Beginning Script
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.5-4
## Loading required package: tcltk
## BiodiversityR 2.11-1: Use command BiodiversityRGUI() to launch the Graphical User Interface;
## to see changes use BiodiversityRGUI(changeLog=TRUE, backward.compatibility.messages=TRUE)
library(reshape)
beetleImport <- read.csv("C:\\Users\\Gabriela Krochmal\\Desktop\\ConBio_R\\groundBeetleAbundanceSP19.csv")
beetleImport$ID <- paste(beetleImport$habitatCode, beetleImport$replicate, sep= "_")
#reshape long form data into a matrix with cast()
beetle <- cast(beetleImport, ID ~ speciesCode, value= 'abundance')
rownames(beetle) <- beetle$ID #name rows with ID
beetle$ID <- NULL #drop ID column
beetle[is.na(beetle)] <- 0 #replace NA with 0
##Estimating alpha diversity with the vegan package
spBeetle <- specnumber(beetle) #species richness
HBeetle <- diversity(beetle, index="shannon") #Shannon(H')
DBeetle <- diversity(beetle, index="invsimpson") #simpson(1/D)
#making a data frame of the results
diversityResult <- data.frame(spBeetle ,HBeetle, DBeetle)
#importing and merging habitat data
habitat <- read.csv("C:\\Users\\Gabriela Krochmal\\Desktop\\ConBio_R\\groundBeetleHabitatSP19.csv")
#paste two columns together with an underscore
habitat$ID <- paste(habitat$habitatCode, habitat$replicate, sep= "_")
#create data frame
diversityResult <- data.frame(spBeetle, HBeetle, DBeetle)
#create ID column
diversityResult$ID <- rownames(diversityResult)
#merge the two data frames by ID column
beetleDiversity <- merge(diversityResult, habitat, by=c("ID"))
head(beetleDiversity)
## ID spBeetle HBeetle DBeetle habitatCode replicate vegHgt habitat
## 1 E_1 17 1.267096 2.571619 E 1 5.5 Edge
## 2 E_2 14 1.424505 3.179289 E 2 15.0 Edge
## 3 E_3 15 1.412179 3.106648 E 3 15.0 Edge
## 4 E_4 25 1.737146 3.871034 E 4 3.0 Edge
## 5 E_5 21 1.636923 3.684945 E 5 2.5 Edge
## 6 E_6 17 1.557477 3.448558 E 6 2.0 Edge
beetle <- data.frame(beetle)
Question 1
#The top four ranked species in the data set are:
#1 Aba.par
#2 Pte.mad
#3 Cal.rot
#4 Pte.nige
RankAbun1 <- rankabundance(beetle)
#The statistics of the species can be found in:
head(RankAbun1)
## rank abundance proportion plower pupper accumfreq logabun
## Aba.par 1 4868 40.6 37.9 43.3 40.6 3.7
## Pte.mad 2 3162 26.4 23.7 29.1 67.0 3.5
## Neb.bre 3 1758 14.7 12.4 17.0 81.7 3.2
## Cal.rot 4 765 6.4 4.6 8.2 88.1 2.9
## Pte.nige 5 246 2.1 1.3 2.8 90.2 2.4
## Pte.str 6 211 1.8 0.9 2.6 91.9 2.3
## rankfreq
## Aba.par 2.1
## Pte.mad 4.2
## Neb.bre 6.2
## Cal.rot 8.3
## Pte.nige 10.4
## Pte.str 12.5
#Plots for abundance and proportion
rankabunplot(RankAbun1, scale="proportion", specnames= c(1:5))
rankabunplot(RankAbun1, scale="abundance", specnames= c(1:5))
#Conclusions I can draw from the plots produced regarding species richness is that this is a pretty diverse data set because there are 48 different species found in all of the ecosystems combined. Although there are alot of species, there are a few that dominate the ecosystem. When looking at the graph you can see that the abundance of all of the other speecies is extremely low (just above 0), while there is an abundance in the thousands of the top ranked species. The evenness can also reflect the abundance because there are so many of certain species so there is not a even distribution of species across the ecosystem.
