A Dynamic Shift in Soil Metal Risk Assessment, It is Time to Shift from Toxicokinetics to Toxicodynamics
Bibliographic record
Abstract
Most ecotoxicological research of the effect of metals on soil organisms focuses on the toxicokinetics of these elements to predict their toxicity to soil invertebrates or microorganisms. Typically, it is thought that free ions cross the cell membrane, are distributed to the site of toxic action, and cause deleterious effects (Peijnenburg and Jager 2003). In this paradigm, metal toxicity is dependent on the availability of metals present in soil porewater. In turn, metal porewater availability is linked to the partitioning of metals between soil and water, the availability of porewater itself (soil moisture), as well as organism traits that influence organism exposure to it. Despite considerable research on this topic, there is still no clear and consistent correlation between metal availability, soil properties, and toxicity. For instance, soil pH is a good predictor of metal availability across soils but not of toxicity for many metals (Smolders et al. 2009). The mismatch between availability and toxicity may arise if porewater ingestion or dermal exposure is not the dominant exposure pathway. It is possible that for invertebrates soil ingestion and subsequent changes of metal availability in the gut explain why metal bioavailability is not always linked to toxicity. Annelids, including both earthworms and enchytraeids, actively ingest soil as a result of their burrowing behavior; but for other invertebrates (oribatid mites and collembolans) it is unclear if or how much soil is ingested. For earthworms only one experiment has tried to distinguish dermal from oral exposure to metals, but more explorations under different conditions/metals/soils/species are needed (Vijver et al. 2003). In this Points of Reference, we argue that the paradigm where toxicokinetics is the sole driver of metal toxicity to soil organisms should be abandoned and that we should look also at toxicodynamic drivers. Toxicodynamics is the dynamic interaction of a toxicant with a site of toxic action and its subsequent biological effects. Typically for metals, toxicodynamic research focuses solely on the site of toxic action, such as reactive oxygen species generation or calcium homeostasis disruption. In the soil environment there are multiple factors that influence soil organism health, such as texture, organic matter composition, pH, and cation exchange capacity. In the past, these parameters have largely been viewed through the lens of toxicokinetics. For example, studies on cation exchange capacity or organic matter focus on how these factors influence metal bioavailability. We argue that we need a different view, in which we explicitly recognize that soil factors not only are key for toxicokinetics but in fact drive toxicodynamic behavior in the organism (Figure 1). Energy is one example by which soil properties drive toxicodynamics of metals within an organism. For example, Oppia nitens has an increased tolerance to metals in high–habitat quality soils compared to those with low habitat quality (Jegede et al. 2019), despite similar Zn bioavailability. An organism's ecological strategies can also affect its responses to metal contamination; for example, some species could reduce reproductive output as a strategy to increase energy allocation for metal resistance and survival (Van Gestel and Hoogerwerf 2001). Although this hypothesis has been suggested (Van Gestel and Hoogerwerf 2001), it has not been experimentally demonstrated, and the mechanisms behind these strategies are not well known. Finally, climate, which has been a major focus in recent years as a result of climate change, is known to affect the response of organisms to contamination through temperature and is expected to function as an added stressor to the organism's biology. Although experimentation has been performed at different temperatures, few studies, if any, report the toxicodynamic mechanisms of how temperature affects organism response to metals. The current risk assessment of metals in soil is broken. Typically, field observations and laboratory toxicity tests show no correspondence. Further, in the real world, metals exist as mixtures; and our existing methods to account for this are theoretically inadequate and empirically not predictive. Yet, field practitioners recognize that increasing the quality of the environment, via amendments or eco-restorative practices, can dramatically increase the abundance of organisms at impacted sites. Our current risk-assessment framework is unable to account for this because it can only account for toxicokinetic changes in metals. A new risk-assessment framework needs a different platform of ecotoxicological research, one that focuses on the key toxicodynamic questions that determine organism response to pollutants. We can build off of the existing literature for how metals interact at the site of toxic action, but additional research is needed on how habitat quality, organism behavior, and organism traits interact during metal impacts. Once built, we can readily adapt existing risk-assessment frameworks to modify predicted risk based on toxicodynamic modifiers. In doing so, we can combine the best of toxicokinetic research with toxicodynamic research and thereby better predict the real risk in our environment. The authors acknowledge the Natural Sciences and Engineering Research Council for funding the Strategic Grant to S.D. Siciliano and the Portuguese Institution Fundação para a Ciência e a Tecnologia for funding the PhD grant of M. Renaud (SFRH/BD/130442/2017). The authors also declare that they have no conflict of interest in the publication of this research. Address correspondence to M. Renaud (jeanmathieubr@gmail.com).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".