ssdtools is an R package to fit and plot Species
Sensitivity Distributions (SSD).
SSDs are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al. (2001). The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-Gumbel (identical to inverse Weibull), log-logistic, log-normal and Weibull to censored and/or weighted data. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by parametric bootstrapping.
To install the latest version from CRAN
install.packages("ssdtools")To install the latest development version:
# install.packages("devtools")
devtools::install_github("bcgov/ssdtools")ssdtools provides a data set for several chemicals
including Boron.
library(ssdtools)
ssddata::ccme_boron
#> # A tibble: 28 × 5
#> Chemical Species Conc Group Units
#> <chr> <chr> <dbl> <fct> <chr>
#> 1 Boron Oncorhynchus mykiss 2.1 Fish mg/L
#> 2 Boron Ictalurus punctatus 2.4 Fish mg/L
#> 3 Boron Micropterus salmoides 4.1 Fish mg/L
#> 4 Boron Brachydanio rerio 10 Fish mg/L
#> 5 Boron Carassius auratus 15.6 Fish mg/L
#> 6 Boron Pimephales promelas 18.3 Fish mg/L
#> 7 Boron Daphnia magna 6 Invertebrate mg/L
#> 8 Boron Opercularia bimarginata 10 Invertebrate mg/L
#> 9 Boron Ceriodaphnia dubia 13.4 Invertebrate mg/L
#> 10 Boron Entosiphon sulcatum 15 Invertebrate mg/L
#> # … with 18 more rowsDistributions are fit using ssd_fit_dists()
fits <- ssd_fit_dists(ssddata::ccme_boron)and can be quickly plotted using autoplot
library(tidyverse)
theme_set(theme_bw())
autoplot(fits) +
scale_colour_ssd()
The goodness of fit can be assessed using ssd_gof
ssd_gof(fits)
#> # A tibble: 6 × 9
#> dist ad ks cvm aic aicc bic delta weight
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 gamma 0.440 0.117 0.0554 238. 238. 240. 0.005 0.357
#> 2 lgumbel 0.829 0.158 0.134 244. 245. 247. 6.56 0.013
#> 3 llogis 0.487 0.0994 0.0595 241. 241. 244. 3.39 0.066
#> 4 lnorm 0.507 0.107 0.0703 239. 240. 242. 1.40 0.177
#> 5 lnorm_lnorm 0.320 0.116 0.0414 240. 243. 247. 4.98 0.03
#> 6 weibull 0.434 0.117 0.0542 238. 238. 240. 0 0.357and the model-averaged 5% hazard concentration estimated by
parametric bootstrapping using ssd_hc
set.seed(99)
hc5 <- ssd_hc(fits, ci = TRUE, nboot = 100) # 100 bootstrap samples for speed
print(hc5)
#> # A tibble: 1 × 10
#> dist percent est se lcl ucl wt method nboot pboot
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 average 5 1.24 0.741 0.510 3.35 1 parametric 100 1Model-averaged predictions complete with confidence intervals can
also be estimated by parametric bootstrapping using the
stats generic predict. To perform
bootstrapping for each distribution in parallel register the future
backend and then select the evaluation strategy.
doFuture::registerDoFuture()
future::plan(future::multisession)
set.seed(99)
boron_pred <- predict(fits, ci = TRUE)and plotted together with the original data using
ssd_plot.
ssd_plot(ssddata::ccme_boron, boron_pred,
shape = "Group", color = "Group", label = "Species",
xlab = "Concentration (mg/L)", ribbon = TRUE
) +
expand_limits(x = 3000) +
scale_colour_ssd()
#> Warning: Ignoring unknown aesthetics: shape
Posthuma, L., Suter II, G.W., and Traas, T.P. 2001. Species Sensitivity Distributions in Ecotoxicology. CRC Press.
To cite ssdtools in publications use:
Thorley, J. and Schwarz C., (2018). ssdtools An R package to fit
Species Sensitivity Distributions. Journal of Open Source Software,
3(31), 1082. https://doi.org/10.21105/joss.01082
A BibTeX entry for LaTeX users is
@Article{,
title = {{ssdtools}: An R package to fit Species Sensitivity Distributions},
author = {Joe Thorley and Carl Schwarz},
journal = {Journal of Open Source Software},
year = {2018},
volume = {3},
number = {31},
pages = {1082},
doi = {10.21105/joss.01082},
}Get started with ssdtools at https://bcgov.github.io/ssdtools/articles/ssdtools.html.
A shiny app to allow non-R users to interface with ssdtools is available at https://github.com/bcgov/shinyssdtools.
The citation for the shiny app:
Dalgarno, S. 2021. shinyssdtools: A web application for fitting Species Sensitivity Distributions (SSDs). JOSS 6(57): 2848. https://joss.theoj.org/papers/10.21105/joss.02848.
The ssdtools package was developed as a result of earlier drafts of:
Schwarz, C., and Tillmanns, A. 2019. Improving Statistical Methods for Modeling Species Sensitivity Distributions. Province of British Columbia, Victoria, BC.
For recent developments in SSD modeling including a review of existing software see:
Fox, D.R., et al. 2021. Recent Developments in Species Sensitivity Distribution Modeling. Environ Toxicol Chem 40(2): 293–308. https://doi.org/10.1002/etc.4925.
The CCME data.csv data file is derived from a factsheet
prepared by the Canadian Council of
Ministers of the Environment. See the data-raw
folder for more information.
To report bugs/issues/feature requests, please file an issue.
If you would like to contribute to the package, please see our CONTRIBUTING guidelines.
Please note that the ssdtools project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
The code is released under the Apache License 2.0
Copyright 2021 Province of British Columbia and Environment and Climate Change Canada
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
ssdtools by the Province of British Columbia and
Environment and Climate Change Canada is licensed under a
Creative Commons Attribution 4.0 International License.