligned to a common 2.5′ grid, clipped to the study extent (the entire land territory of China), and exported in ASCII (ASC) format to ensure spatial and format consistency. To minimize multicollinearity and improve model robustness, we screened the 19 bioclimatic variables using a two-step procedure. First, we ran a preliminary MaxEnt 3.4.4 including all variables and occurrence records, and used the jackknife test to evaluate the relative contribution of each variable. Second, we imported the occurrence points and climate data into ArcGIS 10.8 to extract raster values at the occurrence locations,,exported the resulting dataset to SPSS and calculated Pearson correlation coefficients for all variables pairs. Pairs with |r|≥0.8 were considered highly correlated (Figure S2) [ 33 ]. For each highly correlated pair, the variable with the lower contribution in the preliminary MaxEnt run was excluded. After this stepwise screening, six predictors (Bio2, Bio4, Bio5, Bio15, Bio18, and Bio19) were retained for the final MaxEnt model of B. diaphora . 2.3. MaxEnt Model Optimization and Evaluation Default MaxEnt settings can lead to overfitting and excessive sensitivity to idiosyncrasies in the training data [ 34 ]. To improve model robustness and predictive performance for B. diaphora , we therefore conducted systematic hyperparameter tuning of MaxEnt (v3.4.4) with the ENMeval package in R 4.5.0. We evaluated eight regularization multiplier (RM) levels (0.5-4.0, in 0.5 increments) and eight feature combinations (FC) constructed from linear (L), quadratic (Q), hinge (H), product (P), and threshold (T) features: L, LQ, LQP, QHP, LQH, LQHP, QHPT, and LQHPT[ 35 , 36 ]. These settings produced 64 candidate parameter combinations, each of which was fitted and evaluated in ENMeval. Model complexity and goodness-of-fit were assessed using the corrected Akaike Information Criterion (AICc) [ 37 , 38 ]. To detect potential overfitting, we also monitored the 10% training omission rate (OR10) [ 29 , 39 ]. The optimal parameter set was selected as the configuration with the lowest AICc that also satisfied acceptable OR10, thereby balancing model fit and generalizability for subsequent habitat-suitability projections. Using the 83 filtered occurrence records of B. diaphora and the six selected bioclimatic predictors, we fitted MaxEnt with the optimal feature combination (FC) and regularization multiplier (RM) identified during prior tuning. For each run, 75% of occurrences were randomly assigned to the training set and the remaining 25% to an independent test set. Models were executed in ten replicate runs to implement cross-validation. Model settings included a maximum of 5,000 iterations, 10,000 background points, generation of receiver operating characteristic (ROC) curves, logistic output format, and export of predictions in ASC format [ 40 ]. Variable importance was assessed using the jackknife test, and response curves were plotted for all predictors to characterize their individual relationship with habitat suitability. Model accuracy was assessed by the area under the curve (AUC), which ranges from 0.5 to 1.0. We adopted common interpretive thresholds (AUC > 0.9, excellent; 0.8–0.9, good; 0.7–0.8, fair; 0.6–0.7, poor; and < 0.5, fail) [ 41 ]. Both training and testing AUC values exceeded 0.9, indicating excellent predictive performance and supporting the suitability of the selected bioclimatic variables for projecting the potential distribution of B. diaphora . 2.4. Potential Habitats Predictions and Centroid Shifts Evaluation of B. diaphora The model suitability index was expressed on a continuous scale from 0 to 1, with larger values indicating greater bioclimatic suitability for B. diaphora . The mean suitability raster derived from ten MaxEnt replicates was imported into ArcGIS 10.8 and reclassified into four classes using the Jenks natural breaks method via the Reclassify tool: unsuitable [0.00, 0. 12), low [0.12, 0.33), moderate [0.33, 0.57), and high [0.57, 1.00] [ 42 ]. These raster layers were converted to polygons in ArcGIS, and the total area (km²) of suitable habitat was calculated using the ‘Calculate Geometry’ tool. The resulting maps delineated the potentially suitable-habitat distribution of B. diaphora across China. The geographic centroid of the predicted suitable area was used as a concise indicator of shifts in species ’ spatial distribution in response to bioclimatic change [ 43 , 44 ]. We performed centroid-shift analysis to quantify spatiotemporal changes in suitable habitat for B. diaphora under current and future climate scenarios. First, habitat-suitability rasters for each time period were converted to vector polygon layers in ArcGIS 10.8. The resulting polygon layers for the current and future periods were then entered into the Spatial Statistics toolbox, and the Mean Center function (Measuring Geographic Distributions) was used to calculate centroids for each period. Centroid shifts between periods were quantified as distance and direction of movement from the current to future center. 3. Results 3.1. MaxEnt Model Settings Optimization Under the default MaxEnt parameter settings (RM = 1, FC = LQHP), we used the ENMeval package in R to optimally select two key parameters (FC and RM), resulting in the optimal model configuration (Fig. 2 ). When FC was set to LQP and RM to 0.5, the MaxEnt model achieved the minimum AICc value (i.e., 0). According to the Akaike Information Criterion, this parameter combination minimized both model complexity and overfitting, enabling the most accurate simulations. Additionally, the optimized configuration reduced the average OR₁₀ by 12.22% compared to default parameters. With the optimal parameter configuration, the model produced AUC values > 0.96 across all climate scenarios, indicating exceptionally high predictive accuracy and robust simulation performance (Receiver operating characteristic (ROC) curve in Figure S3). These results confirm the model’s suitability for forecasting B. diaphora ’s potential distribution under future climate conditions. 3.2. Bioclimatic Factors Influencing Distribution and Response Curves of B. diaphora Figure 3 shows that the six bioclimatic variables exhibit different contribution rates and ranked importance in determining B. diaphora suitable habitats. Among these, precipitation of the warmest quarter (Bio18)-consistent with WorldClim bioclimatic variable definitions-stands out significantly, with a contribution rate of 32% and ranked importance of 8.7%, indicating that this variable explained the largest proportion of model gain during the training process. The remaining the warmest month (Bio5, 28.8%), mean diurnal range (Bio2, 20%), temperature seasonality (Bio4, 13.4%), precipitation of the coldest quarter (Bio19, 5%), and precipitation seasonality (Bio15, 0.8%). Figure 4 illustrates the relationship between B. diaphora occurrence probability and selected bioclimaticvariables. Using a suitable-habitat threshold of probability ≥ 0.33, which was derived from a Natural Breaks (Jenks) classification of the continuous MaxEnt suitability outputs, the optimal intervals were as follows: Bio4 (temperature seasonality): 107.99-892.78; Bio5 (maximum temperature of the warmest month): 24.92–37.67°C; Bio15 (precipitation seasonality): 47.36-111.45 mm; Bio18 (precipitation of the warmest quarter): 430.38-1441.24 mm. For Bio4, Bio5, Bio15, and Bio18, B. diaphora occurrence probability initially increases with increasing variable values within the optimal intervals, then decreases after reaching a peak- exhibiting a typical unimodal response.The response curves showed approximately unimodal patterns, reflecting the species’ tolerance range along each climatic gradient. In habitat suitability modeling for new regions, Bio2 and Bio19 were excluded from analysis due to the absence of closed bell-shaped response curves, which are critical for accurately predicting species ’ uninhabited areas. 3.3. Potentially Suitable Habitat for B. diaphora Under Current Climatic Conditions Model predictions (Fig. 5 ) show that under current climatic conditions, B. diaphora ’s potential habitats in China are widely distributed across East, Central, South, and Southwest China, covering a total area of 247.47 × 10⁴ km² (approximately 25.73% of China’s national territory). Highly suitable zones are primarily concentrated in eastern Sichuan, Chongqing, Hubei, Guizhou, Guangxi, Guangdong, and Hainan, covering 42.23 × 10⁴ km² (17.07% of the total suitable area). Moderately suitable zones are mainly located in Shaanxi, Hubei, Hunan, Guizhou, Yunnan, Guangxi, Guangdong, Fujian, and Hainan, covering 59.16 × 10⁴ km² (23.90% of the total suitable area). Low- suitability zones have the largest distribution, encompassing Shandong, Henan, Anhui, Jiangsu, Shaanxi, Hubei, Hunan, Zhejiang, Fujian, Guangdong, Guangxi, and Yunnan, with an area of 146.08 × 10⁴ km² (59.03% of the total suitable area). 3.4. Changes in the Potentially Suitable Habitat Area for B. diaphora Under Future Climate Conditions This study selected four climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods (2030s, 2050s, 2070s, and 2090s) to project B. diaphora ’s future suitable habitats. Compared with current conditions, the locations and extents ofits potential habitats in China are projected to change across different future periods (Figs. 6 , 7 ; Table 1 ). Table 1 Changes in the suitable habitat area for B. diaphora under different climate scenarios. Scenario Decade Predicted area(×104 km2) Comparison with the current distribution(%) Total Suitable Low Suitable Medium Suitable High Suitable Total Suitable Low Suitable Medium Suitable High Suitable - Curren t 247.47 146.08 59.16 42.23 —— —— —— —— SSP1-2.6 2030s 2050s 2070s 2090s 269.80 234.64 293.08 257.03 158.45 141.86 125.15 141.01 64.50 56.73 114.52 67.20 46.84 36.05 53.41 48.82 9.02% -5. 19% 18.43% 3.86% 8.47% -2.89% -14.33% -3.47% 9.03% -4. 11% 93.59% 13.59% 10.91% -14.64% 26.47% 15.60% SSP2-4.5 2030s 2050s 2070s 2090s 246.90 279.70 270.02 253.52 141.49 116.02 125.01 116.89 64.59 104.23 94.74 84.90 40.82 59.44 50.28 51.73 -0.23% 13.02% 9. 11% 2.44% -3. 15% -20.58% -14.43% -19.98% 9. 19% 76.20% 60. 15% 43.52% -3.35% 40.74% 19.04% 22.47% SSP3-7.0 2030s 2050s 2070s 2090s 223.56 240.57 252.09 265.88 134.46 135.00 140.25 155.53 49.24 60.57 64.06 57.13 39.86 45.01 47.78 53.22 -9.66% -2.79% 1.86% 7.44% -7.96% -7.59% -3.99% 6.47% -16.76% 2.38% 8.28% -3.43% -5.62% 6.57% 13. 13% 26.01% SSP5-8.5 2030s 2050s 2070s 2090s 237.31 229.92 232.43 203.26 143.50 138.64 132.22 143.22 53.71 50.87 54.47 60.04 40.11 40.41 45.74 43.70 -4. 11% -7.09% -6.08% -17.87% -1.77% -5.09% -9.49% -1.96% -9.21% -14.01% -7.93% 1.49% -5.04% -4.33% 8.31% 3.48% Under SSP1-2.6: The total suitable habitat area is projected to increase by 9.02% in the 2030s, decrease slightly by 5. 19% in the 2050s, increase significantly by 18.43% in the 2070s, and grow moderately by 3.86% in the 2090s. Under SSP2-4.5: The total suitable habitat area remains nearly unchanged in the 2030s (a slight decrease of 0.23%), then increases markedly by 13.02% from the 2050s onward, with continued growth in the 2070s and 2090s (albeit at a slower rate). Under SSP3-7.0: The suitable habitat area initially decreases by 9.66% in the 2030s and a further 2.79% in the 2050s, then reverses trend with a modest recovery of 1.86% in the 2070s and a 7.44% increase by the 2090s. Under SSP5-8.5: The suitable habitat area exhibits a consistent declining trend throughout the projection period: a 4. 