Climate-driven diversity patterns and range dynamics of Maclura (Moraceae) worldwide

Climate-driven diversity patterns and range dynamics of Maclura (Moraceae) worldwide | 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 Climate-driven diversity patterns and range dynamics of Maclura (Moraceae) worldwide Kexin Zhao, Guodong Li, Yilong Shi, Cancan Li, Lehan Jia, Jinan An, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8309750/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 The genus Maclura (Moraceae), the most widespread and only spiny genus in its family, includes trees, shrubs, and lianas, many with significant medicinal value. However, systematic studies on its global diversity patterns and responses to future climate sce-narios are still lacking. This study analyzed global diversity patterns using species oc-currence data in Geographic Information System (GIS) software. Stepwise regression in R identified environmental drivers, and Maximum Entropy (MaxEnt) modeling pre-dicted current and future suitable habitats. The aim was to elucidate the distribution drivers and predict habitat shifts under climate change. Results show: (1) Maclura has an intercontinental disjunct distribution, concentrated in South America, southern North America, southwestern Europe, and southeastern Asia, with the latter being the richness center and southern North America the endemism center. (2) Diversity is primarily influenced by temperature, solar radiation, water vapor pressure, and soil cation exchange capacity. (3) Current suitable habitats focus on South America, southeastern Asia, and central Africa; future climate scenarios project overall reduction in suitable area. (4) The present distribution centroid is in Ethiopia, Africa, shifting westward and increasingly northwestward under intensified climate forcing. This re-veals Maclura 's global distribution drivers, providing a scientific basis for conserving its germplasm diversity and enabling sustainable use. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The genus Cudrania Tréc., traditionally classified within the family Moraceae, was historically considered sister to Maclura Nutt. (a monotypic genus 1 . However, recent taxonomic revisions by Wu et al. proposed the merger of Asian Cudrania into Maclura due to unstable diagnostic characteristics in Cudrania , thereby redefining the expanded genus Maclura in a broader sense 1 . Currently, the genus comprises approximately 12 species distributed across Africa, Asia, the Americas, Australia, and Pacific islands 2 , 3 , 4 . Maclura represents the most widely distributed genus in Moraceae and is uniquely characterized by the presence of spines, encompassing growth forms ranging from trees and shrubs to lianas (Fig. 1 a-d) 4 . Many species exhibit significant medicinal value, while others are utilized for soil restoration, horticultural cultivation, and timber production, highlighting their substantial economic importance 3 , 5 . Most species in the Maclura genus, such as Maclura tricuspidata Carrière and Maclura cochinchinensis (Lour.) Corner, are rich in bioactive compounds, predominantly flavonoids. These compounds exhibit notable pharmacological properties, including anti-inflammatory, hepatoprotective, anti-lipid peroxidation, and antitumor activities, demonstrating significant medicinal value 6 , 7 . The cultural significance of Maclura is exemplified by Maclura tricuspidata Carrière, a species deeply rooted in Chinese history. Zhecheng County in Henan Province, named after this plant, has been designated by UNESCO as a "Millennium-Old Chinese County" 8 . Nevertheless, overexploitation of genetic resources in regions such as Brazil has pushed certain species toward endangered status 9 . Current research on Maclura primarily focuses on taxonomic revisions, horticultural applications, and phytochemical analyses of individual species. However, comprehensive studies addressing the genus’s evolutionary development, cultivation systems, and adaptive management remain limited, hindering the formulation of effective conservation and utilization strategies 10 . Global biodiversity conservation efforts have intensified in recent decades, with species diversity distribution patterns remaining a central theme in biogeography and macroecology 11 . Current methodologies for analyzing these patterns have reached technical maturity. Stepwise regression analysis within the least squares framework quantitatively assesses relationships between species diversity indices and environmental predictors by optimizing variable weighting 12 , 13 . In parallel, the Maximum Entropy (MaxEnt) model has emerged as a cornerstone tool in ecological niche modeling 14 , 15 , 16 . Widely applied in conservation biology and natural resource management, MaxEnt outputs inform critical decisions regarding species habitat suitability 17 . These investigations not only reveal species-environment adaptations but also provide critical insights into ecological niche partitioning and survival strategies 18 . Spatial distribution analyses elucidate habitat preferences and environmental thresholds governing species viability, while identification of biodiversity hotspots facilitates mechanistic understanding of species assemblage patterns 19 . Such knowledge is indispensable for developing evidence-based conservation strategies, enabling the formulation of strategic frameworks for wild plant germplasm resource preservation and sustainable utilization 13 . Although there have been studies on the taxonomy, medicinal value, and regional distribution of the genus Maclura , there is still a lack of systematic and comprehensive research on its global diversity distribution patterns, dominant environmental factors, and responses to future climate change. Therefore, this study addresses the following scientific questions: (1) What are the global diversity distribution patterns of Maclura species? (2) What are the main environmental factors influencing their distribution? (3) How will future climate change affect the distribution of suitable habitats and the shift in their centroid?This research aims to enhance our understanding of the global distribution patterns of Maclura species, elucidate their underlying causes, and provide a scientific foundation for the conservation of genetic diversity and sustainable utilization of germplasm resources within this genus. The aim is to promote the rational utilization of this genus in ecological restoration, medicinal development, and landscape applications, and to provide references for formulating plant diversity conservation strategies in the context of global change. Results Distribution pattern. This study employed 31,783 filtered occurrence records of Maclura species to construct global diversity distribution patterns (Fig. 1 e). The species richness pattern of Maclura exhibits pronounced intercontinental disjunction, with primary distributions observed in South America, southern North America, southwestern Europe, and southeastern Asia. Notably, southeastern Asia is the richness center of Maclura with the highest species diversity (4 species). In contrast, regions of high endemism are concentrated in southern North America, southeastern South America, and southwestern Europe, with the former two areas identified as endemic centers (Fig. 1 f). Causes of the distribution pattern. The stepwise regression analysis revealed significant correlations between distribution pattern indices (SR and WE) and environmental factors, as detailed in Table 1 Fig. 2 and Figure S1 . The results showed that a strong positive correlation was observed between species richness (SR) and the maximum temperature of the warmest month (Bio5), july vapor pressure (Vapr7). Conversely, significant negative correlations were detected between species richness (SR) and mean diurnal range (Bio2), the minimum temperature of the coldest month (Bio6), july solar radiation (Sard7), soil cation exchange capacity (cecsol). No obvious linear relationship was found between The weighted endemism (WE) and environmental factors. Table 1 The results of single variable ordinary least squares linear regressions model. Variable Species richness (SR) Number Weighted endemism (WE) r²(10 − 2 ) p r²(10 − 2 ) p bio2 2.46 0.0118 * slope5 1.69 <0.0001 *** elevation 1.02 0.0152 * slope8 5.83 <0.0001 *** bio6 0.39 <0.0001 *** cecsol 13.9 <0.0001 *** vapr7 3.58 <0.0001 *** vapr7 3.34 <0.0001 *** bio5 0.04 <0.0001 *** bio5 0.01 0.00504 ** cecsol 1.33 <0.0001 *** -- -- -- sard7 1.12 <0.0001 *** -- -- -- slope7 12.2 <0.0001 *** -- -- -- Notes: r²: coefficient of determination, *: P < 0.05, **༚P < 0.01, ***༚P < 0.001 Key variables affecting the potential distribution. The formal prediction results of the potential distribution model are presented in Figure S2. The AUC values for both training and test datasets reached 0.881 and 0.879, respectively, exceeding the 0.8 threshold for model reliability. These metrics indicate robust predictive accuracy and high credibility of the habitat suitability projections. In the preliminary Maclura distribution model, the contribution rates of environmental factors are summarized in Table S2, with precipitation of the wettest month (bio13) showing zero contribution, leading to its exclusion from subsequent analyses. Correlation analysis of the 19 initial environmental variables is detailed in Figure S3. Based on contribution rankings and correlation outcomes, 10 key bioclimatic variables (bio16, bio18, bio14, bio1, bio8, bio15, bio7, bio19, bio5, bio2) were retained for final distribution modeling. Using MaxEnt v3.4.1 with these 10 contemporary factors, projections identified precipitation of the wettest quarter (bio16, 53.4%), precipitation of the warmest quarter (bio18, 24.2%), and annual mean temperature (bio1, 11.3%) as dominant determinants of current habitat suitability (Table 2 ). Notably, water-related factors collectively accounted for 84.8% of total contribution, while temperature-associated variables constituted 15.2%, underscoring precipitation as the primary driver of Maclura distribution, followed