Next Article in Journal
Humulus lupulus Promoting Osteoblast Activity and Bone Integrity: Effects and Mechanisms
Previous Article in Journal
Genome-Wide-Association-Analysis-Based Identification of Genetic Loci and Candidate Genes Associated with Cold Germination in Sweet Corn
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biology
Volume 14
Issue 5
10.3390/biology14050581
biology-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

Changes in the Distribution Range of the Genus Cardiocrinum in China Under Climate Change and Human Activities

by
Yuxin Zhang
,
Shujian Zhang
,
Haiyan Xiao
,
Heng Li
,
Da Liao
,
Yuxi Xue
,
Xinyi Huang
,
Qitao Su
* and
Yian Xiao
*
Key Laboratory of Jiangxi Province for Biological Invasion and Biosecurity, School of Life Sciences, Jinggangshan University, Ji’an 343009, China
*
Authors to whom correspondence should be addressed.
Biology 2025, 14(5), 581; https://doi.org/10.3390/biology14050581
Submission received: 21 April 2025 / Revised: 17 May 2025 / Accepted: 19 May 2025 / Published: 21 May 2025
Browse Figures
Versions Notes

Simple Summary

The genus Cardiocrinum comprises three recognized species, with their native distributions primarily concentrated in eastern and southwestern China’s forest ecosystems. In this study, the Maxent model was used to study the distribution and suitable area change in Cardiocrinum under climate change. The study findings showed that precipitation, temperature, and human activities significantly impacted the Cardiocrinum distribution. In addition to the relative reduction in the suitable habitat area of Cardiocrinum giganteum var. yunnanense, the suitable habitat area of Cardiocrinum cathayanum and Cardiocrinum giganteum will increase significantly and in the 2081–2100 SSP585 climate, and the area ideal for Cardiocrinum cathayanum habitat will increase greatly. Compared with the current period, this indicates that Cardiocrinum will undergo significant range shifts, with suitable habitats migrating toward higher altitude in the future. The niche consistency test of Cardiocrinum showed that there was niche differentiation between Cardiocrinum cathayanum and the other two, and there was no niche differentiation between Cardiocrinum giganteum and Cardiocrinum giganteum var. yunnanense.

Abstract

Cardiocrinum are perennial herbaceous plants of the Liliaceae family with high ornamental, nutritional, and medicinal value. However, critical knowledge gaps remain regarding the following: (1) the fine-scale habitat preferences of Cardiocrinum; (2) the key ecological drivers influencing their growth and distribution. The MaxEnt software 3.4.1 was used to simulate the current and future suitable habitats of Cardiocrinum, evaluate the impacts of environmental changes on its distribution, and determine the distribution changes under climate change scenarios. The AUC value of the model used in the current study was >0.98, which indicates that the model had good accuracy. The results show that as a typical understory herb, precipitation in the warmest quarter (bio18) and temperature seasonality (bio04) are the main factors affecting the distribution of Cardiocrinum. In addition, Cardiocrinum giganteum and Cardiocrinum giganteum var. yunnanense are also affected by slope and human activity. Under the SSP126, SSP245, and SSP585 climate scenarios, the suitable habitat areas of Cardiocrinum cathayanum and C. giganteum showed an increasing trend. The suitable habitat area of C. giganteum var. yunnanense increased under the SSP126 climate scenario; however, it substantially declined in SSP245 and SSP585 scenarios. The distribution area of Cardiocrinum shifted to higher latitudes. The centroid of C. cathayanum shifted more than 5 degrees of latitude during SSP585 2081s, while the centroid of C. giganteum and C. giganteum var. yunnanense did not shift more than 2 degrees of latitude. In addition, the centroid longitudes of C. giganteum and C. giganteum var. yunnanense shifted westward under the three climate scenarios. There is ecological niche differentiation among C. cathayanum and others, whilst C. giganteum and C. giganteum var. yunnanense have overlapping ecological niches. In the future, we will strengthen the protection of wild Cardiocrinum resources in accordance with environmental factors and suitable habitats for Cardiocrinum.

1. Introduction

Cardiocrinum are perennial herbaceous plants of the family Liliaceae, which are known as the “Prince of Lilies” due to their striking size, beautiful white flowers, and aromatic smell [1]. According to the Flora of China, there are two species of Cardiocrinum in China, namely Cardiocrinum giganteum and Cardiocrinum cathayanum. C. giganteum has a variant of Cardiocrinum giganteum var. yunnanense [2]. C. giganteum var. yunnanense is a near-threatened plant on the Informatin System of Chinese Rare and Endangered Plant (ISCREP) [3], and C. cathayanum is classified as a second-class nationally protected wild plant in China [4]. The plants of Cardiocrinum are perennial herbaceous plants in the forest. They like humid, cold, and shaded environments. They often grow under deciduous broad-leaved forests or beside streams on the edge of forests [1]. Cardiocrinum have esthetic, edible, and medicinal value. Their bulbs and tender leaves are rich in nutrients such as protein, amino acids, lily glycosides, starch, vitamin C, and vitamin B, which can be eaten as vegetables [5]. The biflavonoid compounds (CGY-1 and CGY-2) extracted from the bulbs of Cardiocrinum have good antitussive effects [6]. Field surveys have found that due to the expansion of human activities and the excavation and utilization of wild Cardiocrinum, wild Cardiocrinum resources and habitats have suffered considerable damage [7]. For example, local residents collect Cardiocrinum bulbs to make starch for consumption and sale. In some scenic spots, the inflorescences of common Cardiocrinum are broken by humans, and some Cardiocrinum or their bulbs are eaten by animals. This has resulted in a dramatic decline in both the population size and density of wild Cardiocrinum, with most remaining individuals existing as isolated solitary plants or in small clusters of fewer than a dozen [1]. Therefore, fully understanding the distribution of Cardiocrinum and the ecological environment suitable for their growth will be beneficial to the protection of the diversity of wild Cardiocrinum. This will also allow the development and utilization of their medicinal resources, the domestication and artificial cultivation of Cardiocrinum, the introduction of new species, and the protection of plant resources.
Climate serves as the primary determinant governing species distribution patterns and ecosystem processes [8]. Climate change-induced alterations in temperature and precipitation regimes significantly modify plant physiological processes, thereby influencing growth, developmental patterns, reproductive success, population stability, and geographic distribution ranges [9,10]. Plants may respond to climatic changes through either phenotypic plasticity and local adaptation or by altering their geographic distributions through range shifts and contractions [11,12]. Thus, >80% of plant species may face the alterations in diversity, current distribution, and potential habitats [13]. For example, Forsythia suspensa has shown a northward shift in its potential habitat in China [14], and Haloxylon in Central Asia may move to higher altitudes under a warming climate [15].
Species distribution models (SDMs) are grounded in niche theory, utilizing known occurrence records and associated environmental variables to identify key distribution drivers and predict potential species ranges [16,17]. SDMs mainly include BIOCLIM [18], Ecological Niche Factor Analysis (ENFA) [19], the Generalized Additive Model (GAM) [20], the Generalized Linear Model (GLM) [21], Maximum Entropy (MaxEnt) [22], Classification and Regression Tree (CART) [23], etc. Among these approaches, the Maximum Entropy (MaxEnt) model, grounded in the principle of maximum entropy, represents one of the most widely used and robust methods for modeling species’ geographical distributions [24]. The MaxEnt algorithm utilizes species occurrence records and environmental covariates to estimate a probability distribution with maximum entropy, enabling the analysis and prediction of potential species distribution patterns [25]. Among these models, the MaxEnt model can better combine the distribution of species with environmental and performs well even in the absence of species distribution [26]. It offers computational efficiency and user-friendly implementation, making it particularly suitable for modeling both realized and potential species distributions [27].
To assess the habitat distribution patterns and key environmental determinants of habitat suitability for Cardiocrinum in China, we employed the MaxEnt modeling approach to predict their potential geographic distributions. Therefore, the present study aimed to achieve the following objectives: (1) to determine environmental factors impacting distribution of Cardiocrinum species; (2) to predict suitable habitats for Cardiocrinum species under diverse scenarios by using current simulation and climate change; (3) to determine the distribution of Cardiocrinum species under future climatic conditions to provide an effective basis for its effective conservation, introduction, and utilization.

