CONICET | Buscador de Institutos y Recursos Humanos

1. Species distribution models (SDM) are widely used in diverse research areas because of their simple data requirements and application versatility. However, SDM outcomes are sensitive to data input and methodological choices. Such sensitivity and diverse applications mean that flexibility is necessary to create SDMs with tailored protocols for a given set of data and model use. 2. We introduce the R package flexsdm for supporting flexible species distribution modeling workflows. flexsdm functions and their arguments serve as building blocks to construct a specific modeling protocol for user?s needs. The main flexsdm features are modeling flexibility, integration with other modeling tools, simplicity of the objects returned, and function speed. As an illustration, we used flexsdm to define a complete workflow for California red fir (Abies magnifica). 3. This package provides modeling flexibility by incorporating comprehensive tools structured in three steps: i) The Pre-modeling functions that prepare input, e.g., sampling bias correction, sampling pseudo-absences and background points, data partitioning, and reducing collinearity in predictors. ii) the Modeling functions allow fitting and evaluating different modeling approaches, including individual algorithms, tuned models, ensembles of small models, and ensemble models. iii) the Post-modeling functions include tools related to models? predictions, interpolation, and overprediction correction. 4. Because flexsdm comprises a large part of the SDM process, from outlier detection to overprediction correction, flexsdm users can delineate partial or complete workflows based on the combination functions to meet specific modeling needs.

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