dynamicSDM: Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution (original) (raw)

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.

Version: 1.3.4
Depends: R (≥ 3.5.0)
Imports: dplyr, googledrive, lubridate, magrittr, reticulate, rgee, stats, terra, tidyr, grDevices, graphics, methods, utils, sf, readr
Suggests: ape, CoordinateCleaner, covr, gargle, gbm, ggplot2, knitr, magick, matrixStats, rmarkdown, spThin, stars, testthat (≥ 3.0.0), viridis
Published: 2024-06-28
DOI: 10.32614/CRAN.package.dynamicSDM
Author: Rachel Dobson ORCID iD [aut, cre, ctb], Andy J. Challinor ORCID iD [aut, ctb], Robert A. Cheke ORCID iD [aut, ctb], Stewart Jennings ORCID iD [aut, ctb], Stephen G. Willis ORCID iD [aut, ctb], Martin Dallimer ORCID iD [aut, ctb]
Maintainer: Rachel Dobson
BugReports: https://github.com/r-a-dobson/dynamicSDM/issues
License: GPL (≥ 3)
URL: https://github.com/r-a-dobson/dynamicSDM
NeedsCompilation: no
Citation: dynamicSDM citation info
Materials: README NEWS
CRAN checks: dynamicSDM results

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