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Article

Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model

1
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
2
Advanced Analysis and Testing Center, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(13), 1411; https://doi.org/10.3390/agriculture15131411
Submission received: 26 May 2025 / Revised: 23 June 2025 / Accepted: 25 June 2025 / Published: 30 June 2025
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

Abstract

Phytophthora cinnamomi is a globally distributed plant-pathogenic oomycete that threatens economically important crops, including Lauraceae, Bromeliaceae, Fabaceae, and Solanaceae. Utilizing species occurrence records and 35 environmental variables (|R| < 0.8), we employed the MaxEnt model and ArcGIS spatial analysis to systematically predict the potential geographical distribution of P. cinnamomi under current (1970–2000) and future (2030S, 2050S, 2070S, 2090S) climate scenarios across three Shared Socioeconomic Pathways (SSPs). The results indicate that currently suitable habitats cover the majority of China’s provinces (>50% of their areas), with only sporadic low-suitability zones in Qinghai, Tibet, and Xinjiang. The most influential environmental variables were the mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Under future climate scenarios, new suitable habitats emerged in high-latitude regions, while the highly suitable area expanded significantly, with the distribution centroid shifting northeastward. This study employs predictive modeling to elucidate the future distribution patterns of P. cinnamomi in China, providing a theoretical foundation for establishing a regional-scale disease early warning system and formulating ecological management strategies.

1. Introduction

Global climate change is a pressing crisis and an unprecedented challenge for humanity, exerting profound impacts on both natural ecosystems and socio-economic development [1]. Scientific evidence indicates that climate change is already a significant threat to global biodiversity and is projected to emerge as the dominant driver of species extinction within decades [2]. In contemporary ecological contexts, a diverse array of species is exhibiting adaptive migratory responses to increasingly complex and dynamic environmental conditions, encompassing both plants and animals, as well as invasive pathogens [3]. In addition to climatic factors, topographic features (elevation, slope gradient, and aspect) and edaphic conditions (soil moisture content, pH, and bulk density) also influence the distribution of pathogens [4]. Consequently, multiple studies have reported the geographical distribution of pathogens driven by climate change and edaphic factors [5,6].
The changes in pathogen distribution are attributed to two primary factors. First, global climate change has facilitated the geographic range expansion of numerous biological species, particularly entomopathogenic organisms [7]. Second, the ecological interactions between plants and their parasitic species have intensified in complexity, thereby compromising the efficacy of control measures against pathogenic taxa [8]. Therefore, understanding the impact of climate change on the geographic distribution of insect pathogens is essential for identifying optimal regions to implement effective biological control strategies under current and projected climatic conditions [9].
Ecological niche-based modeling approaches for predicting species distributions have become a cornerstone methodology in contemporary biogeographic research [10,11,12]. The application of ecological niche modeling (ENMs) approaches to analyze invasion patterns has gained significant traction in ecological research [13]. Advances in computational ecology have led to the development of diverse ENMs approaches utilizing distinct algorithmic frameworks, including widely applied methods such as BIOCLIM [14], GARP [15], CLIMEX [16], RF [17] and MaxEnt [18]. These models employ ecological niche theory to analyze empirically derived species distribution data in conjunction with associated environmental covariates. ENMs have demonstrated efficacy in predicting potential species distributions and evaluating extinction risk under varying environmental scenarios [19]. Among these approaches, MaxEnt model exhibits distinct advantages over others, particularly in its capacity for flexible feature selection and optimizing complex interactions [20,21]. Among diverse species distribution models, the Maxent model demonstrates superior predictive accuracy for species exhibiting limited sample sizes, narrow geographic distributions, and constrained environmental tolerances [22,23]. The key advantages of this approach include its operational simplicity, minimal sample size requirements, and superior performance [18]. Consequently, the MaxEnt model has been extensively employed to predict potential geographic distributions of plant species and assess colonization risks posed by invasive species [24,25].
Phytophthora cinnamomi Rands, a member of the genus Phytophthora in the Peronosporaceae family, is a soil prevalent pathogen that affects flowers (Camellia spp., Rhododendron spp. Pelargonium spp.) and crops (Capsicum annuum, Solanum lycopersicum, Ananas comosus) globally, leading to root rot and branch blight [26]. P. cinnamomi has rapidly spread to 68 countries and territories worldwide since its initial detection on cinnamon in Sumatra, Indonesia, in 1922 [27]. The disease was first identified in Jiangsu Province, China, in 1984 and has since expanded to Hainan, Taiwan, Zhejiang, Fujian, Jiangsu, and Yunnan, among other provinces [28]. Currently recognized as one of the top 10 most destructive oomycete pathogens [29], China currently includes it on the List of Imported Plant Quarantine Pests. With the growth of global trade, China’s demand for imported seedlings, timber, and wood packaging materials has surged, increasing the risk of introducing pathogenic oomycetes into the country [30]. China’s vast forest distribution, diverse host plant species, and various climatic conditions provide ideal environment for pathogenic bacterial colonization [31]. The wide-spread occurrence of P. cinnamomi could result in significant agricultural economic losses in China. Therefore, predicting the potential geographic distribution of this pathogen, analyzing the associated risks, implementing monitoring and early warning systems, and enacting preventive and control measures are of paramount im-portance.
This study seeks to address the following: (1) Under current climatic conditions, develop an ecological niche model for P. cinnamomi in China to characterize the spatial distribution patterns of its optimal habitats. (2) Identify and analyze the key environmental drivers to determine the dominant ecological factors governing the geographic distribution of P. cinnamomi. (3) Employ climate scenario modeling to project future changes (expansion/contraction) in the suitable habitat range of P. cinnamomi. (4) Apply centroid shift analysis to quantitatively evaluate the spatiotemporal migration trajectory and directional patterns of high-suitability areas for P. cinnamomi across China.

2. Materials and Methods

2.1. Occurrence Data of P. cinnamomi

We extracted distribution points of P. cinnamomi by utilizing various databases in China, such as the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, https://doi.org/10.15468/dl.x4c2p7, accessed on 7 May 2024), Centre for Agriculture and Bioscience International (CABI, http://www.cabi.org/cpc/, accessed on 8 May 2024), and reading domestic and foreign publications [32,33,34,35]. For locations referenced in published studies that lack latitude and longitude data, we extracted geographic coordinates (WGS84 datum) using Google Earth (https://www.google.com, version 7.1). We utilized ENMTools (version 2.0) to filter out invalid records from the initially collected 152 distribution records of P. cinnamomi [36]. To mitigate potential model overfitting caused by spatial autocorrelation among the species’ distribution points, we applied a 5.0 km buffer-based spatial filtering in ArcGIS (version 10.8) [37]. After this quality control procedure, we retained 135 high-confidence occurrence points, which were exported in CSV format for subsequent ecological niche modeling (Figure 1A).

