ssdtools is an R package to fit Species Sensitivity Distributions (SSDs) using Maximum Likelihood and model averaging.
SSDs are cumulative probability distributions that are used to estimate the percent of species that are affected by a given concentration of a chemical. The concentration that affects 5% of the species is referred to as the 5% Hazard Concentration (\(HC_5\)). For more information on SSDs the reader is referred to Posthuma, Suter II, and Traas (2001).
In order to use
ssdtools you need to install R (see below) or use the Shiny app. The shiny app includes a user guide. This vignette is a user manual for the R package.
ssdtools provides the key functionality required to fit SSDs using Maximum Likelihood and model averaging in R. It is intended to be used in conjunction with tidyverse packages such as
readr to input data,
dplyr to group and manipulate data and
ggplot2 (Wickham 2016) to plot data. As such it endeavors to fulfill the tidyverse manifesto.
In order to install R (R Core Team 2018) the appropriate binary for the users operating system should be downloaded from CRAN and then installed.
Once R is installed, the
ssdtools package can be installed (together with the tidyverse) by executing the following code at the R console
ssdtools package (and key packages) can then be loaded into the current session using
To get additional information on a particular function just type
? followed by the name of the function at the R console. For example
?ssd_gof brings up the R documentation for the
ssdtools goodness of fit function.
For more information on using R the reader is referred to R for Data Science (Wickham and Grolemund 2016).
ssdtools package has been loaded the next task is to input some data. An easy way to do this is to save the concentration data for a single chemical as a column called
Conc in a comma separated file (
.csv). Each row should be the sensitivity concentration for a separate species. If species and/or group information is available then this can be saved as
Group columns. The
.csv file can then be read into R using the following
data <- read_csv(file = "path/to/file.csv")
For the purposes of this manual we use the CCME dataset for boron.
ccme_boron <- ssddata::ccme_boron print(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 rows
ssd_fit_dists() inputs a data frame and fits one or more distributions. The user can specify a subset of the following 10 distributions
ssd_dists_all() #>  "burrIII3" "gamma" "gompertz" "invpareto" #>  "lgumbel" "llogis" "llogis_llogis" "lnorm" #>  "lnorm_lnorm" "weibull"
The estimates for the various terms can be extracted using the tidyverse generic
tidy function (or the base R generic
tidy(fits) #> # A tibble: 6 × 4 #> dist term est se #> <chr> <chr> <dbl> <dbl> #> 1 llogis locationlog 2.63 0.248 #> 2 llogis scalelog 0.740 0.114 #> 3 lnorm meanlog 2.56 0.235 #> 4 lnorm sdlog 1.24 0.166 #> 5 gamma scale 25.1 7.64 #> 6 gamma shape 0.950 0.223
It is generally more informative to plot the fits using the
autoplot generic function (a wrapper on
autoplot returns a
ggplot object it can be modified prior to plotting.
theme_set(theme_bw()) # set plot theme autoplot(fits) + ggtitle("Species Sensitivity Distributions for Boron") + scale_colour_ssd()
Given multiple distributions the user is faced with choosing the “best” distribution (or as discussed below averaging the results weighted by the fit).
ssd_gof(fits) #> # A tibble: 3 × 9 #> dist ad ks cvm aic aicc bic delta weight #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 llogis 0.487 0.0994 0.0595 241. 241. 244. 3.38 0.11 #> 2 lnorm 0.507 0.107 0.0703 239. 240. 242. 1.40 0.296 #> 3 gamma 0.440 0.117 0.0554 238. 238. 240. 0 0.595
ssd_gof() function returns three test statistics that can be used to evaluate the fit of the various distributions to the data.
ks) statistic and
and three information criteria
ssd_gof() is called with
pvalue = TRUE then the p-values rather than the statistics are returned for the ad, ks and cvm tests.
Following Burnham and Anderson (2002) we recommend the
aicc for model selection. The best predictive model is that with the lowest
aicc (indicated by the model with a
delta value of 0.000 in the goodness of fit table). In the current example the best predictive model is the gamma distribution but the lnorm distribution has some support.
For further information on the advantages of an information theoretic approach in the context of selecting SSDs the reader is referred to Fox et al. (2021).
