Similar species ranges may represent outcomes of common biological processes and so form the basis for biogeographical concepts such as areas of endemism and ecoregions. Nevertheless, spatial range congruence is rarely quantified, much less incorporated in bioregionalization methods as an explicit parameter. Furthermore, most available methods suffer from limitations related to the loss, or the excess of range information, or scale bias associated with the use of grids, and the incapacity to recognize independent overlapped patterns or gradients of range distributions. Here, we propose an analytical method, Spatial Congruence Analysis (SCAN), to identify biogeographically meaningful groups of species, called biogeographic elements. Such elements are based on direct and indirect spatial relationships among species’ ranges and vary depending on an explicit measure of range congruence controlled as a numerical parameter in the analysis. A one-layered network connects species (vertices) using pairwise spatial congruence estimates (edges). This network is then analyzed for each species, separately, by an algorithm that accesses the entire web of spatial relationships to the reference species. The method was applied to two datasets: a simulated gradient of ranges and real distributions of birds. The gradient results showed that SCAN can describe gradients of distribution with a high level of detail, without confounding transition zones with true biogeographical units, a frequent pitfall of other methods. The bird dataset showed that only a small portion of range overlaps is biogeographically meaningful, and that there is a large variation in types of patterns that can be found with real distributions. Distinct reference species may converge on similar or identical groups of spatially related species, may lead to recognition of nested species groups, or may even generate similar spatial patterns with no species in common. The biological significance or causal processes of these patterns should be investigated a posteriori. Patterns can vary from simple ones, composed by few highly congruent species, to complex, with numerous alternative component species and spatial configurations, depending on particular parameter settings as determined by the investigator. This approach eliminates or reduces limitations of other methods and permits pattern description without hidden assumptions about processes, and so should make a valuable contribution to the biogeographer’s toolbox.

中文翻译：

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空间一致性分析（SCAN）：一种基于物种范围一致性来检测生物地理模式的客观方法

相似的物种范围可能代表了常见生物过程的结果，因此构成了诸如特有物种和生态区域等生物地理概念的基础。然而，空间范围的一致性很少被量化，更少地纳入生物区域化方法中作为显式参数。此外，大多数可用的方法受到与丢失，距离信息过多或与使用网格相关的比例偏差有关的限制，并且无法识别独立的重叠图案或距离分布梯度。在这里，我们提出一种分析方法，空间同余分析（SCAN），以识别生物地理上有意义的物种组，称为生物地理元素。这些元素基于物种范围之间的直接和间接空间关系，并根据分析中作为数值参数控制的范围一致性的显式度量而变化。一层网络使用成对的空间一致性估计（边缘）连接物种（顶点）。然后，通过访问整个空间关系网络到参考物种的算法，分别为每个物种分析该网络。该方法已应用于两个数据集：模拟的距离梯度和鸟类的实际分布。梯度结果表明，SCAN可以描述高度详细的分布梯度，而不会将过渡带与真实的生物地理单位混淆，这是其他方法的常见陷阱。鸟类数据集显示，范围重叠的一小部分在生物地理意义上是有意义的，并且可以通过实际分布找到的图案类型存在很大差异。不同的参考物种可能会聚集在空间相关物种的相似或相同组上，可能导致对嵌套物种组的识别，甚至可能生成没有共同物种的相似空间模式。这些模式的生物学意义或因果过程应在后验方法上进行研究。模式可能会有所不同，从简单的模式（由很少的高度一致的物种组成）到复杂的模式（取决于研究人员确定的特定参数设置），以及许多替代的组件物种和空间配置。