Electronic Supplementary Material
Modeling invasion risk for coastal marine species utilizing environmental and transport vector
data
Electronic Supplementary Material Figure Captions:
ESM Fig. 1 Projected binary environmental suitability (ES) for models trained with only Global
Biodiversity Information Facility (GBIF) occurrence records and corresponding bias grids
showing suitable environments based on minimum only (yellow) and minimum and 10 percent
(red) training presence logistic threshold. Blue represents areas that are not modeled as suitable.
A: Carcinus maenas; B: Charybdis hellerii; C: Charybdis japonica; D: Hemigrapsus
sanguineus; E: Rhithropanopeus harrisii.
ESM Fig. 2 Projected binary environmental suitability (ES) for models trained with GBIF and
literature occurrence records without the inclusion of a bias grid showing suitable environments
based on minimum only (yellow) and minimum and 10 percent (red) training presence logistic
thresholds. Blue represents areas that are not modeled as suitable. A: Carcinus maenas; B:
Charybdis hellerii; C: Charybdis japonica; D: Hemigrapsus sanguineus; E: Rhithropanopeus
harrisii.
ESM Fig. 3 Occurrence record locations (native and non-native from all sources) for Carcinus
maenas, Charybdis hellerii, Charybdis japonica, Hemigrapsus sanguineus, and Rhithropanopeus
harrisii along with locations of major world ports (N=208; black triangles).
Contents:
GBIF Citations and Acknowledgements
Supplementary Literature Citations
R Code
Please contact the author for information regarding more details concerning port lists, R code,
and specific data used in this manuscript beyond what is included within.
GBIF Citations and acknowledgements
GBIF search term “Carcinus maenas”
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Atlantic Reference Centre Museum of Canadian Atlantic Organisms - Invertebrates and Fishes Data
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1
Buschbaum, Christian (2010): Abundance of macrobenthos organisms in the northern Wadden Sea in 2010. Alfred
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Select R code adapted from this research project. Contact R. Crafton for details or questions
#Load the following libraries (install if necessary)
library(raster)
library(dismo)
library(gdistance)
library(fields)
#Set working directory (fill in parentheses)
setwd()
#import raster of region of interest
r<-paste(file.choose())
r<-raster(r)
projection(r)<-"+proj=longlat +datum=WGS84" # sets the projection
r[!is.na(r)]<-1 # converts all non-NA values to 1
#One option to retrieve data from Global Biodiversity Information Facility for georeferenced (with coordinates).
#Specify Species names in the n-row (numSpp) by 2 column matrix with Genus in col 1 and species in col 2
#Alternately, can download directly from http://www.gbif.org/occurrence
numSpp<- 1 #fill in number of species
SppList<-matrix(nrow=numSpp,ncol=2) #populate species of interest using col 1 for genus and col 2 for species
SppData<-matrix(nrow=0,ncol=0) #create empty matrix to be populated by dataretrieval function gbif
for(w in 1:nrow(SppList)){
temp<-gbif(genus=paste(SppList[w,1]),species=paste(SppList[w,2]),geo=TRUE)
SppData<-rbind(SppData,temp)
} #for loop to retrieve data of interest
rm(temp)
#Creates data.frame of Species Name, Longitude, Latitude from GBIF data
#Alternately import comparable data as csv file from directly downloaded data
occurrences<-data.frame(as.factor(SppData[,1]),as.numeric(SppData[,8]),as.numeric(SppData[,7]))
occurrences<-read.csv(file.choose()) #alternate method to read in data by choosing file, this method is preferred if
using data other than GBIF or if adding supplemental data
#center georeference for each cell and find unique entries
cell<-cellFromXY(r,occurrences[,2:3])
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cellXY<-xyFromCell(r,cell)
occurrences[,2:3]<-cellXY
occurrences<-unique(occurrences) #removes all duplicate values based on identical raster cell
#extracts values from the background raster [r] to ensure that all points are over desired terrain. Binds the longitude
latitude data set with extract value data sets.
occurrences<-cbind(occurrences,extract(r,occurrences[,2:3]))
colnames(occurrences)<-c('Species','Longitude','Latitude','Extract') #renames columns to for clarity
##To correct occurrence records near terrain of interest but not immediately over terrain (extract value of NA)
#subsets the data into those rows that are over terrain of interest (occurrences.not.na) and not (occurrences.na)
occurrences.na<-subset(occurrences,is.na(occurrences[,4]))
occurrences.not.na<-subset(occurrences,!is.na(occurrences[,4]))
#Calculates the closest terrain of interest point for the data not over terrain.
