For decades, we have
known that chemicals affect human and wildlife
behavior. Moreover, due to recent technological and computational
advances, scientists are now increasingly aware that a wide variety
of contaminants and other environmental stressors adversely affect
organismal behavior and subsequent ecological outcomes in terrestrial
and aquatic ecosystems. There is also a groundswell of concern that
regulatory ecotoxicology does not adequately consider behavior, primarily
due to a lack of standardized toxicity methods. This has, in turn,
led to the exclusion of many behavioral ecotoxicology studies from
chemical risk assessments. To improve understanding of the challenges
and opportunities for behavioral ecotoxicology within regulatory toxicology/risk
assessment, a unique workshop with international representatives from
the fields of behavioral ecology, ecotoxicology, regulatory (eco)toxicology,
neurotoxicology, test standardization, and risk assessment resulted
in the formation of consensus perspectives and recommendations, which
promise to serve as a roadmap to advance interfaces among the basic
and translational sciences, and regulatory practices.
This is the accepted version of a paper published in INLAND WATERS. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.
In this study, a trait-based macroinvertebrate
sensitivity modeling
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 predefined 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 trait
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 toward a new predictive ecotoxicology
framework.
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