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• Review Article
• Published: September 2007

Mechanistic approaches to the study of evolution: the functional synthesis

• Antony M. Dean 1 &
• Joseph W. Thornton 2  

Nature Reviews Genetics volume  8 ,  pages 675–688 ( 2007 ) Cite this article

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By combining evolutionary sequence analyses and manipulative molecular experiments, the functional synthesis of molecular evolution provides a powerful framework to elucidate the mechanisms by which historical mutations have altered biochemical processes and produced novel phenotypes. By using this approach, inferred ancestral sequences can be resurrected and their phenotypes and fitness effects assessed experimentally.

The functional synthesis of molecular evolution provides independent corroboration of statistical inferences that have been drawn from sequence analyses.

The functional synthesis of molecular evolution explicitly connects genotype with phenotype to allow mechanistic insights into the causes of adaptive change and evolutionary constraint.

The functional synthesis of molecular evolution provides decisive tests of recent adaptations where genetic variation still segregates in present-day species, and of ancient adaptations where genetic variation is fixed in present-day species.

The functional synthesis of molecular evolution can resolve long-standing questions about evolutionary processes and important evolutionary questions about metabolic, cellular, developmental and behavioural systems.

The functional synthesis of molecular evolution can be used to characterize adaptive landscapes and explore the evolution of complexity.

The functional synthesis of molecular evolution is poised to move beyond studies of single genes to allow the analysis of the evolution of pathways and networks that are made up of multiple genes.

The functional synthesis of molecular evolution should become routine in studies of molecular evolution.

An emerging synthesis of evolutionary biology and experimental molecular biology is providing much stronger and deeper inferences about the dynamics and mechanisms of evolution than were possible in the past. The new approach combines statistical analyses of gene sequences with manipulative molecular experiments to reveal how ancient mutations altered biochemical processes and produced novel phenotypes. This functional synthesis has set the stage for major advances in our understanding of fundamental questions in evolutionary biology. Here we describe this emerging approach, highlight important new insights that it has made possible, and suggest future directions for the field.

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Acknowledgements

We thank M. Borello, M. Travisano, P. Phillips, B. Cresko, P. Rainey, A. Kondrashov, S. Yokoyama, R. Newcomb, D.Weinreich, B. Hall, an anonymous referee and members of the Thornton and Dean laboratories for comments. Supported by the US National Science Foundation (NSF IOB-0546906), the US National Institutes of Health (NIH R01-GM081592), and a Sloan Foundation Fellowship to J.W.T. and NIH R01-GM060,611 to A.M.D.

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The study of the origin and evolution of development, originally restricted to comparative methods, but increasingly using experimental approaches.

A mathematical framework, based on the genealogy of alleles, for estimating population genetic alleles.

A population genetic model in which beneficial mutations are fixed sequentially in the population through a series of selective sweeps, and in which neutral and deleterious mutations can be ignored as having low probabilities of fixation.

A precise assay of the relative growth rates (fitnesses) of competing strains can be obtained in the chemostat, a continuous culture device that is used to impose starvation for a specific resource in a constant environment.

A library of random mutants that have been generated by PCR amplification of a gene is ligated into a plasmid, transformed into a strain and screened for a desired function.

A complex of substrate bound to enzyme just before catalysis.

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Dean, A., Thornton, J. Mechanistic approaches to the study of evolution: the functional synthesis. Nat Rev Genet 8 , 675–688 (2007). https://doi.org/10.1038/nrg2160

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Mechanisms in Science

Around the turn of the twenty-first century, what has come to be called the new mechanical philosophy (or, for brevity, the new mechanism ) emerged as a framework for thinking about the philosophical assumptions underlying many areas of science, especially in sciences such as biology, neuroscience, and psychology. In this entry, we introduce and summarize the distinctive features of this framework, and we discuss how it addresses a range of classic issues in the philosophy of science, including explanation, metaphysics, the relations between scientific disciplines, and the process of scientific discovery. For each of these issues, we show how the mechanistic framework has reoriented philosophical work, what the new mechanism has contributed to the discussion, and what remains to be done.

1. The Rise of the New Mechanism

2.1.1 producing, underlying, and maintaining, 2.1.2 regularity, 2.3.1 conserved quantity accounts, 2.3.2 mechanistic accounts, 2.3.3 activity-based accounts, 2.3.4 counterfactual accounts, 2.4.1 organization and aggregativity, 2.4.2 varieties of organization, 2.4.3 modularity, 2.4.4 jointness, 2.4.5 levels, 2.4.6 stable and ephemeral mechanisms, 2.5.1 what mechanisms are not, 2.5.2 what are not mechanisms, 2.6 philosophical work to be done, 3.1 etiological and constitutive explanations, 3.2 constitutive relevance, 3.3.1 characterizing completeness, 3.3.2 abstraction and idealization, 3.4 philosophical work to be done, 4.1 token mechanisms, type mechanisms, and laws, 4.2 mechanisms, levels, and emergence, 4.3 mechanisms and realization, 4.4 mechanisms and natural kinds, 4.5 mechanisms and functions, 4.6 philosophical work to be done, 5.1 theory reduction, 5.2.1 integrative pluralism, 5.3 philosophical work to be done, 6.1 discovery via a-ha moments, 6.2 discovery via strategies, 6.3 mechanistic evidence in medical discoveries, 6.4 philosophical work to be done, 7. conclusion, other internet resources, related entries.

Twentieth century philosophy of science was largely dominated by logical empiricism. More a framework for doing philosophy of science than any coherent set of doctrines, logical empiricism addressed a range of issues in philosophy of science through the lens of the logical and mathematical structures constitutive of scientific thought and practice (see the entry on logical empiricism ). Logical empiricism tended, by and large, to focus on abstract, epistemic features of science, with little attention to scientific practice. Physics was the dominant exemplar.

The new mechanical philosophy emerged around the turn of the twenty-first century as a new framework for thinking about the philosophy of science. The philosophers who developed this framework were, by comparison with the logical empiricists, practitioners as well of the history of science and tended, by and large, to focus on the biological, rather than physical, sciences. Many new mechanists developed their framework explicitly as a successor to logical empiricist treatments of causation, levels, explanation, laws of nature, reduction, and discovery.

As with logical empiricism, the new mechanical philosophy is less a systematic and coherent set of doctrines than it is an orientation to the subject matter of the philosophy of science. The approach emerged as philosophers and historians of science began to break from the once-standard practice of reconstructing scientific inference with the tools of logic and, instead, to embrace detailed investigation of actual episodes from the history of science. The main tenets of logical empiricism had been under intense criticism for decades, and a new era of historically informed philosophy of science had taken hold through the works of, e.g., Kuhn (1962), Laudan (1977), and Lakatos (1977). To many such scholars raised in this post-logical empiricist milieu, it appeared that much of the practice of contemporary science (both in the laboratory and in print) was driven by the search for mechanisms, that many of the grand achievements in the history of science were discoveries of mechanisms, and that more traditional philosophy of science, for whatever reason, had failed to appreciate this central feature of the scientific worldview.

Aspects of the new mechanical philosophy began to emerge in the late 1960s. Fodor (1968), for example, contrasted mechanistic explanations (dealing with parts and their law-like interactions) with functional explanations in psychology. Wimsatt (1972a, 1976), building on the work of Simon (1962) and Kaufman (1971), argued repeatedly that the abstract and idealized structures of logical empiricism were ill-suited to understanding how scientists discover and explain complex systems at multiple levels of organization. Cummins (1975) provided an account of functional analyses, characterizing a function as a contribution a component part makes to the overall capacity of some system that includes that component. Salmon (1984, 1989) argued that empiricist views of scientific explanation are fundamentally flawed because they neglect causal mechanisms. Cartwright (1989) argued that the logical empiricist conception of a law of nature is, in fact, a philosophical fiction used to describe the search for capacities and nomological machines.