11% decrease in the 2030s, a further 7.09% reduction in the 2050s, a slight recovery to a 6.08% decrease in the 2070s, and a significant decline of 17.87% by the 2090s. Comprehensive analysis indicates that climate warming will significantly affect the B. diaphora suitable habitat area in the coming decades. Potentially suitable habitats are projected to increase in northern and eastern China. Under SSP1-2.6, the total suitable habitat area is expected to peak at 293.08 × 10⁴ km² in the 2070s. Under SSP2-4.5, the highly suitable area is projected to reach its peak at 59.44 × 10⁴ km² in the 2050s. Under SSP3-7.0, the medium suitable habitat area is projected to reach a minimum of 49.24 × 10⁴ km² in the 2030s. Under SSP5-8.5, the total suitable habitat area is expected to reach its minimum at 203.26 × 10⁴ km² in the 2090s. These projections highlight the profound impact of future climate change on B. diaphora distribution, emphasizing the need for proactive adaptation and management measures. 3.5. Centroid Change in the Suitable Habitats of B. diaphora The current centroid of B. diaphora potential habitats is located in Tongren City, Guizhou Province (108.32°E, 28.40°N). An analysis of centroid shifts under different climate change scenarios (Fig. 8 ; Table 2 ) reveals significant variations in migration directions across the four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) over the four time periods. Despite these variations, a general northward and eastward migration trend is evident. Table 2 Shift in Centroid of Potential Habitats for B. diaphora under Current and Future Climate Scenarios. Current Centroid Location Climate Scenario Future Centroid Location 2030s 2050s 2070s 2090s Tongren City (108.326°E, 28.399°N) SSP1-2.6 Xiangxi Tujia and Miao Autonomous Prefecture (110.235 °E, 28.803 °N) Huaihua City (110.628 °E, 28.659 °N) Tongren City (108.626 °E, 28.177 °N) Tongren City (108.776°E, 28.442 °N) SSP2-4.5 Xiangxi Tujia and Miao Autonomous Prefecture (109.384°E, 29.060 °N) Xiangxi Tujia and Miao Autonomous Prefecture (109.43 °E, 28.870°N) Chongqing City (108.826°E, 28.646°N) Chongqing City (108.588°E, 28.541°N) SSP3-7.0 Chongqing City (109.259 °E, 28.498 °N) Chongqing City (108.827 °E, 28.355°N) Zunyi City (107.854 °E, 28.62°N) Zunyi City (107.97 °E, 28.691°N) SSP5-8.5 Chongqing City (109.032°E, 28.680 °N) Tongren City (108.514°E, 28.493°N) Zunyi City (107.643 °E, 28.613°N) Xiangxi Tujia and Miao Autonomous Prefecture (110.162 °E, 28.812°N) 4. Discussion 4.1. Model Optimization and Evaluation Accurate prediction of species distributions is essential for assessing invasion risk and predicting potential range expansions under changing climatic conditions [ 45 ]. In this study, we used the ENMeval package to optimize MaxEnt model complexity through tuning the regularization multiplier and feature combination, thereby reducing overfitting [ 46 ]. To enhance model reliability, cross-validation and Pearson correlation analysis (|r| > 0.8) were combined to remove redundant predictors, retaining six bioclimatic variables for final calibration [ 47 ]. This optimization effectively improved both the stability and the ecological realism of the model[ 34 ]. Previous work has shown that non-optimized MaxEnt models tend to inflate habitat suitability and perform poorly in spatial transferability, supporting the importance of parameter tuning [ 48 ]. In our optimized model, all known occurrence records of B. diaphora were located within areas predicted as suitable under current climatic conditions, and the AUC values consistently exceeded 0.9, indicating excellent predictive performance [ 49 ]. Taken together, these results confirm that the optimized MaxEnt model provides a reliable basis for evaluating the invasion potential and mapping climatically suitable habitats for B. diaphora under current and future scenarios. 4.2 Ecological Implications of Key bioclimatic Variables for B. diaphora As ectotherms, insects are acutely sensitive to hydrothermal fluctuations and extreme climatic events, both of which dictate survival in native habitats and govern the potential for range expansion. For instance, rising temperatures have facilitated the northward expansion of Bactrocera dorsalis (Hendel) in China and augmented the overwintering survival of Bactrocera cucurbitae (Coquillett) in marginal temperate regionsation [ 50 , 51 ]. Concurrently, altered precipitation patterns have modified the population dynamics and outbreak intensity of other congeners, such as Bactrocera tryoni (Froggatt) across subtropical and Mediterranean climates [ 52 ]. Beyond direct physiological impacts, climate change indirectly modulates insect distributions and plant-insect interactions by altering host- plant phenology. For example, warming-induced advances in olive anthesis and fruit maturation have synchronized earlier oviposition and flight periods in Bactrocera oleae (Gmelin), facilitating its expansion toward higher latitudes and altitudes across the Mediterranean Basin[ 53 ]. In our analyses, precipitation of the warmest quarter (Bio18) and maximum temperature of the warmest month (Bio5) emerged as the primary determinants of B. diaphora ’s potential distribution, with Bio18 exerting the greatest influence. The modeled optimal precipitation range for Bio18 was 430.38-1441.24 mm. Notably, the dominant cropping systems in Chongqing particularly in the Three Gorges Reservoir, highly overlap with the host range of B. diaphora [ 54 ]. As a polyphagous pest, it threatens several economically important crops such as citrus, cucurbits, and peaches. Meteorological records for the past two decades indicate that annual precipitation in Chongqing (875.6-1348.2 mm) falls squarely within the modeled optimal range, which reinforces our identification of Chongqing as a highly suitable region for B. diaphora and highlights an elevated risk to local agriculture [ 55 ]. These results highlight the importance of temperature-related variables in shaping the potential distribution of B. diaphora , aligning with previous studies that identifies temperature as a key constraint for tephritid fruit flies [ 56 , 57 ]. Among these, Bio5 was the most influential temperature predictor, with a predicted suitability range of 24.92–37.67°C. Given the scarcity of species-specific data on the thermal biology of B. diaphora , we compared our results with its congener, B. dorsalis , which shares similar ecological niches and host associations. The reported permissive range for B. dorsalis development and reproduction is approximately 15–34°C, with an optimum of 20–28°C [ 58 ]. These thermal traits closely match our predicted suitablility range for B. diaphora . Furthermore, the modeled upper limit (37.67°C) is consistent with the thermal thresholds of other tephritids like B. oleae , where extreme heat impairs survival and reproductive performance [ 53 , 59 ]. The strong correspondence between these experimental observations and our model-derived threshold suggests that our predictions are biologically grounded rather than mere statistical artifact. 4.2. Changes in the Suitable Habitat of B. diaphora Under Current and Future Climate Scenarios Climate warming fundamentally alters regional hydrothermal dynamics, thereby modulating the demographic traits of ectothermic insects and heightening the susceptibility of ecosystems to biological invasions [ 60 ]. Within this context, the evaluation of climate-mediated niche dynamics for B. diaphora is essential for preemptive biosecurity management [ 61 ]. Our models indicate that contemporary suitable areas encompass roughly 247.47×10⁴ km² (25.73% of China), characterized by a distinct latitudinal attenuation (Fig. 5 ). As latitude increases, cooler thermal conditions and less favorable moisture regimes progressively limit the survival, development, and reproduction of tephritid fruit flies, resulting in reduced habitat suitability toward northern regions. Although colder conditions currently limit the establishment of B. diaphora at higher latitudes, there is increasing evidence that tephritid fruit flies may expand poleward as climates warm. For example, ecological niche models for other congeners such as B. tsuneonis project a northward shift in suitable habitat under future climate scenarios, indicating potential spread into regions that are presently marginal or unsuitable [ 44 ]. Highly suitable habitats, covering about 42.23×10⁴ km², are predominantly concentrated in eastern Sichuan, Chongqing, Guangxi, Guangdong, and Hainan. These provinces are among the most important fruit- and vegetable-producing regions in China, with extensive cultivation of citrus, cucurbits, and other known host crops of B. diaphora . The combination of high climatic suitability and abundant host resources suggests a heightened risk of population buildup and outbreaks in these areas, indicating that particular vigilance and strengthened monitoring efforts are warranted. These regions are characterized by a monsoon-influenced climate with abundant host plants (e.g., citrus and cucurbits), thereby providing favorable conditions for establishment and population growth [ 62 , 63 ]. Moderately to marginally suitable areas extend toward the North China Plain, whereas large portions of northern China are predicted to be unsuitable. These non-suitable regions are characterized by climatic conditions that fall outside the modeled tolerance range of B. diaphora , particularly colder winter temperatures and stronger seasonal climatic variability[ 57 ]. Overall, the distribution pattern is strongly constrained by regional climatic conditions. The dominant contributions of temperature- and precipitation-related variables in the optimized MaxEnt models demonstrate that climatic suitability alone is sufficient to capture much of the observed and projected distribution of B. diaphora [ 64 , 65 ]. Future climate change is projected to substantially alter the potential distribution of B. diaphora , but the direction and magnitude of these changes differ markedly among SSP pathways (Figs. 7 – 8 ; Table 2 ). Habitat trends differed by pathway: net expansion was projected under SSP1-2.6 and SSP2-4.5, SSP3-7.0 showed an initial contraction followed by partial recovery; and SSP5-8.5 produced a consistent decline, culminating in a 17.87% reduction by the 2090s. Over the period 2021–2100, SSP1-2.6 and SSP2-4.5 therefore emerge as the most favorable scenarios for range expansion of B. diaphora , reflecting a non-linear climatic response in which more extreme warming increasingly exceeds the species’ climatic tolerance, thereby constraining further expansion. Concurrently, the geographic centroid of suitable habitat shifted northward and eastward, reflecting climate-driven redistribution[ 66 ]. These patterns are consistent with numerous reports that climate change is driving poleward range shifts in insect pests (e.g., Spodoptera frugiperda , Solenopsis invicta , and Culex pipiens pallens ) [ 67 – 69 ]. The divergent trends among SSP pathways highlight the need for scenario-based, regionally targeted surveillance and management strategies that prioritize high-risk areas and