# Analysis of Differences in the Spatial Distribution among Terrestrial Mammals Using Geodetector—A Case Study of China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}; 73°29′ east (E)–135°2′ E, 3°31′ north (N)–53°33′ N), see Figure 1. From east to west, the country extends about 5200 km; from north to south, it spans about 5500 km. The country is characterized by diverse terrain, different climate types, and complex natural conditions. It is the diversity of habitat environments that leads to incredible species abundance. China is one of the countries with the richest biodiversity globally [37]. The geological environment has been subject to events, including tectonic movement and the uplifting of the Qinghai–Tibetan Plateau, which has resulted in clear regional differences to the natural environment and has influenced the geographical pattern in the distribution of animals [38].

#### 2.2. Data Materials

#### 2.2.1. Distribution Data of Terrestrial Mammals

#### 2.2.2. Data Resources of Environmental Factors

#### 2.3. Methods

_{h}and N are the unit numbers of layer h and the whole area. σ

_{h}

^{2}and σ

^{2}are the variances of Y in layer h and the whole area. SSW and SST represent the sum of variance within the layer and the total variance of the whole area. A larger value of q denotes a more obvious spatial heterogeneity of Y and a stronger explanatory power of factor X to variable Y, q ∈ [0, 1].

_{X1}and N

_{X2}represent sample sizes of the two factors X

_{1}and X

_{2}, respectively; L

_{1}and L

_{2}represent the number of layers of X

_{1}and X

_{2}, respectively, where the null hypothesis H

_{0}is SSW

_{X1}= SSW

_{X2}. If H

_{0}is rejected at the level of significance of α, this indicates that there is a significant difference in the effect of the two factors on the spatial distribution of Y.

_{S}to a dependent variable Y. It investigates whether the influence of factors X

_{S}on variable Y are independent and whether they work together to increase or weaken the explanatory power of the Y. First, the q values of the two factors X

_{1}and X

_{2}for Y are calculated separately: q(X

_{1}) and q(X

_{2}). The q value of their interactions is then calculated (by superimposing the new polygon distribution formed by the tangent of the two layers X

_{1}and X

_{2}): q(X

_{1}∩X

_{2}). Then, q(X

_{1}), q(X

_{2}), and q(X

_{1}∩X

_{2}) are compared. If the q(X

_{1}∩X

_{2}) is less than the minimum of q(X

_{1}) and q(X

_{2}), the result is nonlinear weakened. If the q(X

_{1}∩X

_{2}) is between q(X

_{1}) and q(X

_{2}), the result is univariate nonlinear weaken. If the q(X

_{1}∩X

_{2}) is greater than the maximum of q(X

_{1}) and q(X

_{2}) and less than their sum, it is bivariate enhanced. If the q(X

_{1}∩X

_{2}) is the sum of q(X

_{1}) and q(X

_{2}), X

_{1}and X

_{2}are independent. If the q(X

_{1}∩X

_{2}) is the maximum value, it is nonlinear enhanced.

_{h}represents the number of samples in layer h, and Var is the variance. The statistic t approximately obeys the Student’s t distribution [46]. The calculation method of degrees of freedom is

_{0}is ${\overline{Y}}_{h=1}={\overline{Y}}_{h=2}$. If H

_{0}is rejected at the level of significance of α, there is a significant difference in the mean value of attributes between the two subareas [46].

## 3. Results

#### 3.1. Influencing Factors on the Spatial Distribution of Terrestrial Mammalian Richness

#### 3.2. Influencing Factors on the Distribution of Mammalian Orders

#### 3.3. Indication of Environment Factors on the Distribution of Mammal Richness

## 4. Discussion

## 5. Conclusions

- (1)
- The spatial pattern of terrestrial mammals in China showed a low east–west trend and distinct heterogeneity to the north and south. AP and MTCM were the dominant factors affecting the spatial differentiation of mammal richness in China.
- (2)
- The characteristics of the distribution of species richness across taxonomic groups were influenced by different environmental factors. Many mammalian orders were affected by regional freezing tolerance and productivity levels (mainly MTCM and AP). Perissodactyla was mainly influenced by habitat heterogeneity, while regional productivity levels had less impact on Lagomorpha.
- (3)
- Extremely low ambient temperatures had negative impacts on the distribution of animals, with too little precipitation not being conducive to the aggregation of many species. At a certain altitude, mammalian taxonomic richness decreased with increasing altitude. Fewer mammals were present in regions where the altitude was too flat, with most mammals occurring in forest land.
- (4)
- The interactions of any two environmental factors had remarkable bivariate enhancement or nonlinear enhancement effects on the spatial distribution of species richness with respect to individual variables. The synergies of elevation with the minimum temperature of the coldest month and annual precipitation can best explain the regional distribution differences in mammal richness in China.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Influence of environmental factors on the richness distribution of mammalian orders (q values).

