Predicting The Potential Distribution of Campsis Grandiflora In China Under Climate Change

doi:10.21203/rs.3.rs-689719/v1

As the research of geographical distribution of species shows significant influence on people’s understanding of specie protection and utilization, it is important to study the influence of climate change onto the geographical distribution pattern of plants. Based on 166 distribution records as well as 11 climate and terrain variables with low correlation in China, we used MaxEnt (Maximum Entropy) model and ArcGIS software to predict the potential distribution of Campsis grandiflora under climate change and then determine the dominant climate variables which affect the geographical distribution significantly by analysis. The results show that the area under the curve (AUC) of the train is 0.939, which implies our prediction is accurate. Under the current climate condition, the area of potentially suitable habitat is 238.29×10⁴km², mainly distributed in northern China, central China, southern China, and eastern China. The dominant variables affected the geographical distribution of Campsis grandiflora are mean diurnal range, range of annual temperature variation, mean temperature, mean temperature of the coldest season, the driest monthly precipitation, precipitation of the warmest quarter, as well as altitude. In the future climate change scenario, the total area of suitable habitat and highly suitable habitat will increase, whilst the area of moderately suitable habitat and poorly suitable habitat will decrease. In the meantime, the centroid of the potentially suitable area of Campsis grandiflora will migrate to higher latitude areas.

Climatology

MaxEnt model

ArcGIS

Campsis grandiflora

climate variables

the area of potentially suitable habitat

Climate is one of the most predominant factors that greatly determine the distribution of species (Qin et al. 2017). According to the Intergovernmental Panel on Climate Change (IPCC), with the global warming in the past 100 years, the earth's surface temperature has risen by 0. 85 ℃ and it will continue to present an upward trend in the near future (IPCC et al. 2013, Li et al. 2019). Climate change influence the geographical distribution and richness of species, as well as population change and stability to a certain degree (Fitzpatrick et al. 2008, Lenoir et al. 2008). Hence, understanding the change of geographical distribution of species in the future climate environment will significantly contribute to the protection, research, and utilization of species (Yang et al. 2013).

Nowadays the research based on Niche models is a rather popular field. The principle is to predict the actual distribution and potential distribution of species through the Niche model, according to the actual distribution locations of species and related environmental data (Yi et al. 2017). Recently,this model has been widely used in the protection of endangered animals and plants, effects of species invasion and global change on species distribution, as well as diversity pattern, etc. (Franklin and Janet 2013; Zhang et al. 2019). The application of Niche model on predicting the potential distribution of plants under the future climate environment can provide important references for the future distribution, migration and diffusion trend of corresponding species (Warren et al. 2014). The fact that the future distribution area of the species does not overlap with the current distribution area indicates that the species may be affected by global warming (Waltari et al. 2007). At present, the most commonly used Niche models include MaxEnt, CLIMEX, ENFA and GARP, Bioclim, and Domain-based on bioclimatic data (Cao et al. 2010). Compared to other models, MaxEnt is very popular due to its stable results, short running time and exact prediction ability (Ortega-Huerta and Peterson 2008; Phillips et al. 2006).

Campsis grandiflora belongs to Bignoniaceae, Campsis and it is a climbing deciduous vine (Kim et al. 2005). In the theory of traditional Chinese medicine, it belongs to the liver and pericardium meridians, it cools the blood, resolves addiction, dispels wind and is mainly used for irregular menstruation, postpartum breast swelling, rubella redness, skin phlegm itching, seat sore and so on (Xiang et al. 2012). In addition, as a popular flower in garden landscaping, it has gradually been accepted by people (Ueyama et al. 2010). Campsis grandiflora mainly distributes in Hebei, Shandong, Henan, Jiangsu, Zhejiang, Jiangxi, Fujian, Guangdong, Guangxi, Hubei, Hunan, Sichuan, Shaanxi provinces, the main producing areas for medicinal materials cultivation are in Jiangsu, Zhejiang, Jiangxi provinces, most of them grow in valleys, streams, roadsides, sparse forests and other easy observing places, meanwhile, many of them are widely cultivated in courtyards (Ueyama et al. 2010). Recently, however, due to its medicinal value, over-exploitation and blind cultivation has led to a significant decline of its quantity and quality, coming with the fact that the natural habitat of Campanula grandiflora has been seriously degraded with climate change. Nowadays, people mainly focus on the pharmacological activity and chemical composition of Campanula grandiflora, but the research on the potential suitable area of Campanula grandiflora in the future climate change is relatively lacking over the world (Jin et al. 2005). Therefore, it is particularly crucial to use the MaxEnt to predict the potential distribution and migration routes of Campsis grandiflora in China under climate change.

