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Chinese history is often explained in terms of several strategic areas, defined by particular topographic limits. Starting from the Chinese central plain, the former heart of the Han populations, the Han people expanded militarily and then demographically toward the Loess Plateau, the Sichuan Basin, and the Southern Hills (as defined by the map on the left), not without resistance from local populations. Pushed by its comparatively higher demographic growth, the Han continued their expansion by military and demographic waves. The far-south of present-day China, the northern parts of today's Vietnam, and the Tarim Basin were first reached and durably subdued by the Han dynasty's armies. The Northern steppes were always the source of invasions into China, which culminated in the 13th century by Mongolian conquest of the whole China and creation of Mongolian Yuan dynasty. Manchuria, much of today's Northeast China, and Korean Peninsula were usually not under Chinese control, with the exception of some limited periods of occupation. Manchuria became strongly integrated into the Chinese empire during the late Qing dynasty, while the west side of the Changbai Mountains, formerly the home of Korean tribes, thus also entered China.

Understanding the relationship between species richness and the environment is a key issue in ecology and biogeography. Although climate is one of the major factors driving species composition, habitat characteristics are also strongly associated with forest structure and composition (Zellweger et al 2015). In forestry, ensuring species diversity has become one of the important goals which require an understanding of the impacts of disturbance on species diversity and its management concerning other natural drivers (Schmiedinger et al 2012). Multiple drivers like climate, soil conditions, and human influence interact to affect the patterns of species diversity and composition (Ulrich et al 2014). Several hypotheses, such as climatic seasonality, environmental energy, water, and habitat heterogeneity (Currie et al 2004, Shrestha et al 2018), have been suggested to explain the diversity gradient. For example, the water-energy hypothesis suggests that an area with high energy and water availability will promote high species diversity (O'Brien 1998, Francis and Currie 2003). Human disturbance is known to cause habitat fragmentation. It may have a stronger impact on the loss of species than global warming (Schmiedinger et al 2012, Venter et al 2016). The habitat heterogeneity hypothesis attempts to explain species richness through space niches and diversification (Moeslund et al 2013, Stein et al 2014). In addition, on the basis of geological and topographical characteristics for pertaining biodiversity, geodiversity has an effect on biodiversity at landscape and subnational scales (Hjort et al 2012, Gray 2013, Bailey et al 2017). Gray (2013) defined 'geodiversity' as the variability of geological, soil, and geomorphological characteristics and the physical processes that lead to these characteristics. Furthermore, terminology or definitions for describing the geodiversity has not been standardized (Anderson et al 2015). Geodiversity in this article is defined as the variability of geo-traits of morphological, physical, and chemical components representing three hypothesises of topographic heterogeneity, soil texture type, and soil fertility (Keith 2011, Räsänen et al 2016, Bailey et al 2017, Lausch et al 2019). The approach to compile geodiversity information was highly simplified in the current study, but the previous studies e.g. Hjort and Luoto (2012) and Hjort et al (2012) have described geodiversity as the variability in the Earth's surface materials, geomorphological and hydrological variability. Several studies recommend incorporating geodiversity quantitative data such as topographic heterogeneity or soil nutrients into spatial modelling and conservation studies (Mod et al 2016, Bailey et al 2017, Tukiainen et al 2017).

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The climatic variables (MAT, MTCQ, MAP, TS, and PS) were obtained from the WorldClim database (www.worldclim.org, Fick and Hijmans 2017) at a resolution of 30 arc-seconds. The PET, AET, and AI data were downloaded from the CGIAR-CSI Global database (www.cgiar-csi.org, Trabucco et al 2008, Fisher et al 2011) at the same resolution. As topographic heterogeneity is an important driver of species richness in China and particularly southwestern China (Shrestha et al 2018, Liu et al 2019), we represented it by two metrics; elevation range (ELER) and elevation standard deviation (ELE-SD) were extracted from the GTOPO30 digital elevation model using the ArcGIS. ELER is defined as the difference between the maximum and minimum elevation of a grid cell while ELE-SD is the standard deviation of the elevation within each geographical unit (Bailey et al 2017, Shrestha et al 2018).

