In this document, we describe how to use the R package
`specieschrom`

available on Github. The species chromatogram
method only uses three main functions : `chromato_env16.R`

,
`opti_eury_niche2_v2.R`

and `combina_niche3.R`

.
The following packages are required: `abind`

,
`colorRamps`

, `ggplot2`

, `reshape2`

and
`utils`

. Noted that the same procedure is applicable with
Matlab (functions are also available on Github).

See Kléparski and Beaugrand 2022, Ecology and Evolution 12:e8830 (https://doi.org/10.1002/ece3.8830) for further details.

The package can be installed from Github with the
`devtools`

package:

`devtools::install_github("loick-klpr/specieschrom")`

`chromato_env16.R`

The function `chromato_env16.R`

estimates and displays the
chromatogram of a given species. This function takes six arguments in
the following order:

`chromato_env16(z,y,alpha,m,k,order_smth)`

With `z`

a matrix with n samples by p environmental
variables (i.e. the value of each environmental variable in each
sample), `y`

a vector with the abundance of a species in the
n samples, `alpha`

an integer corresponding to the number of
category along each environmental variable, `m`

an integer
corresponding to the lowest number of samples needed in a category in
order to have an estimation of the mean abundance, `k`

an
integer corresponding to the percentage of samples with the highest
abundance values to use to estimate the mean abundance in a given
category and `order_smth`

an integer corresponding to the
order of the simple moving average applied along each niche dimension.
The simple moving average is applied to reduce the noise in the mean
abundance sometimes observed in the chromatograms from a category to
another.

`chromato_env16.R`

used the functions
`nanmean4.R`

, which estimates the mean of the `k`

%
of the samples with the highest abundance and `moymob1.R`

which applies the moving average.

Load the `specieschrom`

R package and the datasets with
the 14 pseudo-species abundances and the associated environmental
variables.

```
library(specieschrom)
data("data_abundance")
data("environment")
```

The `abundance`

dataset contains the abundance of 14
pseudo-species (columns PS1 to PS14) in 100 samples (lines).
Pseudo-species were generated with a beta-niche. Noted that each
pseudo-species has been duplicated (2 x 7 pseudo-species). The
`environment`

dataset contains the values of three fictive
environmental variables (columns x1 to x3) in the 100 samples
(lines).

Apply the function `chromato_env16.R`

on the first
pseudo-species to display its three dimensional niche. Here we used
`alpha`

=50 categories, `m`

=1 sample at least in
each category, `k`

=5 and `order_smth`

=2 :

`sp_chrom_PS1<-chromato_env16(environment,data_abundance[,1],50,1,5,2)`

With the third pseudo-species:

`sp_chrom_PS3<-chromato_env16(environment,data_abundance[,3],50,1,5,2)`

With the fifth pseudo-species:

`sp_chrom_PS5<-chromato_env16(environment,data_abundance[,5],50,1,5,2)`

With the eighth pseudo-species:

`sp_chrom_PS8<-chromato_env16(environment,data_abundance[,8],50,1,5,2)`

`opti_eury_niche2.R`

The function `opti_eury_niche2.R`

estimates the niche
optimums and breadths of each species along each niche dimension,
i.e. each environmental variable. The mean niche breadth is also
estimated. This function takes six arguments in the following order
:

`opti_eury_niche2(sp_chr,T,z,y,k)`

With `sp_chr`

a three dimensional matrices with the
species chromatograms (alpha category by p environmental variables by
species), `T`

the threshold of minimal abundance in a
category for the niche breadth estimation, `z`

a matrix with
n samples by p environmental variables (i.e. the value of each
environmental variable in each sample), `y`

a matrix with the
abundance of the species in the n samples and `k`

the
percentage of samples with the highest abundance used for the mean
abundance estimation.

Combine the multiple species chromatograms along the third dimension
with the function `abind`

:

```
library(abind)
test_PS<-abind::abind(sp_chrom_PS1,sp_chrom_PS3,sp_chrom_PS5,sp_chrom_PS8,along=3)
```

The function `opti_eury_niche2.R`

can then be applied,
with a threshold of abundance `T`

=0 and `k`

=5 (as
in `chromato_env16.R`

:

```
opti_ampli_niche<-opti_eury_niche2(test_PS,0,environment,data_abundance[,c(1,3,5,8)],5)
```

The degree of euryoecy (i.e. niche breadth) of each pseudo-species
along each dimension are stored in
`opti_ampli_niche$amplitudes`

, a table with in line the p
environmental variables (here three) and in column the pseudo-species
(here four, i.e. 1=PS1, 2=PS3, 3=PS5 and 4=PS8):

`opti_ampli_niche$amplitudes`

The mean degree of euryoecy of each pseudo-species is stored in
`opti_ampli_niche$mean_amplitudes`

:

`opti_ampli_niche$mean_amplitudes`

The niche optimums for each species (i.e. four, in column) along each
niche dimension (i.e. three, in line) are stored in
`opti_ampli_niche$optimums`

:

```
opti_ampli_niche$optimums
```

`combina_niche3.R`

The function `combina_niche3.R`

estimates the index of
niche overlapping (D) among species niche. It returns three matrices:
`combi_dim`

which contains the combination of environmental
variables associated with the lowest degree a niche overlapping when 1
to p environmental dimensions are considered, `sp_by_sp`

which contains the degree of niche overlapping species by species when
all the environmental variables are considered and
`dim_alone`

which contains the lowest degree of niche
overlapping when each environmental variable is considered alone. This
function takes two arguments in the following order :

`combina_niche3(sp_chr,T)`

With `sp_chr`

a three dimensional matrix with the species
chromatograms (alpha category by p environmental variables by species)
and `T`

the threshold of minimal abundance in a category for
the niche breadth estimation.

`combina_niche3.R`

used the functions
`niche_difer_sp.R`

and `niche_difer2.R`

.

Apply the function on the previous three-dimensional matrix
`test_PS`

:

`Index_D_PS<-combina_niche3(test_PS,0)`

In `combi_dim`

the first column displays the number of
dimensions considered simultaneously and columns 2 to 4 display the
combinations of dimensions considered. The last column displays the
index D associated with the combination of environmental dimensions.
D=0% when species niches are fully different and D=100% when species
niches are identical; the higher the number of dimensions, the lower the
value of index D. Only the combinations of environmental variables that
minimise values of index D are displayed :

`Index_D_PS$combi_dim`

The matrix `sp_by_sp`

contains the degree of niche
overlapping species by species when all the environmental dimensions are
considered :

`Index_D_PS$sp_by_sp`

The matrix `dim_alone`

contains the lowest degree of niche
overlapping when each environmental variable is considered alone. Here,
as each species has the same niche breadth along each dimension (because
of the use of a beta-niche), the same degree of niche overlapping is
observed along each dimension :

`Index_D_PS$dim_alone`