Species Sensitivity Distributions • ssdtools

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.

Installation

To install the latest version from CRAN

install.packages("ssdtools")

To install the latest development version from GitHub

# install.packages("pak", repos = sprintf("https://r-lib.github.io/p/pak/stable/%s/%s/%s", .Platform$pkgType, R.Version()$os, R.Version()$arch))
pak::pak("bcgov/ssdtools")

Introduction

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 
#> # ℹ 18 more rows

Distributions are fit using ssd_fit_dists()

fits <- ssd_fit_dists(ssddata::ccme_boron, dists = c("lnorm", "llogis"))

and can be quickly plotted using autoplot

library(ggplot2)

theme_set(theme_bw())

autoplot(fits) +
  scale_colour_ssd()

The goodness of fit can be assessed using ssd_gof

ssd_gof(fits)
#> # A tibble: 2 × 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.  0      0.73
#> 2 llogis 0.487 0.0994 0.0595  241.  241.  244.  1.99   0.27

and 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  1.65 0.599 0.923  3.34     1 parametric   100     1

To bootstrap in parallel set future::plan(). For example:

future::multisession(workers = 2)
hc5 <- ssd_hc(fits, ci = TRUE, nboot = 100)

Model-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()

References

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

Information

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.

For the latest changes visit NEWS.

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.

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.

Licensing

The documentation is released under the CC BY 4.0 License

The code is released under the Apache License 2.0