#If there is no desired terrain close to the datum, then NA is returned.
#Example is for a global data set between 70 degrees south and 70 degress north
NAclose<-matrix(nrow=0,ncol=6,data=NA) #create empty data matrix for corrected occurrence records
buffer<- 1 #fill in distance (in coordinate units; degrees in this example) for acceptable buffer for corrected points
colnames(NAclose)<-c('Species','Longitude','Latitude','Extract','x','y')
for(w in 1:nrow(occurrences.na)) {
if(occurrences.na[w,3] >= 70 | occurrences.na[w,3] <= -70 | is.na(occurrences.na[w,3])){ #identifies data points
outside of 70 degrees south - 70 degrees north and categorizes them as NA for final dataset
xy<-cbind(NA,NA)
temp<-cbind(occurrences.na[w,],xy)
colnames(temp)<-c('Species','Longitude','Latitude','Extract','x','y')
NAclose[w,]<-temp}
else{
ext<-extent(c(occurrences.na[w,2]-buffer,occurrences.na[w,2]+buffer,occurrences.na[w,3]buffer,occurrences.na[w,3]+buffer)) #creates correctable window around occurrence record
map<-crop(r,ext)
dist<-distanceFromPoints(map,occurrences.na[w,2:3])
dist<-mask(dist,map)
xy<-Which(dist==cellStats(dist,min),cells=TRUE)
xy<-xy[1]
if(is.na(xy)) {
xy<-cbind(NA,NA)
temp<-cbind(occurrences.na[w,],xy)
colnames(temp)<-c('Species','Longitude','Latitude','Extract','x','y')
NAclose[w,]<-temp}
if(!is.na(xy)){
xy<-xyFromCell(dist,xy)
temp<-cbind(occurrences.na[w,],xy)
colnames(temp)<-c('Species','Longitude','Latitude','Extract','x','y')
NAclose<-rbind(NAclose,temp)}}}
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#compare new and old coordinates, remove any remaining NA data, and combine the non.na data with the corrected
data
occurrences.na.fixed<-occurrences.na
occurrences.na.fixed[,2:3]<-NAclose[,5:6]
occurrences.na.fixed<-subset(occurrences.na.fixed,!is.na(occurrences.na.fixed[,2]))
occurrences.complete<-rbind(occurrences.not.na[,1:3],occurrences.na.fixed[,1:3])
Occurrences.complete<-unique(occurrences.complete)
Occ<-Occurrences.complete #rename fixed occurrence dataset for ease
#to creat bias raster using Gaussian weighted bias grid for Species 1 (SppOne)
points<-cbind(seq(1,ncell(r),by=1),getValues(r))
points<-subset(points,points[,2]==1)
cells<-points[,1]
points.xy<-as.matrix(xyFromCell(r,points[,1]))
SppOne<- paste(SppList[1,1],SppList[1,2])
SppOne.xy<-as.matrix(Occ[Occ[,1]==SppOne,2:3]) #creates a matrix of xy coordinates for Species One
tr<-transition(r, transitionFunction=mean, directions=8) #creates a transition layer using mean value of eight
adjacent cells, see gdistance r package vignette (van Etten 2012)
trC<-geoCorrection(tr,type='c') #corrects the transition layer for being a longlat coordinates, see gdistance vignette
cosDistMatrix<-matrix(nrow=nrow(SppOne.xy),ncol=nrow(points.xy), data=NA) #creates the matrix to be
populated by the least cost distance function
for(w in 1:nrow(SppOne.xy)){
cosDistMatrix[w,]<-costDistance(trC,SppOne.xy[w,],points.xy)
}
unit<-pointDistance(xyFromCell(r,3025958),xyFromCell(r,3025959),longlat=TRUE) #calculates the geographic
distance between two sets of points on a sphere in meters
dist<-costDistance(trC,xyFromCell(r,3025958),xyFromCell(r,3025959)) #calculates the cost for the same distance
as unit
conv<-as.numeric(unit/dist) #conversion factor for cost to meters
#Gaussian bias grid with distance in meters and standard deviation in meters
stdev<- 20000 #fill in standard devation in meters
bgeffort<- 0.01 #fill in background effort level to ensure raster has all values >0 or NA
DMmeters<-cosDistMatrix*conv #converts costs between occurrence records and background cells into meters