**Figure 5.**Interaction factors and single factors mainly affecting the species richness of each order.

**Figure 6.**The appropriate range of environmental factors for the richness distribution of terrestrial mammals.

AP | MTCM | MTWM | AET | NDVI | ST | LT | AMT | Ele | GT | AR | |
---|---|---|---|---|---|---|---|---|---|---|---|

q-statistic | 0.57 | 0.53 | 0.47 | 0.44 | 0.42 | 0.40 | 0.37 | 0.37 | 0.19 | 0.16 | 0.15 |

p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |

Effect direction | + | + | + | + | + | + | − | + | − | + | + |

q(X2) | 0.57 | 0.53 | 0.47 | 0.44 | 0.42 | 0.40 | 0.37 | 0.37 | 0.19 | 0.16 | 0.15 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

q(X1) | q(X1 ∩ X2) | AP | MTCM | MTWM | AET | NDVI | ST | LT | AMT | Ele | GT | AR |

0.57 | AP | bi-E | bi-E | bi-E | bi-E | bi-E | bi-E | bi-E | non-E | bi-E | bi-E | |

0.53 | MTCM | 0.66 | bi-E | bi-E | bi-E | bi-E | bi-E | bi-E | non-E | bi-E | non-E | |

0.47 | MTWM | 0.66 | 0.57 | bi-E | bi-E | bi-E | bi-E | bi-E | non-E | non-E | non-E | |

0.44 | AET | 0.62 | 0.58 | 0.57 | bi-E | bi-E | bi-E | bi-E | non-E | bi-E | bi-E | |

0.42 | NDVI | 0.61 | 0.63 | 0.63 | 0.56 | bi-E | bi-E | bi-E | bi-E | bi-E | non-E | |

0.40 | ST | 0.69 | 0.66 | 0.64 | 0.60 | 0.60 | bi-E | bi-E | non-E | bi-E | non-E | |

0.37 | LT | 0.66 | 0.65 | 0.63 | 0.56 | 0.51 | 0.55 | bi-E | bi-E | bi-E | bi-E | |

0.37 | AMT | 0.63 | 0.64 | 0.57 | 0.54 | 0.56 | 0.59 | 0.57 | non-E | non-E | non-E | |

0.19 | Ele | 0.80 | 0.80 | 0.73 | 0.67 | 0.59 | 0.61 | 0.54 | 0.74 | non-E | non-E | |

0.16 | GT | 0.69 | 0.68 | 0.66 | 0.55 | 0.56 | 0.55 | 0.45 | 0.63 | 0.47 | bi-E | |

0.15 | AR | 0.70 | 0.69 | 0.67 | 0.56 | 0.59 | 0.58 | 0.48 | 0.64 | 0.48 | 0.20 |

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**MDPI and ACS Style**

Chi, Y.; Qian, T.; Sheng, C.; Xi, C.; Wang, J.
Analysis of Differences in the Spatial Distribution among Terrestrial Mammals Using Geodetector—A Case Study of China. *ISPRS Int. J. Geo-Inf.* **2021**, *10*, 21.
https://doi.org/10.3390/ijgi10010021

**AMA Style**

Chi Y, Qian T, Sheng C, Xi C, Wang J.
Analysis of Differences in the Spatial Distribution among Terrestrial Mammals Using Geodetector—A Case Study of China. *ISPRS International Journal of Geo-Information*. 2021; 10(1):21.
https://doi.org/10.3390/ijgi10010021

**Chicago/Turabian Style**

Chi, Yao, Tianlu Qian, Caiying Sheng, Changbai Xi, and Jiechen Wang.
2021. "Analysis of Differences in the Spatial Distribution among Terrestrial Mammals Using Geodetector—A Case Study of China" *ISPRS International Journal of Geo-Information* 10, no. 1: 21.
https://doi.org/10.3390/ijgi10010021