It’s come up now and then in Reviewer’ comments, written in papers, and during conference questions – are you using environmentally relevant concentrations (ERCs)? It is a good question but I think often over-simplistic in that it creates a false-dichotomy i.e. use of only ERCs is a good thing, and any use of non-ERCs is a bad thing.
I put forward that this view has come from a more traditional Ecologist view of experimental design, when papers were dominated by categorical analyses and ANOVAs, and what were okay rules-of-thumb back some time ago, are pretty outdated now. But at the heart of this are differences in philosophical views of the purpose of laboratory experiments. More traditional Ecologists sometimes believe that purpose of running lab experiments is simply to replicate the field (spoiler alert — you can’t), whereas the alternative view is that the main purpose of lab experiments (excluding mechanistic experiments) is to derive causation, through the removal of confounding variables, and observing effects that scale with concentration etc. This occasionally means moving into the realm of artificiality in efforts to improve experimental control. Whether you use all treatment levels in ERCs really depends on quite a few things, but most importantly how many treatment levels you can spare. I’d argue that using some treatment levels outside of ERC is actually good experimental design, for some of the reasons I outline below.
- We need thresholds.
If an effect occurs outside of ERCs i.e. above or below what is environmentally relevant, we still need to know the threshold of an effect for regulation and risk assessment purposes. As Harris et al. 2014 argue (and not just relevant to hazardous substances):
“Indeed, there will be occasions where researchers have to use significantly higher concentrations in order to properly define a LOEC [Low observed effect concentration] for a substance. The LOEC of a substance is, in fact, far more useful in the regulatory sphere than is a conclusion that no effect occurs at environmentally relevant concentrations, because a LOEC enables the regulators to impose more accurate and meaningful safety limits.”
2. We might not know ERCs in other places or in the future
Take work on climate change – more extreme treatment levels are designed for future and often worse-case scenarios such as RCP 8.5. We don’t actually know what will happen in the future but we rely on modelling to get a decent estimate of likely future ERC. (Note: there is some discussion whether RCP 8.5 is actually a future ERC now, given more certainty in our carbon trajectory). It would be a waste of resources that one plans and undertakes an experiment, only to find out that what they thought was an ERC turned out to be only the 50th percentile in another space or time. So the experiment needs to be repeated again.
There are lots of reasons to design experiments with many treatment levels that allow for regression models, rather than categorical designs. Some authors have been ramming this home for 20+yrs and yet categorical derived thresholds (LOEC/NOEC) are still common place in ecology. I won’t go into all the details here but one benefit of regressions is the power of interpolation, which allows us to derive all the responses between the treatment levels tested, rather than solely the treatments used.
4. Modelling fit, and being sure the response you see is real.
Some models improve under a greater response. Take for example the four-parameter logistic equation, a nonlinear model traditionally used for dose-responses. Two parameters in the model are the ‘inflection point’ and the ‘lower asymptote’ of the model. Failing to acquire data near these parameters means their estimations aren’t great, leading to large confidence intervals. We can also make more assumptions about the data if we know its shape, leading to better model selection.
But more than that, we want to know that if we do see a response at the highest ERC, that it is real and not by chance. Seeing a continued response in that extra one-higher treatment level (outside the ERCs) will give you confidence to stand by your data. If however, the response disappears in the one- higher treatment level, you almost certainly have a false-positive. This is a type of Quality Control and should be congratulated.
5. Uncoupling correlating treatments
Often two factors or treatments are closely tied to each other and to understand the relative roles of each treatment, the experiment needs to be designed so each treatment can be assessed independent of each other and in various combinations i.e. a fully-crossed design. But many treatment combinations will be by nature unrealistic. Take for example my study area where I investigate the impacts of sediment and low light on organisms, both of these stressors individually cause effects, but both are also correlated. To uncouple and assess the effects on an organism we would need some unrealistic treatment combinations with low sediment and low light, and some with high sediment and high light. If the organisms died in the high sediment/high light treatment but not in the low sediment/low light treatment, then we would know it was the sediment, not the low-light, that caused mortality.
6. Mechanisms Sometimes mechanisms are difficult to observe at ERC, so you need to increase the ‘concentration’ to get a clear signal. The caveat for this is that there needs to be a clear response in the range of the ERCs first (or that this response is relatively established in the literature). For example, researchers know elevated temperatures cause coral bleaching well within environmentally relevant levels; it is unequivocal. So Researchers may simply skip to high temperatures to enable a strong response to work on physiological mechanisms such as heat-shock proteins. In my work looking at how spectral light profiles impact coral, an artificial (monochromatic) spectral profile that causes a response may further ‘hone in’ on the active wavelengths that cause a response under a broader spectrum, even though monochromatic light is unrealistic.
So what is to stop Researchers using strong responses and significant results outside of ERCs to their advantage such as acceptance into journals? Unfortunately this is common (take early microplastic research as an example) but is relatively easy to fix. Authors should (unless completely unknown) need to provide ERC percentiles with their treatment levels. It should be made very clearly what is likely and what is not.
Conclusion and cautions
A few treatment levels outside EVC can, if chosen correctly, add to quality assurance of experimental design, and make the data more far more useable. However if treatment levels are limited, then there is no point to use non-ERCs, and using them will do more harm than good. This is common in categorical designs where interpolation is not possible. Say we have a two factor, 3 x 3 treatment-level design. If one treatment level is not realistic, then 1/3 of treatment levels are unrealistic. If two treatment levels are unrealistic, then nearly half the experiment is unrealistic. Those using categorical designs really do need to choose their treatment levels very carefully around in situ percentiles of field data. However, in treatments/factors with numerous treatment levels, some unrealistically high or low treatments can often help improve the output of the analysis in ways that using solely ERCs can not.
Harris CA, Scott AP, Johnson AC, Panter GH, Sheahan D, Roberts M, Sumpter JP (2014) Principles of sound ecotoxicology. Environ Sci Technol 48:3100-3111