by thermal conditions. Jackknife validation (Fig. 3 a) revealed three pivotal variables when evaluated individually: precipitation of the wettest quarter (bio16), precipitation of the warmest quarter (bio18), and mean temperature of the wettest quarter (bio8). Conversely, mean diurnal temperature range (bio2) emerged as the most influential factor when other variables were excluded. These results collectively identify four critical climatic determinants: bio16, bio18, bio8, and bio2, with their specific response curves illustrated in Fig. 3 b. Table 2 Contribution rate of each climate variable in MaxEnt model. Variables Description Percent contribution /% bio16 Precipitation of Wettest Quarter 54.1 bio18 Precipitation of Warmest Quarter 24.2 bio1 Annual Mean Temperature 11.3 bio14 Precipitation of Driest Month 4.3 bio8 Mean Temperature of Wettest Quarter 2.4 bio15 Precipitation Seasonality 1.3 bio7 Temperature Annual Range (BIO5-BIO6) 1 bio19 Precipitation of Coldest Quarter 0.9 bio2 Mean Diurnal Range 0.3 bio5 Max Temperature of Warmest Month 0.2 Prediction distribution area of contemporary. The current predicted suitable habitats of Maclura species under contemporary climatic conditions are detailed in Table 3 and Fig. 4 . The total suitable habitat area was estimated to be approximately 33.18 million km² globally. High-suitability areas (6.37 million km², 19.2% of total) were predominantly located in southeastern Asia (particularly China) and southeastern South America; moderate-suitability zones (13.92 million km², 41.9%) occurred primarily in central Africa and northeastern South America; low-suitability regions (12.89 million km², 38.9%) were distributed across southeastern South America, the periphery of central African suitable habitats, and Indonesia. Collectively, the modeled suitable habitats spanned multiple continents, with core distribution areas in South America, southeastern Asia (centered in China), and central Africa, along with significant potential ranges in Mexico and the southeastern United States (North America), Indonesia, and eastern coastal regions of Oceania (Fig. 4 ). Table 3 The area of suitable distribution area in the current and future Suitable area/10 6 km 2 Period Contemporary SSPs126 SSPs245 SSPs585 High suitable area 6.37 6.42 6.34 6.30 Medium suitable area 13.92 13.32 14.32 13.37 Low suitable area 12.89 12.63 11.93 12.42 Total suitable area 33.18 32.37 32.59 32.09 Less than contemporary 0.81 0.59 1.09 Changes of subsistence distribution and spatial pattern in the future. Projected distribution patterns of Maclura species under future climate scenarios (2014–2060) are presented in Table 3 and Fig. 5 a. Compared with contemporary conditions, all three SSP scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) exhibited reductions in total suitable habitat area and individual high/moderate/low-suitability zones. The most severe decline occurred under the high-forcing SSP5-8.5 scenario, with total habitat losses of 10.9 × 10⁴ km², exceeding those of SSP1-2.6 (8.1 × 10⁴ km²) and SSP2-4.5 (5.9 × 10⁴ km²). Spatial dynamics analysis (Table 4 , Fig. 5 b) revealed that > 90% of current suitable habitats remained stable across all scenarios, though stability rates showed a clear declining trend with increasing radiative forcing. Core stable areas persisted in South America, southeastern Asia (particularly China), and central Africa, with additional retention in Mexico/southeastern United States, Indonesia, and eastern Oceania. Habitat expansion areas progressively increased with scenario severity, while contraction peaked under SSP5-8.5. Notably, range shifts primarily occurred at the peripheries of stable zones, with pronounced contraction clusters in southeastern North America. Table 4 Spatial variation of suitable area of Maclura in different climate scenarios Climate scenarios Area /10 5 km 2 Variation /% Extend Stable Shrink Expansion rate Stability rate Shrinkage SSPs126 7.89 315.36 15.96 2.38 95.04 4.81 SSPs245 9.94 315.42 15.91 3.00 95.06 4.80 SSPs585 10.17 310.25 21.05 3.07 93.51 6.34 Centroid migration trend in different situations. Centroid positions of Maclura species distribution under contemporary and future climate scenarios were calculated using the Mean Center tool in ArcGIS (Fig. 6 ). The contemporary centroid was located in Ethiopia, Africa (38°47′6″E, 6°46′28″N). Under future scenarios (2041–2060), all centroids shifted to Sudan, Africa, with coordinates at (33°39′15″E, 6°13′59″N) for SSP1-2.6, (32°54′38″E, 6°40′56″N) for SSP2-4.5, and (32°52′19″E, 7°00′55″N) for SSP5-8.5. Spatial analysis revealed distinct migration patterns: centroids under SSP1-2.6 and SSP2-4.5 exhibited southwestward displacement (Δ longitude: -5°08′-5°53′, Δ latitude: -0°32′-0°54′), whereas SSP5-8.5 showed northwestward movement (Δ longitude: -5°55′, Δ latitude: +0°14′). Collectively, westward centroid migration was observed across all scenarios, with trajectory deflection toward northwest increasing proportionally to radiative forcing intensity. Discussion Distribution pattern of Maclura. Southeast Asia (particularly southwestern China) serves as the diversity center of the genus Maclura underscoring the region's crucial role in species conservation and differentiation of this genus. This finding aligns with taxonomic records in Flora of China, which documents 5 native Maclura species within China's borders - representing 41.7% of the genus' global diversity (12 species worldwide) 20 . The concentration of all five Chinese species in the tropical to subtropical zones of southwestern China further corroborates this pattern, while maintaining biogeographic consistency with recognized diversification centers in Malaysia and India, which reinforces the significant status of Southeast Asia as a diversity hotspot for Maclura 21 , 22 . Contrastingly, southern North America emerged as an endemism center for the genus, characterized by limited species richness but high phylogenetic distinctiveness. Causes of the distribution pattern. According to the results of stepwise regression analysis, species richness of Maclura is significantly correlated with multiple environmental factors, reflecting the complexity of its ecological adaptability. It can be seen that the species richness distribution pattern of Maclura is positively correlated with the highest temperature in the hottest month, July water vapor pressure, and negatively correlated with the mean diurnal range, the minimum temperature of the coldest month, july solar radiation, soil cation exchange capacity. This is consistent with the distribution characteristics of the species of Maclura mainly distributed in the mountain or forest margin area with abundant sunshine at an altitude of 500 -2200m, as well as the living habits of calc-loving soil and drought resistance 22 , 23 . It is hypothesized that changes in geographical location influence the distribution of Maclura species by altering ambient environmental conditions such as humidity, temperature, and solar radiation 24 . These findings indicate that temperature and solar radiation are dominant factors shaping the diversity distribution patterns of Maclura species. Consequently, these dominant factors must be prioritized during the introduction, cultivation, and ex situ conservation of Maclura plants. The dominant variables restricted the distribution pattern. The Jackknife test validation revealed that precipitation of the wettest quarter (bio16), precipitation of the warmest quarter (bio18), and mean temperature of the wettest quarter (bio8) constituted the three most influential variables affecting Maclura distribution when evaluating single environmental factorsThe response curve of Maclura occurrence probability to precipitation in the wettest quarter exhibited a unimodal response curve characterized by an initial increase followed by a plateau phase after reaching maximum probability. It indicates that although Maclura prefers humid conditions, it is intolerant of waterlogging. In contrast, the mean diurnal temperature range (bio2) emerged as the dominant predictor when excluding these three variables. A monotonic negative correlation was observed between survival probability and mean diurnal temperature range (bio2), suggesting reduced viability of Maclura species under conditions of pronounced temperature fluctuations. Using a 0.5 probability threshold, the optimal ranges were quantified as follows: precipitation in the warmest quarter (bio18) 150–2,400 mm, precipitation in the wettest quarter (bio16) 300–2,000 mm, mean temperature of the wettest quarter (bio8) 17–47°C, and mean diurnal temperature range (bio2) -2–19°C. These thresholds provide a quantitative basis for the management of its suitable habitats. The distribution of suitable areas in different future periods and climate scenarios. Under future climate scenarios, the suitable habitat area for Maclura is generally projected to decrease, with the decline being most pronounced under the high forcing scenario (SSP585). This reduction in area may be associated with altered precipitation patterns under global warming. Suitable habitats are expected to expand toward higher latitude temperate zones, while notable contraction is projected in the southeastern United States. This suggests the impact of climate change on aridification in low-latitude regions and changes in hydrothermal conditions at higher latitudes. Despite the overall reduction in area, most of the current suitable habitats are expected to remain stable (stability rate > 90%), indicating strong ecological resilience of Maclura in its core habitats. 