# Multiple data types

#### James T. Thorson

    library(tinyVAST)
    library(sf)
    library(fmesher)
    library(ggplot2)

`tinyVAST` is an R package for fitting vector autoregressive spatio-temporal (VAST) models using a minimal and user-friendly interface. We here show how it can fit a species distribution model fitted to multiple data types. In this specific case, we fit to presence/absence, count, and biomass samples for red snapper in the Gulf of Mexico. However, the interface is quite general and allows combining a wide range of data types.

    # Load data
    data( red_snapper )
    data( red_snapper_shapefile )

    # Plot data extent
    plot( x = red_snapper$Lon,
            y = red_snapper$Lat,
            col = rainbow(3)[red_snapper$Data_type] )
    plot( red_snapper_shapefile, col=rgb(0,0,0,0.2), add=TRUE )
    legend( "bottomleft", bty="n",
            legend=levels(red_snapper$Data_type),
            fill = rainbow(3) )

To fit these data, we first define the `family` for each datum. Critically, we define the cloglog link for the Presence/Absence data, so that the linear predictor is proportional to numerical density for each data type. See (Grüss and Thorson 2019) or (Thorson and Kristensen 2024) Chap-7 for more details

    # Define link and distribution for each data type
    Family = list(
       "Encounter" = binomial(link="cloglog"),
       "Count" = poisson(link="log"),
       "Biomass_KG" = tweedie(link="log")
    )

    # Relevel gear factor so Biomass_KG is base level
    red_snapper$Data_type = relevel( red_snapper$Data_type,
                                     ref = "Biomass_KG" )

We then proceed with the other inputs as per usual:

    # Define mesh
    mesh = fm_mesh_2d( red_snapper[,c('Lon','Lat')], cutoff = 0.5 )

    # define formula with a catchability covariate for gear
    formula = Response_variable ~ Data_type + factor(Year) + offset(log(AreaSwept_km2))

    # make variable column
    red_snapper$var = "logdens"

    # fit using tinyVAST
    fit = tinyVAST( data = red_snapper,
                    formula = formula,
                    space_term = "logdens <-> logdens, sd_space",
                    spacetime_term = "logdens <-> logdens, 0, sd_spacetime",
                    space_columns = c("Lon",'Lat'),
                    spatial_domain = mesh,
                    time_column = "Year",
                    distribution_column = "Data_type",
                    family = Family,
                    variable_column = "var" )

We then plot density estimates for each year:

    # make extrapolation-grid
    sf_grid = st_make_grid( red_snapper_shapefile, cellsize=c(0.2,0.2) )
    sf_grid = st_intersection( sf_grid, red_snapper_shapefile )
    sf_grid = st_make_valid( sf_grid )

    # Extract coordinates for grid
    grid_coords = st_coordinates( st_centroid(sf_grid) )
    areas_km2 = st_area( sf_grid ) / 1e6

    # Calcualte log-density for each year and grid-cell
    index = plot_grid = NULL
    for( year in sort(unique(red_snapper$Year)) ){
      # compile predictive data frame
      newdata = data.frame( "Lat" = grid_coords[,'Y'],
                            "Lon" = grid_coords[,'X'],
                            "Year" = year,
                            "Data_type" = "Biomass_KG",
                            "AreaSwept_km2" = mean(red_snapper$AreaSwept_km2),
                            "var" = "logdens" )

      # predict
      log_dens = predict( fit,
                          newdata = newdata,
                          what = "p_g")
      log_dens = ifelse( log_dens < max(log_dens-log(100)), NA, log_dens )

      # Compile densities
      plot_grid = cbind( plot_grid, log_dens )

      # Estimate and compile total
      total = integrate_output( fit,
                                newdata = newdata,
                                area = areas_km2 )
      index = cbind( index, total[c('Est. (bias.correct)','Std. Error')] )

    }
    colnames(plot_grid) = colnames(index) = sort(unique(red_snapper$Year))

We then plot densities in each year

    # Convert to sf and plot
    plot_grid = st_sf( sf_grid,
                       plot_grid )
    plot( plot_grid,
          border = NA )

Similarly, we can plot the abundance index and standard errors

    ggplot( data.frame("Year"=colnames(index),"Est"=index[1,],"SE"=index[2,]) ) +
      geom_point( aes(x=Year, y=Est)) +
      geom_errorbar( aes(x=Year, ymin=Est-SE, ymax=Est+SE) )

Runtime for this vignette: 1.34 mins

# Works cited

Grüss, Arnaud, and James T. Thorson. 2019. “Developing Spatio-Temporal Models Using Multiple Data Types for Evaluating Population Trends and Habitat Usage.” ICES Journal of Marine Science 76 (6): 1748–61. https://doi.org/10.1093/icesjms/fsz075.
Thorson, James, and Kasper Kristensen. 2024. Spatio-Temporal Models for Ecologists. 1st edition. Boca Raton, FL: Chapman; Hall/CRC.