Question 2
beetleSubE <- beetle[1:6,]
beetleSubG <- beetle[7:12,]
beetleSubW <- beetle[13:18,]
#rank abundance curves for edge habitat
RankAbunSubE1 <- rankabundance(beetleSubE)
rankabunplot(RankAbunSubE1, scale= "proportion", addit= FALSE, specnames=c(1:4))
## rank abundance proportion plower pupper accumfreq logabun
## Aba.par 1 2146 44.4 39.4 49.3 44.4 3.3
## Pte.mad 2 1365 28.2 25.9 30.6 72.6 3.1
## Neb.bre 3 624 12.9 9.7 16.1 85.5 2.8
## Cal.rot 4 314 6.5 2.2 10.8 92.0 2.5
## Pte.str 5 101 2.1 1.5 2.6 94.1 2.0
## Pte.nige 6 45 0.9 0.7 1.2 95.1 1.7
## Pte.mel 7 40 0.8 0.4 1.2 95.9 1.6
## Poe.cup 8 38 0.8 0.2 1.4 96.7 1.6
## Pla.ass 9 27 0.6 0.3 0.8 97.2 1.4
## Pte.nigr 10 27 0.6 0.2 0.9 97.8 1.4
## Bem.man 11 18 0.4 0.1 0.6 98.2 1.3
## Sto.pum 12 17 0.4 0.1 0.6 98.5 1.2
## Lei.ful 13 13 0.3 0.0 0.5 98.8 1.1
## Lei.ruf 14 13 0.3 -0.1 0.6 99.0 1.1
## Cal.vio 15 10 0.2 0.1 0.3 99.3 1.0
## Bem.lam 16 5 0.1 0.0 0.2 99.4 0.7
## Lei.fer 17 5 0.1 -0.2 0.4 99.5 0.7
## Lei.spi 18 4 0.1 0.0 0.2 99.5 0.6
## Lor.pil 19 4 0.1 -0.1 0.2 99.6 0.6
## Ocy.har 20 4 0.1 -0.1 0.2 99.7 0.6
## Pte.obl 21 4 0.1 0.0 0.2 99.8 0.6
## Ago.mue 22 3 0.1 0.0 0.2 99.9 0.5
## Har.ruf 23 2 0.0 -0.1 0.1 99.9 0.3
## Pat.atr 24 2 0.0 -0.1 0.1 99.9 0.3
## Bem.big 25 1 0.0 0.0 0.1 100.0 0.0
## Bem.gut 26 1 0.0 0.0 0.1 100.0 0.0
## Cur.aul 27 1 0.0 0.0 0.1 100.0 0.0
## Acu.dub 28 0 0.0 0.0 0.0 100.0 -Inf
## Ago.afr 29 0 0.0 0.0 0.0 100.0 -Inf
## Ago.ful 30 0 0.0 0.0 0.0 100.0 -Inf
## Ago.vid 31 0 0.0 0.0 0.0 100.0 -Inf
## Ama.aen 32 0 0.0 0.0 0.0 100.0 -Inf
## Ama.com 33 0 0.0 0.0 0.0 100.0 -Inf
## Ama.fam 34 0 0.0 0.0 0.0 100.0 -Inf
## Ama.ple 35 0 0.0 0.0 0.0 100.0 -Inf
## Ama.sim 36 0 0.0 0.0 0.0 100.0 -Inf
## Bad.bul 37 0 0.0 0.0 0.0 100.0 -Inf
## Bem.obt 38 0 0.0 0.0 0.0 100.0 -Inf
## Bra.har 39 0 0.0 0.0 0.0 100.0 -Inf
## Bra.sha 40 0 0.0 0.0 0.0 100.0 -Inf
## Cal.fus 41 0 0.0 0.0 0.0 100.0 -Inf
## Cli.fos 42 0 0.0 0.0 0.0 100.0 -Inf
## Cyc.car 43 0 0.0 0.0 0.0 100.0 -Inf
## Not.big 44 0 0.0 0.0 0.0 100.0 -Inf
## Not.ruf 45 0 0.0 0.0 0.0 100.0 -Inf
## Pte.ver 46 0 0.0 0.0 0.0 100.0 -Inf
## Syn.niv 47 0 0.0 0.0 0.0 100.0 -Inf
## Tre.qua 48 0 0.0 0.0 0.0 100.0 -Inf
## rankfreq
## Aba.par 2.1
## Pte.mad 4.2
## Neb.bre 6.2
## Cal.rot 8.3
## Pte.str 10.4
## Pte.nige 12.5
## Pte.mel 14.6
## Poe.cup 16.7
## Pla.ass 18.8
## Pte.nigr 20.8
## Bem.man 22.9
## Sto.pum 25.0
## Lei.ful 27.1
## Lei.ruf 29.2
## Cal.vio 31.2
## Bem.lam 33.3
## Lei.fer 35.4
## Lei.spi 37.5
## Lor.pil 39.6
## Ocy.har 41.7
## Pte.obl 43.8
## Ago.mue 45.8
## Har.ruf 47.9
## Pat.atr 50.0
## Bem.big 52.1
## Bem.gut 54.2
## Cur.aul 56.2
## Acu.dub 58.3
## Ago.afr 60.4
## Ago.ful 62.5
## Ago.vid 64.6
## Ama.aen 66.7
## Ama.com 68.8
## Ama.fam 70.8
## Ama.ple 72.9
## Ama.sim 75.0
## Bad.bul 77.1
## Bem.obt 79.2
## Bra.har 81.2
## Bra.sha 83.3
## Cal.fus 85.4
## Cli.fos 87.5
## Cyc.car 89.6
## Not.big 91.7
## Not.ruf 93.8
## Pte.ver 95.8
## Syn.niv 97.9
## Tre.qua 100.0
#rank abundance curves for grass habitat
RankAbunSubG1 <- rankabundance(beetleSubG)
rankabunplot(RankAbunSubG1, scale= "proportion", addit= FALSE, specnames=c(1:4))