25 . Analysis of spatial pattern change and centroid transfer of the Maclura. Across three climate scenarios, Maclura habitats consistently exhibited area reductions, with the most pronounced contractions concentrated in southeastern North America, hypothesized that reduced precipitation and increased temperature fluctuations in this region are the main limiting factors.. This pattern implies that accelerating climate warming and rising carbon emissions may induce disproportionate changes in precipitation regimes and thermal conditions across southern North America, ultimately rendering these regions unsuitable for Maclura persistence 21 , 24 . Geospatial analysis of range centroids demonstrates a northwestward migration trajectory, shifting from present-day Ethiopia (Africa) towards Sudan under mid-century projections. This displacement intensifies with heightened climatic forcing across scenarios, aligning with established biogeographic patterns of poleward plant migration under global warming 25 , 26 . This shift suggests that future conservation efforts should focus on the maintenance and monitoring of potential suitable habitats in western and northwestern Africa. Conclusions. This study investigated the global diversity patterns of Maclura species and identify key environmental determinants of their distribution.We projected current and mid-century (2041–2060) potential distributions under three climate scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5), with subsequent analysis of range dynamics and centroid migration. Key findings include: (1) Maclura exhibits a disjunct intercontinental distribution pattern, with primary occurrences in South America, southern North America, southwestern Europe, and southeastern Asia. The latter region constitutes the diversity hotspot, while endemicity centers are located in southern North America and southeastern South America. (2) Six bioclimatic-edaphic variables emerged as dominant distribution drivers: temperature parameters, solar radiation, water vapor pressure, slope gradient, elevation, and soil cation exchange capacity (CEC). (3) Under all climate scenarios, > 90% of current suitable habitats remain stable, though stability rates exhibit inverse correlation with climatic forcing intensity. Geospatial analysis revealed systematic northwestward centroid migration from present-day Ethiopia, with displacement magnitude proportional to scenario severity (SSP1-2.6: 83 km; SSP5-8.5: 217 km). These findings elucidate the macroecological mechanisms shaping Maclura distributions and provide critical baselines for conservation prioritization, particularly regarding ex situ preservation strategies and climate-resilient habitat management. Methods Distribution data acquisition and filtering. Distribution records of Maclura taxa were obtained through global biodiversity repositories including the Global Biodiversity Information Facility (GBIF; http://www.gbif.org ), the National Specimen Information Infrastructure of China (NSII; http://nsii.org.cn/2017/home.php ), and the Chinese Virtual Herbarium (CVH; https://www.cvh.ac.cn/ ). The raw data were cleaned by removing duplicate, incomplete, and invalid distribution records. 17 . Finally, 31,783 georeferenced records of Maclura were obtained for the subsequent distribution pattern analysis. A multi-stage spatial refinement protocol was implemented to mitigate sampling bias in the process of ecological niche analysis. Primary distribution points first underwent a buffer-based spatial thinning procedure using ArcGIS geoprocessing tools, enforcing a 5 km × 5 km exclusion radius to ensure single-point representation per grid unit 27 , 28 . The refined dataset was further processed through the SDM Toolbox v2.4 extension to address spatial autocorrelation artifacts, ultimately generating 3,224 spatially independent occurrence points. These filtered records served as foundational inputs for MaxEnt predictions of potential suitable habitats across the genus. Environmental data acquisition. To comprehensively investigate the determinants underlying the distribution patterns of Maclura species diversity, this study selected 46 environmental variables across four categories (Table S1 ) for subsequent analyses. These comprised: 1) bioclimatic factors, elevation, wind speed, water vapor pressure, and solar radiation indices sourced from WorldClim ( http://www.worldclim.org ); 2) UV-B radiation parameters obtained from the Global UV Database ( https://www.ufz.de/gluv ); and 3) edaphic variables, slope, and aspect derived from the Harmonized World Soil Database ( http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ ) 29, 30, 31 . All environmental data were standardized to a spatial resolution of 2.5 minutes (approximately 5km). The 19 contemporary climate variables represent averages from 1970 to 2000, while future climate data were based on projections for 2041–2060 under three scenarios (SSP126, SSP245, and SSP585) from the BCC-CSM2-MR model. Distribution pattern and cause analysis. Species richness (SR) and weighted endemism (WE) serve as pivotal metrics for analyzing biodiversity distribution patterns. SR quantifies the total number of species within a defined geographical unit 32 , 33 . WE was calculated as the reciprocal of species distribution area (measured by grid cell counts), with spatial weighting applied according to range restriction 34 . WE was calculated as the reciprocal of species distribution area (measured by grid cell counts), with spatial weighting applied according to range restriction 34 . Based on 2°×2° grid cells, ArcGIS 10.8 was used to calculate the number of species and weighted endemism values within each grid, and global distribution heat maps were generated. To investigate the principal factors influencing the diversity distribution pattern of Maclura , stepwise regression analysis based on ordinary least squares (OLS) was performed in R v4.3.1 to model the relationships between species richness, weighted endemism, and environmental factors 33 , 35 . To avoid internal correlations between factors that may affect the stability of the model, the optimal variable combination was identified based on the principle of the minimum Akaike Information Criterion (AIC) 36 . Subsequently, the 46 environmental variables underwent Pearson autocorrelation analysis in R 37 . Following the AIC minimization criteria, highly correlated variables (r>|0.7|) were systematically eliminated 38 (Figure S4, Figure S5). Environmental factors exhibiting statistically significant correlations with the species richness or weighted endemism of Maclura were ultimately identified, and residual analysis plots were generated to visualize these relationships. Suitable area prediction and centroid migration analysis. To prevent overfitting of prediction results and ensure model accuracy and reliability, this study carried out the following treatment 39 . First, Pearson correlation analysis was performed on 19 bioclimatic variables extracted from georeferenced occurrence points of Maclura to evaluate collinearity 40 , 41 . Second, preliminary simulations were conducted using MaxEnt 3.4.1 to quantify variable contributions through permutation importance 38 , 42 . In order to make the regression model more accurate, Variance Inflation Factor (VIF) was used to further eliminate the redundant factors, with a threshold value of 10. Finally, environmental factors with high correlation coefficients and relatively low contribution rates 43 , as well as those with zero contribution rates, were eliminated based on their contribution magnitudes 44 . Finally, the selected environmental variables were applied to predict the suitable habitat distribution of the Maclura genus. The selected bioclimatic variables (.asc format) and species occurrence data (.csv format) were integrated into MaxEnt 3.4.1 with the following parameterization: 75% of occurrence records allocated for model training versus 25% for validation 41 , 45 . Model performance was evaluated through receiver operating characteristic (ROC) analysis using the jackknife method, with predictive accuracy quantified by the area under the curve (AUC) metric 45 . The AUC scale (0–1) reflects model discrimination capacity, where values > 0.8 indicate high predictive performance 46 , 47 , with ascending values corresponding to improved environmental variable-selection congruence 48 , 49 . The prediction results were imported into ArcGIS for reclassification into non-suitable habitats (0 < P < 0.2), low-suitable habitats (0.2 < P < 0.4), medium-suitable habitats (0.4 < P 0.6). 50, 51, 52, 53 . Finally, using the continental layer as a base map, output global potential distribution prediction maps of Maclura under current and future (2041–2060) climate scenarios, along with centroid change diagrams under different climate scenarios. Calculate the area of potential suitable habitats and their changes using layer properties 20 . Declarations Acknowledgements (not compulsory) We thank Zhendong Hong, Jianyong Wang for technical assistance. Thank Yihan Chen and Aixiang Chu for helping to collect photos of Maclura tricuspidata . Author contributions statement K.Z. and L.W. conceived the investigation, G.L., L.J., Y. Z., K.Z., J.A., C.L. conducted the investigation, K.Z., L.J., Y.S. and G.L. analysed the results, Y.S., G.L. and Y.Z. wrote the original draft preparation, K.Z., L.W., and. Y.Z.; reviewed and edited the preparation, L.W. provided fundings. All authors reviewed the manuscript. Additional information Table S2: Pre-simulation contribution rate and importance of bioclimatic variables. Funding declaration: This work was funded by Doctoral Research Foundation of Henan University of Science and Technology [grant number 13480079]; National Undergraduate Training Program for Innovation and Entrepreneurship of Henan University of Science and Technology [grant number 2025457]; National Undergraduate Training Program for Innovation and Entrepreneurship of Henan Province [grant number S202510464065]. These funders provided financial support and contributed to the study design and data collection; the authors retained full responsibility for data analysis, manuscript writing, and the decision to submit for publication. Author Contribution K.Z. and L.W. conceived the investigation, G.L., L.J., Y. Z., K.Z., J.A., C.L. conducted the investigation, K.Z., L.J., Y.S. and G.L. analysed the results, Y.S., G.L. and Y.Z. wrote the original draft preparation, K.Z., L.W., and. Y.Z.; reviewed and edited the preparation, L.W. provided fundings. All authors reviewed the manuscript. Acknowledgement We thank Zhendong Hong, Jianyong Wang for technical assistance. Thank Yihan Chen and Aixiang Chu for helping to collect photos of Maclura tricuspidata. Data Availability Distribution records of Maclura taxa were obtained through global biodiversity repositories including the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), the National Specimen Information Infrastructure of China (NSII; http://nsii.org.cn/2017/home.php), and the Chinese Virtual Herbarium (CVH; https://www.cvh.ac.cn/).Bioclimatic factors, elevation, wind speed, water vapor pressure, and solar radiation indices sourced from WorldClim (http://www.worldclim.org); UV-B radiation parameters obtained from the Global UV Database (https://www.ufz.de/gluv); Edaphic variables, slope, and aspect derived from the Harmonized World Soil Database (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/) References Wu, C. Y., Zhou, Z. K. & Gilbert, M. G. Maclura Nutt Flora China : ; 5 . (2003). Gardner, E. M., Sarraf, P., Williams, E. W. & Zerega, N. J. C. 