Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia ; Department of Zoology, University of Johannesburg, Auckland Park, South Africa.
Centre for Invasion Biology and Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa.
PLoS One. 2014 Jan 15;9(1):e85306. doi: 10.1371/journal.pone.0085306. eCollection 2014.
Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets.
许多生态研究依赖于现有的多物种分布数据集。然而，这些数据集在质量、范围、分辨率或分类涵盖范围上可能存在很大差异。我们提供了一个框架，用于对大规模物种分布数据集中的地理代表性进行空间明确的评估，使用出现地图集与范围地图集数据集的比较作为工作示例。具体来说，我们将广泛使用的《欧洲植物志图集》（AFE）的 3773 个分类群的出现地图与鲜为人知的《北欧维管植物图集》的 2049 个分类群的数字化范围地图进行了比较。我们使用平均纬度和经度物种范围以及占有面积，在 50 公里的空间分辨率下计算了物种分布的一致性水平。使用杰卡德相似性指数和物种丰富度的简化主要轴（RMA）回归分析，对整个图集（总共 5221 个分类群）和共存物种（601 个分类群）之间的物种分布进行了一致性计算和映射。我们发现分布范围或占有面积频率分布没有差异，表明图集之间有足够的重叠，可以进行有效比较。相似性指数图显示了中欧、西欧和北欧的高度一致性。RMA 回归证实，AFE 的地理代表性在数据记录历史稀疏的地区（例如俄罗斯、白俄罗斯和乌克兰）较低。然而，对于东南欧的共存物种，北欧维管植物图集显示出显著更高的丰富度估计。图集数据的地理代表性可能比通常假设的更为多样化。数据集之间的一致性水平可用于评估数据集中的地理代表性。尽管存在方法差异，但将图集合并到单个数据集中是值得的，有助于填补我们对物种分布范围的认识空白。这里举例说明的物种分布数据集合并可以作为全面的物种分布数据集的基线。
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