2.2. Environmental Variables

We obtained 19 bioclimatic variables (BIO1–BIO19) and elevation for the period 1970–2000 from the WorldClim database (version v2.1, https://www.worldclim.org/, accessed on 7 June 2024), with a spatial resolution of 5 km (2.5 arc-min) (Table S1) [38]. The digital elevation model data were processed using ArcGIS to derive slope gradient and aspect orientation raster layers. Additionally, based on the BCC-CSM2-MR climate model (CMIP6) from the National Climate Center, we acquired global bioclimatic variables (BIO01–BIO19) for three scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) across four time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) [39]. The climate data of the future comprises three shared socioeconomic pathways (SSPs): SSP126 represents a low-forcing scenario, SSP370 represents a medium–high-forcing scenario, and SSP585 represents a high-forcing scenario [40,41]. Soil data were sourced from the FAO World Soil Database (version v1.2, https://data.apps.fao.org/, accessed on 8 June 2024), comprising 13 soil attribute variables [42] (Table S1). All environmental variables were spatially processed and standardized using ArcGIS 10.8, resulting in a final dataset of 35 variables encompassing temperature, precipitation, topography, and soil characteristics [38] (Table S1). Due to the lack of future soil data, current soil data were used as a substitute in this study.

2.3. Filter out the Suitable Variables

To construct the ecological model for the current period (1970–2000), we first extracted environmental data corresponding to the geographic coordinates of the target species using the “Spatial Analyst Tools” in ArcGis10.8. Subsequently, we performed Pearson correlation tests on these data using the “Bivariate Analysis Tool” in SPSS software (version IBM SPSS Statistics 25, IBM, Armonk, NY, USA) (Figure S1). In the analysis, we set a Pearson correlation coefficient threshold of 0.8 for variable screening. When the absolute value of the correlation coefficient between two environmental variables exceeded 0.8 (∣R∣ > 0.8), we removed the variable with the lower contribution from the original model while retaining biologically significant variables to mitigate multicollinearity among environmental variables. After completing the variable screening, we input the 135 valid distribution points and environmental factor data into MaxEnt software (version 3.4.4) and conducted 10 simulation runs. Based on the percent contribution analysis, we retained only environmental factors with percent contribution rates greater than 3%. Ultimately, from the initial 35 environmental variables, we selected 7 key variables for modeling (Table 1).

2.4. Model Optimisation

In this study, feature classes (FC) and regularization multipliers (RM) were the two most critical parameters influencing the results of MaxEnt model analysis [43]. Feature classes consist of five types: L (linear), Q (quadratic), P (product), T (threshold), and H (hinge). To optimize the model, we used RStudio software(version RStudio Desktop, Boston, MA, USA) to adjust the RM and FC settings. The RM was tested across a range of 0.5–6.0 at intervals of 0.5, while eight different FC combinations were evaluated: L, H, LQ, LQH, QHP, LQP, LQHP, and LQHPT. Using the checkerboard2 method from the ENMeval package in RStudio, we assessed a total of 96 parameter combinations. Complexity and goodness-of-fit of model were evaluated using delta AICc, with the optimal model selected based on the smallest delta AICc value [44]. The corresponding RM and FC values were then identified as the optimal parameters [45].

2.5. Model Optimization and Accuracy Assessment

Maxent software(version 3.4.1) was utilized in this research to simulate the potential distribution of P. cinnamomi across various climatic conditions worldwide. We randomly allocated 75% of the occurrence records as training data, reserving the remaining 25% for model validation. The model was configured with 50,000 iterations and 10 replicate runs to ensure robustness [46]. The suitability indices of the mean fitness zones were utilized as the final outcomes of the model and presented in a logistic format. The confidence of the predictive model can be assessed by examining the area under the curve (AUC) of the receiver operating characteristic (ROC) curve generated from the validation data [47]. The AUC values range from 0 to 1, with a higher value indicating a stronger correlation between environmental variables and the predicted geographic distribution of species, resulting in a better prediction model. The model’s predictive accuracy was classified into five grades: failing (<0.6), poor (0.6–0.7), fair (0.7–0.8), good (0.8–0.9), and excellent (0.9–1) [48].

2.6. Data Processing

Due to the unavailability of future terrain and soil data, this study utilized current topographic and edaphic parameters as proxies, integrating them with future climate scenario datasets to comprehensively assess the potential distribution of P. cinnamomi. Based on the modeling results, ArcGIS 10.8 was employed to generate predictive maps of potential distribution areas for the current period (1970–2000) and future scenarios (2030s, 2050s, 2070s, and 2090s), as well as to quantify trends and spatial extents of suitable habitats. To construct 12 future predictive distribution scenarios (3 time periods × 4 climate pathways), the optimized projection data from the MaxEnt model and China’s administrative boundary data were first imported into the ArcGIS software(version 10.8). A spatial mask was applied to extract the potential distribution range of the target species within China. Using the ArcGIS10.8, the data output from the MaxEnt model underwent spatial transformation, followed by classification threshold determination via the Natural Breaks (Jenks) method [49]. Based on the suitability index, the potential distribution areas of P. cinnamomi were reclassified into four distinct suitability levels: unsuitable (0–0.125), prooly suitable area (0.125–0.353), moderately suitable area (0.353–0.639), and highly suitable area (0.639–1.0). The area and percentage of each suitability zone relative to the total suitable area were calculated. The distribution data of highly suitability areas for P. cinnamomi were imported into ArcGIS. Using the ‘Centroid Changes (Lines)’ tool in SDMtoolbox(version SDMtoolbox_2_10_4to9), the centroid migration trajectories from the current period to future timeframes were computed [50]. Generate dynamic migration path maps of the centroids in the high-suitability zones of P. cinnamomi to visually illustrate the trends of change.

3. Results

3.1. Model Calibration Optimization and Accuracy Evaluation

In this study, we identified an optimal model that simultaneously meets all predefined selection criteria (Figure 2). As clearly demonstrated in Figure 2A, the Delta.AICc value reached zero when the regularization multiplier (RM) was set to 1.5 with the feature combination (FC) of LQP. This parameter configuration exhibited significantly lower Delta.AICc values compared to all other tested combinations. Our results indicate that this optimal parameter set (RM = 1.5, FC = LQP) effectively reduced model complexity and overfitting while simultaneously enhancing model accuracy.
The MaxEnt model achieved a mean AUC value of 0.940 over 10 replicate runs, outperforming the random prediction model (AUC = 0.5), demonstrating robust predictive capability for identifying potential suitable habitats of P. cinnamomi (Figure 2B). The close agreement between predicted omission rates and test omission rates further confirmed high model accuracy (Figure 2C).

3.2. Factors Influencing the Distribution of P. cinnamomi

Model evaluation results indicated that among the seven environmental variables employed, Elevation demonstrated the highest relative contribution (34.8%) to the potential distribution of P. cinnamomi, followed by BIO12 (24.6%) and BIO02 (13.4%) (Table 1). However, our Jackknife test analysis (Figure 3A) revealed that the key climatic determinants of P. cinnamomi’s geographical distribution were BIO02, BIO10, BIO12, BIO14, and Elevation, respectively. The potential distribution range of species was delineated using a probability threshold of >0.5 for habitat suitability [51]. The optimal survival ranges for P. cinnamomic were determined as follows: BIO02 = 3.48–9.33 °C, BIO10 = 12.20–36.21 °C, BIO12 = 159.29–1051.70 mm, BIO14 = 5.34–206.57 mm, and Elev = 0 to 415.53 m. These ranges represent the suitable environmental conditions for the species’survival.