These strands began to coalesce into an over-arching perspective in the 1990s. The earliest clear statement of the new mechanism was Bechtel and Richardson’s (2010 [1993]) Discovering Complexity . They self-consciously put aside logical empiricist concerns with theory reduction and focused instead on the process by which scientists discover mechanisms (see Section 6 below). Soon after, Glennan argued that mechanisms are the secret connexion Hume sought between cause and effect (1996), a thesis related to and partly inspired by Cartwright’s focus on capacities and nomological machines (Glennan 1997). Likewise, Thagard’s How Scientists Explain Disease centered the search for causes and mechanisms in medicine (Thagard 2000; see also Section 6 below). Machamer, Darden and Craver’s “Thinking about Mechanisms” (Machamer, Darden, and Craver 2000; familiarly known as “MDC”) drew these strands together and became for many the lightening rod of the new mechanist perspective. MDC suggested that the philosophy of biology, and perhaps the philosophy of science more generally, should be restructured around the fundamental idea that many scientists organize their work around the search for mechanisms.

2. The Concept of a Mechanism

The term “mechanism” emerged in the seventeenth century and derived from Greek and Latin terms for “machine” (Dijksterhuis 1961). Descartes understood mechanics as the basic building block of the physical world; in Le Monde , he proposed to explain diverse phenomena in the natural world (such as planetary motion, the tides, the motion of the blood, and the properties of light) in terms of the conservation of inertial motion through contact action (see the entry on René Descartes ). Subsequently, the idea of mechanism has been transformed many times to reflect an evolving understanding of the basic causal forces in the world (besides conserved motion): e.g., attraction and repulsion (du Bois Reymond), conservation of energy (Helmholtz), gravitational attraction (Newton) (Boas 1952; Westfall 1971; see also entries on Hermann von Helmholtz and Isaac Newton ). The concept of mechanism has had an almost separate evolution in the history of the life sciences (Allen 2005; Des Chene 2001, 2005; Nicholson 2012), at times eschewing the metaphysical austerity embraced by Descartes and many early mechanists.

The new mechanists inherit the word “mechanism” from these antecedents, but, in their effort to capture how the term is used in contemporary science, have distanced themselves both from the idea that mechanisms are machines and especially from the austere metaphysical world picture in which all real change involves only one or a limited set of fundamental activities or forces (cf. Andersen 2014a,b).

Mechanists have generally eschewed the effort to spell out necessary and sufficient conditions for something to be a mechanism. Instead, they offer qualitative descriptions designed to capture the way scientists use the term and deploy the concept in their experimental and inferential practices.

Three characterizations are most commonly cited:

• MDC: “Mechanisms are entities and activities organized such that they are productive of regular changes from start or set-up to finish or termination conditions” (2000: 3).
• Glennan: “A mechanism for a behavior is a complex system that produces that behavior by the interaction of a number of parts, where the interaction between parts can be characterized by direct, invariant, change-relating generalizations” (2002: S344).
• Bechtel and Abrahamsen: “A mechanism is a structure performing a function in virtue of its component parts, component operations, and their organization. The orchestrated functioning of the mechanism is responsible for one or more phenomena” (2005: 423).

Each of these characterizations contains four basic features: (1) a phenomenon, (2) parts, (3) causings, and (4) organization. We consider each of these in detail below.

A useful canonical visual representation of a mechanism underlying a phenomenon is shown in Figure 1 (from Craver 2007). At the top is the phenomenon, some system S engaged in behavior ψ. This is the behavior of the mechanism as a whole. Beneath it are the parts (the X s) and their activities (the φs) organized together. The dotted roughly-vertical lines reflect the fact that the parts and activities are contained within, are components of, the mechanism engaged in this behavior. Thus represented, mechanisms are decompositional in the sense that the behavior of the system as a whole can be broken down into organized interactions among the activities of the parts.

Figure 1. A visual representation of a mechanism (adapted from Craver 2007).

In the early literature, these different characterizations were often treated as competitors. Tabery (2004) argued instead that they reflect different, and complementary, emphases and intellectual orientations. Many mechanists have adopted this ecumenical stance. For example, Illari and Williamson offer a “consensus concept” of mechanism:

A mechanism for a phenomenon consists of entities and activities organized in such a way that they are responsible for the phenomenon. (2012: 120)

Likewise, Glennan refers to “minimal mechanism”:

A mechanism for a phenomenon consists of entities (or parts) whose activities and interactions are organized in such a way that they produce the phenomenon. (Glennan forthcoming: Ch. 2)

These ecumenical characterizations each include the four basic elements and are designed to make the characterization more inclusive. MDC’s insistence on the regularity of mechanisms is abandoned, for example, to accommodate mechanisms that work only once or that work irregularly (Skipper and Milstein 2005; Bogen 2005; see also Section 2.1.2 below). Bechtel and Abrahamsen’s emphasis on the “functions” is abandoned to accommodate mechanisms that serve no end and to distance mechanism from this loaded term so often opposed to mechanism (although see Craver 2001a; Garson 2013; Maley and Piccinini forthcoming; see also Section 4.5 below).

These ecumenical characterizations intentionally downplay the fact that the term “mechanism” is used differently in different scientific and philosophical contexts (see Levy 2013 and Andersen 2014a,b for alternative overviews of the differences). Indeed, much of the progress in the early years involved learning to recognize the many ways that the term “mechanism” can be used and the many commitments that can be undertaken in its name. (For still other characterizations of mechanism, see Woodward (2002), Fagan (2012), Nicholson (2012), and Garson (2013)). Taking these ecumenical views as a starting point, we now consider the four basic components: 1) the phenomenon, 2) parts, 3) causings, and 4) organization.

2.1 Phenomenon

The phenomenon is the behavior of the mechanism as a whole. All mechanisms are mechanisms of some phenomenon (Kauffman 1971; Glennan 1996, 2002). The mechanism of protein synthesis synthesizes proteins. The mechanism of the action potential generates action potentials. The boundaries of a mechanism—what is in the mechanism and what is not—are fixed by reference to the phenomenon that the mechanism explains. The components in a mechanism are components in virtue of being relevant to the phenomenon.

MDC (2000) describe mechanisms as working from start- or set-up conditions to termination conditions. They insist that it is impoverished to describe the phenomenon as an input-output relation because there are often many such inputs and outputs from a mechanism and because central features of a phenomenon might be neither inputs nor outputs (but rather details about how the phenomenon unfolds over time). Darden, appealing to the example of protein synthesis, often associates the phenomenon with the end-state: the protein (Darden 2006). Craver (2007), following Cummins (1975) and Cartwright (1989), often speaks of the phenomenon roughly as a capacity or behavior of the mechanism as a whole.

New mechanists speak variously of the mechanism as producing, underlying, or maintaining the phenomenon (Craver and Darden 2013). The language of production is best applied to mechanisms conceived as a causal sequence terminating in some end-product: as when a virus produces symptoms via a disease mechanism or an enzyme phosphorylates a substrate. In such cases, the phenomenon might be an object (the production of a protein), a state of affairs (being phosphorylated), or an activity or event (such as digestion). For many physiological mechanisms, in contrast, it is more appropriate to say that the mechanism underlies the phenomenon. The mechanism of the action potential or of working memory, for example, underlies the phenomenon, here characteristically understood as a capacity or behavior of the mechanism as a whole. Finally, a mechanism might maintain a phenomenon, as when homeostatic mechanisms hold body temperature within tightly circumscribed boundaries. In such cases, the phenomenon is a state of affairs, or perhaps a range of states of affairs, that is held in place by the mechanism. These ways of talking can in many cases be inter-translated (e.g., the product is produced, the production has an underlying mechanism, and the state of affairs is maintained by an underlying mechanism). Yet clearly confusion can arise from mixing these ways of talking.

