Next Article in Journal
European Beech Masting Cycles and the Spatial Distribution of Wisents in the Bieszczady Mountains, Poland
Previous Article in Journal
Impact of Climate Change on Distribution of Suitable Niches for Black Locust (Robinia pseudoacacia L.) Plantation in China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 15
Issue 9
10.3390/f15091617
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impacts of Climate Change on the Potential Distribution of Three Cytospora Species in Xinjiang, China

by
Quansheng Li
1,
Shanshan Cao
2,
Lei Wang
3,
Ruixia Hou
4 and
Wei Sun
2,*
1
College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
2
Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Modern Forestry Institute of Xinjiang Academy of Forestry Sciences, Urumqi 830000, China
4
Research Institute of Resource Information Techniques, CAF, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(9), 1617; https://doi.org/10.3390/f15091617
Submission received: 21 August 2024 / Revised: 10 September 2024 / Accepted: 12 September 2024 / Published: 13 September 2024
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
Xinjiang is an important forest and fruit production area in China, and Cytospora canker, caused by the genus Cytospora Ehrenb., has caused serious losses to forestry production in Xinjiang. In this study, we constructed ensemble models based on Biomod2 to assess the potential geographical distribution of Cytospora chrysosperma, C. nivea, and C. mali in Xinjiang, China and their changes under different climate change scenarios, using species occurrence data and four types of environmental variables: bioclimatic, topographic, NDVI, and soil. The model performance assessment metrics (AUC and TSS) indicated that the ensemble models are highly reliable. The results showed that NDVI had the most important effect on the distribution of all three species, but there were differences in the response patterns, and bioclimatic factors such as temperature and precipitation also significantly affected the distribution of the three species. C. chrysosperma showed the broadest ecological adaptation and the greatest potential for expansion. C. nivea and C. mali also showed expansion trends, but to a lesser extent. The overlapping geographical distribution areas of the three species increased over time and with an intensification of the climate scenarios, especially under the high-emission SSP585 scenario. The centroids of the geographical distribution for all three species generally shifted towards higher latitude regions in the northeast, reflecting their response to climate warming. C. chrysosperma may become a more prevalent forest health threat in the future, and an increase in the overlapping geographical distribution areas of the three species may lead to an increased risk of multiple infections. These findings provide an important basis for understanding and predicting the distribution and spread of the genus Cytospora in Xinjiang and are important for the development of effective forest disease prevention and control strategies.

1. Introduction

The genus Cytospora is an important group of phytopathogenic fungi, causing Cytospora canker in the branches of many woody plants [1,2,3]. With the development of molecular biology and genomics technology, the biological characteristics and pathogenesis of Cytospora have been better understood [4,5]. Different species of Cytospora differ in their morphological and molecular phylogenetic relationships, pathogenesis, and virulence levels. Cytospora canker initially forms lesions on the bark of the trunk or main branches, gradually expanding and penetrating deep into the xylem, causing cankers or decay in the tree, and in severe cases, leading to the wilting of branches or the death of the whole plant [6,7,8]. Cytospora canker occurs widely worldwide, with infections spanning Asia [9], Europe [10], North America [11], Oceania, and other regions [12], and host plants include economic fruit trees such as apple, pear, peach, cherry, and so on [13,14,15], as well as forest tree species like poplar, willow, and eucalyptus [12,16]. In temperate regions, this disease is particularly prevalent and has posed a serious threat to the sustainable development of forestry and fruit tree cultivation.
More than 615 species epithets of Cytospora are listed in Index Fungorum (2018), while Kirk et al. estimated that there are approximately 110 accepted Cytospora species [17]. Different species of Cytospora often have distinct primary host ranges [4,18,19]. In China, species such as Cytospora chrysosperma (Pers.) Fr., Cytospora mali Grove, and Cytospora nivea Fuckel have a wide geographical distribution and diverse host ranges [20,21]. C. chrysosperma mainly affects several genera of trees, such as Salix spp., Populus spp., and Malus spp. [22], and has been recorded in northeastern, northern, and northwestern China [20,22,23]. C. mali, on the other hand, with Malus spp. as the main host, is the main pathogen of apple Valsa canker, causing greater damage in main production areas, such as Shaanxi, Shandong, and Gansu [18,24,25], while C. nivea is mostly found on Populus spp. [26].
Fungal diseases leading to forest degradation are a global issue with impacts far beyond regional boundaries. Ash dieback, caused by the pathogenic fungus Hymenoscyphus fraxineus, has rapidly spread across Europe since its first detection in Poland and Lithuania in the 1990s [27]. This disease has resulted in a significant decline in ash populations, not only altering the structure and function of forest ecosystems but also severely impacting the biodiversity dependent on ash trees [28]. Similarly, in North America, chestnut blight caused by Cryphonectria parasitica has nearly decimated the American chestnut (Castanea dentata) population, profoundly changing the ecological landscape of eastern forests [29]. In Australia, root rot caused by Phytophthora cinnamomi has threatened the survival of multiple native plant species [30]. These cases highlight the enormous threat that fungal diseases pose to global forest health while emphasizing the importance of studying and predicting the distribution dynamics of pathogenic fungi. In this context, our study on Cytospora species in the Xinjiang region is not only of local significance but also represents an important contribution to global plant pathology and forest conservation efforts.
Xinjiang is an important fruit production base in China and a high prevalence area for Cytospora canker. Studies have shown that there are 51 Cytospora species in Xinjiang, including C. chrysosperma, C. nivea, C. mali, C. schulzeri, and C. parasitica, among which C. chrysosperma causes the most severe damage [31,32]. In Xinjiang, Cytospora can infect a wide range of host plants, including 35 genera from 21 families such as Malus spp., Pyrus spp., Zizyphusmill spp., Juglans spp., and Populus spp. [31,33]. During 2003−2005, Cytospora canker occurred in various tree species in Karamay, including Populus spp., Ulmus pumila, Salix sp., Fraxinus chinensis, and Elaeagnus sp., with Populus spp. being the most severely affected by Cytospora canker [34]. The infection rate of Korla fragrant pear orchards was as high as 100% in 2011–2012, which severely affected fruit production and caused huge economic losses to fruit growers [35]. In 2014, the infection rate of artificially planted species such as Populus × beijingensis and Populus alba L. in Altay exceeded 90%, leading to reduced timber quality and patchy tree mortality [36]. In 2015, the infection rate in Juglans regia-producing areas of Xinjiang reached 50% to 90%, becoming a major factor affecting Juglans regia growth, development, and yield decline [37]. Over the past two decades, the high and frequent incidence of Cytospora canker has become a major constraint to the development of the forest and fruit industry in Xinjiang, and has also caused serious damage to the ecological environment.
The occurrence of Cytospora canker is closely related to environmental conditions, with high temperatures, drought stress, suitable temperatures and humidity, and injured trees being favorable factors for its development and spread. Cytospora canker usually develops rapidly after mechanical damage, insect feeding, frost damage, or sunburn in trees, where the pathogen invades through open wounds and rapidly spreads inside the tree, leading to necrosis of cellular tissues [38,39,40,41]. Furthermore, spores of the genus Cytospora can be dispersed in a variety of ways, including by wind and rain splash, making control of the disease more complex and challenging [42]. A comprehensive assessment of the habitat suitability of Cytospora canker in Xinjiang is important for guiding the implementation of preventive and curative measures to maintain forestry security. Currently, ecological niche modeling has been widely used to predict the potential distribution areas of biohazardous species such as pathogenic fungi, plant viruses, and pests at different spatial and temporal scales. Guo et al. (2005) predicted the potential distribution area of Phytophthora ramorum in California using a support vector machine (SVM) method [43]. Puschendorf et al. (2009) used the MaxEnt model to assess the suitable habitat of Batrachochytrium dendrobatidis in Costa Rica [44]. Shirk et al. (2018) used boosted regression tree (BRT), random forest (RF), generalized additive model (GAM), and generalized linear model (GLM) approaches in R to fit a species distribution model of southwestern white pine (Pinus strobiformis, SWWP) for guiding the monitoring of white pine blister rust (WPBR) outbreak monitoring [45]. Shabani et al. (2014) used CLIMEX to predict the future distribution of Fusarium oxysporum f. spp. in agricultural areas of Europe, the Middle East, and North Africa under climate change scenarios [46]. Ejaz et al. (2023) used GLM, MaxEnt, GBM, and BIOCLIM based on Biomod2 to integrate the prediction and comparison of how the global ecological niche of Fusarium spp. will change from now to the 2050s and 2070s, which minimized the uncertainty of the prediction results of individual models and effectively reduced the risk of overfitting [47]. Seidl et al. (2018) used the Biomod2 package in R to model the ecological niches of five harmful organisms, including Phytophthora ramorum (Sudden Oak Death, SOD), Phytophthora kernoviae (Beech Bleeding Canker, BBC), and Fusarium circinatum (Pitch Pine Canker, PPC). They predicted the current and future potential geographical distributions of these organisms, resulting in more reliable and robust predictions [48]. Therefore, ecological niche modeling of C. chrysosperma, C. nivea, and C. mali using occurrence data and environmental variables is an effective way to assess their current and future distribution [49,50].
The aim of this study was to investigate the ecological niche dynamics and potential spread risks of C. chrysosperma, C. nivea, and C. mali in Xinjiang to provide a scientific basis for forestry management and disease prevention and control strategies in Xinjiang, as well as to explore the implications of these findings for global forest health management. The specific objectives of this study include (1) to utilize the Biomod2 platform to screen high-precision ecological niche models suitable for each species by comparing and validating the models, and then constructing the corresponding ensemble model (EM), (2) to analyze the key environmental factors that have a significant impact on the potential geographical distribution of the three Cytospora species, (3) to utilize the constructed EMs to predict the potential geographical distribution and overlap of the three Cytospora species under current and future climate change scenarios, and (4) to analyze the changes and shifting trends of suitable habitats of the three Cytospora species. Building on the aforementioned research, this paper will focus on answering the following core questions: (5) Based on the predicted changes in the distribution of Cytospora fungi in Xinjiang, and in light of the global experience in forest disease management, what specific recommendations can we make for forestry management and disease prevention and control in Xinjiang? How can these recommendations provide new insights into global forest health management strategies?