3.3. Potential Distribution of P. cinnamomi Under Current Climatic Conditions

The application of species occurrence data for ecological niche modeling demonstrates robust predictive performance in delineating its potential geographic distribution (Figure 1). The model (under current climatic conditions) revealed that P. cinnamomi exhibits varying degrees of potential suitability across all provinces in China, with notably limited occurrence records documented in Qinghai Province, Tibet Autonomous Region, and Xinjiang. High-suitability zones were predominantly concentrated in eastern and central China, encompassing the Middle-Lower Yangtze Plain, North China Plain, and Huang-Huai Plain. Moderate-suitability areas formed transitional belts surrounding high-suitability zones, including sections of the Yangtze River Basin, Northeast China Plain, Shandong Peninsula, and selected coastal regions. Low-suitability areas exhibited marginal ecological compatibility, with environmental conditions suboptimal compared to core habitats. Under current environmental conditions, the total predicted suitable habitat area for P. cinnamomi was estimated at 4.2059 million km2 (Table 2), comprising: highly suitable areas: 0.8340 million km2 (19.83% of total suitable habitat); moderately suitable areas: 1.4678 million km2 (34.9%); highly suitable areas: 1.9041 million km2 (45.3%).

3.4. Potential Distribution of P. cinnamomi Under Future Environmental Factors

For future periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100) under three CMIP6 climate scenarios (SSP126, SSP370, and SSP585), a total of 12 climate scenario combinations were analyzed. Compared to the current environmental factors, the total area of potential suitable habitats exhibited a contraction trend, while newly suitable regions displayed a poleward shift (Table 2). Specifically, the area of highly suitable habitats for P. cinnamomi increased, whereas moderately and marginally suitable habitats decreased. Under the SSP370 scenario for 2081–2100, the projected total suitable habitat area reached its maximum (415.5 × 104 km2), yet it still represented a 1.28% reduction compared to current conditions (Figure 4). Conversely, the smallest total suitable area (71.6 × 104 km2) was predicted under the SSP370 scenario for 2021–2040, reflecting a 14.2% decline relative to the present. For moderately suitable habitats, the largest area (129.3 × 104 km2) was projected under the SSP126 scenario for 2021–2040, marking an 11.9% decrease, while the smallest area (113.8 × 104 km2) was forecasted under the SSP585 scenario for 2061–2080, corresponding to a 22.5% reduction. In terms of marginally suitable habitats, the peak area (207.4 × 104 km2) was predicted under the SSP585 scenario for 2081–2100, representing an 8.9% increase, whereas the smallest area (174.3 × 104 km2) was projected under the SSP126 scenario for 2021–2040, indicating an 8.49% decrease.
The projection results indicate that the trends in suitable habitat distribution under the SSP126 and SSP585 scenarios are similar. In regions such as Shandong Province, Jiangsu Province, Heilongjiang, and Jilin, the potential high-suitability areas exhibit significant expansion between 2021 and 2100 under both SSP126 and SSP585 scenarios (Figure 4). As the concentration pathway intensifies and time progresses, the extent of various suitability zones undergoes corresponding changes. During the period of 2041–2060 under both SSP126 and SSP585 scenarios, some areas of moderately suitable areas in Heilongjiang progressively transition into potential highly suitable areas, with an expansion toward higher latitudes. By 2061–2100, the prooly suitable areas begin to emerge within the highly suitable areas of Xinjiang, while the suitable habitat range shifts further eastward. Concurrently, the previously unsuitable areas for P. cinnamomi in Inner Mongolia transition into prooly suitable areas.

3.5. Changes in the P. cinnamomi Habitat Range

Our study clearly delineates the specific geographical locations of both expansion and contraction zones for P. cinnamomi’s suitable habitats in Figure 5. Based on simulations under 12 SSP scenarios from CMIP6, the results indicate that from 2021 to 2100, the contraction of P. cinnamomi’s suitable habitats generally outweighs expansion, with a notable trend of poleward range shifts. Specifically, the expansion zones are primarily concentrated in northern China, including Heilongjiang, Jilin, and Liaoning, with sporadic occurrences in Jiangxi, Taiwan, Jiangsu, Shandong, and Hebei. In contrast, contraction zones predominantly cover northwestern Xinjiang and most of Yunnan, while Inner Mongolia exhibits both expansion and contraction patterns. With the SSP scenario severity increases, this expansion trend demonstrates a more pronounced and systematic spatial shift.

3.6. Core Distributional Shifts

Under various future climate scenarios, the direction and distance of centroid migration for P. cinnamomi’s highly suitable habitats exhibit notable variations (Figure 6; Table 3). Compared to the current climate scenario (where the centroid is located in Zengdu District, Suizhou City, Hubei Province), future projections indicate a northeastward shift of the centroid (except during the 2050s and 2090s under SSP370), with displacement distances generally below 240 km. The centroid remains predominantly within the tri-provincial border region of northwestern Jiangxi, southern Henan, and northeastern Hubei, reflecting the influence of climate warming on habitat suitability preferences. The maximum centroid displacement (234.5 km northeastward) occurs under SSP126 in the 2090s, while the minimum displacement (32.0 km southeastward) is observed under SSP370 in the 2050s.

4. Discussion

4.1. Determinants of the Potential Geographical Distribution of P. cinnamomi

Natural factors play a pivotal role in the distribution of forest diseases. Our analysis reveals the environmental adaptation characteristics of P. cinnamomi: the species exhibits optimal growth under a mean diurnal temperature range (BIO02) of 3.48–9.33 °C, indicating that moderate daily temperature fluctuations favor its proliferation [52,53]. Its broad adaptability to the mean temperature of the warmest quarter (BIO10; 12.20–36.21 °C) suggests a distribution spanning temperate to warm-temperate climates, though extreme temperatures may suppress its survival, consistent with the general patterns observed in oomycete pathogens [54,55]. Moisture conditions are a critical determinant of its distribution: the optimal annual precipitation range (BIO12: 159.29–1051.70 mm) reflects its preference for moderately to highly humid environments, while its tolerance for a minimum precipitation in the driest month (BIO14; 5.34 mm) confirms a degree of short-term drought resistance [56]. These findings align with P. cinnamomi’s dependence on soil moisture for zoospore release, though prolonged aridity significantly restricts its activity [57]. Notably, elevation (0–415.53 m) emerged as the most decisive limiting factor, with its pronounced lowland affinity strongly correlating with the pathogen’s ecological preference for waterlogged or poorly drained root zones [58]. The low-temperature and low-humidity conditions of high-altitude regions likely impede colonization by disrupting physiological mechanisms such as zoospore dispersal [59].

