OBIS is a global open-access data and information clearing-house on marine biodiversity for science, conservation and sustainable development. In order to visualize their marine data OBIS created the package obisindicators.
Obisindicators is an R library developed during the 2022 IOOS Code Sprint. The purpose was to create an ES50 diversity index within hexagonal grids following the diversity indicators notebook by Pieter Provoost linked above. The package includes several examples, limited to 1M occurrences, that demonstrate uses of the package.
This tutorial will guide you on getting obis marine data and processing them in order to calculate and visualize multiple indicators.
This tool for obisindicators is composed of 5 indicators : Number of record, Shannon, Simpson, Es50 and Hill which will be explained in more details later on.
This first step consist of downloading and uploading obis data onto galaxy.
Hands-on: Data upload
Create a new history for this tutorial and give it a name (example: “Obisindicators tutorial”) for you to find it again later if needed.
To create a new history simply click the new-history icon at the top of the history panel:
Hands-on: Choose Your Own Tutorial
This is a "Choose Your Own Tutorial" section, where you can select between multiple paths. Click one of the buttons below to select how you want to follow the tutorial
You can use a tool to download the data you want to study or you can download them from the Obis portal.
Use the galaxy tool
Hands-on: Download your data
OBIS occurences ( Galaxy version 0.0.2) with the following parameters:
“Scientific name of the species”: Empty
“Taxon ID”: Empty
“Input latitude min (+north/-south)”: 41.5
“Input latitude max (+north/-south)”: 45.0
“Input longitude min (+east/-west)”: 7.5
“Input longitude max (+east/-west)”: 10.0
Run Tool
Clean data Advanced Cut
Hands-on: Clean your data
Advanced Cut ( Galaxy version 1.1.0) with the following parameters:
-“File to cut”: Species occurences
“Cut by”: fields
“List of Fields”: basisOfRecorddecimalLatitudedecimalLongitudespeciesindividualCount
You should always have at least the 4 folowing columns: decimalLatitude, decimalLongitude, species and individualCount
The download can take a while depending on the size of your dataset (here less than 15min)
Then click on Download ZIP file
Don’t forget to unzip your file on your machine.
In the downloaded folder you should have your data either csv format (Occurence.csv) and you must have at least 4 columns containing: latitude, longitude, species and record.
Upload obis data
Open the Galaxy Upload Manager galaxy-upload
Select Choose local files
Browse in your computer and get the downloaded zip folder
Press Start (it can take a few seconds to get ready)
Rename the datasets “obis data” for example and preview your dataset
Check the datatype must be csv or tabular
Click on the galaxy-pencilpencil icon for the dataset to edit its attributes
In the central panel, click galaxy-chart-select-dataDatatypes tab on the top
In the galaxy-chart-select-dataAssign Datatype, select datatypes from “New type” dropdown
Tip: you can start typing the datatype into the field to filter the dropdown menu
Click the Save button
Convert data csv-to-tabular
Hands-on: Convert your data
On your data in your history pannel click on param-text
In the top click on galaxy-gear Convert
Press exchange Create Dataset
Clean data Advanced Cut
Hands-on: Clean your data
Advanced Cut ( Galaxy version 9.3+galaxy0) with the following parameters:
-“File to cut”: obis data
“Cut by”: fields
“Delimited by”: Tab
-“Is there a header for the data’s columns ?”: Yes
“List of Fields”: basisOfRecorddecimalLatitudedecimalLongitudespeciesindividualCount
You should always have at least the 4 folowing columns: decimalLatitude, decimalLongitude, species and individualCount
You are now all set to use your obis data in order to do a diversity analysis.
Ocean biodiversity indicators
Hands-on: Ocean biodiversity indicators
Ocean biodiversity indicators ( Galaxy version 0.0.1) with the following parameters:
“Input table”: The result tabular of the Advanced Cut tool
“What character is the separator in your data? (Mostlikely a comma for a csv file and t for a tabular)”: Tabulator (\t)
“Select column containing the decimal value of the longitude “: c2
“Select column containing the decimal value of the latitude “: c3
“Select column containing the species “: c4
“Select column containing the number of records”: c5
“Type of projection for the map : select your coordinate reference system (CRS)”: Robinson Projection
“Choose a resolution for the discrete global grid”: 9
Note that you can rerun the tool and modify the resolution of the maps you want to create.
Click on Execute
You will see 5 outputs appear on the history pannel. one for each of the indicators
The Shannon index expresses the uncertainty associated with the prediction of the species the next sampled individual belongs to. It assumes that individuals are randomly sampled from an infinitely large community, and that all species are represented in the sample.
Warning: OBIS uses records as a proxy for individuals and sampling is generally not random, the community is not infinitely large and not all species are represented in the sample.
The Shannon diversity index, also known as the Shannon-Wiener diversity index, is defined in OBIS as the sum over all species of $-fi*log(fi)$ with fi defined as $n/ni$ with n as the total number of records in the raster cell and ni as the total number of records for the ith-species in the raster cell.
The measure equals the probability that two entities taken at random from the dataset of interest represent the same type. It equals:
where is richness (the total number of types in the dataset) and is the proportional abundances of the types of interest.
