Biodiversity Data Journal :
Research Article
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Corresponding author: Bicai Guan (guanbicai12@163.com), Ding Wu (p_wightiana@126.com)
Academic editor: Anatoliy Khapugin
Received: 24 Jan 2022 | Accepted: 12 Mar 2022 | Published: 16 Mar 2022
© 2022 Xiaosong Dai, Wei Wu, Ling Ji, Shuang Tian, Bo Yang, Bicai Guan, Ding Wu
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Dai X, Wu W, Ji L, Tian S, Yang B, Guan B, Wu D (2022) MaxEnt model-based prediction of potential distributions of Parnassia wightiana (Celastraceae) in China. Biodiversity Data Journal 10: e81073. https://doi.org/10.3897/BDJ.10.e81073
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The maximum entropy (MaxEnt) model for predicting the potential suitable habitat of species has been commonly employed in many ecological and biological applications by using presence-only occurrence records along with associated environmental factors. Parnassia wightiana, a perennial herb, is a cold-adapted plant distributed across three diversity hotspots in China, including the Hengduan Range, Central China and the Lingnan region. The MaxEnt model was used to simulate the historic, current and future distribution trends of P. wightiana, as well as to analyse its distribution pattern in each historical period and explore the causes of species distribution changes. The results of our analysis indicated that annual precipitation, annual temperature range and mean temperature of the warmest quarter were the key bioclimatic variables affecting the distribution of P. wightiana. Most temperate species retracted into smaller refugial areas during glacial periods and experienced range expansion during interglacial periods. Possible refugia of the species were inferred to be located in the Hengduan Range and Qinling Regions.
Parnassia wightiana, bioclimatic variables, MaxEnt, potential suitable region
Climatic oscillations during the Cenozoic period, especially the Quaternary glacial/interglacial cycle, had a great impact on the geographical distribution patterns and genetic structure of species (
Parnassia wightiana Wall. ex Wight & Arn. is an ancient plant species distributed over the China-Himalayan Region (
Species distribution models are an approach that identifies and describes potential suitable habitat for species (
At present, the research on P. wightiana only focuses on the development of taxonomy and medicinal value and there is no report on the change of distribution pattern in different periods under the background of climate change. The field survey found that the wild population size of P. wightiana is becoming smaller and smaller and, due to climate change and human disturbance, the wild living environment has been very bad. There is an urgent need to pay attention to the protection of wild P. wightiana population and study its distribution pattern with climate change. In this study, the cold-adapted species P. wightiana served as the focal species. A MaxEnt model was then used to reconstruct the potential species distribution in five periods, namely the Last Interglacial (LIG), Last Glacial Maximum (LGM), Mid-Holocene (MH), Current (1950-2000s) and Future (2070s) and the simulation results were calculated and visualised by ArcGIS 10.4 (ESRI, Redlands, CA, United States; www.esri.com) (
Distribution data of P. wightiana were sourced from field surveys and the Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn). Based on the National Natural Science Foundation of China (No.4156010383), 45 natural populations of P. wightiana were obtained by extensive field investigation. Additionally, complete distribution information from CVH were first confirmed and then recorded. If there was a detailed collection site, the longitude and latitude were located using Google Earth with reference to the recorded habitat, altitude, along with other information. Voucher photos were checked for records sourced from CVH and NSII (National Specimen Information Infrastructure) to confirm that the species were correctly identified. In total, 48 collection entries from herbarium research were obtained after removing repetitive entries and identification errors.
To prevent model over-fitting caused by data repetition and spatial autocorrelation, as well as to associate model error to the results, the buffer method was used to screen the obtained data. The spatial resolution of environmental variables was 2.5 arc-minutes and spatially coincident data points within 3 km of each other were also eliminated (
The bioclimatic variables (
High correlation and collinearity between bioclimatic variables can easily lead to the over-fitting of the model, thus affecting the accuracy of the resulting predictions, so SPSS 26 was utilised to perform principal component analysis on all bioclimatic variables (
Code |
Environmental variables |
Units |
Bio1 |
Annual Mean Temperature |
℃ |
Bio2 |
Mean Diurnal Range |
℃ |
Bio3 |
Isothermally (BIO2/BIO7) (* 100) |
% |
Bio4 |
Temperature Seasonality (standard deviation *100) |
% |
Bio5 |
Maximum Temperature of Warmest Month |
℃ |
Bio6 |
Minimum Temperature of Coldest Month |
℃ |
Bio7 |
Temperature Annual Range (Bio5-Bio6) |
℃ |
Bio8 |
Mean Temperature of Wettest Quarter |
℃ |
Bio9 |
Mean Temperature of Driest Quarter |
℃ |
Bio10 |
Mean Temperature of Warmest Quarter |
℃ |
Bio11 |
Mean Temperature of Coldest Quarter |
℃ |
Bio12 |
Annual Precipitation |
mm |
Bio13 |
Precipitation of Wettest Period |
mm |
Bio14 |
Precipitation of Driest Period |
mm |
Bio15 |
Precipitation Seasonality (coefficient of variation) |
% |
Bio16 |
Precipitation of Wettest Quarter |
mm |
Bio17 |
Precipitation of Driest Quarter |
mm |
Bio18 |
Precipitation of Warmest Quarter |
mm |
Bio19 |
Precipitation of Coldest Quarter |
mm |
Note: * Bold text indicates the bioclimatic variables used for model construction after screening.
