# set global chunk options:
library(knitr)
opts_chunk$set(cache=FALSE, fig.align='center', fig.width=11.8, fig.height=8)
library("vegan")
library(plyr)
allplot.hab<-read.csv("Sedges_2_all_plots_hab.csv", head=TRUE, row.names=1)
hab.allplot<-allplot.hab[,c(1:9)]
D - Disturbed \ F - Fen \ H - Heath \ LF - Lichen forest \ R - Rocks \ SH - Shoreline \ SM - Spruce moss forest \ T - tundra \ TP - tundra ponds
vegcom.ab<-read.csv("Sedges_2_specialist_generalist_abundance.csv", head=TRUE, row.names=1)
vegcom.ab
## D F H LF R SH SM T TP
## AQU 0 18 1 0 0 1 0 0 0
## BIG 0 0 1 0 0 1 0 18 0
## LEN 0 3 0 0 1 11 2 1 2
## OLI 0 19 0 0 0 1 0 0 0
## ROS 0 20 0 0 0 0 0 0 0
## SAX 0 12 1 0 0 4 0 1 2
## UTR 0 16 0 0 0 4 0 0 0
## VES 0 2 0 0 0 18 0 0 0
## BRU 2 4 2 1 2 2 4 3 0
## CAN 0 15 0 0 1 3 0 1 0
## HEL 0 19 0 0 0 0 1 0 0
## TEN 0 20 0 0 0 0 0 0 0
## TRI 0 15 0 0 0 0 5 0 0
## ANG 0 18 1 0 0 0 0 1 1
## EVA 0 7 0 2 1 2 5 2 1
## VIR 0 20 0 0 0 0 0 0 0
## ALP 0 19 0 0 1 0 0 0 0
## CAP 0 6 0 0 0 0 0 14 0
## CES 0 15 0 0 0 1 0 4 0
## CHO 0 20 0 0 0 0 0 0 0
## DEF 2 0 2 3 3 5 2 3 0
## DIS 0 15 0 0 0 1 4 0 0
## ECH 0 16 0 1 0 3 0 0 0
## EXI 0 20 0 0 0 0 0 0 0
## GYN 0 16 0 0 0 1 3 0 0
## LEP 0 15 0 0 0 2 3 0 0
## LIM 0 19 0 0 0 1 0 0 0
## LIV 0 20 0 0 0 0 0 0 0
## MAG 0 10 0 1 0 3 4 0 1
## PAU 0 19 0 0 0 0 1 0 0
## RAR 0 17 0 0 0 0 1 1 1
## RUS 0 18 0 0 0 1 0 0 1
## SCI 0 2 1 0 0 1 4 12 0
## STY 0 9 0 1 0 7 2 0 1
## VAG 0 4 2 2 0 1 8 2 1
### Calculate relative habitat number
rel.hab.number<-colSums(vegcom.ab)/700
#inverse of relative habitat number
rel.hab.number.inv<-1/rel.hab.number
#Try weighting diversity of inverse of habitat abundances
D<-hab.allplot$D*175
F<-hab.allplot$F* 1.495726
H<-hab.allplot$H*63.636364
LF<-hab.allplot$LF*63.636364
R<-hab.allplot$R*77.777778
SH<-hab.allplot$SH*9.459459
SM<-hab.allplot$SM*14.285714
T<-hab.allplot$T* 11.111111
TP<-hab.allplot$TP*63.636364
#Create new dataframe with rownames based on inverse of habitat abundances
vegcom.inv.weighted<-cbind(D, F, H, LF, R, SH, SM, T, TP)
vegcom.row.names=c("AQU", "BIG", "LEN", "OLI", "ROS", "SAX", "UTR", "VES", "BRU", "CAN", "HEL", "TEN", "TRI", "ANG", "EVA", "VIR", "ALP", "CAP", "CES",
"CHO", "DEF", "DIS", "ECH", "EXI", "GYN","LEP", "LIM", "LIV", "MAG" ,"PAU", "RAR", "RUS", "SCI", "STY" ,"VAG")
vegcom.inv.weighted.names.df<-data.frame(vegcom.inv.weighted, row.names=c ("AQU", "BIG", "LEN", "OLI", "ROS", "SAX", "UTR", "VES", "BRU", "CAN", "HEL", "TEN", "TRI", "ANG", "EVA", "VIR", "ALP", "CAP", "CES",
"CHO", "DEF", "DIS", "ECH", "EXI", "GYN","LEP", "LIM", "LIV", "MAG" ,"PAU", "RAR", "RUS", "SCI", "STY" ,"VAG"))
str(vegcom.inv.weighted.names.df)