2. Materials and Methods

2.1. Species Geographical Distribution Data

In this study, we obtained occurrence data for C. chrysosperma, C. nivea, and C. mali in Xinjiang through field surveys, searching the Global Biodiversity Information Facility (GBIF; https://www.gbif.org, accessed on 17 April 2024), and reviewing the literature.
During 2018 and 2022, we conducted field surveys and sampling in areas such as Aksu, Ili, and Altay, followed by laboratory isolation, purification, and morphological identification, resulting in 368, 20, and 12 distribution records for the three Cytospora species, respectively. To further expand the distribution data, we searched and screened 148, 25, and 24 distribution records of the three species in the study area from the GBIF database. The distribution records of the three species were obtained by searching China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) databases: 30, 21, and 1 records, respectively.
By the above methods we initially obtained distribution records of the three species: 546, 66, and 37 entries. We then cleaned the data, removing entries with incorrect geographical coordinates or insufficient precision. Using ENMTools, we removed redundant data, retaining only one distribution point in each 30 arcsecond grid (approximately 1 km × 1 km, corresponding to the environmental variable data below) [51]. Finally, we obtained 417, 58, and 26 usable distribution records for C. chrysosperma, C. nivea, and C. mali, respectively, in the study area (Figure 1).

2.2. Environmental Variables

We initially selected 33 environmental variables that could potentially influence the distribution of the three Cytospora species, including bioclimatic factors, topography, NDVI, and soil data (Table S1). We obtained 19 bioclimatic variables (bio1 to bio19) from the WorldClim version 2.1 dataset (https://www.worldclim.org, accessed on 17 April 2024), with a spatial resolution of 30 arcseconds. These variables were able to reflect the heat and moisture conditions and their seasonal patterns of change in the study area. This study also incorporated three topographic variables: elevation, aspect, and slope. Among them, the elevation data were directly sourced from WorldClim, while aspect and slope were calculated from the elevation data using the spatial analysis module in ArcGIS.
Considering the biological characteristics of Cytospora and vegetation growth conditions, NDVI and soil factors were introduced into the modeling [52]. Based on the tree growing season in Xinjiang and the occurrence patterns of Cytospora canker [53], we processed C2 level surface reflectance data from Landsat 7 and Landsat 8 (LANDSAT/LC07/C02/T1_L2 and LANDSAT/LC08/C02/T1_L2 with atmospheric correction and radiometric calibration) on the GEE platform. We calculated the multi-year NDVI mean values for March to October from 2011 to 2020 in Xinjiang, with a spatial resolution of 1 km [54,55]. Ten soil variables were extracted from the global soil database HWSD v1.2, including S_CEC_CLAY, S_CEC_SOIL, S_OC, S_PH_H2O, S_TEB, T_CEC_CLAY, T_CEC_SOIL, T_OC, T_PH_H2O, and T_TEB, with a spatial resolution of 30 arcseconds. These variables characterize the distribution of soil properties such as cation exchange capacity and organic carbon content in the topsoil and subsoil layers.
Future bioclimate data of 2050s (2041−2060), 2070s (2061−2080), and 2090s (2081−2100) were used in this study. We selected the BCC-CSM2-MR global climate model [56] and then obtained 19 bioclimatic variables for the corresponding periods under three emission scenarios: SSP245, SSP370, and SSP585. These three emission scenarios cover different possibilities for future socio-economic development pathways and can be used to comparatively analyze the potential impacts of climate changes of varying intensities on species distribution. Due to the lack of data on non-climatic environmental variables and the magnitude of their changes being much lower than that of climatic factors [57,58], it is assumed that the non-climatic variables in the study area will remain relatively stable in the future [59].
Finally, we unified all these variables to a spatial resolution of 30 arcseconds and performed necessary data format conversions to meet the input requirements of the subsequent species distribution models. Meanwhile, to reduce multicollinearity among variables, Pearson correlation coefficient analysis was used in ENMTools to exclude environmental variables with high correlation coefficients (|R| ≥ 0.8) and low contributions, and those with contributions of less than 1% were eliminated based on the Jackknife method [52], which finally resulted in a set of environmental variables used for the construction of models for the three species (Table S1).

2.3. Construction of SDM and Evaluation of Model Accuracy

In the present study, we used an ensemble modeling approach in the Biomod2 package in RStudio to predict suitable habitats for C. chrysosperma, C. nivea, and C. mali [60,61]. We first performed individual model modeling using nine different algorithms in Biomod2, including CTA, FDA, GBM, GLM, MARS, MAXENT, MAXNET, RFT, and SRE. For each modeling process, 80% of the distribution records were randomly selected as the training dataset, with the remaining 20% serving as the test dataset, and this process was repeated 3 times [62]. Additionally, for each of the three Cytospora species, 1000 pseudo-absence points were randomly generated and repeated 10 times. Thus, for each species, we obtained a total of 270 models (9 algorithms × 3 dataset divisions × 10 pseudo-absence point generations). We used the area under the receiver operating characteristic (ROC) curve (AUC) and the True Skill Statistic (TSS) to evaluate model performance. AUC is one of the measures of model performance, and an AUC > 0.9 indicates high model performance [63]. The TSS indicates the net prediction success rate including distribution points and pseudo-distribution points, and the value of TSS is in the range of [−1, 1], and the value of TSS between 0.40 and 0.75 indicates good model performance, and when the value is higher than 0.75, it means the model performance is excellent [64,65]. Based on the outputs of individual models, we retained only the models with an AUC and TSS greater than 0.9 and 0.75, respectively, and then built the ensemble model (EM) using the committee averaging method. We thereafter predicted the potential geographical distributions of C. chrysosperma, C. nivea, and C. mali using the EMs.

2.4. Species Distribution Maps

The prediction results of the EMs produced continuous ASCII raster layers, where each pixel provides a probability (p) value for the presence of Cytospora, ranging from 0 to 1000. Based on the maximum training sensitivity plus specificity threshold to maximize the TSS, we calculated the suitability index binarization threshold. The binary map was then generated by dividing those regions with occurrence probability values below the threshold as unsuitable zones and those above the threshold as suitable zones. Then we further classified the suitable areas into three levels using the natural breakpoint method: lowly suitable habitat, moderately suitable habitat, and highly suitable habitat, mapped the distribution of suitable habitats. Based on the binary maps, we calculated the loss, gain, and stability of suitable habitat areas [66], as well as the shift in the centroid of distribution [67].

3. Results

3.1. Model Accuracy Evaluation

We evaluated the accuracy of the CTA, FDA, GBM, GLM, MARS, MAXENT, MAXNET, RFT, and SRE (Figure 2) models. The results showed differences in the prediction accuracy of the nine models, with RF having the highest accuracy and SRE the lowest. For C. chrysosperma, C. nivea, and C. mali, the values of TSS for the EM were 0.794, 0.901, and 0.960, and the values of AUC were 0.963, 0.987, and 0.995, respectively, which indicated that the use of our EM to predict the potential geographical distribution is accurate and robust.