Comparing with the previous studies of predicting the suitable areas of species based on MaxEnt model, our study not only considered the relationship with carbon dioxide concentration and climate, but also explored the scenario of greenhouse gas emissions under social and economic changes and policy intervention, which was different from the past methods of representative concentration pathways (RCP) (Kriegler et al. 2014; Van Vuuren DP 2012). The impact of social economy and land use on the development of regional climate change is considered in this path (Zhang et al. 2018a). SSP1 is the route of sustainable development, which is a low-material, low-resource, and low-energy green development route. SSP2, the moderate development route, represents the future social and economic development model, will continue to develop along with the current mode. SSP5 assumes that the society and economy are fully developed but based on the fact that energy-intensive is the driving force of economics (Shim et al. 2020). Comparing with RCP, SSP has a higher starting point, its forecast scenario is also smoother and closer to the real value (Riahi 2017). In our study, the highest level greenhouse gas emission scenario and the lowest level greenhouse gas emission scenario routes were chosen to predict the potential distribution areas of Campanula grandiflora in 2050s and 2070s under the climate models of green development (SSP1) and conventional development (SSP5).

The major contributions of our study are as below: (1) a model was put forward to show the relationship between species distribution patterns and our control variables, climate and topographic; (2) the area of suitable habitat for Campanula grandiflora was predicted according to future global climate change scenarios(the 2050s and 2070s); (3) changes about habitat suitability distributions in three different scenarios(the 2000s, 2050s, and 2070s) in China were analyzed, and shifts of the highly suitable area core distributions of Campanula grandiflora were shown. Our study will provide some useful suggestions for authorities to formulate measures for Campanula grandiflora. Some new views about global ecology research will be given in our work, and the distribution patterns in our study provide a basis for a greater understanding of the future propagation, resource utilization, and protection of Campsis grandiflora in China.

2.1. Species occurrence data collection

The distribution records of Campsis grandiflor were obtained from online databases, including the Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn/), the Global Biodiversity Information Facility (GBIF, http://www.gbif.org) and the Specimen Resources Sharing Platform for Education (http://www.gbif.org). We used Google Earth 7.1 (Google Inc., Mountain View, CA, USA) to search the longitude and latitude based on the described geographic location when the available records lacked detailed Geo-coordinates. Records that were geocoding errors and duplicates were deleted. In the end, a total of 166 records of Campsis grandiflor in China were collected and a detailed distribution map was obtained (Fig. 1). As required by MaxEnt software, the distribution records of Campsis grandiflor were imported into Microsoft Excel and sorted into a ". CSV" format file.

2.2. Environmental variables

The critical variables influencing species distribution are environmental variables. Among them, 19 bioclimatic variables are the most typical and main variables to simulate the distribution of potential species. In addition, environmental varianles of altitude, slope, and aspect show important contributions on species distribution, hence we add these 3 factors into our chosen set as 22 variables. Topographic data are derived from the unified world soil database of FAO soil maps and databases V1.2 (http//www.fao.org/soils-portal/soil-survey/soil-maps-and-database/harmonized-world-soil-database-V12-en ). The current climate data (1971–2000) and future climate data (2050a, 2070a) are from the World Climate Database(http://www.worldclim.org/)(Yang et al. 2013). The spatial resolution is 2.5'. The study used 22 initial environmental variables, including 19 biological climate variables and 3 topographic variables (Table 1).

Table 1 Environment variables related to the distribution of Campsis grandiflor.

