‘specleanr': an R package for automated flagging of environmental outliers in ecological data for modeling workflows

作者：Anthony Basooma, Astrid Schmidt‐Kloiber, Sami Domisch, Yusdiel Torres‐Cambas, Marija Smederevac‐Lalić, Vanessa Bremerich, Paul Meulenbroek, Martin Tschikof, Andrea Funk, Thomas Hein, Florian Borgwardt · 发表于：Ecography · 年份：2025 · DOI：10.1002/ecog.08221 · 被引用次数：2 · 研究领域：Fish Ecology and Management Studies、Species Distribution and Climate Change、Environmental DNA in Biodiversity Studies

Developing species distribution models (SDMs) requires high‐quality species occurrence records. These records, stemming from various sources with different sampling procedures, are often archived in open‐access databases, making automated data quality checks inevitable. Temporal, geographic, and taxonomic quality checks are usually conducted in SDM workflows, but checking for records distant in environmental space, i.e. outliers, is often ignored. Here, we present ‘specleanr', an R package that contains 20 outlier detection methods (ODMs) that can be ensembled to identify potential outliers in environmental predictors. These methods are categorized into 1) species‐specific ecological range, 2) univariate, and 3) multivariate ODMs. All potential outliers flagged by the different methods are pooled to identify absolute outliers (records appearing in multiple methods). The local regression (LOESS) method is then used to automatically set a threshold that optimally identifies the absolute outliers. Additionally, clustering records into poor, fair, moderate, very strong, and perfect outliers, as well as non‐outliers, is possible based on each record's likelihood as a potential outlier, which allows expert assessment. We demonstrated the approach to 15 fish species from the Danube River Basin, including native, alien, threatened, and common species. We fitted SDMs using bioclimatic and hydromorphological parameters. We compared the model area under the curve (AUC) before and after ...