3.2. Response Curves of Probability of Presence to Environmental Variables

The response curves of environmental variables reflect the effects of environmental factors on habitat suitability. The top four environmental variables with the most important effects on the distribution of the three Cytospora species are shown in Figure 3. The effect of NDVI on the probability of occurrence of the three species was significant and much greater than that of other environmental variables. As NDVI values increased from about 0.125, the probability of occurrence of C. chrysosperma increased sharply and then remained high, with similar patterns of NDVI response for C. nivea and C. mali. For C. chrysosperma, its habitat suitability was also affected by bio2, slope, and bio14, and its probability of occurrence increased as the values of these three variables increased. The distribution of C. nivea was closely related to bio9, and the probability of occurrence of C. nivea was the lowest when the value of bio9 ranged from −8 to 10 and gradually increased as the value of bio9 decreased or increased. The distribution of C. mali is also affected by bio8, bio3, and S_CEC_CLAY. Among them, the probability of C. mali occurrence tends to decrease with the increase of bio3 and stabilizes when bio3 reaches about 31.

3.3. Current Potential Geographical Distributions of the Three Cytospora Species

The suitability index binarization thresholds were calculated to be 152 (C. chrysosperma), 779.5 (C. nivea), and 547.5 (C. mali), and the specific suitability habitats classified for the three Cytospora species are shown in Table 1.
Figure 4 shows the potential contemporary geographical distribution of C. chrysosperma, C. nivea, and C. mali predicted using an EM. The total area of suitable habitat for C. chrysosperma is 22.31 × 104 km2, of which the area of highly suitable habitat is 11.30 × 104 km2 (Table S2), which is mainly distributed in parts of Tacheng, Ili, and areas surrounding the Tarim Basin, and lowly suitable habitat was mainly distributed in the Altay and Tacheng regions of northern Xinjiang. The suitable habitat for C. nivea was mainly distributed in small areas of Tacheng, Ili, Aksu, Kashgar, and Hotan, with a total suitable habitat area of 9.39 × 104 km2 (Table S3). The suitable habitat for C. mali was mainly distributed in the areas surrounding the Tarim Basin in Bayingolin Mongol Autonomous Prefecture, Aksu, Kashgar, and Hotan, as well as small parts of Ili. The area of lowly suitable habitat was 2.25 × 104 km2, accounting for 46.20% of the total suitable habitat area (Table S4). The overlapping geographical distribution area of the three Cytospora species was 30,215.79 km2, mainly distributed in parts of Tacheng, Ili Valley, Aksu, Hotan, and Kashgar (Figure 4D, Table S5).

3.4. Future Potential Geographical Distributions of the Three Cytospora Species

The potential geographical distributions of C. chrysosperma, C. nivea, and C. mali under different climate scenarios in the future are presented in Figure 5, Figure 6, and Figure 7, respectively.
Under the three emission scenarios of SSP245, SSP370, and SSP585, from the 2050s to the 2070s and 2090s, the highly suitable, moderately suitable, and lowly suitable areas for C. chrysosperma all showed a gradually increasing trend, with the increased amplifying as the emission scenario intensified (SSP245 < SSP370 < SSP585), and the lowly suitable area exhibited the most significant growth (Table S2). In the most extreme scenario (SSP585, 2090s), the highly suitable, moderately suitable, and lowly suitable areas increased to 20.05 × 104 km2, 9.52 × 104 km2, and 19.66 × 104 km2, respectively.
Overall, under different climate scenarios in the future, the suitable areas for C. nivea showed a significant increasing trend, with the highest rate of increase in the lowly suitable areas and a relatively small increase in the highly suitable areas (Table S3). In the 2050s, the increase in the area of all types of suitable habitat was lower in the SSP370 scenario than in the SSP245, and SSP585 scenarios. In the 2090s, under the SSP585 scenario, the highly suitable, moderately suitable, and lowly suitable areas increased to 7.77 × 104 km2, 12.54 × 104 km2, and 12.30 × 104 km2, respectively.
Under different climate scenarios in the future, the highly suitable area of C. mali showed a decreasing trend, while the moderately suitable area and the lowly suitable area showed a gradual increasing trend (Table S4). The total suitable area increased to 8.40 × 104 km2, 10.09 × 104 km2, and 11.00 × 104 km2 under the three emission scenarios in the 2090s, respectively.

3.5. Overlapping Geographical Distribution Areas of the Three Cytospora Species under Different Climate Scenarios

The overlapping geographical distribution areas of the three Cytospora species are mainly in Emin, Gongliu, Xinyuan, Qitai, Korla, Kuqa, Xinhe, Aksu, Alar, Kashgar, and Hotan (Figure 8). The overlapping geographical distribution of the three Cytospora species showed an increasing trend over time and with the intensification of emission scenarios (Table S5). Under the SSP585 scenario, the overlapping geographical distribution areas increased to 54,771.84 km2, 64,132.10 km2, and 76,337.10 km2 in the next three periods, respectively.

3.6. Spatial Variation in the Three Cytospora Species and Transfer of Their Distribution Centroids

As the suitable areas expanded or contracted, the suitable areas of the three Cytospora species changed differently under future scenarios. There was a clear trend toward a net increase in the area of suitable habitat for C. chrysosperma compared to the current climate scenario (Table S6), with all scenarios showing significant expansion. Under the SSP585 scenario, the greatest increase was obtained in the 2090s, amounting to 271,164.10 km2. The suitable area for C. nivea showed an overall expansion trend, but the increase was smaller than that for C. chrysosperma (Table S7). Under the SSP585 scenario, the maximum expansion was obtained in the 2090s, amounting to 96,304.55 km2. The loss of suitable areas for C. nivea showed an overall decreasing trend, reducing from 2000−4000 km2 in the early period to 400−600 km2 in the later period. The area of expansion of suitable habitat for C. mali tended to increase gradually under all scenarios, with a much smaller increase than for C. nivea (Table S8).
Under the current climate scenario, the distribution centroids of C. chrysosperma, C. nivea, and C. mali are located in Gongliu, Wenshu, and Xinhe respectively (Figure 9). Under different scenarios in the future, the centroids of suitable habitats for the three Cytospora species generally shifted towards higher latitude regions in the northeastern direction, with C. nivea and C. mali showing more similar patterns of centroid migration.

4. Discussion

Over the past two decades, Cytospora canker has become a serious and persistent threat to the forest and fruit industry in Xinjiang. This study aimed to assess the potential geographical distribution of the three Cytospora species in Xinjiang and their dynamics under different climate change scenarios. A series of important findings were derived from model performance assessment, habitat suitability response to environmental factors, and the dynamics of the species’ ecological niches. These findings not only help to understand the distributional characteristics of these pathogens but also provide a scientific basis for predicting and managing potential future forest health problems.

4.1. Evaluation of Modeling Performance

Previous studies have shown that EMs constructed using Biomod2 can capture variation between different models [60] and, especially when dealing with complex ecosystems and species distribution issues, an EM typically outperforms any single model [68]. In terms of model evaluation metrics, the TSS values of the EMs for the three species were 0.794, 0.901, and 0.960 respectively, which were higher than the threshold of 0.75 for an excellent model [64], and the AUC values were 0.963, 0.987, and 0.995, which were more than 0.9, respectively, indicating that the EMs had an excellent discriminatory ability [69]. These metrics consistently indicate that the EMs we constructed were highly reliable in predicting the distribution of suitable habitats for the three Cytospora species under different climate change scenarios, providing a solid foundation for subsequent analyses.

4.2. Response of Habitat Suitability to Environmental Variables

In this study, we found that the habitat suitability of the three Cytospora species had both commonalities and differences in their responses to environmental variables, which may be closely related to the unique geographic and climatic characteristics of Xinjiang. Xinjiang is located in the hinterland of the Eurasian continent, and its topographical feature of “three mountains sandwiching two basins” has created a complex and diverse geographical environment and ecosystem [70], which provides a natural laboratory for the study of the ecological adaptations of Cytospora fungi.
First, NDVI showed the most important effect on the distribution of all three Cytospora species, but their response patterns differed significantly. In particular, C. chrysosperma maintained high suitability across a wide range of NDVI values (Figure 3), which is compatible with the diverse ecosystems of Xinjiang, suggesting that this species may be potentially at risk of infection in a wide range of environments from oases to montane forests [71]. In contrast, C. nivea and C. mali were more sensitive to changes in NDVI and may be more restricted to specific ecological regions. This difference may reflect how these pathogens have adapted to the different cover conditions and health of the vegetation in Xinjiang, from the basin to the mountains [72,73].
Temperature-related bioclimatic variables played an important role in the distribution of the three species, which is consistent with the typical temperate continental arid climate characteristics of Xinjiang. The climate of Xinjiang is characterized by aridity and low rainfall, abundant light, large diurnal temperature differences, and large temperature differences between winter and summer [74]. These characteristics explain why C. chrysosperma is particularly sensitive to bio2 (mean diurnal air temperature range) and is more adapted to environments with large diurnal temperature differences. This adaptability suggests that C. chrysosperma may pose potential spread risks across large areas of Xinjiang. C. nivea is significantly influenced by bio6 (mean daily minimum air temperature of the coldest month) and bio9 (mean daily mean air temperatures of the driest quarter), which corresponds to Xinjiang’s climate characteristics of cold winters and hot summers. This suggests that the distribution of C. nivea may be more constrained by Xinjiang’s extreme seasonal temperature variations. C. mali responded significantly to bio8 (mean daily mean air temperatures of the wettest quarter) and bio3 (isothermality), suggesting a preference for extreme temperatures (very cold or very hot) during the wettest season, as well as greater adaptability to habitats with large temperature variability.
Precipitation factors on the distribution of C. chrysosperma (bio14, precipitation amount of the driest month) and C. nivea (bio19, mean monthly precipitation amount of the coldest quarter) also have important implications. The precipitation distribution in Xinjiang shows significant geographical differences, with precipitation gradually increasing from basins to mountainous areas [75]. This precipitation gradient provides diverse moisture conditions for the distribution of different Cytospora species.
Among the topographic soil factors, C. chrysosperma tends to grow on sloping terrain, which may be related to its host plant’s need for moisture conditions or a specific microclimate [76], and S_CEC_CLAY has a significant effect on the distribution of C. mali, which may be related to its host tree’s unique need for specific soil conditions [77]. These findings reveal the impact of environmental factors on species distribution and provide important clues for understanding the ecological adaptability of these pathogens.

