Harnessing Computational Methods to Characterize Chemical Impacts on Biodiversity

Abstract

Chemical pollution can threaten biodiversity at different levels, from genetically diverse populations (genetic diversity) to different species (species diversity) and ecosystem traits/interactions (functional diversity). Most assessments of chemical impacts on different biodiversity levels depend on wet lab and field experiments, including sequencing large numbers of organisms, environmental DNA approaches, single chemical–species–outcome toxicity tests, and trait-based methods. However, it is impossible to assess all chemicals, species, populations, and ecosystems using these methods. Therefore, we advocate that computational methods are necessary to characterize, quantify, and predict chemical impacts on biodiversity. We briefly introduce the current state of research into chemical impacts on genetic diversity, species diversity, and functional diversity and describe new opportunities for computational methods like data integration, machine learning, cross-species/cross-ecosystem extrapolation, adverse outcome pathways, and Bayesian methods to support research in these three areas. By harnessing data and methods currently at our disposal and preparing methods to take advantage of continuously emerging data sets, computational approaches can be paired with environmental monitoring so different levels of biological organization can serve as consecutive warning signs for chemical impacts on biodiversity. This will enable effective ecosystem protection measures to be better developed and implemented to prevent biodiversity loss from chemical pollution.