Next Article in Journal
Effects of Fissure Network Morphology on Soil Organic Carbon Pools in Karst Rocky Habitats
Previous Article in Journal
Diversity and Determinants of Tree-Related Microhabitats in Hemiboreal Forests of Europe Based on National Forest Inventory Data
Previous Article in Special Issue
Dynamics of Greenhouse Gas Fluxes in Açaí Cultivation: Comparing Amazonian Upland and Floodplain Soils
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 17
Issue 1
10.3390/f17010058
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

Spatial and Landscape Fragmentation Pattern of Endemic Symplocos Tree Communities Under Climate Change Scenarios in China

by
Mohammed A. Dakhil
1,2,3,*,†,
Lin Zhang
4,†,
Marwa Waseem A. Halmy
5,*,
Reham F. El-Barougy
6,7,8,
Bikram Pandey
9,
Zhanqing Hao
3,
Zuoqiang Yuan
3,
Lin Liang
10 and
Heba Bedair
11,12
1
Department of Biosciences, University of Exeter, Exeter EX4 4QD, UK
2
Botany and Microbiology Department, Faculty of Science, Helwan University, Cairo 11795, Egypt
3
Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi’an 710072, China
4
CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
5
Department of Environmental Sciences, Faculty of Science, Alexandria University, Alexandria 21511, Egypt
6
Botany and Microbiology Department, Faculty of Science, Damietta University, Damietta 34518, Egypt
7
Department of Biology-Ecology and Evolution, University of Fribourg, Chemin du Musée 10, 1700 Fribourg, Switzerland
8
Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada
9
Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556, USA
10
Xigaze Wetland Conservation Center, Xigaze 857000, China
11
Botany Department, Faculty of Science, Tanta University, Tanta 31527, Egypt
12
Quantitative Landscape Ecology Group, Department of Natural and Environmental Sciences, University of Kaiserslautern-Landau (RPTU), 76829 Landau der Pfalz, Germany
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2026, 17(1), 58; https://doi.org/10.3390/f17010058
Submission received: 12 November 2025 / Revised: 5 December 2025 / Accepted: 9 December 2025 / Published: 31 December 2025
(This article belongs to the Special Issue Forest Dynamics Under Climate and Land Use Change)
Browse Figures
Versions Notes

Abstract

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change.

1. Introduction

The genus Symplocos Jacq. in Enum. Syst. Pl.: 5 (1760), comprising over 300 species that represent principal component of the tropical and subtropical forests globally [1]. China, with its diverse range of ecosystems and climatic zones, is home to numerous endemic Symplocos species, many of which contribute vital ecological roles in forests, including nutrient cycling and provisioning of shelter and habitat for various species [2,3,4,5]. Symplocos is one of the key genera in the mixed evergreen broadleaf forests of China that encompass numerous endemic and cornerstone species [5,6,7,8]. According to Wu and Nooteboom [9] China provides home to 18 endemic Symplocos species. Endemism denotes the ecological condition in which a species is native to a single geographical region [10], often resulting from unique evolutionary pressures [11]. Endemic species, including those within the Symplocos genus, are frequently adapted to specific environmental conditions, making them particularly sensitive to habitat changes. The endemic nature of these species underscores their ecological importance and the need for targeted conservation efforts.
Climate change is considered as among the most serious current environmental concerns, with substantial consequences for biodiversity worldwide [12,13,14]. The increasing temperatures, fluctuating rainfall patterns, and amplified frequency of extreme weather events are modifying habitats and ecosystems, often leading to shifts in species distribution, phenology, and community composition [15,16,17,18]. The rich biodiversity and endemism in China forests contribute significantly to global biodiversity [19,20], but they also present unique conservation challenges, especially under the looming threat of climate change. For endemic species with constrained ranges, such as many Symplocos species, these changes can be especially detrimental, potentially leading to range contractions, population declines, or even extinctions [11]. Recent studies on endemic species highlighted projected negative influences and consequences of climatic changes on endemic taxa across different ecosystems (e.g., [21,22,23,24]).
Richness patterns, which describe the number and diversity of species within a given area, provide critical insights into the spatial pattern of biodiversity distribution [25]. These patterns help identify richness hotspots and areas of high conservation value and prioritize regions for protection and management efforts [26,27]. Understanding how climate change will affect the richness patterns of endemic Symplocos species is essential for developing efficient management and conservation plans [7]. Predictive models that incorporate climate change scenarios can forecast potential shifts in species richness, guiding proactive conservation planning [28,29].
The comprehensive methodological framework that includes species distribution modeling (SDM) and bioclimatic envelope modeling allows the assessment of the susceptibility of endemic species to climatic changes [30,31] and identify potential refugia—localities that may remain suitable for these species despite climatic shifts [32]. Mapping these possible modifications, provide actionable insights for conservation planning, emphasizing the need for adaptive management strategies that can accommodate future [33,34]. A recent study by Dakhil et al. [7] modeled all Sympolocos species diversity in China under climate change scenarios. However, a critical gap remains in the conservation assessment of endemic Symplocos species, as the endemic species may respond differently to climate change scenarios compared with the overall Symplocos diversity [7]. Although the distribution and ecology of forest plant species have been widely studied, research integrating stacked species distribution models, richness mapping, and landscape metrics to assess climate-driven changes in endemic Symplocos species in China is lacking. This study introduces a spatially explicit framework that evaluates both richness dynamics and landscape-level habitat configuration under current and future climates. We identify key ecological drivers of endemic Symplocos species distributions, model potential richness shifts using climate projections, and apply landscape analysis to quantify changes in habitat fragmentation and spatial structure. Together, this integrative approach provides a unique basis for understanding climate impacts on endemic Symplocos and supports more effective conservation planning.
The predicted output maps of changes in species richness suitability would provide a precise afforestation area for priority conservation planning of these endemic tree communities in China. Furthermore, the study seeks to contribute to the broader understanding of how climate change impacts biodiversity, reinforcing the urgency of integrating climate considerations into conservation planning. The findings will inform conservation strategies aimed at safeguarding these unique species against the backdrop of climate change [35], ensuring the preservation of China rich botanical heritage for future generations. By highlighting areas where endemic Symplocos species are most at risk, conservation efforts such as establishing protected areas, restoring degraded habitats, and implementing climate-smart practices can be better informed and prioritized. This study is peculiar in explicitly identifying endemic Symplocos species hotspots, potential climate refugia, and areas most at risk, thereby providing a spatially explicit framework for conservation planning.

2. Materials and Methods

In this study, we provide a spatially explicit and multi-scale assessment (Figure 1) of how climatic change may affect endemic Symplocos richness and habitat configuration. We first examine the current distribution of each species and identify the primary ecological drivers using available occurrence records and environmental datasets. Climate projection models are then applied to simulate future distributional shifts under multiple climate-change scenarios. By integrating key climatic predictors, including temperature and precipitation, the stacked SDMs generate a comprehensive estimate of potential richness dynamics for endemic Symplocos. To detect the potential endemic species richness patterns, we applied a stacked SDM framework, which enables aggregation of continuous suitability outputs from individual species models to estimate spatial patterns of species richness and identify biodiversity hotspots [36,37]. This approach is particularly appropriate for taxa such as endemic Symplocos species, where distributional data are limited and multi-species conservation assessments are required. Furthermore, we used an ensemble modeling strategy to reduce algorithm-specific uncertainty and improve overall predictive accuracy [38,39].

2.1. Species Occurrence Data and Verification

We collected 5333 occurrence records for 11 endemic Symplocos tree species (Table 1) from four databases: GBIF, CVH (Chinese Virtual Herbarium), CNKI (China National Knowledge Infrastructure), and BIEN data (Botanical Information and Ecology Network) ‘SALVIAS’ (Synthesis and Analysis of Vegetation Inventories Across Scales). To ensure accuracy, we verified the taxonomic status (using accepted names) based on the Catalogue of Life (www.catalogueoflife.org/). Additionally, we applied the ‘CoordinateCleaner’ package in R 4.4.1 [40] to remove duplicates and filter out inaccessible areas, such as urban regions, resulting in 762 validated records.

