Building a worklfow for Warblers acorss Pennsylvania state

This vignette illustrates using the intSDM R package for three types of warbler distributed across Pennsylvania on the Eastern side of the United States of America. This case study has been used in numerous other integrated species distribution model analyses (for example, Mostert and O’Hara (2023), Isaac et al. (2020) and Miller et al. (2019)) and includes three datasets: eBird, North American Breeding Bird Survey (BBS) and Pennsylvania Breeding Bird Atlas (BBA). Details on the data and the selection of observation models for each are provided within the references.


library(intSDM)
library(USAboundaries)

We will assume that the BBA and BBS data are not provided on GBIF, and thus will load them directly from the PointedSDMs package.


data("SetophagaData")
BBA <- SetophagaData$BBA
BBA$Species_name <- paste0('Setophaga_', BBA$Species_name)
BBS <- SetophagaData$BBS
BBS$Species_name <- paste0('Setophaga_', BBS$Species_name)


workflow <- startWorkflow(Richness = FALSE,
  Projection = "+proj=utm +zone=17 +datum=WGS84 +units=km",
  Species = c("Setophaga_caerulescens"), 
              #"Setophaga_fusca", "Setophaga_magnolia"),
  saveOptions = list(projectName =  'Setophaga'), Save = FALSE
)

The .$addArea() function only gives us access to country borders. However we can easily add other polygon objects to the workflow using the Object argument from the function.


workflow$addArea(Object = USAboundaries::us_states(states = "Pennsylvania"))

Next we add data to the analysis. The eBird dataset is available to download directly from GBIF, and thus may be downloaded into out workflow using the .$addGBIF function and specifying the relevant datasetKey. We limit our search to only 5000 species observations (per species) using the limit argument, however more species may be added if you wanted. In addition, we only include observations between 2005 and 2009, given that these are the time periods that the other datasets include.

The other two datasets are not directly available on GBIF, but may still be added using the .$addStructured function. This requires us to specify the response name of each dataset (using the responseName argument), and the species name variable (using the speciesName argument).


workflow$addGBIF(datasetName = 'eBird', datasetType = 'PO', limit = 5000,
                 datasetKey = '4fa7b334-ce0d-4e88-aaae-2e0c138d049e',
                 year = '2005,2009')

workflow$addStructured(dataStructured = BBS, datasetType = 'Counts',
                       responseName = 'Counts', 
                       speciesName = 'Species_name')

workflow$addStructured(dataStructured = BBA, datasetType = 'PA',
                       responseName = 'NPres', 
                       speciesName = 'Species_name')

workflow$plot(Species = TRUE)

We can then add the elevation and canopy covariates from the PointedSDMs package. These covariates have already been scaled. If we were planning on using worldClim covariates, we could have used the worldClim argument by specifying which variable we wanted to download.


covariates <- scale(terra::rast(system.file('extdata/SetophagaCovariates.tif', 
                                      package = "PointedSDMs")))
names(covariates) <- c('elevation', 'canopy')

workflow$addCovariates(Object = covariates)
  
workflow$plot(Covariates = TRUE)


workflow$addMesh(cutoff = 0.2 * 5,
                 max.edge = c(0.1, 0.24) * 120,
                 offset = c(0.1, 0.4) * 100)

workflow$plot(Mesh = TRUE)


##Use a correlative structure to share information across the datasets if the standard model does not produce results that we want

workflow$specifySpatial(prior.range = c(15, 0.1),
                        prior.sigma = c(1, 0.1))

workflow$biasFields(datasetName = 'eBird',
                    prior.range = c(15, 0.1),
                    prior.sigma = c(1, 0.1))

workflow$specifyPriors(effectNames = 'Intercept', 
                       Mean = 0, 
                       Precision = 1)

For this case study, we specify the model outcome as Model and cross-validation. This will give us: an R-INLA model outcome for which we could analyse further, and estimates of cross-validation.

The cross-validation we use is spatial-blocking, which will segment our study area into k folds. Here we use the Predict method, which will iteratively fit a training model for all combinations of dataset in all blocks except for one. The linear predictor of the training model is then calculated at the values of the test data (which is the first non-present-only dataset added into the model) in the left out block. A new testing model is then fit using the testing data is fit, using the predicted linear predictor as an offset in the model. The log marginal likelihood is then calculated from the testing model, and the model with the lowest log marginal likelihood across blocks is deemed best.


workflow$workflowOutput(c('Model', 'Cross-validation'))

workflow$crossValidation(Method = 'spatialBlock',  
                         blockOptions = list(k = 4, 
                                             rows_cols = c(20, 20), 
                                             plot = TRUE, seed = 123),
                         blockCVType = "Predict")

We may then estimate the model using sdmWorkflow, and print a summary of the models. Note that the cross-validation may take a long time to estimate, given that the model fits each combination of possible datasets, k times.


Model <- sdmWorkflow(Workflow = workflow, 
                      inlaOptions = list(control.inla=list(int.strategy = 'eb',
                                                    diagonal = 0.1,
                                                    cmin = 0),
                                  safe = TRUE,
                                  verbose = TRUE,
                                  inla.mode = 'experimental'))


Model[[1]]$Model


Model[[1]]$spatialBlock

Isaac, Nick JB, Marta A Jarzyna, Petr Keil, Lea I Dambly, Philipp H Boersch-Supan, Ella Browning, Stephen N Freeman, et al. 2020. “Data Integration for Large-Scale Models of Species Distributions.” Trends in Ecology and Evolution 35 (1): 56–67. https://doi.org/10.1016/j.tree.2019.08.006.

Miller, David AW, 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. https://doi.org/10.1111/2041-210X.13110.

Mostert, Philip S, and Robert B O’Hara. 2023. “PointedSDMs: An R Package to Help Facilitate the Construction of Integrated Species Distribution Models.” Methods in Ecology and Evolution 14 (5): 1200–1207. https://doi.org/10.1111/2041-210X.14091.