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Spatiotemporal Dynamics of Potential Suitable Habitats for Cercidiphyllum japonicum under Climate Change. J. Henan Univ. Sci. Technol. (Nat Sci) . 46 , 88–96. 10.15926/j.cnki.issn1672-6871.2025.04.010 (2025). Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.zip Figure S1: Results of the regression analysis between diversity distribution patterns and envi-ronmental factors. Notes: Purple curve: Smooth fitting line for components + residuals; Blue dot-ted line: Ideal "no-bias" reference line, SR: Species richness, WE: weighted endemism, r2: coeffi-cient of determination, p: significance level; Figure S2: ROC curve prediction results of MaxEnt model; Figure S3 Correlation analysis of bioclimatic variables; Figure S4 Correlation analysis re-sults between species richness distribution patterns and environmental factors; Figure S5 Corre-lation analysis results between weighted endemism distribution patterns and environmental fac-tors; Table S1: List of 46 environmental variables; Table S2: Pre-simulation contribution rate and importance of bioclimatic variables. GraphicalAbstract.jpg 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. 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08:33:13","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36573,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/5a5c0f39ffc15e245f015363.png"},{"id":99313596,"identity":"1cfae53a-5038-4cd8-a6c0-98c0ccbef5bb","added_by":"auto","created_at":"2025-12-31 16:20:19","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119580,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/71d6fd3007e3d1a44e72af9f.png"},{"id":99313808,"identity":"207eeb2f-6473-4519-81cb-8633c6327a3d","added_by":"auto","created_at":"2025-12-31 16:20:31","extension":"png","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68791,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/c6e8a22c313201b682d313f1.png"},{"id":99030613,"identity":"afc7fe33-dd89-4853-bd45-ef0785a1eaac","added_by":"auto","created_at":"2025-12-26 08:33:13","extension":"xml","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136785,"visible":true,"origin":"","legend":"","description":"","filename":"7c6b376fa1ff4a0295cc7fbe21e2df551structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/2f4aa18b9f74248fbb5af5a8.xml"},{"id":99313813,"identity":"2a0f9f1c-846e-4fb4-bea2-32e698adbb10","added_by":"auto","created_at":"2025-12-31 16:20:31","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":154639,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/506c8abcab848708d97660eb.html"},{"id":99313748,"identity":"317802fb-1591-4ccf-a174-206e15ac0cad","added_by":"auto","created_at":"2025-12-31 16:20:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":752106,"visible":true,"origin":"","legend":"\u003cp\u003eThe morphological characteristics and distribution patterns of Maclura species. (a-d) The morphological characteristics of \u003cem\u003eMaclura\u003c/em\u003e. (e) Distribution pattern of species richness of \u003cem\u003eMaclura\u003c/em\u003e. (f) Distribution pattern of weighted endemism of \u003cem\u003eMaclura.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/c1c5b8da8cd3ade6a707d75e.jpg"},{"id":99030576,"identity":"867ee5c4-8604-4251-a72f-e0dce0459c01","added_by":"auto","created_at":"2025-12-26 08:33:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":615724,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the regression analysis between diversity distribution patterns and environmental factors. Notes: Blue dotted line: Ideal \"no-bias\" reference line, SR: Species richness, r\u003csup\u003e2\u003c/sup\u003e: coefficient of determination, p: significance level.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/062ae43b8fd637d4dea4384b.jpg"},{"id":99313053,"identity":"650ea9ec-33e7-4471-9621-2db155befdbc","added_by":"auto","created_at":"2025-12-31 16:19:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1738896,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis results of MaxEnt. (a) The test results of Jackknife. (b) Response curves of main bioclimatic factors.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/86ddd5c296aa4ce34ab96c1c.jpg"},{"id":99313363,"identity":"42f5a103-4d3f-4b5d-a556-6513cdebff0c","added_by":"auto","created_at":"2025-12-31 16:20:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121344,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent potential distribution area of \u003cem\u003eMaclura\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/37bdec7e1e87ea33823141cd.jpg"},{"id":99314101,"identity":"334d5306-d318-406f-9dc6-44b1377a76e2","added_by":"auto","created_at":"2025-12-31 16:20:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2834181,"visible":true,"origin":"","legend":"\u003cp\u003eResults of subsistence distribution and spatial pattern in the future. (a) The suitable area under different climate scenarios in the future. (b) Spatial transformation pattern of the suitable area under different climate scenarios in the future.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/60b213b71190192006d26bad.jpg"},{"id":99313349,"identity":"f7146b4a-b93c-4724-8f80-4200521ae196","added_by":"auto","created_at":"2025-12-31 16:20:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1859190,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of current suitable area and centroid transfer of \u003cem\u003eMaclura\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/fe6aaa99da947f14c4e5d7f9.jpg"},{"id":108093684,"identity":"9597cee6-189c-4381-849d-bc3c519e93a8","added_by":"auto","created_at":"2026-04-29 09:41:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8375375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/efec6f9f-165c-4076-9449-a3cb032d602f.pdf"},{"id":99030616,"identity":"e0a7fb34-fc9b-477f-8877-7523ea84f07a","added_by":"auto","created_at":"2025-12-26 08:33:13","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20992037,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1: Results of the regression analysis between diversity distribution patterns and envi-ronmental factors. Notes: Purple curve: Smooth fitting line for components + residuals; Blue dot-ted line: Ideal \"no-bias\" reference line, SR: Species richness, WE: weighted endemism, r2: coeffi-cient of determination, p: significance level;\u003c/p\u003e\n\u003cp\u003eFigure S2: ROC curve prediction results of MaxEnt model;\u003c/p\u003e\n\u003cp\u003eFigure S3 Correlation analysis of bioclimatic variables;\u003c/p\u003e\n\u003cp\u003eFigure S4 Correlation analysis re-sults between species richness distribution patterns and environmental factors;\u003c/p\u003e\n\u003cp\u003eFigure S5 Corre-lation analysis results between weighted endemism distribution patterns and environmental fac-tors; Table S1: List of 46 environmental variables;\u003c/p\u003e\n\u003cp\u003eTable S2: Pre-simulation contribution rate and importance of bioclimatic variables.\u003c/p\u003e","description":"","filename":"Supplementaryfiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/d5e483f9cd4b84674df8afc6.zip"},{"id":99030578,"identity":"0df2c47b-ec7b-404a-904b-8ec6a8a60faa","added_by":"auto","created_at":"2025-12-26 08:33:12","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3128739,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8309750/v1/f06f63f1c0d1c6425f39bc06.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Climate-driven diversity patterns and range dynamics of Maclura (Moraceae) worldwide","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe genus \u003cem\u003eCudrania\u003c/em\u003e Tr\u0026eacute;c., traditionally classified within the family Moraceae, was historically considered sister to \u003cem\u003eMaclura\u003c/em\u003e Nutt. (a monotypic genus \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, recent taxonomic revisions by Wu et al. proposed the merger of Asian \u003cem\u003eCudrania\u003c/em\u003e into \u003cem\u003eMaclura\u003c/em\u003e due to unstable diagnostic characteristics in \u003cem\u003eCudrania\u003c/em\u003e, thereby redefining the expanded genus \u003cem\u003eMaclura\u003c/em\u003e in a broader sense \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Currently, the genus comprises approximately 12 species distributed across Africa, Asia, the Americas, Australia, and Pacific islands \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMaclura\u003c/em\u003e represents the most widely distributed genus in Moraceae and is uniquely characterized by the presence of spines, encompassing growth forms ranging from trees and shrubs to lianas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-d) \u003csup\u003e4\u003c/sup\u003e. Many species exhibit significant medicinal value, while others are utilized for soil restoration, horticultural cultivation, and timber production, highlighting their substantial economic importance \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Most species in the \u003cem\u003eMaclura\u003c/em\u003e genus, such as \u003cem\u003eMaclura tricuspidata\u003c/em\u003e