Often other distributions will fit the data almost as well as the best distribution as evidenced by
delta values < 2 (Burnham and Anderson 2002). In this situation the recommended approach is to estimate the average fit based on the relative weights of the distributions (Burnham and Anderson 2002). The
aicc based weights are indicated by the
weight column in the goodness of fit table. In the current example, the gamma and log-normal distributions have
delta values < 2.
predict function can be used to generate model-averaged (or if
average = FALSE individual) estimates by parametric bootstrapping. Model averaging is based on
aicc unless the data censored is which case
aicc in undefined. In this situation model averaging is only possible if the distributions have the same number of parameters. Parametric bootstrapping is computationally intensive. To bootstrap 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)
The resultant object is a data frame of the estimated concentration (
est) with standard error (
se) and lower (
lcl) and upper (
ucl) 95% confidence limits (CLs) by percent of species affected (
percent). The object includes the number of bootstraps (
nboot) data sets generated as well as the proportion of the data sets that successfully fitted (
pboot). There is no requirement for the bootstrap samples to converge.
boron_pred #> # A tibble: 99 × 8 #> dist percent est se lcl ucl nboot pboot #> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 average 1 0.379 0.391 0.122 1.58 1000 1 #> 2 average 2 0.624 0.540 0.213 2.25 1000 1 #> 3 average 3 0.856 0.656 0.305 2.78 1000 1 #> 4 average 4 1.08 0.756 0.405 3.27 1000 1 #> 5 average 5 1.31 0.845 0.509 3.74 1000 1 #> 6 average 6 1.53 0.926 0.617 4.17 1000 1 #> 7 average 7 1.76 1.00 0.730 4.55 1000 1 #> 8 average 8 1.98 1.07 0.852 4.92 1000 1 #> 9 average 9 2.21 1.14 0.972 5.30 1000 1 #> 10 average 10 2.44 1.20 1.09 5.66 1000 1 #> # … with 89 more rows
The data frame of the estimates can then be plotted together with the original data using the
ssd_plot() function to summarize an analysis. Once again the returned object is a
ggplot object which can be customized prior to plotting.
ssd_plot(ccme_boron, boron_pred, color = "Group", label = "Species", xlab = "Concentration (mg/L)", ribbon = TRUE) + expand_limits(x = 5000) + # to ensure the species labels fit ggtitle("Species Sensitivity for Boron") + scale_colour_ssd()
In the above plot the model-averaged 95% confidence interval is indicated by the shaded band and the model-averaged 5% Hazard Concentration (\(HC_5\)) by the dotted line. Hazard concentrations are discussed below.
The 5% hazard concentration (\(HC_5\)) is the concentration that affects 5% of the species tested.
ssdtools package provides four ggplot geoms to allow you construct your own plots.
The first is
geom_ssdpoint() which plots species sensitivity data
The second is
geom_ssdsegments() which plots the range of censored species sensitivity data
The third is
geom_xribbon() which plots species sensitivity confidence intervals
And the fourth is
geom_hcintersect() which plots hazard concentrations
They can be combined together as follows
gp <- ggplot(boron_pred, aes_string(x = "est")) + geom_xribbon(aes_string(xmin = "lcl", xmax = "ucl", y = "percent/100"), alpha = 0.2) + geom_line(aes_string(y = "percent/100")) + geom_ssdsegment(data = ccme_boron, aes_string(x = "Conc / 2", xend = "Conc * 2")) + geom_ssdpoint(data = ccme_boron, aes_string(x = "Conc / 2")) + geom_ssdpoint(data = ccme_boron, aes_string(x = "Conc * 2")) + scale_y_continuous("Species Affected (%)", labels = scales::percent) + expand_limits(y = c(0, 1)) + xlab("Concentration (mg/L)") print(gp + geom_hcintersect(xintercept = boron_hc5$est, yintercept = 5 / 100))
To log the x-axis add the following code.
gp <- gp + coord_trans(x = "log10") + scale_x_continuous( breaks = scales::trans_breaks("log10", function(x) 10^x), labels = comma_signif ) print(gp + geom_hcintersect(xintercept = boron_hc5$est, yintercept = 5 / 100))
The most recent plot can be saved as a file using
ggsave(), which also allows the user to set the resolution.
ggsave("file_name.png", dpi = 600)
ssdtools by the Province of British Columbia and Environment and Climate Change Canada is licensed under a Creative Commons Attribution 4.0 International License.
Burnham, Kenneth P., and David R. Anderson, eds. 2002. Model Selection and Multimodel Inference. New York, NY: Springer New York. https://doi.org/10.1007/b97636.
Fox, D. R., R. A. Dam, R. Fisher, G. E. Batley, A. R. Tillmanns, J. Thorley, C. J. Schwarz, D. J. Spry, and K. McTavish. 2021. “Recent Developments in Species Sensitivity Distribution Modeling.” Environmental Toxicology and Chemistry 40 (2): 293–308. https://doi.org/10.1002/etc.4925.
Posthuma, Leo, Glenn W Suter II, and Theo P Traas. 2001. Species Sensitivity Distributions in Ecotoxicology. CRC press. https://www.routledge.com/Species-Sensitivity-Distributions-in-Ecotoxicology/Posthuma-II-Traas/p/book/9781566705783.
R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First edition. Sebastopol, CA: O’Reilly. https://r4ds.had.co.nz.