## rank abundance proportion plower pupper accumfreq logabun
## Aba.par 1 709 37.7 29.7 45.6 37.7 2.9
## Pte.mad 2 382 20.3 16.4 24.2 57.9 2.6
## Neb.bre 3 175 9.3 7.6 11.0 67.2 2.2
## Poe.cup 4 107 5.7 4.2 7.2 72.9 2.0
## Pte.str 5 104 5.5 4.2 6.8 78.4 2.0
## Bem.man 6 97 5.2 3.9 6.4 83.6 2.0
## Pte.nige 7 44 2.3 1.7 3.0 85.9 1.6
## Pte.nigr 8 30 1.6 1.1 2.1 87.5 1.5
## Bem.big 9 29 1.5 0.9 2.2 89.1 1.5
## Cli.fos 10 22 1.2 0.6 1.7 90.2 1.3
## Pla.ass 11 22 1.2 0.3 2.0 91.4 1.3
## Sto.pum 12 22 1.2 0.3 2.0 92.6 1.3
## Ago.afr 13 20 1.1 0.2 1.9 93.6 1.3
## Cal.fus 14 20 1.1 0.8 1.3 94.7 1.3
## Cal.rot 15 20 1.1 0.2 1.9 95.8 1.3
## Bem.lam 16 13 0.7 0.3 1.1 96.4 1.1
## Ama.ple 17 7 0.4 -0.1 0.8 96.8 0.8
## Pte.ver 18 7 0.4 0.1 0.7 97.2 0.8
## Not.ruf 19 6 0.3 -0.1 0.7 97.5 0.8
## Syn.niv 20 5 0.3 0.0 0.5 97.8 0.7
## Bad.bul 21 4 0.2 0.0 0.5 98.0 0.6
## Bra.sha 22 4 0.2 0.1 0.4 98.2 0.6
## Not.big 23 4 0.2 -0.1 0.5 98.4 0.6
## Tre.qua 24 4 0.2 0.1 0.3 98.6 0.6
## Acu.dub 25 3 0.2 0.0 0.3 98.8 0.5
## Ago.ful 26 3 0.2 -0.1 0.4 98.9 0.5
## Bem.obt 27 3 0.2 0.0 0.3 99.1 0.5
## Pte.mel 28 3 0.2 0.0 0.3 99.3 0.5
## Ago.vid 29 2 0.1 -0.1 0.3 99.4 0.3
## Ama.aen 30 2 0.1 -0.2 0.4 99.5 0.3
## Ama.fam 31 2 0.1 -0.2 0.4 99.6 0.3
## Bem.gut 32 2 0.1 -0.2 0.4 99.7 0.3
## Ocy.har 33 2 0.1 -0.2 0.4 99.8 0.3
## Ama.com 34 1 0.1 -0.1 0.2 99.8 0.0
## Ama.sim 35 1 0.1 -0.1 0.2 99.9 0.0
## Bra.har 36 1 0.1 -0.1 0.2 99.9 0.0
## Lei.fer 37 1 0.1 -0.1 0.2 100.0 0.0
## Ago.mue 38 0 0.0 0.0 0.0 100.0 -Inf
## Cal.vio 39 0 0.0 0.0 0.0 100.0 -Inf
## Cur.aul 40 0 0.0 0.0 0.0 100.0 -Inf
## Cyc.car 41 0 0.0 0.0 0.0 100.0 -Inf
## Har.ruf 42 0 0.0 0.0 0.0 100.0 -Inf
## Lei.ful 43 0 0.0 0.0 0.0 100.0 -Inf
## Lei.ruf 44 0 0.0 0.0 0.0 100.0 -Inf
## Lei.spi 45 0 0.0 0.0 0.0 100.0 -Inf
## Lor.pil 46 0 0.0 0.0 0.0 100.0 -Inf
## Pat.atr 47 0 0.0 0.0 0.0 100.0 -Inf
## Pte.obl 48 0 0.0 0.0 0.0 100.0 -Inf
## rankfreq
## Aba.par 2.1
## Pte.mad 4.2
## Neb.bre 6.2
## Poe.cup 8.3
## Pte.str 10.4
## Bem.man 12.5
## Pte.nige 14.6
## Pte.nigr 16.7
## Bem.big 18.8
## Cli.fos 20.8
## Pla.ass 22.9
## Sto.pum 25.0
## Ago.afr 27.1
## Cal.fus 29.2
## Cal.rot 31.2
## Bem.lam 33.3
## Ama.ple 35.4
## Pte.ver 37.5
## Not.ruf 39.6
## Syn.niv 41.7
## Bad.bul 43.8
## Bra.sha 45.8
## Not.big 47.9
## Tre.qua 50.0
## Acu.dub 52.1
## Ago.ful 54.2
## Bem.obt 56.2
## Pte.mel 58.3
## Ago.vid 60.4
## Ama.aen 62.5
## Ama.fam 64.6
## Bem.gut 66.7
## Ocy.har 68.8
## Ama.com 70.8
## Ama.sim 72.9
## Bra.har 75.0
## Lei.fer 77.1
## Ago.mue 79.2
## Cal.vio 81.2
## Cur.aul 83.3
## Cyc.car 85.4
## Har.ruf 87.5
## Lei.ful 89.6
## Lei.ruf 91.7
## Lei.spi 93.8
## Lor.pil 95.8
## Pat.atr 97.9
## Pte.obl 100.0
#rank abundance curves for wood habitat
RankAbunSubW1 <- rankabundance(beetleSubW)
rankabunplot(RankAbunSubW1, scale="proportion", addit= FALSE, specnames =c(1:4))
## rank abundance proportion plower pupper accumfreq logabun
## Aba.par 1 2013 38.3 34.6 41.9 38.3 3.3
## Pte.mad 2 1415 26.9 20.3 33.5 65.2 3.2
## Neb.bre 3 959 18.2 14.4 22.0 83.4 3.0
## Cal.rot 4 431 8.2 6.1 10.2 91.6 2.6
## Lei.ruf 5 176 3.3 1.4 5.3 94.9 2.2
## Pte.nige 6 157 3.0 1.3 4.7 97.9 2.2
## Pla.ass 7 49 0.9 0.2 1.6 98.8 1.7
## Cal.vio 8 23 0.4 0.1 0.8 99.3 1.4
## Not.big 9 19 0.4 0.1 0.6 99.6 1.3
## Cyc.car 10 9 0.2 0.1 0.3 99.8 1.0
## Pte.str 11 6 0.1 0.0 0.2 99.9 0.8
## Lei.spi 12 2 0.0 0.0 0.1 100.0 0.3
## Pte.mel 13 2 0.0 0.0 0.1 100.0 0.3
## Acu.dub 14 0 0.0 0.0 0.0 100.0 -Inf
## Ago.afr 15 0 0.0 0.0 0.0 100.0 -Inf
## Ago.ful 16 0 0.0 0.0 0.0 100.0 -Inf
## Ago.mue 17 0 0.0 0.0 0.0 100.0 -Inf
## Ago.vid 18 0 0.0 0.0 