Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions

Abstract

A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely related species are more likely to have similar sensitivities than are distantly related species. When the response of 1 species can be partially predicted by the response of another species, there is a dependency or autocorrelation in the data set. To date, phylogenetic relationships and the resulting dependencies in input data sets have been ignored in deriving SSDs. In this paper, we explore the importance of the phylogenetic signal in deriving SSDs using a case studies approach. The case studies involved toxicity data sets for aquatic autotrophs exposed to atrazine and aquatic and avian species exposed to chlorpyrifos. Full and partial data sets were included to explore the influences of differing phylogenetic signal strength and sample size. The phylogenetic signal was significant for some toxicity data sets (i.e., most chlorpyrifos data sets) but not for others (i.e., the atrazine data sets, the chlorpyrifos data sets for all insects, crustaceans, and birds). When a significant phylogenetic signal did occur, effective sample size was reduced. The reduction was large when the signal was strong. In spite of the reduced effective sample sizes, significant phylogenetic signals had little impact on fitted SSDs, even in the tails (e.g., hazardous concentration for 5th percentile species [HC5]). The lack of a phylogenetic signal impact occurred even when we artificially reduced original sample size and increased strength of the phylogenetic signal. We conclude that it is good statistical practice to account for the phylogenetic signal when deriving SSDs because most toxicity data sets do not meet the independence assumption. That said, SSDs and HC5s are robust to deviations from the independence assumption. Integr Environ Assess Manag 2019;00:1–13. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

Species Sensitivity to Toxic Substances: Evolution, Ecology and Applications

“…Thus, species that actively assimilated Cl − via gills, such as the channel catfish (Ictalurus punctatus), showed greater sensitive to NO 2 − than species such as killifish (Fundulus heteroclitus), European eel (Anguilla anguilla) and bluegill (Lepomis macrochirus) that access Cl − also through other (e.g., dietary) mechanisms (Brady et al, 2017). Further studies have also provided some support for the presence of a strong phylogenetic signature for some chemical classes, such as organophosphates for fish and other vertebrate species (Hylton et al, 2018;Moore et al, 2020), but only a weak signature for other classes such as organochlorines and metals (Hylton et al, 2018). In a study of energetic compound effects, the overlap of transcriptional network response showed a degree of conservation, but showed greater divergence in increasingly taxonomically distant species (Garcia-Reyero et al, 2011).…”

Recent Developments in Species Sensitivity Distribution Modeling

“…The source code for both has been released under the open source Apache-2.0 Licence (https://github.com/bcgov/ssdtools and https://github.com/bcgov/shinyssdtools), which allows users to modify and/or distribute the code under the same licence. We consider open source software to be preferable to compiled code because it allows code validation and facilitates collaboration and replication (Munafò et al 2017;Mancini et al 2019). SSD Toolbox allows distributions to be fitted using Bayesian methods and can statistically account for multiple datapoints for each species using hierarchical models.…”

“…Despite the limitations, SSDs remain a practical tool and, until a demonstrably better inferential framework is available, developments and enhancements to conventional SSD practice will and should continue. Indeed, numerous studies have attempted to address many of the limitations, including issues of sample size, species representativeness and selection, test endpoints, ecological relevance, phylogenetic relatedness, and routes of exposure (e.g., de Zwart and Posthuma 2005; Dyer et al 2006; Fox 2010; Wang et al 2015; Warne et al 2018; Belanger and Carr 2019; Carr and Belanger 2019; Moore et al 2019; Schwarz and Tillmanns 2019). Although certain improvements to formal SSD methods have recently been adopted (i.e., methods typically approved and recommended for use by national, provincial, and state regulatory bodies; see: Warne et al 2018; British Columbia Ministry of Environment and Climate Change Strategy 2019), in general, few of the outcomes of SSD studies from the past 20 yr have been formally adopted.…”

Integrating Exposure and Effect Distributions with the Ecotoxicity Risk Calculator: Case Studies with Crop Protection Products

“…Like thiamethoxam and chlorothalonil, the maximum value was retained for multiple observations within a 24‐h period at a monitoring location. Following compilation, monitoring exposure distributions were integrated with an SSD for acute toxicity in aquatic plants (Erickson 2012; Moore et al 2020) (Supplemental Data Table S3). Aquatic plants are sensitive to herbicides such as atrazine, and therefore the risk curve is expected to be protective of consumer species (e.g., fish and invertebrates).…”