The survival and distribution of any animal cannot be separated from a certain environment. The negative consequences of human activities restrict the development of resources and the environment, consequently causing detrimental impacts on the natural habitat [1
]. Many species have been annihilated or are in a state of imminent danger. However, how patterns in the distribution of species depend on the environment remain unclear.
How species are distributed is the combined effect of abiotic factors and biotic interactions [2
]. Notably, many scholars have proposed hypotheses to explain the mechanisms of influencing patterns in the geographical distribution of species. Such hypotheses include the productivity hypothesis [6
], ambient energy hypothesis [8
], environmental stability hypothesis [10
], water–energy dynamic hypothesis [12
], freezing tolerance hypothesis [13
], habitat heterogeneity hypothesis [15
], and historical hypothesis [16
]. These hypotheses reveal different forms of energy influencing the spatial patterns of animals from different perspectives.
Landscape, climate, topography, and social processes may affect the distribution of animals [4
]. There have been many available methods in estimating the relationship between the geographical distribution of species and environment variables. Correlation analyses are used to test consistency in the geographical patterns of diversity for different taxonomic groups [18
]. Regression models are often used for quantifying the relationship between one variable and others upon which it depends [19
]. In particular, generalized linear models (GLMs), including linear regression, logistic regression, and Poisson regression, are suitable for analyzing nonlinear relationships between species and environmental variables [20
]. With the development of computer technology, complex models such as machine learning have a strong predictive ability in simulating distributions of species, which can handle complex response relations [20
]. Classification and regression tree (CART) can reveal the interaction between complex predictors [20
]. Multivariate regression trees (MRTs) can be used to explore and predict relationships between species data and environmental characteristics [21
]. Random forests or boosted regression trees (BRTs) are often used to explore and predict which environmental factors influence the distribution of animals [22
]. Ecological niche models also identify the importance of the variables, provide response curves for each variable, and provide a potential distribution range according to the environmental variables associated with species occurrence records [24
Geodetector is a new statistical model to analyze geographical phenomena with spatial stratified heterogeneity, which consists of four detectors [26
]: factor, ecological, interaction, and risk. The factor detector can reveal the relative importance or influence intensity among these key factors without any assumptions or limitations compared to the traditional statistical methods [26
]. The ecological detector identifies the difference in impact between two explanatory variables [26
]. The interaction detector can also analyze the interactions between factors that influence response variables [26
]. The risk detector analyzes whether the mean of the attributes between the two subareas of factors is significantly different [26
]. Geodetector can explore more comprehensively the determinants of stratified heterogeneity of dependent variables from four aspects. It has been utilized in numerous studies, including public health [28
], land use [29
], and ecological environment [30
], and it has been gradually adopted for the research of animal-related fields. Shen et al. [31
] used the Geodetector model to detect the suitability of grasshopper habitat in steppe habitats of Inner Mongolia. Fan et al. [32
] used Geodetector software to study the influence of habitat factors on the distribution pattern of Spermophilus dauricus
in the Manchuria City of China. Chen et al. [33
] analyzed the relationships among the biogeographic patterns of the α-diversity of mangrove mollusks and environmental factors and spatial variables by using the Geodetector software. Liu et al. [34
] assessed the influence of soil, land use, vegetation, and normalized difference vegetation index (NDVI) layers on the distribution of rodent density using Geodetector. Liu et al. [35
] tested the q statistic by Geodetector to avoid the possible confounding caused by spatial stratified heterogeneity.
In China, quantitative studies of the mechanisms influencing the formation of mammalian distribution patterns remain limited due to the limited availability of macrolevel data. In this paper, we aim to present possibilities of using the Geodetector by ecologists, demonstrating its use in an analysis of relationships between mammalian distribution and environments. We used the Geodetector model combined with geospatial data and recent animal taxonomic data to explore the intrinsic relationship between the spatial pattern of terrestrial mammalian richness and environmental factors. The Geodetector model is based on spatial differentiation analysis by combining spatial overlay technology of geographical information systems (GIS). Our study systematically analyzed the relationship between different geographical environmental factors influencing the distribution of species richness to more accurately understand the extent to which species distributions are affected by environmental factors. Our analysis was expected to provide an important reference and scientific basis for the conservation policy of species diversity in China.
Our analysis showed that annual precipitation served as the most influential factor on the spatial distribution of mammalian richness in China. Of note, this view is also supported by Medellín, who suggested that annual precipitation is an essential cause of mammal richness [3
]. Precipitation and vegetation are generally considered to have non-negligible influences on mammal distribution. Most animals in the world depend mainly on grassland vegetation as their source of food [47
]. It is known that plant productivity is influenced by precipitation [48
]. Changes in vegetation in turn affect the distribution and abundance of mammals [50
]. Our findings are similar to those of Lin et al. [51
], who identified that NDVI has an important influence on the spatial pattern of species richness of various groups according to the optimal linear model. The minimum temperature of the coldest month and the maximum temperature of the warmest month (MTWM) have a rather significant positive impact on mammal richness. Most mammals as endothermic vertebrates are dependent on productivity or the environmental climate, partly because of the mechanisms used to regulate temperature. For instance, as the ambient temperature changes, this group needs to consume a lot of energy to maintain a constant body temperature [52
]. At present, much research has shown that MTCM is a remarkable factor affecting the distribution of animals [13
]. Schap et al. [53
] analyzed the relationships between Rodentia and Lagomorpha crown height and diversity with current climate conditions, finding strong correlations of community structure parameters with the maximum temperature of the warmest month and minimum temperature of the coldest month. We found that AET played an important role in the distribution of species richness. As revealed in Torres-Romero and Olalla-Tarraga [54
], actual evapotranspiration is the key factor that affects the distribution of mammalian species richness worldwide.
