Maps and Matrices

Purpose: Create species richness maps of the rodents of Chile for all rodent species and for only rodent species that have ectoparasite associations.

Load packages

Set the directory to where files are, and read in shape files. The file being read in as rodrange is a shapefile with polygons of ranges for 65 rodent species in Chile from IUCN. From this, the rodents which do not have ectoparasites are being exlcuded and named “ectorodents”.

rodrange <- read_sf("./mergedChile/mergedChile.shp", stringsAsFactors = F)

ectorodents <- subset(rodrange, SCI_NAME !="Akodon iniscatus" & SCI_NAME != "Abrothrix jelskii" 
                  & SCI_NAME !="Auliscomys pictus" & SCI_NAME != "Calomys lepidus" 
                  & SCI_NAME !="Cavia tschudii" & SCI_NAME != "Chelemys megalonyx" 
                  & SCI_NAME !="Chinchilla chinchilla" & SCI_NAME != "Chinchilla lanigera"
                  & SCI_NAME !="Ctenomys coyhaiquensis" & SCI_NAME != "Eligmodontia morgani" 
                  & SCI_NAME !="Euneomys petersoni" & SCI_NAME != "Galea musteloides" 
                  & SCI_NAME !="Galenomys garleppi" & SCI_NAME != "Geoxus annectens"
                  & SCI_NAME !="Lagidium wolffsohni" & SCI_NAME != "Microcavia australis" 
                  & SCI_NAME !="Microcavia niata" & SCI_NAME != "Microtus juldaschi"
                  & SCI_NAME != "Neotomys ebriosus"
                  & SCI_NAME !="Octodon pacificus" & SCI_NAME != "Oligoryzomys magellanicus" 
                  & SCI_NAME !="Ondatra zibethicus" & SCI_NAME != "Phyllotis limatus")
ectorodents$SCI_NAME

##  [1] "Ctenomys maulinus"          "Reithrodon auritus"        
##  [3] "Loxodontomys micropus"      "Akodon albiventer"         
##  [5] "Ctenomys opimus"            "Auliscomys boliviensis"    
##  [7] "Abrothrix olivaceus"        "Abrothrix manni"           
##  [9] "Abrothrix lanosus"          "Octodon lunatus"           
## [11] "Auliscomys sublimis"        "Octodon bridgesi"          
## [13] "Phyllotis magister"         "Spalacopus cyanus"         
## [15] "Euneomys chinchilloides"    "Euneomys mordax"           
## [17] "Chinchillula sahamae"       "Octodontomys gliroides"    
## [19] "Chelemys macronyx"          "Abrothrix sanborni"        
## [21] "Loxodontomys pikumche"      "Octodon degus"             
## [23] "Oligoryzomys longicaudatus" "Abrocoma bennettii"        
## [25] "Abrothrix andinus"          "Phyllotis xanthopygus"     
## [27] "Geoxus valdivianus"         "Lagidium viscacia"         
## [29] "Phyllotis osgoodi"          "Abrothrix longipilis"      
## [31] "Mus musculus"               "Abrocoma cinerea"          
## [33] "Eligmodontia puerulus"      "Aconaemys fuscus"          
## [35] "Ctenomys fulvus"            "Aconaemys sagei"           
## [37] "Aconaemys porteri"          "Irenomys tarsalis"         
## [39] "Ctenomys magellanicus"      "Phyllotis darwini"         
## [41] "Andinomys edax"             "Eligmodontia typus"        
## [43] "Myocastor coypus"           "Rattus norvegicus"         
## [45] "Rattus rattus"

Now, we will read to read in the shpefile of Chile, create a raster object, and then rasterize the rodent polygons and the Chile polygon.

chile <- read_sf("./CHL_adm/CHL_adm0.shp") #Chile 
r <- raster(ncol=900, nrow=900) #create a raster object
extent(r) <- extent(chile) # set the extent of chile to the extent of the raster object

poly<-ectorodents ## create a new spatial object of the ectorodents
rp_rod<-rasterize(poly,r) #rasterise the poly to the extent of the raster object
chil_map_ecto<-mask(rp_rod, chile) # mask all the areas outside of chile 

poly3<-rodrange #all rodents same steps
rp_rod3<-rasterize(poly3,r)
chil_map<-mask(rp_rod3, chile)

Now time to make the maps!

color<-colorRampPalette(c("#090387ff","#5084d6bb","skyblue","#fada5fff", "#aa0000")) ##This is the good one :)
par(mfrow=c(1,2)) #split panel into 2
plot(chil_map_ecto, col=color(9), breaks=c(0,5,10,15,20,25,30,35,40,45)
     ,ext=c(-80, -65, -60, 30), main="Rodent Species Richness \n with ectoparasite associations" ) #only with ectoparasites
plot(chil_map, col=color(13), breaks=c(0,5,10,15,20,25,30,35,40,45,50,55,60,65)
     ,ext=c(-80, -65, -60, 30), main="Total Rodent Species Richness" ) #all rodents

The next step is to calculate pairwise range overlap between rodent species. We will do this only for the rodents with ectoparasites

sf::sf_use_s2(FALSE) #turn off spherical geometry

## Spherical geometry (s2) switched off

ectogeom<- st_sfc(ectorodents$geometry)

overlap<- lapply(ectogeom, function(x){
  lapply(ectogeom, function(y) st_intersection( x, y ) %>% st_area(x,y)/(st_area(x)+st_area(y)-st_area(x, y)) )
  })
geolap<- matrix(unlist(overlap), ncol = length(ectorodents$geometry), byrow = TRUE)
colnames(geolap)<- ectorodents$SCI_NAME
rownames(geolap)<- ectorodents$SCI_NAME

write.csv(geolap, "OverlapMatrix.csv")

Now you have a matrix with pairwise range overlap between rodents in a csv file, “OverlapMatrix.csv”.

Calculate the distance between range centroids for pairs of rodents and write a csv file with the distance matrix.

ectorodents <- ectorodents %>% mutate(centroids = st_centroid(st_geometry(.)))
geodist<- ectorodents %>% st_set_geometry('centroids') %>% st_distance()
colnames(geodist) <- ectorodents$SCI_NAME
row.names(geodist)<- ectorodents$SCI_NAME
df<- as.data.frame(geodist)

write.csv(df, "CentroidDist.csv")