2.2. Environmental Data and Variable Selection

At a spatial resolution of 2.5 arcminutes, we obtained 19 bioclimatic variables from WorldClim 2.1 [44]. To assess the impact of climate change scenarios on the endemic species studied, we used the IPSL-CM6A-LR global general circulation model (GCM). An ensemble average of two GCM outputs was used for both the near future (2021–2040) and the distant future (2061–2080), under two socioeconomic scenarios: a low-emission scenario (SSP1-2.6) and a high-emission scenario (SSP5-8.5). The Shared Socioeconomic Pathways (SSPs) were created as alternatives to the Representative Concentration Pathways (RCPs) to incorporate socioeconomic factors [45]. All raster layers were cropped to the study area within the ArcGIS Pro 3.3.0 framework. To prevent model overfitting, multicollinearity analysis and variance inflation factor (VIF) assessments were performed using the ‘usdm’ package, excluding variables with a VIF over 5 and a correlation coefficient threshold of 0.75 [46,47].

2.3. Modeling Approach and Spatial Analysis

To model the 762 occurrence records of the 11 endemic Symplocos species; S. austrosinensis, S. crassilimba, S. fordii, S. fukienensis, S. glandulifera, S. nakaharae, S. ramosissima var. xylopyrena, S. stellaris, S. stellaris var. aenea, S. sumuntia var. modesta, and S. ulotricha—an ensemble model was developed by averaging results from four algorithms: Classification Tree Analysis (CTA), Support Vector Machines (SVM), Generalized Linear Model (GLM), and Random Forest (RF). Models with an AUC score below 0.75 were excluded from the process to ensure reliability.
Endemic Symplocos richness (SR) maps were created by summing continuous habitat suitability maps (pSSDM) using the ‘ssdm’ package in R 4.4.1 [48]. This involved calculating weighted means for each aggregated species model within the ensemble.
The stacked SDM was then projected to predict species distributions under future scenarios and to analyze potential changes in richness and distributional range for the 11 endemic Symplocos species. Differences between current and projected future richness were calculated on a per-pixel basis, with positive values indicating increased richness and negative values signifying declines [49]. The weighted endemism index (WEI) metric within the ‘ssdm’ package was used to map regional species endemism [48].

2.4. Landscape Analysis

To assess the spatial configuration and fragmentation patterns of endemic Symplocos species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The continuous richness maps produced in the previous steps were first reclassified into three discrete richness classes representing low, medium, and high richness levels. Using the landscapemetrics R package version 2.2.1 [50,51], key landscape metrics were computed for each scenario, including the number of patches (NP), edge density (ED), aggregation index (AI), largest patch index (LPI), patch cohesion (PLADJ), contagion index (CONTAG), and Shannon diversity index (SHDI). These metrics quantify different aspects of landscape composition and configuration [52], allowing the detection of fragmentation, aggregation, and dominance patterns among richness classes [53]. All raster layers, representing the current and future projections (SSP126 and SSP585 for 2040 and 2080), were used for calculation of the landscape and class metrics. The computed metrics were summarized by scenario and richness class to evaluate temporal changes in the landscape structure and to identify potential shifts in habitat connectivity and spatial heterogeneity of the endemic Symplocos richness under future climate change projections.

3. Results

3.1. Model Performance and Potential Response to Bioclimatic Variables

Multicollinearity analysis of 19 bioclimatic parameters identified five uncorrelated variables with a VIF greater than 5 and a correlation threshold above 0.75 (Table 2). These selected variables were incorporated into the stacked SDM for the endemic Symplocos species in China. The models demonstrated strong performance, with an average AUC exceeding 0.9 and a TSS greater than 0.75. According to Pearson correlation coefficient, the most influential bioclimatic factors affecting the potential distribution of the endemic Symplocos were precipitation seasonality (Bio15), precipitation of the wettest month (Bio13), and isothermality (Bio3), contributing between 17% and 26.1% in relative importance. Conversely, the mean temperature of the wettest quarter (Bio8) had the lowest influence at 12.1% (Figure 2 and Table 2). Response curve analysis indicated that the probability of presence decreases with increased precipitation seasonality and mean diurnal range, while it increases with higher precipitation during the wettest month (Figure 2).

3.2. Current and Predicted Richness of Endemics Symplocos Species in China

Currently, hotspots for Symplocos endemic species richness and endemism are concentrated in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi (Figure 3a,b). However, projections indicate that these regions could experience up to a 35% reduction in species richness over the next 60 years (Figure 4a–d). Our analysis predicts a significant decline in the richness of the 11 endemic Symplocos species due to climate change. This decline is expected to be especially severe in South China, including Fujian, Guangdong, Guizhou, Yunnan, and southern Shaanxi; moderate in East China, including Heilongjiang, Jilin, Liaoning, and eastern Inner Mongolia; and relatively lower in Central and Western China, including Inner Mongolia, Gansu, Qinghai, central and southern Xinjiang, and northern Tibet, with predicted reductions of over 40%, 35%, 41%, and 46% across the studied climate scenarios, respectively. Notably, there are areas where species richness is expected to increase, particularly in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui (Figure 5a–d). For both current and future projections, the error in our stacked SDM species richness estimates-the difference between observed and predicted species richness was 4.4.

3.3. Landscape Structure and Configuration

The landscape-level metrics revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios (Figure 6). The contagion index (contag) showed a slight increase under future scenarios, particularly in SSP585 by 2080, indicating a trend toward greater landscape aggregation and reduced fragmentation of richness patches over time. Similarly, the largest patch index (lpi) remained consistently high across all scenarios, suggesting that the dominant richness patch will likely persist and maintain its structural dominance under projected climate conditions. In contrast, Shannon diversity index (shdi) exhibited a small decrease under the SSP585 scenario by 2080, implying a potential reduction in landscape diversity and evenness of richness distribution under more extreme warming conditions. Collectively, these results suggest that while the overall spatial configuration of endemic Symplocos richness remains relatively stable, high-emission scenarios may lead to a slight homogenization of richness patterns, with fewer but more aggregated high-richness areas dominating the landscape.
The estimated class-level landscape metrics (Figure 7), which include aggregation index (ai), edge density (ed), number of patches (np), and percentage of like adjacencies (pladj), provided insights into the spatial configuration and fragmentation patterns. The metrics revealed distinct spatial patterns among the richness classes of endemic Symplocos species and their projected changes under future climate scenarios. Across all scenarios, the medium-richness areas consistently exhibited the highest aggregation index (ai), edge density (ed), number of patches (np), and percentage of like adjacencies (pladj), indicating that these zones are the most spatially complex and fragmented, containing multiple small and interconnected patches. In contrast, high-richness areas showed lower values across all metrics, suggesting that regions with high endemic richness are more spatially cohesive and less fragmented, possibly confined to specific environmental niches that will remain relatively stable under future climates.
Temporal comparisons across scenarios (SSP126 and SSP585) for 2040 and 2080 indicate limited structural changes in landscape configuration, suggesting that the spatial distribution of richness classes will remain broadly consistent over time, despite potential shifts in their overall extent. However, under the high-emission scenario SSP585 by 2080, a slight decline in aggregation and increase in fragmentation is predicted, particularly in the medium-richness class, implying that future climatic extremes could lead to reduced habitat connectivity and increased isolation of endemic Symplocos populations.