4.3. Dynamic Ecological Niches of the Three Cytospora Species

Global climate change is profoundly affecting the Earth’s ecosystems, leading to changes in the distribution patterns and hazard levels of many harmful organisms, and posing new challenges to agroforestry production and ecosystem security [78,79]. C. chrysosperma, C. nivea, and C. mali exhibited unique ecological niche dynamics under different climate change scenarios in this study. Notably, the total suitable habitat area and overlapping geographical distribution area of the three Cytospora species increase over time and with the intensification of climate scenarios, especially prominent under the SSP585 high emission scenario. The increase in overlapping geographic distribution areas not only reflects the adaptation of these species to similar environmental conditions but also signals the possibility of more complex fungal interactions in the future. Such changes in distribution patterns may lead to an increased risk of multiple Cytospora infections [80], creating new challenges for forest health management in Xinjiang.
C. chrysosperma’s demonstrated broad tolerance to NDVI and temperature (Figure 3) echoes its known broad host range [81], suggesting that it may become an even more prevalent and serious forest health threat under future climate scenarios. In contrast, C. nivea and C. mali showed more moderate ecological niche changes, but still showed an expansion trend. In terms of the expansion capacity of the three Cytospora species: C. chrysosperma > C. nivea > C. mali. These all provide contrasts for our understanding of the differential response of pathogens to environmental change.
Climate warming has caused some pathogenic fungi, which were originally distributed at low latitudes and low altitudes, to begin to appear at high latitudes and high altitudes and to jeopardize local plant resources [82,83,84]. In recent years, climate change in Xinjiang has shown climate warming and humidification trends [85], and in this study, we found that the distribution centroids of the three Cytospora species show a trend of shifting towards higher latitude regions, which is consistent with the response of many species to climate warming observed globally. This finding not only reflects the sensitivity of biological distribution to temperature changes but also reveals the profound impact that climate change may have on the forest ecosystems of Xinjiang. It has been shown that climate change directly contributes to changes in the activity, spread, and survivability of pathogens, exacerbating the extent of their damage to plants. For example, an increase in temperature may accelerate the germination and spread of fungal spores, while drought stress enhances the ability of pathogens to infect plants. On the other hand, climate change also alters the growth and development patterns of plants, making them more vulnerable to pathogens [86,87,88]. Therefore, compared to many animal and plant species, the genus Cytospora may have a faster rate of distribution range shift, enabling them to quickly occupy new suitable habitats. Furthermore, climate change also affects the relationship between pathogenic fungi and their host plants, altering the generation time and reproductive cycles of fungi, thereby influencing their abundance and distribution in nature [87,89]. These complex interdependencies may result in more complex patterns of distribution changes for the three Cytospora species, sometimes even leading or lagging behind the distribution changes of their hosts.

4.4. Practical Implications and Management Recommendations

Recent studies have been conducted, and Li et al. used the MaxEnt model to predict the potential geographic distribution of C. chrysosperma in Xinjiang under climate change scenarios [90]. Similar to our study, they also found that C. chrysosperma had an expansion trend in Xinjiang. Yan et al. conducted a comparative assessment of three Cytospora species across China [21], and their results showed some similarities with our findings in Xinjiang, such as C. chrysosperma showing the widest range of ecological adaptations. Additionally, Xu et al. studied the potential distribution of Valsa mali in China [91], and their results show some similarities with our predictions for C. mali. However, our study provides more detailed future distribution predictions for multiple Cytospora species by considering multiple climate change scenarios and using an ensemble modeling approach, supporting a more comprehensive disease risk assessment.
The results of this study have important theoretical and practical implications for forestry managers and policymakers in the Xinjiang region. We propose the following management recommendations, which are not only applicable to Xinjiang but can also provide valuable insights for other regions facing similar challenges worldwide. (1) Strengthen monitoring and preventive measures in high-risk areas, especially in highly suitable distribution zones such as Tacheng and Ili. (2) Develop adaptive management strategies, such as adjusting the selection of tree species for afforestation and forest stand structure [92], to address potential changes in disease distribution due to future climate change. (3) Integrate predictive models into forest health monitoring systems to enhance early warning capabilities and achieve precise prevention and control [93].

4.5. Study Limitations and Future Research Directions

The findings of this study are important for understanding the far-reaching impacts that changes in the distribution of Cytospora species may have on the ecosystems of the Xinjiang region. For example, these changes may reduce forest productivity, affect biodiversity, and weaken soil and water conservation [94]. There are some limitations in this study: firstly, the model predictions are subject to uncertainty and limited by the availability and quality of available data [95]. Second, this study failed to fully consider the complex interactions between Cytospora species and their host plants, which may affect the accuracy of the predictions.
Based on these limitations, we propose the following directions for future research: (1) Conducting long-term field monitoring to verify the accuracy of model predictions [96]. (2) Investigating the co-evolution of Cytospora species and host plants in the context of climate change [97], which will contribute to a more comprehensive understanding of the impacts of climate change on forest ecosystems. (3) Combining remote sensing techniques and molecular biology methods to improve the accuracy of disease prediction and early detection [43]. This interdisciplinary approach can provide more comprehensive and timely disease information. (4) Applying models such as FLEXPART and HYSPLIT, which simulate the transport of particulate matter in the atmospheric environment, to the study of spore dispersal in the genus Cytospora [98]. Combining these transmission models with ecological niche models [99], we can comprehensively assess the potential risk distribution and transmission range of Cytospora canker, thus providing a more comprehensive and precise scientific basis for the prevention and control strategy of Cytospora canker.

5. Conclusions

In this study, ensemble models were used to assess the potential geographical distribution of C. chrysosperma, C. nivea, and C. mali in Xinjiang, China, and their dynamics under different climate change scenarios. The results of the study revealed the following key findings: (1) NDVI had the most important effect on the distribution of all three species, but the specific response patterns differed among species. In addition, bioclimatic factors such as temperature and precipitation significantly influenced species distributions. (2) Under the climate change scenarios, the potential distribution areas of the three species showed a general trend of expansion, with distribution centroids shifting towards higher latitude regions in the northeast, reflecting their response to climate warming. (3) C. chrysosperma showed the widest ecological adaptability and the greatest potential for expansion, while C. nivea and C. mali also showed an expansion trend, but to a relatively lesser extent. (4) The overlapping distribution areas of the three species gradually increased over time and with the intensification of the climate change scenarios, especially under the high SSP585 emission scenario. (5) C. chrysosperma may become a more prevalent forest health threat in the future. At the same time, the increase in overlapping geographic distributions may lead to an increased risk of multiple infections. This study not only improves our understanding of the distribution dynamics of Cytospora fungi in Xinjiang but also provides scientific evidence and decision support for forest disease management in the context of climate change, and is an important inspiration for forest health management in other regions of the world.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15091617/s1, Table S1: Environment variables for modeling; Table S2: Predicted potential geographical distribution area of Cytospora chrysosperma under different climate change scenarios; Table S3: Predicted potential geographical distribution area of Cytospora nivea under different climate change scenarios; Table S4: Predicted potential geographical distribution area of Cytospora mali under different climate change scenarios; Table S5: Overlapping area of potential geographical distribution of the three Cytospora species under different climate change scenarios (unit: km2). A: Cytospora chrysosperma. B: C. nivea. C: C. mali; Table S6: The amount of habitat area changes of Cytospora chrysosperma under different climate change scenarios with respect to the current (unit: km2); Table S7: The amount of habitat area changes of Cytospora nivea under different climate change scenarios with respect to the current (unit: km2); Table S8: The amount of habitat area changes of Cytospora mali under different climate change scenarios with respect to the current (unit: km2).