Variables	Description	Unit	Variables	Description	Unit
bio1	Annual mean temperature	℃	bio12	Annual precipitation	mm
bio2	Mean diurnal temperature range	℃	bio13	Precipitation of the wettest month	mm
bio3	Isothermality	-	bio14	Driest monthly precipitation	mm
bio4	Temperature seasonality	℃	bio15	Precipitation seasonality	-
bio5	Max temperature of warmest month	℃	bio16	Precipitation of wettest quarter	mm
bio6	Min temperature of the coldest month	℃	bio17	Precipitation of driest quarter	mm
bio7	Range of annual temperature variation	℃	bio18	Precipitation of the warmest quarter	mm
bio8	Mean temperature of wettest quarter	℃	bio19	Precipitation of coldest quarter	mm
bio9	Mean temperature of driest quarter	℃	Altitude	Altitude	m
bio10	Mean temperature of the warmest quarter	℃	Slope	Slope	°
bio11	Mean temperature of the coldest season	℃	Aspect	Aspect	%

For future climate scenarios, we used the bcc-csm1.1 climate patterns’ change data under four future scenarios(SSP1-2.6,SSP5-8.5 in both the 2050s and 2070s). They are representative pathways of AR5 distribution in the IPCC Assessment Report (Remya et al. 2015). In our study, SSP5-8.5 and SSP1-2.6 were used to simulate the highest and the lowest level greenhouse gas emission scenario (Riahi et al. 2011). Since not all of the environmental variables were conducive to our model prediction, the Pearson correlation coefficient (R) was used to test the multicollinearity and thus remove the relevant environmental variables (Worthington et al. 2016). When the correlation coefficient of the two environmental variables was not less than 0.8, which implies the contribution rate of the environmental variables was small and thus negligible. Finally, 11 environmental variables were used for calculation and analysis of MaxEnt model (Table 2).

Table 2 The contribution rate of environmental variables.

Environmental variables	Percent contribution (%)	Permutation importance (%)
bio14	46.1	0.6
bio18	14.6	9.7
bio10	9.2	14.6
bio11	8.1	14.7
bio7	7.5	22.1
aspect	3.4	4.8
altitude	3.4	22.5
bio2	2.9	2.7
slope	2.1	2.7
bio15	1.7	2.5
bio3	1	3

2.3. Current and future habitat evaluations

In our study, the MaxEnt 3.4.1k software was used to simulate the potential distribution areas of Campsis grandiflor in China under different climatic conditions. To improve the accuracy of our model analysis, 75% of the distribution points were randomly selected as the training data and the remaining 25% of the distribution points were selected as the test data, with 500 iterations and 10 repeated runs. Other parameters were set as the default. ArcMap tool in ArcGIS software was used to convert the output to grid format for further analysis.

In our study, the receiver operating characteristic (ROC) curve of the threshold independent judgment method was used to evaluate the expected results. The area under the curve (AUC) is between 0–1. The closer this value is to 1, the more accurate the prediction is. The prediction accuracy of the model was divided into three grades: very poor (AUC < 0.6), fair (0.7–0.9), and excellent (0.9-1.00). Subsequently, we used the current climate data to simulate the spatial range of suitable habitat of Campsis grandiflora in four future scenarios (SSP1-2.6,SSP5-8.5 in both the 2050s and 2070s). The suitable habitats were divided into four grades: unsuitable habitat (0-0.10), poorly suitable habitat (0.10–0.30), moderately suitable habitat (0.30–0.50) and highly suitable habitat (0.50-1.00). The spatial extents of regions in these four classes were calculated and illustrated.

3.1. The species distribution model and its accuracy

Our MaxEnt model for the Campsis grandiflora performs better than random prediction with the given set of training and test data. The area under the curve (AUC) is 0.939. The results indicate that our model shows a rather good performance (Fig. 2).

3.2. The main environmental variables affecting the distribution of Campsis grandiflora

Among the 11 environmental variables, the environmental variables with top three contribution rate of were precipitation of driest month (bio14, 46.1% contribution), precipitation of the warmest quarter (bio18, 14.6% contribution), and mean temperature of the warmest quarter (bio10, 9.2% contribution), accounted for almost 69.9% of the model prediction (Table 3). Considering the importance of permutation, altitude (22.5% contribution), temperature annual range (bio7, 22.1% contribution) and mean temperature of the coldest quarter (bio11, 14.7% contribution) were much higher than others. Meanwhile, the jackknife test showed that the mean temperature of the coldest quarter (bio11), mean diurnal range (bio2), and precipitation of driest month (bio14) were the main variables (Fig. 3).