Must the relationship between the mechanism and the phenomenon be regular? This is an area of active discussion (DesAutels 2011; Andersen 2011, 2014a,b; Krickel 2014). MDC stipulate that mechanisms are regular in that they work “always or for the most part in the same ways under the same conditions” (2000: 3). Some have understood this (incorrectly in our view) as asserting that there are no mechanisms that work only once, or that a mechanism must work significantly more than once in order to count as a mechanism.

Some argue that mechanisms have to be regular in this factual sense (Andersen 2014a,b); i.e., repeated on many occasions (see Leuridan 2010). This view would seem to require a somewhat arbitrary cut-off point in degree of regularity between things that truly count as mechanisms and those that do not. Some mechanists (Bogen 2005; Glennan 2009) argue that there is no difficulty applying the term “mechanism” to one-off causal sequences, as when an historian speaks of the mechanism that gave rise to World War I. Other mechanists argue that the type-token distinction is too crude a dichotomy to capture the many levels of abstraction at which mechanism types and tokens might be characterized (Darden 1991).

It is possible, however, to read the MDC statement as asserting, not a factual kind of regularity, but as a counterfactual kind of near-determinism: were all the conditions the same, then the mechanism would likely produce the same phenomenon, where “likely” accommodates mechanisms with stochastic elements.

While the MDC account leaves open the possibility that some mechanisms are stochastic, it clearly rules out mechanisms that usually fail to produce their phenomena. Skipper and Millstein (2005) press this point to argue that the MDC account cannot accommodate the idea that natural selection is a mechanism. If, as Gould (1990) argued, one could not reproduce the history of life by rewinding the tapes and letting things play forward again, then natural selection would not be an MDC mechanism (see also Section 2.6 below). It is unclear why MDC would allow for the possibility of stochastic mechanisms and rule out, by definition, the possibility that they might fail more often than they work. Whether any biological mechanisms are truly irregular in this sense (i.e., all the causally relevant factors are the same but the product of the mechanism differs) is a separate question from whether they are mechanisms simpliciter (see Bogen 2005; Machamer 2004; Steel 2008 develops a stochastic account of mechanisms).

Krickel (2014) reviews the many different ways of unpacking the relevant notion of regularity (see also Andersen 2012). Her favored solution, “reverse regularity,” holds that there must be a generalization to the effect that, typically, when the phenomenon occurs, the mechanism was acting.

Mechanists have struggled to find a concise way to express the idea of parthood required of the components in a mechanism. The project is to develop an account that is both sufficiently permissive to include the paradigmatic mechanisms from diverse areas of science and yet not vacuous.

Formal mereologies are difficult to apply to the material parts of biological mechanisms. Axioms of mereology, such as reflexivity (everything is a part of itself) and unrestricted composition (any two things form a whole) do not apply in standard biological uses of the “part” concept.

Glennan (1996) recognized the difficulty of defining parthood very early on. His proposal:

The parts of mechanisms must have a kind of robustness and reality apart from their place within that mechanism. It should in principle be possible to take the part out of the mechanism and consider its properties in another context. (1996: 53)

Yet even this is perhaps too strong, given that some parts of a mechanism might become unstable when removed from their mechanistic context. Later, Glennan (2002: S345) says that the properties of a part must be stable in the absence of interventions, or that parts must be stable enough to be called objects. This notion is perhaps too strong to accommodate the more ephemeral parts of some biochemical mechanisms or of the mechanisms of natural selection (Skipper and Millstein 2005; but see Illari and Williamson 2010).

2.3 Causings

Mechanists have disagreed with one another about how to understand the cause in causal mechanism. New mechanists have in general been at pains both (1) to liberate the relevant causal notion from any overly austere view that restricts causation to only a small class of phenomena (such as collisions, attraction/repulsion, or energy conservation), and (2) to distance themselves from the Humean, regularist conception of causation common among logical empiricists (see also the entry on the the metaphysics of causation ). Four ways of unpacking the cause in causal mechanism have been discussed: conserved quantity accounts, mechanistic accounts, activities accounts, and counterfactual accounts. (It should be noted that some mechanists have evolved in their thinking about causation.)

According to transmission accounts, causation involves the transmission and propagation of marks or conserved quantities (Salmon 1984, 1994; Dowe 1992). The most influential form of this view holds that two causal processes causally interact when they intersect in space-time and exchange some amount of a conserved quantity, such as mass. On this view, causation is local (the processes must intersect) and singular (it is fully instantiated in particular causal processes), though the account relies upon laws of conservation (Hitchcock 1995). Although this view inspired many of the new mechanists, and although it shares their commitment to looking toward science for an account of causation, it has generally been rejected by new mechanists (though see Millstein 2006; Roe 2014).

This view has been unpopular in part because it has little direct application in nonfundamental sciences, such as biology. The causal claims biologists make usually don’t involve explicit reference to conserved quantities (even if they presuppose such notions fundamentally) (Glennan 2002; Craver 2007). Furthermore, biological mechanisms often involve causation by omission, prevention, and double prevention (that is, when a mechanism works by removing a cause, preventing a cause, or inhibiting an inhibitor) (Schaffer 2000, 2004). Such forms of causal disconnection are ubiquitous in the special sciences.

Glennan (1996, 2009) sees causation (at least non-fundamental causation) as derivative from the concept of mechanism: causal claims are claims about the existence of a mechanism. The truth-maker for a causal claim at one level of organization is a mechanism at a lower level. In short, mechanisms are the hidden connexion Hume sought between cause and effect. Like the Salmon-Dowe account, Glennan’s view is singular: particular mechanisms link particular causes and particular effects (Glennan forthcoming)

This view has been charged with circularity: the concept of mechanism ineliminably contains a causal element. However, Glennan replies that many accounts of causation (such as Woodward’s 2003 account, see Section 2.3.4 below) share this flaw. Furthermore, he argues that for at least all non-fundamental causes, a mechanisms clearly explains how a given cause produces its effect.

Whether the analysis succeeds depends on how one deals with the resulting regress (Craver 2007). As Glennan (2009) notes, the decomposition of causes into mechanisms might continue infinitely, in which case there is no point arguing about which notion is more fundamental, or the decomposition might ground out in some basic, lowest-level causal notion that is primitive and so not analyzable into other causal mechanisms. The latter option must confront the widely touted absence of causation in the theories of fundamental physics (Russell 1913); at very small size scales, classical conceptions of objects and properties no longer seem to apply, making it difficult to see what content is left to the idea that there are mechanisms at work (see also Teller 2010; Kuhlman and Glennan 2014).

Still other mechanists, such as Bogen (2005, 2008a) and Machamer (Machamer 2004), embrace an Anscombian, non-reductive view that causation should be understood in terms of productive activities (see also the entry on G.E.M. Anscombe ). Activities are kinds of causing, such as magnetic attraction and repulsion or hydrogen bonding. Defenders of activity-based accounts eschew the need to define the concept, relying on science to say what activities are and what features they might have. This view is a kind of causal minimalism (Godfrey-Smith 2010). Whether an activity occurs is not a matter of how frequently it occurs or whether it would occur always or for the most part in the same conditions (Bogen 2005).

