Three perspectives on the prediction of chemical effects in ecosystems

Abstract

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction

Addressing chemical pollution in biodiversity research

“…In this way, a set of measurements to enable a targeted assessment of chemical pressure could be jointly developed and incorporated into ecological study designs. Collectively, mutual exchanges among these research communities, further supported by advances in computational biology and data science approaches, could allow developing truly innovative study designs providing the long‐needed holistic insights into ecological effects of chemicals across scales and landscapes (Schneeweiss et al, 2023). In turn, this would support development of novel predictive tools that could help inform risk assessment and identify promising policy options.…”

Chemical Mixtures and Multiple Stressors: Same but Different?

“…Compared with a large part of ecotoxicological research, ecological studies more frequently use field experiments and surveys in ecosystems to establish links between stressors and ecological responses, although laboratory studies are also widely used in multiple stressor research. However, these studies have largely ignored chemicals as stressors except for nutrients (Bernhardt et al, 2017; Groh et al, 2022; Schäfer et al, 2016; Schneeweiss et al, 2023; Sigmund et al, 2023). For example, analyses of general and specific (e.g., freshwater) ecological journals found a comparatively low amount of studies on toxic chemicals, and related United States national project funding was negligible (Bernhardt et al, 2017; Persson et al, 2022; Schäfer et al, 2016).…”

“…Several other approaches can be used to tackle the challenge posed by the two complex networks. Rather than ocusing on species, trait‐based approaches group the large number of species in communities and food webs, often based on a few core traits such as body size, resource uptake, and feeding preference (Allhoff et al, 2015; Kiørboe et al, 2018; Litchman & Klausmeier, 2008; Schneeweiss et al, 2023; Williams & Martinez, 2000). Furthermore, bioenergetic models have the potential to integrate metabolism with body size and density‐dependent intra‐ and inter‐specific species interactions to predict how chemical or nonchemical stressors affect the flow of biomass.…”

“…Three basic cases can be delineated (cf. Schneeweiss et al, 2023): (1) Organisms known (e.g., list of terrestrial arthropod species) and stressors known (e.g., list of chemicals and nonchemical stressors). (2) Organisms unknown (e.g., unknown composition of terrestrial arthropods) and stressors known.…”

To Split or Not to Split: Characterizing Chemical Pollution Impacts in Aquatic Ecosystems with Species Sensitivity Distributions for Specific Taxonomic Groups

“…Laboratory-toxicity data-based SSDs are practically used for regulatory purposes and Life Cycle Impact Assessment (LCIA), e.g., to derive protective standards (threshold concentrations) or expected impact levels of ambient chemical pollution. , Recently, their use has expanded to the comprehensive diagnosis of the role of chemical pollution as a driver for biodiversity loss in polluted ecosystems by using SSD-based mixture toxic pressure information (expressed as msPAF, the multisubstance Potentially Affected Fraction of species) as pressure metric, as this resulted in reduced parameters numbers and thus improved statistical power in diagnostic analyses. ,, The choice of required input data and the statistical distribution methods vary among jurisdictions. Models commonly used to fit SSDs include log-normal, log–logistic, or other models that fit the available data well, and commonly, confidence intervals or other metrics of variability and uncertainty are reported. , Crucial to acknowledge is that SSDs are commonly fitted to all available test data per chemicalfollowing the principles developed by the earliest userswhere it is assumed that the SSD describes the exposure-impact relationship for whole field species assemblages.…”