2. Materials and Methods

2.1. The Source and Acquisition of Cardiocrinum Data

The distribution information of Cardiocrinum was obtained by consulting the Global Biodiversity Information Facility (GBIF) (https://www.gbif.org/, accessed on 21 March 2024), the Digital Herbarium of China (https://www.cvh.ac.cn/?from=singlemessage, accessed on 21 March 2024), and the photo distribution map of Flora of China (https://www.iplant.cn/, accessed on 21 March 2024). Finally, 888 effective distribution points of C. cathayanum, 1392 effective distribution points of C. giganteum, and 126 effective distribution points of C. giganteum var. yunnanense in China were obtained (Figure 1). The selected distribution point data were then made into a csv file with species names and the distribution of longitude and latitude points.

2.2. Environmental Parameters

Current and future climate data were obtained from WorldClim data website (https://www.worldclim.org/data/index.html, accessed on 12 April 2024), including 19 environmental variables (bio1-bio19) with a 30 s (ca.1 km) spatial resolution and three terrain variables: elevation (elev), aspect (asp), and slope (slo). These were collected using Computer Network Informations Center of the Chinese Academy of Sciences and the International Scientific Data Website (https://www.gscloud.cn/, accessed on 12 April 2024). As well as this, human activity (ha) data, from the Global Human Impact Index, were from the Socioeconomic Data and Applications Center (http://sedac.ciesin.columbia.edu/wildareas/, accessed on 12 April 2024) (Table 1).
The climate scenarios proposed by CMIP6 based on different socioeconomic development and anthropogenic emission pathways are called “Shared Socioeconomic Pathways” (SSPs). According to the intensity of emissions, three shared socioeconomic pathways were selected, including SSP126 (lowest greenhouse gas emission scenario), SSP245 (moderate greenhouse gas emission scenario), and SSP585 (highest greenhouse gas emission scenario). For future climate modeling, we chose the BCC-CSM2-MR data set to project scenarios for the 2040s (2041–2060) and 2080s (2081–2100). ArcGIS 10.8 was used for the extraction of environmental variables based on the boundaries of the administrative division map of China, and then they were converted into ASCII format for backup.

2.3. Maximum Entropy Model (MaxEnt) Simulation

The predictive performance of the mode was determined by using the Area Under the Receiver Operating Characteristic Curve (AUC). The value range from 0.5 to 1 shows random prediction and perfect discrimination, respectively [28]. The Area Under the Curve (AUC) values ranged from 0.5 (random prediction) to 1.0 (perfect discrimination) [29]. Higher AUC values correspond to greater predictive reliability [30,31]. Following established ecological modeling standards, we classified prediction accuracy into five categories: excellent (0.9–1.0), good (0.8–0.9), fair (0.7–0.8), poor (0.6–0.7), and fail (0.5–0.6) [32]. The MaxEnt model outputs in logistic format were geoprocessed using ArcGIS 10.8. Using China’s administrative boundary layer, we extracted and classified the potential suitable habitats for Cardiocrinum, which were subsequently visualized as a spatial distribution map.
The SDM tool in ArcGIS 10.8 software was used to quantify the suitable habitat of Cardiocrinum based on the results of the MaxEnt model and set the value that excluded 10% of the position with the lowest predicted value as the threshold. Therefore, the potential suitable habitat of this species was divided into three different levels. The first level was non-suitable with a <10 percentile training presence, the second was suitable habitat with a <10 percentile training presence–0.66 and third level was highly suitable habitat with a percentile presence > 0.66. Thereafter, the suitable habitat area for Cardiocrinum was calculated. The ASCII result file output from the MaxEnt model was extracted using ArcGIS 10.8 software. The spatial analysis and visualization mapping were performed using the reclassification tool to calculate the area changes in different levels of suitable habitats of Cardiocrinum under current climate scenarios and future times. Among them, −1 represented a newly added suitable habitat, 0 represented a non-suitable habitat, 1 represented a retained suitable habitat, and 2 represented a lost suitable habitat. The data were converted into binary data using the threshold (suitable habitat and non-suitable habitat), and the “Centroid Changes (Lines)” analysis tool in the SDM Toolbox v2.6 of ArcGIS 10.8 software was used to calculate the location of the distribution center of the suitable habitats and the direction of its spatial migration change.
The niche consistency test among Cardiocrinum was performed using ENMTools [33]. In ENMTools 1.1.2 software, MaxEnt was used to generate a layer under current climate conditions to calculate the actual values of Schoener D [34] and Warren I [35], and the distribution frequency of the expected values of Schoener D and Warren I was obtained by running the pseudo-replication data set 100 times. The statistical significance between observed and expected values of the evaluation index was assessed using a non-parametric Monte Carlo permutation test. When the actual I and D values were significantly lower than the expected values of the pseudo-replication data set (p < 0.01), the hypothesis of niche consistency was rejected, indicating that niche differentiation occurred between the two [36,37].

