María Mercedes Nicolosi Gelis, Belén Sathicq, Joaquín Cochero
DiaThor calculates several diatom-based indices commonly used for water quality assesment, directly from your species’ data
We are bypassing the connection of diaThor to the DiatBarCode project due to some URL changes in the latter. Until that is settled, this version uses the internal DiatBarCode project’s database and will not update to newer versions.
We also fixed some minor issues that made the diat_ips() function crash.
Thanks to Julia Siegmund & Brent Bellinger (WPD, Austin, Texas) for bringing this issues to us and helping us test the solution!
The package calculates multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species’ name using a heuristic search, and their ecological data is retrieved from multiple sources.
Morphological information about the species is retrieved from the ‘Diat.Barcode’ project:
Size class classification is obtained from:
Guild classification is obtained from:
Ecological preferences are obtained form:
Diversity index (Shannons H’) is calculated using the vegan package, following:
Species tolerance and their ecological information to calculate each biotic index is retrieved from their original sources:
IPS: Coste, M. (1982). Étude des méthodes biologiques d’appréciation quantitative de la qualité des eaux. Rapport Cemagref QE Lyon-AF Bassin Rhône Méditerranée Corse.
TDI: Kelly, M. G., & Whitton, B. A. (1995). The trophic diatom index: a new index for monitoring eutrophication in rivers. Journal of Applied Phycology, 7(4), 433-444.
IDP: Gómez, N., & Licursi, M. (2001). The Pampean Diatom Index (IDP) for assessment of rivers and streams in Argentina. Aquatic Ecology, 35(2), 173-181.
DES: Descy, J. P. 1979. A new approach to water quality estimation using diatom. Beih. Nov Hedw. 64:305-323
EPID: Dell’Uomo, A. (1996). Assessment of water quality of an Apennine river as a pilot study for diatom-based monitoring of Italian watercourses. Use of algae for monitoring rivers, 65-72.
IDAP: Prygiel, J., & Coste, M. (1993). The assessment of water quality in the Artois-Picardie water basin (France) by the use of diatom indices. Hydrobiologia, 269(1), 343-349.
ID-CH: Hürlimann J., Niederhauser P. 2007: Méthodes d’analyse et d’appréciation des cours d’eau. Diatomées Niveau R (région). État de l’environnement n° 0740. Office fédéral de l’environnement, Berne. 132 p
ILM: Leclercq, L., & Maquet, B. (1987). Deux nouveaux indices diatomique et de qualité chimique des eaux courantes. Comparaison avec différents indices existants. Cahier de Biology Marine, 28, 303-310.
LOBO:
SLA: Sládeček, V. (1986). Diatoms as indicators of organic pollution. Acta hydrochimica et hydrobiologica, 14(5), 555-566.
SPEAR(herbicides): Wood, R. J., Mitrovic, S. M., Lim, R. P., Warne, M. S. J., Dunlop, J., & Kefford, B. J. (2019). Benthic diatoms as indicators of herbicide toxicity in rivers–A new SPEcies At Risk (SPEARherbicides) index. Ecological Indicators, 99, 203-213.
Sample data included in the package is taken from:
You can install the released version of diathor from CRAN with:
install.packages("diathor")
And the development version from GitHub with:
# install.packages("devtools")
::install_github("limnolab/DiaThor/") devtools
To demonstrate the most common use of DiaThor, the package includes sample data with the abundance of 164 diatom species in 108 sampled sites (Nicolosi Gelis et al., 2020).
Install the package and load it into the R environment
> install.packages("diathor")
> library(diathor)
Load the internally included sample data
> data("diat_sampleData")
Run diaThorAll to get all the outputs from the sample data with the default settings, and store the results into the “results” object, to also retain the output within R
> results <- diaThorAll(diat_sampleData) #If the sample data was used
Note: The package will request the Input file an Output folder through a dialog box
1] "Select Input file"
[1] "Select Results folder" [
After the Results folder is selected, all the calculations conducted will be shown in the console
Optionally, run each individual function with the results of the diat_loadData() function, for instance:
> loadedData <- diat_loadData() # load data with the diat_loadData() function
> results <- diat_ips(loadedData ) # use the diat_ips() function to calculate the IPS index with the loaded data
The package available in the CRAN repository does not automatically update the internal diatom database from the ‘Diat.Barcode’ project. It uses the internal version of such database, which is currently v.10.1 published on 25-06-2021
The GitHub version of the package will update to the most recent database, if it is different to the current internal version.