To summarize, temperature (bio2, bio7, bio10, and bio11), precipitation (bio14, bio18) and altitude played a vital role in predicting the probable distribution of Campsis grandiflora. By the response curve (Fig. 4), we got thresholds (probability of presence > 0.5) for the main bioclimatic parameters. Mean diurnal temperature range ranged from 7.3 to 9.5 ℃, range of annual temperature ranged from 28 to 33 ℃, mean temperature of the warmest quarter ranged from 25.5 to 28 ℃, mean temperature of the coldest season ranged from 5 to 15 ℃, driest monthly precipitation ranged from 30 to 100 mm, precipitation of the warmest quarter ranged from 500 to 720 mm and altitude ranged from 400 to 1200 m.

3.3. Current potential distribution

Campsis grandiflora mainly distributes in northern China, central China, southern China, and eastern China (Fig. 5). Among them, the area of highly suitable habitat mainly distributes in the northeast and southeast parts of Jiangxi and Hunan, the northeast part of Sichuan, and the southeast and northeast part of Hubei, and so on. The area of moderately suitable habitat mainly distributes in the coastal and southern Shandong, western Hunan, eastern Sichuan, and Hubei. The area of poorly suitable habitat mainly distributes in central Yunnan, eastern Guizhou, southern Sichuan, western Guangxi, and northern, western, and eastern Henan.

According to the classification of suitable habitats, the areas of suitable habitat in each province were calculated (Table 3). The suitable distribution area of Campsis grandiflora in China is 238.29×10⁴ km². The area of highly suitable habitat is 50.05×10⁴ km², accounting for 21% of the suitable area. The area of moderately suitable habitat is 72.75×10⁴ km², accounting for 30.53% of the suitable area. The area of poorly suitable habitat is 115.49×10⁴ km², accounting for 48.47% of the suitable area. Jiangxi, Hunan, Sichuan, Guangxi, and Guangdong have relatively large areas of highly suitable habitat. Among them, the suitable distribution area of Campsis grandiflora in Jiangxi is 10.90× 10⁴ km², ranking first in China. Sichuan, Hubei, Hunan, and Shandong have larger moderately suitable habitat areas than other provinces. There is no suitable distribution area of Campsis grandiflora in Inner Mongolia, Jilin, Ningxia, Heilongjiang, Taiwan and Xinjiang.

Table 3 The potential distribution areas for Campsis grandiflora under current climatic conditions.

Province municipality autonomous regions	Poorly suitable habitat(10⁴km²)10-30%	Moderately suitable habitat(10⁴km²)30-50%	highly suitable habitat(10⁴km²)≥50%	Total suitable habitat(10⁴km²)
Beijing	0.92	0.17	0.00	1.09
Tianjin	0.57	0.19	0.03	0.79
Hebei	7.04	0.80	0.02	7.86
Shanxi	1.81	0.00	0.00	1.81
Inner Mongolia	0.00	0.00	0.00	0.00
Liaoning	2.06	0.17	0.01	2.24
Jilin	0.00	0.00	0.00	0.00
Heilongjiang	0.00	0.00	0.00	0.00
Shanghai	0.18	0.10	0.03	0.31
Jiangsu	2.79	4.20	1.06	8.05
Zhejiang	2.93	3.48	2.21	8.62
Anhui	3.63	6.32	2.39	12.34
Fujian	3.77	2.75	3.38	9.9
Jiangxi	1.39	2.83	10.90	15.12
Shandong	6.43	6.57	0.46	13.46
Henan	11.02	3.35	0.21	14.58
Hubei	5.39	6.95	4.37	16.71
Hunan	3.67	6.66	8.68	19.01
Guangdong	6.03	3.49	4.05	13.57
Guangxi	8.36	5.70	4.48	18.54
Hainan	1.09	0.20	0.00	1.29
Chongqing	2.41	2.89	1.97	7.27
Sichuan	9.70	7.56	4.99	22.25
Guizhou	10.11	3.92	0.26	14.29
Yunnan	13.70	2.59	0.47	16.76
Tibet	2.78	0.24	0.03	3.05
Shaanxi	5.71	1.47	0.07	7.25
Gansu	1.49	0.15	0.01	1.65
Ningxia	0.00	0.00	0.00	0.00
Xinjiang	0.00	0.00	0.00	0.00
Taiwan	0.51	0.00	0.00	0.00
Xianggang	0.01	0.00	0.00	0.01
Total(China)	115.49	72.75	50.05	238.29

