| Abstract |
Climate matching, a tool for predicting non‐native species survival in target (recipient) regions, is commonly used in invasive species frameworks such as horizon scanning and screening‐level risk assessment protocols. Screening‐level risk assessments often require the analysis of many species with limited resources, and climate matching can be advantageous to identify a reduced number of species for more detailed analyses. Additionally, risk screening may require examination of non‐native species' source pools where species occurrence records are not used in model training data. In these instances, climate matching is an effective method for assessing the survival of non‐native species or their source pools in a target region and has practical advantages over species distribution models. We introduce the R package ‘Euclimatch' for quantitative climate matching with the Euclidean distance algorithm Climatch. The package provides tools for creating a streamlined data‐agnostic climate‐matching workflow. First, climate data are extracted for species occurrence records or regions. Second, climate match is modelled between two regions as a similarity score per grid cell or summarized across a target region. Third, visualizations of the climate match model outputs are created. We demonstrate the use of the ‘Euclimatch' package with the climate match of two popular aquarium trade species and a region‐to‐region analysis. We also demonstrate differences in results between Euclidean distance metric standardization methods when incorporating climate‐change projections. The scale of each example is global, under historical and projected climates. ‘Euclimatch' provides a scripting interface for Euclidean climate matching for the screening assessment of non‐native species or regions under any climatic conditions. ‘Euclimatch' can be downloaded from the comprehensive R archive network (CRAN).
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