4.2. Reliability of Model Results

This study utilized the ENMeval software (ENMTools.pl) package for parameter optimization, ultimately identifying one optimal model that met all predefined selection criteria. The optimal parameter combination (RM = 1.5, FC = LQH) exhibited a significantly lower Delta. AICc value compared to other tested combinations, indicating its superior performance in reducing model complexity and overfitting risks while significantly enhancing model accuracy. This finding aligns with the conclusions of Phillips et al. [60], further validating the role of parameter optimization in improving ecological niche model performance. Additionally, the high consistency between the predicted omission rate and the test omission rate (Figure 2C) confirmed the model’s accuracy [61], a result comparable to the findings of Wisz et al., who demonstrated the superior performance of MaxEnt models in species distribution prediction.
Compared to previous studies, the enhanced reliability of our predictive results can be attributed to the following factors: First, the use of updated high-precision climate data from WorldClim v2.1 [62], which significantly improved data completeness and accuracy over earlier versions, thereby enhancing the reliability of niche modeling and habitat suitability predictions. Second, the extensive distribution records of P. cinnamomi expanded the model’s applicability and strengthened the generalizability of predictions. Third, through systematic screening, we identified an optimal combination of FC layers and RM layers that significantly enhances model performance, and subsequently fine-tuned the corresponding FC and RM parameters in the model architecture. However, it should be noted that spatial heterogeneity in species distribution may constrain the applicability of ecological niche models, potentially leading to discrepancies in habitat suitability predictions compared to prior studies [63]. Furthermore, our study exclusively employed the MaxEnt model for habitat suitability prediction. We acknowledge that reliance on a single modeling approach may introduce limitations that could affect the robustness of our inferences. To enhance the reliability of future P. cinnamomi ecological niche modeling, we recommend: incorporating ensemble modeling techniques by integrating complementary approaches (e.g., BIOCLIM, Random Forest) for cross-validation. Such methodological improvements would significantly advance both the accuracy and generalizability of predictive outputs.

4.3. Future Shifts in the Potential Distribution Range of P. cinnamomi

Under future climate conditions, the newly acquired habitats of P. cinnamomi exhibit a pronounced northward shift, which aligns with the widely documented response patterns of species to climate change [64,65]. Specifically, the suitable habitat area of P. cinnamomi expanded in the three northeastern provinces and Inner Mongolia but contracted in Yunnan, a spatial pattern consistent with the general trend of species migrating toward higher latitudes/altitudes to adapt to warming [66]. Notably, under the high-emission pathway (SSP585), the most pronounced polar expansion was observed, with a 4–12% increase in highly suitable area in high-latitude regions, directly attributable to the stronger climatic forcing under this scenario. However, it must be emphasized that SSP5-8.5, as a high-forcing scenario, is associated with significant socioeconomic and ecological risks [67]. In contrast, the low-emission pathway (SSP126) exhibited conservative shifts in suitable habitats, primarily characterized by the stability of existing distribution ranges rather than marked polar expansion.
A critical anomaly was identified under SSP370: Despite representing an intermediate socioeconomic development pathway, the centroid migration trajectory of highly suitable habitats in the 2050s and 2090s deviated significantly from the expected warming-driven migration pattern. This discrepancy may stem from nonlinear coupling effects between climatic and socioeconomic drivers under this scenario. We recommend further validation of its reliability using multi-model ensemble approaches in subsequent studies.

4.4. Limitations and Future Research Directions

While ENM has enhanced our understanding of the current and potential suitable habitats for P. cinnamomi, it is important to note that the root rot disease caused by this pathogen is the result of multifactorial interactions. Assessments based solely on climatic, topographic, and edaphic factors exhibit significant limitations. First, P. cinnamomi exhibits a broad host range, encompassing key plant taxa such as Cinnamomum, Quercus, Rhododendron, and Castanea [52]. The spatial distribution patterns of host plants (e.g., density, species composition, and community diversity) can substantially influence the pathogen’s suitable habitat range [68]. Second, anthropogenic activities (e.g., horticultural trade, forestry operations, and ecotourism) may facilitate pathogen dispersal through vector-mediated transmission, while monoculture practices of Cinnamomum spp. in urban greening projects could further exacerbate disease risks [9,69]. Moreover, the regulatory role of soil microbial communities cannot be overlooked. For instance, Bacillus spp. [70,71] and Pseudomonas spp. [72] can directly suppress P. cinnamomi hyphal growth and spore germination by secreting antimicrobial compounds such as antibiotics, cyclic lipopeptides, and surfactants. Similarly, Streptomyces spp. produce specialized antimicrobial metabolites that significantly impair the pathogen’s soil colonization capacity. Additionally, rhizosphere microbes can activate plant systemic resistance (e.g., by inducing defense-related gene expression), thereby indirectly enhancing host resistance through tripartite “soil-microbe-plant” interactions [73].
However, due to technical challenges in acquiring host distribution data, human activity trajectories, and microbiome datasets, this study has not yet integrated these critical parameters. Future research should incorporate the following multidimensional factors: (1) geospatial databases of host plant distributions, (2) models of pathogen dispersal driven by anthropogenic activities, and (3) functional metagenomic data on soil microbial communities. Such multiscale integrative analyses will enable more accurate assessments of P. cinnamomi’s ecological risks and provide a theoretical foundation for developing region-specific disease management strategies.

5. Conclusions

Using the Maximum Entropy (MaxEnt) model, we identified key environmental determinants of P. cinnamomi distribution, including mean diurnal temperature range, mean temperature of the warmest quarter, annual precipitation, precipitation of the driest month, and elevation. Compared to the current scenario, the total suitable habitat area for P. cinnamomi is projected to contract in the future, while its highly suitable range shows a marked expansion. Projections under future climate change scenarios indicate a progressive expansion of its potential suitable habitats toward higher latitudes and colder regions, with significant shifts anticipated in highly suitable areas across the three northeastern provinces and Inner Mongolia. Furthermore, the centroid of highly suitable regions is predicted to migrate northeastward. While this research focused on bioclimatic, soil, and elevation variables, future studies should incorporate additional factors such as human activities and host plant distributions. With advancements in data availability, modeling techniques, and technological capabilities, future predictions of P. cinnamomi distribution are expected to achieve greater precision.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15131411/s1, Figure S1: Pearson correlation analysis of environmental variables. Notes: Elev: Elevation; BIO12: Annual precipitation; BIO02: Mean diurnal temperature range; BIO10: Mean temperature of warmest quarter; Slope: Slope; BIO14: Precipitation of driest month; T_PH: Soil acility and alkalinity. Figure S2: The projected shift in the potential geographic distribution of P. cinnamomic under future scenarios relative to its current suitability range. Table S1: 36 environmental variables for the MaxEnt model.