3. Results

3.1. Model Accuracy Assessment

The MaxEnt model demonstrated exceptional predictive performance, with training set AUC values exceeding 0.98 for all species (Table 2). These results indicate the model achieved excellent discrimination capacity (AUC > 0.9 is considered outstanding) and accurately simulated Cardiocrinum’s potential habitat distribution patterns.

3.2. Key Environmental Variables

The MaxEnt model analysis identified five key environmental variables that significantly affect the distribution habitat of Cardiocrinum. Among the 23 variables examined were the following: warmest-quarter precipitation (bio18), temperature seasonality (bio04), the human activity index (ha), precipitation seasonality (bio15), and slope (slo) (in order of relative contribution). Among these environmental determinants, precipitation emerged as the predominant factor governing Cardiocrinum distribution patterns. (Figure 2). The Jackknife test results showed that for C. cathayanum, the sum of the contribution rates of warmer-quarter rainfall (bio18) and temperature seasonality (bio4) reached 70.8%; the warmest-season precipitation was 446.3–680.8 mm, and the temperature seasonality range was 738.5–912.8. For the C. giganteum, the sum of the precipitation of the warmest quarter (bio18), slope (slo), and precipitation seasonality (bio15) contributed 59.1%; the optimum range of precipitation was 431.6–1995.3 mm, the slope range was 5.45–5.53, and the seasonal variation range of precipitation was 61.69–94.30. The combined contribution of warmest-quarter rainfall and seasonal temperature (bio18) accounted for 56.2% of the observed distribution patterns in C. giganteum var. yunnanense; the optimum range of precipitation was 451.2–754.1 mm, and the seasonal variation range of temperature was 485.48–839.74.

3.3. The Distribution of the Suitable Area of Cardiocrinum Under the Current Climate

The distribution of three species of Cardiocrinum in China at present is shown in Figure 3. C. cathayanum is widely present in central and eastern China, and its most suitable areas are in Hubei, northern Jiangxi, southern Anhui, and central Hunan. The total suitable area is 155.32 × 104 km2; C. giganteum is present in the southwest and east parts of northwest areas and sporadically distributed in central China and east China, with its highly suitable areas mainly in southern Qinghai, northwest and northeast Yunnan, and western Guizhou, with a total suitable area of 165.06 × 104 km2; C. giganteum var. yunnanense is mainly distributed in the southwest, with its highly suitable areas concentrated in Chongqing, Guizhou, and southeastern Qinghai, with a total suitable area of 133.74 × 104 km2.

3.4. Potential Habitat Change for Cardiocrinum in the Future

Compared with the current period, the suitable area of C. cathayanum increased under the 2041–2060 SSP126 climate scenario and then decreased under the 2081–2100 SSP126 scenario. The highly suitable habitat along the Yangtze River Basin first increased under the 2041–2060 SSP126 climate scenario and then decreased under the 2081–2100 SSP126 climate scenario. The total suitable habitat area increased to 206.89 × 104 km2 and then decreased to 196.48 × 104 km2. The highly suitable habitat area increased to 35.66 × 104 km2 and then decreased to 14.74 × 104 km2. The total suitable habitat area of C. giganteum depicted an increasing trend, and its total suitable area increased to 191.30 × 104 km2. Further, the total suitable area of C. giganteum var. yunnanense first declined under the 2041–2060 SSP126 climate scenario. Then, it substantially increased under the 2081–2100 SSP126 climate scenario and reached 138.43 × 104 km2. In general, compared with the current situation, the suitable habitat areas for Cardiocrinum show an increasing trend.
Under the 2041–2060 to 2081–2100 SSP245 climate scenario, the suitable habitat areas of C. cathayanum and C. giganteum increased overall, while the suitable habitat area of C. giganteum var. yunnanense decreased. The total suitable habitat areas of C. cathayanum and C. giganteum increased to 217.53 × 104 km2 and 227.32 × 104 km2, respectively; under the 2041–2060 SSP126 climate scenario, the total suitable habitat area of C. giganteum var. yunnanense increased to 140.14 × 104 km2, while under the 2081–2100 SSP126 climate scenario, it decreased to 128.65 × 104 km2.
Under the 2041–2060 to 2081–2100 SSP585 climate scenario, in general, the suitable habitat area of C. cathayanum and C. giganteum giant increased significantly, while that of C. giganteum var. yunnanense decreased slightly. The area suitable for the C. cathayanum habitat reached 339.29 × 104 km2. The total suitable habitat of C. giganteum increased to 258.57 × 104 km2, and the highly suitable habitat was 98.84 × 104 km2. The total suitable habitat of C. giganteum var. yunnanense finally decreased to 121.38 × 104 km (Figure 4).

3.5. Migration of Centroid of Cardiocrinum-Suitable Area Under Different Climate Scenarios

The current center of mass of C. cathayanum is located in Sangzhi County, Zhangjiajie City, Hunan Province (110°03′46.6596″ E, 29°35′44.9952″ N). The center of mass of C. giganteum is located in Wuchuan County, Zunyi City, Guizhou Province (108°04′44″ E, 28°42′11″ N). The center of mass of C. giganteum var. yunnanense is located in Youyang County (108°57′58.6800″ E, 29°04′26″ N). In the SSP126 and SSP245 scenarios, the centroid of C. cathayanum shifted to the northwest side, while in SSP585 scenario, the centroid shifted to the northeast. In these scenarios, the center of mass of C. giganteum and C. giganteum var. yunnanense shifted to the northwest (Figure 5).

3.6. Ecological Niche Differentiation

The niche consistency test showed results for C. giganteum and C. cathayanum (D: 0.65; I: 0.88), C. giganteum and C. giganteum var. yunnanense (D: 0.72; I: 0.92), and C. cathayanum and C. giganteum var. yunnanense (D: 0.65; I: 0.87). The actual values of Warren I and Schoener D for C. cathayanum and the other two were lower than the expected values, indicating that there was a significant niche change between C. cathayanum and the others. There was an overlapping area between C. giganteum and C. giganteum var. yunnanense in Warren I and Schoener D, indicating that they had overlapping niches (Figure 6).

