SDMtoolbox

SDMtoolbox integrates spatial analysis and species distribution modeling workflows to support spatial ecology, landscape genetics, biogeography, and evolutionary studies by providing Python scripts that leverage ArcGIS (ArcMap) and MaxEnt for processing occurrence data, environmental variables, and SDM parameterization.

Key Features:

• Integration with ArcGIS and MaxEnt: Integrates ArcGIS (ArcMap) and MaxEnt to perform GIS-based spatial analyses and species distribution modeling.
• Automated spatial analyses: Automates repetitive spatial procedures through scripted workflows.
• Species Distribution Modeling (SDM) parameterization: Provides tools for processing occurrence data, environmental variables, and model parameters to minimize overfitting in SDMs.
• Pre- and post-processing tools: Includes universal pre- and post-processing utilities applicable across different modeling software for preparing inputs and handling outputs.
• Extensive script library: Contains 79 scripts for macroecology, landscape genetics, and evolutionary studies enabling biodiversity quantification (species richness, corrected weighted endemism), generation of least-cost paths and corridors among shared haplotypes, assessment of spatial randomizations' significance, and enforcement of dispersal limitations in SDMs projected into future climates.
• Batch processing and data conversion: Provides tools for batch processing and conversion of GIS data types and formats.

Scientific Applications:

• Landscape genetics: Supports analyses such as least-cost paths and corridors among shared haplotypes for landscape genetic inference.
• Biogeography: Enables biodiversity quantification and spatial analyses relevant to biogeographic research.
• Species distribution modeling: Supports SDM parameterization and projection into future climates with enforcement of dispersal limitations via MaxEnt integration.
• Biodiversity assessment: Facilitates calculation of species richness and corrected weighted endemism.
• Evolutionary studies: Provides macroecological and spatial genetics scripts applicable to evolutionary investigations.
• Ecological forecasting: Enables projection of SDMs into future climates with constraints to assess potential distributional changes under environmental change.

Methodology:

Python-based scripts leverage ArcGIS/ArcMap and MaxEnt; include 79 scripts for pre- and post-processing, batch GIS data conversion, biodiversity metrics (species richness, corrected weighted endemism), processing of occurrence data and environmental variables, SDM parameterization to reduce overfitting, generation of least-cost paths and corridors among shared haplotypes, assessment of spatial randomizations' significance, and enforcement of dispersal limitations in SDM projections into future climates.