# Predictive Modeling of Suitable Habitats for Cinnamomum Camphora (L.) Presl Using Maxent Model under Climate Change in China

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.1.1. Available Data

#### 2.1.2. Climate Data

^{2}, which was used to predict the potential suitable range of camphor tree and its response to climate change. We used two greenhouse gas release scenarios, RCP4.5 and RCP8.5, and selected three future time periods, 2025s, 2055s, and 2085s, to represent the three periods of the early, middle, and final phases of the 21st century. The 16 climate variables in this study (Table 1) and the Climate AP client are available from the UBC server (http://asiapacific.forestry.ubc.ca/research-approaches/climate-modeling). To avoid cross-correlation within selected environmental variables, we used the Pearson correlation coefficient in the R language (version 3.5.1) for multicollinearity testing and eliminate variables with a correlation coefficient greater than 0.8 (Figure 2). The principal component analysis (PCA) was then used to select significant bioclimatic variables among the remaining variables [26]. Eventually, eight climate variables were retained for model building: mean annual temperature (MAT), temperature difference between mean warmest month temperature and mean coldest month temperature (TD), mean annual precipitation (MAP), degree days below 0 °C (DD < 0), precipitation as snow between August in previous year and July in current year (PAS), extreme minimum temperature over 30 years (EXT), Hargreaves climatic moisture deficit (CMD), and annual heat moisture index (AHM).

