期刊
METHODS IN ECOLOGY AND EVOLUTION
卷 13, 期 8, 页码 1661-1669出版社
WILEY
DOI: 10.1111/2041-210X.13874
关键词
ecoinformatics; ecological niche modelling; ensemble modelling; model fit for purpose; model tuning; spatial ecology; spatially structured validation
类别
资金
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [165174/2020-0]
- National Science Foundation [1853697]
Species distribution models (SDM) have been widely used due to their simplicity and versatility. However, SDM outcomes are sensitive to data input and methodological choices. To address this issue and meet different modeling needs, we present flexsdm, a package that supports flexible species distribution modeling workflows.
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 arguments serve as building blocks to construct a specific modelling protocol for user's needs. The main flexsdm features are modelling flexibility, integration with other modelling 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. This package provides modelling flexibility by incorporating comprehensive tools structured in three steps: (a) The Pre-modelling functions that prepare input, for example, sampling bias correction, sampling pseudo-absences and background points, data partitioning, and reducing collinearity in predictors. (b) The Modelling functions allow fitting and evaluating different modelling approaches, including individual algorithms, tuned models, ensembles of small models and ensemble models. (c) The Post-modelling functions include tools related to models' predictions, interpolation and overprediction correction. 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 modelling needs.
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