Author Contributions

Investigation, X.Z., H.W. and T.D.; Data curation, X.Z., H.W. and T.D.; Formal analysis, X.Z.; Visualization, X.Z.; Writing—original draft, X.Z.; Writing—review & editing, X.Z., H.W. and T.D.; Project administration, T.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Foundation of China (32471873), the STI 2030-Major Projects (2023ZD0405605), China Postdoctoral Science Foundation (2024M751426), National Key R&D Program of China (2023YFD1401304), Natural Science Foundation of Jiangsu Province, China (BK20231291), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

Institutional Review Board Statement

Not applicable for studies not involving humans or animals.

Data Availability Statement

All data generated or analyzed during this study are included in this article.

Acknowledgments

Thank you to Sixi Lin and Tingting Liao for their technical guidance.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Abbass, K.; Qasim, M.Z.; Song, H.; Murshed, M.; Mahmood, H.; Younis, I. A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environ. Sci. Pollut. Res. 2022, 29, 42539–42559. [Google Scholar] [CrossRef] [PubMed]
  2. Keck, F.; Peller, T.; Alther, R.; Barouillet, C.; Blackman, R.; Capo, E.; Chonova, T.; Couton, M.; Fehlinger, L.; Kirschner, D.; et al. The global human impact on biodiversity. Nature 2025, 641, 395–400. [Google Scholar] [CrossRef] [PubMed]
  3. Ibáñez, A.; Garrido-Chamorro, S.; Barreiro, C. Microorganisms and climate change: A not so invisible effect. Microbiol. Res. 2023, 14, 918–947. [Google Scholar] [CrossRef]
  4. Yu, J.T.; Li, S.Y.; Sun, X.Y.; Zhou, W.Z.; He, L.B.; Zhao, G.Y.; Chen, Z.; Bai, X.T.; Zhang, J.S. The Impact and Determinants of Mountainous Topographical Factors on Soil Microbial Community Characteristics. Microorganisms 2023, 11, 2878. [Google Scholar] [CrossRef]
  5. Alqahtani, M.S.; Shahin, G.; Abdelalim, I.T.; Khalaf, S.M. Evaluation of ecological consequences on the global distribution of Staphylococcus aureus Rosenbach 1884 due to climate change, using Maxent modeling. Sci. Rep. 2025, 15, 11457. [Google Scholar] [CrossRef]
  6. Khalaf, S.M.; Alqahtani, M.S.; Ali, M.R.; Abdelalim, I.T.; Hodhod, M.S. Using MaxEnt modeling to analyze climate change impacts on Pseudomonas syringae van Hall, 1904 distribution on the global scale. Heliyon 2024, 10, e41017. [Google Scholar] [CrossRef] [PubMed]
  7. Carlson, C.J.; Brookson, C.B.; Becker, D.J.; Cummings, C.A.; Gibb, R.; Halliday, F.W.; Heckley, A.M.; Huang, Z.Y.X.; Lavelle, T.; Robertson, H.; et al. Pathogens and planetary change. Nat. Rev. Biodivers. 2025, 1, 32–49. [Google Scholar] [CrossRef]
  8. Qin, X.; Yang, J.; Ni, S. Microbiome-mediated protection against pathogens in woody plants. Int. J. Mol. Sci. 2023, 24, 16118. [Google Scholar] [CrossRef]
  9. Singh, B.K.; Delgado-Baquerizo, M.; Egidi, E.; Guirado, E.; Leach, J.E.; Liu, H.; Trivedi, P. Climate change impacts on plant pathogens, food security and paths forward. Nat. Rev. Microbiol. 2023, 21, 640–656. [Google Scholar] [CrossRef]
  10. Qiao, H.J.; Hu, J.H.; Huang, J.H. Theoretical Basis, Future Directions, and Challenges for Ecological Niche Models. Sci. Sin. (Vitae) 2013, 43, 915–927. [Google Scholar] [CrossRef]
  11. Vasconcelos, R.N.; Cantillo-Pérez, T.; Franca Rocha, W.J.; Aguiar, W.M.; Mendes, D.T.; de Jesus, T.B.; de Santana, C.O.; de Santana, M.M.M.; Oliveira, R.P. Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review. Earth 2024, 5, 963–989. [Google Scholar] [CrossRef]
  12. Thuiller, W. Ecological niche modelling. Curr. Biol. 2024, 34, R225–R229. [Google Scholar] [CrossRef] [PubMed]
  13. Castaño-Quintero, S.; Escobar-Luján, J.; Osorio-Olvera, L.; Peterson, A.T.; Chiappa-Carrara, X.; Martínez-Meyer, E.; Yañez-Arenas, C. Supraspecific units in correlative niche modeling improves the prediction of geographic potential of biological invasions. PeerJ 2020, 8, e10454. [Google Scholar] [CrossRef]
  14. Morozov, A.; Ageel, A.; Bates, A.; Galyov, E. Modelling the effects of climate change on the interaction between bacteria and phages with a temperature-dependent lifecycle switch. Sci. Rep. 2025, 15, 6428. [Google Scholar] [CrossRef]
  15. Joyner, T.A.; Lukhnova, L.; Pazilov, Y.; Temiralyeva, G.; Hugh-Jones, M.E.; Aikimbayev, A.; Blackburn, J.K. Modeling the potential distribution of Bacillus anthracis under multiple climate change scenarios for Kazakhstan. PLoS ONE 2010, 5, e9596. [Google Scholar] [CrossRef] [PubMed]
  16. Rad, S.P.H.; Duque, T.S.; Flory, S.L.; Nascimento, V.G.D.; Mendes, D.S.; Maciel, J.C.; Santos, J.B.D.; Silva, R.S.D.; Shabani, F. Predicting the spread of invasive Imperata cylindrica under climate change: A global risk assessment and future distribution scenarios. PLoS ONE 2025, 20, e0321027. [Google Scholar] [CrossRef] [PubMed]
  17. El-Khalafy, M.M.; El-Kenany, E.T.; Al-Mokadem, A.Z.; Shaltout, S.K.; Mahmoud, A.R. Habitat suitability modeling to improve conservation strategy of two highly-grazed endemic plant species in saint Catherine Protectorate, Egypt. BMC Plant Biol. 2025, 25, 485. [Google Scholar] [CrossRef]
  18. He, Z.; Ali, H.; Wu, J.; Liu, Z.; Wei, X.; Zhuo, Z. Impact of climate change on the distribution of Isaria cicadae Miquel in China: Predictions based on the MaxEnt model. Front. Microbiol. 2025, 16, 1509882. [Google Scholar] [CrossRef]
  19. Lu, H.H.; Zheng, Y.Y.; Qiu, Y.S.; Tang, L.B.; Zhao, Y.C.; Xie, W.G. Comprehensive prediction of potential spatiotemporal distribution patterns, priority planting regions, and introduction adaptability of Elymus sibiricus in the Chinese region. Front. Plant Sci. 2025, 15, 1470653. [Google Scholar] [CrossRef]
  20. Campos, J.C.; Garcia, N.; Alírio, J.; Arenas-Castro, S.; Teodoro, A.C.; Sillero, N. Ecological niche models using MaxEnt in Google Earth Engine: Evaluation, guidelines and recommendations. Ecol. Inform. 2023, 76, 102147. [Google Scholar] [CrossRef]