This account has been criticized as vacuous because it fails to say what activities are (Psillos 2004), to account for the relationship of causal and explanatory relevance (Woodward 2002), and to mark an adequate distinction between activities and correlations (Psillos 2004), though see Bogen (2005, 2008a) for a response. Glennan (forthcoming) argues that these problems can be addressed by recognizing that activities in a mechanism at one level depend on lower-level mechanisms. (See also Persson 2010 for a criticism of activities based on their inability to handle cases of polygenic effects.)

Lastly, some new mechanists, particularly those interested in providing an account of scientific explanation, have gravitated toward a counterfactual view of causal relevance, and in particular, to the manipulationist view expressed in Woodward (2001, 2003) (see, e.g., Glennan 2002; Craver 2007). The central commitment of this view is that models of mechanisms describe variables that make a difference to the values of other variables in the model and to the phenomenon. Difference-making in this manipulationist sense is understood as a relationship between variables in which interventions on cause variables can be used to change the value of effect variables (see the entry on causation and manipulability ).

Unlike the views discussed above, this way of thinking about causation provides a ready analysis of explanatory relevance that comports well with the methods for testing causal claims. Roughly, one variable is causally relevant to a second when there exists an ideal intervention on the first that changes the value of the second via the change induced on the first. The view readily accommodates omissions, preventions, and double preventions—situations that have traditionally proven troublesome for production-type accounts of causation. In short, the claim that C causes E requires only that ideal interventions on C can be used to change the value of E , not that C and E are physically connected to one another. Finally, this view provides some tools for accommodating higher-level causal relations and the non-accidental laws of biology. On the other hand, the counterfactual account is non-reductive (like the mechanistic view), and it inherits challenges faced by other counterfactual views, such as pre-emption and over-determination which are common in biological mechanisms (see the entry on counterfactual theories of causation ).

2.4 Organization

The characteristic organization of mechanisms is itself the subject of considerable discussion.

Wimsatt (1997) contrasts mechanistic organization with aggregation, a distinction that mechanists have used to articulate how the parts of a mechanism are organized together to form a whole (see Craver 2001b). Aggregate properties are properties of wholes that are simple sums of the properties of their parts. In aggregates, the parts can be rearranged and intersubstituted for one another without changing the property or behavior of the whole, the whole can be taken apart and put back together without disrupting the property or behavior of the whole, and the property of the whole changes only linearly with the addition and removal of parts. These features of aggregates hold because organization is irrelevant to the property of the whole. Wimsatt thus conceives organization as non-aggregativity. He also describes it as a mechanistic form of emergence (see Section 4.2 below).

Mechanistic emergence is ubiquitous—truly aggregative properties are rare. Thus mechanists have tended to recognize a spectrum of organization, with aggregates at one end and highly organized mechanisms on the other. Indeed, many mechanisms studied by biologists involve parts and causings all across this spectrum. (For further discussion of mechanistic emergence in relationship to other varieties, see Richardson and Stephan 2007.)

Following Wimsatt, mechanists have detailed several kinds of organization characteristic of mechanisms. A canonical list includes both spatial and temporal organization. Spatial organization includes location, size, shape, position, and orientation; temporal organization includes the order, rate, and duration of the component activities. More recently, mechanists have emphasized organizational patterns in mechanisms as a whole. Bechtel, for example, discusses how mathematical models, and dynamical models in particular, are used to reveal complex temporal organization in interactive mechanisms (Bechtel 2006, 2011, 2013b). Some argue that dynamical models push beyond the limits of the mechanistic framework (e.g., Chemero and Silbestein 2008 and, at times, Bechtel himself; see Kaplan and Bechtel 2011). Others argue that dynamical models are, in fact, often merely descriptive (i.e., non-explanatory models) or, alternatively, that they are used to describe the temporal organization of mechanisms (Kaplan and Bechtel 2011; Kaplan 2012).

Mechanists have also recently borrowed from Alon’s (2006; Milo et al. 2002) work on network motifs, repeated patterns in causal networks, to expand the vocabulary for thinking about abstract patterns of organization (Levy 2014; Levy and Bechtel 2012). Understanding how parts compose wholes is likely to be a growth area in the future of the mechanistic framework. (For some other recent additions, see Kuorikoski and Ylikoski 2013; Kuhlmann 2011; Glennan forthcoming.)

Woodward’s (2001, 2002, 2011, 2014) counterfactual definition of a mechanism (which is indirectly specified via an account of mechanistic models), as well as a descendant elaborated by Menzies (2012), require that models of mechanisms be modular. This means, roughly, that it should be physically possible to intervene on a putative cause variable in a mechanism without disrupting the functional relationships among the other variables in the mechanism. In terms of structural equation models in particular, this means that one should be able to replace the right-hand side of an equation in the model with a particular value (i.e., set the left-hand variable to a value) without needing to change any of the other equations in the model. This is intended to formally capture the sense in which mechanism is composed of separable, interacting parts. For arguments in favor of a modularity condition on mechanistic models see Menzies (2012).

Steel (2008) appeals to a somewhat weaker form of modularity in his probabilistic analysis of mechanisms—one that follows directly from Simon’s (1996 [1962]) idea of nearly decomposable systems. On Simon’s view, the parts of a mechanism have more and stronger causal relations with other components in the mechanism than they do with items outside the mechanism. This gives mechanisms (and parts of mechanisms) a kind of “independence” or “objecthood” defined ultimately in terms of the intensity of interaction among components. Grush (2003), following Haugeland (1998), develops an idea of modularity in terms of the bandwidth of interaction, where modules are high-bandwidth in their internal interactions and low-bandwidth in their external interactions. On this view, modularity is not an all-or-none proposition but a matter of degree; mechanisms are only nearly decomposable. Craver (2007) argues that such a generic notion fails to account for the relevance of different causal interactions for different mechanistic decompositions; what counts as a part of a mechanism can only be defined relative to some prior decision about what one takes the mechanism to be doing. For criticisms of modularity, see Mitchell (2005) and Cartwright (2001, 2002).

Fagan (2012, 2013) emphasizes the interdependent relationship between parts of a mechanism. Components in a mechanism, she points out, often form a more complex unit by virtue of the individual properties that unite them—their “meshing properties”; the complex unit then figures into the mechanism’s behavior. This interdependent relationship—jointness—is exemplified by the lock-and-key model of enzyme action. Fagan applies this notion to research on stem cells (Fagan 2013) but argues that it is a general feature of experimental biology (Fagan 2012).

Many mechanists emphasize the hierarchical organization of mechanisms and the multilevel structure of theories in the special sciences (see especially Craver 2007, Ch. 5). Antecedents of the new mechanism focused almost exclusively on etiological, causal relations. However, the new emphasis on mechanisms in biology and the special sciences demanded an analysis of mechanistic relations across levels of organization.

From a mechanistic perspective, levels are not monolithic divides in the furniture of the universe (as represented by Oppenheim and Putnam 1958), nor are they fundamentally a matter of size or the exclusivity of causal interactions within a level (Wimsatt 1976). Rather, levels of mechanisms are defined locally within a multilevel mechanism: one item is at a lower level of mechanisms than another when the first item is a part of the second and when the first item is organized (spatially, temporally, and actively) with the other components such that together they realize the second item. Thus, the mechanism of spatial memory has multiple levels, some of which include organs such as the hippocampus generating a spatial map, some of which involve the cellular interactions that underlie map generation, and some of which involve the molecular mechanisms that underlie those cellular interactions (Craver 2007). For more on levels, see Section 4.2 below.

