18. Estimating species composition in one or more regions with KnowBr and ModestR (Version ModestR 5.6 or higher)

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Step by step tutorial:
Estimating species composition in
one or more regions with
KnowBR and ModestR

What do you need for this tutorial:
1. ModestR v.5.6 or higher installed
2. Internet connection
3. About 40 minutes
ModestR software can be freely downloaded from http://www.ipez.es/ModestR

We’ll describe how to use ModestR with
KnowBR results to estimate species
composition by region. Follow the next
steps!

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• An estimation of the species richness of those regions. This estimation can
be obtained using KnowBR (in this tutorial we’ll provide a sample result)
KnowBR is a package for R (https://cran.r-project.org/web/packages/KnowBR/index.html)
An explanation of the method employed by KnowBR and how to use it can be found in:
Lobo, J.M. et al. (2018) KnowBR: An application to map the geographical variation of survey effort and identify well-surveyed areas from
biodiversity databases. Ecological Indicators (91), 241-248 https://doi.org/10.1016/j.ecolind.2018.03.077
• The species distribution models for all the species we expect to be present
in the regions to be analyzed. Those models can be calculated using SDM’s
such as Maxent, emsemble models, or NOO (ModestR’s Niche of
Occurrence) (in this tutorial we’ll provide samples)
In this tutorial we’ll explain how to use ModestR to estimate a probable composition of the species present in several
regions (river basins, countries, etc.). To do that we need:
For this tutorial we’ll provide a compressed file with all the sample files you
need! You can download it in THIS LINK. Then uncompress it in a folder. We’ll
use those files along this tutorial.

An estimation of the species richness of those regions obtained using KnowBR should be a CSV file like this:
Species records in each area
(according to provided data)
Area name - In this case areas are river basins, named using the ModestR/KnowBR conventions
(see Tutorial 8- Working with shapes for more information)
Observed richness according
to species records
Expected richness - That is the value we are interested in.
This is an estimation of the real species richness of each
region, using known data and KnowBR estimation method.
This value will often be greater than observed richness,
indicating that there are more species in the region that
those already known.
Therefore, with this information we can try to determine the
probable composition of species of the region!

For this tutorial you’ll find a sample file “South_America_Knowbr_sample_Estimations” among the sample files
provided with this tutorial. You can choose it between two CSV options: Format Spanish / Format English
You may select the format depending on the configuration in the "Options / Preferences" menu of the "CSV Exportation
Options" section of any of the ModestR applications.
For example in ModestR DataBase Manager you can find it:
1) Select menu “Options / Preferences”
2) To use Spanish format (default):
 Decimal separator  “ , “
 Field separator  “ ; “
To use English format :
 Decimal separator  “ . “
 Field separator  “ , “

In this map you can see the regions
corresponding to the river basins
we’ll use in this tutorial
The files contains estimated richness for the river basins of South America.
Take into account that this file does not contain real data! It is just sample data for this tutorial.
To obtain real data, use KnowBR.

We also need the distribution models of the species expected to be present in those regions.
Those models can be calculated using SDM’s such as Maxent, ensemble models, or NOO (ModestR’s Niche of
Occurrence). As each SDM has strengths and weaknesses, you may want to use different SDMs to obtain various
distribution models of each species. But you can also use a single one.
An SDM usually provides as a result a suitability raster
with values ranging from 0 to 1.
The species is expected to be more likely present in the
areas with higher suitability.
Those suitability rasters for each species are what we’ll
use in this tutorial

You’ll find two folders among the sample files provided with this tutorial.
 “SSDM” folder contains species distribution models obtained using ensemble modelling with SSDM package for R
(https://cran.r-project.org/web/packages/SSDM/index.html)
 “NOO2D” folder contains species distribution models obtained using NOO2D, or Niche Of Occurrence, available in
ModestR
Both folders contain suitability rasters for 50 virtual
species (from Virtual Species1 to Virtual Species50).

As we have two suitability rasters for each species, we first need to merge them to obtain a single suitability raster
for each species. We can do that with R or other tools, but here we’ll explain how to do it in ModestR.
2) Go to menu “Layers/Raster merging tool”
NOTE.- If you had a single suitability raster
for each species (because you used a
single SDM to obtain them) you don’t have
to merge several rasters for each species.
Therefore you should skip the next 4 steps.
1) Run ModestR DataManager

In the raster merging tool, select the folders “SSDM” and “NOO2D” where the suitability rasters are
(those folders are among the sample files provided with this tutorial)
Here we added two folders. But any number of
folders/files may be added to the raster merging tool.
1) Click on this button to select a folder and
add all the files of this folder.
Do it for both the folders “SSDM” and
“NOO2D”
The added files will appear here
NOO2D
SSDM

2) In the “First level grouping” box, select
“Species name” option to group all selected
files by species name.
The raster merging tool searches for a
species name at the beginning of the file
names. Then it groups files with the same
species names.
The resulting groups and the files they
contain will be shown in the list
3) Press “Export” button
Then select a folder to save results. We
recommend to create a new folder called
“MergedRasters” in order to save results.