3.4. Future potentially suitable climatic distribution

In the SSP1-2.6 and SSP5-8.5 climate change scenarios for the 2050s and the 2070s, predictions of the future potentially suitable distributions of Campsis grandiflora are illustrated (Fig. 6).

Our study shows that for the 2050s & SSP1-2.6 case, the moderately suitable habitat area of Campsis grandiflora will be 50.05×10⁴ km², the highly suitable habitat area of Campsis grandiflora will be 157.85×10⁴ km², and the total habitat area will be 275.72×10⁴ km². The total suitable habitat of Campsis grandiflora will mainly be located in the north and east part of Guangdong, northeastern Guangxi, northern and southern Yunnan, eastern Sichuan, northern Chongqing, northern Sichuan, southern and eastern Hubei, southern Shanxi, eastern and southern Henan, central and southern Anhui, western Jiangsu, the east part of Shanghai, west and north part of Zhejiang, west and south part of Fujian, central Shandong, south and east part of Hubei, Jiangxi and Hunan.

For the 2050s & SSP5-8.5 case, the moderately suitable habitat area will be 45.06×104 km2, the highly suitable habitat area will be 148.41×104 km², and the total suitable habitat area will be 261.62×10⁴ km². The total suitable habitat will mainly be distributed in northern Guangdong, northern and eastern Guangxi, central and eastern Hunan, eastern Sichuan, eastern Chongqing, eastern Hebei, southern Liaoning, northern and eastern Hunan, northern, southern and eastern of Hubei, southern, and eastern Henan, southern and eastern Anhui, northern and southern Jiangsu, Shandong, Zhejiang, Fujian and Jiangxi.

For the 2070s & SSP1-2.6 case, the moderately suitable habitat area will be 54.54×10⁴ km², the highly suitable habitat area will be 142.88×10⁴ km², and the total suitable habitat area will be 273.64×10⁴ km². The total suitable habitat is not much different from that in the 2050s under SSP1-2.6.

For the 2070s & SSP5-8.5, the moderately suitable habitat area will be 35.57×10⁴ km², the highly suitable habitat area will be 211.51×10⁴ km², and the total suitable habitat area will be 302.91×10⁴ km². The total suitable habitat will mainly be distributed in southern Shaanxi, central and eastern Henan, southern Hebei, southern Hubei, northern Guizhou, Sichuan and Chongqing, Shandong, Jiangsu, Anhui, Zhejiang, Jiangxi, Fujian, Guangdong, Guangxi and Hunan. The main distribution area will be larger than the first three climate scenarios, the total suitable area will be the largest, and the highly suitable habitat will be the largest. However, the moderately suitable habitat area will be the smallest.

Table 4 Suitable areas for Campsis grandiflora under different climatic conditions.

Decades Scenarios	Predicted area/10⁴km²
Decades Scenarios	poorly suitable habitat	moderately suitable habitat	highly suitable habitat	Total suitable habitat
current	115.49	72.75	50.05	238.29
2050s,SSP1-2.6	67.82	50.05	157.85	275.72
2050s,SSP5-8.5	68.15	45.06	148.41	261.62
2070s,SSP1-2.6	76.22	54.54	142.88	273.64
2070s,SSP5-8.5	73.83	35.57	211.51	302.91

Under SSP1-2.6, the lost area of the Campsis grandiflora is 0.18×10⁴ km² during the 2050s, the area obtained will be 83.66×10⁴ km²_, and unchanged area will be 124.33×10⁴ km². The increasing area will be mainly distributed in the southern and eastern Hebei, the northern and the southern Shandong, southern Liaoning, northern Anhui, eastern Jiangsu, southern Fujian, western Sichuan, southern Guangxi, southern Guangdong, and Henan.

