ssdtools

ssdtools is an R package to plot and fit 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 log-normal, gamma, log-logistic, log-Gumbel, Gompertz and Weibull. The user can also provide custom distributions. Multiple distributions can be averaged using Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by parametric bootstrapping.

Installation

To install the latest version from CRAN

install.packages("ssdtools")

To install the latest development version:

install.packages("remotes")
remotes::install_github("bcgov/ssdtools")

Introduction

ssdtools provides a data set for several chemicals including Boron.

library(ssdtools)
boron_data
#> # A tibble: 28 x 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 rows

Multiple distributions can be fit using ssd_fit_dists()

boron_dists <- ssd_fit_dists(boron_data)

and plot using the ggplot2 generic autoplot

library(ggplot2)
theme_set(theme_bw())
autoplot(boron_dists)

The goodness of fit can be assessed using ssd_gof

ssd_gof(boron_dists)
#> # A tibble: 3 x 9
#>   dist      ad     ks    cvm   aic  aicc   bic delta weight
#>   <chr>  <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1 llogis 0.487 0.0993 0.0595  241.  241.  244.  3.38  0.11 
#> 2 gamma  0.440 0.117  0.0554  238.  238.  240.  0     0.595
#> 3 lnorm  0.507 0.106  0.0703  239.  240.  242.  1.40  0.296

and the model-averaged 5% hazard concentration estimated using ssd_hc

set.seed(99)
boron_hc5 <- ssd_hc(boron_dists, ci = TRUE)

print(boron_hc5)
#> # A tibble: 1 x 6
#>   percent   est    se   lcl   ucl dist   
#>     <dbl> <dbl> <dbl> <dbl> <dbl> <chr>  
#> 1       5  1.31 0.808 0.527  3.57 average

Model-averaged predictions complete with confidence intervals can be produced using the stats generic predict

set.seed(99)
boron_pred <- predict(boron_dists, ci = TRUE)

and plotted together with the original data using ssd_plot.

ssd_plot(boron_data, boron_pred,
  shape = "Group", color = "Group", label = "Species",
  xlab = "Concentration (mg/L)", ribbon = TRUE
) + expand_limits(x = 3000)

Citation

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},
  }

Information

Posthuma, L., Suter II, G.W., and Traas, T.P. 2001. Species Sensitivity Distributions in Ecotoxicology. CRC Press.

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:

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:

Fox, D.R., et al. 2021. Recent Developments in Species Sensitivity Distribution Modeling. Environ Toxicol Chem 40(2): 293–308. https://onlinelibrary.wiley.com/doi/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.

Getting Help or Reporting an Issue

To report bugs/issues/feature requests, please file an issue.

How to Contribute

If you would like to contribute to the package, please see our CONTRIBUTING guidelines.

Code of Conduct

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.

License

The code is released under the Apache License 2.0

Copyright 2015 Province of British Columbia

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 is licensed under a Creative Commons Attribution 4.0 International License.