incorporate projected climatic changes 4.3. Limitations of This Study Our study was constrained to temperature and precipitation-derived bioclimatic variables, and did not incorporate other potentially important determinants of B. diaphora distribution such as elevation, host plant distribution, vegetation type, land-use patterns and crop-type information, anthropogenic transport pathways, natural- enemy pressure, or the species ’ adaptive evolutionary potential [ 70 , 71 ]. Among these limitations, the absence of crop-type and land-use layers is particularly noteworthy. Although agricultural structure strongly influences the realized distribution of herbivorous insects, high-resolution, spatially explicit maps of major host crops (e.g., citrus, cucurbits, peach) are not publicly available for China, and existing agricultural statistics lack georeferenced formats usable for SDMs. This simplification introduces uncertainty into model predictions. In particular, the response curves generated by the MaxEnt quantify the marginal effect of each bioclimatic variable on occurrence probability but do not capture interactions among variables or non-additive biotic processes, which frequently shape insect distributions [ 48 ]. In addition, the climate projections relied primarily on the BCC-CSM2-MR model; although this general circulation model performs well at broad scales, systematic biases in its representation of climatic extremes or precipitation seasonality could propagate into the projected suitability maps. The MaxEnt framework itself relies exclusively on available occurrence records and implicitly assumes that those records represent the species ’ accessible and occupied environmental space [ 35 ]. If the species ’ realized distribution is limited by dispersal barriers, recent invasion history, or sampling biases, that assumption may be violated and the model may overestimate the area of climatically suitable habitat. Given these limitations, our results should be interpreted as approximations of the species ’ potential (fundamental) niche rather than definitive predictions of realized occupancy [ 72 ]. Future studies could reduce uncertainty and improve applicability by integrating multiple data sources and modeling approaches: include host-plant and land-cover layers, explicitly model dispersal and anthropogenic pathways, incorporate biotic interactions (for example, through joint-species or mechanistic models), use ensembles of climate models to quantify projection uncertainty, and apply occurrence-data cleaning and spatial filtering to mitigate sampling bias [ 73 , 74 ]. Complementary field surveys and experimental studies of physiological tolerance and population dynamics would also help validate and refine model outputs for more effective risk assessment and management [ 75 ]. 5 Conclusions We used the optimized MaxEnt model to simulate the potential geographical distribution of B. diaphora under current climate conditions and four future periods (2030s, 2050s, 2070s, and 2090s) across four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Under present-day climate, the pest was predicted mainly in eastern Sichuan, Chongqing, Hubei, Guizhou, Guangxi, Guangdong, and Hainan, with a highly suitable area of approximately 42.23×10 4 km². Precipitation of the Warmest Quarter (Bio18) and the Maximum Temperature of Warmest Month (Bio5) were identified as the dominant factors shaping its distribution. Compared to the current climate, the suitable habitat area is projected to expand under the lower-emission scenario (SSP1-2.6 and SSP2-4.5) but to decline progressively under the high-emission scenario (SSP5-8.5). These spatial-temporal projections provide data-driven guidance for prioritizing surveillance and control efforts and contribute new perspectives on the potential dynamics of B. diaphora under climate change. Declarations Ethics approval and consent to participate Not applicable Consent for publication All authors have approved this publication Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by Nanping Academy of Resource Industrialization Chemistry Project (N2023Z007;N2024Z014), Key Project of the Nanping Natural Fund (N2023J004), Key Technological Innovation and Industrialization Project (2023XQ019), and Fujian Provincial Natural Science Foundation Program (2024J01917). Author Contribution RL: Methodology, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. HG: Methodology, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. PC: Conceptualization, Supervision, Validation, Writing – review & editing WW: Investigation, Visualization, Writing – review & editing. ZL: Investigation, Writing – review & editing. QH: Investigation, Writing – review & editing. YH: Investigation, Writing – review & editing. CL: Investigation, Writing – review & editing. XD: Investigation, Writing – review & editing. YS: Investigation, Writing – review & editing. 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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-8832813","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592185442,"identity":"26ce6589-06b8-440b-b2da-395d6702d963","order_by":0,"name":"Ruijun Liu","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Ruijun","middleName":"","lastName":"Liu","suffix":""},{"id":592185443,"identity":"d41fc801-0ba2-47c0-a375-f6a98fc98860","order_by":1,"name":"Hengchuang Gao","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Hengchuang","middleName":"","lastName":"Gao","suffix":""},{"id":592185444,"identity":"7a4d7730-9c3e-4674-84e4-220d54986e38","order_by":2,"name":"Wensheng Wu","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Wensheng","middleName":"","lastName":"Wu","suffix":""},{"id":592185445,"identity":"9e478f41-52a0-4335-b4f4-cecde4512078","order_by":3,"name":"Ziyu Lin","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Ziyu","middleName":"","lastName":"Lin","suffix":""},{"id":592185446,"identity":"a3c3471b-1274-4832-ada4-490c70733fa5","order_by":4,"name":"Qianran Hong","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Qianran","middleName":"","lastName":"Hong","suffix":""},{"id":592185447,"identity":"05a22d9a-44d8-476f-8cac-51326924e41b","order_by":5,"name":"Chengcong Lu","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Chengcong","middleName":"","lastName":"Lu","suffix":""},{"id":592185448,"identity":"eb23abbf-cfa1-4080-9e33-f3625eb2ce3d","order_by":6,"name":"Xi Du","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Du","suffix":""},{"id":592185449,"identity":"b9f753da-f572-4ca7-91b0-71ab4d93ecfb","order_by":7,"name":"Yunzhe Song","email":"","orcid":"","institution":"Wuyi University","correspondingAuthor":false,"prefix":"","firstName":"Yunzhe","middleName":"","lastName":"Song","suffix":""},{"id":592185450,"identity":"1bf4c180-c9cc-4966-9917-2ceb8d47ed15","order_by":8,"name":"Pumo Cai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFACxgYQKQciDsC5xGgxJkULBCTCVBLWwt9+uO0xb45N+oYbOYaHeRhsZDccYH72AJ8WiTOJ7ca829JygVoMgFrSjDccYDM3wKfFgCGxTZp32+HcbRAthxM3HOBhk8Crhf8hWEu6GUTLfyK0SEBsSYBqOUBYi8SNh+2Gc7elGe4/86zg4ByDZOOZh9nM8Grh709/9uDtNht5yfbkzR/eVNjJ9h1vfoZXCxCwQWkOA1BoMDAwE1CPpIX9AWG1o2AUjIJRMCIBAACBS4ShGLNJAAAAAElFTkSuQmCC","orcid":"","institution":"Wuyi University","correspondingAuthor":true,"prefix":"","firstName":"Pumo","middleName":"","lastName":"Cai","suffix":""},{"id":592185451,"identity":"029e25f8-0bad-4a76-bcac-8d5e737148fd","order_by":9,"name":"Yongcong Hong","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yongcong","middleName":"","lastName":"Hong","suffix":""}],"badges":[],"createdAt":"2026-02-09 16:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8832813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8832813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050326,"identity":"3a1ae7e9-5e05-4670-a993-98397aea0994","added_by":"auto","created_at":"2026-02-20 07:49:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214840,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of \u003cem\u003eB. diaphora \u003c/em\u003eoccurrence points.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/3be1c14c7cc0637abd07c6db.png"},{"id":103029190,"identity":"5f053866-1670-4898-ba39-ccd72a5c7085","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80906,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation results of the MaxEnt model under different settings. (a) delta.AICc values; (b) OR10 Values. Legend for feature classes: L = linear, Q = quadratic, H = hinge, P = product,T=threshold.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/4998293e91d181535d5dab7e.jpeg"},{"id":103029195,"identity":"d661d6b0-2818-47a7-9f49-8c2871775d85","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147149,"visible":true,"origin":"","legend":"\u003cp\u003eThe percent contribution (a) and permutation importance (b) of six main bioclimatic variables.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/e03e769f2e48172e38e65753.png"},{"id":103029194,"identity":"3862c53b-c060-417b-b233-6051089c2a47","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92300,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of \u003cem\u003eB. diaphora \u003c/em\u003eunder various key bioclimatic variables.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/203af9c5a5d7205b58214a32.jpeg"},{"id":103050557,"identity":"447ecfd2-d03a-48c1-8ac0-43bbcd1bf9de","added_by":"auto","created_at":"2026-02-20 07:50:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":217759,"visible":true,"origin":"","legend":"\u003cp\u003ePotential suitable areas for \u003cem\u003eB. diaphora \u003c/em\u003eunder China,s current climatic conditions.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/e523743725f3771964126f0a.png"},{"id":103029191,"identity":"ce5d4ea0-56db-4d0f-b8b5-4263de90b5cc","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":164715,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in suitable habitat area for China under different climate change scenarios during the future period.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/bfdc35a67dbc78fcddb69d84.png"},{"id":103050190,"identity":"7b2bd3b7-901a-41d5-a8b8-dd1745da138b","added_by":"auto","created_at":"2026-02-20 07:48:41","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":376877,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in suitable habitat area for China under different climate change scenarios during the future period.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/3f8164efd6f2b1db353b7c67.png"},{"id":103029197,"identity":"a44a7a7a-2d96-456e-9a33-5fc6bcb240aa","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":242720,"visible":true,"origin":"","legend":"\u003cp\u003eShift in the Centroid of the Potential Habitat of \u003cem\u003eB. diaphora \u003c/em\u003eunder Current and Future Climate Scenarios.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/4450cc0ab803413eee19c106.png"},{"id":103051245,"identity":"20b1ea09-675a-445f-945a-beab921247ec","added_by":"auto","created_at":"2026-02-20 07:59:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2561894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/58d90150-f106-42af-8ae8-802d625822d8.pdf"},{"id":103029198,"identity":"141e7a77-374b-481b-ac73-63cdcfc7291c","added_by":"auto","created_at":"2026-02-19 21:37:20","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3115236,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-8832813/v1/1926311db92bbda3f9545727.