Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution
Test for spatial and temporal bias in species occurrence records
Fit boosted regression tree models to species distribution or abundanc...
Reformats GBIF data into dynamicSDM data frame
Combine explanatory variable rasters into covariates for each projecti...
Generate vector of dates for dynamic projections
Create GIF of dynamic species distribution and abundance projections
Project species distribution and abundance models onto dynamic environ...
Split occurrence records into spatial and temporal blocks for model fi...
Check species occurrence record formatting, completeness and validity.
dynamicSDM: Species Distribution and Abundance Modelling at High Spati...
Extract spatially buffered and temporally dynamic explanatory variable...
Extract spatially buffered and temporally dynamic rasters of explanato...
Combine extracted explanatory variable data for occurrence records int...
Extract temporally dynamic explanatory variable data for occurrence re...
Extract temporally dynamic rasters of explanatory variables.
Extract explanatory variables from static rasters
Generate a “moving window” matrix of optimal size
Pipe operator
Test for spatial and temporal autocorrelation in species distribution ...
Filter species occurrence records by a given spatial and temporal exte...
Generate pseudo-absence record coordinates and dates
Filter species occurrence records by given spatial and temporal resolu...
Thin species occurrence records by spatial and temporal proximity.
Calculate sampling effort across spatial and temporal buffer from spec...
A collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.
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