Author Contributions

Conceptualization, W.S.; Methodology, Q.L.; Software, L.W.; Formal analysis, S.C.; Investigation, Q.L. and L.W.; Data curation, R.H.; Writing—original draft, Q.L.; Writing—review & editing, R.H. and W.S.; Supervision, S.C.; Project administration, R.H. and W.S.; Funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (NO. 32060321), Special Project for the Creation of an Environment (Talents and Bases)—Construction of Science and Technology Innovation Bases (Construction of Resource Sharing Platform) of Xinjiang Province, China (NO. PT2012).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Li, J.-T.; Li, J.-R.; Jiang, N. Identification of Cytospora Species Isolated from Branch Canker Diseases of Woody Plants in Tibet, China. Forests 2024, 15, 121. [Google Scholar] [CrossRef]
  2. Pan, M.; Zhu, H.; Bonthond, G.; Tian, C.; Fan, X. High Diversity of Cytospora Associated With Canker and Dieback of Rosaceae in China, With 10 New Species Described. Front. Plant Sci. 2020, 11, 690. [Google Scholar] [CrossRef] [PubMed]
  3. Thorpe, K. Diseases of Trees and Shrubs. Forestry 2006, 79, 612–613. [Google Scholar] [CrossRef]
  4. Han, Z.; Yu, R.; Xiong, D.; Tian, C. A Sge1 Homolog in Cytospora Chrysosperma Governs Conidiation, Virulence and the Expression of Putative Effectors. Gene 2021, 778, 145474. [Google Scholar] [CrossRef]
  5. Yu, L.; Xiong, D.; Han, Z.; Liang, Y.; Tian, C. The Mitogen-Activated Protein Kinase Gene CcPmk1 Is Required for Fungal Growth, Cell Wall Integrity and Pathogenicity in Cytospora chrysosperma. Fungal Genet. Biol. 2019, 128, 1–13. [Google Scholar] [CrossRef]
  6. Christensen, C. Studies on the Biology of Valsa Sordida and Cytospora Chrysosperma [sic]. Phytopathology 1940, 30, 459–475. [Google Scholar]
  7. Yin, Z.; Liu, H.; Li, Z.; Ke, X.; Dou, D.; Gao, X.; Song, N.; Dai, Q.; Wu, Y.; Xu, J.; et al. Genome Sequence of Valsa Canker Pathogens Uncovers a Potential Adaptation of Colonization of Woody Bark. New Phytol. 2015, 208, 1202–1216. [Google Scholar] [CrossRef]
  8. Ke, X.; Huang, L.; Han, Q.; Gao, X.; Kang, Z. Histological and Cytological Investigations of the Infection and Colonization of Apple Bark by Valsa mali var. mali. Australas. Plant Pathol. 2013, 42, 85–93. [Google Scholar] [CrossRef]
  9. Liu, X.; Li, X.; Bozorov, T.A.; Ma, R.; Ma, J.; Zhang, Y.; Yang, H.; Li, L.; Zhang, D. Characterization and Pathogenicity of Six Cytospora Strains Causing Stem Canker of Wild Apple in the Tianshan Forest, China. For. Pathol. 2020, 50, e12587. [Google Scholar] [CrossRef]
  10. Eken, C.; Sevindik, E. Molecular Phylogeny of Cytospora Species Associated with Canker Diseases of Apple Trees in Türkiye. Erwerbs-Obstbau 2023, 65, 2249–2257. [Google Scholar] [CrossRef]
  11. Lawrence, D.P.; Travadon, R.; Pouzoulet, J.; Rolshausen, P.E.; Wilcox, W.F.; Baumgartner, K. Characterization of Cytospora Isolates from Wood Cankers of Declining Grapevine in North America, with the Descriptions of Two New Cytospora Species. Plant Pathol. 2017, 66, 713–725. [Google Scholar] [CrossRef]
  12. Adams, G.C.; Roux, J.; Wingfield, M.J. Cytospora Species (Ascomycota, Diaporthales, Valsaceae): Introduced and Native Pathogens of Trees in South Africa. Austral. Plant Pathol. 2006, 35, 521–548. [Google Scholar] [CrossRef]
  13. Zhang, J.; Gu, Y.; Chi, F.; Ji, Z.; Wu, J.; Dong, Q.; Zhou, Z. Bacillus amyloliquefaciens GB1 Can Effectively Control Apple Valsa Canker. Biol. Control 2015, 88, 1–7. [Google Scholar] [CrossRef]
  14. Lawrence, D.P.; Holland, L.A.; Nouri, M.T.; Travadon, R.; Abramians, A.; Michailides, T.J.; Trouillas, F.P. Molecular Phylogeny of Cytospora Species Associated with Canker Diseases of Fruit and Nut Crops in California, with the Descriptions of Ten New Species and One New Combination. IMA Fungus 2018, 9, 333–369. [Google Scholar] [CrossRef] [PubMed]
  15. Trouillas, F.P.; Peduto, F.; Lorber, J.D.; Sosnowski, M.R.; Grant, J.; Coates, W.W.; Anderson, K.K.; Caprile, J.; Gubler, W.D. Calosphaeria Canker of Sweet Cherry Caused by Calosphaeria pulchella in California and South Australia. Plant Dis. 2012, 96, 648–658. [Google Scholar] [CrossRef]
  16. Adams, G.; Wingfield, M.; Common, R.; Roux, J. Phylogenetic Relationships and Morphology of Cytospora Species and Related Teleomorphs (Ascomycota, Diaporthales, Valsaceae) from Eucalyptus. Stud. Mycol. 2004, 52, 1–3. [Google Scholar]
  17. Pan, M.; Zhu, H.-Y.; Tian, C.-M.; Alvarez, L.V.; Fan, X.-L. Cytospora piceae sp. nov. Associated with Canker Disease of Picea crassifolia in China. Phytotaxa 2018, 383, 181–196. [Google Scholar] [CrossRef]
  18. Wang, X.; Shi, C.-M.; Gleason, M.L.; Huang, L. Fungal Species Associated with Apple Valsa Canker in East Asia. Phytopathol. Res. 2020, 2, 35. [Google Scholar] [CrossRef]
  19. Azizi, R.; Ghosta, Y.; Ahmadpour, A. Apple Crown and Collar Canker and Necrosis Caused by Cytospora balanejica sp. nov. in Iran. Sci. Rep. 2024, 14, 6629. [Google Scholar] [CrossRef]
  20. Fan, X.-L.; Liang, Y.-M.; Ma, R.; Tian, C.-M. Morphological and Phylogenetic Studies of Cytospora (Valsaceae, Diaporthales) Isolates from Chinese Scholar Tree, with Description of a New Species. Mycoscience 2014, 55, 252–259. [Google Scholar] [CrossRef]
  21. Yan, C.; Hao, H.; Sha, S.; Wang, Z.; Huang, L.; Kang, Z.; Wang, L.; Feng, H. Comparative Assessment of Habitat Suitability and Niche Overlap of Three Cytospora Species in China. JoF 2024, 10, 38. [Google Scholar] [CrossRef] [PubMed]
  22. Wang, Y.-L.; Lu, Q.; Decock, C.; Li, Y.-X.; Zhang, X.-Y. Cytospora Species from Populus and Salix in China with C. davidiana sp. nov. Fungal Biol. 2015, 119, 420–432. [Google Scholar] [CrossRef] [PubMed]
  23. Fan, X.-L.; Tian, C.-M.; Yang, Q.; Liang, Y.-M.; You, C.-J.; Zhang, Y.-B. Cytospora from Salix in Northern China. Mycotaxon 2015, 129, 303–315. [Google Scholar] [CrossRef]
  24. Wang, X.; Wei, J.; Huang, L.; Kang, Z. Re-Evaluation of Pathogens Causing Valsa Canker on Apple in China. Mycologia 2011, 103, 317–324. [Google Scholar] [CrossRef]
  25. Liang, X.; Zhang, R.; Gleason, M.L.; Sun, G. Sustainable Apple Disease Management in China: Challenges and Future Directions for a Transforming Industry. Plant Dis. 2022, 106, 786–799. [Google Scholar] [CrossRef]
  26. Lin, L.; Pan, M.; Bezerra, J.D.P.; Tian, C.; Fan, X. Re-Evaluation of the Fungal Diversity and Pathogenicity of Cytospora Species from Populus in China. Plant Dis. 2023, 107, 83–96. [Google Scholar] [CrossRef]
  27. Pautasso, M.; Aas, G.; Queloz, V.; Holdenrieder, O. European Ash (Fraxinus Excelsior) Dieback—A Conservation Biology Challenge. Biol. Conserv. 2013, 158, 37–49. [Google Scholar] [CrossRef]
  28. Mitchell, R.J.; Beaton, J.K.; Bellamy, P.E.; Broome, A.; Chetcuti, J.; Eaton, S.; Ellis, C.J.; Gimona, A.; Harmer, R.; Hester, A.J.; et al. Ash Dieback in the UK: A Review of the Ecological and Conservation Implications and Potential Management Options. Biol. Conserv. 2014, 175, 95–109. [Google Scholar] [CrossRef]