Carri\u0026egrave;re and \u003cem\u003eMaclura cochinchinensis\u003c/em\u003e (Lour.) Corner, are rich in bioactive compounds, predominantly flavonoids. These compounds exhibit notable pharmacological properties, including anti-inflammatory, hepatoprotective, anti-lipid peroxidation, and antitumor activities, demonstrating significant medicinal value \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The cultural significance of \u003cem\u003eMaclura\u003c/em\u003e is exemplified by \u003cem\u003eMaclura tricuspidata\u003c/em\u003e Carri\u0026egrave;re, a species deeply rooted in Chinese history. Zhecheng County in Henan Province, named after this plant, has been designated by UNESCO as a \"Millennium-Old Chinese County\" \u003csup\u003e8\u003c/sup\u003e. Nevertheless, overexploitation of genetic resources in regions such as Brazil has pushed certain species toward endangered status \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Current research on \u003cem\u003eMaclura\u003c/em\u003e primarily focuses on taxonomic revisions, horticultural applications, and phytochemical analyses of individual species. However, comprehensive studies addressing the genus\u0026rsquo;s evolutionary development, cultivation systems, and adaptive management remain limited, hindering the formulation of effective conservation and utilization strategies \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobal biodiversity conservation efforts have intensified in recent decades, with species diversity distribution patterns remaining a central theme in biogeography and macroecology \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Current methodologies for analyzing these patterns have reached technical maturity. Stepwise regression analysis within the least squares framework quantitatively assesses relationships between species diversity indices and environmental predictors by optimizing variable weighting \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In parallel, the Maximum Entropy (MaxEnt) model has emerged as a cornerstone tool in ecological niche modeling \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Widely applied in conservation biology and natural resource management, MaxEnt outputs inform critical decisions regarding species habitat suitability \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These investigations not only reveal species-environment adaptations but also provide critical insights into ecological niche partitioning and survival strategies \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Spatial distribution analyses elucidate habitat preferences and environmental thresholds governing species viability, while identification of biodiversity hotspots facilitates mechanistic understanding of species assemblage patterns \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Such knowledge is indispensable for developing evidence-based conservation strategies, enabling the formulation of strategic frameworks for wild plant germplasm resource preservation and sustainable utilization \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough there have been studies on the taxonomy, medicinal value, and regional distribution of the genus \u003cem\u003eMaclura\u003c/em\u003e, there is still a lack of systematic and comprehensive research on its global diversity distribution patterns, dominant environmental factors, and responses to future climate change. Therefore, this study addresses the following scientific questions: (1) What are the global diversity distribution patterns of \u003cem\u003eMaclura\u003c/em\u003e species? (2) What are the main environmental factors influencing their distribution? (3) How will future climate change affect the distribution of suitable habitats and the shift in their centroid?This research aims to enhance our understanding of the global distribution patterns of \u003cem\u003eMaclura\u003c/em\u003e species, elucidate their underlying causes, and provide a scientific foundation for the conservation of genetic diversity and sustainable utilization of germplasm resources within this genus. The aim is to promote the rational utilization of this genus in ecological restoration, medicinal development, and landscape applications, and to provide references for formulating plant diversity conservation strategies in the context of global change.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDistribution pattern.\u003c/b\u003e This study employed 31,783 filtered occurrence records of \u003cem\u003eMaclura\u003c/em\u003e species to construct global diversity distribution patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). The species richness pattern of \u003cem\u003eMaclura\u003c/em\u003e exhibits pronounced intercontinental disjunction, with primary distributions observed in South America, southern North America, southwestern Europe, and southeastern Asia. Notably, southeastern Asia is the richness center of \u003cem\u003eMaclura\u003c/em\u003e with the highest species diversity (4 species). In contrast, regions of high endemism are concentrated in southern North America, southeastern South America, and southwestern Europe, with the former two areas identified as endemic centers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCauses of the distribution pattern.\u003c/b\u003e The stepwise regression analysis revealed significant correlations between distribution pattern indices (SR and WE) and environmental factors, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The results showed that a strong positive correlation was observed between species richness (SR) and the maximum temperature of the warmest month (Bio5), july vapor pressure (Vapr7). Conversely, significant negative correlations were detected between species richness (SR) and mean diurnal range (Bio2), the minimum temperature of the coldest month (Bio6), july solar radiation (Sard7), soil cation exchange capacity (cecsol). No obvious linear relationship was found between The weighted endemism (WE) and environmental factors.\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\u003eThe results of single variable ordinary least squares linear regressions model.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSpecies richness (SR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eWeighted endemism (WE)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026sup2;(10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u0026sup2;(10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0118 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eslope5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0152 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eslope8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ececsol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evapr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003evapr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00504 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ececsol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esard7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eslope7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.0001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: r\u0026sup2;: coefficient of determination, *: P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **༚P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***༚P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eKey variables affecting the potential distribution.\u003c/b\u003e The formal prediction results of the potential distribution model are presented in Figure S2. The AUC values for both training and test datasets reached 0.881 and 0.879, respectively, exceeding the 0.8 threshold for model reliability. These metrics indicate robust predictive accuracy and high credibility of the habitat suitability projections. In the preliminary \u003cem\u003eMaclura\u003c/em\u003e distribution model, the contribution rates of environmental factors are summarized in Table S2, with precipitation of the wettest month (bio13) showing zero contribution, leading to its exclusion from subsequent analyses. Correlation analysis of the 19 initial environmental variables is detailed in Figure S3. Based on contribution rankings and correlation outcomes, 10 key bioclimatic variables (bio16, bio18, bio14, bio1, bio8, bio15, bio7, bio19, bio5, bio2) were retained for final distribution modeling.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing MaxEnt v3.4.1 with these 10 contemporary factors, projections identified precipitation of the wettest quarter (bio16, 53.4%), precipitation of the warmest quarter (bio18, 24.2%), and annual mean temperature (bio1, 11.3%) as dominant determinants of current habitat suitability (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, water-related factors collectively accounted for 84.8% of total contribution, while temperature-associated variables constituted 15.2%, underscoring precipitation as the primary driver of \u003cem\u003eMaclura\u003c/em\u003e distribution, followed by thermal conditions.\u003c/p\u003e \u003cp\u003eJackknife validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) revealed three pivotal variables when evaluated individually: precipitation of the wettest quarter (bio16), precipitation of the warmest quarter (bio18), and mean temperature of the wettest quarter (bio8). Conversely, mean diurnal temperature range (bio2) emerged as the most influential factor when other variables were excluded. These results collectively identify four critical climatic determinants: bio16, bio18, bio8, and bio2, with their specific response curves illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eb.