Merging process will start. The
suitability rasters for each species will
be merged calculating a mean raster
for each species
4) Once ended, press “Close”

If you see the content for the selected output folder
(that we proposed to call “MergedRasters”), you will
have a single suitability raster for each species. That is,
something like this.

Now we are ready to continue!
You’ll find a file named “VirtualSpecies for tutorial 18.MODESTRDB” among the sample files provided with this tutorial.
Open it in DataManager
This step is not mandatory. You can continue just opening any
database (even an empty one). It is not necessary to have a
database containing the species to be used. But it’ll be useful to
provide a taxonomy structure for the results we’ll obtain later.
1) Select menu “File / Open Database” then select
VirtualSpecies for tutorial 18.MODESTRDB
This database contains just a sample taxonomy for the 50 virtual species we
are working with (no distribution maps, just a taxonomy)

2) Select menu “Mapping/ Estimate species distribution
using KnowBR+SDM results”

A step-by-step assistant will guide you through the process. Just follow the indications
The file will be read and the regions will be extracted, along
with the richness estimated by KnowBR for each one. A dialog
box will indicate how many regions were read from file.
If errors were detected in the file, they will be shown
1) Click on “Select” then select the file
“South_America_Knobr_sample_Estimations_format_[Spanish or English].csv”
(which is among the sample files provided with this tutorial). This file
contains a sample output from KnowBR, as explained previously.
NOTE.- Remember, see CSV import settings in "Options / Preferences / CSV Exportation Options”
3) Click on “Continue”
2) Click on “Accept”

In this step you have to provide the species distribution models (that is, the suitability rasters) for each species expected to
be present in the regions to be analyzed.
1) Click on “Select” (SMD input files) then select the
folder that contains the rasters we previously obtained
merging the different suitability rasters we have for each
species (we proposed to save them in a folder named
“MergedRasters”)
2) Click on “Select” (Output setting) then select a folder
to save the results. We recommend creating a new
folder to save results
This option will create a new database copying the
taxonomy of the current one and saving in it the
estimated distribution of each species. That is why it is
useful to provide a database with the taxonomy, as
commented previously
3) Click on “Continue”

In this step you can define the species preference index (SPI). That is, how it’ll be decided that a species is more likely
present in an area than others. By default the species with the maximum suitability in the area (according to the provided
SDM results) will be selected. But other criteria are possible, such as prioritizing the species by the percent of surface where
they are more suitable in a region; or a weighted average of suitability and percent of surface, etc.
For this tutorial we’ll use the defaults: prioritizing the species with the maximum suitability in the area.
Click on “Continue”
Click on this button to see an explanation about
how SPI is calculated and how you can modify it

The process will start. Each species suitability raster will be read, the SPI is calculated for each area and each species, and
the species with a higher value are assigned to the areas until reaching the richness estimated in the KnowBR file provided
previously.

This last step, when generating and saving result, can take some time… Be patient!

Once finished, go to the folder you selected to save results. Lets examine them one by one.
You will find those files and folders:

In the folder “Presence_by_species” there are the rasters of estimated presence for each species.
A raster for a species X will contain a value of “1” in the regions where the species X is one of those with a higher SPI.
You can visualize those rasters using MapMaker for example.
To display a raster, you must run MapMaker, go to
menu “Layers/Rasters/Load raster from ESRI ASC”

The file “distributionreport.csv” contains the estimated composition of species for each analyzed region.
Area or region List of species assigned to each region
Suitability of the species for the region
Percent of the area where the species is suitable
(according to an entered threshold).
This value is 0 if this value is not used to calculate SPI.
SPI assigned to the species in this region, according to
the user settings

The file “summaryreport.csv” contains the expected richness (according to KnowBR estimations imported initially) and the
resulting richness for each region. That is, the number of species assigned to each region.
In ideal conditions both expected and resulting
richness will be the same for a region. But it may
happen that resulting richness is lower than expected
richness. This can be an indication that there are
missing species to discover in this region.

The raster “totalrichness.asc” contains the resulting richness by region
To display a raster in MapMaker, go to menu
“Layers/Rasters/Load raster from ESRI ASC”

The file “db_knowbrbaseddistribution.modestrdb” contains the estimated distribution maps for each species basing on
the provided data from KnowBR and the SDM’s. The distribution of each species will be the sum of the regions where the
species is one of the more likely ones to be present
Note.- This database is created by default, but you can select in the setting
whether to create it or not.
You can use MRMapping to visually explore this database,
make multidistribution maps, etc.
See Tutorial 5 about MRMapping in
http://www.ipez.es/ModestR/Manual_Tutorial.html
You can open and explore it using DataManager,
go to menu “File / Open Database”

It was the Step by step tutorial:
Estimating species composition in one or more regions with
KnowBR and ModestR
Thank you for your interest.
You can find this one and other tutorials in http://www.ipez.es/ModestR
By the ModestR team

18. Estimating species composition in one or more regions with KnowBr and ModestR (Version ModestR 5.6 or higher)

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