Biogeographic regionalization of the Amazon using highly diverse horse flies (Diptera: Tabanidae): insights from three decades of data

Delimiting biogeographic regions based on occurrence data is an interesting approach to investigating processes behind biodiversity distribution patterns. Comparing spatial scales and identifying predictor variables of biogeographic regions have wide application for biodiversity conservation. In this study we used a comprehensive database containing more than thirty years of horse fly records to estimate species richness, endemism, and species composition, and regionalize the Amazon biogeographically. We compared five spatial scales defined by grid size (1–5º), and test five hypotheses (elevation, climate, vegetation cover, and two regionalizations from the literature) to identify predictors of the biogeographic regions. Endemism, species richness and composition were predicted by different sets of predictor variables, although the models were highly dependent on spatial scale. We identified three well-defined biogeographic regions, which have been formed by a combination of geographic distance, climate and historical factors converging with some theories proposed for mammals. Our models indicated dispersal as a key factor for regionalization, as it can be constrained by a combination of climate and historical processes changing habitats over time, although this finding was highly dependent on spatial scale. We showed that horse flies are interesting models for biogeography although they have been historically neglected.

基于物种发生数据（occurrence data）界定生物地理区域（biogeographic regions），是探究生物多样性分布格局（biodiversity distribution patterns）背后驱动过程的有效研究路径。对比不同空间尺度（spatial scales）、明确生物地理区域的预测变量（predictor variables），在生物多样性保护领域具有广泛应用价值。本研究依托涵盖三十余年虻类（horse flies）记录的综合数据库，估算了物种丰富度（species richness）、特有性（endemism）与物种组成（species composition），并对亚马逊地区开展生物地理分区。我们对比了由1°至5°网格尺寸（grid size）定义的五种空间尺度，同时检验了五项假说：海拔（elevation）、气候、植被覆盖（vegetation cover），以及两项来自文献的生物地理分区方案（regionalizations from the literature），以识别生物地理区域的驱动因子。尽管模型结果高度依赖空间尺度，但特有性、物种丰富度与物种组成各自对应的预测变量集并不相同。本研究识别出三个界限清晰的生物地理区域，其形成是地理距离、气候与历史因素共同作用的结果，且与部分针对哺乳动物提出的生物地理理论相契合。本研究的模型显示，扩散（dispersal）是生物地理分区的关键驱动因子：扩散过程可受到气候与随时间改变栖息地的历史过程的共同约束，但该结论同样高度依赖空间尺度。本研究证实，尽管虻类长期以来被生物地理学研究忽视，但其不失为生物地理学研究的优质模式类群。