Package: dynamicSDM 1.3.4

dynamicSDM: Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution

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.

Authors:Rachel Dobson [aut, cre, ctb], Andy J. Challinor [aut, ctb], Robert A. Cheke [aut, ctb], Stewart Jennings [aut, ctb], Stephen G. Willis [aut, ctb], Martin Dallimer [aut, ctb]

dynamicSDM_1.3.4.tar.gz
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dynamicSDM.pdf |dynamicSDM.html✨
dynamicSDM/json (API)
NEWS

# Install 'dynamicSDM' in R:
install.packages('dynamicSDM', repos = c('https://r-a-dobson.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/r-a-dobson/dynamicsdm/issues

Datasets:

On CRAN:

dynamicsdmgoogle-earth-enginegoogledrivesdmspatiotemporalspatiotemporal-data-analysisspatiotemporal-forecastingspecies-distribution-modellingspecies-distributions

5.86 score 6 stars 15 scripts 270 downloads 23 exports 106 dependencies

Last updated 4 months agofrom:a0c4253504. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxWARNINGOct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macWARNINGOct 31 2024
R-4.3-winWARNINGOct 31 2024
R-4.3-macWARNINGOct 31 2024

Exports:%>%brt_fitconvert_gbifdynamic_projdynamic_proj_covariatesdynamic_proj_datesdynamic_proj_GIFextract_buffered_coordsextract_buffered_rasterextract_coords_combineextract_dynamic_coordsextract_dynamic_rasterextract_static_coordsget_moving_windowspatiotemp_autocorrspatiotemp_biasspatiotemp_blockspatiotemp_checkspatiotemp_extentspatiotemp_pseudoabsspatiotemp_resolutionspatiotemp_thinspatiotemp_weights

Dependencies:askpassbase64encbitbit64bslibcachemclassclassIntclicliprcolorspacecpp11crayoncrosstalkcurlDBIdigestdplyre1071evaluatefansifarverfastmapfontawesomefsgarglegenericsgeojsonsfgeometriesgluegoogledriveherehighrhmshtmltoolshtmlwidgetshttrjquerylibjsonifyjsonliteKernSmoothknitrlabelinglatticelazyevalleafemleafletleaflet.providerslifecyclelubridatemagrittrMASSMatrixmemoisemimemunsellopensslpillarpkgconfigpngprettyunitsprocessxprogressproxypspurrrR6rapidjsonrrappdirsrasterRColorBrewerRcppRcppTOMLreadrreticulatergeerlangrmarkdownrprojrootrstudioapis2sassscalessfsfheadersspstringistringrsysterratibbletidyrtidyselecttimechangetinytextzdbunitsutf8uuidvctrsviridisLitevroomwithrwkxfunyaml

dynamicSDM: Dynamic distribution projections

Rendered fromvignette4_projecting.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-06-10
Started: 2023-02-05

dynamicSDM: Explanatory variable data

Rendered fromvignette2_explanatory_data.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-03-16
Started: 2023-02-05

dynamicSDM: Model fitting and autocorrelation

Rendered fromvignette3_modelling.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-06-09
Started: 2023-02-05

dynamicSDM: Response variable data

Rendered fromvignette1_response_data.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2023-06-09
Started: 2023-02-05

Readme and manuals

Help Manual

Help pageTopics
Fit boosted regression tree models to species distribution or abundance data.brt_fit
Reformats GBIF data into 'dynamicSDM' data frameconvert_gbif
Project species distribution and abundance models onto dynamic environmental covariates.dynamic_proj
Combine explanatory variable rasters into covariates for each projection date.dynamic_proj_covariates
Generate vector of dates for dynamic projectionsdynamic_proj_dates
Create GIF of dynamic species distribution and abundance projectionsdynamic_proj_GIF
Extract spatially buffered and temporally dynamic explanatory variable data for occurrence records.extract_buffered_coords
Extract spatially buffered and temporally dynamic rasters of explanatory variable data.extract_buffered_raster
Combine extracted explanatory variable data for occurrence records into single data frame.extract_coords_combine
Extract temporally dynamic explanatory variable data for occurrence records.extract_dynamic_coords
Extract temporally dynamic rasters of explanatory variables.extract_dynamic_raster
Extract explanatory variables from static rastersextract_static_coords
Generate a “moving window” matrix of optimal sizeget_moving_window
Sample projection covariates three variables across for southern Africa.sample_cov_data
Sample e-Bird sampling event recordssample_events_data
Sample species occurrence records with associated dynamic explanatory variablessample_explan_data
MULTIPOLYGON object for the extent of southern Africasample_extent_data
Sample of filtered species occurrence recordssample_filt_data
Sample species occurrence recordssample_occ_data
Test for spatial and temporal autocorrelation in species distribution model explanatory data.spatiotemp_autocorr
Test for spatial and temporal bias in species occurrence recordsspatiotemp_bias
Split occurrence records into spatial and temporal blocks for model fitting.spatiotemp_block
Check species occurrence record formatting, completeness and validity.spatiotemp_check
Filter species occurrence records by a given spatial and temporal extent.spatiotemp_extent
Generate pseudo-absence record coordinates and datesspatiotemp_pseudoabs
Filter species occurrence records by given spatial and temporal resolutionspatiotemp_resolution
Thin species occurrence records by spatial and temporal proximity.spatiotemp_thin
Calculate sampling effort across spatial and temporal buffer from species occurrence recordsspatiotemp_weights