  21. Lissovsky, A.A.; Dudov, S.V. Species-distribution modeling: Advantages and limitations of its application. 2. MaxEnt. Biol. Bull. Rev. 2021, 11, 265–275. [Google Scholar] [CrossRef]
  22. Kaky, E.; Nolan, V.; Alatawi, A.; Gilbert, F. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecol. Inform. 2020, 60, 101150. [Google Scholar] [CrossRef]
  23. Ray, R.; Gururaja, K.V.; Ramchandra, T.V. Predictive distribution modeling for rare himalayan medicinal plant Berberis aristata DC. J. Environ. Biol. 2011, 32, 725–730. [Google Scholar]
  24. Yang, J.; Huang, Y.; Jiang, X.; Chen, H.; Liu, M.; Wang, R. Potential geographical distribution of the edangred plant Isoetes under human activities using MaxEnt and GARP. Glob. Ecol. Conserv. 2022, 38, e02186. [Google Scholar] [CrossRef]
  25. Aidoo, O.F.; Souza, P.G.C.; da Silva, R.S.; Júnior, P.A.S.; Picanço, M.C.; Osei-Owusu, J.; Sétamou, M.; Ekesi, S.; Borgemeister, C. A machine learning algorithm-based approach (MaxEnt) for predicting invasive potential of Trioza erytreae on a global scale. Ecol. Inform. 2022, 71, 101792. [Google Scholar] [CrossRef]
  26. Kharel, A.; Rookes, J.; Ziemann, M.; Cahill, D. Viable protoplast isolation, organelle visualization and transformation of the globally distributed plant pathogen Phytophthora cinnamomi. Protoplasma 2024, 261, 1073–1092. [Google Scholar] [CrossRef]
  27. Rands, R.D. Stripe canker of Cinnamon caused by Phytophthora cinnamomi n. sp. Meded. Van Het Inst. Voor Plantenziekten 1922, 40, 991–992. [Google Scholar] [CrossRef]
  28. Wang, M.S.; Zhang, S.H.; Chen, X.D.; Lu, J.; Wang, J.J.; Zhu, J.J. Research progress on biological characteristics and control techniques of Phytophthora cinnamomic. J. Anhui Agric. Sci. 2018, 46, 21–24+35. [Google Scholar] [CrossRef]
  29. Engelbrecht, J.; Duong, T.A.; Prabhu, S.A.; Seedat, M.; van den Berg, N. Genome of the destructive oomycete Phytophthora cinnamomi provides insights into its pathogenicity and adaptive potential. BMC Genom. 2021, 22, 302. [Google Scholar] [CrossRef]
  30. Zhao, J.; Hu, K.; Chen, K.; Shi, J. Quarantine supervision of wood packaging materials (WPM) at Chinese ports of entry from 2003 to 2016. PLoS ONE 2021, 16, e0255762. [Google Scholar] [CrossRef]
  31. Zhang, W.D.; Liu, Y.Y.; Li, M.M.; Du, H.; Huang, K.Y.; Feng, Y.Y.; Ma, C.W.; Wei, X.X.; Wang, X.Q.; Ran, J.H. Decoding endosperm endophytes in Pinus armandi: A crucial indicator for host response to climate change. BMC Microbiol. 2025, 25, 239. [Google Scholar] [CrossRef] [PubMed]
  32. Ho, H.H.; Zhuang, W.Y.; Liang, Z.R.; Yu, N.; Wang, Y.J. Phytophthora cinnamomi on black locust (Robinia pseudoacacia) in Jiangsu Province, China. Mycologia 1984, 75, 881–886. [Google Scholar] [CrossRef]
  33. Zhou, X.G.; Zhu, Z.Y.; Wu, L.Z.; Fa, J.P.; Wang, S.J. Phytophthora cinnamomi causes root rot of camellia (Camellia spp.). Acta Agric. Shanghai 1993, 71–75. [Google Scholar]
  34. de Andrade Lourenço, D.; Branco, I.; Choupina, A. Phytopathogenic oomycetes: A review focusing on Phytophthora cinnamomi and biotechnological approaches. Mol. Biol. Rep. 2020, 47, 9179–9188. [Google Scholar] [CrossRef]
  35. Burgess, T.I.; Scott, J.K.; Mcdougall, K.L.; Stukely, M.J.; Crane, C.; Dunstan, W.A.; Brigg, F.; Andjic, V.; White, D.; Rudman, T.; et al. Current and projected global distribution of Phytophthora cinnamomi, one of the world’s worst plant pathogens. Glob. Change Biol. 2017, 23, 1661–1674. [Google Scholar] [CrossRef]
  36. Brown, J.L. SDM toolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 2014, 5, 694–700. [Google Scholar] [CrossRef]
  37. Zhao, Y.; Liu, J.; Wang, Q.; Huang, R.; Nie, W.; Yang, S.; Cheng, X.F.; Li, M. Occurrence Data Sources Matter for Species Distribution Modeling: A Case Study of Quercus variabilis Based on Biomod2. Ecol. Evol. 2025, 15, e71390. [Google Scholar] [CrossRef]
  38. Pacifici, K.; Reich, B.J.; Miller, D.A.; Gardner, B.; Stauffer, G.; Singh, S.; Singh, S.; McKerrow, A.; Collazo, J.A. Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology 2017, 98, 840–850. [Google Scholar] [CrossRef]
  39. Riahi, K.; Van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’neill, B.C.; Fujimori, S.; Bauer, N.; Dellink, R.; Fricko, O.; Lutz, W.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 2017, 42, 153–168. [Google Scholar] [CrossRef]
  40. Liu, L.; Zhang, Y.Y.; Huang, Y.; Zhang, J.D.; Mou, Q.Y.; Qiu, J.; Wang, R.L.; Li, Y.J.; Zhang, D.Q. Simulation of potential suitable distribution of original species of Fritillariae Cirrhosae Bulbus in China under climate change scenarios. Environ. Sci. Pollut. Res. 2022, 29, 22237–22250. [Google Scholar] [CrossRef]
  41. Zhao, Z.; Guo, Y.; Zhu, F.; Jiang, Y. Prediction of the impact of climate change on fast-growing timber trees in China. For. Ecol. Manag. 2021, 501, 119653. [Google Scholar] [CrossRef]
  42. Wang, Y.J.; Wu, K.F.; Zhao, R.X.; Xie, L.Y.; Li, Y.L.; Zhao, G.H.; Zhang, F.G. Prediction of potential suitable habitats in the 21st century and GAP analysis of priority conservation areas of Chionanthus retusus based on the MaxEnt and Marxan models. Front. Plant Sci. 2024, 15, 1304121. [Google Scholar] [CrossRef]
  43. Morales, N.S.; Fernández, I.C.; Baca-González, V. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ 2017, 5, e3093. [Google Scholar] [CrossRef]
  44. Cavanaugh, J.E.; Neath, A.A. The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. Wiley Interdiscip. Rev. Comput. Stat. 2019, 11, e1460. [Google Scholar] [CrossRef]
  45. Wang, X.; Li, Z.; Zhang, L.; Wang, Y.; Liu, Y.; Ma, Y. The optimized Maxent model reveals the pattern of distribution and changes in the suitable cultivation areas for Reaumuria songarica being driven by climate change. Ecol. Evol. 2024, 14, e70015. [Google Scholar] [CrossRef] [PubMed]
  46. Song, P.; Peng, D.L.; Li, Y.M.; Chen, Z.J.; Zhai, Y.Y.; Chen, L.; Bo, H. Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling. J. Integr. Agric. 2023, 22, 2138–2150. [Google Scholar] [CrossRef]
