Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for multiple species. The ssdtools package uses Maximum Likelihood to fit log-normal, log-logistic, log-Gumbel, Gompertz, gamma or Weibull distributions. Multiple distributions can be averaged using Information Criteria. Confidence intervals can be calculated for the fitted cumulative distribution function or specific hazard concentrations (percentiles). Confidence intervals are currently produced by bootstrapping.

ssdtools is an R package to plot and fit Species Sensitivity Distributions (SSD). It uses Maximum Likelihood and Information-Theoretic based model averaging.

Installation

To quickly install the latest version:

# install.packages("devtools")
devtools::install_github("bcgov/ssdtools")

To install the latest version and build its vignette:

# install.packages("devtools")
devtools::install_github("bcgov/ssdtools", force = TRUE, build_vignettes = TRUE)

To view the vignette

vignette("ssdtools")

Introduction

ssdtools loads fitdistrplus and ggplot2, both of which it extends, as well as several other packages.

library(ssdtools)
#> Loading required package: VGAM
#> Loading required package: stats4
#> Loading required package: splines
#> Loading required package: ggplot2
#> Loading required package: fitdistrplus
#> Loading required package: MASS
#> Loading required package: survival
#> Loading required package: npsurv
#> Loading required package: lsei

ssdtools provides a data set for several chemicals including Boron.

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

theme_set(theme_bw()) # set plotting theme
autoplot(boron_dists)

The goodness of fit can be assessed using ssd_gof

ssd_gof(boron_dists)
#> # A tibble: 6 x 9
#>   dist        ad     ks    cvm   aic  aicc   bic delta weight
#>   <chr>    <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
#> 1 lnorm    0.507 0.107  0.0703  239.  240.  242. 1.42   0.133
#> 2 llog     0.487 0.0993 0.0595  241.  241.  244. 3.40   0.049
#> 3 gompertz 0.602 0.120  0.0822  238.  238.  240. 0      0.271
#> 4 lgumbel  0.829 0.158  0.134   244.  245.  247. 6.58   0.01 
#> 5 gamma    0.441 0.117  0.0555  238.  238.  240. 0.019  0.268
#> 6 weibull  0.435 0.117  0.0543  238.  238.  240. 0.014  0.269

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

ssd_hc(boron_dists)
#> # A tibble: 1 x 5
#>   percent   est    se   lcl   ucl
#>     <int> <dbl> <dbl> <dbl> <dbl>
#> 1       5  1.25 0.717 0.619  3.13

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

boron_pred <- predict(boron_dists)

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)")

For more information and examples of how to use ssdtools in conjuction with fitdistrplus to assess alternative fits and deal with weighted and censored data see the vignette ssdtools. The vignette also demonstrates how to use ssdtools in conjunction with ggplot2 to produce custom plots.

A shiny webpage developed by Seb Dalgaro of Poisson Consulting for non-users of R is available at https://poissonconsulting.shinyapps.io/ssdtools/.

The data included in ssdtools are sourced from the Canadian environmental quality guidelines published by the Canadian Council of Ministers of the Environment. See the data-raw folder for more information.

Citation

To cite package 'ssdtools' in publications use:

  Joe Thorley and Carl Schwarz (2018). ssdtools: Species
  Sensitivity Distributions. R package version 0.0.1.9003.
  https://github.com/bcgov/ssdca

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {ssdtools: Species Sensitivity Distributions},
    author = {Joe Thorley and Carl Schwarz},
    year = {2018},
    note = {R package version 0.0.1.9003},
    url = {https://github.com/bcgov/ssdca},
  }

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.

Please note that this project is released with a Contributor Code of Conduct. By participating in 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 

   http://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.