中国蚂蚁丰富度地理分布格局及其与环境因子的关系

物种丰富度分布格局及其形成机制的研究对于生物多样性保护具有重要意义。为了了解中国蚂蚁物种丰富度分布格局,利用中国省级尺度蚂蚁物种分布数据和环境信息,结合GIS和数理统计方法,探讨蚂蚁物种丰富度的地理分布格局与环境因子之间的关系。研究结果表明:(1)蚂蚁丰富度随纬度增加呈逐渐递减趋势,但缺乏显著的经度梯度。丰富度最高的地区主要集中在南方省份,我国北方、西北干旱区和青藏高原北部地区丰富度较低;(2)简单线性回归分析表明,能量、水分和季节性因素中,影响蚂蚁物种丰富度最强的因子分别为最冷月均温(TEMmin)(R2adj=0.532)、年均降水量(PREC)(R2adj=0.376)和年温度变化范围(TEMvar)(R2adj=0.539),而单个生境异质性因子对蚂蚁物种丰富度的影响均不显著;(3)最优模型由年均温(TEM)、海拔变化范围(ELErange)和年温度变化范围(TEMvar)组成,能够解释68.4%的蚂蚁丰富度地理分异。鉴于海拔变化范围更多地反映与温度相关的生境异质性,因此温度是限制中国蚂蚁分布的最重要因素。另外,分析结果还表明,海南、贵州、江西、四川、安徽和山西等6省蚂蚁区系调查最不充分,是未来发现蚂蚁新分布的热点地区。

Relationships between geographic patterns of ant species richness and environmental factors in China

Understanding spatial patterns of species richness is a hot topic in macroecology because of its significance in biodiversity conservation. In many previous studies, current environmental variables representing energy and water availability have been considered as the main drivers of geographic diversity patterns. However, the roles of different environmental variables vary for different taxonomic groups and distinct biogeographic extents; therefore, there is no consensus among ecologists on which environmental variables are primary drivers of the spatial variation in species richness. China covers a wide range of latitudes and longitudes and has a wide range of variation in climate and vegetation (e.g., from tropical rain forest in the south to boreal forest in the northwest and desert in the north and northwest). Thus, China offers great opportunity for testing the relationships between species richness and environment. In the present study, geographic pattern of ant species richness in China and its relationships with environmental factors were investigated based on ant distribution data and environmental variables at a provincial scale. Several studies on the distribution of ants in other geographical areas show that temperature (energy) is the most important factor that determines ant species richness patterns. This conclusion might also apply to China. Therefore, four hypotheses were tested in the present study. (1) Compared to the mean annual precipitation, ant species richness pattern was more likely affected by the mean annual temperature. (2) Compared to the habitat heterogeneity associated with precipitation, ant species richness pattern was more affected by habitat heterogeneity associated with temperature. Since ants are ectotherms and their survivorship under low temperature in winter is critical for their populations and distribution, we further assumed that (3) compared to the mean annual temperature, mean temperature of the coldest month better explains the spatial differentiation of ant species richness. Given that precipitation seasonality and temperature seasonality are mainly determined by annual temperature range and annual precipitation range, we inferred that, (4) compared to precipitation seasonality, temperature seasonality can better explain spatial ant species richness pattern. To test these hypotheses, GIS was used to map ant species diversity and simple and multiple linear regressions were used to determine the relative roles of different environmental variables. The results showed that:(1) Ant species richness decreases significantly with latitude but not longitude; the species richness is higher in southern provinces of China than in northern and northwestern regions of China. (2) Simple linear regression analyses showed that, mean temperature of the coldest month (TEM_min, R_adj²=0.532), annual precipitation (PREC, R_adj²=0.376), and annual temperature range (TEM_var, R_adj²=0.539) are the variables that best fit energy, water, and seasonality, respectively. However, none of the factors reflecting habitat heterogeneity have significant effect on ant species richness when assessed independently. (3) Our results indicate that the best model based on the Akaike information criterion (AIC) includes the mean annual temperature (TEM), the range of elevation within a province (ELEV_range), and the annual temperature range (TEM_var). This model can explain 68.4% of the geographic variance in ant species richness across different provinces in China. The above four hypotheses were confirmed, and we conclude that temperature is the most important factor controlling ant distribution in China. In addition, our analysis revealed that ant fauna in Hainan, Guizhou, Jiangxi, Sichuan, Anhui, and Shanxi is poorly sampled, and these provinces are potential hot spots for new ant records.