  47. Blanc, M.; Martínez-Rincón, R.O. Global scale study of the environmental preferences and distribution of Orcinus orca. J. Coast. Conserv. 2023, 27, 60. [Google Scholar] [CrossRef]
  48. Wu, Z.; Gao, T.; Luo, Y.; Shi, J. Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model. Ecosyst 2022, 9, 100042. [Google Scholar] [CrossRef]
  49. He, Y.L.; Ma, J.M.; Chen, G.S. Potential geographical distribution and its multi-factor analysis of Pinus massoniana in China based on the maxent model. Ecol. Indic. 2023, 154, 110790. [Google Scholar] [CrossRef]
  50. Xue, N.; Li, K.; Chen, K.; Li, P.; Ji, X.; Ma, Z.; Ji, W. Predicting climate change impacts on distribution and conservation of critically endangered Picea neoveitchii using MaxEnt. Front. For. Glob. Change 2024, 7, 1472857. [Google Scholar] [CrossRef]
  51. Koç, D.E.; Ustaoğlu, B.; Biltekin, D. Effect of climate change on the habitat suitability of the relict species Zelkova carpinifolia Spach using ensembled species distribution modelling. Sci. Rep. 2024, 14, 27967. [Google Scholar] [CrossRef]
  52. Hardham, A.R.; Blackman, L.M. Phytophthora cinnamomi. Mol. Plant Pathol. 2018, 19, 260–285. [Google Scholar] [CrossRef]
  53. Phillips, D.; Weste, G. Growth rates of four Australian isolates of Phytophthora cinnamomi in relation to temperature. Trans. Br. Mycol. Soc. 1985, 84, 183–185. [Google Scholar] [CrossRef]
  54. Linde, C.; Drenth, A.; Kemp, G.H.J.; Wingfield, M.J.; Broembsen, S.L.V. Population structure of Phytophthora cinnamomi in south africa. Phytopathology 1997, 87, 822–827. [Google Scholar] [CrossRef] [PubMed]
  55. Khaliq, I.; Hardy, G.E.S.J.; Burgess, T.I. Phytophthora cinnamomi exhibits phenotypic plasticity in response to cold temperatures. Mycol. Prog. 2020, 19, 405–415. [Google Scholar] [CrossRef]
  56. Sena, K.; Crocker, E.; Vincelli, P.; Barton, C. Phytophthora cinnamomi as a driver of forest change: Implications for conservation and management. For. Ecol. Manag. 2018, 409, 799–807. [Google Scholar] [CrossRef]
  57. Nesbitt, H.J.; Malajczuk, N.; Glenn, A.R. Effect of soil moisture and temperature on the survival of Phytophthora cinnamomi Rands in soil. Soil Biol. Biochem. 1979, 11, 137–140. [Google Scholar] [CrossRef]
  58. Lin, Z.Y.; Halliday, F.W.; Zhang, P.; Wang, X.X.; Chen, F.; Shi, A.Y.; Shi, J.J.; Yao, X.; Liu, X. Above- and belowground plant pathogens along elevational gradients: Patterns and potential mechanisms. Oikos 2025, 2025, e10455. [Google Scholar] [CrossRef]
  59. Serrano, M.S.; Romero, M.Á.; Homet, P.; Gómez-Aparicio, L. Climate change impact on the population dynamics of exotic pathogens: The case of the worldwide pathogen Phytophthora cinnamomi. Agric. For. Meteorol. 2022, 322, 109002. [Google Scholar] [CrossRef]
  60. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  61. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2011, 17, 43–57. [Google Scholar] [CrossRef]
  62. Wisz, M.S.; Hijmans, R.J.; Li, J.; Peterson, A.T.; Graham, C.H.; Guisan, A. Effects of sample size on the performance of species distribution models. Divers. Distrib. 2008, 14, 763–773. [Google Scholar] [CrossRef]
  63. Hirzel, A.H.; Hausser, J.; Chessel, D.; Perrin, N. Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology 2002, 83, 2027–2036. [Google Scholar] [CrossRef]
  64. Chen, I.C.; Hill, J.K.; Ohlemüller, R.; Roy, D.B.; Thomas, C.D. Rapid range shifts of species associated with high levels of climate warming. Science 2011, 333, 1024–1026. [Google Scholar] [CrossRef]
  65. Jetz, W.; Wilcove, D.S.; Dobson, A.P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 2007, 5, e157. [Google Scholar] [CrossRef]
  66. Change, I.P.O.C. Climate change 2007: The physical science basis. Agenda 2007, 6, 333. [Google Scholar] [CrossRef]
  67. O’Neill, B.C.; Tebaldi, C.; van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Kriegler, E. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 2017, 9, 3461–3482. [Google Scholar] [CrossRef]
  68. Sena, K.L.; Yang, J.; Kohlbrand, A.J.; Dreaden, T.J.; Barton, C.D. Landscape variables influence Phytophthora cinnamomi distribution within a forested Kentucky watershed. For. Ecol. Manag. 2019, 36, 39–44. [Google Scholar] [CrossRef]
  69. Tazerji, S.S.; Nardini, R.; Safdar, M.; Shehata, A.A.; Duarte, P.M. An overview of anthropogenic actions as drivers for emerging and re-emerging zoonotic diseases. Pathogens 2022, 11, 1376. [Google Scholar] [CrossRef]
  70. Xie, C.S.; Wu, Y.T.; Wu, Z.H.; Cao, H.; Cao, X.H.; Cui, F.; Meng, S.; Chen, J. Bacillus velezensis TCS001 Enhances the Resistance of Hickory to Phytophthora cinnamomi and Reshapes the Rhizosphere Microbial Community. Agriculture 2025, 15, 193. [Google Scholar] [CrossRef]
  71. Miljaković, D.; Marinković, J.; Balešević-Tubić, S. The significance of Bacillus spp. in disease suppression and growth promotion of field and vegetable crops. Microorganisms 2020, 8, 1037. [Google Scholar] [CrossRef] [PubMed]
  72. Muthukumar, A.; Raj, T.S.; Prabhukarthikeyan, S.R.; Kumar, R.N.; Keerthana, U. Pseudomonas and Bacillus: A biological tool for crop protection. In New and Future Developments in Microbial Biotechnology and Bioengineering; Elsevier: Amsterdam, The Netherlands, 2022; pp. 145–158. [Google Scholar] [CrossRef]
  73. Dow, L.; Gallart, M.; Ramarajan, M.; Law, S.R.; Thatcher, L.F. Streptomyces and their specialised metabolites for phytopathogen control–comparative in vitro and in planta metabolic approaches. Front. Plant Sci. 2023, 14, 1151912. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Geographical distribution pattern of P. cinnamomi in China (A) and potential suitable areas for P. cinnamomi in China under current environmental variable (B).
Figure 1. Geographical distribution pattern of P. cinnamomi in China (A) and potential suitable areas for P. cinnamomi in China under current environmental variable (B).
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Figure 2. Accuracy assessment of the species distribution model for predicting the potential geographical range of Phytophthora cinnamomi. Notes: (A) Optimization of feature combination and regularization; (B) Receiver operating characteristic curve (ROC curve); (C) Omission rate.