4. Discussion

4.1. Influence of Bioclimatic Variable on Endemic Symplocos Species Distribution

The stacked species distribution models (SDMs) utilized in this study based on ensemble models combining four algorithms namely BRT, GLM, SVM and RF demonstrated good fit and excellent predictive performance, as indicated by the used measures of accuracy assessment (mean AUC > 0.9 and TSS > 0.75). The robust predictive outcomes of the used algorithms particularly the Random Forest and Support Vector Machine algorithms highlights their suitability and efficacy for predicting the distribution patterns of endemic Symplocos species in China [7]. The effectiveness of these models underscores the reliability of the produced projections and the importance of selecting appropriate modeling techniques in biodiversity studies [54,55].
The multicollinearity analysis identified seven uncorrelated bioclimatic variables as significant predictors of endemic Symplocos distribution. Notably, seasonality of precipitation (Bio15), wettest month precipitation (Bio13), isothermality (Bio3) and mean diurnal temperature (Bio2). These variables highlight the critical role of both temperature and precipitation in shaping the richness pattern [56], and distribution of the endemic Symplocos species. This is in accordance with findings of other studies that revealed that the seasonal pattern of precipitation is a main determinant for the distributional patterns of the tree species in China particularly the southwestern part (e.g., [57,58,59]). The response curves further clarify the relationship between these bioclimatic variables and Symplocos occurrence. Through these curves it was revealed that with the upsurge of the precipitation seasonality, the likelihood of presence of the species decreases, which suggests the sensitivity of endemic Symplocos species to the fluctuations in precipitation. The positive correlation with precipitation variables, specifically the precipitation of wettest month indicates that adequate moisture availability is essential for their survival and distribution [56]. Symplocos species tend to avoid arid regions [5], favoring warm-temperate and tropical regions, particularly moist to wet mixed areas within evergreen broadleaf rainforests [6,8].
Additionally, the likelihood of occurrence of the endemic Symplocos species increases with the increase in temperature isothermality. A high isothermality or a more stable temperature range across seasons can improve their occurrence and abundance by reducing the need to adapt to rapid temperature changes [60,61]. This can be also confirmed through the negative correlation between mean diurnal temperature range and species presence that suggests that endemic Symplocos species are more likely to thrive in regions with less temperature fluctuation [62]. These findings align with the ecological requirements of Symplocos species, which typically prefer humid and stable climatic conditions [7,56,63].

4.2. Changes in the Endemic Symplocos Species Richness

The outcomes indicate that current hotspots for Symplocos endemics are concentrated in the southeastern provinces of Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi, aligning with the distribution of subtropical and tropical evergreen broadleaf forests in China [1]. These regions are characterized by diversity in habitats and ecological conditions that are favorable to Symplocos species [6,7]. The high species richness in these areas underscores their ecological importance, highlighting the necessity for adopting climate-smart conservation actions to protect these biodiversity-rich regions.
Current hotspots of Symplocos endemism in southeastern China are predicted to experience considerable declines in species richness due to climate change, with southern China facing the highest risk. Significant declines are anticipated in Fujian, Guangdong, Guizhou, Yunnan, and southern Shaanxi, with a projected loss of up to 35% of species richness in these areas over the next 60 years. Mid-level declines are expected in East China (Heilongjiang, Jilan, Liaoning, and eastern parts of Inner Mongolia), while lower declines are projected for West and Central China, illustrating the widespread impact of climate change across different regions. These findings align with global and regional patterns where climate-sensitive species face heightened risks of habitat loss and population decline [8,64,65]. The projected loss aligns with previous studies on the effects of climatic changes on forest species and richness, which highlights the alarming risk to subtropical and tropical forests in southern China, primarily due to increased dryness (e.g., [7,66,67,68]). A decline by more than 30% of the vastly suitable regions for tree species in China has also been predicted (e.g., [69]).
Interestingly, the models predict potential increases in species richness in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui. This suggests that the endemic Symplocos species may undergo range shifts to the north and west, and upward shifts in vegetation zones in mountainous species due to climate change have been reported by previous research [69,70]. Evidence based on fossil data from the paleobiogeographic studies on the historical biogeography and diversification of Symplocaceae provided valuable insight into how Symplocos diversity responded to past climatic regimes, the outcomes suggest that the Symplocos genus experienced significant shifts in distribution during earlier warm periods [71], which aligns with some of our projected patterns under future climate scenarios. Fossil and palaeobotanical studies revealed that Symplocos had a broad and more northerly expanded distribution during the Neogene (late Oligocene-Miocene-Pliocene), with abundant fruit and endocarp records in Europe, East Asia and the Americas, indicating high regional diversity in warmer intervals (e.g., [71,72,73]). Comparisons with paleoclimate benchmarks indicate the Mid-Pliocene which is the closest past analog for projected near-future warming, supported warmer and often wetter conditions that likely allowed thermophilous taxa such as Symplocos to occupy a larger range and higher latitudes than during the Last Glacial Maximum; however, modern landscape fragmentation and rapid warming could limit the capacity for similar range expansions in the future [74]. Areas of gain in richness may serve as future refugia for endemic Symplocos species, offering critical habitats as current hotspots become less suitable. The identification of these potential refugia underscores the dynamic nature of species distributions and the importance of adaptive conservation strategies.
Our results provide new insights into the theoretical understanding of climate refugia. The identification of stable habitats and potential contraction areas for endemic Symplocos species supports the idea that mountainous and topographically complex regions can act as long-term refugia under changing climatic conditions [5,75,76]. By integrating stacked species distribution modeling with richness mapping and landscape analysis, this study demonstrates how spatially explicit approaches can empirically test and refine theoretical frameworks on species, environmental persistence buffering, and the role of refugia in maintaining biodiversity under climate change scenarios [45,77].

4.3. Landscape Structure and Conservation Implications

The landscape analysis of the suitable areas for endemic Symplocos species richness under current and future climate scenarios revealed important insights into spatial structure and potential ecological resilience. Estimating landscape metrics such as aggregation index (ai), number of patches (np), edge density (ed), largest patch index (lpi), percentage of like adjacencies (pladj), Shannon diversity index (shdi), and contagion (contag) can provide insight into the compositional and configurational changes in richness classes [77,78].
At the landscape level, metrics such as contag and lpi remained relatively high across scenarios, suggesting that richness hotspot patches are likely to remain spatially dominant and connected even under moderate (SSP126) and higher emission (SSP585) scenarios. This suggests that large patches of high endemic richness may act as stable refugia despite climate change, consistent with findings that intact or aggregated patches support biodiversity more robustly than fragmented systems [52,78,79].
However, when stratified by richness class, the medium-richness read consistently exhibited a higher number of patches (np) and edge density (ed) alongside lower aggregation index (ai) and percentage of like adjacencies (pladj), indicating increased fragmentation and lower connectivity relative to the high-richness areas. This pattern aligns with literature showing that increased patch number and edge density often reflect habitat fragmentation and pose risks for biodiversity persistence [79,80,81]. Notably, under the SSP585 scenario by 2080, the medium class showed a more pronounced decrease in ai and increase in np than other classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. The decline in shdi observed in this scenario further suggests a reduction in richness-class heterogeneity and spatial diversity, consistent with studies linking lower shdi values to declining biodiversity and landscape resilience [82,83].
From a conservation perspective, these findings highlight the importance of maintaining connectivity among high-richness patches and prioritizing management of medium-richness zones that are more vulnerable to fragmentation. The relative stability of high-richness zones under future scenarios may provide key nodes for ecological networks, but the vulnerability of the medium class underscores a need for proactive landscape planning. Moreover, the use of landscape metrics in this context has enabled a quantitative evaluation of spatial patterns and potential changes in richness structure, aligning with the broader ecological usage of these tools to monitor spatial heterogeneity and fragmentation over time [84,85,86].
The outcomes of the present study revealed important implications for the conservation of endemic Symplocos species in China. The revealed spatial variability in predicted changes necessitates a comprehensive approach to conservation planning. Multi-taxa spatial conservation planning recommended for improving protected area representation in reaction to the climatic changes [87]. Protecting the current hotspots of endemic Symplocos species in southeastern China should remain a priority for their high species richness and ecological significance. Additionally, the potential refugia identified in by the current study, particularly in northern and western Xinjiang and western Tibet, require proactive conservation measures. These areas could serve as future strongholds for Symplocos species, and efforts should be made to maintain their suitability through habitat management and protection. Conservation efforts should focus on habitat preservation, restoration, and mitigation of the consequences of the negative influences of the climatic changes through adaptive management practices, which include site-based climate-smart reforestation [84,87] and redesigning of current protected areas [7]. Particularly, those in the moist evergreen broad-leave forests in China subtropics, which host various Symplocos species [2].

