No open-access location is known for this record yet.
No open-access location is known for this record yet.
Theory is a critical component of the biological research process, and complements observational and experimental approaches. However, most biologists receive little training on how to frame a theoretical question and, thus, how to evaluate when theory has successfully answered the research question. Here we develop a guide with six verbal framings for theoretical models in biology. These correspond to different personas one might adopt as a theorist: “Advocate”, “Explainer”, “Instigator”, “Mediator”, “Semantician”, and “Tinkerer”. These personas are drawn from combinations of two starting points (pattern or mechanism) and three foci (novelty, robustness, or conflict). We illustrate each of these framings with examples of specific theoretical questions, by drawing on recent theoretical papers in the fields of ecology and evolutionary biology. We show how the same research topic can be approached from slightly different perspectives, using different framings. We show how clarifying a model’s framing can debunk common misconceptions of theory: that simplifying assumptions are bad, more detail is always better, models show anything you want, and modelling requires substantial math knowledge. Finally, we provide a roadmap that researchers new to theoretical research can use to identify a framing to serve as a blueprint for their own theoretical research projects.Keywords:mathematical biology, methodology, narratives, pedagogy, scientific writing, theoretical ecologyTake the theorist personality quiz here: https://z.umn.edu/theorypersona Introduction Theory is a critical component of how biology (and science broadly) is conducted, and complements experimental and observational approaches. Theory serves many purposes, including to explore logical consistency of ideas, to identify the simplest model that can predict observed phenomena, to demonstrate the complexity of a situation, to suggest ways of looking at empirical data, to generate novel hypotheses, and to explore possible ranges of behaviour of a system [1,2]. Theory can take a range of forms including verbal, conceptual, computational, and mathematical. Broadly, theory serves as scaffolding [3] that helps us make sense of observations and experiments. Yet, papers with primarily theoretical approaches make up a small portion of the overall biological literature; only 18% of papers in the most theory-heavy journals within ecology and evolutionary biology presented primarily theoretical findings [4,5]. Perhaps due to this small representation, most biologists receive little training on how to design and interpret theory (particularly mathematical theory) [6], especially compared to the amount of training they receive on experimental approaches. Lack of training could result from an absence of conversation among biologists about best practices for designing and interpreting theory. However, this is clearly not the case; for example, people have debated how to do theory for as long as theoretical ecology has been a field. Levins’ seminal paper [7] argued that the three key aims for models are realism, precision, and generality. Since no model can accomplish all three aims simultaneously, we need different sets of models to prioritise different aims so that we can find true understanding at the point(s) where the results intersect [7]. May [8] cautioned against having an uneven balance of detail in models; including extensive detail in some model aspects while keeping others vague can convey a false sense of how much realism the model includes. In contrast to Levins and May, some researchers have called for prioritising more of one type of theoretical model over others. For example, Holling [9] argued that the field had enough of what he called ‘strategic’ models (that sacrifice precision to focus on generality), but needed more ‘tactical’ models. Evans et al. [10] similarly called for embracing complex models as a means of achieving generality through generating testable predictions. The opposite argument has also been made: Marquet et al. [11] called for the development of more ‘efficient’ theories that have fewer parameters and do not need to be precise. In addition, much has been written arguing for the value of theory in biology as a whole, drawing parallels between how theoretical and empirical studies are conducted in both ecology [1] and evolutionary biology [12]. Yet, conversations within these fields about designing and interpreting theory have not translated into guidance for newcomers on how to conduct and communicate theory. This lack of guidance creates a barrier for scientists new to theoretical research. In response, there have been a number of recent ‘how-to’ guides aimed at breaking down this barrier for researchers working with theoretical approaches. For example, recent guides on how to communicate theory to broad audiences include suggestions like clearly stating context and assumptions, reducing irrelevant complexities (adjusting math to the target audience), using clear and standardised mathematical notation, and using analogies and narratives to facilitate links between new and existing information [13,14]. In another how-to guide, Edwards and Auger-Méthé [15] provide advice for choosing mathematical notation. There have also been recent guides on how to read and use mathematical theory in ecology. Shoemaker et al. [13] suggest that readers spend extra time engaging with the math, including breaking down equations into components and working through them with peers, connecting specific equations to a general class of models, and reconstructing models or exploring parameter space to get a better handle on them. Other guides show how to use theoretical frameworks to guide empirical lab and field work, use mathematical equations to make empirically-based calculations, and test either the assumptions or predictions of theory [16,17]. Overall, these suggestions provide guidance for researchers who are either reading broadly before they start a project, or have completed a theoretical research project and want to communicate it clearly. In contrast, there is less guidance for the middle stage in the theory development process: how to choose – and frame – a theoretical research question. This is a critical gap. Even researchers who never pose theoretical research questions themselves will use and evaluate theory (e.g., as guides for experimental or observational work). Thus, we all benefit from understanding how theory is framed in order to help evaluate when theory has been successful. Here, we fill this gap by providing guidance for how to frame theoretical research in biology. Theoretical research often starts out as a verbal model, using reasoning to set up an argument about what is expected to occur. Verbal arguments can only get us so far, and relying on common sense and intuition often leads us astray [18]. It is at these points that turning the verbal (or narrative-based) argument into a mathematical (or computational) form can provide clarity and help extend a verbal argument [19]. Here, we argue that the converse is also true: a clear verbal framing can help improve the usefulness of a mathematical or computational model. Below, we present six ways to frame theoretical research, describing each as a persona one might adopt as a theorist: the “Advocate”, “Explainer”, “Instigator”, “Mediator”, “Semantician”, and “Tinkerer”. We show that these personas are not mutually exclusive; the same question or idea can be framed using multiple personas, and the appropriate choice will often depend on the research goal or intended audience. Finally, we demonstrate how the ways of thinking that we present can be used to address common misconceptions about theory. Framing theory Theoretical models are fundamentally about understanding the link between outcomes and assumptions [50]. Those assumptions will include what biological details to focus on and what to ignore. In particular, many models aim to link ‘patterns’ (outcomes of interest) to ‘mechanisms’ (processes that can generate those outcomes) (Figure 1). When a researcher starts developing a theoretical model, they typically have a sense of both what mechanism(s) they want to include and what pattern(s) to expect. The core part of the modelling process is concretely stating the specifics of patterns and mechanisms and determining the conditions under which mechanisms and patterns are linked. Here, we propose that when writing about theory for general readers (e.g., in grant proposals or manuscripts), it can help to focus on either the pattern or mechanism as a starting point and then connect it to the other. For example, a theoretical project could start by describing a pattern and develop theory to better understand the mechanisms that cause it (i.e., exploring causes). Alternatively, a theoretical project could start by considering a mechanism and use theory to better understand the patterns it can generate (i.e., exploring consequences). In addition to these two starting points, we suggest that the goal of theory can be pitched with a specific focus: novelty, robustness, or conflict. Theory with a novelty goal presents new (or previously overlooked) ideas, while robustness-oriented theory aims to examine the boundaries and context-dependence of previously described relationships, and theory focused on conflict endeavours to evaluate contradictory observations or opposing explanations. Taken together, these two starting points and three foci lead to six different ways to frame theory, or six different personas one might adopt as a theorist (Figure 2).
Loading…
Stored only when both papers are in the corpus — 8 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.