4. Discussion

Temperature and rainfall are the most impact factors regulating plant growth and development [38]. Cardiocrinum are typical understory herbaceous plants, and the growth of understory herbaceous plants is largely affected by environmental factors and stand factors [39]. Environmental factors affect forest stand factors in the environment by affecting the height and density of trees, namely water and heat factors, soil nutrients, and understory light, which, in turn, affect understory herbaceous plants [40]. Light availability, soil nutrient content, and water supply emerge as the principal abiotic factors governing understory plant distribution patterns and diversity dynamics [41,42,43]. Previous studies have shown that Cardiocrinum mostly grow on natural forest slopes below 1000 m above sea level, while C. giganteum grows on natural forest steep slopes at 1300–1600 m above sea level [44]. There are usually mountain streams and creeks near their wild habitats, and the air humidity is very high. Judging from the habitat of wild Cardiocrinum, they can tolerate a local extreme minimum temperature of −10–6 °C, and the soil requires good drainage and is not resistant to strong direct sunlight [2]. Therefore, Cardiocrinum need appropriate moisture for growth. Appropriate precipitation can promote the growth and reproduction of Cardiocrinum. Insufficient precipitation may lead to the insufficient development of their bulbs, while excessive precipitation may cause bulb rot. Previous studies indicate that Cardiocrinum species thrive in regions with annual rainfall of 600–2200 nm and the annual average relative humidity is above 70%. High precipitation is not conducive to the development of fruits, while higher annual average temperatures are more conducive to the development of fruits and seeds [1]. The sensitivity to temperature may be due to the morphological physiological dormancy (MPD) of their seeds [45]. After the seeds of Cardiocrinum mature, the embryo is not fully developed, so a complex temperature stratification from high temperature to low temperature is required to effectively break the dormancy of Cardiocrinum seeds [46]. Therefore, temperature changes are of great significance to the growth of Cardiocrinum.
The future distribution of the C. giganteum and C. giganteum var. yunnanense will also be affected by slope and human activity factors. The influence of slope may be related to the fact that these two Cardiocrinum are distributed in high-altitude areas. The slope will affect the light of the plants, the water content and saturation of the land, and excessive precipitation will form runoff, leading to soil erosion [47]. Therefore, the decrease in soil moisture and fertility will affect the growth and reproduction of them, thereby affecting their distribution.
Cardiocrinum serve multiple purposes as ornamental plants for garden landscaping and pot cultivation [5]. Future expansion and northward shifts in the suitable habitat range of C. cathayanum may be influenced by human cultivation. Additionally, these plants have medicinal and edible applications [48]. C. giganteum and C. giganteum var. yunnanense are primarily found in the Yunnan–Guizhou–Sichuan region, where their bulbs have traditionally been consumed. This practice, along with economic development, agricultural expansion, and habitat destruction, threatens wild populations by depleting local resources and degrading their natural habitats.
C. cathayanum exhibits a primary distribution in eastern and central China, with core populations concentrated in the provinces of Hubei, Hunan, Jiangxi, Zhejiang, Anhui, and Jiangsu [49]. C. giganteum is primarily distributed across southwestern to central China, with its core range encompassing Yunnan, Tibet, Sichuan, Shaanxi, Hubei, and Henan Provinces. Sichuan is the place with the most densely distributed C. giganteum in China [50]. C. giganteum var. yunnanense is predominantly distributed in the southwestern regions of China, with its core range encompassing Sichuan and Yunnan Provinces. Model fitting results indicate the medium- and high-suitability habitat area of C. cathayanum is about 155.32 × 104 km2, the medium- and high-suitability habitat area of C. giganteum is about 165.06 × 104 km2, and the medium- and high-suitability habitat area of C. giganteum var. yunnanense is about 133.74 × 104 km2. The suitable area is basically consistent with the model fitting, and the model fitting degree is high. Under the SSP126, SSP245, and SSP585 climate scenarios, projections for 2041–2060 and 2081–2100 indicate that the models project an expansion of suitable habitats for both C. cathayanum and C. giganteum across all climate scenarios, with C. cathayanum demonstrating particularly significant gains. The results show that the suitable area for C. giganteum increased in SSP126 climate scenario, while it decreased in SSP245 and SSP585 scenarios. This decrease in suitable area might be linked with its narrow ecological adaptability. Narrowly distributed plant species typically exhibit limited ecological plasticity, rendering them more vulnerable to climate change impacts compared to widely distributed taxa [51].
From the perspective of the future changes in the centroid migration, the centroids of Cardiocrinum all showed a trend of migrating to higher latitudes. The centroids of C. giganteum and C. giganteum var. yunnanense had a smaller range of movement, and the upward migration did not exceed 2 degrees of latitude, while the centroid of C. cathayanum leaves had a significant northward migration under the 2081s SSP585 scenario, exceeding 5 degrees of latitude. The reason for this difference may be that C. cathayanum is more affected by temperature and precipitation, and plants that are more sensitive to temperature will migrate more significantly to the north [52]. In addition to these two factors, C. giganteum and C. giganteum var. yunnanense are also affected by many other factors. In addition, the centroid longitude of the C. giganteum and C. giganteum var. yunnanense moved westward in all three climate scenarios. This may be because the longitude of alpine plants moves westward when the distribution of alpine plants moves upward in altitude [53]. It is speculated that due to climate warming, plants will migrate to higher altitudes and higher latitudes [54,55,56]. In the future climate warming, northern China will become humid and warm [57]. The projected increase in warm, humid areas is expected to significantly expand suitable habitats for Cardiocrinum. Model simulations indicate that these favorable conditions will facilitate both northeastward and altitudinal range shifts in the species’ distribution. In addition, under the condition of global warming, China‘s forest vegetation will tend to move north [58]. As understory plants, Cardiocrinum may move northward with vegetation in order to survive better. This observed migration pattern aligns with well-documented biogeographic responses to climate change, where species increasingly shift toward higher latitudes and elevations under warming conditions [59].
Interspecific niche differentiation primarily arises from divergence in both fundamental niches and species-specific environmental contexts [35]. There is no overlapping niche between C. cathayanum and the other two species. This may be due to the differences in influencing factors and suitable habitats between C. cathayanum and the others. C. cathayanum is primarily not affected by slope, while C. cathayanum and C. giganteum var. yunnanense are greatly affected by slope. The highest habitat suitability for C. cathayanum occurs predominantly in lowland floodplains of the middle and lower Yangtze River Basin, while the high-suitability areas of C. giganteum and C. giganteum var. yunnanense are around the Sichuan Basin. Studies have shown that the interaction between geological changes in the eastern part of the Qinghai–Tibet Plateau and climate change in the late Miocene induced differences among C. cathayanum and C. giganteum [60,61]. Therefore, environmental heterogeneity may cause significantly different microenvironments in different populations, which, in turn, leads to differences in local genetic variation and differentiation in local environmental adaptability [62]. This shows that environmental factors play a crucial role in species differentiation. C. giganteum and C. giganteum var. yunnanense have overlapping ecological niches, which may be because C. giganteum var. yunnanense is a variant of C. giganteum. The distribution of the area of both these species overlaps because of the same environmental factors. This overlap may increase the competitive pressure between the two species, especially when resources are limited or the environment is volatile. However, niche overlap does not necessarily lead to direct competitive exclusion, because species can also reduce direct competition and achieve coexistence through niche differentiation [63].