Simpson’s index expresses the probability that any two individuals drawn at random from an infinitely large community belong to the same species. Note that small values are obtained in cells of high diversity and large values in cells of low diversity. This counterintuitive behavior is adressed with the Hill 2 number, which is the inverse of the Simpson index.
The Simpson biodiversity index is defined in OBIS as the sum over all species of $(ni/n)^2$ with n as the total number of records in the cell and ni the total number of records for the ith species.
Warning: The Simpson index has the same assumptions as the Shannon index.
The expected number of marine species in a random sample of 50 individuals (records) is an indicator on marine biodiversity richness.
The ES50 is defined in OBIS as the \(sum(esi)\) over all species of the following per species calculation:
when $$ n - ni$ <= 50 $$ (with n as the total number of records in the cell and ni the total number of records for the ith-species)
$$ esi = 1 - exp(lngamma(n-ni+1) + lngamma(n-50+1) - lngamma(n-ni-50+1) - lngamma(n+1)) $$
when $$ n >= 50 $$
$$ esi = 1 $$
else
$$ esi = NULL $$
Warning: ES50 assumes that individuals are randomly distributed, the sample size is sufficiently large, the samples are taxonomically similar, and that all of the samples have been taken in the same manner.
Maxp is the maximum of the total number of records for the ith-species ni divided by the total number of records in the cell n, ie, \(Maxp = max(ni / n)\).
The Hill biodiversity index accounts for species’ relative abundance (number of records in OBIS) and Hill1 can be roughly interpreted as the number of species with “typical” abundances, and is a commonly used indicator for marine biodiversity richness. It is defined as:
Warning: The Simpson index has the same assumptions as the Shannon index.
Hill 2
The Hill biodiversity index accounts for species’ relative abundance (number of records in OBIS) and discounts rare species, so Hill2 can be interpreted as the equivalent to the number of more dominant species and so is less sensitive to sample size than Hill1. The Hill index is a commonly used indicator for marine biodiversity richness. It is defined as:
Warning: The Simpson index has the same assumptions as the Shannon index.
They are calculated as shown below:
- \(hill\_1 = exp(shannon)\)
- \(hill\_2 = 1 / simpson\)
- \(hill\_inf = 1 / maxp\)
Index file
You also have a tabular file that sums up each indicators.
Nb: the column sp is the count of the number of observations in a dataset. It is the number of records in the dataset.
Conclusion
You here learn how to select and download OBIS dataset for your region of interest, to handle data to finally compute diversity indicators and display it in maps.
You've Finished the Tutorial
Please also consider filling out the Feedback Form as well!
Further information, including links to documentation and original publications, regarding the tools, analysis techniques and the interpretation of results described in this tutorial can be found here.
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Hiltemann, Saskia, Rasche, Helena et al., 2023 Galaxy Training: A Powerful Framework for Teaching! PLOS Computational Biology 10.1371/journal.pcbi.1010752
Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012
@misc{ecology-obisindicators,
author = "Marie Josse and Yvan Le Bras",
title = "Obis marine indicators (Galaxy Training Materials)",
year = "",
month = "",
day = ""
url = "\url{https://training.galaxyproject.org/training-material/topics/ecology/tutorials/obisindicators/tutorial.html}",
note = "[Online; accessed TODAY]"
}
@article{Hiltemann_2023,
doi = {10.1371/journal.pcbi.1010752},
url = {https://doi.org/10.1371%2Fjournal.pcbi.1010752},
year = 2023,
month = {jan},
publisher = {Public Library of Science ({PLoS})},
volume = {19},
number = {1},
pages = {e1010752},
author = {Saskia Hiltemann and Helena Rasche and Simon Gladman and Hans-Rudolf Hotz and Delphine Larivi{\`{e}}re and Daniel Blankenberg and Pratik D. Jagtap and Thomas Wollmann and Anthony Bretaudeau and Nadia Gou{\'{e}} and Timothy J. Griffin and Coline Royaux and Yvan Le Bras and Subina Mehta and Anna Syme and Frederik Coppens and Bert Droesbeke and Nicola Soranzo and Wendi Bacon and Fotis Psomopoulos and Crist{\'{o}}bal Gallardo-Alba and John Davis and Melanie Christine Föll and Matthias Fahrner and Maria A. Doyle and Beatriz Serrano-Solano and Anne Claire Fouilloux and Peter van Heusden and Wolfgang Maier and Dave Clements and Florian Heyl and Björn Grüning and B{\'{e}}r{\'{e}}nice Batut and},
editor = {Francis Ouellette},
title = {Galaxy Training: A powerful framework for teaching!},
journal = {PLoS Comput Biol} Computational Biology}
}
Funding
These individuals or organisations provided funding support for the development of this resource
5 stars:
Liked: Reusability and ease of execution, it's really neat!
Disliked: There seems to be more columns in OBIS data now, so it is maybe better to cut based on column names and not based on hard coded column number. May I also suggest to include the link to obisindicators in the text https://github.com/marinebon/obisindicators ? The text states "Obisindicators is an R library developed during the 2022 IOOS Code Sprint. The purpose was to create an ES50 diversity index within hexagonal grids following the diversity indicators notebook by Pieter Provoost linked above. " but I didn't manage to find the link. Thank you so much!!
Marie Josse
Yvan Le Bras
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