Figure 2. Accuracy assessment of the species distribution model for predicting the potential geographical range of Phytophthora cinnamomi. Notes: (A) Optimization of feature combination and regularization; (B) Receiver operating characteristic curve (ROC curve); (C) Omission rate.
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Figure 3. Response curves of the dominant environmental variables of Phytophthora cinnamomi. Note: (A) The regularized training gain values of the seven environmental factors; (B) Mean diurnal temperature range; (C) Mean temperature warmest quartrt; (D) Annual precipitation; (E) Precipitation of driest month; (F) Elev.
Figure 3. Response curves of the dominant environmental variables of Phytophthora cinnamomi. Note: (A) The regularized training gain values of the seven environmental factors; (B) Mean diurnal temperature range; (C) Mean temperature warmest quartrt; (D) Annual precipitation; (E) Precipitation of driest month; (F) Elev.
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Figure 4. Potential distribution of Phytophthora cinnamomi under future environmental scenarios.
Figure 4. Potential distribution of Phytophthora cinnamomi under future environmental scenarios.
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Figure 5. Spatial pattern changes in the suitable areas of P. cinnamomi under SSP126, SSP370 and SSP585 climate scenarios.
Figure 5. Spatial pattern changes in the suitable areas of P. cinnamomi under SSP126, SSP370 and SSP585 climate scenarios.
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Figure 6. The distributional shifts of P. cinnamomi at its centroids.
Figure 6. The distributional shifts of P. cinnamomi at its centroids.
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Table 1. The contribution and importance of preliminary selected environmental variables.
Table 1. The contribution and importance of preliminary selected environmental variables.
Environmental VariablesDescriptionPercent ContributionPermutation Importance
ElevElevation34.82.1
BIO12Annual precipitation(mm)24.619.8
BIO02Mean diurnal temperature range (°C)13.427.6
BIO10Mean temperature of warmest quarter (°C)8.640.7
SlopeSlope7.74.3
BIO14Precipitation of driest month(mm)7.43.7
T_PHSoil acility and alkalinity3.41.7
Table 2. Suitable areas for P. cinnamomi in China under current environmental factors and future environmental scenarios.
Table 2. Suitable areas for P. cinnamomi in China under current environmental factors and future environmental scenarios.
Shared Socio-Economic Pathways, SSPs|DecadesPredicted Area (×104 km2) and % of the Corresponding Current Area
Total Suitable AreaPoorly Suitable AreaModerately Suitable AreaHighly Suitable Area
1970–2000420.59190.41146.7883.40
SSP1262030S391.393.0%174.391.5%129.388.1%87.7105.2%
2050S394.093.7%185.497.3%116.679.4%92.0110.3%
2070S395.894.1%183.796.5%123.183.9%89.0106.7%
2090S396.794.3%187.098.2%124.484.7%85.3102.3%
SSP3702030S371.888.4%181.595.3%118.780.9%71.685.9%
2050S388.892.5%177.393.1%119.181.1%92.4110.8%
2070S390.592.8%189.999.7%113.877.5%86.8104.1%
2090S415.598.8%197.7103.8%124.684.9%93.2111.8%
SSP5852030S392.193.2%175.992.4%126.586.2%89.7107.5%
2050S398.994.9%186.197.7%121.782.9%91.1109.2%
2070S407.773.2%207.4108.9%111.776.1%88.6106.2%
2090S408.897.2%188.298.8%128.087.2%92.6111.1%
Table 3. Changes in centroids of suitable areas for P. cinnamomi under current climate conditions and future climate scenarios.
Table 3. Changes in centroids of suitable areas for P. cinnamomi under current climate conditions and future climate scenarios.
Shared Socio-Economic Pathways, SSPs|DecadesCoordinateCentroid PositionMigration DirectionMigration Distance/km
1970–2000114.150836° E, 31.593743° NZengdu District, Suizhou City, Hubei Province
SSP1262030S115.698358° E, 33.141402° NQiaocheng District, Bozhou City, Anhui ProvinceNortheast178.5
2050S114.759967° E, 32.289301° NPingqiao District, Xinyang City, Henan ProvinceNortheast81.2
2070S114.472419° E, 31.671662° NGuangshui City, Suizhou City, Hubei ProvinceNortheast34.2
2090S115.826678° E, 33.488854° NGuoyang County, Bozhou City, Anhui ProvinceNortheast234.5
SSP3702030S114.489599° E, 31.96863° NGuangshui City, Suizhou City, Hubei ProvinceNortheast44.3
2050S114.415436° E, 31.399209° NDawu County, Xiaogan City, Hubei ProvinceSoutheast32.0
2070S115.419042° E, 32.076438° NXi County, Xinyang City, Henan ProvinceNortheast83.3
2090S113.561336° E, 30.730627° NHanchuan City, Xiaogan City, Hubei ProvinceSouthwest111.3
SSP5852030S114.682466° E, 31.959892° NGuangshui City, Suizhou City, Hubei ProvinceNortheast62.3
2050S114.785718° E, 32.13088° NPingqiao District, Xinyang City, Henan ProvinceNortheast82.0
2070S116.175465° E, 33.044489° NMengcheng County, Bozhou City, Anhui ProvinceNortheast223.8
2090S115.22764° E, 32.741617° NXincai County, Zhumadian City, Henan ProvinceNortheast159.3
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Zhang, X.; Wang, H.; Dai, T. Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model. Agriculture 2025, 15, 1411. https://doi.org/10.3390/agriculture15131411

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Zhang X, Wang H, Dai T. Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model. Agriculture. 2025; 15(13):1411. https://doi.org/10.3390/agriculture15131411

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Zhang, Xiaorui, Haiwen Wang, and Tingting Dai. 2025. "Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model" Agriculture 15, no. 13: 1411. https://doi.org/10.3390/agriculture15131411

APA Style

Zhang, X., Wang, H., & Dai, T. (2025). Predicting the Potential Geographic Distribution of Phytophthora cinnamomi in China Using a MaxEnt-Based Ecological Niche Model. Agriculture, 15(13), 1411. https://doi.org/10.3390/agriculture15131411

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