5. Conclusions

Our findings indicate that current C. cathayanum distribution is primarily concentrated in the middle and lower Yangtze River Basin, while C. giganteum and C. giganteum var. yunnanense are distributed radially along the Sichuan Basin. The distributions of C. cathayanum and C. giganteum are predominantly constrained by seasonal temperature and the rainfall of the warmest quarter (bio18). In addition, the suitable habitats of C. giganteum and C. giganteum var. yunnanense are also affected by slope and human activity. Under future climate scenarios (SSP126, SSP245, and SSP585) for both the 2041–2060 and 2081–2100 periods, the suitable habitat areas of C. giganteum and C. giganteum will gradually increase, while that of C. giganteum var. yunnanense will decrease. C. cathayanum will have a tendency to migrate northward, while the distribution of C. giganteum will radiate along the current suitable habitat. There is niche differentiation between C. cathayanum and C. giganteum and C. giganteum var. yunnanense, while there is no niche differentiation between C. giganteum and C. giganteum var. yunnanense. This may be caused by differences in the environment. In order to better protect the resources of Cardiocrinum in China, we must first avoid uncontrolled excavation. Secondly, we can establish corresponding seed and seedling breeding bases in their suitable habitats and actively carry out the investigation, preservation, and collection of wild germ-plasm resources. At the same time, we can also promote research on the reproductive model of Cardiocrinum growth in its highly suitable habitats through various means such as tissue culture, to provide guarantees for the sustainable use and protection of Cardiocrinum.