0.0 100.0 -Inf
## Ama.aen 19 0 0.0 0.0 0.0 100.0 -Inf
## Ama.com 20 0 0.0 0.0 0.0 100.0 -Inf
## Ama.fam 21 0 0.0 0.0 0.0 100.0 -Inf
## Ama.ple 22 0 0.0 0.0 0.0 100.0 -Inf
## Ama.sim 23 0 0.0 0.0 0.0 100.0 -Inf
## Bad.bul 24 0 0.0 0.0 0.0 100.0 -Inf
## Bem.big 25 0 0.0 0.0 0.0 100.0 -Inf
## Bem.gut 26 0 0.0 0.0 0.0 100.0 -Inf
## Bem.lam 27 0 0.0 0.0 0.0 100.0 -Inf
## Bem.man 28 0 0.0 0.0 0.0 100.0 -Inf
## Bem.obt 29 0 0.0 0.0 0.0 100.0 -Inf
## Bra.har 30 0 0.0 0.0 0.0 100.0 -Inf
## Bra.sha 31 0 0.0 0.0 0.0 100.0 -Inf
## Cal.fus 32 0 0.0 0.0 0.0 100.0 -Inf
## Cli.fos 33 0 0.0 0.0 0.0 100.0 -Inf
## Cur.aul 34 0 0.0 0.0 0.0 100.0 -Inf
## Har.ruf 35 0 0.0 0.0 0.0 100.0 -Inf
## Lei.fer 36 0 0.0 0.0 0.0 100.0 -Inf
## Lei.ful 37 0 0.0 0.0 0.0 100.0 -Inf
## Lor.pil 38 0 0.0 0.0 0.0 100.0 -Inf
## Not.ruf 39 0 0.0 0.0 0.0 100.0 -Inf
## Ocy.har 40 0 0.0 0.0 0.0 100.0 -Inf
## Pat.atr 41 0 0.0 0.0 0.0 100.0 -Inf
## Poe.cup 42 0 0.0 0.0 0.0 100.0 -Inf
## Pte.nigr 43 0 0.0 0.0 0.0 100.0 -Inf
## Pte.obl 44 0 0.0 0.0 0.0 100.0 -Inf
## Pte.ver 45 0 0.0 0.0 0.0 100.0 -Inf
## Sto.pum 46 0 0.0 0.0 0.0 100.0 -Inf
## Syn.niv 47 0 0.0 0.0 0.0 100.0 -Inf
## Tre.qua 48 0 0.0 0.0 0.0 100.0 -Inf
## rankfreq
## Aba.par 2.1
## Pte.mad 4.2
## Neb.bre 6.2
## Cal.rot 8.3
## Lei.ruf 10.4
## Pte.nige 12.5
## Pla.ass 14.6
## Cal.vio 16.7
## Not.big 18.8
## Cyc.car 20.8
## Pte.str 22.9
## Lei.spi 25.0
## Pte.mel 27.1
## Acu.dub 29.2
## Ago.afr 31.2
## Ago.ful 33.3
## Ago.mue 35.4
## Ago.vid 37.5
## Ama.aen 39.6
## Ama.com 41.7
## Ama.fam 43.8
## Ama.ple 45.8
## Ama.sim 47.9
## Bad.bul 50.0
## Bem.big 52.1
## Bem.gut 54.2
## Bem.lam 56.2
## Bem.man 58.3
## Bem.obt 60.4
## Bra.har 62.5
## Bra.sha 64.6
## Cal.fus 66.7
## Cli.fos 68.8
## Cur.aul 70.8
## Har.ruf 72.9
## Lei.fer 75.0
## Lei.ful 77.1
## Lor.pil 79.2
## Not.ruf 81.2
## Ocy.har 83.3
## Pat.atr 85.4
## Poe.cup 87.5
## Pte.nigr 89.6
## Pte.obl 91.7
## Pte.ver 93.8
## Sto.pum 95.8
## Syn.niv 97.9
## Tre.qua 100.0
#Rank Abundance curves for all three habitats using proportion
rankabuncomp(beetle, y=habitat, factor="habitatCode", scale="proportion", legend=FALSE)
#rank abundance curves for all three habitats using raw abundance
rankabuncomp(beetle, y=habitat, factor="habitatCode", scale="abundance", legend=FALSE)
#The top ranked species in the Edge habitat are Aba.par, Pte.mad, Neb.bre, and Cal.rot. The abundance for Aba.park is 2,146.. 1,365 for Pte.mad, 624 for Neb.bre, and 314 for Cal.rot. 44% of this ecosystem is dominated by Aba.par, which provides insight that this ecosystem does not have an equal spread of species. In the Grassland habitat, the top 4 ranked species are Aba.par, Pte.mad, Neb.bre, and Poe.cup. The first ranked species Aba.par has a total abundance of 709, followed by 382 for Pte.mad, 175 for Neb.bre, and 107 for Poe.cup. This habitat has a large diversity but again only a few species dominate so it is not that even. In the Woodland habitat, the top ranked species are Aba.par, Pte.mad, Neb.bre, and Cal.rot. Their abundances are.. 2,013 for Aba.par, 1,415 for Pte.mad, 959 for Neb.bre, and 431 for Cal.rot. As in all of the other ecosystem, Aba.par dominates with a proportion of 38.3%. Comparing all of the top ranked species in the ecosystems, we can see that Aba.par by far is the most abundant species that is typically followed by Pte.mad and Neb.bre.