  29. Anagnostakis, S.L. Chestnut Blight: The Classical Problem of an Introduced Pathogen. Mycologia 1987, 79, 23–37. [Google Scholar] [CrossRef]
  30. Cahill, D.M.; Rookes, J.E.; Wilson, B.A.; Gibson, L.; McDougall, K.L. Phytophthora cinnamomi and Australia’s Biodiversity: Impacts, Predictions and Progress towards Control. Aust. J. Bot. 2008, 56, 279–310. [Google Scholar] [CrossRef]
  31. He, T.; Cai, G.; Jia, H.; Zhai, Y.; Ma, R. Distribution Characteristics of Cytospora spp. in Xinjiang. Xinjiang Agric. Sci. 2022, 59, 2696–2706. (In Chinese) [Google Scholar] [CrossRef]
  32. Zhao, Y.; Ma, R.; Yin, Y.X.; Zhang, Z.D.; Tian, C.M. Diversity of Cytospora chrysosperma from different hosts in Xinjiang. Biodivers. Sci. 2019, 27, 1122–1131. [Google Scholar] [CrossRef]
  33. Liu, C.; Dong, Y.; Li, Y.; Jiang, N.; Zhu, T.; Li, Z.; Song, S.; Li, J.; Luo, L. Identification of causal agent of apple, walnut and poplar Valsa canker disease in partial areas of Xinjiang. Acta Phytopathol. Sin. 2020, 50, 267–275. (In Chinese) [Google Scholar] [CrossRef]
  34. Kereman; Jiang, H.; Zhang, X.; Wang, C.; Jiao, S.; Wang, M.; Liu, J.; Liu, A. Records on Species of Forest Disease in Agricultural Development Area of Karamay City. Xinjiang Agric. Sci. 2006, 03, 192–194. (In Chinese) [Google Scholar]
  35. Wu, F.; Liu, H.; Hou, S.; Wen, J. Spatial Distribution Characteristics of Valsa Canker on Fragrant Pear. Chin. Agric. Sci. Bull. 2012, 28, 277–281. (In Chinese) [Google Scholar] [CrossRef]
  36. Gemingguli, M. Occurrence Regularity of Poplar Rot in Altai Region and Control Measures. J. Agric. Catastrophology 2014, 4, 13–14. (In Chinese) [Google Scholar] [CrossRef]
  37. Yue, C.; Kong, T.; Ayixiamu, Y.; Jiao, S.; Zhang, X. Main Factors Affecting Walnut Rot Disease. J. Northwest For. Univ. 2015, 30, 154–157. (In Chinese) [Google Scholar] [CrossRef]
  38. Tekauz, A. The Role of Twig Infections on the Incidence of Perennial Canker of Peach. Phytopathology 1974, 64, 683. [Google Scholar] [CrossRef]
  39. Biggs, A.R. Integrated Approach to Controlling Leucostoma Canker of Peach in Ontario. Plant Dis. 1989, 73, 869. [Google Scholar] [CrossRef]
  40. Kepley, J.B.; Jacobi, W.R. Pathogenicity of Cytospora Fungi on Six Hardwood Species. AUF 2000, 26, 326–333. [Google Scholar] [CrossRef]
  41. Minter, D.W. IMI Descriptions of Fungi and Bacteria. Mycopathologia 1996, 136, 147–185. [Google Scholar] [CrossRef] [PubMed]
  42. Bertrand, P.F. Release and Dispersal of Conidia and Ascospores of Valsa Leucostoma [sic]. Phytopathology 1976, 66, 987. [Google Scholar] [CrossRef]
  43. Guo, Q.; Kelly, M.; Graham, C.H. Support Vector Machines for Predicting Distribution of Sudden Oak Death in California. Ecol. Modell. 2005, 182, 75–90. [Google Scholar] [CrossRef]
  44. Puschendorf, R.; Carnaval, A.C.; VanDerWal, J.; Zumbado-Ulate, H.; Chaves, G.; Bolanos, F.; Alford, R.A. Distribution Models for the Amphibian Chytrid Batrachochytrium Dendrobatidis in Costa Rica: Proposing Climatic Refuges as a Conservation Tool. Divers. Distrib. 2009, 15, 401–408. [Google Scholar] [CrossRef]
  45. Shirk, A.; Cushman, S.; Waring, K.; Wehenkel, C.; Leal-Saenz, A.; Toney, C.; Lopez-Sanchez, C. Southwestern White Pine (Pinus strobiformis) Species Distribution Models Project a Large Range Shift and Contraction Due to Regional Climatic Changes. For. Ecol. Manag. 2018, 411, 176–186. [Google Scholar] [CrossRef]
  46. Shabani, F.; Kumar, L.; Esmaeili, A. Future Distributions of Fusarium oxysporum f. spp. in European, Middle Eastern and North African Agricultural Regions under Climate Change. Agric. Ecosyst. Environ. 2014, 197, 96–105. [Google Scholar] [CrossRef]
  47. Ejaz, M.R.; Jaoua, S.; Ahmadi, M.; Shabani, F. An Examination of How Climate Change Could Affect the Future Spread of Fusarium spp. around the World, Using Correlative Models to Model the Changes. Environ. Technol. Innov. 2023, 31, 103177. [Google Scholar] [CrossRef]
  48. Seidl, R.; Klonner, G.; Rammer, W.; Essl, F.; Moreno, A.; Neumann, M.; Dullinger, S. Invasive Alien Pests Threaten the Carbon Stored in Europe’s Forests. Nat. Commun. 2018, 9, 1626. [Google Scholar] [CrossRef]
  49. Fuchs, A.J.; Gilbert, C.C.; Kamilar, J.M. Ecological Niche Modeling of the Genus Papio. Am. J. Phys. Anthr. 2018, 166, 812–823. [Google Scholar] [CrossRef]
  50. Batista, E.; Lopes, A.; Miranda, P.; Alves, A. Can Species Distribution Models Be Used for Risk Assessment Analyses of Fungal Plant Pathogens? A Case Study with Three Botryosphaeriaceae Species. Eur. J. Plant Pathol. 2023, 165, 41–56. [Google Scholar] [CrossRef]
  51. Warren, D.L.; Glor, R.E.; Turelli, M. ENMTools: A Toolbox for Comparative Studies of Environmental Niche Models. Ecography 2010, 33, 607–611. [Google Scholar] [CrossRef]
  52. Zhang, B.; Chen, B.; Zhou, X.; Zou, H.; Duan, D.; Zhang, X.; Zhang, X. Distribution and Protection of Thesium Chinense Turcz. under Climate and Land Use Change. Sci. Rep. 2024, 14, 6475. [Google Scholar] [CrossRef] [PubMed]
  53. Xu, S. The Occurrence, Pathogenic Structural Composition and Genetic Diversity of Walnut Canker Disease in Xingjiang. Master’s Thesis, Tarim University, Aral, China, 3 June 2022. (In Chinese) [Google Scholar] [CrossRef]
  54. Huete, A.; Justice, C.; Van, L.W. MODIS vegetation index (MOD13). Algorithm Theor. Basis Doc. 1999, 3, 295–309. [Google Scholar]
  55. Roy, D.P.; Kovalskyy, V.; Zhang, H.K.; Vermote, E.F.; Yan, L.; Kumar, S.S.; Egorov, A. Characterization of Landsat-7 to Landsat-8 Reflective Wavelength and Normalized Difference Vegetation Index Continuity. Remote Sens. Environ. 2016, 185, 57–70. [Google Scholar] [CrossRef] [PubMed]
  56. Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geosci. Model Dev. 2016, 9, 1937–1958. [Google Scholar] [CrossRef]
  57. Shabani, F.; Ahmadi, M.; Peters, K.J.; Haberle, S.; Champreux, A.; Saltré, F.; Bradshaw, C.J.A. Climate-driven Shifts in the Distribution of Koala-browse Species from the Last Interglacial to the near Future. Ecography 2019, 42, 1587–1599. [Google Scholar] [CrossRef]
  58. Wu, Y.-M.; Shen, X.-L.; Tong, L.; Lei, F.-W.; Mu, X.-Y.; Zhang, Z.-X. Impact of Past and Future Climate Change on the Potential Distribution of an Endangered Montane Shrub Lonicera Oblata and Its Conservation Implications. Forests 2021, 12, 125. [Google Scholar] [CrossRef]
  59. Fan, Z.; Zhou, B.; Ma, C.; Gao, C.; Han, D.; Chai, Y. Impacts of Climate Change on Species Distribution Patterns of Polyspora Sweet in China. Ecol. Evol. 2022, 12, e9516. [Google Scholar] [CrossRef]
  60. Thuiller, W.; Lafourcade, B.; Engler, R.; Araújo, M.B. BIOMOD—A Platform for Ensemble Forecasting of Species Distributions. Ecography 2009, 32, 369–373. [Google Scholar] [CrossRef]
