dynamicsdm

An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

https://github.com/r-a-dobson/dynamicsdm

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An R package for species geographical distribution and abundance modelling at high spatiotemporal resolution

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# dynamicSDM


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# 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")
```

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