Question 5
#species accumulation curves for the full dataset
beetleAccum <- specaccum(beetle, method="random", permutation = 1000)
plot(beetleAccum, ci.type="poly", col="blue", lwd=2, ci.lty=0, ci.col="lightblue")
boxplot(beetleAccum, col="yellow", add=TRUE)
## Species Accumulation Curve
## Accumulation method: random, with 1000 permutations
## Call: specaccum(comm = beetle, method = "random", permutations = 1000)
##
##
## Sites 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000
## Richness 18.149000 25.210000 29.516000 32.885000 35.507000 37.536000
## sd 6.054871 6.713641 6.491832 6.163584 5.612972 5.128255
##
## Sites 7.000000 8.000000 9.000000 10.000000 11.000000 12.000000
## Richness 39.117000 40.490000 41.739000 42.806000 43.747000 44.534000
## sd 4.687353 4.311901 3.845213 3.326474 2.932847 2.561915
##
## Sites 13.000000 14.000000 15.000000 16.000000 17.000000 18
## Richness 45.217000 45.893000 46.488000 47.011000 47.499000 48
## sd 2.295033 1.970643 1.603255 1.298678 0.872215 0
#mean and sd richness should be similar to sampling one site
mean(diversityResult$spBeetle)
## [1] 17.944444
sd(diversityResult$spBeetle)
## [1] 6.2823021
#When looking at our species accumulation curve we can ensure that we had a good sampling effort because we hit our asymtote relatively quickly. My sampling conclusions for the individual habitats are not different than my conclusions for the whole data set because each site had 6 plots with richness numbers in the same range.
Question 6
beetlePool <- specpool(beetle)
#species column: number of unique species
#chao column: total species estimator
#n column: number of sites in the sample
#estimated species richness by habitat
beetlePoolSub <- specpool(beetle, habitat$habitatCode)
specnumber(beetle) #species richness per site
## E_1 E_2 E_3 E_4 E_5 E_6 G_1 G_2 G_3 G_4 G_5 G_6 W_1 W_2 W_3 W_4 W_5 W_6
## 17 14 15 25 21 17 28 22 18 28 26 24 12 11 11 12 12 10
specpool2vect(beetlePoolSub) #estimated total species per habitat
## [1] 32.000000 32.000000 32.000000 32.000000 32.000000 32.000000 43.666667
## [8] 43.666667 43.666667 43.666667 43.666667 43.666667 13.000000 13.000000
## [15] 13.000000 13.000000 13.000000 13.000000
#ratio of observed richness/estimated total species richness
unobsSpRatio <- specnumber(beetle) / specpool2vect(beetlePoolSub)
#boxplot
boxplot(unobsSpRatio ~ habitat$habitatCode)
#Conclusions that I can make regarding the sampling effort and the estimation of unobserved species in each beetle habitat is that we did a really good job for woodlands because our estimated amount of species was 13 and we actually found 13. For grasslands we were a bit off because we estimated that we would observe about 50 when we actually observed 37, so our sampling wasn't great here. For Edge we estimated that we would find 27 species and we found 32, so our sampling was decent here. These numbers make sense when reevaluating the curves I produced earlier because we had good enough sampling to see an asymtote earlier.
Question 8
#Compare and contrast the beta-diversity results using the Jaccard Index vs the Bray-Curtis index
library(betapart) #new library for assessing jaccard index
beetleJac <- ifelse(beetle > 0,1,0) #presence/absence data
beetleBeta <- beta.pair(beetleJac, index.family="jaccard")
attributes(beetleBeta) #gives us information on the object
## $names
## [1] "beta.jtu" "beta.jne" "beta.jac"
beetleBeta$beta.jtu #turnover partition between sites
## E_1 E_2 E_3 E_4 E_5
## E_2 0.000000000
## E_3 0.000000000 0.000000000
## E_4 0.111111111 0.000000000 0.000000000
## E_5 0.000000000 0.000000000 0.000000000 0.090909091
## E_6 0.300000000 0.133333333 0.235294118 0.111111111 0.111111111
## G_1 0.380952381 0.250000000 0.333333333 0.648648649 0.551724138
## G_2 0.380952381 0.352941176 0.421052632 0.625000000 0.551724138
## G_3 0.454545455 0.352941176 0.421052632 0.500000000 0.500000000
## G_4 0.454545455 0.352941176 0.421052632 0.611111111 0.600000000
## G_5 0.380952381 0.250000000 0.333333333 0.648648649 0.551724138
## G_6 0.454545455 0.352941176 0.421052632 0.666666667 0.600000000
## W_1 0.500000000 0.500000000 0.500000000 0.285714286 0.285714286
## W_2 0.428571429 0.428571429 0.428571429 0.307692308 0.307692308
## W_3 0.533333333 0.533333333 0.533333333 0.307692308 0.307692308
## W_4 0.400000000 0.400000000 0.400000000 0.285714286 0.285714286
## W_5 0.400000000 0.400000000 0.400000000 0.285714286 0.285714286
## W_6 0.461538462 0.461538462 0.461538462 0.333333333 0.333333333
## E_6 G_1 G_2 G_3 G_4
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1 0.300000000
## G_2 0.380952381 0.240000000
## G_3 0.380952381 0.105263158 0.285714286
## G_4 0.380952381 0.352941176 0.240000000 0.105263158
## G_5 0.300000000 0.266666667 0.307692308 0.105263158 0.322580645
## G_6 0.380952381 0.222222222 0.240000000 0.105263158 0.153846154
## W_1 0.285714286 0.500000000 0.500000000 0.588235294 0.500000000
## W_2 0.307692308 0.428571429 0.428571429 0.533333333 0.428571429
## W_3 0.307692308 0.533333333 0.533333333 0.625000000 0.533333333
## W_4 0.285714286 0.400000000 0.500000000 0.500000000 0.500000000
## W_5 0.285714286 0.400000000 0.500000000 0.500000000 0.500000000
## W_6 0.333333333 0.461538462 0.461538462 0.571428571 0.461538462
## G_5 G_6 W_1 W_2 W_3
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6 0.153846154
