Assigning occurrence data to cryptic taxa improves climatic niche assessments: biodecrypt, a new tool tested on European butterflies

Aim
Occurrence data are fundamental to macroecology, but accuracy is often compromised when multiple units are lumped together (e.g. in recently separated cryptic species or citizen science records). Using amalgamated data leads to inaccuracy in species mapping, to biased beta-diversity assessments and to potentially erroneously predicted responses to climate change. We provide a set of R functions (biodecrypt) to objectively attribute undetermined occurrences to the most probable taxon based on a subset of identified records.

Innovation
Biodecrypt assumes that unknown occurrences can only be attributed at certain distances from areas of sympatry. The function draws concave hulls based on the subset of identified records; subsequently, based on hull geometry, it attributes (or not) unknown records to a given taxon. Concavity can be imposed with an alpha value and sea or land areas can be excluded. A cross-validation function tests attribution reliability and another function optimi...

目标
物种出现数据是宏观生态学研究的基石，但当多个分类单元被合并（例如在近期分离的隐存种或公民科学记录中）时，其准确性常受影响。使用合并数据会导致物种分布图绘制不准确、β多样性评估产生偏差，以及可能错误预测物种对气候变化的响应。我们提供一套R函数（biodecrypt），基于已鉴定记录的子集，将未确定的分布记录客观归因于最可能的分类单元。

创新点
Biodecrypt假设未知分布记录只能在与同域分布区域相距特定距离内被归因。该函数基于已鉴定记录的子集绘制凹包；随后，根据凹包的几何特征，将未知记录归因于（或不归因于）特定分类单元。凹度可通过α值设定，且可排除海洋或陆地区域。一个交叉验证函数用于测试归因可靠性，另一个函数则用于优化...