Evolutionary patterns and physicochemical properties explain macroinvertebrate sensitivity to heavy metals

Malaj, Egina; Guénard, Guillaume; Schäfer, Ralf B.; Ohe, Peter Carsten von der

doi:10.1890/15-0346.1

Cited by 9 publications

(13 citation statements)

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“…Yet, extending the model beyond this relatively basic parameterization is straightforward; as the joint modeling framework, in general, can accommodate a wide range of study designs (e.g., hierarchical, temporal, spatial), data types (e.g., dichotomous, continuous), and sampling strategies (e.g., estimating detection probability; Beissinger et al, 2016 ; Warton et al, 2016 ; Clark et al, 2017 ; Ovaskainen et al, 2017 ). In a comprehensive review of the joint modeling framework for use in community ecology research, for example, Ovaskainen et al (2017) recently discussed applying model-based constraints to the species response matrix on the basis of phylogeny or ecological traits to further improve prediction and inference; an approach which could be similarly applied to stressor responses (e.g., based on physicochemical properties; Malaj et al, 2015 ). Moreover, with spatially or temporally structured data, relevant extensions have been discussed that could facilitate more explicit evaluations of species, receptor, and environmental associations across different spatial and/or temporal scales ( Ovaskainen et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Martin

Waits

Nietch

2018

Science of The Total Environment

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Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence of stressors and receptors using empirical data, open-source statistical software, and Geographic Information Systems tools and data. To illustrate the approach, we apply the framework to bioassessment data on stream fishes and nutrients collected from a watershed in southwestern Ohio. The results highlighted the joint model’s ability to parse and exploit statistical dependencies in order to provide empirical insight into the potential environmental and ecotoxicological interactions influencing co-occurrence. We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences. For practitioners, we believe that this data-driven approach to modeling and mapping co-occurrence can lead to more quantitatively transparent and robust assessments of ecological risk.

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Section: Discussionmentioning

confidence: 99%

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Martin

Waits

Nietch

2018

Science of The Total Environment

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“…Several studies show that complementing trait data with data on phylogenetics greatly enhances model performance, and that both traits and phylogenetic indicators explain a distinct part of species sensitivity (Poteat, Jacobus et al 2015, Pilière, Verberk et al 2016. Other attempts combining phylogenetics and physiochemical properties into predictive models have also proven successful (Guénard, von der Ohe et al 2014, Malaj, Guénard et al 2016, although phylogenetics is still impossible to include in an inclusive approach as we performed in this study. Nevertheless, we suggest to further explore the potential of species traits for sensitivity predictions, as they enable large scale applicability and increased mechanistic understanding.…”

Section: Data Gaps and Potential For Improvementmentioning

confidence: 88%

“…Relatedness-based (RB) models use the extent of evolutionary relatedness between organisms as a proxy for the similarity in their response to chemical stressors (e.g. Craig 2013, Guénard, von der Ohe et al 2014, Malaj, Guénard et al 2016. The underlying principle of these models is that closely related species exhibit high correlation of sensitivity to chemicals.…”

Section: Overview Of Methodsmentioning

confidence: 99%

“…Other studies (Malaj, Guénard et al 2016) concerned with determining which species were most sensitive to chemical stressors, combined phylogenetic information with chemical properties. They were to a great extent (R 2 of ~0.8) capable of predicting species sensitivity to pesticides (Guénard, von der Ohe et al 2014) and heavy metals (Malaj, Guénard et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Predictive methodologies extrapolating existing toxicity data to untested organisms can help understand spatial-temporal variation in species sensitivity by predicting sensitivity values for a wide range of species (e.g. Malaj, Guénard et al 2016, Raimondo and Barron 2019, Van den Berg, Baveco et al 2019. However, although several predictive methods have been developed in the last decades, a clear overview of which extrapolation methodologies are currently available, along with a description of their considerations, assumptions, merits, and pitfalls, is still lacking.…”

Section: Introductionmentioning

confidence: 99%

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Improving cross-species extrapolation of chemical sensitivity

Berg¹

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In this study, a trait-based macroinvertebrate sensitivity modelling tool is presented that provides two main outcomes: (1) it constructs a macroinvertebrate sensitivity ranking and, subsequently, a predictive trait model for each one of a diverse set of pre-defined Modes of Action (MOAs) and (2) it reveals data gaps and restrictions, helping with the direction of future research. Besides revealing taxonomic patterns of species sensitivity, we find that there was not one genus, family or class which was most sensitive to all MOAs, and that common test taxa were often not the most sensitive at all. Traits like life cycle duration and feeding mode were identified as important in explaining species sensitivity. For 71% of the species, no or incomplete traits data were available, making the lack of trait data the main obstacle in model construction. Research focus should therefore be on completing trait databases, and enhancing them with finer morphological traits, focusing on the toxicodynamics of the chemical (e.g. target site distribution). Further improved sensitivity models can help with the creation of ecological scenarios by predicting the sensitivity of untested species. Through this development, our approach can help reduce animal testing and contribute towards a new predictive ecotoxicology framework.

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Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions

Moore¹,

Priest²,

Galić

et al. 2019

Integr Envir Assess & Manag

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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)

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Evolutionary patterns and physicochemical properties explain macroinvertebrate sensitivity to heavy metals

Cited by 9 publications

References 0 publications

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors

Improving cross-species extrapolation of chemical sensitivity

Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions

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