## W_1 0.588235294 0.588235294
## W_2 0.533333333 0.533333333 0.000000000
## W_3 0.625000000 0.625000000 0.000000000 0.166666667
## W_4 0.500000000 0.588235294 0.153846154 0.000000000 0.166666667
## W_5 0.500000000 0.588235294 0.153846154 0.000000000 0.166666667
## W_6 0.571428571 0.571428571 0.000000000 0.000000000 0.181818182
## W_4 W_5
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6
## W_1
## W_2
## W_3
## W_4
## W_5 0.000000000
## W_6 0.000000000 0.000000000
beetleBeta$beta.jne #nestedness partition between sites
## E_1 E_2 E_3 E_4 E_5
## E_2 0.1764705882
## E_3 0.1176470588 0.0666666667
## E_4 0.2735042735 0.4400000000 0.4000000000
## E_5 0.1904761905 0.3333333333 0.2857142857 0.1398601399
## E_6 0.0000000000 0.1444444444 0.0804953560 0.2735042735 0.1616161616
## G_1 0.2127976190 0.3500000000 0.2795698925 0.0263513514 0.0871647510
## G_2 0.1190476190 0.2070588235 0.1558704453 0.0321428571 0.0149425287
## G_3 0.0237154150 0.1232492997 0.0789473684 0.1129032258 0.0555555556
## G_4 0.1818181818 0.2922201139 0.2351973684 0.0299145299 0.0756756757
## G_5 0.1857142857 0.3214285714 0.2528735632 0.0092460882 0.0659229209
## G_6 0.1316614420 0.2396514161 0.1860902256 0.0090090090 0.0363636364
## W_1 0.1190476190 0.0555555556 0.0789473684 0.3439153439 0.2795031056
## W_2 0.1714285714 0.1008403361 0.1269841270 0.3589743590 0.3010033445
## W_3 0.1333333333 0.0777777778 0.0982456140 0.3589743590 0.3010033445
## W_4 0.1500000000 0.0705882353 0.1000000000 0.3439153439 0.2795031056
## W_5 0.1500000000 0.0705882353 0.1000000000 0.3439153439 0.2795031056
## W_6 0.1884615385 0.1266968326 0.1495726496 0.3703703704 0.3188405797
## E_6 G_1 G_2 G_3 G_4
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1 0.2483870968
## G_2 0.1190476190 0.1470967742
## G_3 0.0281385281 0.3085299456 0.1142857143
## G_4 0.2127976190 0.0000000000 0.1470967742 0.3085299456
## G_5 0.2172413793 0.0458333333 0.0923076923 0.2651072125 0.0410557185
## G_6 0.1547619048 0.1003584229 0.0562962963 0.2147368421 0.1128205128
## W_1 0.1879699248 0.2500000000 0.1923076923 0.1074168798 0.2500000000
## W_2 0.2186234818 0.3133640553 0.2514285714 0.1484848485 0.3133640553
## W_3 0.2186234818 0.2479166667 0.1974358974 0.1141304348 0.2479166667
## W_4 0.1879699248 0.3096774194 0.1923076923 0.1363636364 0.2500000000
## W_5 0.1879699248 0.3096774194 0.1923076923 0.1363636364 0.2500000000
## W_6 0.2456140351 0.3126550868 0.2584615385 0.1558441558 0.3126550868
## G_5 G_6 W_1 W_2 W_3
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6 0.0604395604
## W_1 0.1859582543 0.1703853955
## W_2 0.2333333333 0.2166666667 0.0833333333
## W_3 0.1814516129 0.1681034483 0.0833333333 0.0000000000
## W_4 0.2333333333 0.1703853955 0.0000000000 0.0833333333 0.0641025641
## W_5 0.2333333333 0.1703853955 0.0000000000 0.0833333333 0.0641025641
## W_6 0.2285714286 0.2142857143 0.1666666667 0.0909090909 0.0681818182
## W_4 W_5
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6
## W_1
## W_2
## W_3
## W_4
## W_5 0.0000000000
## W_6 0.1666666667 0.1666666667
beetleBeta$beta.jac #beta diversity between sites
## E_1 E_2 E_3 E_4 E_5
## E_2 0.176470588
## E_3 0.117647059 0.066666667
## E_4 0.384615385 0.440000000 0.400000000
## E_5 0.190476190 0.333333333 0.285714286 0.230769231
## E_6 0.300000000 0.277777778 0.315789474 0.384615385 0.272727273
## G_1 0.593750000 0.600000000 0.612903226 0.675000000 0.638888889
## G_2 0.500000000 0.560000000 0.576923077 0.657142857 0.566666667
## G_3 0.478260870 0.476190476 0.500000000 0.612903226 0.555555556
## G_4 0.636363636 0.645161290 0.656250000 0.641025641 0.675675676
## G_5 0.566666667 0.571428571 0.586206897 0.657894737 0.617647059
## G_6 0.586206897 0.592592593 0.607142857 0.675675676 0.636363636
## W_1 0.619047619 0.555555556 0.578947368 0.629629630 0.565217391
## W_2 0.600000000 0.529411765 0.555555556 0.666666667 0.608695652
## W_3 0.666666667 0.611111111 0.631578947 0.666666667 0.608695652
## W_4 0.550000000 0.470588235 0.500000000 0.629629630 0.565217391
## W_5 0.550000000 0.470588235 0.500000000 0.629629630 0.565217391
## W_6 0.650000000 0.588235294 0.611111111 0.703703704 0.652173913
## E_6 G_1 G_2 G_3 G_4
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1 0.548387097
## G_2 0.500000000 0.387096774
## G_3 0.409090909 0.413793103 0.400000000
## G_4 0.593750000 0.352941176 0.387096774 0.413793103
## G_5 0.517241379 0.312500000 0.400000000 0.370370370 0.363636364
## G_6 0.535714286 0.322580645 0.296296296 0.320000000 0.266666667
## W_1 0.473684211 0.750000000 0.692307692 0.695652174 0.750000000
## W_2 0.526315789 0.741935484 0.680000000 0.681818182 0.741935484
## W_3 0.526315789 0.781250000 0.730769231 0.739130435 0.781250000
## W_4 0.473684211 0.709677419 0.692307692 0.636363636 0.750000000
## W_5 0.473684211 0.709677419 0.692307692 0.636363636 0.750000000
## W_6 0.578947368 0.774193548 0.720000000 0.727272727 0.774193548
## G_5 G_6 W_1 W_2 W_3
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6 0.214285714