Finally, mechanists have found it necessary to distinguish between stable mechanisms, which rely fundamentally upon the more or less fixed arrangement of parts and activities, and ephemeral mechanisms, which involve a process evolving through time without fixed spatial and temporal arrangement (Glennan 2009). The time-keeping mechanism in a clock, for example, is a relatively stable assemblage of components in relatively fixed locations that work the same way, with the same organizational features, each time it works. Ephemeral mechanisms, in contrast, involve a much looser kind of organization: items still interact in space and time, but they do not do so in virtue of robust, stable structures. Many chemical mechanisms in a cell are like that (Richardson and Stephan 2007). Ephemeral mechanisms are surely a primary focus of historical sciences, such as archaeology, history, and evolutionary biology (Glennan 2009).

2.5 What Mechanisms Are Not and What Are Not Mechanisms

The term “mechanism” has been used in many different ways to express many different ideas. The new mechanists’ appropriation of the term is thus likely to cause unhelpful associations, and their liberalization of the term is likely to raise worries that the notion of mechanism has thereby been trivialized (see, e.g., Moss 2012 and Nicholson 2012). Here, we first distinguish the new mechanism from other doctrines with which it shares both name and family resemblance. We then discuss some things to which the new use of the term “mechanism” does not apply.

New mechanists have explicitly eschewed the following associations with the term “mechanism”:

• Mechanisms are not necessarily deterministic . Mechanisms might be stochastic if, for example, they are composed of stochastic activities (Bogen 2005, 2008a), or, in a more mundane sense (i.e., one consistent with determinism), because it is always possible for one or more factors to interfere with the working of a mechanism; one of the parts might be broken, or an unexpected preventer might interfere with the operation of a mechanism. The truth or falsity of determinism, and its relevance to understanding the special sciences, is an independent issue from the question of whether something is a mechanism.
• Appeal to mechanisms is not necessarily reductionistic . Mechanisms are often described as multi-level, with activities at different levels being equally essential to how a mechanism works. Mechanistic explanations might look, up, down, or around depending on the choice of an explanandum and the presuppositions of the explanatory context (Bechtel 2009a). Mechanists might be reductionists or anti-reductionists. That said, many mechanists opt for some form of explanatory anti-reductionism, emphasizing the importance of multilevel and upward-looking explanations, without rejecting the central ideas that motivate a broadly physicalist world-picture (e.g., McCauley and Bechtel 2001; Craver 2007). (For further discussion, see Theurer 2013; see also Sections 3.1 and 5 and the entry on reductionism in biology .)
• Not all mechanisms are machines . Machines are human-made contrivances with each part added and organized by a designer to perform a function; biological and social mechanisms, in contrast, are products of evolution, broadly construed (Darden 2006), and so display ornate forms of organization in comparison with contrivances. One machine might contain multiple mechanisms (a car, for example, has mechanisms for braking, propulsion, playing music and climate control). Machines are also capable of being both active and passive (a stopped clock is still a machine); mechanisms, in contrast, have a productive aspect and are always doing something.
• Mechanisms are not necessarily sequential or linear . Mechanisms can have feedback loops and cycles wherein the output of the mechanism or components in turn influences the input of the mechanism or components in a subsequent iteration (Bechtel 2011). Also, the interactions among components in a mechanism need not be describable by a linear equation.
• Mechanisms are not necessarily localizable (Bechtel and Richardson 2010 [1993]). Components of mechanisms might be widely distributed (as are many brain mechanisms) and might violate our intuitive or tutored sense of the boundaries of objects (as an action potential violates the cell boundary). The assumption of localization is often an important heuristic in the search for mechanisms; however, this heuristic often must be abandoned as the mechanism’s organization reveals itself.
• Mechanisms are not limited to push-pull dynamics . Descartes’ mechanism had this feature, but (as noted above) the new mechanism explicitly liberalizes the notion to account for other kinds of causing.
• Mechanisms are not just fictions/metaphors . When a scientist says that there is a mechanism that makes proteins in living organisms, she is not just using a machine metaphor; rather, she is saying that there are in fact parts and activities organized in living organisms such that they produce proteins.

One might object that there’s nothing left of mechanism once it sheds these historical associations. One might suspect that it has been trivialized (Dupré 2013).

The idea of mechanism is a central part of the explanatory ideal of understanding the world by learning its causal structure. The history of science contains many other conceptions of scientific explanation and understanding that are at odds with this commitment. Some have held that the world should be understood in terms of divine motives. Some have held that natural phenomena should be understood teleologically. Others have been convinced that understanding the natural world is nothing more than being able to predict its behavior. Commitment to mechanism as a framework concept is commitment to something distinct from and, for many, exclusive of, these alternative conceptions. If this appears trivial, rather than a central achievement in the history of science, it is because the mechanistic perspective now so thoroughly dominates our scientific worldview.

Yet there are many ways of organizing phenomena besides revealing mechanisms. Some scientists are concerned with physical structures and their spatial relations without regard to how they work: an anatomist might be interested in the spatial organization of parts within the body with minimal interest in how those parts articulate together to do something. Many scientists build predictive models of systems without any pretense that these models in fact reveal the causal structures by which the systems work. Some scientists are concerned with taxonomy, sorting like with like without regard to how the sorted items came about or how they work. Finally, in many areas of science, there is a widely recognized and practically significant distinction between knowing that C (e.g., smoking) is a cause of E (lung cancer) and knowing how C causes E . This is not so much an ontological difference as it is a difference in the grain with which one seeks to understand a system’s causal structure. In short, there are many framework concepts in science, and not all of them can be assimilated to mechanisms.

But what, the critic might push further, does not count as a mechanism? Here are some contrast classes:

• Entities (or objects ) are not mechanisms. Mechanisms do things. If an object is not doing anything (i.e., if there is no phenomenon), then it is not a mechanism.
• Correlations are not mechanisms. Mechanisms explain at least many correlations, and many correlations can be used to characterize causal or mechanistic relations, but correlations themselves are not mechanistic. The same can be said of mere temporal sequences of events.
• Inferences , reasons , and arguments are not mechanisms. Though there are mechanisms of inference and reasoning, what makes something an inference or a reason is logical relation and not (merely) a causal relationship between premise and conclusion.
• Symmetries are not mechanisms. Many kind of symmetry are of fundamental importance in different areas of physics (e.g., translational symmetries, rotational symmetries). These are features of physical systems that are highly general facts or assumptions, not mechanisms.
• Fundamental laws and fundamental causal relations are not mechanisms. If a law or causal relation is fundamental, then (by definition) there is no mechanism for it.
• Relations of logical and mathematical necessity are not mechanisms. Such truths hold in all possible worlds and so do not depend for their truth on facts about the causal structure of this world.

This is not an exhaustive list of non-mechanisms or non-mechanistic framework concepts. Yet it demonstrates that even the liberalized concept of mechanism is neither vacuous nor trivial.

Much of the early new mechanical philosophy has focused on the special sciences, such as neuroscience and molecular biology. In the years since, philosophers have extended the mechanistic framework to other scientific disciplines, such as cell biology (Bechtel 2006), cognitive science (Bechtel 2008; Thagard 2006), neuroeconomics (Craver and Alexandrova 2008), organic chemistry (Ramsey 2008), physics (Teller 2010), astrophysics (Illari and Williamson 2012), behavior genetics (Tabery 2014a), and phylogenetics (Matthews forthcoming). Philosophers continue to test the limits of this framework, with the expectation that alternative organizing frameworks might play central roles in other sciences. For example, a debate has emerged in the philosophy of biology over whether or not natural selection is a mechanism (see, for example, Skipper and Millstein 2005; Baker 2005; Barros 2008; Illari and Williamson 2012; Havstad 2011; and Matthewson and Calcott 2011). Similar debates have emerged concerning mechanistic explanation in cognitive science (Bechtel 2008; Piccinini and Craver 2011; Weiskopf 2011; Povich forthcoming).