#### 2.2. Model Establishment and Evaluation Methods

## 3. Results

#### 3.1. Model Performance and Evaluation

#### 3.2. Potential Distribution Range

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

Latitude | Longitude |
---|---|

21.99°N | 100.75°E |

21.93°N | 101.26°E |

24.07°N | 101.97°E |

23.57°N | 102.15°E |

27.86°N | 102.30°E |

25.65°N | 103.38°E |

30.68°N | 104.09°E |

28.75°N | 104.64°E |

28.87°N | 105.42°E |

32.42°N | 105.68°E |

23.14°N | 106.42°E |

26.85°N | 106.60°E |

22.35°N | 106.86°E |

23.74°N | 106.92°E |

25.00°N | 107.50°E |

28.88°N | 107.60°E |

31.29°N | 107.68°E |

23.16°N | 108.28°E |

27.72°N | 109.18°E |

18.77°N | 109.54°E |

27.43°N | 109.70°E |

26.58°N | 109.70°E |

28.00°N | 110.00°E |

29.89°N | 110.04°E |

19.37°N | 110.11°E |

20.03°N | 110.35°E |

21.22°N | 110.40°E |

29.14°N | 110.43°E |

26.66°N | 110.64°E |

32.63°N | 110.78°E |

26.45°N | 110.84°E |

25.93°N | 111.07°E |

30.70°N | 111.08°E |

29.40°N | 111.20°E |

23.49°N | 111.28°E |

27.57°N | 111.58°E |

25.78°N | 111.68°E |

22.18°N | 111.80°E |

23.44°N | 111.89°E |

22.56°N | 112.01°E |

32.04°N | 112.12°E |

24.78°N | 112.38°E |

24.47°N | 112.65°E |

27.24°N | 112.75°E |

28.67°N | 112.82°E |

25.29°N | 112.88°E |

22.91°N | 112.89°E |

24.94°N | 112.93°E |

28.18°N | 112.94°E |

25.75°N | 112.98°E |

23.67°N | 113.07°E |

28.82°N | 113.08°E |

24.78°N | 113.29°E |

24.43°N | 113.53°E |

34.79°N | 113.67°E |

25.12°N | 113.67°E |

23.01°N | 113.77°E |

27.05°N | 113.93°E |

24.96°N | 114.07°E |

30.63°N | 114.07°E |

24.36°N | 114.13°E |

22.58°N | 114.18°E |

22.42°N | 114.22°E |

26.32°N | 114.53°E |

26.61°N | 114.54°E |

25.70°N | 114.54°E |

27.41°N | 114.57°E |

28.05°N | 114.64°E |

31.30°N | 114.67°E |

24.87°N | 114.74°E |

28.87°N | 114.85°E |

30.48°N | 114.89°E |

28.34°N | 114.90°E |

29.26°N | 115.09°E |

25.40°N | 115.20°E |

26.07°N | 115.23°E |

26.97°N | 115.31°E |

28.90°N | 115.49°E |

27.83°N | 115.53°E |

27.96°N | 115.74°E |

27.36°N | 115.83°E |

29.73°N | 115.83°E |

28.77°N | 115.84°E |

25.73°N | 115.95°E |

29.56°N | 116.00°E |

24.30°N | 116.12°E |

26.91°N | 116.22°E |

25.05°N | 116.42°E |

25.75°N | 116.46°E |

27.81°N | 116.62°E |

28.80°N | 116.66°E |

23.36°N | 116.73°E |

25.72°N | 116.76°E |

35.42°N | 117.01°E |

23.67°N | 117.01°E |

24.99°N | 117.03°E |

27.79°N | 117.07°E |

34.24°N | 117.19°E |

31.88°N | 117.21°E |

29.89°N | 117.30°E |

27.30°N | 117.50°E |

24.50°N | 117.71°E |

30.48°N | 117.83°E |

28.16°N | 117.84°E |

28.48°N | 117.93°E |

29.40°N | 118.04°E |

27.76°N | 118.05°E |

26.71°N | 118.08°E |

24.49°N | 118.11°E |

28.52°N | 118.31°E |

27.07°N | 118.40°E |

27.92°N | 118.53°E |

31.67°N | 118.54°E |

32.07°N | 118.82°E |

25.00°N | 118.90°E |

29.60°N | 119.03°E |

27.60°N | 119.07°E |

34.85°N | 119.11°E |

28.20°N | 119.20°E |

34.61°N | 119.24°E |

26.09°N | 119.24°E |

28.62°N | 119.38°E |

31.43°N | 119.48°E |

29.82°N | 119.55°E |

27.97°N | 119.64°E |

29.12°N | 119.65°E |

27.56°N | 119.72°E |

30.01°N | 119.90°E |

30.29°N | 120.15°E |

33.37°N | 120.16°E |

31.69°N | 120.28°E |

22.63°N | 120.32°E |

23.48°N | 120.46°E |

31.26°N | 120.63°E |

24.15°N | 120.67°E |

28.25°N | 120.70°E |

28.85°N | 120.71°E |

30.75°N | 120.77°E |

31.56°N | 120.81°E |

31.97°N | 120.88°E |

28.13°N | 120.93°E |

24.81°N | 120.98°E |

29.15°N | 121.01°E |

22.77°N | 121.15°E |

29.69°N | 121.26°E |

31.19°N | 121.44°E |

25.03°N | 121.52°E |

29.81°N | 121.80°E |

29.20°N | 121.95°E |

**Figure A1.**This picture is the receiver operating characteristic (ROC) curve of the camphor tree habitat distribution model. The red curves shown are the averages over 10 replicate runs. The average test AUC for the replicate runs is 0.923, and the standard deviation is 0.010.

**Figure A2.**This picture shows the results of the jackknife test of variable importance in the camphor tree habitat distribution model. The environmental variable with highest gain when used in isolation is MAT, which therefore appears to have the most useful information by itself. Values shown are averages over replicate runs.

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**Figure 1.**Distribution of the camphor presence points in major provinces with mean annual temperature.

**Figure 2.**(

**a**) Results of jackknife test for the area under the curve (AUC) of individual environmental variable importance relative to eight environmental variables for MaxEnt model. (

**b**) Correlation analysis of the independent variables. The red squares are for positive correlations and the blue ones for negative correlations. The stronger the correlation, the darker the color.

**Figure 3.**The response curves of eight environmental variables in camphor habitats distribution model. The logistic probability of presence is represented by the vertical axis and the climate variable by the horizontal axis. The values of probability presence range from 0 to 1, with values >0.5 meaning a better than random fit. The red curves shown are the averages over 10 replicate runs; blue margins show ±1 standard deviation (SD) calculated over 10 replicates.

**Figure 4.**Potential habitat suitability of camphor tree projected by MaxEnt model. (

**a**) Current occurrence; (

**b**) based on RCP4.5 in 2025 s; (

**c**) based on RCP4.5 in 2055 s; (

**d**) based on RCP4.5 in 2085 s; (

**e**) based on RCP8.5 in 2025 s; (

**f**) based on RCP8.5 in 2055 s; (

**g**) based on RCP8.5 in 2085 s.

**Figure 5.**Change in habitat suitability of camphor tree in different periods in the future. RCP4.5: (

**a**) 2025 s, (

**b**) 2055 s, (

**c**) 2085 s; RCP8.5: (

**d**) 2025 s, (

**e**) 2055 s, (

**f**) 2085 s. The columnar graph shows the suitability changes in the area of different periods.

**Table 1.**Climate variables provided by Climate AP and their percentage contribution. The variables with bold fonts were used in the model.

Code | Climate Variables | Units | % Contribution |
---|---|---|---|

MAT | Mean annual temperature | °C | 2.0 |

MWMT | Mean warmest month temperature | °C | |

MCMT | Mean coldest month temperature | °C | |

TD | Temperature difference between MWMT and MCMT, or continentality | °C | 0.6 |

MAP | Mean annual precipitation | mm | 0.7 |

EXT | Extreme maximum temperature over 30 years | °C | 12.3 |

AHM | Annual heat moisture index (MAT+10)/(MAP/1000)) | - | 0.0 |

DD > 5 | Degree-days above 5 °C, growing degree-days | °C | |

DD < 0 | Degree-days below 0 °C, chilling degree-days | °C | 81.1 |

NFFD | The number of frost-free days | day | |

PAS | Precipitation as snow between August in previous year and July in current year | mm | 0.2 |

EMT | Extreme minimum temperature over 30 years | °C | |

Eref | Hargreaves reference evaporation | - | |

CMD | Hargreaves climatic moisture deficit | - | 3.0 |

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## Share and Cite

**MDPI and ACS Style**

Zhang, L.; Jing, Z.; Li, Z.; Liu, Y.; Fang, S.
Predictive Modeling of Suitable Habitats for *Cinnamomum Camphora* (L.) Presl Using Maxent Model under Climate Change in China. *Int. J. Environ. Res. Public Health* **2019**, *16*, 3185.
https://doi.org/10.3390/ijerph16173185

**AMA Style**

Zhang L, Jing Z, Li Z, Liu Y, Fang S.
Predictive Modeling of Suitable Habitats for *Cinnamomum Camphora* (L.) Presl Using Maxent Model under Climate Change in China. *International Journal of Environmental Research and Public Health*. 2019; 16(17):3185.
https://doi.org/10.3390/ijerph16173185

**Chicago/Turabian Style**

Zhang, Lei, Zhinong Jing, Zuyao Li, Yang Liu, and Shengzuo Fang.
2019. "Predictive Modeling of Suitable Habitats for *Cinnamomum Camphora* (L.) Presl Using Maxent Model under Climate Change in China" *International Journal of Environmental Research and Public Health* 16, no. 17: 3185.
https://doi.org/10.3390/ijerph16173185