## W_1 0.774193548 0.758620690
## W_2 0.766666667 0.750000000 0.083333333
## W_3 0.806451613 0.793103448 0.083333333 0.166666667
## W_4 0.733333333 0.758620690 0.153846154 0.083333333 0.230769231
## W_5 0.733333333 0.758620690 0.153846154 0.083333333 0.230769231
## W_6 0.800000000 0.785714286 0.166666667 0.090909091 0.250000000
## W_4 W_5
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6
## W_1
## W_2
## W_3
## W_4
## W_5 0.000000000
## W_6 0.166666667 0.166666667
plotBeta <- betadisper(beetleBeta$beta.jac, habitat$habitatCode)
plot(plotBeta)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.0482585 0.02412923 5.31927 0.017947 *
## Residuals 15 0.0680429 0.00453619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## G-E 0.039835908 -0.061167441 0.140839256 0.57341463
## W-E -0.084362641 -0.185365990 0.016640707 0.10934484
## W-G -0.124198549 -0.225201897 -0.023195200 0.01567444
beetleBray <- bray.part(beetle)
attributes(beetleBray)
## $names
## [1] "bray.bal" "bray.gra" "bray"
beetleBray$bray #beta diversity with abundance data
## E_1 E_2 E_3 E_4 E_5
## E_2 0.118012422
## E_3 0.143277002 0.085507246
## E_4 0.151706700 0.109306683 0.153998678
## E_5 0.157769870 0.147214076 0.207173779 0.085963003
## E_6 0.133663366 0.146177370 0.190691661 0.075791855 0.081196581
## G_1 0.470370370 0.472247498 0.430267062 0.509740260 0.544910180
## G_2 0.526946108 0.523996082 0.489817792 0.558058925 0.588235294
## G_3 0.632608696 0.633652822 0.588719154 0.662313433 0.688775510
## G_4 0.541389153 0.543925234 0.509164969 0.552784705 0.596021423
## G_5 0.480446927 0.487648673 0.446766169 0.512234910 0.550375940
## G_6 0.529636711 0.524882629 0.488229273 0.540901503 0.583717358
## W_1 0.248478141 0.234392114 0.291139241 0.189382338 0.115850703
## W_2 0.189920098 0.157958688 0.209242619 0.156829680 0.144981413
## W_3 0.256427159 0.184895833 0.214088398 0.213900539 0.218274112
## W_4 0.257142857 0.155137676 0.177730193 0.166461159 0.173812283
## W_5 0.265459088 0.170370370 0.194516971 0.155733029 0.134087237
## W_6 0.216481360 0.136950904 0.160273973 0.158834027 0.163025210
## E_6 G_1 G_2 G_3 G_4
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1 0.532385466
## G_2 0.579124579 0.217791411
## G_3 0.683544304 0.291228070 0.207317073
## G_4 0.578011318 0.243937233 0.146067416 0.256931608
## G_5 0.534920635 0.165745856 0.148606811 0.287234043 0.125179856
## G_6 0.563311688 0.235632184 0.132686084 0.253731343 0.103448276
## W_1 0.146011039 0.633493480 0.676577230 0.759444873 0.684873950
## W_2 0.169332598 0.581832420 0.628023353 0.720680394 0.639423077
## W_3 0.247210804 0.544130249 0.594123049 0.692154916 0.606326889
## W_4 0.196859903 0.519642857 0.570057582 0.670833333 0.589367553
## W_5 0.162842753 0.571542766 0.619778346 0.712190651 0.633387889
## W_6 0.194169096 0.552162850 0.596730245 0.695780177 0.619130435
## G_5 G_6 W_1 W_2 W_3
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6 0.124637681
## W_1 0.647139904 0.669711876
## W_2 0.597167585 0.621882542 0.169660679
## W_3 0.560723514 0.586937335 0.240760296 0.093348891
## W_4 0.536804309 0.572744015 0.223605847 0.139772046 0.201027617
## W_5 0.587148594 0.618734593 0.166835187 0.134593993 0.190758294
## W_6 0.568627451 0.603493450 0.203567681 0.069524913 0.110148515
## W_4 W_5
## E_2
## E_3
## E_4
## E_5
## E_6
## G_1
## G_2
## G_3
## G_4
## G_5
## G_6
## W_1
## W_2
## W_3
## W_4
## W_5 0.083485679
## W_6 0.107711918 0.104705882
plotBray <- betadisper(beetleBray$bray, habitat$habitatCode)
plot(plotBray)
## Analysis of Variance Table
##
## Response: Distances
## Df Sum Sq Mean Sq F value Pr(>F)
## Groups 2 0.0046519 0.00232597 1.05675 0.37209
## Residuals 15 0.0330159 0.00220106
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = distances ~ group, data = df)
##
## $group
## diff lwr upr p adj
## G-E 0.0371786930 -0.033178127 0.107535513 0.37936206
## W-E 0.0073514026 -0.063005417 0.077708222 0.96031491
## W-G -0.0298272904 -0.100184110 0.040529529 0.52792832
#The Jaccard Index is looking at the list of species richness, and they will get a 1 or 0 if they are present or not. If our different habitat types are similar they will be close to eachother or on top of eachother;as they get further away from eachother then the more different they are. The ANOVA actually tells us that its different and the Tukey HSD test can tell us the differences between ecosystems. Grasslands and Edge are very similar because the pvalue is 0.57. Woods and Edge are a little similar because the pvalue is 0.10, while Wood and Grasslands are very different because the pvalue is 0.01. Using the Bray-Curtis Index we get different results. The Grasslands and Edge are similar because the p-value is 0.37, Woods and Edge are also very similar because the pvalue is 0.96, and lastly woods and grasslands are also similar with a pvalue of 0.52.