One area that has received particular attention is the effort to understand computational mechanisms. On some accounts, computational mechanisms form a proper subclass of mechanisms that can be defined explicitly in terms of the kinds of entities, properties, and activities involved in mechanisms in that class (Piccinini 2007; Milkowski 2013). According to this view, computational mechanisms are mechanisms that have the function to manipulate medium independent vehicles in accordance with a general rule that applies to all vehicles and depends on the inputs for its application (Piccinini and Scarantino 2011). Digital computers are distinctive in that their vehicles are digits (Piccinini 2007). Proponents of this account hope to demarcate computing mechanisms from non-computing mechanisms by appeal to the distinctive components proprietary to computing mechanisms. This view contrasts both with a semantic view, according to which computation is essentially a matter of manipulating symbols or representations, and with perspectivalist views, according to which whether a mechanism counts as computing is a matter of whether it is so described (Churchland 1986; Churchland and Sejnowski 1992; Shagrir 2010).

Philosophers of the social sciences have also emphasized and debated the importance of mechanistic knowledge (e.g., Elster 1989; for a useful review of these connections, see Hedström and Ylikoski 2010). In that context, appeals to mechanisms are intended to remedy the relative uninformativeness of social (or macro-level) explanations of social phenomena (such as widespread norms, persistent inequalities, network and institutional structures) by insisting that these explanations ultimately be grounded in mechanistic details about individual agents and actors, their desires and motivations, and, importantly, their relations to one another. The emphasis on relations among actors distances this mechanistic view from methodological individualism (see the entry on methodological individualism ). Mechanists in the social sciences have also tended to shy away from grand, overarching theories and toward more local explanations: scientific knowledge grows by adding items to a toolbox of mechanisms and showing how items from that toolbox can be combined to provide an explanation for a particular phenomenon. Frederica Russo (2009) discusses a number of strategies for modeling social mechanisms (see also Little 1991, 1998; Hedström 2005; Hedström and Swedberg 1998).

3. Explanation: From Formal Analyses to Material Structures

The covering-law model of explanation was a centerpiece of the logical empiricist conception of science. According to that model, explanations are arguments showing that the event to be explained (the explanandum event) was to have been expected on the basis of laws of nature and the antecedent and boundary conditions (the explanans ). For advocates of the covering-law model, the philosophical problem of explanation is thus largely a matter of analyzing the formal structure of explanatory arguments (Hempel and Oppenheim 1948; Hempel 1965). A rainbow, for example, is explained under the covering-law model by reference to laws of reflection and refraction alongside conditions concerning the position of the sun and the nature of light, the position of the raindrops, and the position of the person seeing the rainbow. The description of the rainbow is the conclusion of a deductive argument with law statements and descriptions of conditions as premises, and so the rainbow was to be expected in light of knowledge of the laws and conditions.

Mechanists, in contrast, insist explanation is a matter of elucidating the causal structures that produce, underlie, or maintain the phenomenon of interest. For mechanists, the philosophical problem is largely about characterizing or describing the worldly or ontic structures to which explanatory models (including arguments) must refer if they are to count as genuinely explanatory. A rainbow, for the mechanist, is explained by situating that phenomenon in the causal structure of the world; the explanation is an account of how the phenomenon was produced by entities (like rain drops and eyeballs) with particular properties (like shapes and refractive indices) that causally interact with light propagating from the sun. Mechanists typically distinguish several ways of situating a phenomenon within the causal structure of the world.

Most mechanists recognize two main aspects of mechanistic explanation: etiological and constitutive. Salmon (1984) describes them as two different ways of situating an explanandum phenomenon in the causal nexus (see also Craver 2001b; Glennan 2009). Etiological explanations reveal the causal history of the explanandum phenomenon, as when one says a virus explains a disease. Constitutive explanations, in contrast, explain a phenomenon by describing the mechanism that underlies it, as when one says brain regions, muscles, and joints explain reaching.

Philosophical arguments against the covering law model often focused on its inability to deal with causal, etiological explanations. The model failed to deliver the right verdict on a variety of problem cases precisely because it attempted to provide an account of explanation without any explicit mention of causation (Bromberger 1966; Salmon 1984; Scriven 1959)

New mechanists extend these kinds of criticism to the covering law model of intertheoretic, micro-reduction. According to the covering law model of reductive explanation, a theory about parts reduces, and so explains, a theory about wholes when it is possible to derive the second from the first given bridge laws to connect the two (see Nagel 1961; Schaffner 1993).

Some mechanists argue that the covering law model of constitutive explanation has problems analogous to those that beset the covering-law model of etiological explanations. Action potentials cannot be explained by mere temporal sequences of events utterly irrelevant to the phenomenon, but one can derive a description of the action potential from descriptions of such irrelevant phenomena. Action potentials cannot be explained by mere patterns of correlation that are not indicative of an underlying causal relation. Irrelevant byproducts of a mechanism might be correlated with the behavior of the mechanism, even perfectly correlated such that one could form bridge laws between levels, but would not thereby explain the relationship. Merely finding a neural correlate of consciousness, for example, would not, and is not taken by anyone to, constitute an explanation of consciousness. So mechanists argue that micro-reductive explanations must satisfy causal constraints just as surely as etiological explanations must (Craver 2007).

The covering law model also fails to distinguish models that merely re-describe the phenomenon in general terms from explanations that, in addition to predicting aspects of the phenomenon, reveal the mechanisms that produce it (Craver 2006; Kaplan and Craver 2011; but see Weiskopf 2011). For example, Snell’s law allows one to predict how light bends when passing from one medium to another, but it does not explain why the light bends. New mechanists also argue that the covering law model fails to distinguish predictively adequate but fictional models from explanatory models. Finally, mechanists argue that the intertheoretic model of reduction fails to capture an important dimension of explanatory quality: depth. An implication of the covering law model is that any true law statements that allow one to derive the explanandum law (with suitable corrections and assumptions) will count as a complete explanation. Yet it seems one can deepen an explanation by opening black boxes and revealing how things work down to whatever level one takes as relatively fundamental for the purposes at hand. Such criticisms suggest that the covering-law model of constitutive explanation is too weak to capture the norms of explanation in the special sciences.

Other mechanists have argued that the covering law model is too strong. Philosophers of biology have long argued that there are no laws of the sort the logical empiricist described in biology and other special sciences (Beatty 1995; Mitchell 1997, 2000; Woodward 2001). One might conclude from this that there are no explanations in biology (Rosenberg 1985), but such a radical conclusion is difficult to square with obvious advances in understanding, e.g., protein synthesis, action potentials, cell signaling, and a host of other biological phenomena. In such cases, one finds that scientists appeal to mechanisms to do the explanatory work, even in cases where nothing resembling a law appears to be available.

With increased attention to constitutive explanation, mechanists realized the need for an account of constitutive relevance, a principal for sorting relevant from irrelevant factors in a mechanism (Craver 2007; Ylikoski 2013). A system ( S ) exhibiting phenomenon (ψ) is composed of many different entities ( x ), with various properties, engaging in myriad activities (φ) organized together (see Figure 1 above in Section 2). One central research problem is to say which of these entities, activities, and organizational features contribute to the phenomenon and which do not. In a sense, this is a challenge of defining the boundaries of a mechanism: of saying what is and is not in the mechanism.