Author Contributions

Q.S. and Y.X. (Yian Xiao), conceptualization; Y.Z., Q.S., and S.Z., research methods; Y.Z., H.X., and S.Z., procedure; Y.Z., Q.S., and Y.X. (Yian Xiao), check confirmation; Y.Z., H.X., formal analysis; H.L. and D.L., investigation; Y.X. (Yian Xiao), resources; Y.Z., S.Z., H.X., H.L., Y.X. (Xue Yuxi), and X.H., data curation; Y.Z., writing—original preparation; X.H., Q.S., and Y.X. (Yian Xiao), writing—reviewing and editing; Q.S. and Y.X. (Yian Xiao), visualization; Y.X. (Yian Xiao), project management; Y.X. (Yian Xiao), sourcing of funds. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (42467035) and Key Laboratory of Jiangxi Province for Biological Invasion and Biosecurity (2023SSY02111).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be made available on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Dong, Z.Y.; Zhang, L.; Zhang, H.; Wei, Y. Study on Phenotypic Diversity of Fruitsand Seeds of Wild Cardiocrinum giganteum in 5 Regions. Seed 2024, 43, 93–102. [Google Scholar]
  2. Zhang, C.B.; Chen, F. Cardiocrinum giganteum var. yunnanense (Elwes) Stearn, A newly Recorded Vascular Plants of Liliaceae from Chongqing. Till. Cult. 2023, 43, 112–113. [Google Scholar]
  3. Cardiocrinum giganteum var. yunnanense. Informatin System of Chinese Rare and Endangered Plant. Available online: https://www.iplant.cn/rep/prot/Cardiocrinum%20giganteum%20var.%20yunnanense (accessed on 13 May 2024).
  4. National Forestry and Grassland Administration; Ministry of Agriculture and Rural Affairs of the People’s Republic of China. List of National Key Protected Wild Plants; National Forestry and Grassland Administration: Beijing, China, 2021. [Google Scholar]
  5. Wang, Z.Y.; Li, R.C.; Huang, X.L.; Liao, J.M.; Zhao, Z.H. Distribution Characteristics and Network Pharmacological Analysis of Regalosides in Cardiocrinum giganteum Bulbs. Guangxi For. Sci. 2024, 53, 554–566. [Google Scholar]
  6. Shou, J.W.; Zhang, R.R.; Wu, H.Y.; Xia, X.; Nie, H.; Jiang, R.W.; Shaw, P.C. Isolation of novel biflavonoids from Cardiocrinum giganteum seeds and characterization of their antitussive activities. J. Ethnopharmacol. 2018, 222, 171–176. [Google Scholar] [CrossRef]
  7. Guan, W.L. Mountain wildflowers-Cardiocrinum giganteum var. yunnanense. Landsc. Archit. Acad. J. 2007, 2, 37. [Google Scholar]
  8. Williams, J.W.; Jackson, S.T.; Kutzbach, J.E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl. Acad. Sci. USA 2007, 104, 5738–5742. [Google Scholar] [CrossRef]
  9. Nguyen, T.T.; Gliottone, I.; Pham, M.P. Current and future predicting habitat suitability map of Cunninghamia konishii Hayata using MaxEnt model under climate change in Northern Vietnam. Eur. J. Ecol. 2021, 7, 1–17. [Google Scholar] [CrossRef]
  10. Graham, E.M.; Reside, A.E.; Atkinson, I.; Baird, D.; Hodgson, L.; James, C.S.; Vanderwal, J.J. Climate change and biodiversity in Australia: A systematic modelling approach to nationwide species distributions. Australas. J. Environ. Manag. 2019, 26, 112–123. [Google Scholar] [CrossRef]
  11. Wang, G.H.; Xie, C.P.; Wei, L.J.; Gao, Z.Q.; Yang, H.L.; Jim, C.Y. Predicting Suitable Habitats for China’s Endangered Plant Handeliodendron bodinieri (H. Lév.) Rehder. Mol. Divers. 2023, 15, 1033. [Google Scholar] [CrossRef]
  12. Wei, L.; Wang, G.; Xie, C.; Gao, Z.; Huang, Q.; Jim, C.Y. Predicting suitable habitat for the endangered tree Ormosia microphylla in China. Sci. Rep. 2024, 14, 10330. [Google Scholar] [CrossRef]
  13. Root, T.L.; Price, J.T.; Hall, K.R.; Schneider, S.H.; Rosennzweig, C.; Pounds, J.A. Fingerprints of global warming on wild animals and plants. Nature 2003, 421, 57–60. [Google Scholar] [CrossRef] [PubMed]
  14. Wang, E.; Lu, Z.; Rohani, E.R.; Ou, J.; Tong, X.; Han, R. Current and future distribution of Forsythia suspensa in China under climate change adopting the MaxEnt model. Front. Plant Sci. 2024, 15, 1394799. [Google Scholar] [CrossRef] [PubMed]
  15. Li, J.; Chang, H.; Liu, T.; Zhang, C. The potential geographical distribution of Haloxylon across Central Asia under climate change in the 21st century. Conf. Agric. For. Meteorol. 2019, 275, 243–254. [Google Scholar] [CrossRef]
  16. Zhu, G.; Liu, G.; Bu, W.; Gao, Y. Ecological niche modeling and its applications in biodiversity conservation. Biodivers. Sci. 2013, 21, 90–98. [Google Scholar]
  17. Rahmanian, S.; Nasiri, V.; Amindin, A.; Karami, S.; Maleki, S.; Pouyan, S.; Borz, S.A. Prediction of plant diversity using multi-seasonal remotely sensed and geodiversity data in a mountainous area. Remote Sens. 2023, 15, 387. [Google Scholar] [CrossRef]
  18. Busby, J.R. BIOCLIM: A bioclimate analysis and prediction system. Plant Prot. Q. 1991, 6, 8–9. [Google Scholar]
  19. Hirzel, A.H.; Hausser, J.; Chessel, D.; Perrin, N. Ecological-niche factor analysis: How to compure habitat-suitability maps without absence sata? Ecology 2002, 83, 2027–2036. [Google Scholar] [CrossRef]
  20. Yee, T.W.; Mackenzie, M. Vector generalized additive models in plant ecology. Ecol. Modell. 2002, 157, 141–156. [Google Scholar] [CrossRef]
  21. Lehmann, A.; Overton, J.M.; Leathwick, J.R. GRASP: Generalized regression analysis and spatial prediction. Ecol. Modell. 2002, 160, 165–183. [Google Scholar] [CrossRef]
  22. Harte, J.; Newman, E.A. Maximum information entropy: A foundation for ecological theory. Trends Ecol. Evol. 2014, 29, 384–389. [Google Scholar] [CrossRef]
  23. Vayssières, M.P.; Plant, R.E.; Allen-Diaz, B.H. Classification trees: An alternative non-parametric approach for predicting species distributions. J. Veg. Sci. 2000, 11, 679–694. [Google Scholar] [CrossRef]
  24. Zhang, K.; Yao, L.; Meng, J.; Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 2018, 634, 1326–1334. [Google Scholar] [CrossRef] [PubMed]
  25. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  26. Amindin, A.; Pourghasemi, H.R.; Safaeian, R.; Rahmanian, S.; Tiefenbacher, J.P.; Naimi, B. Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change. Rangel. Ecol. Manag. 2024, 94, 149–162. [Google Scholar] [CrossRef]
  27. Yi, Y.J.; Cheng, X.; Yang, Z.F.; Wieprecht, S.; Zhang, S.H.; Wu, Y.J. Evaluating the ecological influence of hydraulic projects: A review of aquatic habitat suitability models. Renew. Sustain. Energy Rev. 2017, 68, 748–762. [Google Scholar] [CrossRef]
  28. Liu, D.; Xie, C.; Jim, C.Y.; Liu, Y.; Hou, S. Predicting the Potential Distribution of the Alien Invasive Alligator Gar Atractosteus spatula in China. Sustainability. 2023, 15, 6419. [Google Scholar] [CrossRef]
  29. Gastón, A.; García-Vinas, J.I. Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models. Ecol. Modell. 2011, 222, 2037–2041. [Google Scholar] [CrossRef]
  30. Elith, J.; Graham, C.H.; Anderson, R.P.; Dudik, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  31. Wang, Y.J.; Xie, L.Y.; Zhou, X.Y.; Chen, R.F.; Zhao, G.H.; Zhang, F.G. Prediction of the potentially suitable areas of Leonurus japonicus in China based on future climate change using the optimized MaxEnt model. Ecol. Evol. 2023, 13, e10597. [Google Scholar] [CrossRef]
  32. Zhao, Y.; Zhao, M.; Zhang, L.; Wang, C.; Xu, Y. Predicting Possible Distribution of Tea (Camellia sinensis L.) under Climate Change Scenarios Using MaxEnt Model in China. Agriculture 2021, 11, 1122. [Google Scholar] [CrossRef]
  33. Warren, D.L.; Glor, R.E.; Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 2010, 33, 607–611. [Google Scholar] [CrossRef]
  34. Schoener, T.W. The Anolis Lizards of Bimini: Resource Partitioning in a Complex Fauna. Ecology 1968, 33, 607–611. [Google Scholar] [CrossRef]
  35. Warren, D.L.; Glor, R.E.; Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 2008, 11, 2868–2883. [Google Scholar] [CrossRef]
  36. Li, L.; Zhang, J.; Lu, Z.Q.; Zhao, J.L.; Li, Q.J. Genomic data reveal two distinct species from the widespread alpine ginger Roscoea tibetica Batalin (Zingiberaceae). Acta Phytotaxon. Sin. 2020, 59, 1232–1243. [Google Scholar] [CrossRef]
  37. Zhao, J.L.; Gugger, P.F.; Xia, Y.M.; Li, Q.J. Ecological divergence of two closely related Roscoea species associated with late Quaternary climate change. J. Biogeog. 2016, 43, 1990–2001. [Google Scholar] [CrossRef]
  38. Dong, R.; Hua, L.M.; Hua, R.; Ye, G.H.; Bao, D.; Cai, X.C.; Cai, B.; Zhao, X.C.; Chu, B.; Tang, Z.S. Prediction of the potentially suitable areas of Ligularia virgaurea and Ligularia sagitta on the Qinghai–Tibet Plateau based on future climate change using the MaxEnt model. Front. Plant Sci. 2023, 14, 1193690. [Google Scholar] [CrossRef]
