Bob O'Hara & Lea Dambly 8/7/2019
This is the repository for the code for the integrated modelling example for Isaac et al. (submitted), fitting a model for the black-throated blue warbler (Setophaga caerulescens) in Pennsylvania, USA. This is an extension of the analysis in Miller et al. (2019).
We use the following observation data:
- Pennsylvania Breeding Bird Atlas point count data for part of Pennsylvania. This is taken straight from the SI of Miller et al. (2019)
- eBird data from 2006-2009, downloaded from GBIF, using the spocc package (Chamberlain 2018)
- North American BBS data (Pardieck and Hudson. 2018) for 2005-2009
These data are in the Data directory.
For environmental data we use elevation and canopy cover, both imported directly into R.
- Elevation is imported using the elevatr package which utilises Amazon Web Services terrain tiles (Hollister and Shah 2017)
- Canopy cover is imported using the FedData package which downloads the USGS's NLCD canopy data from 2011 (Bocinsky 2019)
In addition, we use population density from the U.S. Census Bureau (Recht 2019), as a covariate on the eBird data. If you wish to download this data directly, you will need a census key, which you can get by signing up here. The data used in the model fitting has already been downloaded.
Note that the files should run "as is", but the predictions will be at a coarser scale than used in final version. This is to ensure it runs fairly quickly (high performance computers were used for the final version). If you want the predictions at the scale used in the final version, set Nxy.scale <- 0.01 before you run MakeStacks.R.
The workflow is as follows:
- extract the observation data, with ExtractData.R. Note that you will need a key for the US census to run this. This has already been done, and the results are the .csv files in Data/
- make the INLA stacks, using MakeStacks.R. This includes the covariate data, and creates some large objects that we don't save in this repository.
- Fit the model, with FitWarblerModel.R. This takes some time (overnight with the fine-scaled prediction surface), and creates another big .RData file.
- Look at and plot the results, with WarblerResults.R. This produces some maps
This Directory
- ExtractData.R: Code to extract data from different sources, and save as .csvs.
- FitWarblerModel.R: Code to, um, fit the warbler model
- IM_warbler.Rproj: R project file
- MakeStacks.R: Code to make INLA stacks from data.
- README.md: This file, unless you're looking at...
- README.Rmd: ... this file (the R Markdown file to make the Markdown file)
- References.bib: BibTex file with references
- WarblerResults.R: Code to plot & poke the results
- FitWarblerModelTwoRFs.R: Code to fit model with a random field on eBird observation effort. This is in development, so doesn't do anything useful (yet)
Data Folder
- BBA.csv: Pennsylvania breeding bird atlas data
- BBS.csv: North American breeding bird survey data
- eBird.csv: eBird data, downloaded from GBIF, plus census data from the US Census Bureau
Functions Folder
Some of these functions are from the current version of the PointedSDms package. As that package is in early development, we cannot guarantee the functions will be the same when you read this.
- Add2010Census.R: Functions to add 2010 census data to a data frame
- FitModel.R: Function to fit a model with INLA (from Pointed SDMs)
- GetBBSData.R: A couple of functions to update rBBS package
- GetNearestCovariate.R: Function to get covariate values nearest the data/integration point (from Pointed SDMs)
- MakeBinomStack.R: Function to create stack for presence only points (from Pointed SDMs)
- MakeIntegrationStack.R: Function to create stack for integration points (from Pointed SDMs)
- MakePointsStack.R: Function to create stack for presence only points (from Pointed SDMs)
- MakeProjectionGrid.R: Function to create stack for predictions (from Pointed SDMs)
- MakeSpatialRegion.R: Function to set up spatial structure for region (from Pointed SDMs)
Fitting the model with the FitModel.R function and the fully-scaled prediction layer can take >24 hours and can potentially crash due to the large amount of memory required. Thus a lower-resolution prediction is made, but see the note in MakeStacks.R about creating the full predictions.
If you want teh predictions at a high resolution and hit memory problems, there are a couple of possible solutions:
- reduce the number of threads used via the nthreads argument - this will increase runtime but reduce memory requirements.
- run the code on a larger machine or remote server, or ma.
Most of the code was written by Lea Dambly, and then changed by Bob O'Hara. Additional comments and help from Nick Isaac, Nick Golding and Colin Beale.
Bocinsky, R. Kyle. 2019. FedData: Functions to Automate Downloading Geospatial Data Available from Several Federated Data Sources. https://CRAN.R-project.org/package=FedData.
Chamberlain, Scott. 2018. Spocc: Interface to Species Occurrence Data Sources. https://CRAN.R-project.org/package=spocc.
Hollister, Jeffrey, and Tarak Shah. 2017. Elevatr: Access Elevation Data from Various Apis. http://github.com/usepa/elevatr.
Miller, David A. W., Krishna Pacifici, Jamie S. Sanderlin, and Brian J. Reich. 2019. “The Recent Past and Promising Future for Data Integration Methods to Estimate Species’ Distributions.” Methods in Ecology and Evolution 10 (1): 22–37. doi:10.1111/2041-210X.13110.
Pardieck, D.J. Ziolkowski Jr., K.L., and M.-A.R. Hudson. 2018. North American Breeding Bird Survey Dataset 1966 - 2017, Version 2017.0. U.S. Geological Survey, Patuxent Wildlife Research Center. https://doi.org/10.5066/F76972V8.
Recht, Hannah. 2019. Censusapi: Retrieve Data from the Census Apis. https://CRAN.R-project.org/package=censusapi.