The sorted distribution point data and the screened bioclimatic variable data were imported into MaxEnt 3.4.1 and the bioclimatic variables were evaluated by the Jackknife test. The models obtained were calibrated using 75% of the available records for each species as training (calibration) data and the remaining 25% were used for model validation as test data. The Bootstrap method was implemented with 10 repeats, a maximum of 5000 iterations and default selected parameters (
Model performance was evaluated by calculating the Area Under the Receiver Operator Curve (AUC), where models with AUC values larger than 0.7 were considered satisfactory for our study (
A MaxEnt model was used to predict the distribution of P. wightiana in different periods, which were then visualised using ArcGIS. The prediction of the MaxEnt model, based on the current climate data, is very consistent with the actual distribution area and the AUC values of each period are greater than 0.9, indicating that the model has a good predicting ability (Fig.
To distinguish unsuitable habitat from suitable habitat, a reclassification of the probability maps was performed using a threshold, which establishes the minimum level below which a given distribution should be excluded. The threshold of automatic generation, based on the model, is 0.277, that is, the range of 0-0.277 represents the non-suitable area of P. wightiana. Suitable areas are divided into the following grades: low suitable areas (0.277-0.518), medium suitable areas (0.518-0.759) and high suitable areas (0.759-1). Total counts of the grid numbers of each grade were used to calculate the area of each suitable region (Table
Period |
Prediction areas (×104 km2) |
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Low suitable area |
Medium suitable area |
High suitable area |
Total |
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LIG |
53.10 |
22.55 |
3.52 |
79.17 |
LGM |
53.47 |
24.43 |
4.18 |
82.08 |
MH |
52.21 |
21.71 |
3.45 |
77.37 |
Current |
55.56 |
25.96 |
3.52 |
85.04 |
Future |
54.38 |
24.99 |
3.52 |
82.89 |
The most influential environmental variables (Table
Major climatic factors |
Contribution rate (%) |
||||
LIG |
LGM |
MH |
Current |
Future |
|
Bio12 |
37.2 |
39.8 |
36.3 |
37.9 |
39.2 |
Bio7 |
28 |
20.9 |
29.7 |
29.8 |
28.6 |
Bio10 |
19 |
17.8 |
19.2 |
17.2 |
18.8 |
Bio4 |
8.1 |
12.2 |
5.6 |
8.2 |
6.6 |
Bio2 |
6.4 |
7.2 |
7.6 |
5.3 |
5.5 |
Bio3 |
1.2 |
2.2 |
1.5 |
1.5 |
1.4 |
An estimate of the current distribution of P. wightiana was developed using the same six bioclimatic variables for current climate (1950-2000s). The total predicted suitability area was 85.04×104 km2, of which the high suitability area was 3.52×104 km2, the medium suitability area was 25.96×104 km2 and the low suitability area was 55.56×104 km2 (Table
The entire geographical range of P. wightiana follows very closely to the area of China's mountains. Sichuan Basin is characterised as a non-suitable area, which is consistent with the modern habitat distribution of P. wightiana. Compared to the actual distribution, it is found that the range predicted by the model is in general agreement with the actual distribution area. Although there are minor deviations, the core distribution area is consistent with the current distribution.
Our SDM analysis provides a detailed picture of the last glacial cycle (Fig.
The potential suitable habitat for P. wightiana increased slightly during the Last Glacial Maximum, with a total area of 82.08×104 km2, while the high climatic suitability area increased from 3.52×104 km2 to 4.18×104 km2 (Table
During the Holocene, as temperatures generally increased, the distribution range for P. wightiana shrank and suitable areas at all levels decreased. The high suitability area was 3.45×104 km2 and the total area decreased by 4.71×104 km2 (Fig.
The results from these analyses predicted that the future distribution range of P. wightiana will shrink and that the total area may be reduced to 82.89×104 km2. The medium and low suitability areas are predicted to be reduced, but the high suitability areas will remain stable. Numerous suitability areas that were previously identified in Sichuan Basin are predicted to no longer be suitable habitat for the survival of P. wightiana. Under continued global warming into the future, we observe a migration to higher elevations and the habitat becoming fragmented in Wumeng Mountain of Yunnan-Guizhou Plateau. The medium suitable areas in central Guizhou are predicted to shift to low suitable areas.