Question 9
library(cooccur)
beetleCOImport <- t(beetle)
beetleCO <- ifelse(beetleCOImport > 1, 1, beetleCOImport)
cooccur.beetles <- cooccur(mat = beetleCO, type= "spp_site", thresh= TRUE, spp_names=TRUE)
##
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head(cooccur.beetles$results)
## sp1 sp2 sp1_inc sp2_inc obs_cooccur prob_cooccur exp_cooccur p_lt p_gt
## 1 1 2 18 3 3 0.167 3 1 1
## 2 1 3 18 6 6 0.333 6 1 1
## 3 1 4 18 2 2 0.111 2 1 1
## 4 1 5 18 2 2 0.111 2 1 1
## 5 1 6 18 2 2 0.111 2 1 1
## 6 1 7 18 1 1 0.056 1 1 1
## sp1_name sp2_name
## 1 Aba.par Acu.dub
## 2 Aba.par Ago.afr
## 3 Aba.par Ago.ful
## 4 Aba.par Ago.mue
## 5 Aba.par Ago.vid
## 6 Aba.par Ama.aen
plot(cooccur.beetles) #heat map plot
#pair--wise investigation using species codes
pair(mod= cooccur.beetles, "Cli.fos")
## Species:
## [1] "Cli.fos"
## with 19 associations
##
## sp2 sp2_inc obs_cooccur prob_cooccur exp_cooccur p_lt p_gt
## 411 Cyc.car 6 0 0.111 2.0 0.04977 1.00000
## 413 Lei.ruf 9 0 0.167 3.0 0.00452 1.00000
## 417 Not.ruf 4 4 0.074 1.3 1.00000 0.00490
## 420 Poe.cup 12 6 0.222 4.0 1.00000 0.04977
## 424 Pte.nigr 12 6 0.222 4.0 1.00000 0.04977
## 427 Pte.ver 4 4 0.074 1.3 1.00000 0.00490
## 429 Syn.niv 4 4 0.074 1.3 1.00000 0.00490
## 430 Tre.qua 4 4 0.074 1.3 1.00000 0.00490
## 55 Acu.dub 3 3 0.056 1.0 1.00000 0.02451
## 78 Ago.afr 6 6 0.111 2.0 1.00000 0.00005
## 162 Ama.ple 4 4 0.074 1.3 1.00000 0.00490
## 187 Bad.bul 3 3 0.056 1.0 1.00000 0.02451
## 207 Bem.big 7 6 0.130 2.3 1.00000 0.00038
## 249 Bem.lam 10 6 0.185 3.3 1.00000 0.01131
## 277 Bem.man 12 6 0.222 4.0 1.00000 0.04977
## 303 Bem.obt 3 3 0.056 1.0 1.00000 0.02451
## 323 Bra.sha 4 4 0.074 1.3 1.00000 0.00490
## 340 Cal.fus 6 6 0.111 2.0 1.00000 0.00005
## 388 Cal.vio 12 0 0.222 4.0 0.00005 1.00000
#p_lt = p-value < 0.5 detects negative associations
#p_gt = p-value < 0.5 detecs of positive associations
#After looking at cooccurrence patterns among species pairs, the conclusions I can make are that whenever there is a positive relationship you can find those species together, but when there is a negative relationship you will not find those species together at all (probably because they like different habitats). For example we would find species Cli.fos and Cal.fus in the same ecosystem because they have a positive relationship, but you would not find Cli.fos and Cal.vio in the same ecosystem because they have a negative relationship.
Question 10
pair(mod= cooccur.beetles, "Aba.par")
## Species:
## [1] "Aba.par"
## with 0 associations
##
## [1] sp2 sp2_inc obs_cooccur prob_cooccur exp_cooccur
## [6] p_lt p_gt
## <0 rows> (or 0-length row.names)
pair(mod= cooccur.beetles, "Pte.mad")
## Species:
## [1] "Pte.mad"
## with 0 associations
##
## [1] sp2 sp2_inc obs_cooccur prob_cooccur exp_cooccur
## [6] p_lt p_gt
## <0 rows> (or 0-length row.names)
pair(mod= cooccur.beetles, "Cal.rot")
## Species:
## [1] "Cal.rot"
## with 0 associations
##
## [1] sp2 sp2_inc obs_cooccur prob_cooccur exp_cooccur
## [6] p_lt p_gt
## <0 rows> (or 0-length row.names)
pair(mod= cooccur.beetles, "Pte.nige")
## Species:
## [1] "Pte.nige"
## with 0 associations
##
## [1] sp2 sp2_inc obs_cooccur prob_cooccur exp_cooccur
## [6] p_lt p_gt
## <0 rows> (or 0-length row.names)
#The highest ranked species in the data set all have no associations because they are found everywhere.