Under SSP5-8.5, the lost area of the Campsis grandiflora will be 0.07×10⁴ km² during the 2050s, the increasing area will be 99.18 ×10⁴ km² and the unchanged area will be 124.33×10⁴ km². The distribution range of increasing area will be similar to that in SSP1-2.6 for the 2050s.

Under SSP1-2.6, the loss area of the Campsis grandiflora will be 0.12×10⁴ km² during the 2070s, the area obtained will be 72.48×10⁴ km² and the unchanged area will be 124.71×10⁴ km². The distribution range of increased area will be similar to that under SSP1-2.6 in the 2050s.

Under SSP5-8.5, the loss area of the Campsis grandiflora will be 0.14×10⁴ km² during the 2070s, the area obtained will be 123.41×10⁴ km² and the unchanged area will be 123.71×10⁴ km². The increasing areas will mainly be distributed in southern and eastern Hebei, southern Liaoning, southern Shaanxi, northern and southern Shandong, southern Sichuan, northern Guizhou, central of Yunnan, southern Guangxi, southern Zhejiang, eastern Fujian, southern Guangdong, northern Hainan Island, and Hubei.

Table 5 Future changes in suitable habitat area.

Decades Scenarios	Predicted area/10⁴km²
Decades Scenarios	Loss	Gain	Unchanged
2050s,SSP1-2.6	0.18	83.66	124.33
2050s,SSP5-8.5	0.07	99.18	124.33
2070s,SSP1-2.6	0.12	72.48	124.71
2070s,SSP5-8.5	0.14	123.41	123.71

3.5. The core distributional shifts

The centroid of the current habitat of Campsis grandiflora is located at the position of 112°88’ E and 29°04’ N in the central of Hunan. The centroid of the suitable area will shift to 112°67 ’E, 29°65’ N during the 2050s in SSP1-2.6, and to 112°47’ E, 29°81’ N during the 2050s in SSP5-8.5. During the 2070s in SSP1-2.6, the centroid of the future suitable area will locate at a northeast position (112°73’ E, 29°81’ N), but during the 2070s in SSP5-8.5, the centroid of the suitable area will shift to the northwest (112°40’ E, 30°09’ N). Overall, by our prediction, in the future, the centroid of the potentially suitable area of Campsis grandiflora will migrate to higher latitude areas.

Evaluation indicators of the used model are accurate, sensitive, specific, and are able to get accurate statistical results (Allouche and Kadmon 2006; Wang 2007). Being regarded as the best evaluated indicator nowadays, the ROC curve could be drawn by MaxEnt software immediately and the area under the curve(AUC) of the model could be also calculated directly, which contributes to judging the effect of prediction much easier. So ROC curves has been extensively used in evaluating MaxEnt models. For instance, Zhang et al. (2018) used ROC curves to evaluate the prediction of the potential geographical distribution of two peony species under Climate change (Zhang et al. 2018a). Zhao et al. (2020) used ROC curves to study Taiwania cryptomerioides under Climate change (Zhao et al. 2020). Consequently, ROC curves are used to estimate the predictive accuracy of the MaxEnt model. In our study, the average area under the curve (AUC) was 0.988, which indicated that the simulation effect was ‘excellent’ and proved that the model could be used to predicate the current and future distributions of Campsis grandiflora under Climate change in China.

The results of the MaxEnt model prediction and analysis show that many environmental variables have a great influence on the potential distribution of Campsis grandiflora. Such as mean diurnal temperature range, range of annual temperature variation, mean temperature of the warmest quarter, mean temperature of the coldest season, driest monthly precipitation, precipitation of the warmest quarter, and altitude. The suitable ranges are 7.3–9.5 ℃, 28–33 ℃, 25.5–28 ℃, 5–15 ℃, 30–100 mm, 500–720 mm and 400–1200 m respectively. It showed that temperature, rainfall, and altitude were the main variables affecting the distribution of Campsis grandiflora. The humid and warm climate all year round is very important for the distribution of Campsis grandiflora. Campsis grandiflora likes a warm, humid and sunny environment, which makes it more resistant to shade, and requires good drainage, leeward and sunny, fertile, and loose soil. Drought resistance, avoiding water, and water availability are important factor affecting the germination of seeds (Poorter and Nagel 2000; Wang et al. 2012). The roots of Campsis grandiflora are fresh when it has enough water. Previous researches have proved that the optimum relative soil moisture content is 75% (Zhang et al. 2018b). Water validity can directly affect the emergence. Too much water cause root rot and increase the risk of plant diseases and insect pests (Zhang et al. 2018b). These hydrothermal factors would affect the formation of the ecological adaptation of Campsis grandiflora and the species distribution. Altitude includes the drastic changes of many environmental variables, such as temperature, water, soil fertility, and so on, and the environmental heterogeneity caused by altitude gradient often affects the vertical distribution pattern of plants(Chang-Han et al. 2005).

Some studies show that under the influence of global climate change, the continuous increase of global temperature and the change of precipitation pattern in the future will lead the migration of many plants to high latitudes and high altitudes (Sun et al. 2020). Zhang et al. (2018) used MaxEnt to predict the potential geographical distribution of two peony species under Climate Change and the results showed that a shift in the distribution of suitable habitat to higher elevations would gradually become more significant (Zhang et al. 2018a). Mckenney et al. (2007) predicted under two different assumptions of 130 tree species to future climate change in North America and the results showed that the distribution areas would migrate northward by 700 and 330 km respectively (Mckenney et al. 2007). The results of our study are consistent with these findings. Based on the environmental variables of four different emission scenarios under the future climate change scenario, combined with the modern climate conditions, the potential geographical distribution of Campsis grandiflora in China under the future climate change scenario was predicted by the MaxEnt model. Its results show with the global climate change, the potentially suitable area of Campsis grandiflora will increase in the future. In detail, the results indicate that the potential geographical distributions of Campsis grandiflora in 2050s and 2070s show an increasing trend in total suitable habitats and an increasing trend in highly suitable habitats. However, poorly suitable habitats and moderately suitable habitats show a decreasing trend. In the future, the core area of potential distribution Campsis grandiflora will migrate to higher latitude areas. It means that in the future climate change scenario, there may be new areas suitable for the growth of Campsis grandiflora. With the global temperature rising, some species may migrate to higher altitudes or dimensions (Lenoir et al. 2008; Parmesan et al. 2015). However, some species may adapt to this change in physiology or phenology (Hu et al. 2014).

A model of potential distribution of Campsis grandiflora with g environmental variables changing was successfully established in the study. With the model representing the distribution of observed occurrence records, the impacts of climate change were estimated on suitable habitats of Campsis grandiflora in China. The results indicate that the area of total suitable habitat and highly suitable habitat will increase in the future climate change scenario. However, the area of moderately and the poorly suitable habitat will decrease. Besides climate and topographic variables, other variables such like soil, biotic factors, and human activity also affect the distribution of Campsis grandiflora. The further study of these variables in the model will further strengthen the prediction of the distribution change of the Campsis grandiflora in China caused by climate change.

Contribution Xianheng Ouyang analyzed the data and wrote and revised the manuscript. Anliang Chen supervised the study and revised the paper. Jiangling Pan analyzed the data. Zhitao Wu revised the paper.

Funding The work was supported by Science and Technology Plan Projects of Zhejiang Province [grant numbers 2019C02024].

Data availability Not applicable.

Ethics approval and consent to participate Not applicable.

Consent for publication Not applicable.

Competing interests The authors declare no conflicts of interest.

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Predicting The Potential Distribution of Campsis Grandiflora In China Under Climate Change

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

2.1. Species occurrence data collection

2.2. Environmental variables

2.3. Current and future habitat evaluations

Results

3.1. The species distribution model and its accuracy

3.2. The main environmental variables affecting the distribution of Campsis grandiflora

3.3. Current potential distribution

3.4. Future potentially suitable climatic distribution

3.5. The core distributional shifts

Discussion

Conclusion

Declarations

References

Status:

Journal Publication

Version 1