4.4. Limitations and Prospects

The study utilized stacked species distribution modeling (SDM) to project species richness patterns. While species distribution models (SDMs) and stacked SDMs are powerful tools for predicting species richness and identifying biodiversity hotspots, they have inherent limitations. Model accuracy can be affected by the quality and completeness of occurrence data, sampling bias, and the resolution of environmental predictors [88,89]. Algorithmic assumptions and differences between modeling approaches also introduce uncertainty, which may influence predicted distributions [38,39]. Future studies could improve model reliability by incorporating additional ecological and biotic variables, such as species interactions, dispersal limitations, and functional traits [37,90]. The relatively low species richness error of 4.4 for the present and future forecasts suggest robust model performance, yet it is crucial to recognize the innate uncertainties in climate projections and species distribution models. Projections under future climate scenarios are subject to uncertainties associated with climate models and emission scenarios [91]. Future research should incorporate multiple climate models and scenarios to account for these uncertainties and refine predictions. Additionally, we did not account for the current trends of other human-induced changes on the studied species, which might have more serious impacts on these species. Therefore, future research should incorporate multiple factors that account for the land use changes and other anthropogenic factors in the predictive distribution models to refine predictions. Additionally, long-term monitoring of Symplocos populations and their habitats is essential to validate model projections and adapt conservation strategies accordingly.

5. Conclusions

The richness patterns and potential shifts in 11 endemic Symplocos species in China under climate change scenarios present critical insights for conservation planning. Our study highlights significant spatial variations in current and projected endemic Symplocos species richness, emphasizing the pressing requirement for targeted climate-smart conservation strategies including site-based climate-smart reforestation efforts and redesigning of existing protected areas. The study underscores the significant impacts of climate change on the richness patterns of endemic Symplocos species in China. A decline is expected to be especially severe in South China, with predicted reductions of over 35% in richness across the studied climate scenarios over the next 60 years. The projected declines in endemic Symplocos species richness highlight the vulnerability of current hotspots, while the identification of potential refugia richness in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui offers hope for future conservation efforts. The increase in species richness in these areas suggests that they may become suitable habitats for Symplocos species currently occupying other regions, underscoring the dynamic nature of species distribution under climate change. The landscape analysis revealed that while the spatial structure of endemic Symplocos richness appears relatively resilient under moderate climate change, the increased fragmentation risk under the more extreme SSP585 scenario warrants attention. Continued monitoring of patch connectivity, edge effects, and richness class transitions will be critical for conservation strategies aimed at safeguarding endemic species in a changing climate.
By focusing on endemic species in China’s subtropical evergreen broad-leaved forests, the study contributes to filling knowledge gap and informing strategies to preserve species richness under future environmental change. Effective conservation planning must integrate these findings to safeguard the ecological integrity and biodiversity of endemic Symplocos species amid the challenging consequences inflicted by the climatic changes. By prioritizing both current hotspots and potential future refugia, we can enhance the resilience of these species and their ecosystems in a changing climate.

Author Contributions

M.A.D., L.Z. and R.F.E.-B.: Conceptualization and Data collection. M.A.D., M.W.A.H. and H.B.: Data curation and Data analysis. All authors: Drafting and editing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