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Distributional Changes of Bactrocera diaphora (Hendel) (Diptera: Tephritidae) in China Using an Optimized MaxEnt Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eBactrocera\u003c/em\u003e (Zeugodacus) \u003cem\u003ediaphora\u003c/em\u003e (Hendel) (Diptera: Tephritidae) is a quarantine pest that causes considerable damage on a variety of fruit and vegetable crops including citrus, cucumbers, luffa, and navel oranges etc. Damage results primarily from female oviposition: hatched larvae bore into host tissues and remain largely concealed within the fruit, which complicates detection and control. This cryptic habit also facilitates long-distance passive dispersal, primarily via eggs and larvae transported inside infested fruits and via pupae moved with packing materials or vehicles. With the intensification of international trade in fresh produce, \u003cem\u003eB. diaphora\u003c/em\u003e has demonstrated high invasive potential and was listed as a quarantine invasive pest in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].First intercepted at Huizhou Port (Guangdong Province) in 2017, the species has since been detected in multiple provinces and regions, including Hainan, Yunnan, Sichuan, Chongqing, and Taiwan, indicating its capacity for rapid establishment and dispersal. Nevertheless, its overall distribution remains relatively limited due to ongoing official control measures. In Chongqing, outbreaks have mainly affected cucumbers, luffa, and navel oranges production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent management of \u003cem\u003eB. diaphora\u003c/em\u003e in China relies on generalized strategies extrapolated from other \u003cem\u003eBactrocera\u003c/em\u003e species due to a lack of species-specific research. These strategies comprise: (1) monitoring and early warning via food-based lures [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]; (2) quarantine measures to prevent movement through trade [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; (3) adult suppression using mass trapping and attract-and-kill methods [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; (4) biological control, including SIT and natural enemies release [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; and (5) chemical control via bait sprays and soil treatments [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, increasing trap captures in related species, indicate that \u003cem\u003eB. diaphora\u003c/em\u003e could similarly increase in abundance and expand its geographical range [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Given this invasive potential and the likehood of population growth, further research on risk assessment and correspondingly targeted control strategies is urgently needed.\u003c/p\u003e \u003cp\u003eBiological invasions represent an escalating threat to China, where the number of invasive insect species continues to rise, resulting in annual economic losses estimated at 119.876\u0026nbsp;billion yuan [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This challenge is further compounded by global climate change, which is reshaping ecosystems and agricultural sustainability through rising temperatures and altered hydrological regimes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Over recent decades, China has experienced pronounced warming and destabilized agro-ecosystems, heightening the country\u0026rsquo;s vulnerability to invasion risks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Because insects are ectotherms, fluctuations in temperature and moisture directly influence their physiology and phenology, often facilitating range expansion, enhanced overwinter survival, and accelerated development [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While empirical research specifically targeting \u003cem\u003eB. diaphora\u003c/em\u003e remains limited, its tropical origin and ectothermic biology suggest high sensitivity to thermal shifts and significant potential for population outbreaks under warming scenarios. Consequently, proactive, science-based monitoring and risk assessments are essential to anticipate and mitigate the heightened threats posed by \u003cem\u003eB. diaphora\u003c/em\u003e and other emerging pests [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecies Distribution Models (SDMs) provide a quantitative framework to predict potentially suitable habitats by relating occurrence records to environmental variables (e.g., climate, hosts, natural enemies, altitude, and human activities). Among available algorithms such as BIOCLIM, GLM, GARP, ENFA, and MaxEnt, the MaxEnt algorithm, which is based on the principle of maximum entropy, has proven particularly effective for presence only data and limited sample sizes and is widely used to project invasive pest habitat under climate change [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. MaxEnt has consistently demonstrated strong performance in identifying climate-driven distribution patterns and has been validated in studies of species such as \u003cem\u003eGrapholita dimorpha\u003c/em\u003e (Komai) (Lepidoptera: Tortricidae), \u003cem\u003eAnthonomus eugenii\u003c/em\u003e (Cano) (Coleoptera: Curculionidae), \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e (Smith) (Lepidoptera: Noctuidae), \u003cem\u003eTuta absoluta\u003c/em\u003e (Meyrick) (Lepidoptera: Pyralidae), and \u003cem\u003eOphelimus maskelli\u003c/em\u003e (Ashmead) (Hymenoptera: Ichneumonidae) [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These studies have provided essential information for pest risk analysis and have supported the development of scientifically informed, spatially tailored control strategie [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious suitability studies for \u003cem\u003eB. diaphora\u003c/em\u003e have indicated strong thermal and winter-cold constraints but often suffer from methodological limitations, such as coarse-resolution climate data or default MaxEnt settings that can overlook microclimatic heterogeneity and promote model overfitting [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To address these issues, the present study compiles comprehensive occurrence records and high- resolution climate data and uses the ENMeval package in R (v.4.5.0) to optimize MaxEnt feature combinations (FC) and regularization multipliers (RM) based on Akaike Information Criterion (AICc), thereby improving ecological realism and reducing overfitting [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Accordingly, this study aims to (1) map the current and future potential distribution of \u003cem\u003eB. diaphora\u003c/em\u003e across China under multiple climate-change scenarios, and (2) identify the key bioclimatic drivers of its habitat suitability. The results are intended to provide quantitative guidance for surveillance, early warning and regionally targeted management of this quarantine pest.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Collection and Processing of distribution data of \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eWe compiled presence-only occurrence data for \u003cem\u003eB. diaphora\u003c/em\u003e from multiple online biodiversity repositories and the primary literature. Specifically, records were downloaded from GBIF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gbif.org/\u003c/span\u003e\u003cspan address=\"http://www.gbif.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 19 June 2025), Bold Systems v4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.boldsystems.org/\u003c/span\u003e\u003cspan address=\"http://www.boldsystems.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 20 January 2025), iNaturalist (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inaturalist.org/\u003c/span\u003e\u003cspan address=\"https://www.inaturalist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 20 June 2025), CABI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plantwiseplusknowledgebank.org/\u003c/span\u003e\u003cspan address=\"https://plantwiseplusknowledgebank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 20 June 2025), iDigBio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idigbio.org/\u003c/span\u003e\u003cspan address=\"https://www.idigbio.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 20 January 2025), and the National Catalogue of Administrative Regions for the Distribution of Quarantine Pests of Agricultural Plants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.moa.gov.cn/nybgb/\u003c/span\u003e\u003cspan address=\"https://www.moa.gov.cn/nybgb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on 20 June 2025). In parallel, we conducted a structured literature search in Web of Science and CNKI using the standardized taxonomic name \u003cem\u003eBactrocera diaphora\u003c/em\u003e and geographic filters. Duplicate entries, records with missing coordinates, and records located in marine areas were removed, yielding 112 raw occurrence records (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All records were merged and de-duplicated, and geographic coordinates were validated. We excluded records with missing or obviously erroneous coordinates (e.g., zero) and records with ambiguous locality descriptions, those providing only coarse administrative levels (e.g., \"China\" or \"Yunnan Province\") without specific site details (e.g., city or town level), which precluded accurate georeferencing. To reduce spatial sampling bias and to match the resolution of the bioclimatic layers, we applied grid-based spatial thinning in R using the raster and sp packages: occurrences were downsampled so that no more than one presence fell within each ~\u0026thinsp;5 km \u0026times; 5 km grid cell (2.5 arc-minute resolution) of the WorldClim version 2.1 climate surfaces. After these quality-control and thinning procedures, the final dataset comprised 83 georeferenced presences, with the complete set of filtered occurrence and their spatial distribution provided in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.Locations were pinpointed using Google Earth Pro.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Collection and Screening of Bioclimatic Variables in \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eWe obtained the 19 standard bioclimatic variables (Bio1-Bio19) from the WorldClim version 2.1 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) at 2.5 arc-minutes resolution, referenced to the 1970\u0026ndash;2000 baseline. These high-resolution, long-term averages are widely used as standard predictors in species distribution modeling, and we used them as the present-day bioclimatic predictors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Future climate projections were derived from the BCC-CSM2-MR general circulation model (CMIP6) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) for four time slices (2021\u0026ndash;2040, 2041\u0026ndash;2060, 2061\u0026ndash;2080, 2081\u0026ndash;2100). The SSPs span low to very-high greenhouse-gas forcing by 2100 (\u0026asymp;\u0026thinsp;26, 45, 70, and 85 W\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, respectively) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All climate rasters (current and future) were aligned to a common 2.5\u0026prime; grid, clipped to the study extent (the entire land territory of China), and exported in ASCII (ASC) format to ensure spatial and format consistency.