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Article

Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios

by
Shuhan Chen
,
Chengming You
,
Zheng Zhang
and
Zhenfeng Xu
*
Forestry Ecological Engineering in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province & National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & The College of Forestry of Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Forests 2024, 15(6), 1033; https://doi.org/10.3390/f15061033
Submission received: 11 May 2024 / Revised: 12 June 2024 / Accepted: 12 June 2024 / Published: 14 June 2024
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Versions Notes

Abstract

:
Global climate changes are expected to profoundly shape species distribution. Quercus oxyphylla, a valuable evergreen broad-leaved tree species, is rigorously conserved and managed in China owing to its substantial scientific, economic, and ecological value. However, the impact of projected climate change on its future distribution and potential climatic drivers remains unclear. Here, a maximum entropy model (MaxEnt) was used to explore the distribution of Q. oxyphylla in China under current conditions and three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the 2050s and 2070s. We optimized the model using the ‘ENMeval’ package to obtain the best parameter combination (RM = 1, FC = LQHPT), and multiple evaluation metrics (AUC ≥ 0.9; TSS ≥ 0.6; Kappa ≥ 0.75) verified the high accuracy of the model and the reliability of the prediction results. We found the following: (1) The potential distribution of Q. oxyphylla spans across 28 provinces in China under current climatic conditions, predominantly in southern regions, with Sichuan exhibiting the largest suitable area for survival. The total suitable habitat covers 244.98 × 104 km2, comprising highly, moderately, and poorly suitable habitats of 51.66 × 104 km2, 65.98 × 104 km2, and 127.34 × 104 km2, respectively. (2) Under future climate conditions, the overall geographical boundaries of Q. oxyphylla are predicted to remain similar to the present one, with an increase of 10.29% in the 2050s and 11.31% in the 2070s. In the 2050s, the total suitable habitats for Q. oxyphylla under the three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) might increase by 8.83%, 9.62%, and 12.42%, while in the 2070s they might increase by 10.39%, 17.21%, and 6.33%, respectively. (3) Moreover, the centroid of the suitable area is expected to migrate southwestward under the three scenarios in the future. (4) Annual precipitation, isothermality, and temperature annual range emerged as the main factors influencing the distribution of Q. oxyphylla, with contributions of 55.9%, 25.7%, and 13.5%, respectively. Our findings refined the spatial arrangement of Q. oxyphylla growth and revealed its climate resilience. This suggested that under climate change, Sichuan and Shaanxi are the optimal regions for cultivation and management, while appropriate conservation strategies should be formulated in Tibet and Hubei.

1. Introduction

Understanding species distribution is essential for comprehending ecosystem structure, safeguarding biodiversity, and evaluating the effects of environmental changes [1]. In recent decades, global warming has heightened climate system instability, evidenced by rising global surface temperatures and annual precipitation [2,3]. Additionally, the frequency and severity of extreme weather, including heatwaves and heavy rainfall, have also increased [2]. These changes can lead to higher frequencies of pest and disease outbreaks, and cause temporary reproductive isolation, significantly altering species distributions [4,5]. For example, rising temperatures and abnormal precipitation patterns at high altitudes and latitudes affect seed dispersal and life cycles, ultimately shaping species’ geographic distribution patterns. Therefore, forecasting potential species’ distribution amid global climate challenges provides vital insights for devising effective conservation strategies.
Climate change profoundly impacts ecosystems by altering species composition, reorganizing ecological niches, and disrupting food chains [5,6]. Research indicated that modified precipitation patterns and rising temperatures are the primary abiotic factors influencing species distribution at regional scales [7,8]. These changes significantly affect species’ responses, including reproduction, seed germination, growth, transpiration, respiration, and photosynthesis [5,9]. For instance, lower annual mean temperatures can cause freezing damage, severely impacting seedling emergence and survival rates, thus limiting species’ geographical distributions [10]. Conversely, higher annual precipitation can increase soil water content and availability, mitigate surface temperatures, and create optimal growth conditions for species [11]. Studies have primarily focused on annual mean temperature and annual precipitation [10,12], which influence species distribution through soil water dynamics and seed germination. However, the impact of dynamic factors like temperature annual range and isothermality have been largely overlooked.
Ecological niche modeling is essential for predicting species distribution patterns at regional scales [13]. Prominent ecological niche models encompass the Genetic Algorithm for Rule Set Prediction (GARP) [14], Ecological Niche Factor Analysis (ENFA) [15], Bioclimatic Envelope Modeling (BioClimLim) [16], and Maximum Entropy (MaxEnt) [17]. Among these, MaxEnt stands out for its excellent predictive performance, high simulation accuracy, and rapid processing [18]. Consequently, MaxEnt is extensively utilized in research on imperiled species and biological invasions. For example, it has successfully forecasted the spread of invasive ants like Iridomyrmex in New Zealand [19] and identified suitable habitats for the endangered species Canacomyrica monticola in New Caledonia [20]. Additionally, MaxEnt has been employed to study species of high conservation significance at various spatial scales, such as Gentiana rhodantha globally [21], Pinus gerardiana in South Asia [9], and Nilaparvata lugens in India [22]. Therefore, utilizing the MaxEnt model to simulate species’ geographical distribution is scientifically robust.
Q. oxyphylla, an evergreen broad-leaved tree species native to China, thrives in mountainous and hilly regions, exhibiting remarkable adaptability to diverse environmental conditions. It has been rigorously conserved and managed in China for its ecological, economic, and social value, significantly contributing to soil and water conservation and air purification. However, its natural habitat has rapidly declined due to human activities and dramatic climate change. Despite its importance, comprehensive studies on its geographic distribution and ecological requirements are lacking. Refining its spatial distribution and assessing its resilience to climate change are critical for the introduction, cultivation, ecological conservation, and management of Q. oxyphylla.
In this study, we utilized MaxEnt to forecast Q. oxyphylla’s potential distribution in China across existing and forthcoming climate scenarios, employing 42 distribution points and 20 bioclimatic factors. With global change, precipitation intensity and surface temperatures have been continuously rising, particularly at high altitudes. Additionally, research indicates that global warming will raise the optimal altitude for species [23,24]. Therefore, we hypothesize that Q. oxyphylla’s suitable habitat will shift to higher altitudes in China. Our study aims to (1) forecast Q. oxyphylla’s potential distribution across present and future climate conditions in China, and (2) explore the primary climatic drivers influencing suitable habitats.

2. Materials and Methods

2.1. Gathering Species Distribution Points and a Base Map for Analysis

The occurrence data for Q. oxyphylla were obtained from the Global Biodiversity Information Facility (GBIF, http://www.gbif.org, accessed on 25 January 2024), the National Plant Specimen Resource Center (NPSRC; http://www.cvh.ac.cn, accessed on 25 January 2024), and relevant published literature [25]. We meticulously reviewed 61 occurrence records to ensure accuracy and eliminate redundancy. After excluding duplicate data, uncertain data, and erroneous data (e.g., longitude and latitude of zero, inverted coordinates, or incorrect locations), we retained 42 occurrence points for model construction and stored them in CSV format.
A base map for analysis was sourced from China’s National Basic Geographic Information System (http://nfgis.nsdi.gov.cn, accessed on 26 January 2024). Using ArcGIS 10.8, we created a sighting point map illustrating the distribution of Q. oxyphylla in China (Figure 1).

2.2. Climatic Factors

We selected 19 bioclimatic variables from the WorldClim database (www.worldclim.org, accessed on 27 January 2024) (2.5 arc-min). These variables, which are derived from monthly temperature and precipitation data, are extensively utilized to create species distribution models. Future projection indicators of the periods 2041–2060 and 2061–2080, based on a BCC-CSM2-MR model with a 2.5 arc-min resolution, were obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These projections encompass three climate scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5 [26], corresponding to global effective radiative forcing values of about 2.6 W/m2, 4.5 W/m2, and 8.5 W/m2, respectively [21]. The Shared Socioeconomic Pathways (SSPs) framework facilitates comprehensive analyses of future climate impacts, vulnerabilities, adaptations, and mitigations [12].
Altitude data were sourced from the Geospatial Data Cloud (GDC, http://www.gscloud.cn/, accessed on 27 January 2024). The data were standardized by filling in missing values, removing invalid data, and harmonizing the resolution (30′) and projection (WGS1984) using ArcGIS 10.8 for subsequent analysis [13].

2.3. Screening of Environmental Variables

Correlation among environmental factors can cause overfitting and reduce prediction accuracy. Initially, we modeled with 20 environmental variables and calculated their contribution rates, removing variables with a rate of zero (Table S1). The remaining variables were then analyzed for correlation using ArcGIS 10.8. For pairs of variables with Pearson’s correlation coefficients above 0.8 in absolute value, we kept the one with the higher contribution [27]. We utilized the ‘corrplot’ package in R 4.3.2 software to visualize correlation coefficients (Figure 2). Ultimately, we selected six environmental factors for the model (Table 1).

2.4. Model Development and Statistical Evaluation

After eliminating the influence of highly correlated environmental variables, we further analyzed the impact of the selected background points, which cover approximately one-fourth of China and are sparsely distributed. This spatial bias in occurrence points was addressed using the ‘ENMeval’, ‘SDMtune’, and ‘rJava’ packages in R. We generated 5000 random points and a species bias file based on existing distribution data, incorporating climatic factors, to create a two-dimensional kernel density raster [9]. This approach effectively manipulated environmental background bias and enhanced the model’s accuracy.
To enhance accuracy, we optimized the model using the ’dismo’, ‘ENMeval’, and ‘raster’ packages in R to examine and visualize the regularization multiplier (RM) and feature combination (FC), selecting the best parameter setup. FCs include Linear (L), Quadratic (Q), Hinge (H), Product (P), and Threshold (T). We developed six FCs: L, LQ, H, LQH, LQHP, and LQHPT. We assessed 72 combinations derived from these FCs and twelve RMs (ranging from 0.5 to 6 in 0.5 increments). The model’s fit and complexity with various parameter combinations were evaluated using the corrected Akaike Information Criterion (AICc) [28]. Overfitting was checked through a 10% training omission rate (OR10) and AUC difference between training and testing (AUC.diff) [29]. The combination with the lowest AICc value was chosen as the optimal model configuration.
We produced RLDMs using the MaxEnt Java application. First, we loaded the selected dataset of 42 Q. oxyphylla occurrence points and six climatic factors into MaxEnt. Then, we configured the model parameters as follows: (1) The data were divided into a dataset, with 75% for training and 25% for evaluating. (2) To improve accuracy, we performed 10 repetitions, averaging the results. (3) To interpret the impact of individual variables, we used the options ‘Do jackknife to assess variable importance’, ‘Make pictures of predictions’, and ‘Create response curves’. (4) We selected the logistic output and cross-validation method to estimate the probability of the presence of environmental variables. (5) Additionally, the ‘skip if output exists’ option was set to exclude distribution points outside China, with the rest set by default.
The model’s accuracy was assessed using the AUC value under the receiver operating characteristic curve (ROC) [21]. However, AUC has inherent limitations [30]. Therefore, we also employed partial AUC (p-AUC-ROC), True Skill Statistics (TSS), Kappa, and AUC ratios to evaluate accuracy. Using the ‘ENMeval’, ‘pROC’, ‘PresenceAbsence’, and ‘spm’ packages in R, we assessed the p-AUC-ROC curve values with a 95% confidence interval [9]. The AUC ratio was calculated by the AUC value at an error rate of 5% (E = 0.05), where a ratio greater than one indicated good model performance [31]. Predictive performance was categorized as good (TSS ≥ 0.6; Kappa ≥ 0.75; AUC ≥ 0.9), moderate (0.2 ≤ TSS ≤ 0.6; 0.4 ≤ Kappa ≤ 0.75; 0.7 ≤ AUC ≤ 0.9), or poor (TSS ≤ 0.2; Kappa ≤ 0.4; AUC ≤ 0.7) [32].
The average results from MaxEnt simulations were loaded into ArcGIS 10.8 to classify and visualize the distribution regions. For Q. oxyphylla, an endemic species, we utilized the ‘alphahull’ package in R to develop an Extent of Occurrence (EOO) polygon using the Convex Hull method and visualized the results with the ‘ggplot2’ package [9]. Suitable habitats were classified into four levels: unsuitable (p < 0.1), poorly suitable (0.1 ≤ p < 0.3), moderately suitable (0.3 ≤ p < 0.5), and highly suitable (p ≥ 0.5), using the natural breaks classification (Jenks) method [33]. To analyze area changes, the ‘distribution changes between binary SDMs’ tool was utilized to identify expansion, stability, and contraction areas [21]. Additionally, the ‘centroid changes (lines)’ tool calculated shifts in the geometric center of suitable habitats over different periods [34].

3. Results

3.1. Assessment of the MaxEnt Model

Based on the analysis, selecting the LQHPT feature class and an RM value of one is the best model configuration, minimizing the AICc value (AICc = 0). Further evaluation of RO10 and AUC.diff values indicated a low degree of overfitting and reasonable parameter settings (Figure S2).
The correlation coefficients among the six variables were below 0.8 (Figure 2), satisfying the accuracy criteria for habitat suitability assessment. The model’s precision was evaluated using the AUC under ROC, with the AUC value reaching 0.955 (Figure S3). Additional evaluation indices included TSS (0.903), p-AUC-ROC (0.90), AUC ratio (1.96), and Kappa (0.909) (Table 2), which all demonstrated excellent model performance. These results suggest that the model accurately forecast the potential spatial distribution of Q. oxyphylla.

3.2. Contribution of Important Climatic Variables

After confirming the model’s feasibility, we used the Jackknife test in MaxEnt to analyze climate factors (Figure S1). Among the six selected climatic variables, annual precipitation (PC = 50.5%, PI = 3.9%) (Table 1) was the dominant factor influencing its distribution. This was succeeded by temperature annual range and isothermality, with a cumulative contribution of 45.7% and a permutation importance of 89% (Table 1). In other words, temperature-related and precipitation-related variables were the most significant predictors for Q. oxyphylla distribution.
Response curves indicated the thresholds (existence probability > 0.5) for key bioclimatic parameters: isothermality (Bio3) was 26.67~30.67 °C, peaking at 28.33 °C (Figure 3A); and temperature annual range (Bio7) was 25.91~30.91 °C, peaking at 28.18 °C (Figure 3B). Additionally, habitat suitability for Q. oxyphylla showed a positive correlation with annual precipitation (Bio12) (Figure 3C).

3.3. Predicting Potential Distribution under Current Conditions

Based on MaxEnt simulation results, Q. oxyphylla’s potential distribution is widespread in 28 provinces in China under present climatic conditions. It is predominantly found in southern China, with Sichuan exhibiting the most suitable area for survival (Figure 4). The total suitable habitat covers 244.98 × 104 km2. Highly suitable habitat covers 51.66 × 104 km2 (5.38% of the study region), primarily in Sichuan (15%), Shaanxi (13%), and Guizhou (12%). Moderately suitable habitat spans 65.98 × 104 km2, representing 6.87% of the study area, with the largest areas in Sichuan (13%), Guangxi (11%), and Hunan (10%). Poorly suitable habitat is concentrated in Tibet (15%), Sichuan (13%), Anhui (8%), and Henan (8%), covering 127.34 × 104 km2 and accounting for 13.26% of the study area (Figure 4 and Table S2).

3.4. Forecast of Future Potential Habitat

Suitable areas for Q. oxyphylla are projected to experience a net increase in accordance with diverse climate change simulations. The geographical boundaries of Q. oxyphylla are expected to remain largely the same as they are currently. The suitable habitat area is projected to expand by 10.29% in the 2050s and an additional 11.31% in the 2070s (Figure 5).
In comparison to the present, the 2050s show an overall expansion trend. Across the three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), the total suitable area for Q. oxyphylla in the 2050s is expected to grow by 8.83%, 9.62%, and 12.42%, respectively, with highly suitable habitats increasing significantly by 14.94%, 27.39%, and 28.86% (Figure 5A,C,E). In 2070, regions of highly and moderately suitable habitat are projected to rise significantly, whereas poorly suitable habitats decrease. The total suitable area is predicted to grow by 10.39% (SSP1-2.6), 17.21% (SSP2-4.5), and 6.33% (SSP5-8.5), with highly suitable areas increasing by 35.35%, 46.79%, and 26.48% (Figure 5B,D,F).

3.5. Shift of the Geometric Center across Various Climate Scenarios

The center of Q. oxyphylla’s potential habitat is currently in Tongnan District, Chongqing (30.60° N, 105.57° E). In the SSP1-2.6 scenario, the centroid is projected to shift southwestward by about 54.83 km to Anyue County, Sichuan (2050s), and then southwestward by about 130.02 km to Jingyan County (2070s). In the SSP2-4.5 scenario, the centroid is expected to move southwestward by about 58.01 km to Dazu District, Chongqing (2050s), and then by about 70.70 km to Da’an District, Sichuan (2070s). For SSP5-8.5, the centroid is anticipated to relocate southwestward to Yongchuan District (2050s), with a migration distance of 67.26 km, and then southwestward (2070s) by the shortest distance (37.27 km). Overall, the habitat center consistently shifts southwestward in China under various climate scenarios, moving progressively further west over time (Figure 6).

4. Discussion

4.1. Significance of Model Predictions

Given the ongoing rise in global temperatures and changing precipitation patterns, simulating the potential distribution of plants is crucial for the ecological and practical management of species and forest ecosystems [35]. MaxEnt is frequently employed for its ability to quickly and easily generate detailed information about the present and future occurrence of target species [36], such as Drosophila suzukii [10], Pinus Massoniana [13], and others [9,37]. Our research is the first to utilize MaxEnt to model potentially suitable areas for Q. oxyphylla, using 42 distribution points and 6 of 20 environmental variables. To ensure accuracy and stability, we optimized MaxEnt with the ‘ENMeval’ package to minimize overfitting and sampling bias, and enhance prediction accuracy [9]. We also utilized R to examine Pearson correlation within the data, removing highly correlated and low-contribution variables to mitigate multicollinearity effects (Figure 2). In comparison, the AUC values for Alnus cremastogyne [35], Cunninghamia lanceolata [28], and Vaccinium uliginosum [23] were 0.945, 0.885, and 0.948, respectively, which are relatively lower than our AUC value of 0.955. This indicates that MaxEnt is appropriate for forecasting the potential geographical distribution of Q. oxyphylla.

4.2. Prospective Changes in Q. oxyphylla’s Suitable Habitat

Climate change is anticipated to modify the geographic distribution of forest ecosystems in a species-specific manner [35,38]. Currently, the potential distribution of Q. oxyphylla covers 28 provinces in China, primarily in the southern region, consistent with established survey regions [25]. Q. oxyphylla’s suitable habitat is predominantly found in the subtropical monsoon climate zone, characterized by high seasonal temperatures and ample rainfall. Litsea cubeba, which prefers humid and rainy conditions, is also found in these regions, indicating a similar ecological preference as Q. oxyphylla [39]. Highly suitable habitat is located in northern Sichuan and Guangxi, southern Shaanxi and Jiangxi, and central Guizhou, featuring abundant sunshine, optimal annual precipitation (330–2000 mm), a temperature annual range (23–33 °C), and isothermality (26–36 °C). These regions offer ideal conditions for the cultivation and introduction of Q. oxyphylla. Moderately suitable habitat, adjacent to highly suitable areas, have abundant precipitation (230–330 mm) and greater temperature seasonality (36–45 °C). Poorly suitable habitat, including eastern Tibet, western Sichuan, and most of Henan, experience cold and windy winters, hot and rainy summers, relatively low precipitation (94–230 mm), and significant climatic variation. Therefore, we speculate that the probability of Q. oxyphylla occurrence in these areas is very low, necessitating appropriate conservation strategies.
The Chengdu Plain, known as the ‘Land of Abundance’, exhibits the largest highly suitable habitat, likely due to its mild, spring-like climate year-round [40]. Q. oxyphylla also thrives in the plateau climate regions of eastern Tibet, western Sichuan, and northern Yunnan, probably due to artificial cultivation. These regions are distinguished by significant daily temperature variation, intense solar radiation, and a unique cold environment, demonstrating Q. oxyphylla’s strong adaptability [41]. In contrast, northern China is unsuitable for Q. oxyphylla due to its temperate continental and monsoon climates, featuring limited rainfall and harsh winters [39].
Global warming significantly impacts species distribution, causing expansions, shifts, or contractions in their habitats [36]. Some species face threats from climate change and become endangered [20], while others benefit and expand their distribution [35]. The suitable habitat for Q. oxyphylla is projected to increase across various SSP scenarios during the 2050s and 2070s (Table 3), suggesting that future habitats could be available for artificial cultivation [36]. Typical expansions are concentrated in Chongqing, Sichuan, Guizhou, Hunan, and Jiangxi (Figure 7), possibly resulting from anticipated increases in temperature and precipitation [42]. In the 2050s, the highly suitable area under SSP5-8.5 is significantly higher than under SSP1-2.6 and SSP2-4.5 (Table 3), mainly due to improved hydrothermal conditions [39]. This aligns with prior research suggesting that plant species’ habitat suitability is expected to enhance in certain regions under climate change [36]. However, in the 2070s, the total highly suitable area is larger under SSP2-4.5 compared to SSP1-2.6 and SSP5-8.5 (Table 3). Similar trends were observed in the study of Keteleeria davidiana [43], indicating that continuous temperature rises could exceed plant tolerance, prolonging bud differentiation and postponing growth, and eventually causing dormancy [44]. Notably, the overall distribution pattern under future climate scenarios remains comparable to the present one, suggesting limited range movement of the vegetation belt during peak warm periods [45].
The core of the suitable habitat is expected to shift southwest within China under various climate scenarios (Figure 6). This finding aligns with research on Alnus cremastogyne Burk, and is attributed to projected warmer and more humid conditions [35]. For instance, global warming leads to a wetter climate at high latitudes and a drier climate at mid-latitudes, causing some habitats to shrink and migrate to higher altitudes as species adapt to the changing environment. Our study on the prospective change in suitable habitat reveals that future climate change matches Q. oxyphylla’s characteristics and habitat preferences, indicating a potential expansion of its suitable area.

4.3. Impacts of Climatic Parameters on the Distribution of Q. oxyphylla

Climatic factors are crucial for the regeneration and spread of natural populations [36]. Identifying the precise environmental elements that shape and sustain species distribution is indispensable [21]. The study indicated that Q. oxyphylla’s distribution is predominantly influenced by precipitation-related (Bio12) and temperature-related (Bio3 and Bio7) climate variables (Table 1).
In the study, annual precipitation (PC = 50.5%, PI = 3.9%) had the most significant effect on Q. oxyphylla’s growth, aligning with studies on Carex alatauensis [46] and Haloxylon [47]. Alterations in precipitation patterns impact plant physiological and ecological processes by influencing soil moisture and nutrient levels, thereby controlling plant growth and development [21,48].
Furthermore, temperature annual range (PC = 27.6%, PI = 74.1%) had the second-largest contribution, consistent with a study on H. riparia [47]. Temperature influences plant growth and distribution through its effects on plant morphology and biochemical processes, like photosynthesis, respiration, and material transfer [21].
Isothermality (PC = 18.1%, PI = 14.9%) contributed the third-largest percentage, which aligns with a study on Paeonia delavayi [49]. Larger isothermality benefits nutrient accumulation, allowing Q. oxyphylla to use the relatively high daytime temperatures for photosynthesis and the relatively low nighttime temperatures for respiration energy consumption [49].
The relatively minor contribution of the mean temperature of wettest and driest quarters indicates that Q. oxyphylla exhibits significant resilience to extreme weather conditions [33].

4.4. Study Limitations and Future Directions

While we modeled MaxEnt for the first time to forecast Q. oxyphylla’s potential habitat in China under present and future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the 2050s and the 2070s, there are still some limitations. (1) Our model excluded nonclimatic factors such as biotic interactions, dispersal mechanisms, land-use changes, and other anthropogenic influences, which may significantly affect model outcomes [21]. (2) The sample data of Q. oxyphylla were obtained from the Global Biodiversity Information Facility, the National Plant Specimen Resource Center, and published literature [25]. The limited and dispersed distribution points increased uncertainty in our model, potentially leading to potential discrepancies from the actual suitable areas for Q. oxyphylla [33]. (3) The resolution of the environmental data used may not adequately capture microhabitat variations that are crucial for the survival of Q. oxyphylla, possibly leading to an oversimplified prediction. (4) The model’s reliance on climate scenarios assumes a linear progression of climate change impacts, which may not account for abrupt shifts or extreme events, thereby limiting the robustness of the projections. (5) The evaluation metrics used may not fully capture the model’s performance, potentially leading to an overestimation or underestimation of the model’s accuracy and reliability.
Thus, we suggest that future studies should (1) collect more detailed data by conducting field investigations, and (2) supply missing impact factors to enhance the precision and reliability of the predictive outcomes.

5. Conclusions

In this study, 42 distribution points of Q. oxyphylla and 20 bioclimatic factors were utilized in MaxEnt modeling to conduct a thorough examination of its potential distribution in both present and projected climate scenarios.
(1)
The predicted suitable habitat for Q. oxyphylla is primarily in southern China, characterized by a subtropical monsoon climate, with Sichuan province displaying the largest area of suitability. Additionally, a well-defined suitable range is identified within the plateau climates of Yunnan and Tibet, indicating that Q. oxyphylla can thrive in high-altitude regions with unique climatic conditions. The overall geographical boundaries of Q. oxyphylla are predicted to remain similar to the current ones, with a southwestward migration and expansion.
(2)
The distribution is significantly influenced by temperature- and precipitation-derived variables, particularly annual precipitation, isothermality, and temperature annual range, underscoring the importance of these climatic factors in determining Q. oxyphylla’s habitat suitability.
Our research shows significant expansion in Q. oxyphylla’s suitable habitat due to climate change. These findings offer a scientific basis for future conservation, afforestation, reforestation, and management strategies in China. The study emphasizes the need to consider both temperature and precipitation variables in habitat modeling and highlights the importance of adaptive management strategies to mitigate climate change impacts and ensure Q. oxyphylla’s long-term survival.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f15061033/s1: Figure S1: A Jackknife test for evaluating the relative importance of major environmental variables for Q. oxyphylla; Figure S2: The AICc, RO10, and AUC.diff values for each parameter combination; Figure S3: ROC analysis of MaxEnt model; Table S1: The variables used by MaxEnt, along with the contribution and permutation rates from the initial modeling; Table S2: Predicted potential distribution areas for Q. oxyphylla under current climatic conditions.

Author Contributions

Conceptualization, S.C., C.Y. and Z.X.; methodology, C.Y., Z.Z. and Z.X.; software, S.C. and Z.Z.; validation, S.C., C.Y. and Z.Z.; formal analysis, C.Y.; investigation, Z.Z.; data curation, S.C. and Z.Z.; writing—original draft preparation, S.C. and Z.Z.; writing—review and editing, C.Y. and Z.X.; visualization, S.C.; supervision, C.Y. and Z.X.; project administration, Z.X.; funding acquisition, Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Research and Development Projects in Sichuan Province (grant no. 24ZDYF0001) and the National Natural Science Foundation of China (grant no. U23A2051, 32071745).

Data Availability Statement

The bioclimatic variables are available from the WorldClim-Global Climate Database (www.worldclim.org, accessed on 27 January 2024).

Acknowledgments

We are particularly grateful to the authors’ contribution, and editors and reviewers for their valuable comments on the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Geographic distribution of Q. oxyphylla.
Figure 1. Geographic distribution of Q. oxyphylla.
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Figure 2. Correlation analysis of environmental factors. In the figure, * indicates low significance, ** indicates medium significance, and *** indicates high significance.
Figure 2. Correlation analysis of environmental factors. In the figure, * indicates low significance, ** indicates medium significance, and *** indicates high significance.
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Figure 3. Response curves of presence probability of Q. oxyphylla to major environmental factors. (A) Isothermality (Bio3); (B) temperature annual range (Bio7); (C) annual precipitation (Bio12); (D) precipitation seasonality (Bio15).
Figure 3. Response curves of presence probability of Q. oxyphylla to major environmental factors. (A) Isothermality (Bio3); (B) temperature annual range (Bio7); (C) annual precipitation (Bio12); (D) precipitation seasonality (Bio15).
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Figure 4. Simulation of Q. oxyphylla’s present planting range in China.
Figure 4. Simulation of Q. oxyphylla’s present planting range in China.
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Figure 5. Potentially suitable habitat of Q. oxyphylla in China under future climate scenarios for the 2050s (left panels (A,C,E)) and 2070s (right panels (B,D,F)).
Figure 5. Potentially suitable habitat of Q. oxyphylla in China under future climate scenarios for the 2050s (left panels (A,C,E)) and 2070s (right panels (B,D,F)).
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Figure 6. Modeled migration pathways of core suitable habitats for Q. oxyphylla under various climate scenarios.
Figure 6. Modeled migration pathways of core suitable habitats for Q. oxyphylla under various climate scenarios.
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Figure 7. Change in the potential suitable habitats for Q. oxyphylla under different climate scenarios in the 2050s (left panel (A,C,E)) and the 2070s (right panel (B,D,F)).
Figure 7. Change in the potential suitable habitats for Q. oxyphylla under different climate scenarios in the 2050s (left panel (A,C,E)) and the 2070s (right panel (B,D,F)).
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Table 1. Contribution and significance of climatic variables.
Table 1. Contribution and significance of climatic variables.
Environmental VariablesDescriptionPC/%PI/%
Bio12Annual Precipitation50.53.9
Bio7Temperature Annual Range27.674.1
Bio3Isothermality18.114.9
Bio15Precipitation Seasonality2.14.7
Bio8Mean Temperature of Wettest Quarter0.90.3
Bio9Mean Temperature of Driest Quarter0.82
Table 2. Different prediction accuracies of the MaxEnt model for future climate change scenarios by the 2050s and 2070s.
Table 2. Different prediction accuracies of the MaxEnt model for future climate change scenarios by the 2050s and 2070s.
DecadesScenariosAUCmeanAUC RatioTSSp-ROC-AUCKappa
Current-0.9551.960.9030.900.909
2050sSSP1-2.60.9561.970.8980.890.914
SSP2-4.50.9541.960.8900.880.899
SSP5-8.50.9541.920.8890.890.909
2070sSSP1-2.60.9581.980.9050.900.918
SSP2-4.50.9541.970.8850.870.895
SSP5-8.50.9581.990.9080.900.915
Table 3. Change in Q. oxyphylla’s suitable habitats across various climate scenarios.
Table 3. Change in Q. oxyphylla’s suitable habitats across various climate scenarios.
DecadesScenariosPredicted Area/104 km2Increase/Decrease Rate (%) [Compared to the Current Distribution]
Total Poorly Suitable
Habitat		Total
Moderately
Suitable Habitat		Total Highly Suitable
Habitat		Total Poorly Suitable HabitatTotal
Moderately
Suitable Habitat		Total Highly Suitable Habitat
Current-127.3465.9851.66---
2050sSSP1-2.6138.1769.0559.388.504.6514.94
SSP2-4.5132.0870.6665.813.727.0927.39
SSP5-8.5136.4872.3666.577.189.6728.86
2070sSSP1-2.6124.2676.2669.92-2.4215.5835.35
SSP2-4.5125.2786.0375.83-1.6330.3946.79
SSP5-8.5123.8371.3265.34-2.768.0926.48
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Chen, S.; You, C.; Zhang, Z.; Xu, Z. Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios. Forests 2024, 15, 1033. https://doi.org/10.3390/f15061033

AMA Style

Chen S, You C, Zhang Z, Xu Z. Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios. Forests. 2024; 15(6):1033. https://doi.org/10.3390/f15061033

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Chen, Shuhan, Chengming You, Zheng Zhang, and Zhenfeng Xu. 2024. "Predicting the Potential Distribution of Quercus oxyphylla in China under Climate Change Scenarios" Forests 15, no. 6: 1033. https://doi.org/10.3390/f15061033

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