2022
DOI: 10.1111/2041-210x.13874
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flexsdm: An r package for supporting a comprehensive and flexible species distribution modelling workflow

Abstract: 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. We introduce the r package flexsdm for supporting flexible species distribution modelling workflows. flexsdm functions and their argume… Show more

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Cited by 48 publications
(35 citation statements)
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References 36 publications
(40 reference statements)
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“…This distance threshold was defined as the maximum nearest neighbour distance among pairs of occurrences of the respective species (the ‘OBR’ approach in Mendes et al, 2020). Computations were performed in R 4.1.0 (R Core Team, 2021) using the package ENMTML (Andrade, Velazco, et al, 2020) for non‐rare species and the package flexsdm (Velazco et al, 2022) for rare species.…”
Section: Methodsmentioning
confidence: 99%
“…This distance threshold was defined as the maximum nearest neighbour distance among pairs of occurrences of the respective species (the ‘OBR’ approach in Mendes et al, 2020). Computations were performed in R 4.1.0 (R Core Team, 2021) using the package ENMTML (Andrade, Velazco, et al, 2020) for non‐rare species and the package flexsdm (Velazco et al, 2022) for rare species.…”
Section: Methodsmentioning
confidence: 99%
“…To reduce over fitting, we filtered the data in both geographic and environmental space. We retained one record per km 2 grid cell, then used the ‘occfilt_env’ function from the ‘flexsdm’ package version 1.3.2 (Velazco et al, 2022) with 12 bins to remove records with similar values across all environmental predictor variables. We also tested using 5 and 20 bins (more bins exclude less records), but settled on 12 as this appeared to achieve the best balance between the number of records returned and the evenness of environmental values.…”
Section: Methodsmentioning
confidence: 99%
“…We therefore cleaned the pooled occurrence records by removing duplicates, masking to terrestrial landmasses, and removing occurrences intersecting within a 2 km radius of zoo, herbaria and country capital centroids with the CoordinateCleaner R package (Zizka et al, 2019). Lastly, to further correct for sampling biases in our presence records we applied spatial thinning to include one presence per 1 km 2 grid cell to match the spatial resolution of our environmental variables with the FLEXSDM v1.3.2 R package (Velazco et al, 2022). For distribution modelling, we produced an equal sample of pseudo-absences to cleaned presences and randomly distributed them outside of a 1 km buffer of each presence point for each model (Velazco et al, 2022).…”
Section: Methodsmentioning
confidence: 99%