The National Natural Science Foundation of China (32171581, 32230067, 32301361), the Key Research and Development Program of Shaanxi (2024SF-YBXM-558, 2024SF-YBXM-551), the Natural Science Basic Research Program of Shaanxi (22JHQ036). Also, The West Light Foundation of Chinese Academy of Sciences (CAS) (2022XBZG_XBQNXZ_A_003).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Ashton, P.; Zhu, H. The Tropical-Subtropical Evergreen Forest Transition in East Asia: An Exploration. Plant Divers. 2020, 42, 255–280. [Google Scholar] [CrossRef] [PubMed]
  2. Peng, H. Biological Characteristic. In China Danxia; Peng, H., Ed.; Springer: Singapore, 2020; pp. 75–88. ISBN 978-981-13-5959-0. [Google Scholar]
  3. Nooteboom, H.P. Additions to Symplocaceae of the Old World Including New Caledonia. Blumea-Biodivers. Evol. Biogeogr. Plants 2005, 50, 407–410. [Google Scholar] [CrossRef]
  4. Måren, I.E.; Bhattarai, K.R.; Chaudhary, R.P. Forest Ecosystem Services and Biodiversity in Contrasting Himalayan Forest Management Systems. Environ. Conserv. 2014, 41, 73–83. [Google Scholar] [CrossRef]
  5. Nooteboom, H.P. Symplocaceae. In Flowering Plants Dicotyledons: Celastrales, Oxalidales, Rosales, Cornales, Ericales; Kubitzki, K., Ed.; Springer: Berlin, Heidelberg, 2004; pp. 443–449. ISBN 978-3-662-07257-8. [Google Scholar]
  6. Nooteboom, H. Symplocaceae. Flora Malesiana—Ser. 1 Spermatophyta 1974, 8, 205–274. [Google Scholar]
  7. Dakhil, M.A.; Zhang, L.; El-Barougy, R.F.; Bedair, H.; Hao, Z.; Yuan, Z.; Feng, Y.; Halmy, M.W.A. Diversity Pattern of Symplocos Tree Species in China under Climate Change Scenarios: Toward Conservation Planning. Glob. Ecol. Conserv. 2024, 54, e03198. [Google Scholar] [CrossRef]
  8. Yang, X.; Yan, H.; Li, B.; Han, Y.; Song, B. Spatial Distribution Patterns of Symplocos Congeners in a Subtropical Evergreen Broad-Leaf Forest of Southern China. J. For. Res. 2018, 29, 773–784. [Google Scholar] [CrossRef]
  9. Wu, R.-F.; Nooteboom, H.P.  Symplocaceae. In Flora of China; Wu, Z.-Y., Raven, P.H., Eds.; Science Press & Missouri Botanical Garden Press: Beijing, China, 1996; Volume 15, pp. 235–252. [Google Scholar]
  10. Scheiner, S.M. Encyclopedia of Biodiversity, 3rd ed.; Academic Press: London, UK, 2024; ISBN 978-0-12-822562-2. [Google Scholar]
  11. Manes, S.; Costello, M.J.; Beckett, H.; Debnath, A.; Devenish-Nelson, E.; Grey, K.-A.; Jenkins, R.; Khan, T.M.; Kiessling, W.; Krause, C.; et al. Endemism Increases Species’ Climate Change Risk in Areas of Global Biodiversity Importance. Biol. Conserv. 2021, 257, 109070. [Google Scholar] [CrossRef]
  12. Weiskopf, S.R.; Rubenstein, M.A.; Crozier, L.G.; Gaichas, S.; Griffis, R.; Halofsky, J.E.; Hyde, K.J.W.; Morelli, T.L.; Morisette, J.T.; Muñoz, R.C.; et al. Climate Change Effects on Biodiversity, Ecosystems, Ecosystem Services, and Natural Resource Management in the United States. Sci. Total Environ. 2020, 733, 137782. [Google Scholar] [CrossRef] [PubMed]
  13. Shivanna, K.R. Climate Change and Its Impact on Biodiversity and Human Welfare. Proc. Indian Natl. Sci. Acad. 2022, 88, 160–171. [Google Scholar] [CrossRef]
  14. Abbass, K.; Qasim, M.Z.; Song, H.; Murshed, M.; Mahmood, H.; Younis, I. A Review of the Global Climate Change Impacts, Adaptation, and Sustainable Mitigation Measures. Environ. Sci. Pollut. Res. 2022, 29, 42539–42559. [Google Scholar] [CrossRef]
  15. Morecroft, M.D.; Paterson, J.S. Effects of Temperature and Precipitation Changes on Plant Communities. In Plant Growth and Climate Change; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2006; pp. 146–164. ISBN 978-0-470-98869-5. [Google Scholar]
  16. Lavergne, S.; Thuiller, W.; Ronce OLavergne, S.; Mouquet, N.; Thuiller, W.; Ronce, O. Biodiversity and Climate Change: Integrating Evolutionary and Ecological Responses of Species and Communities. Annu. Rev. Ecol. Evol. Syst. 2010, 41, 321–350. [Google Scholar] [CrossRef]
  17. Gornish, E.S.; Tylianakis, J.M. Community Shifts under Climate Change: Mechanisms at Multiple Scales. Am. J. Bot. 2013, 100, 1422–1434. [Google Scholar] [CrossRef]
  18. Leathers, K.; Herbst, D.; de Mendoza, G.; Doerschlag, G.; Ruhi, A. Climate Change Is Poised to Alter Mountain Stream Ecosystem Processes via Organismal Phenological Shifts. Proc. Natl. Acad. Sci. USA 2024, 121, e2310513121. [Google Scholar] [CrossRef]
  19. Wang, W.; Feng, C.; Liu, F.; Li, J. Biodiversity Conservation in China: A Review of Recent Studies and Practices. Environ. Sci. Ecotechnol. 2020, 2, 100025. [Google Scholar] [CrossRef]
  20. Mi, X.; Feng, G.; Hu, Y.; Zhang, J.; Chen, L.; Corlett, R.T.; Hughes, A.C.; Pimm, S.; Schmid, B.; Shi, S.; et al. The Global Significance of Biodiversity Science in China: An Overview. Natl. Sci. Rev. 2021, 8, nwab032. [Google Scholar] [CrossRef]
  21. Yousefzadeh, H.; Amirchakhmaghi, N.; Naseri, B.; Shafizadeh, F.; Kozlowski, G.; Walas, Ł. The Impact of Climate Change on the Future Geographical Distribution Range of the Endemic Relict Tree Gleditsia caspica (Fabaceae) in Hyrcanian Forests. Ecol. Inform. 2022, 71, 101773. [Google Scholar] [CrossRef]
  22. Abro, T.W.; Desta, A.B.; Debie, E.; Alemu, D.M. Endemic Plant Species and Threats to Their Sustainability in Ethiopia: A Systematic Review. Trees For. People 2024, 17, 100634. [Google Scholar] [CrossRef]
  23. Abrha, H.; Dodiomon, S.; Ongoma, V.; Hagos, H.; Birhane, E.; Gebresamuel, G.; Manaye, A. Response of Plant Species to Impact of Climate Change in Hugumbrda Grat-Kahsu Forest, Tigray, Ethiopia: Implications for Domestication and Climate Change Mitigation. Trees For. People 2024, 15, 100487. [Google Scholar] [CrossRef]
  24. Meru, L.B.; Pandey, R. Climate Change Ecological Vulnerability and Hotspot Analysis of Himalayan Forests in North-Eastern Region, India. Environ. Sustain. Indic. 2024, 24, 100472. [Google Scholar] [CrossRef]
  25. White, H.J.; McKeon, C.M.; Pakeman, R.J.; Buckley, Y.M. The Contribution of Geographically Common and Rare Species to the Spatial Distribution of Biodiversity. Glob. Ecol. Biogeogr. 2023, 32, 1730–1747. [Google Scholar] [CrossRef]
  26. Carrasco, J.; Price, V.; Tulloch, V.; Mills, M. Selecting Priority Areas for the Conservation of Endemic Trees Species and Their Ecosystems in Madagascar Considering Both Conservation Value and Vulnerability to Human Pressure. Biodivers. Conserv. 2020, 29, 1841–1854. [Google Scholar] [CrossRef]
  27. Shipley, B.R.; McGuire, J.L. Interpreting and Integrating Multiple Endemism Metrics to Identify Hotspots for Conservation Priorities. Biol. Conserv. 2022, 265, 109403. [Google Scholar] [CrossRef]
  28. Thuiller, W.; Albert, C.; Araújo, M.B.; Berry, P.M.; Cabeza, M.; Guisan, A.; Hickler, T.; Midgley, G.F.; Paterson, J.; Schurr, F.M.; et al. Predicting Global Change Impacts on Plant Species’ Distributions: Future Challenges. Perspect. Plant Ecol. Evol. Syst. 2008, 9, 137–152. [Google Scholar] [CrossRef]
  29. Franklin, J. Species Distribution Modelling Supports the Study of Past, Present and Future Biogeographies. J. Biogeogr. 2023, 50, 1533–1545. [Google Scholar] [CrossRef]
  30. Bagaria, P.; Thapa, A.; Sharma, L.K.; Joshi, B.D.; Singh, H.; Sharma, C.M.; Sarma, J.; Thakur, M.; Chandra, K. Distribution Modelling and Climate Change Risk Assessment Strategy for Rare Himalayan Galliformes Species Using Archetypal Data Abundant Cohorts for Adaptation Planning. Clim. Risk Manag. 2021, 31, 100264. [Google Scholar] [CrossRef]
  31. Benavides Rios, E.; Sadler, J.; Graham, L.; Matthews, T.J. Species Distribution Models and Island Biogeography: Challenges and Prospects. Glob. Ecol. Conserv. 2024, 51, e02943. [Google Scholar] [CrossRef]
  32. Beaumont, L.J.; Esperón-Rodríguez, M.; Nipperess, D.A.; Wauchope-Drumm, M.; Baumgartner, J.B. Incorporating Future Climate Uncertainty into the Identification of Climate Change Refugia for Threatened Species. Biol. Conserv. 2019, 237, 230–237. [Google Scholar] [CrossRef]
  33. Tallis, H.; Fargione, J.; Game, E.; McDonald, R.; Baumgarten, L.; Bhagabati, N.; Cortez, R.; Griscom, B.; Higgins, J.; Kennedy, C.M.; et al. Prioritizing Actions: Spatial Action Maps for Conservation. Ann. N. Y. Acad. Sci. 2021, 1505, 118–141. [Google Scholar] [CrossRef]
  34. van Kerkhoff, L.; Munera, C.; Dudley, N.; Guevara, O.; Wyborn, C.; Figueroa, C.; Dunlop, M.; Hoyos, M.A.; Castiblanco, J.; Becerra, L. Towards Future-Oriented Conservation: Managing Protected Areas in an Era of Climate Change. Ambio 2019, 48, 699–713. [Google Scholar] [CrossRef] [PubMed]
  35. Jeschke, J.M.; Strayer, D.L. Usefulness of Bioclimatic Models for Studying Climate Change and Invasive Species. Ann. N. Y. Acad. Sci. 2008, 1134, 1–24. [Google Scholar] [CrossRef]
  36. Calabrese, J.M.; Certain, G.; Kraan, C.; Dormann, C.F. Stacking Species Distribution Models and Adjusting Bias by Linking Them to Macroecological Models. Glob. Ecol. Biogeogr. 2014, 23, 99–112. [Google Scholar] [CrossRef]
  37. D’Amen, M.; Dubuis, A.; Fernandes, R.F.; Pottier, J.; Pellissier, L.; Guisan, A. Using Species Richness and Functional Traits Predictions to Constrain Assemblage Predictions from Stacked Species Distribution Models. J. Biogeogr. 2015, 42, 1255–1266. [Google Scholar] [CrossRef]
  38. Araújo, M.B.; New, M. Ensemble Forecasting of Species Distributions. Trends Ecol. Evol. 2007, 22, 42–47. [Google Scholar] [CrossRef] [PubMed]
  39. 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]
  40. Zizka, A.; Silvestro, D.; Andermann, T.; Azevedo, J.; Duarte Ritter, C.; Edler, D.; Farooq, H.; Herdean, A.; Ariza, M.; Scharn, R.; et al. CoordinateCleaner: Standardized Cleaning of Occurrence Records from Biological Collection Databases. Methods Ecol. Evol. 2019, 10, 744–751. [Google Scholar] [CrossRef]
  41. Liu, B.; Botanic Gardens Conservation International (BGCI); IUCN SSC Global Tree Specialist Group. Symplocos austrosinensis. IUCN Red List. Threat. Species 2019, e.T152823799A152841577. [Google Scholar] [CrossRef]
  42. Liu, B.; Botanic Gardens Conservation International (BGCI); IUCN SSC Global Tree Specialist Group. Symplocos crassilimba. IUCN Red List. Threat. Species 2019, e.T152824007A152836393. [Google Scholar] [CrossRef]
  43. Liu, B.; Botanic Gardens Conservation International (BGCI); IUCN SSC Global Tree Specialist Group. Symplocos glandulifera. IUCN Red List. Threat. Species 2019, e.T152824032A152834290. [Google Scholar] [CrossRef]
  44. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-Km Spatial Resolution Climate Surfaces for Global Land Areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  45. Hausfather, Z. Explainer: The High-Emissions ‘RCP8.5’Global Warming Scenario. Carbon Brief 22. 2019. Available online: https://www.carbonbrief.org/explainer-the-high-emissions-rcp8-5-global-warming-scenario/ (accessed on 25 October 2025).
  46. Naimi, B. Usdm: Uncertainty Analysis for Species Distribution Models. Available online: https://cran.r-project.org/web/packages/usdm/usdm.pdf (accessed on 8 December 2025).
  47. Guisan, A.; Thuiller, W.; Zimmermann, N.E. Habitat Suitability and Distribution Models: With Applications in R; Cambridge University Press: Cambridge, UK, 2017; ISBN 978-0-521-76513-8. [Google Scholar]
  48. Schmitt, S.; Pouteau, R.; Justeau, D.; de Boissieu, F.; Birnbaum, P. Ssdm: An r Package to Predict Distribution of Species Richness and Composition Based on Stacked Species Distribution Models. Methods Ecol. Evol. 2017, 8, 1795–1803. [Google Scholar] [CrossRef]
  49. Karthik; Shivakumar, B.R. Change Detection Using Image Differencing: A Study over Area Surrounding Kumta, India. In Proceedings of the 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 22–24 February 2017; pp. 1–5. [Google Scholar]
  50. Hesselbarth, M.H.K.; Sciaini, M.; With, K.A.; Wiegand, K.; Nowosad, J. Landscapemetrics: An Open-Source R Tool to Calculate Landscape Metrics. Ecography 2019, 42, 1648–1657. [Google Scholar] [CrossRef]
  51. McGarigal, K.S.; Cushman, S.; Neel, M.; Ene, E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps; University of Massachusetts: Amherst, MA, USA, 2002. [Google Scholar]
  52. Nowosad, J.; Stepinski, T.F. Information Theory as a Consistent Framework for Quantification and Classification of Landscape Patterns. Landsc. Ecol 2019, 34, 2091–2101. [Google Scholar] [CrossRef]
  53. Sothe, C.; De Almeida, C.M.; Schimalski, M.B.; La Rosa, L.E.C.; Castro, J.D.B.; Feitosa, R.Q.; Dalponte, M.; Lima, C.L.; Liesenberg, V.; Miyoshi, G.T.; et al. Comparative Performance of Convolutional Neural Network, Weighted and Conventional Support Vector Machine and Random Forest for Classifying Tree Species Using Hyperspectral and Photogrammetric Data. GISci. Remote Sens. 2020, 57, 369–394. [Google Scholar] [CrossRef]
  54. Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; da Fonseca, G.A.B.; Kent, J. Biodiversity Hotspots for Conservation Priorities. Nature 2000, 403, 853–858. [Google Scholar] [CrossRef]
  55. Yao, Z.; Xin, Y.; Yang, L.; Zhao, L.; Ali, A. Precipitation and Temperature Regulate Species Diversity, Plant Coverage and Aboveground Biomass through Opposing Mechanisms in Large-Scale Grasslands. Front. Plant Sci. 2022, 13, 999636. [Google Scholar] [CrossRef]
  56. Bräuning, A.; Grießinger, J.; Hochreuther, P.; Wernicke, J. Dendroecological Perspectives on Climate Change on the Southern Tibetan Plateau. In Climate Change, Glacier Response, and Vegetation Dynamics in the Himalaya: Contributions Toward Future Earth Initiatives; Singh, R., Schickhoff, U., Mal, S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 347–364. ISBN 978-3-319-28977-9. [Google Scholar]
  57. Dakhil, M.A.; Halmy, M.W.A.; Liao, Z.; Pandey, B.; Zhang, L.; Pan, K.; Sun, X.; Wu, X.; Eid, E.M.; El-Barougy, R.F. Potential Risks to Endemic Conifer Montane Forests under Climate Change: Integrative Approach for Conservation Prioritization in Southwestern China. Landsc. Ecol 2021, 36, 3137–3151. [Google Scholar] [CrossRef]
  58. Yang, J.; Wu, Q.; Dakhil, M.A.; Halmy, M.W.A.; Bedair, H.; Fouad, M.S. Towards Forest Conservation Planning: How Temperature Fluctuations Determine the Potential Distribution and Extinction Risk of Cupressus Funebris Conifer Trees in China. Forests 2023, 14, 2234. [Google Scholar] [CrossRef]
  59. Amissah, L.; Mohren, G.M.J.; Bongers, F.; Hawthorne, W.D.; Poorter, L. Rainfall and Temperature Affect Tree Species Distributions in Ghana. J. Trop. Ecol. 2014, 30, 435–446. [Google Scholar] [CrossRef]
  60. La Montagna, D.; Attorre, F.; Hamdiah, S.; Maděra, P.; Malatesta, L.; Vahalík, P.; Van Damme, K.; De Sanctis, M. Climate Change Effects on the Potential Distribution of the Endemic Commiphora Species (Burseraceae) on the Island of Socotra. Front. For. Glob. Change 2023, 6, 1183858. [Google Scholar] [CrossRef]
  61. Gallou, A.; Jump, A.S.; Lynn, J.S.; Field, R.; Irl, S.D.H.; Steinbauer, M.J.; Beierkuhnlein, C.; Chen, J.-C.; Chou, C.-H.; Hemp, A.; et al. Diurnal Temperature Range as a Key Predictor of Plants’ Elevation Ranges Globally. Nat. Commun. 2023, 14, 7890. [Google Scholar] [CrossRef]
  62. Sanjeewani, N.; Samarasinghe, D.; Jayasinghe, H.; Ukuwela, K.; Wijetunga, A.; Wahala, S.; De Costa, J. Variation of Floristic Diversity, Community Composition, Endemism, and Conservation Status of Tree Species in Tropical Rainforests of Sri Lanka across a Wide Altitudinal Gradient. Sci. Rep. 2024, 14, 2090. [Google Scholar] [CrossRef] [PubMed]
  63. Jiménez-García, D.; Peterson, A.T. Climate Change Impact on Endangered Cloud Forest Tree Species in Mexico. Rev. Mex. Biodivers. 2019, 90, e902781. [Google Scholar] [CrossRef]
  64. Zhao, Y.; Cao, H.; Xu, W.; Chen, G.; Lian, J.; Du, Y.; Ma, K. Contributions of Precipitation and Temperature to the Large Scale Geographic Distribution of Fleshy-Fruited Plant Species: Growth Form Matters. Sci. Rep. 2018, 8, 17017. [Google Scholar] [CrossRef]
  65. Yin, Y.; Ma, D.; Wu, S. Climate Change Risk to Forests in China Associated with Warming. Sci. Rep. 2018, 8, 493. [Google Scholar] [CrossRef]
  66. Huang, Z.; Liu, S.; Bradford, K.J.; Huxman, T.E.; Venable, D.L. The Contribution of Germination Functional Traits to Population Dynamics of a Desert Plant Community. Ecology 2016, 97, 250–261. [Google Scholar] [CrossRef]
  67. Xie, Z.; Sun, X.; Lux, J.; Chen, T.-W.; Potapov, M.; Wu, D.; Scheu, S. Drivers of Collembola Assemblages along an Altitudinal Gradient in Northeast China. Ecol. Evol. 2022, 12, e8559. [Google Scholar] [CrossRef] [PubMed]
  68. Tian, P.; Liu, Y.; Ou, J. Meta-Analysis of the Impact of Future Climate Change on the Area of Woody Plant Habitats in China. Front. Plant Sci. 2023, 14, 1139739. [Google Scholar] [CrossRef]
  69. Thang, T.H.; Thu, A.M.; Chen, J. Tree Species of Tropical and Temperate Lineages in a Tropical Asian Montane Forest Show Different Range Dynamics in Response to Climate Change. Glob. Ecol. Conserv. 2020, 22, e00973. [Google Scholar] [CrossRef]
  70. Xu, S.-L.; Kodrul, T.; Romanov, M.S.; Bobrov, A.V.F.C.; Maslova, N.; Li, S.-F.; Fu, Q.-Y.; Huang, W.-Y.; Quan, C.; Jin, J.-H.; et al. Diversity of Symplocos (Symplocaceae, Ericales) at Low Latitudes in Asia during Late Oligocene and Miocene. Plant Divers. 2024, 46, 812–816. [Google Scholar] [CrossRef]
  71. Mai, D.H.; Martinetto, E. A Reconsideration of the Diversity of Symplocos in the European Neogene on the Basis of Fruit Morphology. Rev. Palaeobot. Palynol. 2006, 140, 1–26. [Google Scholar] [CrossRef]
  72. Manchester, S.R.; Lott, T.A.; Herrera, F.; Hooghiemstra, H.; Wijninga, V.M.; Fritsch, P.W. Symplocos Fruits from the Pliocene of Colombia. Syst. Bot. 2021, 46, 416–421. [Google Scholar] [CrossRef]
  73. Turner, M.G.; Gardner, R.H. Landscape Ecology in Theory and Practice: Pattern and Process; Springer: New York, NY, USA, 2015; ISBN 978-1-4939-2793-7. [Google Scholar]
  74. Keppel, G.; Van Niel, K.P.; Wardell-Johnson, G.W.; Yates, C.J.; Byrne, M.; Mucina, L.; Schut, A.G.T.; Hopper, S.D.; Franklin, S.E. Refugia: Identifying and Understanding Safe Havens for Biodiversity under Climate Change. Glob. Ecol. Biogeogr. 2012, 21, 393–404. [Google Scholar] [CrossRef]
  75. Ashcroft, M.B. Identifying Refugia from Climate Change. J. Biogeogr. 2010, 37, 1407–1413. [Google Scholar] [CrossRef]
  76. Dawson, T.P.; Jackson, S.T.; House, J.I.; Prentice, I.C.; Mace, G.M. Beyond Predictions: Biodiversity Conservation in a Changing Climate. Science 2011, 332, 53–58. [Google Scholar] [CrossRef]
  77. McGarigal, K.; Marks, B.J. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure; Gen. Tech. Rep. PNW-GTR-351; Department of Agriculture, Forest Service, Pacific Northwest Research Station: Portland, OR, USA, 1995; 122p. [CrossRef]
