https://github.com/r-a-dobson/dynamicsdm
An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution
https://github.com/r-a-dobson/dynamicsdm
dynamicsdm google-earth-engine googledrive sdm spatiotemporal spatiotemporal-data-analysis spatiotemporal-forecasting species-distribution-modelling species-distributions
Last synced: 5 months ago
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An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution
- Host: GitHub
- URL: https://github.com/r-a-dobson/dynamicsdm
- Owner: r-a-dobson
- Created: 2022-11-25T15:56:47.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-07-14T06:50:14.000Z (about 1 year ago)
- Last Synced: 2025-12-09T11:05:48.020Z (8 months ago)
- Topics: dynamicsdm, google-earth-engine, googledrive, sdm, spatiotemporal, spatiotemporal-data-analysis, spatiotemporal-forecasting, species-distribution-modelling, species-distributions
- Language: R
- Homepage: https://github.com/r-a-dobson/dynamicSDM
- Size: 28.7 MB
- Stars: 6
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# dynamicSDM
[](http://www.gnu.org/licenses/gpl-3.0.html)
[](https://app.codecov.io/gh/r-a-dobson/dynamicSDM?branch=main)
[](https://github.com/r-a-dobson/dynamicSDM/actions)
[](https://doi.org/10.5281/zenodo.7405906)
[](https://doi.org/10.1111/2041-210X.14101)
[](https://CRAN.R-project.org/package=dynamicSDM)
# Added features dynamicSDM v1.3
* Added function `extract_static_coords` for extracting spatially-buffered co-ordinate data from static datasets.
* Added arguments to `dynamic_proj_covariates()` for adding static rasters to covariates for each data (e.g. static elevation raster)
* Removed package dependency on `raster`, `sp`, `geodist` and `geosphere`.
* All functions are now `terra` and `sf` compatible.
* The package has since been published in the **Open Access journal "Methods in Ecology and Evolution"**
[*Dobson, R., Challinor, A.J., Cheke, R.A., Jennings, S., Willis, S.G. and Dallimer, M., 2023. dynamicSDM: An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution. Methods in Ecology and Evolution, 14, 1190-1199.*](https://doi.org/10.1111/2041-210X.14101)
# Summary
Across ecological research fields, species distribution and abundance modelling (SDM) is a major tool for understanding the drivers and patterns of species occurrence. To advance our ability to model species inhabiting dynamic ecosystems worldwide, **dynamicSDM** facilitates the incorporation of explanatory variables that are dynamic in both space and time.
Our functions are:
* **user-friendly** - requiring only simple inputs and outputs;
* **highly flexible** - offering diverse and open arguments for targeting study specifics;
* **computer friendly** - utilising Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with high spatiotemporal resolution modelling.
# Package structure
dynamicSDM functions are split into four key modelling stages: response data, explanatory variables, modelling relationships and dynamic projections. See the package manual [here](https://github.com/r-a-dobson/dynamicSDM/blob/main/man/figures/dynamicSDM_1.3.pdf) for more details on each function.
### 1) Response data functions
Functions for preparing species distribution or abundance model input data for modelling with spatiotemporally dynamic explanatory variables.
* `convert_gbif()` Transform Global Biodiversity Information Facility occurrence records to `dynamicSDM` compatible.
* `spatiotemp_check()` Check species occurrence record formatting, completeness and validity.
* `spatiotemp_extent()` Filter species occurrence records by a given spatial and temporal extent.
* `spatiotemp_resolution()` Filter species occurrence records by given spatial and temporal resolution.
* `spatiotemp_bias()` Test for spatial and temporal bias in species occurrence records.
* `spatiotemp_thin()` Thin species occurrence records by spatial and temporal proximity.
* `spatiotemp_pseudoabs()` Generate pseudo-absence record coordinates and dates.
* `spatiotemp_weights()` Calculate sampling effort across spatial and temporal buffer from occurrence records.
### 2) Explanatory variable functions
Functions for extracting spatiotemporally dynamic explanatory variable data for species occurrence record co-ordinates and dates using Google Earth Engine.
* `extract_dynamic_coords()` Extract temporally dynamic explanatory variable data for occurrence records.
* `get_moving_window()` Generate a “moving window” matrix of optimal size for spatial buffering of explanatory variable data.
* `extract_buffered_coords()` Extract spatially buffered and temporally dynamic explanatory variable data for occurrence records.
* `extract_coords_combine()` Combine extracted explanatory variable data for occurrence records into single data frame for model fitting.
* `extract_static_coords` Extract spatially buffered data from static rasters for occurrence record co-ordinates (no temporal dimension).
### 3) Modelling relationship functions
Functions for generating species distribution or abundance models that account for spatial and temporal autocorrelation in dynamic explanatory variables.
* `spatiotemp_autocorr()` Test for spatial and temporal autocorrelation in species distribution model explanatory data.
* `spatiotemp_block()` Split occurrence records into spatial and temporal blocks for model fitting.
* `brt_fit()` Fit boosted regression tree models to species distribution or abundance data.
### 4) Dynamic projection functions
Functions for generating explanatory variable projection data frames at given spatiotemporal extent and resolution, and projecting species dynamic distribution and abundance patterns onto these.
* `dynamic_proj_dates()` Generate vector of dates for dynamic projections
* `extract_dynamic_raster()` Extract temporally dynamic rasters of explanatory variable data.
* `extract_buffered_raster()` Extract spatially buffered and temporally dynamic rasters of explanatory variable data.
* `dynamic_proj_covariates()` Combine explanatory variable rasters into a covariate data frame for each projection date.
* `dynamic_proj()` Project species distribution and abundance models onto dynamic environmental covariates.
* `dynamic_proj_GIF()` Create GIF of dynamic species distribution and abundance projections
# Installation
``` r
# Install using Github
install_github("r-a-dobson/dynamicSDM")
```
# Common installation errors
dynamicSDM depends on a range of spatial and graphic R packages, which may result in some persistent
errors on installation or running of certain functions.
If you encounter an error or bug when installing and using dynamicSDM, please post a comment
[here](https://github.com/r-a-dobson/dynamicSDM/issues) for guidance and support from us.
Below we have outlined common errors and typical solutions to try, depending on your operating
system
#### 1) Error with rgl
```{r,eval=F}
# Loading rgl's DLL failed. This build of rgl depends on XQuartz, which failed to load.
options(rgl.useNULL = TRUE)
library(rgl)
```
#### 2) Dependency package terra
On Homebrew (macOS) run:
```{Homebrew,eval=F}
brew install pkg-config
brew install gdal
```
On Linux run:
```{Linux,eval=F}
sudo apt-get install libgdal-dev libproj-dev libgeos-dev libudunits2-dev netcdf-bin
```
Then in R run:
```{r,eval=F}
install.packages("Rcpp")
install.packages('terra', repos='https://rspatial.r-universe.dev')
```
#### 3) Dependency package magick
On Homebrew (macOS) run:
```{Homebrew,eval=F}
brew install imagemagick@6
```
On Linux run:
```{Linux,eval=F}
sudo apt-get install -y libmagick++-dev
```
Then in R run:
```{r,eval=F}
install.packages("magick")
```