Data from: SpeciesGeoCoder: fast categorization of species occurrences for analyses of biodiversity, biogeography, ecology and evolution

Understanding the patterns and processes underlying the uneven distribution of biodiversity across space constitutes a major scientific challenge in systematic biology and biogeography, which largely relies on effectively mapping and making sense of rapidly increasing species occurrence data. There is thus an urgent need for making the process of coding species into spatial units faster, automated, transparent, and reproducible. Here we present SpeciesGeoCoder, an open-source software package written in Python and R, that allows for easy coding of species into user-defined operational units. These units may be of any size and be purely spatial (i.e., polygons) such as countries and states, conservation areas, biomes, islands, biodiversity hotspots, and areas of endemism, but may also include elevation ranges. This flexibility allows scoring species into complex categories, such as those encountered in topographically and ecologically heterogeneous landscapes. In addition, SpeciesGeoCoder can be used to facilitate sorting and cleaning of occurrence data obtained from online databases, and for testing the impact of incorrect identification of specimens on the spatial coding of species. The various outputs of SpeciesGeoCoder include quantitative biodiversity statistics, global and local distribution maps, and files that can be used directly in many phylogeny-based applications for ancestral range reconstruction, investigations of biome evolution, and other comparative methods. Our simulations indicate that even datasets containing hundreds of millions of records can be analyzed in relatively short time using a standard computer. We exemplify the use of SpeciesGeoCoder by inferring the historical dispersal of birds across the Isthmus of Panama, showing that lowland species crossed the Isthmus about twice as frequently as montane species with a marked increase in the number of dispersals during the last 10 million years.

厘清生物多样性在空间分布上的不均模式与形成机制，是系统生物学与生物地理学领域的重大科学挑战，而该领域的研究高度依赖对快速增长的物种出现数据（species occurrence data）进行有效映射与解读。因此，亟需实现将物种编码至空间单元这一流程的快速化、自动化、透明化与可复现性。本文介绍了SpeciesGeoCoder——一款基于Python与R语言开发的开源软件包，可便捷地将物种编码至用户自定义的空间单元（operational units）。此类单元尺寸灵活可调，既可为纯粹的空间实体（即多边形区域），例如国家、省级行政区、保护地、生物群区、岛屿、生物多样性热点地区以及特有分布区，也可涵盖海拔梯度范围。这种灵活性支持将物种划分至复杂类别中，适配地形与生态异质性较强的复杂景观场景。此外，SpeciesGeoCoder还可辅助完成从在线数据库获取的物种出现数据的筛选与清洗工作，亦可用于检验标本鉴定错误对物种空间编码的影响。SpeciesGeoCoder的各类输出结果包含量化生物多样性统计数据、全球与局部分布地图，以及可直接应用于多种基于系统发育的分析流程的文件，可用于祖先分布区重建、生物群区演化研究以及其他比较生物学分析方法。我们的模拟实验表明，即便包含数亿条记录的数据集，也可在普通计算机上于较短时间内完成分析。我们以巴拿马地峡跨洋鸟类的历史扩散事件分析为例，演示了SpeciesGeoCoder的使用方法，结果显示低地物种跨越地峡的频率约为山地物种的两倍，且在过去1000万年中扩散事件的数量出现了显著增长。