\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\u003eContribution rate of each climate variable in MaxEnt model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent contribution /%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Wettest Quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Warmest Quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Mean Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Driest Month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of Wettest Quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\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\u003ePrecipitation Seasonality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\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\u003eTemperature Annual Range (BIO5-BIO6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Coldest Quarter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Diurnal Range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebio5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax Temperature of Warmest Month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\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 \u003cb\u003ePrediction distribution area of contemporary.\u003c/b\u003e The current predicted suitable habitats of \u003cem\u003eMaclura\u003c/em\u003e species under contemporary climatic conditions are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The total suitable habitat area was estimated to be approximately 33.18\u0026nbsp;million km\u0026sup2; globally. High-suitability areas (6.37\u0026nbsp;million km\u0026sup2;, 19.2% of total) were predominantly located in southeastern Asia (particularly China) and southeastern South America; moderate-suitability zones (13.92\u0026nbsp;million km\u0026sup2;, 41.9%) occurred primarily in central Africa and northeastern South America; low-suitability regions (12.89\u0026nbsp;million km\u0026sup2;, 38.9%) were distributed across southeastern South America, the periphery of central African suitable habitats, and Indonesia. Collectively, the modeled suitable habitats spanned multiple continents, with core distribution areas in South America, southeastern Asia (centered in China), and central Africa, along with significant potential ranges in Mexico and the southeastern United States (North America), Indonesia, and eastern coastal regions of Oceania (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\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\u003eThe area of suitable distribution area in the current and future\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuitable area/10\u003csup\u003e6\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e Period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContemporary\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSPs126\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSSPs245\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSSPs585\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh suitable area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium suitable area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow suitable area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal suitable area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than contemporary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\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 \u003cp\u003e \u003cb\u003eChanges of subsistence distribution and spatial pattern in the future.\u003c/b\u003e Projected distribution patterns of \u003cem\u003eMaclura\u003c/em\u003e species under future climate scenarios (2014\u0026ndash;2060) are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003ea. Compared with contemporary conditions, all three SSP scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) exhibited reductions in total suitable habitat area and individual high/moderate/low-suitability zones. The most severe decline occurred under the high-forcing SSP5-8.5 scenario, with total habitat losses of 10.9 \u0026times; 10⁴ km\u0026sup2;, exceeding those of SSP1-2.6 (8.1 \u0026times; 10⁴ km\u0026sup2;) and SSP2-4.5 (5.9 \u0026times; 10⁴ km\u0026sup2;).\u003c/p\u003e \u003cp\u003eSpatial dynamics analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) revealed that \u0026gt;\u0026thinsp;90% of current suitable habitats remained stable across all scenarios, though stability rates showed a clear declining trend with increasing radiative forcing. Core stable areas persisted in South America, southeastern Asia (particularly China), and central Africa, with additional retention in Mexico/southeastern United States, Indonesia, and eastern Oceania. Habitat expansion areas progressively increased with scenario severity, while contraction peaked under SSP5-8.5. Notably, range shifts primarily occurred at the peripheries of stable zones, with pronounced contraction clusters in southeastern North America.\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\u003eSpatial variation of suitable area of \u003cem\u003eMaclura\u003c/em\u003e in different climate scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClimate scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eArea /10\u003csup\u003e5\u003c/sup\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eVariation /%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShrink\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpansion rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStability rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eShrinkage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e315.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.34\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 \u003cp\u003e \u003cb\u003eCentroid migration trend in different situations.\u003c/b\u003e Centroid positions of \u003cem\u003eMaclura\u003c/em\u003e species distribution under contemporary and future climate scenarios were calculated using the Mean Center tool in ArcGIS (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The contemporary centroid was located in Ethiopia, Africa (38\u0026deg;47\u0026prime;6\u0026Prime;E, 6\u0026deg;46\u0026prime;28\u0026Prime;N). Under future scenarios (2041\u0026ndash;2060), all centroids shifted to Sudan, Africa, with coordinates at (33\u0026deg;39\u0026prime;15\u0026Prime;E, 6\u0026deg;13\u0026prime;59\u0026Prime;N) for SSP1-2.6, (32\u0026deg;54\u0026prime;38\u0026Prime;E, 6\u0026deg;40\u0026prime;56\u0026Prime;N) for SSP2-4.5, and (32\u0026deg;52\u0026prime;19\u0026Prime;E, 7\u0026deg;00\u0026prime;55\u0026Prime;N) for SSP5-8.5. Spatial analysis revealed distinct migration patterns: centroids under SSP1-2.6 and SSP2-4.5 exhibited southwestward displacement (Δ longitude: -5\u0026deg;08\u0026prime;-5\u0026deg;53\u0026prime;, Δ latitude: -0\u0026deg;32\u0026prime;-0\u0026deg;54\u0026prime;), whereas SSP5-8.5 showed northwestward movement (Δ longitude: -5\u0026deg;55\u0026prime;, Δ latitude: +0\u0026deg;14\u0026prime;). Collectively, westward centroid migration was observed across all scenarios, with trajectory deflection toward northwest increasing proportionally to radiative forcing intensity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cb\u003eDistribution pattern of Maclura.\u003c/b\u003e Southeast Asia (particularly southwestern China) serves as the diversity center of the genus \u003cem\u003eMaclura\u003c/em\u003e underscoring the region's crucial role in species conservation and differentiation of this genus. This finding aligns with taxonomic records in Flora of China, which documents 5 native \u003cem\u003eMaclura\u003c/em\u003e species within China's borders - representing 41.7% of the genus' global diversity (12 species worldwide) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The concentration of all five Chinese species in the tropical to subtropical zones of southwestern China further corroborates this pattern, while maintaining biogeographic consistency with recognized diversification centers in Malaysia and India, which reinforces the significant status of Southeast Asia as a diversity hotspot for \u003cem\u003eMaclura\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Contrastingly, southern North America emerged as an endemism center for the genus, characterized by limited species richness but high phylogenetic distinctiveness.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCauses of the distribution pattern.\u003c/b\u003e According to the results of stepwise regression analysis, species richness of \u003cem\u003eMaclura\u003c/em\u003e is significantly correlated with multiple environmental factors, reflecting the complexity of its ecological adaptability. It can be seen that the species richness distribution pattern of \u003cem\u003eMaclura\u003c/em\u003e is positively correlated with the highest temperature in the hottest month, July water vapor pressure, and negatively correlated with the mean diurnal range, the minimum temperature of the coldest month, july solar radiation, soil cation exchange capacity. This is consistent with the distribution characteristics of the species of \u003cem\u003eMaclura\u003c/em\u003e mainly distributed in the mountain or forest margin area with abundant sunshine at an altitude of 500 -2200m, as well as the living habits of calc-loving soil and drought resistance \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. It is hypothesized that changes in geographical location influence the distribution of \u003cem\u003eMaclura\u003c/em\u003e species by altering ambient environmental conditions such as humidity, temperature, and solar radiation \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. These findings indicate that temperature and solar radiation are dominant factors shaping the diversity distribution patterns of \u003cem\u003eMaclura\u003c/em\u003e species. Consequently, these dominant factors must be prioritized during the introduction, cultivation, and ex situ conservation of \u003cem\u003eMaclura\u003c/em\u003e plants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe dominant variables restricted the distribution pattern.