Our result indicated that most mammalian orders were mainly affected by regional freezing tolerance and productivity levels (especially MTCM and AP). Topography, climate, vegetation, and other factors act together on mammals, resulting in different types of mammals adapting to various environments, in terms of morphological structure and living habits. The Chiroptera are not poikilothermic animals, divided into Megachiroptera and Microchiroptera. All species of Megachiroptera studied are homeothermic animals, with constant body temperatures regardless of the temperature of the surrounding environment to a large extent. Species of Microchiroptera on the other hand show different degrees of heterothermy [55
]. In many species of Microchiroptera, body temperature and metabolic rate rise only during activity. This makes them homeothermic when they are active and poikilothermic when they are at rest [56
]. Thus, the survival of Microchiroptera is closely related to the environment, especially temperature. Primates are widely distributed in tropical and subtropical regions, mostly inhabiting forests [57
], and the extremely low temperature limits their activities. In reverse, Perissodactyla was mainly affected by habitat heterogeneity, and regional productivity levels had less impact on Lagomorpha. Perissodactyla includes mainly large-scale herbivores [58
], which occur in the steppe, semi-desert, and desert environments. They inhabit mountainous areas or open areas of plateaus, which are strongly influenced by elevation and soil type. Most species of Lagomorpha occupy alpine habitats, mainly affected by elevation. The degree of influence was higher than the environmental heat factor. Most species of Eulipotyphla feed on insects and small animals, while others feed on plant rhizome and leaf fruits, which are closely associated with the vegetation index.
Analyses of the spatial distribution of animals have shifted from qualitative to quantitative by combining GIS spatial analysis with statistical methods. By taking advantage of GIS spatial data analysis, we used the latest data and measured the spatial distribution patterns of terrestrial mammals in China on 10 by 10 km geographic grids. Compared with previous studies limited to administrative districts or smaller areas, the area of the study unit eliminated the influence of area on species richness, controlled for single variables, and we compared other factors to animal distribution under the same conditions.
The main distinctions among methods involve the type of data they use. Ecological niche models perform well in predicting species potential distribution and provide response curves for each variable. However, most of them are based on the presence data of species. We aimed to quantitatively analyze the influence of geographical factors on the spatial distribution of terrestrial mammalian richness in China. However, there is no systematic collection of the presence–absence data of China’s mammals. In the study, mammal species distribution data were obtained from China’s Mammal Diversity and Geographic Distribution
], which is more systematic, organized by many zoologists. At the same time, we selected categorical variables, including soil, landform, and land use as environmental factors. Geodetector analysis is based on a statistical relationship [59
], which does not provide a spatial output. There are differences with ecological niche models. However, the Geodetector model is more conducive to the spatial stratification of different types of qualitative data. It can quantify the type of soil, landform, and land use to explain the stratification of mammalian distribution.
We aimed to present possibilities of using Geodetector to analyze the environmental factors that influence the distribution of species richness. Geodetector can identify appropriate types and ranges of the determinants of stratified heterogeneity of dependent variables more comprehensively. The factor detector in Geodetector reveals the relative importance or influence of variables related to the species richness without any assumptions or limitations compared to the traditional statistical methods. The ecological detector in Geodetector identifies the difference in impact between the two explanatory variables. The risk detector in Geodetector can screen out the range of geographical environments suitable for the survival of multiple animals. It provides a better comparison of species distribution with different stratifications of different environmental factors. Using the interaction detector in Geodetector to explore the interaction of two factors, a new perspective is provided in studies of mammalian distributions. The results show that two-factor interaction significantly enhances the influence of independent environmental factors on mammalian richness distribution.
The variable must be categorical in the Geodetector models. However, to comprehensively analyze the effects of environmental factors on mammalian species richness, we must select numerical variables and categorical variables. Numerical variables should be transformed to a category, which inevitably leads to information loss. A more detailed stratification of numerical environmental factors leads to less information loss and denote a more significant impact on the target factor. We divided the numerical variables into 10 categories, 20 categories, 30 categories, and 50 categories, and the q value of each numerical variable did not change much. The results show that, no matter how each numerical variable is classified, its influence on species richness is within a certain range. The relative influence of various numerical variables on species richness did not change. The soil type consisted of 13 soil orders, and the landform type and land-use type had eight categories. To keep small differences in categories between numerical variables and categorical variables, we classified the values of eight numerical variables into 10 classes. There are also other potential factors having major effects on mammalian richness. In subsequent analysis, more variables will be selected for a more detailed discrete classification to comprehensively evaluate the influence of environmental factors on the distribution of mammalian species richness. In conclusion, we discovered that Geodetector is an important and promising tool for ecologists to analyze and study species richness.
We evaluated the spatial distribution patterns of terrestrial mammal richness and analyzed the main factors influencing distribution differences in species richness. This research contributes to identifying spatial differences in mammalian richness, comprehensively considering climate, precipitation, topography, and vegetation factors, using the Geodetector model to discuss the influence strength and synergistic effect between key factors. The main conclusions are as follows:
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.
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.
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.
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.
Our study provides a more accurate understanding of the extent to which species distributions are influenced by individual environmental factors and their pairs. We aim to facilitate the wider use of Geodetector by ecologists. Consequently, our findings can be used for the remote sensing monitoring of animal protection, which could help toward understanding the distribution range of terrestrial mammals and key control zones in protected areas from the macro perspective. It could also help give some reference evidence for the conservation policy of species diversity. Furthermore, some human activities such as overgrazing and deforestation might affect the distribution of animals by destroying existing habitats. The effects of human activities on the spatial distribution patterns of terrestrial mammals must be accounted for in future studies to mitigate the loss of biodiversity.