{"paper_id":"0006ce1a-7529-483a-9c82-cd8a0817e12e","body_text":"Based on the MaxEnt model, the potential suitable areas for Pythium helicoides in China are predicted | 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 Article Based on the MaxEnt model, the potential suitable areas for Pythium helicoides in China are predicted Yuzhe Kong, Binbin Jiao, Size Dai, Chun Yang, Qing Chen, Tingting Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9173871/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A nationwide assessment of the climatic suitability for Pythium helicoides provides a scientific basis for developing preventive strategies against this pathogen in China. Using the Maximum Entropy (MaxEnt) model combined with ArcGIS, the potential geographic distribution and suitable habitats of P. helicoides were predicted based on 37 occurrence records (from GBIF and literature) and 37 environmental variables. The potential distribution was simulated under current and future climate conditions (2021–2100) across low (SSP1-2.6), medium (SSP3-7.0), and high (SSP5-8.5) emission scenarios. Key environmental variables influencing habitat suitability were identified. The results show: (1) The MaxEnt model performed reliably, with AUC values exceeding 0.9 across all periods. (2) The distribution of suitable areas was mainly affected by Bio14 (precipitation of the driest month), Bio7 (temperature annual range), and elevation, which together contributed 86.2% of the cumulative influence. (3) Under current conditions, suitable habitats were classified into high (37.11×10⁴ km²), medium (65.66×10⁴ km²), and low suitability (87.24×10⁴ km²), with highly suitable areas concentrated in eastern Hainan and throughout Taiwan, characterized by tropical/subtropical monsoon climates. In future scenarios, suitable habitats are projected to occur mainly in tropical, subtropical, and warm temperate regions, with an overall declining trend and a northward shift in latitude. Plains, basin floors, and valleys are highly suitable due to large catchment areas and slow drainage, while windward slopes in mountains are generally unsuitable. However, lower-lying depressions or terraced areas with gentler slopes may form scattered medium-suitable habitats. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Maxent P. helicoides Suitable habitat analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction P. helicoides , an oomycete within the kingdom Oomycota, is a significant plant pathogen. It is mainly distributed in Hebei, Shandong, Henan, Jiangsu, Guangdong, Fujian and other provinces in China, and has attracted considerable attention in agricultural production. This pathogen infects a broad range of hosts and has caused severe damage to numerous economically important crops. This pathogen primarily infects a variety of economically important aquatic and terrestrial crops, including Fragaria × ananassa , Nelumbo nucifera , and Brasenia schreberi J. F. Gmel., leading to diseases such as root rot and stem base rot. This limitation severely reduces crop yield and quality, resulting in substantial economic losses in agricultural production. Morphologically, the colonies on CMA medium exhibit a radial pattern with cottony aerial mycelia. The mycelia are well-developed and highly branched, measuring 1.5–9.2 µm in diameter. Sporangia are predominantly ovoid, rarely nearly spherical, and possess a single apical pore; intercalary sporangia occur occasionally. Nearly spherical sporangia range from 17 to 36 µm in diameter. It should be noted that this species has recently been reclassified as Phytopythium helicoides based on phylogenetic studies; however, as the majority of occurrence records obtained from GBIF and the literature are archived under Pythium helicoides , and to maintain consistency with the input data and existing references, the name Pythium helicoides is retained throughout this manuscript. This terminology choice does not affect the taxonomic identity or the validity of the distribution modeling. The ecological niche model (ENM) serves as an important tool in species distribution modeling and biogeography research. This model can predict potentially suitable areas for a species, evaluate habitat suitability, and infer potential transmission pathways based on known species distribution data and relevant environmental variables( 1 ). Among various ecological niche models, the MaxEnt model has become a mainstream method for species habitat analysis because it maintains high prediction accuracy and stability even with relatively limited data. Compared to earlier models such as Climex( 2 ), Bioclim( 3 ), and Garp( 4 ), the MaxEnt model exhibits greater adaptability and explanatory power in analyzing relationships between species distribution data and environmental factors, especially under limited sample sizes. In recent years, the MaxEnt model has been widely used to predict species’ potential suitable habitats, assess alien species invasion risks, and evaluate the impacts of climate change on biodiversity( 5 ). It has also yielded satisfactory results in predicting the distribution and suitable habitats of species such as tangerine pomelo( 6 ), kiwi fruit( 7 ), breadfruit( 8 ), locust tree( 9 ), and wolfberry( 10 ), demonstrating the model’s good applicability for predicting geographically suitable habitats of species in China. Currently, systematic studies predicting potential suitable areas for P. helicoides in China remain relatively limited. However, clarifying the current and future spatial distribution patterns of this pathogen is crucial for early warning, targeted regional prevention and control, and the development of quarantine strategies. Based on 37 valid distribution records collected across China and 11 key environmental variables, this study employed the MaxEnt model alongside GIS spatial analysis to simulate potential suitable areas for P. helicoides under current and future climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for four periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The main environmental factors influencing its distribution were also identified, providing a scientific basis for monitoring, early warning, regional management, and ecological risk control of this pathogen ( 11 ). Despite the recognized impact of P. helicoides on key agricultural systems in China, a comprehensive, spatially explicit assessment of its current and future climatic suitability at the national scale remains scarce( 12 ). Most existing studies have focused on local outbreaks or specific host–pathogen interactions, with limited integration of multi-environmental predictors under climate change scenarios. Such a gap hinders the development of proactive and regionally tailored management strategies( 13 ). To address this, the present study employs the Maximum Entropy (MaxEnt) model, coupled with Geographic Information System (GIS) spatial analysis, to ( 1 ) identify the key environmental drivers shaping the current distribution of P. helicoides in China( 14 ), ( 2 ) predict its potential suitable habitats under current and future climate scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) across four time periods (2021–2040 to 2081–2100)( 15 ), and ( 3 ) quantify shifts in suitable areas and distribution centroids under climate change( 16 ). By integrating climatic, topographic, and edaphic variables, this work aims to provide a high-resolution, forward-looking perspective on the pathogen’s biogeographic dynamics( 17 ). The findings are expected to offer a scientific basis for early warning systems, regional quarantine policies, and targeted mitigation measures, thereby supporting sustainable agriculture and forest management in southern China and beyond( 18 ). It should be noted that the model outputs generated in this study represent environmental suitability for Pythium helicoides based on climatic and environmental variables, rather than direct estimates of disease occurrence probability or epidemic intensity. The term “risk” used herein refers to spatial prioritization derived from suitability maps, serving as an extension for management implications. While true disease risk assessment would ideally incorporate additional biological and socioeconomic factors such as host distribution, cropping patterns, or trade pathways—which are not included in the current modeling framework—this study focuses primarily on the geospatial dimension of potential suitability. These limitations are acknowledged in the Discussion, and the findings are intended to provide a preliminary spatial guide for monitoring and quarantine planning, rather than a comprehensive risk forecast. This study provides information on environmental suitability and potential risks, and the risk management recommendations are derived from the suitability maps. 2. Materials and methods 2.1. Species occurrence data The geographical distribution records of P. helicoides were obtained from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/ ) and relevant published literature. A total of 37 occurrence records were collected, predominantly located in eastern China. To minimize spatial autocorrelation and sampling bias, a two-step spatial filtering procedure was implemented in ArcGIS 10.8: Spatial thinning: Given the spatial resolution of the climate data (approx. 5 km), a 5 km × 5 km grid was overlaid on the occurrence points. Within each grid cell, only one record was retained to ensure spatial independence. Bias correction using Target-Group Background (TGB): To account for potential sampling bias (e.g., uneven survey effort), background points were selected from occurrence records of closely related Pythium species within the same geographic region. This approach helps to create a more ecologically meaningful background for model training. 2.2 Data acquisition and processing A total of 37 environmental variables were initially considered, including 19 bioclimatic variables from WorldClim version 2.1 ( http://www.worldclim.org ), The data used are from WorldClim and the spatial resolution is 2.5′. 3 topographic variables (elevation, slope, aspect) from the same source, and 15 soil variables from the FAO Soil Database ( https://www.fao.org/faostat/ ). Future climate projections for four periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5) were also downloaded. Variable screening was performed to avoid multicollinearity and overfitting. Pearson correlation coefficients were calculated for all variable pairs. When \\(\\:\\mid\\:r\\mid\\:\\ge\\:0.85\\) , the variable with lower contribution in a preliminary MaxEnt run was excluded. After screening, 11 variables with low correlation and high ecological relevance were retained for final modeling (Table 2 )( 19 ). 2.3. Study area and background extent (M region) The study area (M region) was defined as the geographic extent within which the species is presumed to be able to disperse and establish under current and future climatic conditions and based on the buffer zone of the known distribution points (500 km) and the superimposition with the related ecological areas, an ecologically reasonable area has been obtained. Based on the known distribution of P. helicoides and its host plants in China, we delineated the M region as mainland China plus Taiwan and Hainan Island, bounded by 73°–135°E and 18°–54°N. This region encompasses all known occurrence points and represents a plausible accessible area for the pathogen under natural and human-assisted dispersal scenarios. 2.4. Background point selection strategy To improve model realism and reduce geographic bias, we adopted a target-group background (TGB) approach. Background points (n = 10,000) were randomly sampled from the occurrence records of multiple Pythium species within the same genus and geographic region, obtained from GBIF. The background points are derived from the distribution records of the same species (Pythium) in East Asia, thereby matching the sampling efforts and enabling the model to focus on differentiating environmental suitability rather than sampling bias.This strategy ensures that background points reflect similar survey effort and environmental coverage as the presence data, thereby reducing model overfitting to sampling artifacts. 2.5 Construction of the MaxEnt model The screened geographical distribution data of P. helicoides and corresponding environmental variables were imported into Maxent v.3.4.1. Twenty-five percent of the species occurrence records were randomly selected as the test dataset. The model was run 10 times, and the ROC curve was generated. The replication method was configured as \"Bootstrap\". The prediction results were output in logistic format as an ASC file ( 20 ), and the contributions of environmental variables to the model were evaluated. All other parameters remained at their default settings. 2.4 Model accuracy testing The area under the curve (AUC) value for the ROC curve was calculated using MaxEnt software. An AUC value ≤ 0.5 suggests a reverse prediction (i.e., a negative correlation between observed and predicted values), ≤ 0.6 indicates failure, ≤ 0.7 reflects poor performance, and ≤ 0.8 denotes acceptable performance, an AUC value ≤ 0.9 indicates good performance, while a value > 0.9 indicates favourable performance. Finally, environmental factors were selected based on their contribution rate and correlation. Specifically, factors with an absolute correlation coefficient (|r|) ≥ 0.85 and the factors with a higher contribution rate were retained for further analysis. Among these, temperature annual range (BIO7), precipitation of the wettest month (BIO13), precipitation of the driest month (BIO14), and precipitation seasonality coefficient (BIO15) are the 11 environmental variables with relatively high contribution rates selected for subsequent model construction ( 21 ). 2.5 Optimization of the MaxEnt Model The unoptimized MaxEnt model is prone to prediction errors, which may compromise the transferability of results. To improve model accuracy and reproducibility, the following systematic optimization procedure was adopted in this study: Variable Screening Prior to modeling, environmental variables were preprocessed to remove highly correlated variables (Pearson correlation coefficient > 0.8), ensuring variable independence and reducing the risk of overfitting. Parameter Optimization The R package ENMeval was used to tune the feature class (FC) and regularization multiplier (RM). Specific settings were as follows Feature classes Twelve common combinations were tested, including L (linear), Q (quadratic), H (hinge), P (product), and T (threshold) in various combinations (e.g., H, L, LQ, LQH, LQHP, LQHPT, LQP, LQPT, etc.). Regularization multipliers Twelve values (0.5, 1.0, …, 6.0) were tested, resulting in a total of 144 candidate models. Model run settings The model was trained for 50,000 iterations using 10,000 background points, with 10-fold cross-validation. A random selection of 75% of the data was used as the training set, while the remaining 25% was reserved for testing. The final results are presented as mean values. Model selection was based on the difference between training and testing AUC (AUC diff) and the 10% training omission rate (OR10), with the model exhibiting the smallest ΔAICc value chosen as the optimal model ( 22 )(random seed). 2.6 Classification of suitable areas The output results of the MaxEnt model were imported into ArcGIS 10.8 and reclassified according to the specified format to predict the potential suitable areas for P. helicoides . Combined with the geographical distribution data, suitability levels were classified into four categories using the natural breakpoint method: non-suitable area (0–0.078), low-suitable area (0.078–0.249), medium-suitable area (0.249–0.462), and high-suitable area (0.462–1.000). The areas of these zones were then calculated ( 23 ). 2.7 Changes in potential suitable areas and center of gravity shift Using ArcGIS, the prediction results were binarized and reclassified to distinguish suitable areas from unsuitable ones(10% training percentile threshold). Subsequently, the raster data was converted to polygons with the \"create multipart feature\" option enabled, followed by an intersect operation for overlay analysis ( 24 ). Finally, the polygon-to-raster conversion method was applied to quantify changes in potentially suitable areas. Based on projected shifts in species distributions under climate change, the grid cells were categorized as: contraction areas (currently occupied but potentially lost in the future), expansion areas (currently unoccupied but potentially gained in the future), and stable areas (persistent under both current and future conditions). ArcGIS was employed to visualize the compositional changes in P. helicoides . A comparison of its current and projected future distributions reveals clear trends in the shifts of its range. The \"Average Center\" tool within the spatial statistics module was used to compute the central position, migration direction, and displacement distance of P. helicoides across different periods. To some extent, the shift in geographic distribution centers reflects how future climate conditions may affect P. helicoides ( 25 ). 3. Results and analysis 3.1 Model performance results The model parameters show that the LQHPT combination with an RM value of 2.5 yields the lowest delta AICc (Fig. 1 ), and was therefore selected for the final model. The ROC curve was derived from the MaxEnt model simulation results. The feature curve achieved an AUC of 0.960 (Fig. 2 ), demonstrating strong discriminative ability of the model. The AUC (Area Under the Curve) value ranges from 0 to 1, with a higher value indicating greater model accuracy and reliability. An AUC below 0.6 is generally considered to reflect an unqualified prediction performance; the predictive performance is categorized as follows: poor for 0.6 ≤ AUC < 0.7, average for 0.7 ≤ AUC < 0.8, good for 0.8 ≤ AUC < 0.9, and favourable for 0.9 ≤ AUC < 1.0. An AUC of 1 indicates a perfect match between predicted and actual values. The results demonstrate an AUC of 0.960 for the ROC curve, indicating strong model performance in analysis and prediction ( 26 ). The predictive accuracy of the MaxEnt model was assessed using the AUC value, statistical significance, AIC value, and a 5% training omission rate. The AUC values exceeded 0.9, with a delta AICc of 0 and a 5% training omission rate of 0 (Table 1 ). The suitability predicted by the model based on the above data is both credible and accurate ( 27 ), making it applicable for forecasting the potentially suitable areas for P. helicoides . Table 1 AUC-based Predictions of P. helicoides Habitat under Climate Scenarios Climate change scenario Year AUC value Current 1970–2000 0.974 2021–2040 0.976 2041–2060 0.978 scenario SSP1-2.6 2061–2080 0.976 2081–2100 0.977 2021–2040 0.975 2041–2060 0.979 scenario SSP3-7.0 2061–2080 0.978 2081–2100 0.978 2021–2040 0.979 2041–2060 0.975 scenario SSP5-8.5 2061–2080 0.979 2081–2100 0.976 3.2 Selection of key environmental factors The jackknife test results indicate that, when considered individually, the regularization training gain scores for Bio7 (annual temperature range), Bio13 (precipitation of the wettest month), Bio14 (precipitation of the driest month), and elev (elevation) all exceed 0.80 (Fig. 3 ). This suggests that these four environmental variables exert the greatest influence on the distribution of P. helicoides . Based on the contribution rate and Jackknife test results, four key climate variables were selected and used to plot response curves.The key environmental variables influencing the distribution of P. helicoides are as follows: annual temperature range 9.32–35.09°C, precipitation of the wettest month 196.24–802.61 mm, precipitation of the driest month 19.18–210.11 mm, and elevation − 684.91 to 394.13 m. A. Jackknife analysis result. B. Annual range of temperature. C. The monthly precipitation of the wettest month. D. The lowest monthly precipitation. E. Elevation. 3.3 Key environmental drivers of P. helicoides distribution Combining Pearson correlation analysis(Fig. 4 ) with model contribution rate, 11 environmental variables were selected (Table 1 ). Through MaxEnt modeling, simulations at multiple temporal scales were conducted to assess potential suitable areas for P. helicoides . The results indicate that its distribution pattern is significantly influenced by climatic factors. Among these, Bio14 (minimum monthly precipitation), Bio7 (annual temperature range), and elev (elevation) exhibited the highest relative contributions, collectively accounting for 86.2% of the total—significantly greater than the sum of the remaining environmental factors( 28 ). The ranking results indicate that Bio14 (lowest monthly precipitation), elev (elevation), and t_gravel (percentage of crushed stone volume) are the primary variables, accounting for a cumulative importance of 81.6%. From this analysis, it is evident that Bio14 (minimum monthly precipitation) and elev (elevation) are the principal environmental factors shaping the geographic distribution of P. helicoides under current climatic conditions ( 29 ). Table 2 Key environmental drivers in MaxEnt modeling Environmental variable Decription Perent contribution Permutation importance Bio14 The lowest monthly precipitation 72.4 35.8 Bio7 Annual range of temperature 8.1 5.3 elev elevation 5.7 32 t_gravel Percentage of crushed stone volume 5.4 13.8 Bio13 The monthly precipitation of the wettest month 3.2 4.2 t_texture Topsoil texture 1.9 2.5 slope Slope gradient 1.9 2.1 t_esp Slope orientation 0.4 1.8 bio15 Seasonal variation of precipitation 0.4 0.9 aspect Aspect 0.3 1 t_silt Silt content 0.2 0.6 3.4 Present suitability map for P. helicoides The current suitable distribution area of P. helicoides was predicted using the MaxEnt model (Fig. 5 ). The current total suitable area for P. helicoides is 1,900,100 km², accounting for 19.79% of China’s total land area. The total suitable area comprises 87.24 × 10⁴ km² of low suitability, 65.66 × 10⁴ km² of medium suitability, and 37.11 × 10⁴ km² of high suitability. P. helicoides is primarily distributed in low-suitability regions of China, occupying approximately 9.91% of the total land area. It is primarily distributed in western Hebei, Shandong, eastern Henan, central and eastern Hubei, eastern Sichuan, Chongqing, and southern Guizhou. Areas of moderate severity account for 6.84% of the country's land area, primarily distributed in Beijing, Tianjin, Hebei, Zhejiang, Anhui, central Guangxi, central Guangdong, and western Hunan. The highly suitable areas, comprising 3.87% of the country's land area, are primarily concentrated in eastern Hainan and Taiwan, demonstrating a distinct pattern of geographical clustering ( 30 ). From the perspective of climate zones, suitable habitats are primarily distributed in tropical and subtropical monsoon climate regions. Note The map is based on the standard map of GS (2022) 1873, and the map has not been modified. 3.5 Future climate distribution of P. helicoides Table 3 summarizes the total suitable area and its grade composition in four periods (2021–2040, 2041–2060, 2061–2080, 2081–2100) under three SSP scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5). Generally speaking, compared with the current situation, the total suitable area in all scenarios shows a downward trend in the future, and the reduction in high and medium suitable areas is particularly significant. Under future climate scenarios, the suitable habitat range of P. helicoides in China is projected to gradually contract spatially, while unsuitable areas are expected to expand accordingly ( 31 ). Table 3 Habitat shifts of P. helicoides under climate change scenarios Climate scenario Area Unit(×10 4 km 2 ) Unsuitable habitat Sightly suitable habitat habitat Moderately habitat habitat Highly suitable habitat Total suitable habitat SSP1-2.6 2021–2040 755.03 87.49 62.84 33.08 183.41 SSP1-2.6 2041–2060 772.90 82.75 52.15 30.64 165.54 SSP1-2.6 2061–2080 755.81 90.75 57.13 34.75 182.63 SSP1-2.6 2081–2100 762.40 85.53 53.73 36.77 176.03 SSP3-7.0 2021–2040 739.56 86.49 67.00 45.38 198.87 SSP3-7.0 2041–2060 774.10 79.93 54.65 29.76 164.34 SSP3-7.0 2061–2080 756.34 94.19 54.12 33.77 182.08 SSP3-7.0 2081 − 2040 761.67 91.59 53.03 32.15 176.77 SSP5-8.5 2021–2040 752.47 90.99 60.65 34.32 185.96 SSP5-8.5 2041–2060 753.97 88.94 60.73 34.80 184.47 SSP5-8.5 2061–2080 748.46 92.86 58.32 38.79 189.97 SSP5-8.5 2081–2100 763.71 84.95 56.80 32.99 174.74 The current suitable habitat area is 1.9001×10 6 km². Under future stress scenarios, this area is projected to decline. Under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios, the suitable habitat area gradually declines (Fig. 6 ). Under the SSP1-2.6 scenario, the suitable habitat area first decreased to 165.54 × 10⁴ km² before expanding to 176.04 × 10⁴ km². Under the SSP3-7.0 scenario, it initially declined to 164.34 × 10⁴ km² and then increased to 176.77 × 10⁴ km² (Table 3 ). However, the extent of both moderately and highly suitable habitats decreased across all scenarios. From 2021 to 2040 under SSP1‑2.6, moderately to highly suitable areas were predominantly located in the middle – upper Yangtze River Plain (Jianghan–Dongting–Poyang Lake floodplain; 30°–32°N, 113°–120°E) and in the Pearl River Delta (23°–25°N, 110°–115°E). Under the SSP3‑7.0 and SSP5‑8.5 scenarios, the initial distribution resembled that of SSP1‑2.6, but highly suitable areas became more fragmented. Under the SSP1-2.6 scenario from 2041 to 2060, highly suitable areas extended westward along the Yangtze River into the eastern Sichuan Basin (30°N, 105°–107°E) and eastward toward the coastal regions of the Yangtze River Delta. The northern boundary of the red zone in the Pearl River Estuary extends across the Nanling Mountains, reaching approximately 25°N in southern Hunan. Under the SSP3-7.0 scenario, the area of high suitability was expected to expand; however, it contracted northward instead—specifically, the orange zone north of 32°N disappeared, while the red zone in the Sichuan Basin decreased by more than 30%. Under the SSP5-8.5 scenario, the high-suitability area exhibited a northward shift: the Shandong Peninsula (36°N, 118°–122°E) transitioned to high suitability, while coastal regions in southern China shifted from high- to low-suitability areas. From 2061 to 2080, expansion peaked under the SSP1-2.6 scenario. Under the SSP3-7.0 scenario, the overall suitable area shifted southward by 1–1.5 degrees of latitude in mid-to-high latitudes, from approximately 32°N to around 30.5°N. In the coastal areas of southern China, increased frequency of extreme summer temperatures (> 35°C) has caused a shift in suitability zones, with high suitability areas being replaced by medium suitability areas, and medium suitability areas by low suitability areas. In the SSP5-85 scenario, mid-to-high altitude settlement areas extend eastward along the 35°N latitude to the Korean Peninsula border, and westward into the Guanzhong–Tianshui Basin (34°–35°N, 105°–108°E). Under the SSP1-2.6 scenario, the suitable habitat area during 2081–2100 remained nearly unchanged compared to that in 2061–2080, with no notable shift in spatial distribution. This stability can be attributed to temperature and precipitation increases approaching their upper limits under low radiative forcing by the 2080s, leading to a sustained equilibrium in habitat suitability for the species. Under the SSP3-7.0 scenario, the suitable mid- and high-altitude habitats are reduced to the hilly basin of \"Xiangjiang–Northern Guangxi–Northern Guangdong,\" situated between 24°–30°N and 108°–118°E. The area decreased by approximately 25% relative to the 2061–2080 period. Under medium forcing scenarios, temperature increases significantly, while precipitation changes exhibit greater variability. High temperatures inhibit mycelial growth, while rapid drainage after heavy rain reduces habitat suitability in the formerly humid Pearl River Estuary and Yangtze River Delta regions. Under the SSP5-8.5 scenario, the northernmost extent of the highly suitable zone shifts to the region between 33.5°N–36°N and 105°E–122°E, encompassing the Huang-Huai-Hai Plain and the southern portion of the Shandong-Liaoning Peninsula. The southern region (22°–26°N) is almost entirely unsuitable, leading to a \"north–south inversion\" phenomenon. Under high emissions, northern precipitation increases by 15–20%, and winter temperatures rise by 4–5°C. This prevents soil freezing during winter dormancy and leads to a spring ground temperature increase exceeding 15°C, conditions that favor bacterial overwintering and subsequent spring outbreaks. In contrast, extreme heat and drought in southern regions suppress bacterial activity. All scenarios (Fig. 7 ) indicate a contraction of suitable habitat areas south of 25°N. This provides a counterexample to the \"climate refuge\" concept, demonstrating that not all warming events result in northward disease migration; instead, suitability may decline in tropical marginal zones due to threshold exceedance. Table 4 quantifies the dynamic changes of each scenario compared to the current suitable area. The overall trend is characterized by \"overall contraction and local northward expansion\". Under the SSP1-2.6 scenario: The suitable areas exhibit a \"contraction - recovery - re-contraction\" fluctuation pattern. The expansion areas mainly occur in the middle and lower reaches of the Yangtze River and the Pearl River Delta.The spatial change is characterized by a slow northward movement of the boundaries within a stable core area. The Jianghan Plain and the middle and lower reaches of the Yangtze River have always been stable and contiguous high suitability core regions. The northward movement is mainly manifested as the gradual and slow northward expansion of the distribution boundary. By the end of this century, the north boundary of the suitable area will move from the current Huai River line to the southern part of the Huai-Huai region, and sporadic medium and low suitability patches will begin to appear on the Shandong Peninsula. The south boundary remains relatively stable, and the high suitability areas in Hainan and Taiwan are maintained. This pattern is a relatively mild, gradual expansion based on the original core area. Under the SSP3-7.0 scenario:In the initial period (2021–2040), the expansion was significant; in the middle period (2041–2060), the contraction was intense; in the later period, there was a recovery, but the total area was lower than the current level.The spatial pattern evolution shows the formation and strengthening of a \"mid-latitude expansion corridor\". The most notable feature is the formation of a stable and continuous expansion area within the band from 30° to 35°N (including the southern part of North China, the Guanzhong Plain, and the Han River Valley), which is called the \"mid-latitude expansion corridor\". This area has been relieved of the low-temperature constraint due to the warming in winter, and the water and heat conditions in summer still remain within the tolerance window of pathogenic bacteria, thus becoming a new favorable habitat hotspot. At the same time, the contraction in the south is extremely intense. The favorable habitat grades in the traditional high-incidence areas of South China have generally declined, and the fragmented or even disappeared high-favorable areas. The final result of the pattern evolution is a significant northward shift of the spatial center of the favorable habitats, forming a \"dual-core north-south\" or \"strong north and weak south\" new pattern. Under the SSP5-8.5 scenario:Both contraction and expansion occur simultaneously. In the middle and later stages (2061–2080), there is a significant northward expansion in the North China Plain, the southern part of Northeast China, and the northern foothills of the Tianshan Mountains in Xinjiang. The spatial changes are the most dramatic, presenting a pattern reversal of \"north-south inversion\". By the end of this century, the traditional high-suited areas - most of South China (22°–26°N) - will shrink to areas that are not suitable or only moderately suitable, while the North China Plain, the Huaihai region, and the southern part of the Liaodong Peninsula (33.5°–38°N) will transform into contiguous high-suited areas. The northern boundary of the suitable areas will shift significantly northward, reaching as far as the Liaohe River Basin in Northeast China (approximately 42°N). The mountain-front oases in the northern part of the Tianshan Mountains in Xinjiang have also emerged new suitable patches due to changes in the water and heat combinations. This \"northward advance and southward retreat\" reversal pattern is mainly caused by the deterioration of habitats due to extreme high-temperature heat waves and changes in precipitation patterns in the south, while the significant increase in winter temperatures and precipitation in the north has created unprecedented overwintering and growth conditions. Under this scenario, the northward shift is no longer the movement of boundaries, but a spatial replacement of the entire suitable area, which has a overturning impact on the pattern of agricultural pest and disease control. 3.6 Centroid migration of P. helicoides Analysis of the distribution changes of P. helicoides across past, present, and future periods indicates that, under different climate scenarios, the migration distance and direction of its suitable habitat centroid vary. Overall, however, a southward shift is projected over time. The change in the center of mass is influenced by the area weight. The area in the north has increased while that in the south has decreased more significantly(Table 4 ). The center of mass may still shift southward.The suitable habitat for P. helicoides is currently concentrated in the core area of Wuchang District, Wuhan City, with a central coordinate of 114°18′E, 30°36′N (Fig. 8 ). Under the SSP1-2.6 scenario, the centroid of the suitable area shifted initially toward the southwest, followed by an eastward movement. Under SSP3-7.0, the centroid moved first eastward then westward, ultimately remaining within Wuhan City (114°218′E, 30°659′N). Under SSP5-8.5, the centroid shifted eastward and was finally located in Ezhou City (114°349′E, 30°468′N). Table 4 Future climate suitability projections for P. helicoides Decades scenarios Predicted area Unit:(×10 4 km 2 ) Suitable Contraction Gain 2021–2040 SSP1-2.6 vs current 172.804 11.005 10.380 2041–2060 SSP1-2.6 vs current 161.250 22.498 4.619 2061–2080 SSP1-2.6 vs current 172.493 11.347 9.949 2081–2100 SSP1-2.6 vs current 168.389 15.418 7.256 2021–2040 SSP3-7.0 vs current 178.929 4.974 19.304 2041–2060 SSP3-7.0 vs current 161.517 22.252 2.670 2061–2080 SSP3-7.0 vs current 169.203 14.616 12.635 2081–2100 SSP3-7.0 vs current 169.158 14.597 7.651 2021–2040 SSP5-8.5 vs current 174.012 9.813 11.597 2041–2060 SSP5-8.5 vs current 173.517 10.347 10.849 2061–2080 SSP5-8.5 vs current 173.042 10.800 16.842 2081–2100 SSP5-8.5 vs current 168.188 15.599 6.701 4. Discussion 4.1. Dominance of Bio14: The dry-month precipitation bottleneck Our results identify precipitation of the driest month (Bio14) as the predominant factor governing the distribution of P. helicoides , contributing 72.4% to the model. This contrasts with studies on other Pythium species (e.g., P. aphanidermatum ), which are often more limited by growing-season moisture (Bio18) or mean temperature. The critical role of Bio14 underscores a survival bottleneck specific to the East Asian monsoon climate: the winter-spring drought period. As an oomycete, P. helicoides depends on persistent soil moisture for oospore survival and germination. Bio14 directly determines the severity and duration of soil moisture deficit during this vulnerable stage. Regions with moderate dry-season rainfall (e.g., eastern Hainan and Taiwan) retain sufficient residual moisture to sustain the pathogen, whereas areas with pronounced winter-spring drought cannot support stable populations, even under humid summer conditions. This insight shifts the monitoring focus from total annual precipitation to seasonal moisture stress and suggests that irrigation or soil moisture conservation during dry intervals could mitigate establishment in marginal zones. 4.2 Southern contraction under high-emission scenarios Contrary to the expectation that warming uniformly drives poleward expansion, our projections under SSP5-8.5 show a pronounced contraction of suitable habitat south of 25°N. This “southern loss” occurs because future climate conditions in these tropical margins exceed the pathogen’s thermal and hydrological tolerance thresholds. Increasing frequency of extreme summer temperatures (> 35°C) likely inhibits mycelial growth, while more intense but episodic rainfall enhances surface runoff, shortening the soil saturation period required for infection. Consequently, combined heat stress and altered hydrology render these regions unsuitable for P. helicoides establishment. This pattern aligns with the “high-temperature escape” hypothesis noted in other pathosystems. Practically, traditional disease hotspots in South China may experience reduced pathogen pressure under high-emission futures, permitting a strategic reallocation of monitoring resources. 4.3 Local expansion within overall contraction Although the total suitable area for P. helicoides is projected to decline across all scenarios, we detected notable northward expansion into specific regions such as the Huang-Huai Plain, Jianghan Basin, and parts of Northeast China. This “local expansion amid overall contraction” arises because climate change differentially alleviates former constraints in these areas: warmer winters reduce cold-induced mortality, and modified precipitation regimes provide adequate soil moisture during key infection windows without the extreme drought or heat stress observed in the south. However, these newly suitable habitats are often fragmented and cannot fully offset losses in core southern areas. The result is a more fragmented and dynamic risk landscape. Enhanced surveillance and quarantine measures are therefore warranted along major agricultural corridors in northern China, where the pathogen could colonize previously unaffected cropping systems. 4.4 Differences and similarities with traditional views The conventional view suggests that climate warming drives pathogenic bacteria toward higher latitudes, creating \"climate refuges.\" However, in the South Asian tropical region within 22–26°N, extensive range contraction was observed. This contrasts with the northward expansion pattern of P. ultimum reported in Europe, suggesting that: ( 1 ) When temperatures rise beyond the pathogen's upper thermal limit (approximately 36°C), the combined influence of heat and moisture reduces its survival. ( 2 ) The rise in winter temperatures at the tropical margin disrupts the “low-temperature disinfection” mechanism, while extreme summer heat and humidity shorten the soil moisture accumulation window, leading to a net increase in climatic discomfort. This finding supports the “high-temperature escape” hypothesis, indicating that under high-emission scenarios, traditional disease-prone regions in South China may shift toward becoming a “climate filter zone,” whereas the warm temperate zone within 32–38°N would emerge as the primary potentially suitable area. 4.4. Stable centroid versus shifting boundaries A notable finding is the relative stability of the habitat centroid in the Jianghan Plain (Wuhan region) across all climate scenarios, despite considerable shifts in distribution boundaries. This indicates that the central Yangtze River basin maintains an optimal, buffered combination of moderate temperature seasonality (Bio7) and seasonally balanced moisture (Bio14, Bio13). In contrast, the northern distribution boundary exhibits greater mobility, advancing into warmer temperate zones. This decoupling between a stable core and a dispersing front suggests that long-term monitoring and research should remain concentrated in endemic core areas like the Jianghan Plain to track pathogen evolution and epidemic behavior. Simultaneously, adaptive early-warning networks must be extended into the expanding northern zones to detect incursions and nascent outbreaks. 4.5. Refining traditional views on pathogen range shifts Our findings both align with and complicate the traditional narrative that climate warming promotes poleward pathogen movement. While a northward expansion component exists, the dominant pattern is an overall range contraction driven by southern habitat loss. This contrasts with reports of certain temperate pathogens (e.g., P. ultimum in Europe) and underscores that range shifts are species-specific and mediated by local climatic thresholds. For P. helicoides in monsoonal China, the future distribution appears constrained from the south by excessive heat and from the west/north by moisture limitations, converging toward a narrower optimal belt. This supports a more nuanced model in which climate change acts as a “filter,” reshaping pathogen geography through simultaneous expansion and contraction. Such a perspective aids in prioritizing regions facing emergent risks (northern expansion zones) versus those where threats may stabilize or diminish (southern contraction zones). 5. Considerations of Model Uncertainty Although the MaxEnt model has demonstrated good performance in species distribution predictions, several sources of uncertainty remain in this study, which should be considered when interpreting the results. 5.1 Climate Model Source and Limitations The future climate projections used in this study are based on output from a single global climate model (GCM), MIROC6. While MIROC6 performs well in simulating climatic variables over East Asian monsoon regions, structural differences among GCMs can lead to uncertainties in future climate projections. Future studies could employ multi-model ensembles or probabilistic frameworks to reduce biases associated with any single GCM. 5.2 Extrapolation Risk and Model Transferability Under future climate scenarios, some environmental variables may fall outside the range present in the current training data, introducing extrapolation risk. Although the “clamping” function in MaxEnt was enabled to limit extreme extrapolation, uncertainty remains for projections into novel environmental space. Subsequent studies could incorporate multivariate extrapolation detection (e.g., MESS analysis) to identify and flag regions with high extrapolation risk. 5.3 Static Assumptions for Non‑Climatic Variables Soil properties and topographic variables were assumed to remain constant over time. This static assumption may overlook long‑term pedogenic processes or anthropogenic land‑surface changes that could influence species distributions. Future work could integrate dynamic soil or land‑use models to improve ecological realism. 5.4 Small‑Sample Bias and Model Generalization Only 37 validated occurrence records were available for modeling, a relatively small sample size with uneven spatial coverage. This may lead to biased estimates of certain environmental response curves. Although the Target‑Group Background approach was used to partially correct sampling bias, the model’s generalizability to unsampled regions should be interpreted cautiously. Updating and validating the model with more comprehensive survey data in the future is recommended. 6. Conclusion In this study, the MaxEnt model was used to predict the suitable habitat distribution of P. helicoides across China. The model performed reliably, with AUC values consistently above 0.9. The results identified the key environmental variables governing its distribution: Bio14 (precipitation of the driest month), Bio7 (temperature annual range), and elevation, which together accounted for 86.2% of the cumulative contribution. These variables capture essential climatic and topographic constraints for the pathogen. Under current conditions, highly suitable habitats are concentrated in eastern Hainan and throughout Taiwan, regions characterized by tropical and subtropical monsoon climates. In future climate scenarios (2021–2100) across multiple SSPs, the overall suitable habitat range for P. helicoides is projected to decline. However, a northward expansion of suitable areas toward higher latitudes is expected, accompanied by a southward shift of the habitat centroid. This indicates a contraction in core suitable areas alongside a dispersal of marginal habitats northward. Plains, basins, and river valleys remain highly suitable due to favorable water retention, while mountainous areas generally show lower suitability except in localized depressions or terraces with improved catchment conditions. Given the projected shifts and potential for latitudinal spread, strict phytosanitary inspections and reinforced dual quarantine measures are recommended to limit the further introduction and expansion of P. helicoides in China. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding This work was supported by National Natural Science Foundation of China (32471873), the STI 2030- Major Projects (2023ZD0405605), the China Postdoctoral Science Foundation(2024M751426), National Key R&D Program of China (2023YFD1401304), Natural Science Foundation of Jiangsu Province, China (BK20231291), and the Priority Academic Program Development of Jiangsu Higher Education Institutions. Author contributions Yuzhe Kong: conceptualization, methodology, formal analysis, data curation, and writing (original draft, review, and editing). Jiao Binbin: Conceptualization, Data curation, Formal analysis, writing (review and editing). Size Dai: formal analysis and data curation. Chun Yang: writing (review and editing). Qing chen: Investigation and Project administration. TingTing Dai: methodology, project administration, supervision, writing (review and editing). Conflict of interest All authors declare that there are no conflicts of interest. Data availability In this study, the original data used for analysis can all be obtained from the internet. The biological and climatic variables as well as the topographical variables are from WorldClim version 2.1 (http://www.worldclim.org), with a spatial resolution of 2.5'. The soil variables are from the FAO Soil Database (https://www.fao.org/faostat/). References Gadelha L, Dalcin E, Silva L, Augusto D, Krempser E, Affe H, et al. 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Prediction of global potential distribution and assessment of habitat suitability for Xanthium spinosum driven by climate change. Frontiers in Plant Science. 2025;Volume 16 - 2025. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9173871\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":616928519,\"identity\":\"4ab53e2b-1cb9-4ae7-addb-ace48c02629a\",\"order_by\":0,\"name\":\"Yuzhe Kong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanjing Forestry University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yuzhe\",\"middleName\":\"\",\"lastName\":\"Kong\",\"suffix\":\"\"},{\"id\":616928520,\"identity\":\"8e0dc41d-4b45-4e28-8ede-bdca447d0730\",\"order_by\":1,\"name\":\"Binbin Jiao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Shanghai Customs University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Binbin\",\"middleName\":\"\",\"lastName\":\"Jiao\",\"suffix\":\"\"},{\"id\":616928521,\"identity\":\"338b8aea-71dd-4201-994c-24dc47a10a59\",\"order_by\":2,\"name\":\"Size Dai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanjing Forestry University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Size\",\"middleName\":\"\",\"lastName\":\"Dai\",\"suffix\":\"\"},{\"id\":616928522,\"identity\":\"d067bd83-3f2b-4cf1-880a-efcfab2b5c77\",\"order_by\":3,\"name\":\"Chun Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanjing Forestry University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Chun\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":616928523,\"identity\":\"ec675aaf-64ed-4472-85fb-d7a26e01aa11\",\"order_by\":4,\"name\":\"Qing Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nanjing Forestry University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qing\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":616928524,\"identity\":\"d0c3aafc-6445-40db-b8b0-bb9ece3a8ffc\",\"order_by\":5,\"name\":\"Tingting Dai\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3QvWrDMBSG4SMEJ4tsrwoU+xYEBV/PMQZvhYwaAhHE2EN/Mmdpb6FjRpuCJ2XPmExdk6neGu8tlrNl0DPrRZ8E4Hl3CKPNT08aV9Wsbo+kl+4klI2SZxuyN2FzdbSdO4mB1HxbxuxdUjo/lXzCMGjoMTApR6BCZwYhqp9pPOGmyYNdgQhtd8h2DyDt/tNxS2u+AtsJZKY4ZBZBySdXkrN1UP1K5JAusopPSQrOthWqYVkK0xI5bDlbJBQilzQsdL4l2bwMf6WRko/v9tLrZRzVr+PJH+K2457ned6/rucURpbNq3vUAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Nanjing Forestry University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Tingting\",\"middleName\":\"\",\"lastName\":\"Dai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-20 02:08:43\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9173871/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9173871/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106223257,\"identity\":\"4ecf916f-0629-4ded-a5a0-0277d8218787\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 10:17:04\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71448,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eOptimized MaxEnt: aicc vs. feature and regularization\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/8761be3c536fedcb4d3173f0.png\"},{\"id\":106403330,\"identity\":\"b52e6df1-0da3-4ec6-b5d6-686107afd143\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:14:05\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":77137,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eROC-AUC of MaxEnt\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/950c8be33337e3635452030e.png\"},{\"id\":106402940,\"identity\":\"7766e9d6-e66f-4bf2-b541-a7040ee2e82e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:13:14\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":138199,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDominant-factor response curve\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA. Jackknife analysis result. B. Annual range of temperature. C. The monthly precipitation of the wettest month. D. The lowest monthly precipitation. E. Elevation.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/fca618e0b9df8c09f40f1d84.png\"},{\"id\":106223259,\"identity\":\"3ef041cc-300a-4b09-bd0e-204007bdb3df\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 10:17:04\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":50563,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePearson correlation analysis\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/63ec9636000fdb911d410500.png\"},{\"id\":106403241,\"identity\":\"161ca437-7292-4f28-8ef5-46497d26ef4e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:13:57\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":278700,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe current distribution of potential suitable areas for \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eP. helicoides\\u003c/strong\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNote: The map is based on the standard map of GS (2022) 1873, and the map has not been modified.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/8618073a7e9812e18a067955.png\"},{\"id\":106223262,\"identity\":\"aea52282-3a07-4f52-b9dd-33a14b469a9b\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 10:17:04\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":297686,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eClimatic suitability of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eP. helicoides\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e in future china\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/e5af96570b8b1aecbec0df6d.png\"},{\"id\":106403169,\"identity\":\"36056c7d-f4dd-4cd6-b3ee-60be4183827e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-08 09:13:45\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":293058,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eClimate‐driven range shift of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eP. helicoides\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e in China\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/8494b1deecb4be98b90572f3.png\"},{\"id\":106223264,\"identity\":\"f56b35d1-84a2-40a0-973a-18e50c69ba2e\",\"added_by\":\"auto\",\"created_at\":\"2026-04-06 10:17:04\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":124374,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eShifts in the Suitable Habitat Centroid of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eP. helicoides\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e under Climate Change\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/bd856bc1188460a6cf2a948c.png\"},{\"id\":106976030,\"identity\":\"67bda298-9bd2-4a65-bc22-186c3d6b134c\",\"added_by\":\"auto\",\"created_at\":\"2026-04-15 10:44:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2578776,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9173871/v1/4e51e24c-25b9-49e8-8e33-f17116e1c2df.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Based on the MaxEnt model, the potential suitable areas for Pythium helicoides in China are predicted\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003e \\u003cem\\u003eP. helicoides\\u003c/em\\u003e, an oomycete within the kingdom Oomycota, is a significant plant pathogen. It is mainly distributed in Hebei, Shandong, Henan, Jiangsu, Guangdong, Fujian and other provinces in China, and has attracted considerable attention in agricultural production. This pathogen infects a broad range of hosts and has caused severe damage to numerous economically important crops. This pathogen primarily infects a variety of economically important aquatic and terrestrial crops, including \\u003cem\\u003eFragaria \\u0026times; ananassa\\u003c/em\\u003e, \\u003cem\\u003eNelumbo nucifera\\u003c/em\\u003e, and \\u003cem\\u003eBrasenia schreberi\\u003c/em\\u003e J. F. Gmel., leading to diseases such as root rot and stem base rot. This limitation severely reduces crop yield and quality, resulting in substantial economic losses in agricultural production. Morphologically, the colonies on CMA medium exhibit a radial pattern with cottony aerial mycelia. The mycelia are well-developed and highly branched, measuring 1.5\\u0026ndash;9.2 \\u0026micro;m in diameter. Sporangia are predominantly ovoid, rarely nearly spherical, and possess a single apical pore; intercalary sporangia occur occasionally. Nearly spherical sporangia range from 17 to 36 \\u0026micro;m in diameter. It should be noted that this species has recently been reclassified as \\u003cem\\u003ePhytopythium helicoides\\u003c/em\\u003e based on phylogenetic studies; however, as the majority of occurrence records obtained from GBIF and the literature are archived under \\u003cem\\u003ePythium helicoides\\u003c/em\\u003e, and to maintain consistency with the input data and existing references, the name \\u003cem\\u003ePythium helicoides\\u003c/em\\u003e is retained throughout this manuscript. This terminology choice does not affect the taxonomic identity or the validity of the distribution modeling.\\u003c/p\\u003e \\u003cp\\u003eThe ecological niche model (ENM) serves as an important tool in species distribution modeling and biogeography research. This model can predict potentially suitable areas for a species, evaluate habitat suitability, and infer potential transmission pathways based on known species distribution data and relevant environmental variables(\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Among various ecological niche models, the MaxEnt model has become a mainstream method for species habitat analysis because it maintains high prediction accuracy and stability even with relatively limited data. Compared to earlier models such as Climex(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e), Bioclim(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e), and Garp(\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e), the MaxEnt model exhibits greater adaptability and explanatory power in analyzing relationships between species distribution data and environmental factors, especially under limited sample sizes. In recent years, the MaxEnt model has been widely used to predict species\\u0026rsquo; potential suitable habitats, assess alien species invasion risks, and evaluate the impacts of climate change on biodiversity(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). It has also yielded satisfactory results in predicting the distribution and suitable habitats of species such as tangerine pomelo(\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e), kiwi fruit(\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), breadfruit(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e), locust tree(\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e), and wolfberry(\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e), demonstrating the model\\u0026rsquo;s good applicability for predicting geographically suitable habitats of species in China.\\u003c/p\\u003e \\u003cp\\u003eCurrently, systematic studies predicting potential suitable areas for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e in China remain relatively limited. However, clarifying the current and future spatial distribution patterns of this pathogen is crucial for early warning, targeted regional prevention and control, and the development of quarantine strategies. Based on 37 valid distribution records collected across China and 11 key environmental variables, this study employed the MaxEnt model alongside GIS spatial analysis to simulate potential suitable areas for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e under current and future climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for four periods (2021\\u0026ndash;2040, 2041\\u0026ndash;2060, 2061\\u0026ndash;2080, and 2081\\u0026ndash;2100). The main environmental factors influencing its distribution were also identified, providing a scientific basis for monitoring, early warning, regional management, and ecological risk control of this pathogen (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite the recognized impact of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e on key agricultural systems in China, a comprehensive, spatially explicit assessment of its current and future climatic suitability at the national scale remains scarce(\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Most existing studies have focused on local outbreaks or specific host\\u0026ndash;pathogen interactions, with limited integration of multi-environmental predictors under climate change scenarios. Such a gap hinders the development of proactive and regionally tailored management strategies(\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo address this, the present study employs the Maximum Entropy (MaxEnt) model, coupled with Geographic Information System (GIS) spatial analysis, to (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) identify the key environmental drivers shaping the current distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e in China(\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e), (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) predict its potential suitable habitats under current and future climate scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) across four time periods (2021\\u0026ndash;2040 to 2081\\u0026ndash;2100)(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e), and (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) quantify shifts in suitable areas and distribution centroids under climate change(\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). By integrating climatic, topographic, and edaphic variables, this work aims to provide a high-resolution, forward-looking perspective on the pathogen\\u0026rsquo;s biogeographic dynamics(\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe findings are expected to offer a scientific basis for early warning systems, regional quarantine policies, and targeted mitigation measures, thereby supporting sustainable agriculture and forest management in southern China and beyond(\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). It should be noted that the model outputs generated in this study represent environmental suitability for \\u003cem\\u003ePythium helicoides\\u003c/em\\u003e based on climatic and environmental variables, rather than direct estimates of disease occurrence probability or epidemic intensity. The term \\u0026ldquo;risk\\u0026rdquo; used herein refers to spatial prioritization derived from suitability maps, serving as an extension for management implications. While true disease risk assessment would ideally incorporate additional biological and socioeconomic factors such as host distribution, cropping patterns, or trade pathways\\u0026mdash;which are not included in the current modeling framework\\u0026mdash;this study focuses primarily on the geospatial dimension of potential suitability. These limitations are acknowledged in the Discussion, and the findings are intended to provide a preliminary spatial guide for monitoring and quarantine planning, rather than a comprehensive risk forecast. This study provides information on environmental suitability and potential risks, and the risk management recommendations are derived from the suitability maps.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Species occurrence data\\u003c/h2\\u003e \\u003cp\\u003eThe geographical distribution records of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e were obtained from the Global Biodiversity Information Facility (GBIF, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.gbif.org/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.gbif.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and relevant published literature. A total of 37 occurrence records were collected, predominantly located in eastern China. To minimize spatial autocorrelation and sampling bias, a two-step spatial filtering procedure was implemented in ArcGIS 10.8:\\u003c/p\\u003e \\u003cp\\u003eSpatial thinning: Given the spatial resolution of the climate data (approx. 5 km), a 5 km \\u0026times; 5 km grid was overlaid on the occurrence points. Within each grid cell, only one record was retained to ensure spatial independence.\\u003c/p\\u003e \\u003cp\\u003eBias correction using Target-Group Background (TGB): To account for potential sampling bias (e.g., uneven survey effort), background points were selected from occurrence records of closely related Pythium species within the same geographic region. This approach helps to create a more ecologically meaningful background for model training.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Data acquisition and processing\\u003c/h2\\u003e \\u003cp\\u003eA total of 37 environmental variables were initially considered, including 19 bioclimatic variables from WorldClim version 2.1 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.worldclim.org\\u003c/span\\u003e\\u003cspan address=\\\"http://www.worldclim.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), The data used are from WorldClim and the spatial resolution is 2.5\\u0026prime;. 3 topographic variables (elevation, slope, aspect) from the same source, and 15 soil variables from the FAO Soil Database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.fao.org/faostat/\\u003c/span\\u003e\\u003cspan address=\\\"https://www.fao.org/faostat/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). Future climate projections for four periods (2021\\u0026ndash;2040, 2041\\u0026ndash;2060, 2061\\u0026ndash;2080, 2081\\u0026ndash;2100) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5) were also downloaded.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eVariable screening\\u003c/b\\u003e was performed to avoid multicollinearity and overfitting. Pearson correlation coefficients were calculated for all variable pairs. When \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\mid\\\\:r\\\\mid\\\\:\\\\ge\\\\:0.85\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, the variable with lower contribution in a preliminary MaxEnt run was excluded. After screening, 11 variables with low correlation and high ecological relevance were retained for final modeling (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e)(\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Study area and background extent (M region)\\u003c/h2\\u003e \\u003cp\\u003eThe study area (M region) was defined as the geographic extent within which the species is presumed to be able to disperse and establish under current and future climatic conditions and based on the buffer zone of the known distribution points (500 km) and the superimposition with the related ecological areas, an ecologically reasonable area has been obtained. Based on the known distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e and its host plants in China, we delineated the M region as mainland China plus Taiwan and Hainan Island, bounded by 73\\u0026deg;\\u0026ndash;135\\u0026deg;E and 18\\u0026deg;\\u0026ndash;54\\u0026deg;N. This region encompasses all known occurrence points and represents a plausible accessible area for the pathogen under natural and human-assisted dispersal scenarios.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Background point selection strategy\\u003c/h2\\u003e \\u003cp\\u003eTo improve model realism and reduce geographic bias, we adopted a target-group background (TGB) approach. Background points (n\\u0026thinsp;=\\u0026thinsp;10,000) were randomly sampled from the occurrence records of multiple Pythium species within the same genus and geographic region, obtained from GBIF. The background points are derived from the distribution records of the same species (Pythium) in East Asia, thereby matching the sampling efforts and enabling the model to focus on differentiating environmental suitability rather than sampling bias.This strategy ensures that background points reflect similar survey effort and environmental coverage as the presence data, thereby reducing model overfitting to sampling artifacts.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Construction of the MaxEnt model\\u003c/h2\\u003e \\u003cp\\u003eThe screened geographical distribution data of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e and corresponding environmental variables were imported into Maxent v.3.4.1. Twenty-five percent of the species occurrence records were randomly selected as the test dataset. The model was run 10 times, and the ROC curve was generated. The replication method was configured as \\\"Bootstrap\\\". The prediction results were output in logistic format as an ASC file (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e), and the contributions of environmental variables to the model were evaluated. All other parameters remained at their default settings.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Model accuracy testing\\u003c/h2\\u003e \\u003cp\\u003eThe area under the curve (AUC) value for the ROC curve was calculated using MaxEnt software. An AUC value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.5 suggests a reverse prediction (i.e., a negative correlation between observed and predicted values), \\u0026le; 0.6 indicates failure, \\u0026le; 0.7 reflects poor performance, and \\u0026le;\\u0026thinsp;0.8 denotes acceptable performance, an AUC value\\u0026thinsp;\\u0026le;\\u0026thinsp;0.9 indicates good performance, while a value\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.9 indicates favourable performance. Finally, environmental factors were selected based on their contribution rate and correlation. Specifically, factors with an absolute correlation coefficient (|r|)\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.85 and the factors with a higher contribution rate were retained for further analysis. Among these, temperature annual range (BIO7), precipitation of the wettest month (BIO13), precipitation of the driest month (BIO14), and precipitation seasonality coefficient (BIO15) are the 11 environmental variables with relatively high contribution rates selected for subsequent model construction (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Optimization of the MaxEnt Model\\u003c/h2\\u003e \\u003cp\\u003eThe unoptimized MaxEnt model is prone to prediction errors, which may compromise the transferability of results. To improve model accuracy and reproducibility, the following systematic optimization procedure was adopted in this study:\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eVariable Screening\\u003c/strong\\u003e \\u003cp\\u003ePrior to modeling, environmental variables were preprocessed to remove highly correlated variables (Pearson correlation coefficient\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.8), ensuring variable independence and reducing the risk of overfitting.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eParameter Optimization\\u003c/strong\\u003e \\u003cp\\u003eThe R package ENMeval was used to tune the feature class (FC) and regularization multiplier (RM). Specific settings were as follows\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eFeature classes\\u003c/strong\\u003e \\u003cp\\u003eTwelve common combinations were tested, including L (linear), Q (quadratic), H (hinge), P (product), and T (threshold) in various combinations (e.g., H, L, LQ, LQH, LQHP, LQHPT, LQP, LQPT, etc.).\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eRegularization multipliers\\u003c/strong\\u003e \\u003cp\\u003eTwelve values (0.5, 1.0, \\u0026hellip;, 6.0) were tested, resulting in a total of 144 candidate models.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eModel run settings\\u003c/strong\\u003e \\u003cp\\u003eThe model was trained for 50,000 iterations using 10,000 background points, with 10-fold cross-validation. A random selection of 75% of the data was used as the training set, while the remaining 25% was reserved for testing. The final results are presented as mean values. Model selection was based on the difference between training and testing AUC (AUC diff) and the 10% training omission rate (OR10), with the model exhibiting the smallest ΔAICc value chosen as the optimal model (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e)(random seed).\\u003c/p\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Classification of suitable areas\\u003c/h2\\u003e \\u003cp\\u003eThe output results of the MaxEnt model were imported into ArcGIS 10.8 and reclassified according to the specified format to predict the potential suitable areas for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e. Combined with the geographical distribution data, suitability levels were classified into four categories using the natural breakpoint method: non-suitable area (0\\u0026ndash;0.078), low-suitable area (0.078\\u0026ndash;0.249), medium-suitable area (0.249\\u0026ndash;0.462), and high-suitable area (0.462\\u0026ndash;1.000). The areas of these zones were then calculated (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 Changes in potential suitable areas and center of gravity shift\\u003c/h2\\u003e \\u003cp\\u003eUsing ArcGIS, the prediction results were binarized and reclassified to distinguish suitable areas from unsuitable ones(10% training percentile threshold). Subsequently, the raster data was converted to polygons with the \\\"create multipart feature\\\" option enabled, followed by an intersect operation for overlay analysis (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Finally, the polygon-to-raster conversion method was applied to quantify changes in potentially suitable areas. Based on projected shifts in species distributions under climate change, the grid cells were categorized as: contraction areas (currently occupied but potentially lost in the future), expansion areas (currently unoccupied but potentially gained in the future), and stable areas (persistent under both current and future conditions). ArcGIS was employed to visualize the compositional changes in \\u003cem\\u003eP. helicoides\\u003c/em\\u003e. A comparison of its current and projected future distributions reveals clear trends in the shifts of its range. The \\\"Average Center\\\" tool within the spatial statistics module was used to compute the central position, migration direction, and displacement distance of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e across different periods. To some extent, the shift in geographic distribution centers reflects how future climate conditions may affect \\u003cem\\u003eP. helicoides\\u003c/em\\u003e (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results and analysis\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Model performance results\\u003c/h2\\u003e \\u003cp\\u003eThe model parameters show that the LQHPT combination with an RM value of 2.5 yields the lowest delta AICc (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), and was therefore selected for the final model.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe ROC curve was derived from the MaxEnt model simulation results. The feature curve achieved an AUC of 0.960 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), demonstrating strong discriminative ability of the model. The AUC (Area Under the Curve) value ranges from 0 to 1, with a higher value indicating greater model accuracy and reliability. An AUC below 0.6 is generally considered to reflect an unqualified prediction performance; the predictive performance is categorized as follows: poor for 0.6\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.7, average for 0.7\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.8, good for 0.8\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.9, and favourable for 0.9\\u0026thinsp;\\u0026le;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.0. An AUC of 1 indicates a perfect match between predicted and actual values. The results demonstrate an AUC of 0.960 for the ROC curve, indicating strong model performance in analysis and prediction (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). The predictive accuracy of the MaxEnt model was assessed using the AUC value, statistical significance, AIC value, and a 5% training omission rate. The AUC values exceeded 0.9, with a delta AICc of 0 and a 5% training omission rate of 0 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The suitability predicted by the model based on the above data is both credible and accurate (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e), making it applicable for forecasting the potentially suitable areas for \\u003cem\\u003eP. helicoides\\u003c/em\\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\\u003eAUC-based Predictions of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e Habitat under Climate Scenarios\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eClimate change scenario\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYear\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAUC value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1970\\u0026ndash;2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.974\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.976\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.978\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003escenario SSP1-2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.976\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.977\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.975\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.979\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003escenario SSP3-7.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.978\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.978\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.979\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.975\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003escenario SSP5-8.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.979\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.976\\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\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Selection of key environmental factors\\u003c/h2\\u003e \\u003cp\\u003eThe jackknife test results indicate that, when considered individually, the regularization training gain scores for Bio7 (annual temperature range), Bio13 (precipitation of the wettest month), Bio14 (precipitation of the driest month), and elev (elevation) all exceed 0.80 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). This suggests that these four environmental variables exert the greatest influence on the distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e. Based on the contribution rate and Jackknife test results, four key climate variables were selected and used to plot response curves.The key environmental variables influencing the distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e are as follows: annual temperature range 9.32\\u0026ndash;35.09\\u0026deg;C, precipitation of the wettest month 196.24\\u0026ndash;802.61 mm, precipitation of the driest month 19.18\\u0026ndash;210.11 mm, and elevation\\u0026thinsp;\\u0026minus;\\u0026thinsp;684.91 to 394.13 m.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eA. Jackknife analysis result. B. Annual range of temperature. C. The monthly precipitation of the wettest month. D. The lowest monthly precipitation. E. Elevation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Key environmental drivers of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e distribution\\u003c/h2\\u003e \\u003cp\\u003eCombining Pearson correlation analysis(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) with model contribution rate, 11 environmental variables were selected (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Through MaxEnt modeling, simulations at multiple temporal scales were conducted to assess potential suitable areas for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e. The results indicate that its distribution pattern is significantly influenced by climatic factors. Among these, Bio14 (minimum monthly precipitation), Bio7 (annual temperature range), and elev (elevation) exhibited the highest relative contributions, collectively accounting for 86.2% of the total\\u0026mdash;significantly greater than the sum of the remaining environmental factors(\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). The ranking results indicate that Bio14 (lowest monthly precipitation), elev (elevation), and t_gravel (percentage of crushed stone volume) are the primary variables, accounting for a cumulative importance of 81.6%. From this analysis, it is evident that Bio14 (minimum monthly precipitation) and elev (elevation) are the principal environmental factors shaping the geographic distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e under current climatic conditions (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\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\\u003eKey environmental drivers in MaxEnt modeling\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnvironmental\\u003c/p\\u003e \\u003cp\\u003evariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDecription\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePerent\\u003c/p\\u003e \\u003cp\\u003econtribution\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePermutation importance\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThe lowest monthly precipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e72.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnnual range of temperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eelev\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eelevation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e32\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003et_gravel\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePercentage of crushed stone volume\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBio13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eThe monthly precipitation of the wettest month\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003et_texture\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTopsoil texture\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eslope\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSlope gradient\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003et_esp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSlope orientation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ebio15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSeasonal variation of precipitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003easpect\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAspect\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003et_silt\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSilt content\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Present suitability map for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThe current suitable distribution area of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e was predicted using the MaxEnt model (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The current total suitable area for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e is 1,900,100 km\\u0026sup2;, accounting for 19.79% of China\\u0026rsquo;s total land area. The total suitable area comprises 87.24 \\u0026times; 10⁴ km\\u0026sup2; of low suitability, 65.66 \\u0026times; 10⁴ km\\u0026sup2; of medium suitability, and 37.11 \\u0026times; 10⁴ km\\u0026sup2; of high suitability. \\u003cem\\u003eP. helicoides\\u003c/em\\u003e is primarily distributed in low-suitability regions of China, occupying approximately 9.91% of the total land area. It is primarily distributed in western Hebei, Shandong, eastern Henan, central and eastern Hubei, eastern Sichuan, Chongqing, and southern Guizhou. Areas of moderate severity account for 6.84% of the country's land area, primarily distributed in Beijing, Tianjin, Hebei, Zhejiang, Anhui, central Guangxi, central Guangdong, and western Hunan. The highly suitable areas, comprising 3.87% of the country's land area, are primarily concentrated in eastern Hainan and Taiwan, demonstrating a distinct pattern of geographical clustering (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). From the perspective of climate zones, suitable habitats are primarily distributed in tropical and subtropical monsoon climate regions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eNote\\u003c/strong\\u003e \\u003cp\\u003eThe map is based on the standard map of GS (2022) 1873, and the map has not been modified.\\u003c/p\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Future climate distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e summarizes the total suitable area and its grade composition in four periods (2021\\u0026ndash;2040, 2041\\u0026ndash;2060, 2061\\u0026ndash;2080, 2081\\u0026ndash;2100) under three SSP scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5). Generally speaking, compared with the current situation, the total suitable area in all scenarios shows a downward trend in the future, and the reduction in high and medium suitable areas is particularly significant. Under future climate scenarios, the suitable habitat range of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e in China is projected to gradually contract spatially, while unsuitable areas are expected to expand accordingly (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eHabitat shifts of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e under climate change 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eClimate scenario\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c6\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eArea Unit(\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUnsuitable habitat\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSightly suitable habitat habitat\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModerately habitat habitat\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHighly suitable habitat\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eTotal suitable habitat\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP1-2.6\\u003c/p\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e755.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e87.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e183.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP1-2.6\\u003c/p\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e772.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e82.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e165.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP1-2.6\\u003c/p\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e755.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e182.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP1-2.6\\u003c/p\\u003e \\u003cp\\u003e2081\\u0026ndash;2100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e762.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e85.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e36.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e176.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP3-7.0\\u003c/p\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e739.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e67.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e198.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP3-7.0\\u003c/p\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e774.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e79.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e54.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e29.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e164.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP3-7.0\\u003c/p\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e756.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e94.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e54.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e182.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP3-7.0\\u003c/p\\u003e \\u003cp\\u003e2081\\u0026thinsp;\\u0026minus;\\u0026thinsp;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e761.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e91.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e176.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5\\u003c/p\\u003e \\u003cp\\u003e2021\\u0026ndash;2040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e752.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e185.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5\\u003c/p\\u003e \\u003cp\\u003e2041\\u0026ndash;2060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e753.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e88.94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e184.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5\\u003c/p\\u003e \\u003cp\\u003e2061\\u0026ndash;2080\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e748.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e92.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e58.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e189.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSSP5-8.5\\u003c/p\\u003e \\u003cp\\u003e2081\\u0026ndash;2100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e763.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e84.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e56.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32.99\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e174.74\\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\\u003eThe current suitable habitat area is 1.9001\\u0026times;10\\u003csup\\u003e6\\u003c/sup\\u003e km\\u0026sup2;. Under future stress scenarios, this area is projected to decline. Under the SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios, the suitable habitat area gradually declines (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Under the SSP1-2.6 scenario, the suitable habitat area first decreased to 165.54 \\u0026times; 10⁴ km\\u0026sup2; before expanding to 176.04 \\u0026times; 10⁴ km\\u0026sup2;. Under the SSP3-7.0 scenario, it initially declined to 164.34 \\u0026times; 10⁴ km\\u0026sup2; and then\\u003c/p\\u003e \\u003cp\\u003eincreased to 176.77 \\u0026times; 10⁴ km\\u0026sup2; (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). However, the extent of both moderately and highly suitable habitats decreased across all scenarios.\\u003c/p\\u003e \\u003cp\\u003eFrom 2021 to 2040 under SSP1‑2.6, moderately to highly suitable areas were predominantly located in the middle \\u0026ndash; upper Yangtze River Plain (Jianghan\\u0026ndash;Dongting\\u0026ndash;Poyang Lake floodplain; 30\\u0026deg;\\u0026ndash;32\\u0026deg;N, 113\\u0026deg;\\u0026ndash;120\\u0026deg;E) and in the Pearl River Delta (23\\u0026deg;\\u0026ndash;25\\u0026deg;N, 110\\u0026deg;\\u0026ndash;115\\u0026deg;E). Under the SSP3‑7.0 and SSP5‑8.5 scenarios, the initial distribution resembled that of SSP1‑2.6, but highly suitable areas became more fragmented.\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP1-2.6 scenario from 2041 to 2060, highly suitable areas extended westward along the Yangtze River into the eastern Sichuan Basin (30\\u0026deg;N, 105\\u0026deg;\\u0026ndash;107\\u0026deg;E) and eastward toward the coastal regions of the Yangtze River Delta. The northern boundary of the red zone in the Pearl River Estuary extends across the Nanling Mountains, reaching approximately 25\\u0026deg;N in southern Hunan. Under the SSP3-7.0 scenario, the area of high suitability was expected to expand; however, it contracted northward instead\\u0026mdash;specifically, the orange zone north of 32\\u0026deg;N disappeared, while the red zone in the Sichuan Basin decreased by more than 30%. Under the SSP5-8.5 scenario, the high-suitability area exhibited a northward shift: the Shandong Peninsula (36\\u0026deg;N, 118\\u0026deg;\\u0026ndash;122\\u0026deg;E) transitioned to high suitability, while coastal regions in southern China shifted from high- to low-suitability areas.\\u003c/p\\u003e \\u003cp\\u003eFrom 2061 to 2080, expansion peaked under the SSP1-2.6 scenario. Under the SSP3-7.0 scenario, the overall suitable area shifted southward by 1\\u0026ndash;1.5 degrees of latitude in mid-to-high latitudes, from approximately 32\\u0026deg;N to around 30.5\\u0026deg;N. In the coastal areas of southern China, increased frequency of extreme summer temperatures (\\u0026gt;\\u0026thinsp;35\\u0026deg;C) has caused a shift in suitability zones, with high suitability areas being replaced by medium suitability areas, and medium suitability areas by low suitability areas. In the SSP5-85 scenario, mid-to-high altitude settlement areas extend eastward along the 35\\u0026deg;N latitude to the Korean Peninsula border, and westward into the Guanzhong\\u0026ndash;Tianshui Basin (34\\u0026deg;\\u0026ndash;35\\u0026deg;N, 105\\u0026deg;\\u0026ndash;108\\u0026deg;E).\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP1-2.6 scenario, the suitable habitat area during 2081\\u0026ndash;2100 remained nearly unchanged compared to that in 2061\\u0026ndash;2080, with no notable shift in spatial distribution. This stability can be attributed to temperature and precipitation increases approaching their upper limits under low radiative forcing by the 2080s, leading to a sustained equilibrium in habitat suitability for the species.\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP3-7.0 scenario, the suitable mid- and high-altitude habitats are reduced to the hilly basin of \\\"Xiangjiang\\u0026ndash;Northern Guangxi\\u0026ndash;Northern Guangdong,\\\" situated between 24\\u0026deg;\\u0026ndash;30\\u0026deg;N and 108\\u0026deg;\\u0026ndash;118\\u0026deg;E. The area decreased by approximately 25% relative to the 2061\\u0026ndash;2080 period. Under medium forcing scenarios, temperature increases significantly, while precipitation changes exhibit greater variability. High temperatures inhibit mycelial growth, while rapid drainage after heavy rain reduces habitat suitability in the formerly humid Pearl River Estuary and Yangtze River Delta regions.\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP5-8.5 scenario, the northernmost extent of the highly suitable zone shifts to the region between 33.5\\u0026deg;N\\u0026ndash;36\\u0026deg;N and 105\\u0026deg;E\\u0026ndash;122\\u0026deg;E, encompassing the Huang-Huai-Hai Plain and the southern portion of the Shandong-Liaoning Peninsula. The southern region (22\\u0026deg;\\u0026ndash;26\\u0026deg;N) is almost entirely unsuitable, leading to a \\\"north\\u0026ndash;south inversion\\\" phenomenon. Under high emissions, northern precipitation increases by 15\\u0026ndash;20%, and winter temperatures rise by 4\\u0026ndash;5\\u0026deg;C. This prevents soil freezing during winter dormancy and leads to a spring ground temperature increase exceeding 15\\u0026deg;C, conditions that favor bacterial overwintering and subsequent spring outbreaks. In contrast, extreme heat and drought in southern regions suppress bacterial activity.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll scenarios (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e) indicate a contraction of suitable habitat areas south of 25\\u0026deg;N. This provides a counterexample to the \\\"climate refuge\\\" concept, demonstrating that not all warming events result in northward disease migration; instead, suitability may decline in tropical marginal zones due to threshold exceedance.\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e quantifies the dynamic changes of each scenario compared to the current suitable area. The overall trend is characterized by \\\"overall contraction and local northward expansion\\\".\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP1-2.6 scenario: The suitable areas exhibit a \\\"contraction - recovery - re-contraction\\\" fluctuation pattern. The expansion areas mainly occur in the middle and lower reaches of the Yangtze River and the Pearl River Delta.The spatial change is characterized by a slow northward movement of the boundaries within a stable core area. The Jianghan Plain and the middle and lower reaches of the Yangtze River have always been stable and contiguous high suitability core regions. The northward movement is mainly manifested as the gradual and slow northward expansion of the distribution boundary. By the end of this century, the north boundary of the suitable area will move from the current Huai River line to the southern part of the Huai-Huai region, and sporadic medium and low suitability patches will begin to appear on the Shandong Peninsula. The south boundary remains relatively stable, and the high suitability areas in Hainan and Taiwan are maintained. This pattern is a relatively mild, gradual expansion based on the original core area.\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP3-7.0 scenario:In the initial period (2021\\u0026ndash;2040), the expansion was significant; in the middle period (2041\\u0026ndash;2060), the contraction was intense; in the later period, there was a recovery, but the total area was lower than the current level.The spatial pattern evolution shows the formation and strengthening of a \\\"mid-latitude expansion corridor\\\". The most notable feature is the formation of a stable and continuous expansion area within the band from 30\\u0026deg; to 35\\u0026deg;N (including the southern part of North China, the Guanzhong Plain, and the Han River Valley), which is called the \\\"mid-latitude expansion corridor\\\". This area has been relieved of the low-temperature constraint due to the warming in winter, and the water and heat conditions in summer still remain within the tolerance window of pathogenic bacteria, thus becoming a new favorable habitat hotspot. At the same time, the contraction in the south is extremely intense. The favorable habitat grades in the traditional high-incidence areas of South China have generally declined, and the fragmented or even disappeared high-favorable areas. The final result of the pattern evolution is a significant northward shift of the spatial center of the favorable habitats, forming a \\\"dual-core north-south\\\" or \\\"strong north and weak south\\\" new pattern.\\u003c/p\\u003e \\u003cp\\u003eUnder the SSP5-8.5 scenario:Both contraction and expansion occur simultaneously. In the middle and later stages (2061\\u0026ndash;2080), there is a significant northward expansion in the North China Plain, the southern part of Northeast China, and the northern foothills of the Tianshan Mountains in Xinjiang. The spatial changes are the most dramatic, presenting a pattern reversal of \\\"north-south inversion\\\". By the end of this century, the traditional high-suited areas - most of South China (22\\u0026deg;\\u0026ndash;26\\u0026deg;N) - will shrink to areas that are not suitable or only moderately suitable, while the North China Plain, the Huaihai region, and the southern part of the Liaodong Peninsula (33.5\\u0026deg;\\u0026ndash;38\\u0026deg;N) will transform into contiguous high-suited areas. The northern boundary of the suitable areas will shift significantly northward, reaching as far as the Liaohe River Basin in Northeast China (approximately 42\\u0026deg;N). The mountain-front oases in the northern part of the Tianshan Mountains in Xinjiang have also emerged new suitable patches due to changes in the water and heat combinations. This \\\"northward advance and southward retreat\\\" reversal pattern is mainly caused by the deterioration of habitats due to extreme high-temperature heat waves and changes in precipitation patterns in the south, while the significant increase in winter temperatures and precipitation in the north has created unprecedented overwintering and growth conditions. Under this scenario, the northward shift is no longer the movement of boundaries, but a spatial replacement of the entire suitable area, which has a overturning impact on the pattern of agricultural pest and disease control.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Centroid migration of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eAnalysis of the distribution changes of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e across past, present, and future periods indicates that, under different climate scenarios, the migration distance and direction of its suitable habitat centroid vary. Overall, however, a southward shift is projected over time. The change in the center of mass is influenced by the area weight. The area in the north has increased while that in the south has decreased more significantly(Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The center of mass may still shift southward.The suitable habitat for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e is currently concentrated in the core area of Wuchang District, Wuhan City, with a central coordinate of 114\\u0026deg;18\\u0026prime;E, 30\\u0026deg;36\\u0026prime;N (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). Under the SSP1-2.6 scenario, the centroid of the suitable area shifted initially toward the southwest, followed by an eastward movement. Under SSP3-7.0, the centroid moved first eastward then westward, ultimately remaining within Wuhan City (114\\u0026deg;218\\u0026prime;E, 30\\u0026deg;659\\u0026prime;N). Under SSP5-8.5, the centroid shifted eastward and was finally located in Ezhou City (114\\u0026deg;349\\u0026prime;E, 30\\u0026deg;468\\u0026prime;N).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFuture climate suitability projections for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDecades scenarios\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003ePredicted area Unit:(\\u0026times;10\\u003csup\\u003e4\\u003c/sup\\u003e km\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSuitable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eContraction\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGain\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040 SSP1-2.6 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e172.804\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.380\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060 SSP1-2.6 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e161.250\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.498\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.619\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080 SSP1-2.6 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e172.493\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.949\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100 SSP1-2.6 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e168.389\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.418\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.256\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040 SSP3-7.0 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e178.929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.974\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e19.304\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060 SSP3-7.0 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e161.517\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.252\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.670\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080 SSP3-7.0 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e169.203\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.616\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.635\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100 SSP3-7.0 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e169.158\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.597\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.651\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2021\\u0026ndash;2040 SSP5-8.5 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e174.012\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.813\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.597\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2041\\u0026ndash;2060 SSP5-8.5 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e173.517\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.849\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2061\\u0026ndash;2080 SSP5-8.5 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e173.042\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.800\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.842\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2081\\u0026ndash;2100 SSP5-8.5 vs current\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e168.188\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.599\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.701\\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=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Dominance of Bio14: The dry-month precipitation bottleneck\\u003c/h2\\u003e \\u003cp\\u003eOur results identify precipitation of the driest month (Bio14) as the predominant factor governing the distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e, contributing 72.4% to the model. This contrasts with studies on other Pythium species (e.g., \\u003cem\\u003eP. aphanidermatum\\u003c/em\\u003e), which are often more limited by growing-season moisture (Bio18) or mean temperature. The critical role of Bio14 underscores a survival bottleneck specific to the East Asian monsoon climate: the winter-spring drought period. As an oomycete, \\u003cem\\u003eP. helicoides\\u003c/em\\u003e depends on persistent soil moisture for oospore survival and germination. Bio14 directly determines the severity and duration of soil moisture deficit during this vulnerable stage. Regions with moderate dry-season rainfall (e.g., eastern Hainan and Taiwan) retain sufficient residual moisture to sustain the pathogen, whereas areas with pronounced winter-spring drought cannot support stable populations, even under humid summer conditions. This insight shifts the monitoring focus from total annual precipitation to seasonal moisture stress and suggests that irrigation or soil moisture conservation during dry intervals could mitigate establishment in marginal zones.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Southern contraction under high-emission scenarios\\u003c/h2\\u003e \\u003cp\\u003eContrary to the expectation that warming uniformly drives poleward expansion, our projections under SSP5-8.5 show a pronounced contraction of suitable habitat south of 25\\u0026deg;N. This \\u0026ldquo;southern loss\\u0026rdquo; occurs because future climate conditions in these tropical margins exceed the pathogen\\u0026rsquo;s thermal and hydrological tolerance thresholds. Increasing frequency of extreme summer temperatures (\\u0026gt;\\u0026thinsp;35\\u0026deg;C) likely inhibits mycelial growth, while more intense but episodic rainfall enhances surface runoff, shortening the soil saturation period required for infection. Consequently, combined heat stress and altered hydrology render these regions unsuitable for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e establishment. This pattern aligns with the \\u0026ldquo;high-temperature escape\\u0026rdquo; hypothesis noted in other pathosystems. Practically, traditional disease hotspots in South China may experience reduced pathogen pressure under high-emission futures, permitting a strategic reallocation of monitoring resources.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Local expansion within overall contraction\\u003c/h2\\u003e \\u003cp\\u003eAlthough the total suitable area for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e is projected to decline across all scenarios, we detected notable northward expansion into specific regions such as the Huang-Huai Plain, Jianghan Basin, and parts of Northeast China. This \\u0026ldquo;local expansion amid overall contraction\\u0026rdquo; arises because climate change differentially alleviates former constraints in these areas: warmer winters reduce cold-induced mortality, and modified precipitation regimes provide adequate soil moisture during key infection windows without the extreme drought or heat stress observed in the south. However, these newly suitable habitats are often fragmented and cannot fully offset losses in core southern areas. The result is a more fragmented and dynamic risk landscape. Enhanced surveillance and quarantine measures are therefore warranted along major agricultural corridors in northern China, where the pathogen could colonize previously unaffected cropping systems.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Differences and similarities with traditional views\\u003c/h2\\u003e \\u003cp\\u003eThe conventional view suggests that climate warming drives pathogenic bacteria toward higher latitudes, creating \\\"climate refuges.\\\" However, in the South Asian tropical region within 22\\u0026ndash;26\\u0026deg;N, extensive range contraction was observed. This contrasts with the northward expansion pattern of \\u003cem\\u003eP. ultimum\\u003c/em\\u003e reported in Europe, suggesting that: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) When temperatures rise beyond the pathogen's upper thermal limit (approximately 36\\u0026deg;C), the combined influence of heat and moisture reduces its survival. (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) The rise in winter temperatures at the tropical margin disrupts the \\u0026ldquo;low-temperature disinfection\\u0026rdquo; mechanism, while extreme summer heat and humidity shorten the soil moisture accumulation window, leading to a net increase in climatic discomfort. This finding supports the \\u0026ldquo;high-temperature escape\\u0026rdquo; hypothesis, indicating that under high-emission scenarios, traditional disease-prone regions in South China may shift toward becoming a \\u0026ldquo;climate filter zone,\\u0026rdquo; whereas the warm temperate zone within 32\\u0026ndash;38\\u0026deg;N would emerge as the primary potentially suitable area.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Stable centroid versus shifting boundaries\\u003c/h2\\u003e \\u003cp\\u003eA notable finding is the relative stability of the habitat centroid in the Jianghan Plain (Wuhan region) across all climate scenarios, despite considerable shifts in distribution boundaries. This indicates that the central Yangtze River basin maintains an optimal, buffered combination of moderate temperature seasonality (Bio7) and seasonally balanced moisture (Bio14, Bio13). In contrast, the northern distribution boundary exhibits greater mobility, advancing into warmer temperate zones. This decoupling between a stable core and a dispersing front suggests that long-term monitoring and research should remain concentrated in endemic core areas like the Jianghan Plain to track pathogen evolution and epidemic behavior. Simultaneously, adaptive early-warning networks must be extended into the expanding northern zones to detect incursions and nascent outbreaks.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5. Refining traditional views on pathogen range shifts\\u003c/h2\\u003e \\u003cp\\u003eOur findings both align with and complicate the traditional narrative that climate warming promotes poleward pathogen movement. While a northward expansion component exists, the dominant pattern is an overall range contraction driven by southern habitat loss. This contrasts with reports of certain temperate pathogens (e.g., \\u003cem\\u003eP. ultimum\\u003c/em\\u003e in Europe) and underscores that range shifts are species-specific and mediated by local climatic thresholds. For \\u003cem\\u003eP. helicoides\\u003c/em\\u003e in monsoonal China, the future distribution appears constrained from the south by excessive heat and from the west/north by moisture limitations, converging toward a narrower optimal belt. This supports a more nuanced model in which climate change acts as a \\u0026ldquo;filter,\\u0026rdquo; reshaping pathogen geography through simultaneous expansion and contraction. Such a perspective aids in prioritizing regions facing emergent risks (northern expansion zones) versus those where threats may stabilize or diminish (southern contraction zones).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Considerations of Model Uncertainty\",\"content\":\"\\u003cp\\u003eAlthough the MaxEnt model has demonstrated good performance in species distribution predictions, several sources of uncertainty remain in this study, which should be considered when interpreting the results.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Climate Model Source and Limitations\\u003c/h2\\u003e \\u003cp\\u003eThe future climate projections used in this study are based on output from a single global climate model (GCM), MIROC6. While MIROC6 performs well in simulating climatic variables over East Asian monsoon regions, structural differences among GCMs can lead to uncertainties in future climate projections. Future studies could employ multi-model ensembles or probabilistic frameworks to reduce biases associated with any single GCM.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Extrapolation Risk and Model Transferability\\u003c/h2\\u003e \\u003cp\\u003eUnder future climate scenarios, some environmental variables may fall outside the range present in the current training data, introducing extrapolation risk. Although the \\u0026ldquo;clamping\\u0026rdquo; function in MaxEnt was enabled to limit extreme extrapolation, uncertainty remains for projections into novel environmental space. Subsequent studies could incorporate multivariate extrapolation detection (e.g., MESS analysis) to identify and flag regions with high extrapolation risk.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec29\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.3 Static Assumptions for Non‑Climatic Variables\\u003c/h2\\u003e \\u003cp\\u003eSoil properties and topographic variables were assumed to remain constant over time. This static assumption may overlook long‑term pedogenic processes or anthropogenic land‑surface changes that could influence species distributions. Future work could integrate dynamic soil or land‑use models to improve ecological realism.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec30\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.4 Small‑Sample Bias and Model Generalization\\u003c/h2\\u003e \\u003cp\\u003eOnly 37 validated occurrence records were available for modeling, a relatively small sample size with uneven spatial coverage. This may lead to biased estimates of certain environmental response curves. Although the Target‑Group Background approach was used to partially correct sampling bias, the model\\u0026rsquo;s generalizability to unsampled regions should be interpreted cautiously. Updating and validating the model with more comprehensive survey data in the future is recommended.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"6. Conclusion\",\"content\":\"\\u003cp\\u003eIn this study, the MaxEnt model was used to predict the suitable habitat distribution of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e across China. The model performed reliably, with AUC values consistently above 0.9. The results identified the key environmental variables governing its distribution: Bio14 (precipitation of the driest month), Bio7 (temperature annual range), and elevation, which together accounted for 86.2% of the cumulative contribution. These variables capture essential climatic and topographic constraints for the pathogen.\\u003c/p\\u003e \\u003cp\\u003eUnder current conditions, highly suitable habitats are concentrated in eastern Hainan and throughout Taiwan, regions characterized by tropical and subtropical monsoon climates. In future climate scenarios (2021\\u0026ndash;2100) across multiple SSPs, the overall suitable habitat range for \\u003cem\\u003eP. helicoides\\u003c/em\\u003e is projected to decline. However, a northward expansion of suitable areas toward higher latitudes is expected, accompanied by a southward shift of the habitat centroid. This indicates a contraction in core suitable areas alongside a dispersal of marginal habitats northward.\\u003c/p\\u003e \\u003cp\\u003ePlains, basins, and river valleys remain highly suitable due to favorable water retention, while mountainous areas generally show lower suitability except in localized depressions or terraces with improved catchment conditions.\\u003c/p\\u003e \\u003cp\\u003eGiven the projected shifts and potential for latitudinal spread, strict phytosanitary inspections and reinforced dual quarantine measures are recommended to limit the further introduction and expansion of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e in China.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by National Natural Science Foundation of China (32471873), the STI 2030- Major Projects (2023ZD0405605), the China Postdoctoral Science Foundation(2024M751426), National Key R\\u0026amp;D Program of China (2023YFD1401304), Natural Science Foundation of Jiangsu Province, China (BK20231291), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eYuzhe Kong: conceptualization, methodology, formal analysis, data curation, and writing (original draft, review, and editing).\\u0026nbsp;Jiao Binbin: Conceptualization, Data curation, Formal analysis, writing (review and editing). Size Dai: formal analysis and data curation. Chun Yang: writing (review and editing). Qing chen: Investigation and Project administration. TingTing Dai: methodology, project administration, supervision, writing (review and editing).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors declare that there are no conflicts of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn this study, the original data used for analysis can all be obtained from the internet. The biological and climatic variables as well as the topographical variables are from WorldClim version 2.1 (http://www.worldclim.org), with a spatial resolution of 2.5\\u0026apos;. The soil variables are from the FAO Soil Database (https://www.fao.org/faostat/).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eGadelha L, Dalcin E, Silva L, Augusto D, Krempser E, Affe H, et al. A survey of biodiversity informatics: Concepts, practices, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020;11:e1394.\\u003c/li\\u003e\\n \\u003cli\\u003eHayat U, Shi J, Wu Z, Rizwan M, Haider MS. Which SDM Model, CLIMEX vs. MaxEnt, Best Forecasts Aeolesthes sarta Distribution at a Global Scale under Climate Change Scenarios? Insects. 2024;15.\\u003c/li\\u003e\\n \\u003cli\\u003eChiou K, Blair M. 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Plants. 2025;14(17):2797.\\u003c/li\\u003e\\n \\u003cli\\u003eJaturapruek R, Fontaneto D, Maiphae S. The influence of environmental variables on bdelloid rotifers of the genus \\u0026lt;em\\u0026gt;Rotaria\\u0026lt;/em\\u0026gt; in Thailand. Journal of Tropical Ecology. 2020;36(6):267\\u0026ndash;74.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang P, Xiao Y, Sun Y, Huang H, Gong Z, Zhu Y. Distribution changes of Ormosia microphylla under different climatic scenarios. Scientific Reports. 2025;15(1):2607.\\u003c/li\\u003e\\n \\u003cli\\u003eLuo J, Ma Y, Liu Y, Zhu D, Guo X. Predicting Polygonum capitatum distribution in China across climate scenarios using MaxEnt modeling. Scientific Reports. 2024;14(1):20020.\\u003c/li\\u003e\\n \\u003cli\\u003eFan Y, Zhang X, Yang J, Yang J, Zhang H, Yang B, et al. Prediction of global potential distribution and assessment of habitat suitability for Xanthium spinosum driven by climate change. Frontiers in Plant Science. 2025;Volume 16 - 2025.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Maxent, P. helicoides, Suitable habitat analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9173871/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9173871/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eA nationwide assessment of the climatic suitability for \\u003cem\\u003ePythium helicoides\\u003c/em\\u003eprovides a scientific basis for developing preventive strategies against this pathogen in China. Using the Maximum Entropy (MaxEnt) model combined with ArcGIS, the potential geographic distribution and suitable habitats of \\u003cem\\u003eP. helicoides\\u003c/em\\u003e were predicted based on 37 occurrence records (from GBIF and literature) and 37 environmental variables. The potential distribution was simulated under current and future climate conditions (2021–2100) across low (SSP1-2.6), medium (SSP3-7.0), and high (SSP5-8.5) emission scenarios. Key environmental variables influencing habitat suitability were identified. The results show: (1) The MaxEnt model performed reliably, with AUC values exceeding 0.9 across all periods. (2) The distribution of suitable areas was mainly affected by Bio14 (precipitation of the driest month), Bio7 (temperature annual range), and elevation, which together contributed 86.2% of the cumulative influence. (3) Under current conditions, suitable habitats were classified into high (37.11×10⁴ km²), medium (65.66×10⁴ km²), and low suitability (87.24×10⁴ km²), with highly suitable areas concentrated in eastern Hainan and throughout Taiwan, characterized by tropical/subtropical monsoon climates. In future scenarios, suitable habitats are projected to occur mainly in tropical, subtropical, and warm temperate regions, with an overall declining trend and a northward shift in latitude. Plains, basin floors, and valleys are highly suitable due to large catchment areas and slow drainage, while windward slopes in mountains are generally unsuitable. However, lower-lying depressions or terraced areas with gentler slopes may form scattered medium-suitable habitats.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Based on the MaxEnt model, the potential suitable areas for Pythium helicoides in China are predicted\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-06 10:17:00\",\"doi\":\"10.21203/rs.3.rs-9173871/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a7285d4e-d29c-4cda-9492-ca9f342d3cef\",\"owner\":[],\"postedDate\":\"April 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":65653982,\"name\":\"Earth and environmental sciences/Climate sciences\"},{\"id\":65653983,\"name\":\"Biological sciences/Ecology\"},{\"id\":65653984,\"name\":\"Earth and environmental sciences/Ecology\"},{\"id\":65653985,\"name\":\"Earth and environmental sciences/Environmental sciences\"}],\"tags\":[],\"updatedAt\":\"2026-04-15T10:43:22+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-06 10:17:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9173871\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9173871\",\"identity\":\"rs-9173871\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}