\u003c/b\u003e The Jackknife test validation revealed that precipitation of the wettest quarter (bio16), precipitation of the warmest quarter (bio18), and mean temperature of the wettest quarter (bio8) constituted the three most influential variables affecting \u003cem\u003eMaclura\u003c/em\u003e distribution when evaluating single environmental factorsThe response curve of \u003cem\u003eMaclura\u003c/em\u003e occurrence probability to precipitation in the wettest quarter exhibited a unimodal response curve characterized by an initial increase followed by a plateau phase after reaching maximum probability. It indicates that although \u003cem\u003eMaclura\u003c/em\u003e prefers humid conditions, it is intolerant of waterlogging. In contrast, the mean diurnal temperature range (bio2) emerged as the dominant predictor when excluding these three variables. A monotonic negative correlation was observed between survival probability and mean diurnal temperature range (bio2), suggesting reduced viability of \u003cem\u003eMaclura\u003c/em\u003e species under conditions of pronounced temperature fluctuations. Using a 0.5 probability threshold, the optimal ranges were quantified as follows: precipitation in the warmest quarter (bio18) 150\u0026ndash;2,400 mm, precipitation in the wettest quarter (bio16) 300\u0026ndash;2,000 mm, mean temperature of the wettest quarter (bio8) 17\u0026ndash;47\u0026deg;C, and mean diurnal temperature range (bio2) -2\u0026ndash;19\u0026deg;C. These thresholds provide a quantitative basis for the management of its suitable habitats.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe distribution of suitable areas in different future periods and climate scenarios.\u003c/b\u003e Under future climate scenarios, the suitable habitat area for \u003cem\u003eMaclura\u003c/em\u003e is generally projected to decrease, with the decline being most pronounced under the high forcing scenario (SSP585). This reduction in area may be associated with altered precipitation patterns under global warming. Suitable habitats are expected to expand toward higher latitude temperate zones, while notable contraction is projected in the southeastern United States. This suggests the impact of climate change on aridification in low-latitude regions and changes in hydrothermal conditions at higher latitudes. Despite the overall reduction in area, most of the current suitable habitats are expected to remain stable (stability rate\u0026thinsp;\u0026gt;\u0026thinsp;90%), indicating strong ecological resilience of \u003cem\u003eMaclura\u003c/em\u003e in its core habitats. \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of spatial pattern change and centroid transfer of the Maclura.\u003c/b\u003e Across three climate scenarios, \u003cem\u003eMaclura\u003c/em\u003e habitats consistently exhibited area reductions, with the most pronounced contractions concentrated in southeastern North America, hypothesized that reduced precipitation and increased temperature fluctuations in this region are the main limiting factors.. This pattern implies that accelerating climate warming and rising carbon emissions may induce disproportionate changes in precipitation regimes and thermal conditions across southern North America, ultimately rendering these regions unsuitable for \u003cem\u003eMaclura\u003c/em\u003e persistence \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Geospatial analysis of range centroids demonstrates a northwestward migration trajectory, shifting from present-day Ethiopia (Africa) towards Sudan under mid-century projections. This displacement intensifies with heightened climatic forcing across scenarios, aligning with established biogeographic patterns of poleward plant migration under global warming \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This shift suggests that future conservation efforts should focus on the maintenance and monitoring of potential suitable habitats in western and northwestern Africa.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions.\u003c/b\u003e This study investigated the global diversity patterns of \u003cem\u003eMaclura\u003c/em\u003e species and identify key environmental determinants of their distribution.We projected current and mid-century (2041\u0026ndash;2060) potential distributions under three climate scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5), with subsequent analysis of range dynamics and centroid migration. Key findings include:\u003c/p\u003e \u003cp\u003e(1) \u003cem\u003eMaclura\u003c/em\u003e exhibits a disjunct intercontinental distribution pattern, with primary occurrences in South America, southern North America, southwestern Europe, and southeastern Asia. The latter region constitutes the diversity hotspot, while endemicity centers are located in southern North America and southeastern South America.\u003c/p\u003e \u003cp\u003e(2) Six bioclimatic-edaphic variables emerged as dominant distribution drivers: temperature parameters, solar radiation, water vapor pressure, slope gradient, elevation, and soil cation exchange capacity (CEC).\u003c/p\u003e \u003cp\u003e(3) Under all climate scenarios, \u0026gt;\u0026thinsp;90% of current suitable habitats remain stable, though stability rates exhibit inverse correlation with climatic forcing intensity. Geospatial analysis revealed systematic northwestward centroid migration from present-day Ethiopia, with displacement magnitude proportional to scenario severity (SSP1-2.6: 83 km; SSP5-8.5: 217 km).\u003c/p\u003e \u003cp\u003eThese findings elucidate the macroecological mechanisms shaping \u003cem\u003eMaclura\u003c/em\u003e distributions and provide critical baselines for conservation prioritization, particularly regarding ex situ preservation strategies and climate-resilient habitat management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eDistribution data acquisition and filtering.\u003c/b\u003e Distribution records of \u003cem\u003eMaclura\u003c/em\u003e taxa were obtained through global biodiversity repositories including the Global Biodiversity Information Facility (GBIF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gbif.org\u003c/span\u003e\u003cspan address=\"http://www.gbif.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the National Specimen Information Infrastructure of China (NSII; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://nsii.org.cn/2017/home.php\u003c/span\u003e\u003cspan address=\"http://nsii.org.cn/2017/home.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Chinese Virtual Herbarium (CVH; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cvh.ac.cn/\u003c/span\u003e\u003cspan address=\"https://www.cvh.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The raw data were cleaned by removing duplicate, incomplete, and invalid distribution records. \u003csup\u003e17\u003c/sup\u003e. Finally, 31,783 georeferenced records of \u003cem\u003eMaclura\u003c/em\u003e were obtained for the subsequent distribution pattern analysis.\u003c/p\u003e \u003cp\u003eA multi-stage spatial refinement protocol was implemented to mitigate sampling bias in the process of ecological niche analysis. Primary distribution points first underwent a buffer-based spatial thinning procedure using ArcGIS geoprocessing tools, enforcing a 5 km \u0026times; 5 km exclusion radius to ensure single-point representation per grid unit \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The refined dataset was further processed through the SDM Toolbox v2.4 extension to address spatial autocorrelation artifacts, ultimately generating 3,224 spatially independent occurrence points. These filtered records served as foundational inputs for MaxEnt predictions of potential suitable habitats across the genus.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEnvironmental data acquisition.\u003c/b\u003e To comprehensively investigate the determinants underlying the distribution patterns of \u003cem\u003eMaclura\u003c/em\u003e species diversity, this study selected 46 environmental variables across four categories (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) for subsequent analyses. These comprised: 1) bioclimatic factors, elevation, wind speed, water vapor pressure, and solar radiation indices sourced from WorldClim (\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); 2) UV-B radiation parameters obtained from the Global UV Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ufz.de/gluv\u003c/span\u003e\u003cspan address=\"https://www.ufz.de/gluv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); and 3) edaphic variables, slope, and aspect derived from the Harmonized World Soil Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/\u003c/span\u003e\u003cspan address=\"http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e29, 30, 31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll environmental data were standardized to a spatial resolution of 2.5 minutes (approximately 5km). The 19 contemporary climate variables represent averages from 1970 to 2000, while future climate data were based on projections for 2041\u0026ndash;2060 under three scenarios (SSP126, SSP245, and SSP585) from the BCC-CSM2-MR model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDistribution pattern and cause analysis.\u003c/b\u003e Species richness (SR) and weighted endemism (WE) serve as pivotal metrics for analyzing biodiversity distribution patterns. SR quantifies the total number of species within a defined geographical unit \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. WE was calculated as the reciprocal of species distribution area (measured by grid cell counts), with spatial weighting applied according to range restriction \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. WE was calculated as the reciprocal of species distribution area (measured by grid cell counts), with spatial weighting applied according to range restriction \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Based on 2\u0026deg;\u0026times;2\u0026deg; grid cells, ArcGIS 10.8 was used to calculate the number of species and weighted endemism values within each grid, and global distribution heat maps were generated.