  39. Sander, J.; Wardell-Johnson, G. Impacts of soil fertility on species and phylogenetic turnover in the high-rainfall zone of the southwest Australian global biodiversity hotspot. Plant Soil 2011, 345, 103–124. [Google Scholar] [CrossRef]
  40. Li, J.; Yin, Z.; Cheng, F.; Gadow, K.V.; Hao, M.; Fan, C.; Zhao, X.; Zhang, C. Effects of stand structure, individual dominant species and environment on herb diversity in a temperate forest region. Ecol. Indic. 2025, 172, 113262. [Google Scholar] [CrossRef]
  41. Murphy, S.J.; Salpeter, K.; Comita, L.S. Highe beta-diversity observed for herbs over woody plants is driven by stronger habitat filtering in a tropical understory. Ecology 2016, 97, 2074–2084. [Google Scholar] [CrossRef]
  42. Beck, J.J.; Givnish, T.J. Fine: Cale environmental heterogeneity and spatial niche partitioning among spring-flowering forest herbs. Am. J. Bot. 2021, 108, 63–73. [Google Scholar] [CrossRef]
  43. Ni, M.; Vellend, M. Soil properties constrain forest understory plant distributions along an elevation gradient. Philos. Trans. R. Soc. B 2024, 379, 20230373. [Google Scholar] [CrossRef] [PubMed]
  44. Zhang, L.; Dong, Z.Y.; Zhang, H.; Chi, M.; Deng, J.Y.; Wei, Y. Study on Introduction and Culture of Cardiocrinum in Beijing. In Proceedings of the 2020 China Botanical Garden Academic Annual Conference, Shenyang, China, 10–13 October 2020; pp. 65–70. [Google Scholar]
  45. Li, Y.F.; Song, J.; Guan, W.L.; Li, F.R. Seed dormancy and germination in Cardiocrinum giganteum var. yunnanense, a perennial herb in China with post-dispersal embryo growth. Seed Sci. Technol. 2020, 48, 303–314. [Google Scholar]
  46. Guan, W.L.; Li, S.F.; Chen, X.; Li, Y.F.; He, F.M. Dormancy Characteristics and Dormancy Break of Cardiocrinum giganteum Seed. Acta Bot. Boreali-Occident. Sin. 2010, 30, 2479–2483. [Google Scholar]
  47. Zhu, P.Z.; Zhang, G.H.; Wang, H.X.; Zhang, B.J.; Liu, Y.N. Soil moisture variations in response to precipitation properties and plant communities on steep gully slope on the Loess Plateau. Agric. Manage. Water Qual. 2021, 256, 107086. [Google Scholar] [CrossRef]
  48. Li, M.; Ling, K.H.; Lam, H.; Shaw, P.C.; Cheng, L.; Techen, N.; Khan, I.A.; Chang, Y.S.; But, P.P.H. Cardiocrinum seeds as a replacement for Aristolochia fruits in treating cough. J. Ethnopharmacol. 2010, 130, 429–432. [Google Scholar] [CrossRef]
  49. Wang, B.; Qian, X.M.; Zhou, J.; Li, L.; Wang, J.L.; Feng, S.L. Investigation and Application of Wild Plant Resources of Cardiocrinum in Northern Foot of Qinling Mountains. Shanxi Agric. Sci. 2023, 69, 34–38. [Google Scholar]
  50. Wan, Z.Z.; Long, C.L.; Chen, Z.Y.; Yang, D.; Jin, Z.H. Advances in Study of Cardiocrinum. J. Yunnan Agric. Univ. Nat. Sci. 2007, 22, 30–34. [Google Scholar]
  51. Ma, B.; Sun, J. Predicting the distribution of Stipa purpurea across the Tibetan Plateau via the MaxEnt model. BMC Ecol. 2018, 18, 10. [Google Scholar] [CrossRef]
  52. Habibullah, M.S.; Din, B.H.; Tan, S.H.; Zahid, H. Impact of climate change on biodiversity loss: Global evidence. Environ. Sci. Pollut. Res. 2021, 29, 1073–1086. [Google Scholar] [CrossRef]
  53. Liang, Q.; Xu, X.; Mao, K.; Wang, M.; Wang, K.; Xi, Z.; Liu, J. Shifts in plant distributions in response to climate warming in a biodiversity hotspot, the Hengduan Mountains. J. Biogeogr. 2018, 45, 1334–1344. [Google Scholar] [CrossRef]
  54. Su, Q.T.; Du, Z.X.; Xue, Y.X.; Li, H.; Zhang, Y.X.; Zhang, S.J.; Huang, X.Y.; Zhou, B.; Qian, H.; Xiao, Y.A.; et al. Habitat Suitability Modeling of Endemic Genus Chimonanthus in China under Climate Change. Forests 2024, 15, 1625. [Google Scholar] [CrossRef]
  55. Thomas, Z.A.; Mooney, S.; Cadd, H.; Baker, A.; Turney, C.; Schneider, L.; Hogg, A.; Haberle, S.; Green, K.; Weyrich, L.S. Late Holocene climate anomaly concurrent with fire activity and ecosystem shifts in the eastern Australian Highlands. Sci. Total Environ. 2022, 802, 149542. [Google Scholar] [CrossRef]
  56. Su, Q.T.; Du, Z.X.; Luo, Y.; Zhou, B.; Xiao, Y.A.; Zhou, Z.R. MaxEnt Modeling for Predicting the Potential Geographical Distribution of Hydrocera triflora since the Last Interglacial and under Future Climate Scenarios. Biology 2024, 13, 745. [Google Scholar] [CrossRef]
  57. Yang, S.; Ding, Z.; Li, Y.; Wang, X.; Jiang, W.; Huang, X. Warming-induced northwestward migration of the East Asian monsoon rain belt from the Last Glacial Maximum to the mid-Holocene. Proc. Natl. Acad. Sci. USA 2015, 112, 13178–13183. [Google Scholar] [CrossRef]
  58. Jiao, K.W.; Gao, J.B.; Wu, S.H.; Hou, W.J. Research progress on the response processes of vegetation activity to climate change. Acta Ecol. Sin. 2018, 38, 2229–2238. [Google Scholar]
  59. Gu, C.J.; Tu, Y.L.; Liu, L.S.; Bo, W.; Zhang, Y.L.; Yu, H.B.; Wang, X.L.; Zhuoga, Y.J.; Zhang, B.H.; Cui, B.H. Predicting the potential global distribution of Ageratina adenophora under current and future climate change scenarios. Ecol. Evol. 2021, 11, 12092–12113. [Google Scholar]
  60. Lu, R.S.; Chen, Y.; Tamaki, I.; Sakaguchi, S.; Ding, Y.Q.; Takahashi, D.; Li, P.; Isaji, Y.; Chen, J.; Qiu, Y.X. Pre-quaternary diversification and glacial demographic expansions of Cardiocrinum (Liliaceae) in temperate forest biomes of Sino-Japanese Floristic Region. Mol. Phylogenet. Evol. 2020, 143, 106693. [Google Scholar] [CrossRef]
  61. Yang, L.Q.; Hu, H.Y.; Xie, C.; Lai, C.X.; Yang, M.; He, X.J.; Zhou, S.D. Molecular phylogeny, biogeography and ecological niche modelling of Cardiocrinum (Liliaceae): Insights into the evolutionary history of endemic genera distributed across the Sino-Japanese floristic region. Ann. Bot. 2017, 119, 59–72. [Google Scholar] [CrossRef]
  62. Zhang, H.X.; Wang, Q.; Jia, S.W. Genomic Phylogeography of Gymnocarpos przewalskii (Caryophyllaceae): Insights into Habitat Fragmentation in Arid Northwestern China. Mol. Divers. 2020, 12, 335. [Google Scholar] [CrossRef]
  63. Carretero, M.A.; Sillero, N. Evaluating how species niche modelling is affected by partial distributions with an empirical case. Acta Oecologica 2016, 77, 207–216. [Google Scholar] [CrossRef]
Biology 14 00581 g001
Figure 1. Distribution map of Cardiocrinum in China.
Figure 1. Distribution map of Cardiocrinum in China.
Biology 14 00581 g001
Biology 14 00581 g002
Figure 2. The percentage contributions of environmental factors for Cardiocrinum.
Figure 2. The percentage contributions of environmental factors for Cardiocrinum.
Biology 14 00581 g002
Biology 14 00581 g003
Figure 3. The distribution of Cardiocrinum under the current climate.
Figure 3. The distribution of Cardiocrinum under the current climate.
Biology 14 00581 g003
Biology 14 00581 g004
Figure 4. The change in the suitable habitat area of Cardiocrinum under future climatic conditions.
Figure 4. The change in the suitable habitat area of Cardiocrinum under future climatic conditions.
Biology 14 00581 g004
Biology 14 00581 g005
Figure 5. Trajectory changes in the centroid in the future for Cardiocrinum.
Figure 5. Trajectory changes in the centroid in the future for Cardiocrinum.
Biology 14 00581 g005
Biology 14 00581 g006
Figure 6. Niche identity test for Cardiocrinum. Note: The vertical dotted lines show the empirical values of Schoener D and Warren I, and the histograms represent the frequency of the expected Schoener D and Warren I.
Figure 6. Niche identity test for Cardiocrinum. Note: The vertical dotted lines show the empirical values of Schoener D and Warren I, and the histograms represent the frequency of the expected Schoener D and Warren I.
Biology 14 00581 g006
Table 1. The 23 environmental variables used for model prediction.
Table 1. The 23 environmental variables used for model prediction.
VariablesAbbreviations
Annual mean temperaturebio1
Mean diurnal rangebio2
Isothermalitybio3
Temperature seasonalitybio4
Max. temperature of warmest monthbio5
Min. temperature of coldest monthbio6
Temperature annual rangebio7
Mean temperature of wettest quarterbio8
Mean temperature of driest quarterbio9
Mean temperature of warmest quarterbio10
Mean temperature of coldest quarterbio11
Annual precipitationbio12
Precipitation of wettest monthbio13
Precipitation of driest monthbio14
Precipitation seasonalitybio15
Precipitation of wettest quarterbio16
Precipitation of driest quarterbio17
Precipitation of warmest quarterbio18
Precipitation of coldest quarterbio19
Elevationelev
Human activityha
Slopeslo
Aspectasp
Table 2. AUC and TSS value for Cardiocrinum.
Table 2. AUC and TSS value for Cardiocrinum.
SpeciesAUC TrainingAUC TestTSS
C. cathayanum0.9890.9830.913
C. giganteum0.9820.9800.936
C. giganteum var. yunnanense0.9950.9960.989
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