## 'data.frame': 35 obs. of 9 variables:
## $ D : num 175 0 0 0 0 0 0 0 350 0 ...
## $ F : num 282.7 16.5 12 55.3 98.7 ...
## $ H : num 190.9 63.6 0 0 0 ...
## $ LF: num 63.6 63.6 0 0 0 ...
## $ R : num 77.8 0 77.8 0 0 ...
## $ SH: num 104.1 18.9 132.4 28.4 0 ...
## $ SM: num 114.3 42.9 57.1 0 0 ...
## $ T : num 33.3 344.4 11.1 0 0 ...
## $ TP: num 63.6 127.3 127.3 0 0 ...
vegcom.inv.weighted.names.df
## D F H LF R SH SM
## AQU 175 282.692214 190.90909 63.63636 77.77778 104.054049 114.28571
## BIG 0 16.452986 63.63636 63.63636 0.00000 18.918918 42.85714
## LEN 0 11.965808 0.00000 0.00000 77.77778 132.432426 57.14286
## OLI 0 55.341862 0.00000 0.00000 0.00000 28.378377 0.00000
## ROS 0 98.717916 0.00000 0.00000 0.00000 0.000000 0.00000
## SAX 0 29.914520 63.63636 0.00000 0.00000 66.216213 0.00000
## UTR 0 82.264930 0.00000 0.00000 0.00000 75.675672 28.57143
## VES 0 16.452986 0.00000 0.00000 77.77778 227.027016 28.57143
## BRU 350 5.982904 127.27273 63.63636 155.55556 75.675672 71.42857
## CAN 0 70.299122 63.63636 127.27273 155.55556 132.432426 42.85714
## HEL 0 37.393150 0.00000 0.00000 0.00000 0.000000 14.28571
## TEN 0 80.769204 0.00000 0.00000 0.00000 56.756754 14.28571
## TRI 0 62.820492 63.63636 63.63636 0.00000 47.297295 128.57143
## ANG 0 65.811944 127.27273 0.00000 0.00000 56.756754 14.28571
## EVA 0 14.957260 0.00000 254.54546 77.77778 18.918918 71.42857
## VIR 0 53.846136 0.00000 0.00000 77.77778 0.000000 0.00000
## ALP 0 104.700820 0.00000 0.00000 77.77778 0.000000 28.57143
## CAP 0 11.965808 63.63636 63.63636 0.00000 0.000000 28.57143
## CES 0 237.820434 0.00000 63.63636 77.77778 66.216213 85.71428
## CHO 0 65.811944 0.00000 0.00000 0.00000 9.459459 14.28571
## DEF 525 1.495726 127.27273 190.90909 233.33333 47.297295 28.57143
## DIS 0 38.888876 0.00000 0.00000 0.00000 9.459459 57.14286
## ECH 0 68.803396 0.00000 63.63636 0.00000 66.216213 14.28571
## EXI 0 43.376054 0.00000 0.00000 0.00000 0.000000 0.00000
## GYN 0 91.239286 63.63636 63.63636 77.77778 18.918918 100.00000
## LEP 0 92.735012 63.63636 63.63636 0.00000 28.378377 100.00000
## LIM 0 242.307612 0.00000 0.00000 0.00000 28.378377 14.28571
## LIV 0 113.675176 0.00000 0.00000 0.00000 0.000000 14.28571
## MAG 0 219.871722 63.63636 254.54546 77.77778 236.486475 214.28571
## PAU 0 98.717916 0.00000 63.63636 0.00000 47.297295 28.57143
## RAR 0 76.282026 63.63636 0.00000 77.77778 0.000000 71.42857
## RUS 0 82.264930 0.00000 0.00000 0.00000 56.756754 0.00000
## SCI 0 8.974356 127.27273 0.00000 0.00000 18.918918 57.14286
## STY 0 22.435890 0.00000 63.63636 0.00000 85.135131 28.57143
## VAG 0 130.128162 190.90909 254.54546 155.55556 113.513508 342.85714
## T TP
## AQU 33.33333 63.63636
## BIG 344.44444 127.27273
## LEN 11.11111 127.27273
## OLI 0.00000 0.00000
## ROS 0.00000 0.00000
## SAX 22.22222 190.90909
## UTR 0.00000 63.63636
## VES 11.11111 63.63636
## BRU 33.33333 63.63636
## CAN 44.44444 63.63636
## HEL 0.00000 0.00000
## TEN 0.00000 63.63636
## TRI 