How to devide my dataset for using permanova - r

Question

Hello everyone :) I have a data set with individuals that correspond in 5 different species in one column, and their presence/absence in different landscapes (7 other columns).
data.frame': 1212 obs. of 10 variables:
$ latitude : num 34.5 34.7 34.7 34.8 34.8 ...
$ longitude : num 127 127 127 127 127 ...
$ species : Factor w/ 5 levels "Bufo gargarizans",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Built : int 0 0 0 0 0 0 0 0 0 0 ...
$ Agriculture: int 1 1 0 1 0 0 1 0 0 0 ...
$ Forested : int 0 0 1 0 0 0 0 1 1 1 ...
$ Grassland : int 0 0 0 0 0 0 0 0 0 0 ...
$ Wetland : int 0 0 0 0 0 0 0 0 0 0 ...
$ Bare : int 0 0 0 0 1 0 0 0 0 0 ...
$ Water : int 1 0 0 0 0 1 0 0 0 0 ...
I try to use permanova and then Tukey test to see if the species use the landscape differently or not. My supervisor did it on SPSS and it worked very well, so I have to do it on R.
I saw I need 2 csv files for running permanova on R but I have only one. I will give you the script that I found on internet and I want to use for my analysis.
library(vegan)
data(dune)
data(dune.env)
# default test by terms
adonis2(dune ~ Management*A1, data = dune.env)
In my case, I should have 1 dataframe with species and 1 dataframe with environmental variables, if I understand well.
However my presence/absence are inside the environmental categories (see the str of my table above). So if I create 1 dataframe with species only, I will not have numerical values in the dataframe with species.
So I am totally lost. I don't know how to process. Can someone help me please ? Thank you !

Answer 1

I will split my answer into two parts. The one where I know what I am talking about and one where I am brainstorming ;)
Here is the first part on how to split your data into two data.frames
# Set seed
seed(1312)
# Some sample data
sample_data <- dplyr::tibble(species=sample(LETTERS[1:5],size=500,replace=T),
Built=sample(c(0,1),size=500,replace=T),
Agriculture=sample(c(0,1),size=500,replace=T),
Forested=sample(c(0,1),size=500,replace=T),
Grassland=sample(c(0,1),size=500,replace=T),
Wetland=sample(c(0,1),size=500,replace=T),
Bare=sample(c(0,1),size=500,replace=T),
Water=sample(c(0,1),size=500,replace=T))
# Split data
df1 <- sample_data %>%
dplyr::select(species)
df2 <- sample_data %>%
dplyr::select(Built:Water)
Now part two: as it says in ?adonis2 the first part of adnonis2 is a formula where the left part of the formula must be a community data matrix or a dissimilarity matrix
Eventhough I am not sure if it does make sense, I went wild and followed the instructions :D
df2_dist <- dist(df2)
vegan::adonis2(df2_dist~species, data=df1)
Permutation test for adonis under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999
vegan::adonis2(formula = d2 ~ species, data = d1)
Df SumOfSqs R2 F Pr(>F)
species 4 5.333 0.15802 0.7038 0.88
Residual 15 28.417 0.84198
Total 19 33.750 1.00000
Of course this might be nonsense in terms of content as I took a purely technical approach here, but maybe it helps you to shape your data as required

Answer 2

So I made this code :
setwd("C:/Users/Johan/Downloads/memoire Johanna (1)/memoire Johanna")
xdata=read.csv(file="all10_reduce_focal species1.csv", header=T, sep=",")
str(xdata)
xdata$species <- as.factor(xdata$species)
sample_data <- dplyr::tibble(species=sample(LETTERS[1:5],size=1212,replace=T),
Built=sample(c(0,1),size=1212,replace=T),
Agriculture=sample(c(0,1),size=1212,replace=T),
Forested=sample(c(0,1),size=1212,replace=T),
Grassland=sample(c(0,1),size=1212,replace=T),
Wetland=sample(c(0,1),size=1212,replace=T),
Bare=sample(c(0,1),size=1212,replace=T),
Water=sample(c(0,1),size=1212,replace=T))
library(dplyr)
df1 <- sample_data %>%
dplyr::select(species)
df2 <- sample_data %>%
dplyr::select(Built:Water)
df1_dist <- dist(df1)
vegan::adonis2(df1_dist~Built+Agriculture+Grassland+Forested+Wetland+Bare+Water, data=df2)
Species should be the response as I try to see the landscape on the species. When I do this I have :
Error in vegdist(as.matrix(lhs), method = method, ...) : input data must be numeric
It's because the "species" variable has only characters. So I changed to make it numeric :
sample_data <- dplyr::tibble(species=sample(c(1:5),size=1212,replace=T),
Built=sample(c(0,1),size=1212,replace=T),......
But the result I got is different as the result from SPSS, as I don't have any significant variable (in SPSS Built, Agriculture, Forested and Water are significant).
I think my code is wrong