  78. Forman, R.T.T. Land Mosaics. The Ecology of Landscapes and Regions; Cambridge University Press: Cambridge, UK, 1995; Available online: https://www.scirp.org/reference/referencespapers?referenceid=2127483 (accessed on 9 November 2025).
  79. Flowers, B.; Huang, K.-T.; Aldana, G.O. Analysis of the Habitat Fragmentation of Ecosystems in Belize Using Landscape Metrics. Sustainability 2020, 12, 3024. [Google Scholar] [CrossRef]
  80. Fletcher, R.J., Jr.; Smith, T.A.H.; Kortessis, N.; Bruna, E.M.; Holt, R.D. Landscape Experiments Unlock Relationships among Habitat Loss, Fragmentation, and Patch-Size Effects. Ecology 2023, 104, e4037. [Google Scholar] [CrossRef] [PubMed]
  81. Fahrig, L. Effects of Habitat Fragmentation on Biodiversity. Annu. Rev. Ecol. Evol. Syst. 2003, 34, 487–515. [Google Scholar] [CrossRef]
  82. de Lima Filho, J.A.; Vieira, R.J.A.G.; de Souza, C.A.M.; Ferreira, F.F.; de Oliveira, V.M. Effects of Habitat Fragmentation on Biodiversity Patterns of Ecosystems with Resource Competition. Phys. A Stat. Mech. Its Appl. 2021, 564, 125497. [Google Scholar] [CrossRef]
  83. Botequilha Leitão, A.; Ahern, J. Applying Landscape Ecological Concepts and Metrics in Sustainable Landscape Planning. Landsc. Urban Plan. 2002, 59, 65–93. [Google Scholar] [CrossRef]
  84. Cullum, C.; Rogers, K.H.; Brierley, G.; Witkowski, E.T.F. Ecological Classification and Mapping for Landscape Management and Science: Foundations for the Description of Patterns and Processes. Prog. Phys. Geogr. Earth Environ. 2016, 40, 38–65. [Google Scholar] [CrossRef]
  85. Alaei, N.; Mostafazadeh, R.; Esmali Ouri, A.; Hazbavi, Z.; Sharari, M.; Huang, G. Spatial Comparative Analysis of Landscape Fragmentation Metrics in a Watershed with Diverse Land Uses in Iran. Sustainability 2022, 14, 14876. [Google Scholar] [CrossRef]
  86. Critchlow, R.; Cunningham, C.A.; Crick, H.Q.P.; Macgregor, N.A.; Morecroft, M.D.; Pearce-Higgins, J.W.; Oliver, T.H.; Carroll, M.J.; Beale, C.M. Multi-Taxa Spatial Conservation Planning Reveals Similar Priorities between Taxa and Improved Protected Area Representation with Climate Change. Biodivers. Conserv. 2022, 31, 683–702. [Google Scholar] [CrossRef]
  87. Xu, W.; Shrestha, A.; Wang, G.; Wang, T. Site-Based Climate-Smart Tree Species Selection for Forestation under Climate Change. Clim. Smart Agric. 2024, 1, 100019. [Google Scholar] [CrossRef]
  88. Elith, J.; Leathwick, J.R. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 2009, 40, 677–697. [Google Scholar] [CrossRef]
  89. Guisan, A.; Thuiller, W. Predicting Species Distribution: Offering More than Simple Habitat Models. Ecol. Lett. 2005, 8, 993–1009. [Google Scholar] [CrossRef] [PubMed]
  90. Guisan, A.; Tingley, R.; Baumgartner, J.B.; Naujokaitis-Lewis, I.; Sutcliffe, P.R.; Tulloch, A.I.T.; Regan, T.J.; Brotons, L.; McDonald-Madden, E.; Mantyka-Pringle, C.; et al. Predicting Species Distributions for Conservation Decisions. Ecol. Lett. 2013, 16, 1424–1435. [Google Scholar] [CrossRef] [PubMed]
  91. Masson-Delmotte, V.; Zhai, P.; Pirani, A.; Connors, S.L.; Péan, C.; Berger, S.; Caud, N.; Chen, Y.; Goldfarb, L.; Gomis, M.I.; et al. Climate Change 2021—The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Cambridge University Press: Cambridge, UK, 2023; ISBN 978-1-009-15789-6. [Google Scholar]
Forests 17 00058 g001
Figure 1. Flowchart illustrating the step-by-step spatial and ensemble modelling workflow used to generate potential endemic Symplocos species richness and maps of projected changes.
Figure 1. Flowchart illustrating the step-by-step spatial and ensemble modelling workflow used to generate potential endemic Symplocos species richness and maps of projected changes.
Forests 17 00058 g001
Forests 17 00058 g002
Figure 2. Relative importance (a) and response curves (b) for the selected predictor variables explaining the potential endemic Symplocos species distribution. Abbreviations are shown in Table 2.
Figure 2. Relative importance (a) and response curves (b) for the selected predictor variables explaining the potential endemic Symplocos species distribution. Abbreviations are shown in Table 2.
Forests 17 00058 g002
Forests 17 00058 g003aForests 17 00058 g003b
Figure 3. Maps of (a) present species richness, and (b) weighted endemism index of 11 Symplocos endemics in China based on the Stacked Species Distribution Models (SSDM) generated by the “ssdm” package.
Figure 3. Maps of (a) present species richness, and (b) weighted endemism index of 11 Symplocos endemics in China based on the Stacked Species Distribution Models (SSDM) generated by the “ssdm” package.
Forests 17 00058 g003aForests 17 00058 g003b
Forests 17 00058 g004aForests 17 00058 g004b
Figure 4. Projected endemic Symplocos richness under (a) SSP126, (b) SSP585 for the near future (2021–2040), (c) SSP126, and (d) SSP585 for the future (2061–2080).
Figure 4. Projected endemic Symplocos richness under (a) SSP126, (b) SSP585 for the near future (2021–2040), (c) SSP126, and (d) SSP585 for the future (2061–2080).
Forests 17 00058 g004aForests 17 00058 g004b
Forests 17 00058 g005aForests 17 00058 g005b
Figure 5. Change in the endemic Symplocos richness under (a) SSP126, (b) SSP585 for the near future (2021–2040), (c) SSP126, and (d) SSP585 for the far future (2061–2080).
Figure 5. Change in the endemic Symplocos richness under (a) SSP126, (b) SSP585 for the near future (2021–2040), (c) SSP126, and (d) SSP585 for the far future (2061–2080).
Forests 17 00058 g005aForests 17 00058 g005b
Forests 17 00058 g006
Figure 6. The landscape-level metrics (contag = contagion Index, lpi = largest patch index, and shdi = Shannon diversity index) of the endemic Symplocos species richness under current and future climate scenarios (SSP126 and SSP585 for 2040 and 2080).
Figure 6. The landscape-level metrics (contag = contagion Index, lpi = largest patch index, and shdi = Shannon diversity index) of the endemic Symplocos species richness under current and future climate scenarios (SSP126 and SSP585 for 2040 and 2080).
Forests 17 00058 g006
Forests 17 00058 g007
Figure 7. The class-level landscape metrics (ai = aggregation index, ed = edge density, np = number of patches, pladj= percentage of like adjacencies) for the endemic Symplocos species richness classes for the current climate and future climate scenarios (SSP126 and SSP585 for 2040 and 2080).
Figure 7. The class-level landscape metrics (ai = aggregation index, ed = edge density, np = number of patches, pladj= percentage of like adjacencies) for the endemic Symplocos species richness classes for the current climate and future climate scenarios (SSP126 and SSP585 for 2040 and 2080).
Forests 17 00058 g007
Table 1. List of the studied endemic Symplocos species, their main ecosystem, distribution, IUCN Red List of Threatened Species conservation status (LC = least concern; NE= not evaluated), and global population status.
Table 1. List of the studied endemic Symplocos species, their main ecosystem, distribution, IUCN Red List of Threatened Species conservation status (LC = least concern; NE= not evaluated), and global population status.
SpeciesMain EcosystemDistribution
According to
Wu & Nooteboom [9]		IUCN Red List Status Global Population StatusSource
S. austrosinensis Handel-Mazzetti Mixed forests; up to 1000 m. N Guangdong, Guangxi, Guizhou, Hunan LC stable Liu [41]
S. crassilimba Merrill, Lingnan Mixed forests (400–1000 m) Hainan LC stable Liu [42]
S. fordii Hance Mixed forests; up to 500 m. South Guangdong NE unknown
S. fukienensis Ling Mixed forests; up to 900 m. Fujian
S. glandulifera Brand in Engler Mixed forests slopes (1400–2000 m) Guangxi, Hunan, Yunnan LC Stable Liu [43]
S. nakaharae (Hayata) Masam. LC unknown
S. ramosissima var. xylopyrena Mixed forests (1800–2000 m) Xizang, Yunnan
S. stellaris Brand in Diels Mixed forests (100–2000 m) Anhui, Guangdong, Fujian, Guizhou, Guangxi, Jiangsu, Sichuan, Zhejiang, Yunnan, NE unknown
S. stellaris var. aenea (Handel-Mazzetti) Nooteboom Mixed forests (1000–2000 m) South Sichuan, Yunnan NE unknown
S. sumuntia var. modesta Mixed forests up yo 1000 m Taiwan. NE unknown
S. ulotricha Ling Mixed forests slopes (900–1100 m) Fujian, Guangdong NE unknown
Table 2. An overview of the model and the relative significance of the chosen predictor variables that describe the possible range of endemic Symplocos species in China. To prevent multicollinearity issues, correlated variables with variance inflation factor (VIF) values more than 10 and a correlation threshold of 0.75 were eliminated. The accuracy of the SSDMs is shown by the averages of the area under the curve (AUC) with value > 0.9 and true skill statistic (TSS) > 0.75.
Table 2. An overview of the model and the relative significance of the chosen predictor variables that describe the possible range of endemic Symplocos species in China. To prevent multicollinearity issues, correlated variables with variance inflation factor (VIF) values more than 10 and a correlation threshold of 0.75 were eliminated. The accuracy of the SSDMs is shown by the averages of the area under the curve (AUC) with value > 0.9 and true skill statistic (TSS) > 0.75.
Variable CodeRelative
Importance		VIF
Bio15 (Precipitation seasonality)26.16.6
Bio13 (Precipitation of wettest month)205.6
Bio3 (Isothermality)17.69.2
Bio2 (Mean diurnal range)14.68.8
Bio8 (Mean temperature of wettest quarter)12.16.7
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

