In James-Thorson/FishLife: Predict Life History Parameters For Any Fish

Description

We here show how to replicate and extract the archetypes estimated in Thorson et al. 2023. We first load packages and data:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
# Build
#  setwd(R'(C:\Users\James.Thorson\Desktop\Git\FishLife)'); devtools::build_rmd("vignettes/Archetypes.Rmd"); rmarkdown::render( "vignettes/Archetypes.Rmd", rmarkdown::pdf_document())

# Load packages and data
library(archetypes)
library(FishLife)
data(FishBase_and_Morphometrics)

# Set seed so that archetypes are labeled the same way when replicating
suppressWarnings(RNGversion("3.5.0"))
set.seed(1986)

Next we re-run the archetype analysis and plot the resulting simplex of archetypes

# Load results
beta_iv = FishBase_and_Morphometrics$beta_gv[ FishBase_and_Morphometrics$g_i, ]
a <- archetypes(beta_iv, 3, verbose = TRUE)

# Labels
Labels = c("Equil.","Oppor.","Per.")[c(1,3,2)]
Col_names = colnames(FishBase_and_Morphometrics$Y_ij)
  Col_names = gsub("habitat", "H: ", Col_names )
  Col_names = gsub("body_shape", "BS: ", Col_names )
  Col_names = gsub("feeding_mode", "FM: ", Col_names )
  Col_names = gsub("spawning_type", "ST: ", Col_names )

# Col function
mycol = function(x,alpha){
  y = col2rgb(x)
  rgb( y[1,1]/255, y[2,1]/255, y[3,1]/255, alpha=alpha)
}

# Plot by taxa
Z_ik = FishBase_and_Morphometrics$Z_ik
simplex = simplexplot(a, show_points=FALSE, labels=Labels) #, projection=atypes_projection)
points( simplex$proj_h[which(Z_ik$Family=="Gobiidae"), ], col=mycol("brown",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Percidae"), ], col=mycol("orange",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Salmonidae"), ], col=mycol("red",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Scorpaenidae"), ], col=mycol("grey",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Clupeidae"), ], col=mycol("green",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Class=="Elasmobranchii"), ], col=mycol("black",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Poeciliidae"), ], col=mycol("blue",0.5), pch=20 )
points( simplex$proj_h[which(Z_ik$Family=="Atherinidae"), ], col=mycol("purple",0.5), pch=20 )
legend( "topleft", bty="n", ncol=2, legend=c("Gobiidae","Salmonidae","Poeciliidae",
        "Atherinidae","Percidae","Scorpaenidae","Clupeidae","Elasmobranchii"),
        fill=c("brown","red","blue","purple","orange","grey","green","black"))

If interested, we can pull out values for individual species based on their Latin binomial. We here show a few examples:

# Pull out examples
Z_ik = FishBase_and_Morphometrics$Z_ik
species_to_plot = data.frame(
  c("Gadus_macrocephalus", "Hippoglossus_stenolepis", "Sebastes_alutus", "Thaleichthys_pacificus",
     "Oncorhynchus_tshawytscha", "Somniosus_pacificus", "Mallotus_villosus", "Boreogadus_saida",
     "Bothrocara_brunneum", "Tarletonbeania_taylori", "Gambusia_affinis", "Callogobius_sclateri",
     "Acanthurus_xanthopterus", "Eviota_jewettae"),
  c( "Pacific cod", "Halibut", "Paciric Ocean perch", "Eulachon", "Chinook", "Sleeper shark",
     "Capelin", "Arctic cod", "twoline eelpout", "North Pacific lanternfish",
     "Western mosquitofish", "Pacific goby", "Yellowfin surgeonfish", "Jewett's dwarfgoby")
)
species_to_plot$color = rainbow(nrow(species_to_plot))
# Plot by taxa
simplex = simplexplot(a, show_points=FALSE, labels=Labels)
for( r in seq_len(nrow(species_to_plot)) ){
  which_row = which(paste0(Z_ik$Genus,"_",Z_ik$Species)==species_to_plot[r,1])
  text( x=simplex$proj_h[which_row,1], y=simplex$proj_h[which_row,2],
        col=species_to_plot[r,3], labels=letters[r] )
}
legend( "topleft", bty="n", ncol=2, legend=species_to_plot[,2], fill=species_to_plot[,3])

Finally, we can plot the distribution of predicted traits for all species, along with the predicted mean for each archetype:

Col_names = colnames(FishBase_and_Morphometrics$beta_gv)
  Col_names = gsub("habitat", "H: ", Col_names )
  Col_names = gsub("body_shape", "BS: ", Col_names )
  Col_names = gsub("feeding_mode", "FM: ", Col_names )
  Col_names = gsub("spawning_type", "ST: ", Col_names )
dims = c(6,6)
par( mfrow=dims, mgp=c(2,0.5,0), mar=c(1,1,2,0), tck=-0.02 )
for( cI in 1:ncol(FishBase_and_Morphometrics$beta_gv) ){
  hist( FishBase_and_Morphometrics$beta_gv[,cI],
        main=Col_names[cI], xlab="", ylab="", yaxt="n" )
  abline( v=parameters(a)[,cI], col=viridisLite::viridis(3), lwd=3 )
}