The glm.nb() function was used for GLMs in the 'MASS' R package (Venables and Ripley 2013) followed by stepwise regression to explore the combined effect of predictor variables on richness pattern and determine the best group of predictors with the highest explanatory power (Shrestha et al 2018). Due to high correlation, one variable from each predictor category was selected to build a model, and finally, seven predictors were selected (Faraway 2016) (see appendix A, table S4). We used all possible combinations of predictors using four energy, three water, two seasonality, two topographic heterogeneity, one human influence, four soil-texture types, and seven soil fertility variables, which yielded 1344 models (4 3 2 2 1 4 7) for each richness category. We selected the best model based on the lowest Akaike information criterion (AIC) for each richness group. Within each selected model, we calculated the variance inflation factor (VIF) for all predictors using the 'car' package in R v. 3.6.1 to evaluate the significance of multicollinearity (Legendre and Legendre 2012, Fox et al 2016), where collinearity is considered to be significant when VIF > 5. Next, we compared the explanatory power of each predictor individually using GLM, and their combined effect using stepwise regression. Similarly, we used a hierarchical partitioning model to compare the proportion of variance explained by each predictor category (Liu et al 2019). We first conducted a principal component analysis (PCA) with the varimax rotation in SPSS v. 21.0 (IBM 2012) within each of the predictor categories. We then extracted the first axis to represent each category (hypothesis). We standardized the human influence variable because the PCA cannot be applied to a group containing only one variable (Liu et al 2019). Then, we used the PCA-extracted first axes to apply hierarchical partitioning analysis using the 'hier.part' R package (Walsh and Mac Nally 2013).

Of the 23 variables, the explanatory variables of topographic heterogeneity were the strongest predictors of species richness and showed positive relationships with all richness groups (table 1). Most of the soil fertility variables showed positive and stronger relationships than those of soil texture variables (table 1). The contribution of the human-influence variable was low for all groups and showed negative relationships among groups (table 1). The proportion of variance using the extracted principal components explained by the individual environmental and human-influence predictors highlighted the significant roles of topographic heterogeneity and soil fertility in determining species richness (figure 3).

Hierarchical partitioning modelling showed similar results to those of the GLM models, suggesting that topographic heterogeneity and soil fertility had the highest independent effects on all conifer-richness groups. In contrast, these variables had the highest joint effect on the richness of endemic-threatened species (see appendix A, figures S1a and S1b). The output of the stepwise GLM combined models showed that three models of the all conifer richness group, were rejected due to multicollinearity (i.e. VIF > 5), while for threatened richness group, only one model with the lowest AIC was rejected due to multicollinearity. For endemic or endemic-threatened richness groups, there was no model rejected because the predictor variables, of the model with the lowest AIC, has no multicollinearity. The combined models produced using stepwise regression (GLM) showed that ELER and soil nitrogen were consistently significant predictors (table 2). The developed models moderately predicted the conifer richness for all groups (table 2).

Our results support strong positive topographic heterogeneity-diversity relationships (see table 1 and figure 3) that promote the coexistence and diversification of species by offering niche spaces, which agrees with the findings of several studies (e.g. Stein et al 2014, Lee and Chun 2016, Shrestha et al 2018, Sharma et al 2019, Wang et al 2019). Topographic heterogeneity was the principle and key driver affecting conifer richness patterns. This result has been reported in several studies for Rhododendron tree species in south-west China (Shrestha et al 2018), rare woody plant species in the Inner Asian drylands (Liu et al 2019), and conifer species in southeast Alaska (Bisbing et al 2016). The high richness of endemic conifers in Qionglai-Minshan and the Hengduan Mountains may be rooted in the complex topography that creates a wide range of habitats and provides more resources that support a high number of species (Bisbing et al 2016, Shrestha et al 2018). Furthermore, historically high rates of plant diversification in south-west China have been due to topographical isolation based on a recent study of molecular phylogeny (Xing and Ree 2017). Hence, heterogeneity is thought to have a significant effect on the structure and dynamics of plant communities (Moeslund et al 2013, Lee and Chun 2016). Moreover, topographic heterogeneity can be used as a proxy of habitat heterogeneity through the control of temperature, the distribution of soil water, and nutrient availability, particularly at high elevations, thereby controlling the occurrence and distribution patterns of species (Bisbing et al 2016, Tashi et al 2016). These results support our findings that temperature was negatively correlated with conifer richness. At the same time, topographic heterogeneity and soil fertility were greatly correlated with species richness (see tables 1 and 2).

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