GD<-exp(-(DMmeters^2)/(2*stdev^2)) #function to calculate matrix of Gaussian weights for distance between all
background cells and occurrence records. Each row represents a single occurrence record while each column is one
background point
CS<-colSums(GD) #sums guassian distances from each occurrence by background raster point to determine final
bias weight for each background point
BiasMatrix<-cbind(cells,CS) #binds cell number with weight to allow for creation of raster
BiasRaster<-r
BiasRaster[BiasMatrix[,1]]<-BiasMatrix[,2] #creates Bias raster based on calculated weights
BiasRaster<-BiasRaster+bgeffort
writeRaster(BiasRaster,'BiasRaster.asc') #write bias raster in ascii format to use in MaxEnt
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## Use calculated occurrence records, environmental data, and bias raster to run MaxEnt model in MaxEnt software.
Options are available to run in R, but are not presented here.
##To calculate Introduction likelihood based on distance to port
ports<-read.csv(file.choose()) #select csv file that has port list including columns for port name, longitude, latitude
ports.xy<-as.matrix(ports[,2:3])
PortDistmat<-costDistance(trC,ports.xy,points.xy) #calculate distance betweeen ports and background points
PortDistmat_Meters<-PortDistmat*conv
PortDist_Meters.min<-apply(PortDistmat_Meters,2,min)
PortDistRaster<-r
PortDistRaster[cells]<-PortDist_Meters.min
inv<-read.csv(file.choose()) #import invasive occurrence records for all species
SppOne.inv<-inv[inv[,1]==SppOne,]
SppOne.inv.xy<-as.matrix(SppOne.inv[,2:3]) #subset invasive records for Species One
SppOne.PortDist<-costDistance(trC,SppOne.inv.xy,ports.xy) #calculates least cost distance between every Species
One invasive occurrence record and port
SppOne.PortDist_Meters<-SppOne.PortDist*conv #converts least cost distance into meters
SppOne.inv$PortDist<-apply(SppOne.PortDist_Meters,1,min) #finds the closest port to each occurrence record
write.csv(SppOne.inv,'SppOneInvasive_PortDistance.csv')
# Repeat for additional species
## Calculate inverse cumulative probability for each species and aggregated across all species (done in Excell for
ease of visualization)
#Create raster of inverse cumulative probably of observing occurrence records farther from port
InverseProb<-read.csv(file.choose()) #Import csv file of inverse cumulative probability into r with two columns
(distance from port in meters, inverse cumulative probablity of observing an occurrence record aggregated for all
species)
PortDistRaster.Scale<-PortDistRaster
for (w in 1:(nrow(InverseProb)-1)){
PortDistRaster.Scale[PortDistRaster.Scale>=InverseProb[w,1]&PortDistRaster.Scale<InverseProb[w+1,1]]<InverseProb[w,2]
}
PortDistRaster.Scale[PortDistRaster.Scale>=InverseProb[nrow(InverseProb),1]]<-0
writeRaster(PortDistRaster.Scale,'PortDistRaster_Scale.asc')
## Import ascii output from MaxEnt and plot as desired using Raster package, retrieve threshold values from
MaxentResults.csv to plot threshold plots (e.g. Fig 1 and 2)
## Plot PortDistRaster.Scale and underlying data as desired (e.g. Fig 3 and Fig 4)
## Combine data from MaxEnt output and Introduction likelihood data to determine invasion risk and plot as desired
(e.g. Fig 5)
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ESM Supplementary Material Citations for occurrence data for Global Biodiversity Information Facility (GBIF)
and literature sources along with select R code modified from this project.
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