\u003c/p\u003e \u003cp\u003eTo minimize multicollinearity and improve model robustness, we screened the 19 bioclimatic variables using a two-step procedure. First, we ran a preliminary MaxEnt 3.4.4 including all variables and occurrence records, and used the jackknife test to evaluate the relative contribution of each variable. Second, we imported the occurrence points and climate data into ArcGIS 10.8 to extract raster values at the occurrence locations,,exported the resulting dataset to SPSS and calculated Pearson correlation coefficients for all variables pairs. Pairs with |r|\u0026ge;0.8 were considered highly correlated (Figure S2) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For each highly correlated pair, the variable with the lower contribution in the preliminary MaxEnt run was excluded. After this stepwise screening, six predictors (Bio2, Bio4, Bio5, Bio15, Bio18, and Bio19) were retained for the final MaxEnt model of \u003cem\u003eB. diaphora\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. MaxEnt Model Optimization and Evaluation\u003c/h2\u003e \u003cp\u003eDefault MaxEnt settings can lead to overfitting and excessive sensitivity to idiosyncrasies in the training data [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To improve model robustness and predictive performance for \u003cem\u003eB. diaphora\u003c/em\u003e, we therefore conducted systematic hyperparameter tuning of MaxEnt (v3.4.4) with the ENMeval package in R 4.5.0. We evaluated eight regularization multiplier (RM) levels (0.5-4.0, in 0.5 increments) and eight feature combinations (FC) constructed from linear (L), quadratic (Q), hinge (H), product (P), and threshold (T) features: L, LQ, LQP, QHP, LQH, LQHP, QHPT, and LQHPT[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These settings produced 64 candidate parameter combinations, each of which was fitted and evaluated in ENMeval. Model complexity and goodness-of-fit were assessed using the corrected Akaike Information Criterion (AICc) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To detect potential overfitting, we also monitored the 10% training omission rate (OR10) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The optimal parameter set was selected as the configuration with the lowest AICc that also satisfied acceptable OR10, thereby balancing model fit and generalizability for subsequent habitat-suitability projections.\u003c/p\u003e \u003cp\u003eUsing the 83 filtered occurrence records of \u003cem\u003eB. diaphora\u003c/em\u003e and the six selected bioclimatic predictors, we fitted MaxEnt with the optimal feature combination (FC) and regularization multiplier (RM) identified during prior tuning. For each run, 75% of occurrences were randomly assigned to the training set and the remaining 25% to an independent test set. Models were executed in ten replicate runs to implement cross-validation. Model settings included a maximum of 5,000 iterations, 10,000 background points, generation of receiver operating characteristic (ROC) curves, logistic output format, and export of predictions in ASC format [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVariable importance was assessed using the jackknife test, and response curves were plotted for all predictors to characterize their individual relationship with habitat suitability. Model accuracy was assessed by the area under the curve (AUC), which ranges from 0.5 to 1.0. We adopted common interpretive thresholds (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9, excellent; 0.8\u0026ndash;0.9, good; 0.7\u0026ndash;0.8, fair; 0.6\u0026ndash;0.7, poor; and \u0026lt;\u0026thinsp;0.5, fail) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Both training and testing AUC values exceeded 0.9, indicating excellent predictive performance and supporting the suitability of the selected bioclimatic variables for projecting the potential distribution of \u003cem\u003eB. diaphora\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Potential Habitats Predictions and Centroid Shifts Evaluation of \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe model suitability index was expressed on a continuous scale from 0 to 1, with larger values indicating greater bioclimatic suitability for \u003cem\u003eB. diaphora\u003c/em\u003e. The mean suitability raster derived from ten MaxEnt replicates was imported into ArcGIS 10.8 and reclassified into four classes using the Jenks natural breaks method via the Reclassify tool: unsuitable [0.00, 0. 12), low [0.12, 0.33), moderate [0.33, 0.57), and high [0.57, 1.00] [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These raster layers were converted to polygons in ArcGIS, and the total area (km\u0026sup2;) of suitable habitat was calculated using the \u0026lsquo;Calculate Geometry\u0026rsquo; tool. The resulting maps delineated the potentially suitable-habitat distribution of \u003cem\u003eB. diaphora\u003c/em\u003e across China.\u003c/p\u003e \u003cp\u003eThe geographic centroid of the predicted suitable area was used as a concise indicator of shifts in species \u0026rsquo; spatial distribution in response to bioclimatic change [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. We performed centroid-shift analysis to quantify spatiotemporal changes in suitable habitat for \u003cem\u003eB. diaphora\u003c/em\u003e under current and future climate scenarios. First, habitat-suitability rasters for each time period were converted to vector polygon layers in ArcGIS 10.8. The resulting polygon layers for the current and future periods were then entered into the Spatial Statistics toolbox, and the Mean Center function (Measuring Geographic Distributions) was used to calculate centroids for each period. Centroid shifts between periods were quantified as distance and direction of movement from the current to future center.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. MaxEnt Model Settings Optimization\u003c/h2\u003e \u003cp\u003eUnder the default MaxEnt parameter settings (RM\u0026thinsp;=\u0026thinsp;1, FC\u0026thinsp;=\u0026thinsp;LQHP), we used the ENMeval package in R to optimally select two key parameters (FC and RM), resulting in the optimal model configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When FC was set to LQP and RM to 0.5, the MaxEnt model achieved the minimum AICc value (i.e., 0). According to the Akaike Information Criterion, this parameter combination minimized both model complexity and overfitting, enabling the most accurate simulations. Additionally, the optimized configuration reduced the average OR₁₀ by 12.22% compared to default parameters.\u003c/p\u003e \u003cp\u003eWith the optimal parameter configuration, the model produced AUC values\u0026thinsp;\u0026gt;\u0026thinsp;0.96 across all climate scenarios, indicating exceptionally high predictive accuracy and robust simulation performance (Receiver operating characteristic (ROC) curve in Figure S3). These results confirm the model\u0026rsquo;s suitability for forecasting \u003cem\u003eB. diaphora\u003c/em\u003e\u0026rsquo;s potential distribution under future climate conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Bioclimatic Factors Influencing Distribution and Response Curves of \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the six bioclimatic variables exhibit different contribution rates and ranked importance in determining \u003cem\u003eB. diaphora\u003c/em\u003e suitable habitats. Among these, precipitation of the warmest quarter (Bio18)-consistent with WorldClim bioclimatic variable definitions-stands out significantly, with a contribution rate of 32% and ranked importance of 8.7%, indicating that this variable explained the largest proportion of model gain during the training process. The remaining the warmest month (Bio5, 28.8%), mean diurnal range (Bio2, 20%), temperature seasonality (Bio4, 13.4%), precipitation of the coldest quarter (Bio19, 5%), and precipitation seasonality (Bio15, 0.8%).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the relationship between \u003cem\u003eB. diaphora\u003c/em\u003e occurrence probability and selected bioclimaticvariables. Using a suitable-habitat threshold of probability\u0026thinsp;\u0026ge;\u0026thinsp;0.33, which was derived from a Natural Breaks (Jenks) classification of the continuous MaxEnt suitability outputs, the optimal intervals were as follows: Bio4 (temperature seasonality): 107.99-892.78; Bio5 (maximum temperature of the warmest month): 24.92\u0026ndash;37.67\u0026deg;C; Bio15 (precipitation seasonality): 47.36-111.45 mm; Bio18 (precipitation of the warmest quarter): 430.38-1441.24 mm. For Bio4, Bio5, Bio15, and Bio18, \u003cem\u003eB. diaphora\u003c/em\u003e occurrence probability initially increases with increasing variable values within the optimal intervals, then decreases after reaching a peak- exhibiting a typical unimodal response.The response curves showed approximately unimodal patterns, reflecting the species\u0026rsquo; tolerance range along each climatic gradient. In habitat suitability modeling for new regions, Bio2 and Bio19 were excluded from analysis due to the absence of closed bell-shaped response curves, which are critical for accurately predicting species \u0026rsquo; uninhabited areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Potentially Suitable Habitat for \u003cem\u003eB. diaphora\u003c/em\u003e Under Current Climatic Conditions\u003c/h2\u003e \u003cp\u003eModel predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e) show that under current climatic conditions, \u003cem\u003eB. diaphora\u003c/em\u003e\u0026rsquo;s potential habitats in China are widely distributed across East, Central, South, and Southwest China, covering a total area of 247.47 \u0026times; 10⁴ km\u0026sup2; (approximately 25.73% of China\u0026rsquo;s national territory).\u003c/p\u003e \u003cp\u003eHighly suitable zones are primarily concentrated in eastern Sichuan, Chongqing, Hubei, Guizhou, Guangxi, Guangdong, and Hainan, covering 42.23 \u0026times; 10⁴ km\u0026sup2; (17.07% of the total suitable area). Moderately suitable zones are mainly located in Shaanxi, Hubei, Hunan, Guizhou, Yunnan, Guangxi, Guangdong, Fujian, and Hainan, covering 59.16 \u0026times; 10⁴ km\u0026sup2; (23.90% of the total suitable area). Low- suitability zones have the largest distribution, encompassing Shandong, Henan, Anhui, Jiangsu, Shaanxi, Hubei, Hunan, Zhejiang, Fujian, Guangdong, Guangxi, and Yunnan, with an area of 146.08 \u0026times; 10⁴ km\u0026sup2; (59.03% of the total suitable area).