\u003c/p\u003e \u003cp\u003eTo investigate the principal factors influencing the diversity distribution pattern of \u003cem\u003eMaclura\u003c/em\u003e, stepwise regression analysis based on ordinary least squares (OLS) was performed in R v4.3.1 to model the relationships between species richness, weighted endemism, and environmental factors \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. To avoid internal correlations between factors that may affect the stability of the model, the optimal variable combination was identified based on the principle of the minimum Akaike Information Criterion (AIC) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Subsequently, the 46 environmental variables underwent Pearson autocorrelation analysis in R \u003csup\u003e37\u003c/sup\u003e. Following the AIC minimization criteria, highly correlated variables (r\u0026gt;|0.7|) were systematically eliminated \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e (Figure S4, Figure S5). Environmental factors exhibiting statistically significant correlations with the species richness or weighted endemism of \u003cem\u003eMaclura\u003c/em\u003e were ultimately identified, and residual analysis plots were generated to visualize these relationships.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSuitable area prediction and centroid migration analysis.\u003c/b\u003e To prevent overfitting of prediction results and ensure model accuracy and reliability, this study carried out the following treatment \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. First, Pearson correlation analysis was performed on 19 bioclimatic variables extracted from georeferenced occurrence points of \u003cem\u003eMaclura\u003c/em\u003e to evaluate collinearity \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Second, preliminary simulations were conducted using MaxEnt 3.4.1 to quantify variable contributions through permutation importance \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In order to make the regression model more accurate, Variance Inflation Factor (VIF) was used to further eliminate the redundant factors, with a threshold value of 10. Finally, environmental factors with high correlation coefficients and relatively low contribution rates \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, as well as those with zero contribution rates, were eliminated based on their contribution magnitudes \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Finally, the selected environmental variables were applied to predict the suitable habitat distribution of the \u003cem\u003eMaclura\u003c/em\u003e genus.\u003c/p\u003e \u003cp\u003eThe selected bioclimatic variables (.asc format) and species occurrence data (.csv format) were integrated into MaxEnt 3.4.1 with the following parameterization: 75% of occurrence records allocated for model training versus 25% for validation \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Model performance was evaluated through receiver operating characteristic (ROC) analysis using the jackknife method, with predictive accuracy quantified by the area under the curve (AUC) metric \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The AUC scale (0\u0026ndash;1) reflects model discrimination capacity, where values\u0026thinsp;\u0026gt;\u0026thinsp;0.8 indicate high predictive performance \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, with ascending values corresponding to improved environmental variable-selection congruence \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prediction results were imported into ArcGIS for reclassification into non-suitable habitats (0\u0026thinsp;\u0026lt;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.2), low-suitable habitats (0.2\u0026thinsp;\u0026lt;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.4), medium-suitable habitats (0.4\u0026thinsp;\u0026lt;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.6), and high-suitable habitats (P\u0026thinsp;\u0026gt;\u0026thinsp;0.6). \u003csup\u003e50, 51, 52, 53\u003c/sup\u003e. Finally, using the continental layer as a base map, output global potential distribution prediction maps of \u003cem\u003eMaclura\u003c/em\u003e under current and future (2041\u0026ndash;2060) climate scenarios, along with centroid change diagrams under different climate scenarios. Calculate the area of potential suitable habitats and their changes using layer properties \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements (not compulsory)\u003c/h2\u003e\n\u003cp\u003eWe thank Zhendong Hong, Jianyong Wang for technical assistance. Thank Yihan Chen and Aixiang Chu for helping to collect photos of \u003cem\u003eMaclura tricuspidata\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions statement\u003c/h2\u003e\n\u003cp\u003eK.Z. and L.W. conceived the investigation, G.L., L.J., Y. Z., K.Z., J.A., C.L. conducted the investigation, K.Z., L.J., Y.S. and G.L. analysed the results, Y.S., G.L. and Y.Z. wrote the original draft preparation, K.Z., L.W., and. Y.Z.; reviewed and edited the preparation, L.W. provided fundings. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAdditional information\u003c/h2\u003e\n\u003cp\u003eTable S2: Pre-simulation contribution rate and importance of bioclimatic variables.\u003c/p\u003e\n\u003ch2\u003eFunding declaration:\u003c/h2\u003e\n\u003cp\u003eThis work was funded by Doctoral Research Foundation of Henan University of Science and Technology [grant number 13480079]; National Undergraduate Training Program for Innovation and Entrepreneurship of Henan University of Science and Technology [grant number 2025457]; National Undergraduate Training Program for Innovation and Entrepreneurship of Henan Province [grant number S202510464065].\u003c/p\u003e\n\u003cp\u003eThese funders provided financial support and contributed to the study design and data collection; the authors retained full responsibility for data analysis, manuscript writing, and the decision to submit for publication.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eK.Z. and L.W. conceived the investigation, G.L., L.J., Y. Z., K.Z., J.A., C.L. conducted the investigation, K.Z., L.J., Y.S. and G.L. analysed the results, Y.S., G.L. and Y.Z. wrote the original draft preparation, K.Z., L.W., and. Y.Z.; reviewed and edited the preparation, L.W. provided fundings. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank Zhendong Hong, Jianyong Wang for technical assistance. Thank Yihan Chen and Aixiang Chu for helping to collect photos of Maclura tricuspidata.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eDistribution records of Maclura taxa were obtained through global biodiversity repositories including the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), the National Specimen Information Infrastructure of China (NSII; http://nsii.org.cn/2017/home.php), and the Chinese Virtual Herbarium (CVH; https://www.cvh.ac.cn/).Bioclimatic factors, elevation, wind speed, water vapor pressure, and solar radiation indices sourced from WorldClim (http://www.worldclim.org); UV-B radiation parameters obtained from the Global UV Database (https://www.ufz.de/gluv); Edaphic variables, slope, and aspect derived from the Harmonized World Soil Database (http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu, C. 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Spatiotemporal Dynamics of Potential Suitable Habitats for \u003cem\u003eCercidiphyllum japonicum\u003c/em\u003e under Climate Change. \u003cem\u003eJ. Henan Univ. Sci. Technol. (Nat Sci)\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e, 88\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15926/j.cnki.issn1672-6871.2025.04.010\u003c/span\u003e\u003cspan address=\"10.15926/j.cnki.issn1672-6871.2025.04.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8309750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8309750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe genus \u003cem\u003eMaclura\u003c/em\u003e (Moraceae), the most widespread and only spiny genus in its family, includes trees, shrubs, and lianas, many with significant medicinal value. However, systematic studies on its global diversity patterns and responses to future climate sce-narios are still lacking. This study analyzed global diversity patterns using species oc-currence data in Geographic Information System (GIS) software. Stepwise regression in R identified environmental drivers, and Maximum Entropy (MaxEnt) modeling pre-dicted current and future suitable habitats. The aim was to elucidate the distribution drivers and predict habitat shifts under climate change. Results show: (1) \u003cem\u003eMaclura\u003c/em\u003e has an intercontinental disjunct distribution, concentrated in South America, southern North America, southwestern Europe, and southeastern Asia, with the latter being the richness center and southern North America the endemism center. (2) Diversity is primarily influenced by temperature, solar radiation, water vapor pressure, and soil cation exchange capacity. (3) Current suitable habitats focus on South America, southeastern Asia, and central Africa; future climate scenarios project overall reduction in suitable area. (4) The present distribution centroid is in Ethiopia, Africa, shifting westward and increasingly northwestward under intensified climate forcing. This re-veals \u003cem\u003eMaclura\u003c/em\u003e's global distribution drivers, providing a scientific basis for conserving its germplasm diversity and enabling sustainable use.\u003c/p\u003e","manuscriptTitle":"Climate-driven diversity patterns and range dynamics of Maclura (Moraceae) worldwide","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-26 08:33:08","doi":"10.21203/rs.3.rs-8309750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dbbca742-8ed8-4d29-b654-9c70f0d32c4b","owner":[],"postedDate":"December 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60186901,"name":"Earth and environmental sciences/Climate sciences"},{"id":60186902,"name":"Biological sciences/Ecology"},{"id":60186903,"name":"Earth and environmental sciences/Ecology"},{"id":60186904,"name":"Earth and environmental sciences/Environmental sciences"},{"id":60186905,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-29T09:40:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-26 08:33:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8309750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8309750","identity":"rs-8309750","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}