The predicted suitable habitats of P. wightiana under current conditions were generally in agreement with the actual observed species distribution (Fig.
In the present study, we found that the MaxEnt model was able to provide robust results with small, sparse and irregularly sampled data and our results are visualised using ArcGIS. The operation was simple and intuitive and the sample requirement was small and other previous studies have also highlighted the great performance of similar methods. For example,
In this study, the environmental variables used in the MaxEnt model are all climate factors. Nineteen climatic variables, based on temperature and rainfall, were chosen according to the different needs of computing performance. However, there are many factors in addition to climate that affect the distribution of species, such as interspecific interactions, microtopography and local microclimates. For example, the uplift of the Qinghai-Tibet Plateau, along with the environmental destruction caused by human activities may together lead to species extinction (
The predicted distribution of P. wightiana in each period showed that the suitable areas were concentrated in the mountainous areas of south-western China, mainly in the Himalayan, Hengduan and Qinling Mountains. According to the existing collection along with specimen records, Daming Mountain in Wuming District in Guangxi represents the southern distributional limit, the Weiyuan Weihe River in Gansu represents the northern, Huanggang Mountain in Jiangxi the eastward and Jipu Village in Jilong Town in Tibet constitutes the westward limit. Additionally, Nepal, Bhutan, northern Myanmar, Thailand, Sikkim and northeast India are also distribution areas for P. wightiana (
Based on the results of the MaxEnt model, the key factors affecting species geographical distribution are not completely consistent. For example, a previous study on Stipa purpurea in the Tibetan Plateau found that precipitation was a key factor affecting its distribution (
Understanding species distributional patterns is a fundamental question in biogeography and conservation biology. The middle and high suitable areas of P. wightiana were mainly located in the Hengduan Mountains, Yunnan-Guizhou Plateau, Qinling Mountains and Daba Mountain at high altitudes. During the Last Glacial Maximum, as temperatures gradually began to decline, some inland areas of Sichuan Basin began to transform into suitable areas. The Weihe River source in Gansu Province was a residual area of the Qinling Mountains and also began to transform into a medium suitable area. For the cold-adapted P. wightiana, the decline in temperature makes the environment more suitable for survival, most likely during the glacial expansion and postglacial contraction.
The study of potential plant refuges during the Quaternary glaciation is of great significance for understanding current plant distribution patterns along with future evolution. A MaxEnt model was employed to simulate the distribution of highly suitable areas over different time periods, allowing for the inference of potential refugia (
In this study, the distribution range of P. wightiana in the future was predicted. The results showed that the suitable area of P. wightiana would continue to shrink in the next few decades. Although the highly suitable area remained stable, the prospect of population development was not optimistic. In view of the small size of wild populations and serious human disturbance, we put forward some suggestions: (1) in situ conservation of the known P. wightiana community, minimise the adverse effects of human activities on species survival; (2) at present, the research on biological characteristics, artificial cultivation methods and genetic shape improvement of P. wightiana is relatively weak and systematic research in this area should be strengthened; (3) artificial harvesting and planting can be carried out around some populations with less human activity to maximise the size of their wild populations.
The simulation results show that the current suitable area is basically consistent with the actual distribution area and the AUC values in each period are greater than 0.9, indicating that the results are accurate. In the change of distribution pattern, the P. wightiana accorded with the pattern of glacial expansion and interglacial contraction, but the overall change of area and scope was not obvious. The main environmental factors affecting the distribution of P. wightiana are annual precipitation (Bio12), annual temperature range (Bio7) and average temperature in the warmest quarter (Bio10). P. wightiana may have multiple refuges, distributed in Hengduan Mountains, Qinling Mountains and other high mountains. The future climate is not ideal for the survival of P. wightiana and local protection and artificial breeding are important ways to protect wild populations of P. wightiana.
We are grateful to Yifan Ma, Fangmin Hu, Xiaodong Tang and Dr. Ranran Cheng for their fieldwork and suggestions regarding the paper’s arrangement of ideas and materials. We would like to thank Dr. Shannon Elliot at Michigan State University for his assistance with English language and grammatical editing. This work was supported by the National Natural Science Foundation of China (41561014, 32060309) and the Science and Technology Projects of Jiangxi Provincial Department of Education (GJJ191163, GJJ191177).
National Natural Science Foundation of China (32160314, 41561014)
The Science and Technology Projects of Jiangxi Provincial Department of Education (GJJ191163, GJJ191177)
Ding Wu and Bicai Guan designed and conceived the project. Xiaosong Dai and Wei Wu participated in the analysis and processing of data and the writing of manuscripts. Ling Ji, Shuang Tian and Bo Yang participated in the production of the chart and the revision of the manuscript. All co-authors commented the manuscript. All co-authors read and approved the final manuscript.
The authors declare that they have no competing interests.