Dakhil, M.A.; Zhang, L.; Halmy, M.W.A.; El-Barougy, R.F.; Pandey, B.; Hao, Z.; Yuan, Z.; Liang, L.; Bedair, H. Spatial and Landscape Fragmentation Pattern of Endemic Symplocos Tree Communities Under Climate Change Scenarios in China. Forests 2026, 17, 58. https://doi.org/10.3390/f17010058

AMA Style

Dakhil MA, Zhang L, Halmy MWA, El-Barougy RF, Pandey B, Hao Z, Yuan Z, Liang L, Bedair H. Spatial and Landscape Fragmentation Pattern of Endemic Symplocos Tree Communities Under Climate Change Scenarios in China. Forests. 2026; 17(1):58. https://doi.org/10.3390/f17010058

Chicago/Turabian Style

Dakhil, Mohammed A., Lin Zhang, Marwa Waseem A. Halmy, Reham F. El-Barougy, Bikram Pandey, Zhanqing Hao, Zuoqiang Yuan, Lin Liang, and Heba Bedair. 2026. "Spatial and Landscape Fragmentation Pattern of Endemic Symplocos Tree Communities Under Climate Change Scenarios in China" Forests 17, no. 1: 58. https://doi.org/10.3390/f17010058

APA Style

Dakhil, M. A., Zhang, L., Halmy, M. W. A., El-Barougy, R. F., Pandey, B., Hao, Z., Yuan, Z., Liang, L., & Bedair, H. (2026). Spatial and Landscape Fragmentation Pattern of Endemic Symplocos Tree Communities Under Climate Change Scenarios in China. Forests, 17(1), 58. https://doi.org/10.3390/f17010058

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

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop