1. Biologists often seek to geographically provenance organisms using their traits. This is typically achieved by defining spatial groups using distinct patterns of trait variation.
2. Here, we present a new spatial provenancing and trait boundary identification methodology, based on correlations between geographic and trait distances that require no a priori group assumptions. We apply this to three datasets where spatial provenance is sought: morphological rat and vole dentition data (human commensal translocation datasets); and birdsong data (cultural transmission dataset). We also present the results of cross‐validation testing.
3. Spatial provenancing is possible with differing degrees of accuracy for each dataset, with birdsong providing the most accurate geographic origin (identifying an average spatial region of 0.22 km² as the area of origin with 99.9% confidence).
4. Our method has a wide range of potential applications to diverse data types—including phenotypic, genetic and cultural—to identify trait boundaries and spatially provenance the origin of unknown or translocated specimens where trait differences are geographically structured and correlated with spatial separation.

中文翻译：

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geoorigins：一种无分类约束的标本特征映射和地理出处的新方法和软件包

1. 生物学家经常试图利用它们的性状对地理来源的生物进行鉴定。这通常是通过使用特质变化的不同模式定义空间组来实现的。
2. 在这里，我们提出了一种新的空间出处和特征边界识别方法，该方法基于不需要先验组假设的地理距离和特征距离之间的相关性。我们将其应用于寻求空间出处的三个数据集：形态大鼠和田鼠齿列数据（人类共生易位数据集）；和Birdong数据（文化传播数据集）。我们还介绍了交叉验证测试的结果。
3. 每个数据集的空间出处都可能具有不同的准确度，而Birdong可以提供最准确的地理起源（将0.22 km ²的平均空间区域识别为99.9％的置信区域）。
4. 我们的方法对包括表型，遗传和文化在内的各种数据类型具有广泛的潜在应用，以识别特征边界和空间出处，即未知或易位标本的起源，特征差异在地理上是结构化的并且与空间分隔相关。