  61. Thuiller, W.; Georges, D.; Gueguen, M.; Engler, R.; Breiner, F.; Lafourcade, B.; Patin, R.; Blancheteau, H. biomod2: Ensemble Platform for Species Distribution Modeling. R Package Version 4.2-6-1, 2024, 1–133. Available online: https://biomodhub.github.io/biomod2/ (accessed on 12 July 2024).
  62. Rather, Z.A.; Ahmad, R.; Dar, T.-U.-H.; Khuroo, A.A. Ensemble Modelling Enables Identification of Suitable Sites for Habitat Restoration of Threatened Biodiversity under Climate Change: A Case Study of Himalayan Trillium. Ecol. Eng. 2022, 176, 106534. [Google Scholar] [CrossRef]
  63. Resquin, F.; Duque-Lazo, J.; Acosta-Muñoz, C.; Rachid-Casnati, C.; Carrasco-Letelier, L.; Navarro-Cerrillo, R.M. Modelling Current and Future Potential Habitats for Plantations of Eucalyptus Grandis Hill Ex Maiden and E. dunnii Maiden in Uruguay. Forests 2020, 11, 948. [Google Scholar] [CrossRef]
  64. Allouche, O.; Tsoar, A.; Kadmon, R. Assessing the Accuracy of Species Distribution Models: Prevalence, Kappa and the True Skill Statistic (TSS). J. Appl. Ecol. 2006, 43, 1223–1232. [Google Scholar] [CrossRef]
  65. Liu, C.; White, M.; Newell, G. Measuring and Comparing the Accuracy of Species Distribution Models with Presence-Absence Data. Ecography 2011, 34, 232–243. [Google Scholar] [CrossRef]
  66. Zhao, G.; Cui, X.; Sun, J.; Li, T.; Wang, Q.; Ye, X.; Fan, B. Analysis of the Distribution Pattern of Chinese Ziziphus jujuba under Climate Change Based on Optimized Biomod2 and MaxEnt Models. Ecol. Indic. 2021, 132, 108256. [Google Scholar] [CrossRef]
  67. Xian, X.; Zhao, H.; Wang, R.; Huang, H.; Chen, B.; Zhang, G.; Liu, W.; Wan, F. Climate Change Has Increased the Global Threats Posed by Three Ragweeds (Ambrosia L.) in the Anthropocene. Sci. Total Environ. 2023, 859, 160252. [Google Scholar] [CrossRef]
  68. Hao, T.; Elith, J.; Guillera-Arroita, G.; Lahoz-Monfort, J.J. A Review of Evidence about Use and Performance of Species Distribution Modelling Ensembles like BIOMOD. Divers. Distrib. 2019, 25, 839–852. [Google Scholar] [CrossRef]
  69. Fielding, A.H.; Bell, J.F. A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
  70. Zhang, R.; Guo, J.; Liang, T.; Feng, Q. Grassland Vegetation Phenological Variations and Responses to Climate Change in the Xinjiang Region, China. Quat. Int. 2019, 513, 56–65. [Google Scholar] [CrossRef]
  71. Wang, J.; Zhang, F.; Jim, C.-Y.; Chan, N.W.; Johnson, V.C.; Liu, C.; Duan, P.; Bahtebay, J. Spatio-Temporal Variations and Drivers of Ecological Carrying Capacity in a Typical Mountain-Oasis-Desert Area, Xinjiang, China. Ecol. Eng. 2022, 180, 106672. [Google Scholar] [CrossRef]
  72. Du, J.; Shu, J.; Yin, J.; Yuan, X.; Jiaerheng, A.; Xiong, S.; He, P.; Liu, W. Analysis on Spatio-Temporal Trends and Drivers in Vegetation Growth during Recent Decades in Xinjiang, China. Int. J. Appl. Earth Obs. Geoinf. 2015, 38, 216–228. [Google Scholar] [CrossRef]
  73. van Leeuwen, W.J.D.; Orr, B.J.; Marsh, S.E.; Herrmann, S.M. Multi-Sensor NDVI Data Continuity: Uncertainties and Implications for Vegetation Monitoring Applications. Remote Sens. Environ. 2006, 100, 67–81. [Google Scholar] [CrossRef]
  74. Zhang, H.; Song, J.; Wang, G.; Wu, X.; Li, J. Spatiotemporal Characteristic and Forecast of Drought in Northern Xinjiang, China. Ecol. Indic. 2021, 127, 107712. [Google Scholar] [CrossRef]
  75. Yao, J.; Chen, Y.; Zhao, Y.; Guan, X.; Mao, W.; Yang, L. Climatic and Associated Atmospheric Water Cycle Changes over the Xinjiang, China. J. Hydrol. 2020, 585, 124823. [Google Scholar] [CrossRef]
  76. Lin, L.; Pan, M.; Tian, C.; Fan, X. Fungal Richness of Cytospora Species Associated with Willow Canker Disease in China. J. Fungi 2022, 8, 377. [Google Scholar] [CrossRef]
  77. Bertrand, P.F.; English, H.; Carlson, R.M. Relation of soil physical and fertility properties to the occurrence of Cytospora canker in French prune orchards. Phytopathology 1976, 66, 1321–1324. [Google Scholar] [CrossRef]
  78. Parmesan, C.; Yohe, G. A Globally Coherent Fingerprint of Climate Change Impacts across Natural Systems. Nature 2003, 421, 37–42. [Google Scholar] [CrossRef]
  79. Juroszek, P.; von Tiedemann, A.; Duveiller, E.; Singh, R.P.; Nicol, J.M. Climate Change and Potential Future Risks through Wheat Diseases: A Review. Eur. J. Plant Pathol. 2013, 136, 21–33. [Google Scholar] [CrossRef]
  80. Dudley, M.M.; Tisserat, N.A.; Jacobi, W.R.; Negrón, J.; Stewart, J.E. Pathogenicity and Distribution of Two Species of Cytospora on Populus Tremuloides in Portions of the Rocky Mountains and Midwest in the United States. For. Ecol. Manag. 2020, 468, 118168. [Google Scholar] [CrossRef]
  81. Han, Z.; Xiong, D.; Xu, Z.; Liu, T.; Tian, C. The Cytospora chrysosperma Virulence Effector CcCAP1 Mainly Localizes to the Plant Nucleus To Suppress Plant Immune Responses. mSphere 2021, 6, e00883-20. [Google Scholar] [CrossRef]
  82. Duveiller, E.; Singh, R.P.; Nicol, J.M. The Challenges of Maintaining Wheat Productivity: Pests, Diseases, and Potential Epidemics. Euphytica 2007, 157, 417–430. [Google Scholar] [CrossRef]
  83. Wollan, A.K.; Bakkestuen, V.; Kauserud, H.; Gulden, G.; Halvorsen, R. Modelling and Predicting Fungal Distribution Patterns Using Herbarium Data. J. Biogeogr. 2008, 35, 2298–2310. [Google Scholar] [CrossRef]
  84. Trebicki, P. Climate Change and Plant Virus Epidemiology. Virus Res. 2020, 286, 198059. [Google Scholar] [CrossRef] [PubMed]
  85. Fang, S.; Yan, J.; Che, M.; Zhu, Y.; Liu, Z.; Pei, H.; Zhang, H.; Xu, G.; Lin, X. Climate Change and the Ecological Responses in Xinjiang, China: Model Simulations and Data Analyses. Quat. Int. 2013, 311, 108–116. [Google Scholar] [CrossRef]
  86. Chakraborty, S.; Newton, A.C. Climate Change, Plant Diseases and Food Security: An Overview. Plant Pathol. 2011, 60, 2–14. [Google Scholar] [CrossRef]
  87. Thomson, L.J.; Macfadyen, S.; Hoffmann, A.A. Predicting the Effects of Climate Change on Natural Enemies of Agricultural Pests. Biol. Control 2010, 52, 296–306. [Google Scholar] [CrossRef]
  88. Gutierrez, A.P.; Ponti, L.; d’Oultremont, T.; Ellis, C.K. Climate Change Effects on Poikilotherm Tritrophic Interactions. Clim. Chang. 2008, 87, 167–192. [Google Scholar] [CrossRef]
  89. Büntgen, U.; Egli, S.; Tegel, W.; Stobbe, U.; Sproll, L.; Elburg, R.; Peter, M.; Nievergelt, D.; Cherubini, P.; Stenseth, N.C. Illuminating the Mysterious World of Truffles. Front. Ecol Env. 2012, 10, 462–463. [Google Scholar] [CrossRef]
  90. Li, Q.; Cao, S.; Sun, W.; Zhang, Z. Prediction of the Potential Geographical Distribution of Cytospora Chrysosperma in Xinjiang, China under Climate Change Scenarios. Front. For. Glob. Change 2024, 7, 1370365. [Google Scholar] [CrossRef]
  91. Xu, W.; Sun, H.; Jin, J.; Cheng, J. Predicting the Potential Distribution of Apple Canker Pathogen (Valsa Mali) in China under Climate Change. Forests 2020, 11, 1126. [Google Scholar] [CrossRef]
  92. Sturrock, R.N.; Frankel, S.J.; Brown, A.V.; Hennon, P.E.; Kliejunas, J.T.; Lewis, K.J.; Worrall, J.J.; Woods, A.J. Climate Change and Forest Diseases. Plant Pathol. 2011, 60, 133–149. [Google Scholar] [CrossRef]