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Changes in the Potentially Suitable Habitat Area for \u003cem\u003eB. diaphora\u003c/em\u003e Under Future Climate Conditions\u003c/h2\u003e \u003cp\u003eThis study selected four climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) and four future time periods (2030s, 2050s, 2070s, and 2090s) to project \u003cem\u003eB. diaphora\u003c/em\u003e\u0026rsquo;s future suitable habitats. Compared with current conditions, the locations and extents ofits potential habitats in China are projected to change across different future periods (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e; 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\u003eChanges in the suitable habitat area for \u003cem\u003eB. diaphora\u003c/em\u003e under different climate scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDecade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePredicted area(\u0026times;104 km2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eComparison with the current distribution(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurren\u003c/p\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030s\u003c/p\u003e \u003cp\u003e2050s\u003c/p\u003e \u003cp\u003e2070s\u003c/p\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269.80\u003c/p\u003e \u003cp\u003e234.64\u003c/p\u003e \u003cp\u003e293.08\u003c/p\u003e \u003cp\u003e257.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158.45\u003c/p\u003e \u003cp\u003e141.86\u003c/p\u003e \u003cp\u003e125.15\u003c/p\u003e \u003cp\u003e141.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.50\u003c/p\u003e \u003cp\u003e56.73\u003c/p\u003e \u003cp\u003e114.52\u003c/p\u003e \u003cp\u003e67.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.84\u003c/p\u003e \u003cp\u003e36.05\u003c/p\u003e \u003cp\u003e53.41\u003c/p\u003e \u003cp\u003e48.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.02%\u003c/p\u003e \u003cp\u003e-5. 19%\u003c/p\u003e \u003cp\u003e18.43%\u003c/p\u003e \u003cp\u003e3.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.47%\u003c/p\u003e \u003cp\u003e-2.89%\u003c/p\u003e \u003cp\u003e-14.33%\u003c/p\u003e \u003cp\u003e-3.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.03%\u003c/p\u003e \u003cp\u003e-4. 11%\u003c/p\u003e \u003cp\u003e93.59%\u003c/p\u003e \u003cp\u003e13.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10.91%\u003c/p\u003e \u003cp\u003e-14.64%\u003c/p\u003e \u003cp\u003e26.47%\u003c/p\u003e \u003cp\u003e15.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP2-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030s\u003c/p\u003e \u003cp\u003e2050s\u003c/p\u003e \u003cp\u003e2070s\u003c/p\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246.90\u003c/p\u003e \u003cp\u003e279.70\u003c/p\u003e \u003cp\u003e270.02\u003c/p\u003e \u003cp\u003e253.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.49\u003c/p\u003e \u003cp\u003e116.02\u003c/p\u003e \u003cp\u003e125.01\u003c/p\u003e \u003cp\u003e116.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.59\u003c/p\u003e \u003cp\u003e104.23\u003c/p\u003e \u003cp\u003e94.74\u003c/p\u003e \u003cp\u003e84.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.82\u003c/p\u003e \u003cp\u003e59.44\u003c/p\u003e \u003cp\u003e50.28\u003c/p\u003e \u003cp\u003e51.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.23%\u003c/p\u003e \u003cp\u003e13.02%\u003c/p\u003e \u003cp\u003e9. 11%\u003c/p\u003e \u003cp\u003e2.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3. 15%\u003c/p\u003e \u003cp\u003e-20.58%\u003c/p\u003e \u003cp\u003e-14.43%\u003c/p\u003e \u003cp\u003e-19.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9. 19%\u003c/p\u003e \u003cp\u003e76.20%\u003c/p\u003e \u003cp\u003e60. 15%\u003c/p\u003e \u003cp\u003e43.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.35%\u003c/p\u003e \u003cp\u003e40.74%\u003c/p\u003e \u003cp\u003e19.04%\u003c/p\u003e \u003cp\u003e22.47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP3-7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030s\u003c/p\u003e \u003cp\u003e2050s\u003c/p\u003e \u003cp\u003e2070s\u003c/p\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223.56\u003c/p\u003e \u003cp\u003e240.57\u003c/p\u003e \u003cp\u003e252.09\u003c/p\u003e \u003cp\u003e265.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134.46\u003c/p\u003e \u003cp\u003e135.00\u003c/p\u003e \u003cp\u003e140.25\u003c/p\u003e \u003cp\u003e155.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.24\u003c/p\u003e \u003cp\u003e60.57\u003c/p\u003e \u003cp\u003e64.06\u003c/p\u003e \u003cp\u003e57.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.86\u003c/p\u003e \u003cp\u003e45.01\u003c/p\u003e \u003cp\u003e47.78\u003c/p\u003e \u003cp\u003e53.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.66%\u003c/p\u003e \u003cp\u003e-2.79%\u003c/p\u003e \u003cp\u003e1.86%\u003c/p\u003e \u003cp\u003e7.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-7.96%\u003c/p\u003e \u003cp\u003e-7.59%\u003c/p\u003e \u003cp\u003e-3.99%\u003c/p\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-16.76%\u003c/p\u003e \u003cp\u003e2.38%\u003c/p\u003e \u003cp\u003e8.28%\u003c/p\u003e \u003cp\u003e-3.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.62%\u003c/p\u003e \u003cp\u003e6.57%\u003c/p\u003e \u003cp\u003e13. 13%\u003c/p\u003e \u003cp\u003e26.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2030s\u003c/p\u003e \u003cp\u003e2050s\u003c/p\u003e \u003cp\u003e2070s\u003c/p\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e237.31\u003c/p\u003e \u003cp\u003e229.92\u003c/p\u003e \u003cp\u003e232.43\u003c/p\u003e \u003cp\u003e203.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e143.50\u003c/p\u003e \u003cp\u003e138.64\u003c/p\u003e \u003cp\u003e132.22\u003c/p\u003e \u003cp\u003e143.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53.71\u003c/p\u003e \u003cp\u003e50.87\u003c/p\u003e \u003cp\u003e54.47\u003c/p\u003e \u003cp\u003e60.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.11\u003c/p\u003e \u003cp\u003e40.41\u003c/p\u003e \u003cp\u003e45.74\u003c/p\u003e \u003cp\u003e43.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4. 11%\u003c/p\u003e \u003cp\u003e-7.09%\u003c/p\u003e \u003cp\u003e-6.08%\u003c/p\u003e \u003cp\u003e-17.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.77%\u003c/p\u003e \u003cp\u003e-5.09%\u003c/p\u003e \u003cp\u003e-9.49%\u003c/p\u003e \u003cp\u003e-1.96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-9.21%\u003c/p\u003e \u003cp\u003e-14.01%\u003c/p\u003e \u003cp\u003e-7.93%\u003c/p\u003e \u003cp\u003e1.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.04%\u003c/p\u003e \u003cp\u003e-4.33%\u003c/p\u003e \u003cp\u003e8.31%\u003c/p\u003e \u003cp\u003e3.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnder SSP1-2.6: The total suitable habitat area is projected to increase by 9.02% in the 2030s, decrease slightly by 5. 19% in the 2050s, increase significantly by 18.43% in the 2070s, and grow moderately by 3.86% in the 2090s.\u003c/p\u003e \u003cp\u003eUnder SSP2-4.5: The total suitable habitat area remains nearly unchanged in the 2030s (a slight decrease of 0.23%), then increases markedly by 13.02% from the 2050s onward, with continued growth in the 2070s and 2090s (albeit at a slower rate).\u003c/p\u003e \u003cp\u003eUnder SSP3-7.0: The suitable habitat area initially decreases by 9.66% in the 2030s and a further 2.79% in the 2050s, then reverses trend with a modest recovery of 1.86% in the 2070s and a 7.44% increase by the 2090s.\u003c/p\u003e \u003cp\u003eUnder SSP5-8.5: The suitable habitat area exhibits a consistent declining trend throughout the projection period: a 4. 11% decrease in the 2030s, a further 7.09% reduction in the 2050s, a slight recovery to a 6.08% decrease in the 2070s, and a significant decline of 17.87% by the 2090s.\u003c/p\u003e \u003cp\u003eComprehensive analysis indicates that climate warming will significantly affect the \u003cem\u003eB. diaphora\u003c/em\u003e suitable habitat area in the coming decades. Potentially suitable habitats are projected to increase in northern and eastern China. Under SSP1-2.6, the total suitable habitat area is expected to peak at 293.08 \u0026times; 10⁴ km\u0026sup2; in the 2070s. Under SSP2-4.5, the highly suitable area is projected to reach its peak at 59.44 \u0026times; 10⁴ km\u0026sup2; in the 2050s. Under SSP3-7.0, the medium suitable habitat area is projected to reach a minimum of 49.24 \u0026times; 10⁴ km\u0026sup2; in the 2030s. Under SSP5-8.5, the total suitable habitat area is expected to reach its minimum at 203.26 \u0026times; 10⁴ km\u0026sup2; in the 2090s. These projections highlight the profound impact of future climate change on \u003cem\u003eB. diaphora\u003c/em\u003e distribution, emphasizing the need for proactive adaptation and management measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Centroid Change in the Suitable Habitats of \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe current centroid of \u003cem\u003eB. diaphora\u003c/em\u003e potential habitats is located in Tongren City, Guizhou Province (108.32\u0026deg;E, 28.40\u0026deg;N). An analysis of centroid shifts under different climate change scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reveals significant variations in migration directions across the four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) over the four time periods. Despite these variations, a general northward and eastward migration trend is evident.\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\u003eShift in Centroid of Potential Habitats for \u003cem\u003eB. diaphora\u003c/em\u003e under Current and Future Climate Scenarios.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent Centroid Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClimate\u003c/p\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eFuture Centroid Location\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2030s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2050s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2070s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2090s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTongren City\u003c/p\u003e \u003cp\u003e(108.326\u0026deg;E,\u003c/p\u003e \u003cp\u003e28.399\u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP1-2.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXiangxi Tujia\u003c/p\u003e \u003cp\u003eand Miao\u003c/p\u003e \u003cp\u003eAutonomous\u003c/p\u003e \u003cp\u003ePrefecture\u003c/p\u003e \u003cp\u003e(110.235 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.803 \u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuaihua City\u003c/p\u003e \u003cp\u003e(110.628 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.659 \u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTongren City\u003c/p\u003e \u003cp\u003e(108.626 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.177 \u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTongren City\u003c/p\u003e \u003cp\u003e(108.776\u0026deg;E,\u003c/p\u003e \u003cp\u003e28.442 \u0026deg;N)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP2-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXiangxi Tujia\u003c/p\u003e \u003cp\u003eand Miao\u003c/p\u003e \u003cp\u003eAutonomous\u003c/p\u003e \u003cp\u003ePrefecture\u003c/p\u003e \u003cp\u003e(109.384\u0026deg;E,\u003c/p\u003e \u003cp\u003e29.060 \u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eXiangxi Tujia\u003c/p\u003e \u003cp\u003eand Miao\u003c/p\u003e \u003cp\u003eAutonomous\u003c/p\u003e \u003cp\u003ePrefecture\u003c/p\u003e \u003cp\u003e(109.43 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.870\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChongqing City\u003c/p\u003e \u003cp\u003e(108.826\u0026deg;E,\u003c/p\u003e \u003cp\u003e28.646\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChongqing City\u003c/p\u003e \u003cp\u003e(108.588\u0026deg;E,\u003c/p\u003e \u003cp\u003e28.541\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP3-7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChongqing City\u003c/p\u003e \u003cp\u003e(109.259 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.498 \u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChongqing City\u003c/p\u003e \u003cp\u003e(108.827 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.355\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZunyi City\u003c/p\u003e \u003cp\u003e(107.854 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.62\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZunyi City\u003c/p\u003e \u003cp\u003e(107.97 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.691\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP5-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChongqing City (109.032\u0026deg;E, 28.680 \u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTongren City\u003c/p\u003e \u003cp\u003e(108.514\u0026deg;E,\u003c/p\u003e \u003cp\u003e28.493\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZunyi City\u003c/p\u003e \u003cp\u003e(107.643 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.613\u0026deg;N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXiangxi Tujia\u003c/p\u003e \u003cp\u003eand Miao\u003c/p\u003e \u003cp\u003eAutonomous\u003c/p\u003e \u003cp\u003ePrefecture\u003c/p\u003e \u003cp\u003e(110.162 \u0026deg;E,\u003c/p\u003e \u003cp\u003e28.812\u0026deg;N)\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"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Model Optimization and Evaluation\u003c/h2\u003e \u003cp\u003eAccurate prediction of species distributions is essential for assessing invasion risk and predicting potential range expansions under changing climatic conditions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this study, we used the ENMeval package to optimize MaxEnt model complexity through tuning the regularization multiplier and feature combination, thereby reducing overfitting [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. To enhance model reliability, cross-validation and Pearson correlation analysis (|r| \u0026gt; 0.8) were combined to remove redundant predictors, retaining six bioclimatic variables for final calibration [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This optimization effectively improved both the stability and the ecological realism of the model[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Previous work has shown that non-optimized MaxEnt models tend to inflate habitat suitability and perform poorly in spatial transferability, supporting the importance of parameter tuning [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our optimized model, all known occurrence records of \u003cem\u003eB. diaphora\u003c/em\u003e were located within areas predicted as suitable under current climatic conditions, and the AUC values consistently exceeded 0.9, indicating excellent predictive performance [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Taken together, these results confirm that the optimized MaxEnt model provides a reliable basis for evaluating the invasion potential and mapping climatically suitable habitats for \u003cem\u003eB. diaphora\u003c/em\u003e under current and future scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Ecological Implications of Key bioclimatic Variables for \u003cem\u003eB. diaphora\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAs ectotherms, insects are acutely sensitive to hydrothermal fluctuations and extreme climatic events, both of which dictate survival in native habitats and govern the potential for range expansion. For instance, rising temperatures have facilitated the northward expansion of \u003cem\u003eBactrocera dorsalis\u003c/em\u003e (Hendel) in China and augmented the overwintering survival of \u003cem\u003eBactrocera cucurbitae\u003c/em\u003e (Coquillett) in marginal temperate regionsation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Concurrently, altered precipitation patterns have modified the population dynamics and outbreak intensity of other congeners, such as \u003cem\u003eBactrocera tryoni\u003c/em\u003e (Froggatt) across subtropical and Mediterranean climates [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Beyond direct physiological impacts, climate change indirectly modulates insect distributions and plant-insect interactions by altering host- plant phenology. For example, warming-induced advances in olive anthesis and fruit maturation have synchronized earlier oviposition and flight periods in \u003cem\u003eBactrocera oleae\u003c/em\u003e (Gmelin), facilitating its expansion toward higher latitudes and altitudes across the Mediterranean Basin[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our analyses, precipitation of the warmest quarter (Bio18) and maximum temperature of the warmest month (Bio5) emerged as the primary determinants of \u003cem\u003eB. diaphora\u003c/em\u003e\u0026rsquo;s potential distribution, with Bio18 exerting the greatest influence. The modeled optimal precipitation range for Bio18 was 430.38-1441.24 mm. Notably, the dominant cropping systems in Chongqing particularly in the Three Gorges Reservoir, highly overlap with the host range of \u003cem\u003eB. diaphora\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. As a polyphagous pest, it threatens several economically important crops such as citrus, cucurbits, and peaches. Meteorological records for the past two decades indicate that annual precipitation in Chongqing (875.6-1348.2 mm) falls squarely within the modeled optimal range, which reinforces our identification of Chongqing as a highly suitable region for \u003cem\u003eB. diaphora\u003c/em\u003e and highlights an elevated risk to local agriculture [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. These results highlight the importance of temperature-related variables in shaping the potential distribution of \u003cem\u003eB. diaphora\u003c/em\u003e, aligning with previous studies that identifies temperature as a key constraint for tephritid fruit flies [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Among these, Bio5 was the most influential temperature predictor, with a predicted suitability range of 24.92\u0026ndash;37.67\u0026deg;C. Given the scarcity of species-specific data on the thermal biology of \u003cem\u003eB. diaphora\u003c/em\u003e, we compared our results with its congener, \u003cem\u003eB. dorsalis\u003c/em\u003e, which shares similar ecological niches and host associations. The reported permissive range for \u003cem\u003eB. dorsalis\u003c/em\u003e development and reproduction is approximately 15\u0026ndash;34\u0026deg;C, with an optimum of 20\u0026ndash;28\u0026deg;C [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. These thermal traits closely match our predicted suitablility range for \u003cem\u003eB. diaphora\u003c/em\u003e. Furthermore, the modeled upper limit (37.67\u0026deg;C) is consistent with the thermal thresholds of other tephritids like \u003cem\u003eB. oleae\u003c/em\u003e, where extreme heat impairs survival and reproductive performance [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The strong correspondence between these experimental observations and our model-derived threshold suggests that our predictions are biologically grounded rather than mere statistical artifact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Changes in the Suitable Habitat of \u003cem\u003eB. diaphora\u003c/em\u003e Under Current and Future Climate Scenarios\u003c/h2\u003e \u003cp\u003eClimate warming fundamentally alters regional hydrothermal dynamics, thereby modulating the demographic traits of ectothermic insects and heightening the susceptibility of ecosystems to biological invasions [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Within this context, the evaluation of climate-mediated niche dynamics for \u003cem\u003eB. diaphora\u003c/em\u003e is essential for preemptive biosecurity management [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur models indicate that contemporary suitable areas encompass roughly 247.47\u0026times;10⁴ km\u0026sup2; (25.73% of China), characterized by a distinct latitudinal attenuation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As latitude increases, cooler thermal conditions and less favorable moisture regimes progressively limit the survival, development, and reproduction of tephritid fruit flies, resulting in reduced habitat suitability toward northern regions. Although colder conditions currently limit the establishment of \u003cem\u003eB. diaphora\u003c/em\u003e at higher latitudes, there is increasing evidence that tephritid fruit flies may expand poleward as climates warm. For example, ecological niche models for other congeners such as \u003cem\u003eB. tsuneonis\u003c/em\u003e project a northward shift in suitable habitat under future climate scenarios, indicating potential spread into regions that are presently marginal or unsuitable [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Highly suitable habitats, covering about 42.23\u0026times;10⁴ km\u0026sup2;, are predominantly concentrated in eastern Sichuan, Chongqing, Guangxi, Guangdong, and Hainan. These provinces are among the most important fruit- and vegetable-producing regions in China, with extensive cultivation of citrus, cucurbits, and other known host crops of \u003cem\u003eB. diaphora\u003c/em\u003e. The combination of high climatic suitability and abundant host resources suggests a heightened risk of population buildup and outbreaks in these areas, indicating that particular vigilance and strengthened monitoring efforts are warranted. These regions are characterized by a monsoon-influenced climate with abundant host plants (e.g., citrus and cucurbits), thereby providing favorable conditions for establishment and population growth [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModerately to marginally suitable areas extend toward the North China Plain, whereas large portions of northern China are predicted to be unsuitable. These non-suitable regions are characterized by climatic conditions that fall outside the modeled tolerance range of \u003cem\u003eB. diaphora\u003c/em\u003e, particularly colder winter temperatures and stronger seasonal climatic variability[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Overall, the distribution pattern is strongly constrained by regional climatic conditions. The dominant contributions of temperature- and precipitation-related variables in the optimized MaxEnt models demonstrate that climatic suitability alone is sufficient to capture much of the observed and projected distribution of \u003cem\u003eB. diaphora\u003c/em\u003e [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuture climate change is projected to substantially alter the potential distribution of \u003cem\u003eB. diaphora\u003c/em\u003e, but the direction and magnitude of these changes differ markedly among SSP pathways (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e8\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Habitat trends differed by pathway: net expansion was projected under SSP1-2.6 and SSP2-4.5, SSP3-7.0 showed an initial contraction followed by partial recovery; and SSP5-8.5 produced a consistent decline, culminating in a 17.87% reduction by the 2090s. Over the period 2021\u0026ndash;2100, SSP1-2.6 and SSP2-4.5 therefore emerge as the most favorable scenarios for range expansion of \u003cem\u003eB. diaphora\u003c/em\u003e, reflecting a non-linear climatic response in which more extreme warming increasingly exceeds the species\u0026rsquo; climatic tolerance, thereby constraining further expansion. Concurrently, the geographic centroid of suitable habitat shifted northward and eastward, reflecting climate-driven redistribution[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These patterns are consistent with numerous reports that climate change is driving poleward range shifts in insect pests (e.g., \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e, \u003cem\u003eSolenopsis invicta\u003c/em\u003e, and \u003cem\u003eCulex pipiens pallens\u003c/em\u003e) [\u003cspan additionalcitationids=\"CR68\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The divergent trends among SSP pathways highlight the need for scenario-based, regionally targeted surveillance and management strategies that prioritize high-risk areas and incorporate projected climatic changes\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations of This Study\u003c/h2\u003e \u003cp\u003eOur study was constrained to temperature and precipitation-derived bioclimatic variables, and did not incorporate other potentially important determinants of \u003cem\u003eB. diaphora\u003c/em\u003e distribution such as elevation, host plant distribution, vegetation type, land-use patterns and crop-type information, anthropogenic transport pathways, natural- enemy pressure, or the species \u0026rsquo; adaptive evolutionary potential [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Among these limitations, the absence of crop-type and land-use layers is particularly noteworthy. Although agricultural structure strongly influences the realized distribution of herbivorous insects, high-resolution, spatially explicit maps of major host crops (e.g., citrus, cucurbits, peach) are not publicly available for China, and existing agricultural statistics lack georeferenced formats usable for SDMs. This simplification introduces uncertainty into model predictions. In particular, the response curves generated by the MaxEnt quantify the marginal effect of each bioclimatic variable on occurrence probability but do not capture interactions among variables or non-additive biotic processes, which frequently shape insect distributions [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, the climate projections relied primarily on the BCC-CSM2-MR model; although this general circulation model performs well at broad scales, systematic biases in its representation of climatic extremes or precipitation seasonality could propagate into the projected suitability maps. The MaxEnt framework itself relies exclusively on available occurrence records and implicitly assumes that those records represent the species \u0026rsquo; accessible and occupied environmental space [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. If the species \u0026rsquo; realized distribution is limited by dispersal barriers, recent invasion history, or sampling biases, that assumption may be violated and the model may overestimate the area of climatically suitable habitat. Given these limitations, our results should be interpreted as approximations of the species \u0026rsquo; potential (fundamental) niche rather than definitive predictions of realized occupancy [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Future studies could reduce uncertainty and improve applicability by integrating multiple data sources and modeling approaches: include host-plant and land-cover layers, explicitly model dispersal and anthropogenic pathways, incorporate biotic interactions (for example, through joint-species or mechanistic models), use ensembles of climate models to quantify projection uncertainty, and apply occurrence-data cleaning and spatial filtering to mitigate sampling bias [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Complementary field surveys and experimental studies of physiological tolerance and population dynamics would also help validate and refine model outputs for more effective risk assessment and management [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eWe used the optimized MaxEnt model to simulate the potential geographical distribution of \u003cem\u003eB. diaphora\u003c/em\u003e under current climate conditions and four future periods (2030s, 2050s, 2070s, and 2090s) across four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Under present-day climate, the pest was predicted mainly in eastern Sichuan, Chongqing, Hubei, Guizhou, Guangxi, Guangdong, and Hainan, with a highly suitable area of approximately 42.23\u0026times;10\u003csup\u003e4\u003c/sup\u003e km\u0026sup2;. Precipitation of the Warmest Quarter (Bio18) and the Maximum Temperature of Warmest Month (Bio5) were identified as the dominant factors shaping its distribution. Compared to the current climate, the suitable habitat area is projected to expand under the lower-emission scenario (SSP1-2.6 and SSP2-4.5) but to decline progressively under the high-emission scenario (SSP5-8.5). These spatial-temporal projections provide data-driven guidance for prioritizing surveillance and control efforts and contribute new perspectives on the potential dynamics of \u003cem\u003eB. diaphora\u003c/em\u003e under climate change.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eAll authors have approved this publication\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by Nanping Academy of Resource Industrialization Chemistry Project (N2023Z007;N2024Z014), Key Project of the Nanping Natural Fund (N2023J004), Key Technological Innovation and Industrialization Project (2023XQ019), and Fujian Provincial Natural Science Foundation Program (2024J01917).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRL: Methodology, Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. HG: Methodology, Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. PC: Conceptualization, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing WW: Investigation, Visualization, Writing \u0026ndash; review \u0026amp; editing. ZL: Investigation, Writing \u0026ndash; review \u0026amp; editing. QH: Investigation, Writing \u0026ndash; review \u0026amp; editing. YH: Investigation, Writing \u0026ndash; review \u0026amp; editing. CL: Investigation, Writing \u0026ndash; review \u0026amp; editing. XD: Investigation, Writing \u0026ndash; review \u0026amp; editing. YS: Investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the funding agencies for their financial support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYang XJ, Zhang JF, Cheng B, Ding S, Liu XM, Hu Y. First record of \u003cem\u003eBactrocera nigrifacia\u003c/em\u003e Zhang intercepted at Huizhou Port. Plant Quarantine. 2017;31:65. [in Chinese].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi XZ. 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Ecol Lett. 2010;13:1041\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1461-0248.2010.01479.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1461-0248.2010.01479.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"invasive insect, optimized MaxEnt, climate change, Potential distribution, Pest risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-8832813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8832813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eBactrocera\u003c/em\u003e (\u003cem\u003eZeugodacus\u003c/em\u003e) \u003cem\u003ediaphora\u003c/em\u003e (Hendel) is a quarantine invasive pest that causes substantial damage to fruit and vegetable crops. To assess its potential range in China under climate change, we compiled 83 occurrence records and calibrated a MaxEnt model using an optimized feature combination (LQP) and a regularization multiplier (RM\u0026thinsp;=\u0026thinsp;0.5). Model validation yielded a mean AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.96, indicating excellent performance. Using current climate data and four CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) for four future periods (2021\u0026ndash;2040, 2041\u0026ndash;2060, 2061\u0026ndash;2080, and 2081\u0026ndash;2100), we projected current and future suitable habitats in China. Six key bioclimatic variables were retained after Pearson correlation analysis and jackknife testing: mean diurnal temperature range (bio2), temperature seasonality (bio4), maximum temperature of warmest month (bio5), precipitation seasonality (bio15), precipitation of warmest quarter (bio18), and precipitation of coldest quarter (bio19); bio18 and bio5 contributed most (32% and 28.8%, respectively). Under current climate conditions, total suitable habitat covered 25.73% of China (247.47\u0026times;10⁴ km\u0026sup2;), with highly suitable areas concentrated in eastern Sichuan, Chongqing, and the coastal areas of South China. Future projections showed divergent trends among SPPs: total suitable area expanded under SSP1-2.6 and SSP2-4.5 (notably in the 2050s and 2070s), declined then increased under SSP3-7.0, and contracted steadily under SSP5-8.5 (a 17.87% decrease by the 2090s). The current geographical centroid of suitability was located in Tongren, Guizhou (108.326\u0026deg;E, 28.399\u0026deg;N) and is projected to shift northward and eastward. These findings provide a quantitative foundation for targeted monitoring, early warning, and region-specific management of \u003cem\u003eB. diaphora\u003c/em\u003e under future climate scenarios.\u003c/p\u003e","manuscriptTitle":"Predicting Distributional Changes of Bactrocera diaphora (Hendel) (Diptera: Tephritidae) in China Using an Optimized MaxEnt Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 21:37:15","doi":"10.21203/rs.3.rs-8832813/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"63795897515010247873668798330738789842","date":"2026-04-06T05:37:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336126302904064935980594479514882181889","date":"2026-03-04T09:38:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T03:35:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214498807836754943313049142990510033584","date":"2026-02-21T05:19:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T05:43:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-10T22:04:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T05:49:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T05:46:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ecology and Evolution","date":"2026-02-09T16:25:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ecology-and-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evob","sideBox":"Learn more about [BMC Ecology and Evolution](http://bmcevolbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/evob/default.aspx","title":"BMC Ecology and Evolution","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b8d02de2-fef1-405e-a5b1-1bd3e7d041dd","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T21:37:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 21:37:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8832813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8832813","identity":"rs-8832813","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}