0.00000 63.63636
## ANG 22.22222 127.27273
## EVA 22.22222 63.63636
## VIR 11.11111 0.00000
## ALP 22.22222 63.63636
## CAP 166.66666 0.00000
## CES 144.44444 190.90909
## CHO 0.00000 0.00000
## DEF 44.44444 0.00000
## DIS 0.00000 0.00000
## ECH 0.00000 63.63636
## EXI 0.00000 0.00000
## GYN 22.22222 127.27273
## LEP 33.33333 127.27273
## LIM 11.11111 0.00000
## LIV 0.00000 0.00000
## MAG 44.44444 254.54546
## PAU 0.00000 63.63636
## RAR 11.11111 63.63636
## RUS 0.00000 63.63636
## SCI 288.88889 63.63636
## STY 11.11111 127.27273
## VAG 111.11111 190.90909
# Not corrected for habitat type
vegcom.hab<-colSums(vegcom.ab)
vegcom.hab
## D F H LF R SH SM T TP
## 4 468 11 11 9 74 49 63 11
# Corrected of different "sampling efforts" per habitat type
vegcom.hab.weighted<-colSums(vegcom.inv.weighted.names.df)
vegcom.hab.weighted
## D F H LF R SH SM T
## 1050.000 2737.179 1463.636 1781.818 1477.778 1872.973 1857.143 1466.667
## TP
## 2481.818
#dev.new(width=11.8, height=6)
par(mfrow=c(1,2))
barplot(vegcom.hab, names=c("Disturbed", "Fen", "Heath", "Lichen forest", "Rocks",
"Shoreline", "Spruce Moss", "Tundra", "Tundra Pond"), las=2, col="gray40", ylab="Frequency",
main="Frequency of sampling in \ndifferent habitat types", cex.axis=0.8,
cex=0.8, cex.main=1)
barplot(vegcom.hab.weighted, names=c("Disturbed", "Fen", "Heath", "Lichen forest", "Rocks",
"Shoreline", "Spruce Moss", "Tundra", "Tundra Pond"), las=2, col="gray40", ylab="Frequency",
main="Frequency of sampling in different habitat \ntypes corrected by sampling effort", cex.axis=0.8,
cex=0.8, cex.main=1)
H.inv<-diversity(vegcom.inv.weighted.names.df)
H.inv.rescale<-rescale(H.inv, to=c(0,1), range(H.inv, na.rm = TRUE))
These are the results that are inputed into further analyzes.
H.inv.no.def<-H.inv[c(1:20, 22:35)]
H.inv.nodef.rescale<-rescale(H.inv.no.def, to=c(0,1), range(H.inv.no.def, na.rm = TRUE))
H.inv.nodef.sort<-sort(H.inv.nodef.rescale)
H.low.high.names.nodef.inv<-names(H.inv.nodef.sort)
#write.csv(file="C:/Users/TLElliott/My Documents/PhD/Year4/Schefferville_sedges_2/Analyzes/H.inv.rescale.no.def.allplots.csv", x= H.inv.nodef.rescale)
H.inv.sort<-sort(H.inv.nodef.rescale)
H.low.high.names.inv<-names(H.inv.sort)
#plot(H.inv.sort, pch=16, cex=0.8, col="black")
#text(H.inv.sort, labels=H.low.high.names.inv, cex=0.4, pos=1, offset=0.2)
plot(H.inv.sort, type="n", axes=FALSE, ann=FALSE,xlim=c(0,35), ylim=c(0,1), cex.lab=2, font.lab=4, pch=16)
axis(1, cex.axis=1.25)
axis(2, las=2, cex.axis=1.25)
mtext("Species rank", side=1, line=3)
mtext("Rank value", side=2, line=3)
text(H.inv.sort, labels=H.low.high.names.inv, cex=0.6, pos=1, offset=0.2)
mtext("Sedge species ranked based on \nhabitat classification of specialization", side=3, line=1.5, cex=1.25)
points(H.inv.sort, cex=0.9, col="black", pch=16)
box(bty="l", lwd=2)
#write.csv(file="C:/Users/TLElliott/My Documents/PhD/Year4/Schefferville_sedges_2/Analyzes/H.inv.rescale.no.alp.no.def.allplots.csv", x=H.inv.noalp.nodef.rescale)