  93. Bosso, L.; Di Febbraro, M.; Cristinzio, G.; Zoina, A.; Russo, D. Shedding Light on the Effects of Climate Change on the Potential Distribution of Xylella Fastidiosa in the Mediterranean Basin. Biol. Invasions 2016, 18, 1759–1768. [Google Scholar] [CrossRef]
  94. Jactel, H.; Petit, J.; Desprez-Loustau, M.-L.; Delzon, S.; Piou, D.; Battisti, A.; Koricheva, J. Drought Effects on Damage by Forest Insects and Pathogens: A Meta-Analysis. Glob. Chang. Biol. 2012, 18, 267–276. [Google Scholar] [CrossRef]
  95. 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] [PubMed]
  96. Merow, C.; Smith, M.J.; Edwards, T.C.; Guisan, A.; McMahon, S.M.; Normand, S.; Thuiller, W.; Wüest, R.O.; Zimmermann, N.E.; Elith, J. What Do We Gain from Simplicity versus Complexity in Species Distribution Models? Ecography 2014, 37, 1267–1281. [Google Scholar] [CrossRef]
  97. Pautasso, M.; Döring, T.F.; Garbelotto, M.; Pellis, L.; Jeger, M.J. Impacts of Climate Change on Plant Diseases—Opinions and Trends. Eur. J. Plant Pathol. 2012, 133, 295–313. [Google Scholar] [CrossRef]
  98. Savage, D.; Barbetti, M.J.; MacLeod, W.J.; Salam, M.U.; Renton, M. Temporal Patterns of Ascospore Release in Leptosphaeria Maculans Vary Depending on Geographic Region and Time of Observation. Microb. Ecol. 2013, 65, 584–592. [Google Scholar] [CrossRef]
  99. Meentemeyer, R.K.; Haas, S.E.; Václavík, T. Landscape Epidemiology of Emerging Infectious Diseases in Natural and Human-Altered Ecosystems. Annu. Rev. Phytopathol. 2012, 50, 379–402. [Google Scholar] [CrossRef]
Forests 15 01617 g001
Figure 1. Distribution records of Cytospora chrysosperma, C. nivea and C. mali in Xinjiang, China.
Figure 1. Distribution records of Cytospora chrysosperma, C. nivea and C. mali in Xinjiang, China.
Forests 15 01617 g001
Forests 15 01617 g002
Figure 2. Evaluate the predictive performance of each model for the three Cytospora species using the area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS).
Figure 2. Evaluate the predictive performance of each model for the three Cytospora species using the area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS).
Forests 15 01617 g002
Forests 15 01617 g003
Figure 3. Response curves of the top 4 most important environmental variables for the three Cytospora species.
Figure 3. Response curves of the top 4 most important environmental variables for the three Cytospora species.
Forests 15 01617 g003
Forests 15 01617 g004
Figure 4. Current geographical distributions of Cytospora chrysosperma, C. nivea, and C. mali predicted using ensemble models. Potential geographical distributions of (A) C. chrysosperma, (B) C. nivea, and (C) C. mali. (D) Overlapping geographical distribution areas of the three Cytospora species.
Figure 4. Current geographical distributions of Cytospora chrysosperma, C. nivea, and C. mali predicted using ensemble models. Potential geographical distributions of (A) C. chrysosperma, (B) C. nivea, and (C) C. mali. (D) Overlapping geographical distribution areas of the three Cytospora species.
Forests 15 01617 g004
Forests 15 01617 g005
Figure 5. Potential geographical distribution of Cytospora chrysosperma under different climate scenarios predicted using ensemble model.
Figure 5. Potential geographical distribution of Cytospora chrysosperma under different climate scenarios predicted using ensemble model.
Forests 15 01617 g005
Forests 15 01617 g006
Figure 6. Potential geographical distribution of Cytospora nivea under different climate scenarios predicted using ensemble model.
Figure 6. Potential geographical distribution of Cytospora nivea under different climate scenarios predicted using ensemble model.
Forests 15 01617 g006
Forests 15 01617 g007
Figure 7. Potential geographical distribution of Cytospora mali under different climate scenarios predicted using ensemble model.
Figure 7. Potential geographical distribution of Cytospora mali under different climate scenarios predicted using ensemble model.
Forests 15 01617 g007
Forests 15 01617 g008
Figure 8. Overlapping geographical distribution areas of Cytospora chrysosperma, C. nivea, and C. mali under different climate scenarios in the future.
Figure 8. Overlapping geographical distribution areas of Cytospora chrysosperma, C. nivea, and C. mali under different climate scenarios in the future.
Forests 15 01617 g008
Forests 15 01617 g009
Figure 9. Centroid shifts of potential suitable area for Cytospora chrysosperma, C. nivea, and C. mali under different climate scenarios. (A) Location of the centroids of potential suitable areas for the three Cytospora species in the study area. (B) Centroid shifts of potential suitable area for C. chrysosperma. (C) Centroid shifts of potential suitable area for C. nivea. (D) Centroid shifts of potential suitable area for C. mali.
Figure 9. Centroid shifts of potential suitable area for Cytospora chrysosperma, C. nivea, and C. mali under different climate scenarios. (A) Location of the centroids of potential suitable areas for the three Cytospora species in the study area. (B) Centroid shifts of potential suitable area for C. chrysosperma. (C) Centroid shifts of potential suitable area for C. nivea. (D) Centroid shifts of potential suitable area for C. mali.
Forests 15 01617 g009
Table 1. Classification of suitable habitats for Cytospora chrysosperma, C. nivea, and C. mali.
Table 1. Classification of suitable habitats for Cytospora chrysosperma, C. nivea, and C. mali.
SpecieUnsuitable HabitatLowly Suitable HabitatModerately Suitable HabitatHighly Suitable Habitat
C. chrysosperma0 ≤ p < 152152 ≤ p < 443443 ≤ p < 787787 ≤ p ≤ 1000
C. nivea0 ≤ p < 779.5779.5 ≤ p < 857857 ≤ p < 932932 ≤ p ≤ 1000
C. mali0 ≤ p < 547.5547.5 ≤ p < 672672 ≤ p < 802802 ≤ p ≤ 1000
p: a probability value for the presence of Cytospora.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, Q.; Cao, S.; Wang, L.; Hou, R.; Sun, W. Impacts of Climate Change on the Potential Distribution of Three Cytospora Species in Xinjiang, China. Forests 2024, 15, 1617. https://doi.org/10.3390/f15091617

AMA Style

Li Q, Cao S, Wang L, Hou R, Sun W. Impacts of Climate Change on the Potential Distribution of Three Cytospora Species in Xinjiang, China. Forests. 2024; 15(9):1617. https://doi.org/10.3390/f15091617

Chicago/Turabian Style

Li, Quansheng, Shanshan Cao, Lei Wang, Ruixia Hou, and Wei Sun. 2024. "Impacts of Climate Change on the Potential Distribution of Three Cytospora Species in Xinjiang, China" Forests 15, no. 9: 1617. https://doi.org/10.3390/f15091617

APA Style

Li, Q., Cao, S., Wang, L., Hou, R., & Sun, W. (2024). Impacts of Climate Change on the Potential Distribution of Three Cytospora Species in Xinjiang, China. Forests, 15(9), 1617. https://doi.org/10.3390/f15091617

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop