Establishing causality in experiments: how deep is deep enough?
Is there a trend towards exaggerated mechanistic resolution?
Reading some papers these days, one sometimes marvels at the depth to which researchers follow the chain of causality. Perhaps it is even becoming a recipe for unlocking high-impact publications, making a paper more likely to be accepted or at least sent for review if it presents an overwhelming chain of causality as evidence.
I don’t think anybody would say that following the chain of causality across various levels is a bad thing; it certainly isn’t bad science. But is it necessary or is it overkill? And is the increasing frequency of such papers generating an expectation that this is what you need to do if you want to produce top-level science?
Of course we do experiments in order to establish causality, that is the entire point. Normally, I would say, the mechanism is a thing that resides at the level immediately below the level at which a phenomenon is observed. For example, a plant grows better, because it has more mycorrhizal fungi in its roots and in the soil, taking up P and giving it to the plant. Or soil aggregates are less stable because something degraded the binding agents or impacted fungal hyphae.
It seems that some recent papers go much further. There are more and more experiments that pinpoint the effect to ever greater levels of detail, usually ending up at some sort of molecular-level mechanism, all the way from an organismal response of a plant, for example.
The question is, do we need that in ecology and the environmental sciences. Do we need to get right down to the molecular-level mechanism of ecological phenomena? What specific advantages does this offer? Or should this only be done if it is really clear that there are clear advantages of pursuing the chain of causality right to its end? For example, this could open up targets for breeding or genetic manipulation, if the molecular culprit has been identified. But it seems to me that this is also done for reasons of “because we can”. And maybe that is not a good enough reason.
Has anyone else noticed this trend? Is it even a trend? Is there a trend towards exaggerated mechanistic resolution? If there really is such a phenomenon, is this more prevalent in papers in high-impact journals?
What do you think? Let me know in the comments. :)
I believe there is indeed a growing trend in ecology and environmental science that the more deeply a study investigates a question—and the more comprehensive its technical approaches are—the more likely it is to convince editors and reviewers. Multilevel mechanistic validation, omics analyses, and global-scale datasets, which were once regarded as additional strengths, are gradually becoming implicit requirements for publication in high-impact journals. Studies that do not trace a causal chain down to the molecular level or lack large-scale data may therefore be considered insufficiently complete or important.
As a result, scientific publishing is increasingly turning into an arms race in which researchers compete by accumulating more resources, technologies, and layers of evidence.
Large research groups have the funding, personnel, technical platforms, and collaborative networks needed to combine multiple established methods, expand sample sizes, and add layers of omics evidence in order to construct an apparently comprehensive causal chain. Researchers with fewer resources, however, may struggle to publish in leading journals even when they have developed genuinely important scientific questions, simply because they cannot afford such extensive validation.
Truly original and important ideas are difficult to generate, whereas adding analytical layers using established techniques is comparatively straightforward when sufficient funding and personnel are available. When technical complexity and resource investment are treated as equivalent to scientific depth, this further reinforces the Matthew effect in science, allowing already powerful groups to become even stronger and making resource-intensive, technology-driven research increasingly disadvantageous for the majority of researchers.
This is a really important observation. There can be a tradeoff between reaching a high level of mechanistic resolution and external validity. In ecology, achieving a high level of mechanistic resolution often requires well-replicated experiments. These have a greater chance of getting published in high-impact journals than observational field studies. However, how often do we lose track of how relevant a signal picked up in the lab really is in nature? I think that our recent work on the novel weapons hypothesis and allelopathy can serve as a good example of how going down highly mechanistic approaches may be misleading in the true ecological sense.