Zhang, Y.; Zhang, S.; Xiao, H.; Li, H.; Liao, D.; Xue, Y.; Huang, X.; Su, Q.; Xiao, Y. Changes in the Distribution Range of the Genus Cardiocrinum in China Under Climate Change and Human Activities. Biology 2025, 14, 581. https://doi.org/10.3390/biology14050581

AMA Style

Zhang Y, Zhang S, Xiao H, Li H, Liao D, Xue Y, Huang X, Su Q, Xiao Y. Changes in the Distribution Range of the Genus Cardiocrinum in China Under Climate Change and Human Activities. Biology. 2025; 14(5):581. https://doi.org/10.3390/biology14050581

Chicago/Turabian Style

Zhang, Yuxin, Shujian Zhang, Haiyan Xiao, Heng Li, Da Liao, Yuxi Xue, Xinyi Huang, Qitao Su, and Yian Xiao. 2025. "Changes in the Distribution Range of the Genus Cardiocrinum in China Under Climate Change and Human Activities" Biology 14, no. 5: 581. https://doi.org/10.3390/biology14050581

APA Style

Zhang, Y., Zhang, S., Xiao, H., Li, H., Liao, D., Xue, Y., Huang, X., Su, Q., & Xiao, Y. (2025). Changes in the Distribution Range of the Genus Cardiocrinum in China Under Climate Change and Human Activities. Biology, 14(5), 581. https://doi.org/10.3390/biology14050581

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

Article Metrics

Back to TopTop