Three proposals have been considered. The first, the mutual manipulability account , understands constitutive relevance in terms of the experimental manipulations used to test interlevel relations. According to this account, if it can be shown (i) that the putative components are contained within S , (ii) that some ideal interventions on the putative component ( x ’s φ-ing) change the phenomenon ( S ’s ψ-ing), and (iii) that some ideal interventions on S ’s ψ-ing change x ’s φ-ing, that is sufficient to establish that x is a component in the mechanism. The notion of an ideal intervention in this account is explicitly indebted to and a proposed extension of Woodward’s theory of causal relevance to the constitutive domain (see Craver 2007; see also Kaplan 2012). A concern with the mutual manipulability account, though, is that it is best an epistemic guide to constitutive relevance, not an account of what constitutive relevance is (Couch 2011). The account offers, at best, a sufficient condition of relevance. Also, the notion of an “ideal” intervention, borrowed from Woodward’s account of causal relevance, cannot be applied straightforwardly to constitutive explanations. An ideal intervention on a system cannot intervene on both the independent and the dependent variable at the same time. However, when one intervenes to make S ψ (or prevent S from ψ-ing), one invariably also intervenes on the components of S ’s ψ-ing. And when one intervenes on the components of S ’s ψ-ing, one often intervenes on S ’s ψ-ing. Because x ’s φ-ing and S ’s ψ-ing are related as part to whole, they are not independent, and so require another way to think about ideal interventions (see Baumgartner 2010; 2013; Leuridan 2011; yet see Menzies 2012; Woodward 2014.

A second proposal offers a regularity account of constitutive relevance modeled on Mackie’s notion of understanding a cause as an INUS condition: an Insufficient but Non-redundant part of an Unnecessary but Sufficient condition for the effect in question (Mackie 1974; see also Cummins 1983). On this account, a constitutively relevant component is an insufficient but non-redundant part of an unnecessary but sufficient mechanism for a given phenomenon (Couch 2011; see also Harbecke 2010, 2014). Allow that any number of mechanisms might suffice to bring about S ’s ψ-ing; each possible sufficient mechanism is then unnecessary for ψ-ing. Each of these mechanisms is made of components, none of which is alone sufficient to produce the behavior of the mechanism as a whole, but each of which is necessary in the context of the mechanism for S to ψ. This account presupposes the idea of being necessary in context, and one might reasonably worry about sorting accidentally correlated X s from X s that in fact make a difference to S ’s ψ-ing.

A third approach to constitutive relevance dispenses with the interlevel framing enforced by the mutual manipulability account and attempts to analyze relevance using causal notions only. According to accounts of this sort, constitutive relevance is a kind of causal between-ness. If S ’s ψ-ing is understood as an input-output relationship of some sort, then mechanistic relevance could be understood as being a necessary link in the causal chain between the input and the output (see Harinen forthcoming; Menzies 2012). The putatively interlevel experiments in the mutual manipulability account can then be recast as different kinds of unilevel causal experiments. Romero (forthcoming) provides a helpful framing of these issues and offers the novel suggestion that putatively high-level interventions are in fact fat-handed interventions relative to their lower-level counterparts.

3.3 Mechanisms and Models

The philosophical literature on mechanisms also overlaps with the philosophical literature on scientific models (see the entry on models in science ). Here we distinguish mechanical models from models of mechanisms and we discuss varieties of non-mechanical models.

Glennan (2005) proposed a definition of a mechanical model as follows:

(MM) A mechanical model consists of (i) a description of the mechanism’s behavior (the behavioral description); and (ii) a description of the mechanism that accounts for that behavior (the mechanical description). (446)

Such models can be represented in many different ways (see also Giere 2004). They are evaluated in terms of their ability to predict the features of the phenomenon and in terms of the mapping between items in the model and the entities, activities, and organizational features in the mechanism (Glennan 2005: 17; Kaplan and Craver 2011). Glennan emphasizes that there is no hard line between complete and incomplete models; rather models are continually in the process of articulation and refinement. Whether a model is complete enough is determined by pragmatic considerations.

This last point is related to Darden’s distinction between mechanism schemas and mechanism sketches (Darden and Cain 1989; Darden 2002). In discovering a mechanism, it is often crucial to identify gaps that have to be filled in one’s model. While no model is ever complete in the absolute sense, some models have lacunae that must be filled before the model is complete enough

Mechanism schemas are abstract descriptions of mechanisms that can be filled in with details to yield a specific type or token mechanism. Thus, the schema:

DNA → RNA → Protein

can be filled in with a specific sequence of bases in DNA, its complement in RNA, and a corresponding amino acid sequence in the protein. The arrows can be filled in, showing how transcription and translation work. A mechanism sketch is an incomplete representation of a mechanism that specifies some of the relevant entities, activities, and organizational features but leaves gaps that cannot yet be filled. Black boxes, question marks, and filler-terms (such as “activate”, “cause”, or “inhibitor”) hold the place for some entity, activity or process yet to be discovered. The distinction between sketches and schemas is a matter of completeness: schemas are more complete than sketches in the sense that a sketch omits one or more stages of the mechanism that have to be understood if one really wants to solve one’s discovery problem.

Mechanists also emphasize the distinction between a how-possibly schema and a how-actually-enough schema (Craver and Darden 2013). A how-possibly schema describes how entities and activities might be organized to produce a phenomenon. A how possibly model is n hypothesis about how the mechanism works. Such models might be true (enough) or false. A true (enough) how-possibly model is (though we may not know it) also a how-actually (enough) model. A how-actually-enough schema describes how entities and activities are in fact organized to produce the phenomenon. The term “how-actually-enough” captures the idea that the requisite “accuracy” of a mechanistic model can vary considerably from one pragmatic context to another (Weisberg 2013).A false how possibly model is merely a how possibly model; just-so-stories are merely how possibly models (Dray 1957; Brandon 1985). Used in this way, the term “how possibly model” is similar to the term “hypothesis”: it is entertained as a possibility but not necessarily endorsed.

In contrast to mechanism schemas and sketches, some models of mechanisms work not by describing all of the parts, causal interactions, and organizational features, but rather by abstracting away from such potentially obfuscating details (Craver and Darden 2013; Strevens 2008; Levy and Bechtel 2012). In such cases, idealizing assumptions can be introduced to bring the relevant feature of the mechanism most clearly into view: infinite populations, frictionless planes, perfect geometrical shapes are presumed in order to strip the model of detail that does not matter for, or would only obstruct, the intended purposes of model.

Critics of the new mechanical philosophy have pushed on the importance of abstraction in science, drawing attention to the above discussions of completeness. The goals of completeness and accuracy are taken to conflict with the common practice of being satisfied with models that sacrifice detail and truth for clarity and generality (Strevens 2008; Woodward 2014). The normative distinction between a schema and a sketch, for example, seems to suggest that science progresses by moving from incomplete to complete models. And the distinction between how-possibly and how-actually-enough likewise seems to privilege accuracy over other goals of modeling, which often require distortion and falsity (see Wimsatt 2007; Weisberg 2007; Levy and Bechtel 2012; Batterman and Rice 2014; Chirimuuta 2014; Levy 2014).

Yet mechanists can surely allow that not all models of mechanisms are mechanical models or mechanism schemas. Often other sorts of model are useful for isolating central aspects of a mechanism’s functioning. Dynamical models, for example, can be used to characterize the temporal dynamics of a mechanism (Bechtel 2013a,b; Kaplan and Bechtel 2011). Network models can be used to characterize patterns of connectivity regardless of what units are connected and regardless of what kinds of connections one is particularly interested in characterizing (Hunneman 2010). Minimal models can be used to capture something fundamental about the dynamics of a broad class of mechanisms that share no entities and activities in common (Batterman 2002). A model of a mechanism is a model that describes a mechanism. It need not be a mechanical model or a mechanism schema, in the above sense, to play that role.

Some mechanists reserve the term “mechanical models” for models that describe the entities, activities, and organizational features of a system. According to Glennan’s (2005) account, a mechanical model that leaves out some relevant features is, ipso facto , incomplete and sketchy. One specific instantiation of this debate concerns the explanatory force of functional models in psychology. Piccinini and Craver (2011) argue that such models should be understood as mechanistic sketches, black-box models to be evaluated and filled in as details about the underlying mechanism are discovered. Black box models are incomplete in virtue of leaving out details about underling mechanisms and that those models ultimately depend for their explanatory force on the promise that the functional models do, in fact, correspond to how the mechanism works. (See Weiskopf 2011 for a criticism of this account and Povich forthcoming for a response)

One might talk about mechanistic explanation in a way that abstracts from the kind of model used to describe the mechanism: the commitment to mechanistic explanation is not a commitment about the form of the model but rather a commitment about what such models must represent: namely, causal and mechanistic structures. Models are explanatory in virtue of the fact that they represent the causal/mechanistic structures that produce, underlie, or maintain the phenomenon. They are non-mechanistic if they refer to some non-causal, non-mechanistic kind of relation (Salmon 1984; Craver 2014).

To date, much of the work on mechanistic explanation has been driven by the goal of providing a descriptively and normatively adequate theory of mechanistic explanation. Some claim there are kinds of explanation that rely very little on a precise understanding of the mechanistic details of a system (Woodward 2014; Weiskopf 2011) or that work fundamentally by removing all such details from one’s model (Batterman and Rice 2014). Resolving such debates will require being very clear about precisely what one expects out of a philosophical theory of scientific explanation and what one takes a scientific explanation to be (Strevens 2008; Craver 2014). Research is required to understand the diverse representational forms that scientists use to represent mechanisms (Burnston et al. forthcoming), and to understand the role of idealization in mechanistic explanation (Levy and Bechtel 2012; Huneman 2010). Further work is also required to limn the boundaries between mechanistic explanation and other putative varieties of explanation and to say, as perspicuously as Hempel or the causal-mechanical theory, what a model must do to count as explanatory and precisely how good explanations are to be distinguished from bad.

4. Metaphysics of Mechanisms

In this section, we review some of the ways that the concept of mechanism has been used in diverse areas of metaphysics. Of all the areas we have discussed, this is likely the most in need of future development. Here we discuss the relationship between mechanisms and laws, emergence, realization, natural kinds, and functions.

In much of the early literature on mechanisms, mechanisms are contrasted explicitly with laws of nature (Bechtel 1988; Bechtel and Abrahamsen 2005; MDC 2000). This contrast clearly grew out of an emerging consensus in philosophy that there are few, or perhaps no, laws of biology (see Section 3.1 above). The empirical generalizations one finds in biology tend to be hedged by ceteris paribus clauses; whether they hold or not depends on background conditions that might not hold and on conditions internal to the mechanism that might fail. These generalizations, in short, are mechanistically explicable; what necessity they have derives from a mechanism (Cummins 2000; Glennan 1996). Mechanisms thus seem to play the role of laws in the biological sciences: we seek mechanisms to explain, predict, and control phenomena in nature even if mechanisms lack many of the characteristics definitive of laws in the logical empiricist framework (such as universality, inviolable necessity, or unrestricted scope).

One specific strand of this discussion emerged from consideration of Weber’s (2005) argument that biology is heteronymous , i.e., that it ultimately borrows its explanatory power from the laws of physics and chemistry. Weber uses Hodgkin and Huxley’s model of the action potential as an exemplar of the reducing biological phenomena to physical laws (such as Ohm’s law and the Nernst equation). Craver (2006) responds that the explanatory force of Hodgkin and Huxley’s model, in fact, requires a grasp of the distinctly biological properties of ion channels, which properties were black-boxed in Hodgkin and Huxley’s total current equation (see also Craver 2007; Bogen 2008b; Weber 2008).

Yet the contrast between laws and mechanisms has not always been entirely clear. Some, such as Bogen (2005), Machamer (2004), and Glennan (forthcoming) emphasize that causes and mechanisms are, at base, singular, not general or universal. Leuridan (2010), building on the work of Mitchell (2000), objects that mechanisms cannot replace laws of nature in our conceptual understanding of explanation and the metaphysics of science. Scientists rarely investigate token mechanisms, one might think, but are much more interested in types. And once one starts talking about types of mechanisms, one is back in the business of formulating general regularities about how mechanisms work. So it would appear that the concept of mechanism cannot supplant the work that generalization was supposed to do, but requires the idea of regularity, and so something akin to laws, if it is to do that explanatory work (see Andersen 2011, 2012, 2014a,b; Krickel 2014). For a reply to Leuridan, see Kaiser and Craver (2013).

Work on mechanisms has also helped to clarify the idea of levels of organization and its relation to other forms of organization and non-mechanistic forms of emergence.

Many mechanists, following Simon (1996 [1962]), emphasize that biological systems are hierarchically organized into near-decomposable structures: mechanisms within mechanisms, within mechanism. Using the parable of Tempus and Hora, Simon (1962) argued that a watchmaker who builds hierarchically decomposable watches (Tempus) will make more watches than one who builds holistic watches (Hora). This parable led Simon to the conclusion that evolved structures are more likely to be nearly decomposable into hierarchically organized, more or less stable structures and sub-structures. Some have objected that the story is misleading because evolution does not construct organisms from scratch, piece by piece (Bechtel 2009b). Steel (2008), building on the work of others (Schlosser and Wagner 2004), therefore attempts to reconstruct this argument as a way of showing that evolved systems are more likely to be modular: systems made of independently manipulable parts can quarantine the effects of changes to specific parts, giving them added flexibility to make local changes without causing catastrophic side-effects.

The near decomposability of mechanisms is directly related to the idea that mechanisms span multiple levels of organization. The behavior of the whole is explained in terms of the activities and interactions among the component parts. These activities and interactions are themselves sustained by underlying activities and interactions among component parts, and so on (see Bechtel and Richardson 2010 [1993]). Craver (2007) defines levels of mechanisms in terms of a relationship between the behavior (ψ) exhibited by a system ( S ) and the activity (φ) of some component part ( X ) of that system. On this account, X ’s φ-ing is at a lower level of mechanistic organization than S ’s ψ-ing if and only if (i) X is a part of S , and (ii) X ’s φ-ing is a component in S ’s ψ-ing. In short, to say that something is at a lower mechanistic level than the mechanism as a whole is to say that it is a working part of the mechanism. Though the term “level” is used in many legitimate ways, levels of mechanisms seem to play a central role in structuring the relations among many different models in contemporary biology (e.g., between Mendelian and molecular genetics (Darden 2006), between learning and memory and channel physiology (Craver 2007), and between population-level variation and developmental mechanisms (Tabery 2009, 2014a)).

One implication of this view of levels, combined with certain familiar assumptions about causal relations, is that there can be no causal relationships between items at different levels of mechanisms. There can be causal relationships between things of different sizes, and there can be causal relationships between things described in very different vocabularies; but (again, conjoined with certain assumptions about the temporal asymmetry of cause and effect and the independence of cause and effect) there cannot be causal relationships between the behavior of a mechanism and the activities of the parts that jointly constitute that behavior. Claims about interlevel causation, which are ubiquitous in the scientific literature, are best understood either as targeting a different sense of levels or, concerning levels of mechanisms, as expressing hybrid claims combining constitutive claims about the relationship between the behavior of the mechanism as a whole and the activities of its parts, and causal claims concerning relationships between things not related as part and whole (Craver and Bechtel 2007). For alternative interpretations of levels, see Fehr 2004; Leuridan 2011; Thalos 2013; Eronen 2013; 2015; Baumgartner and Gebhardter forthcoming; Romero forthcoming. For reflections on the metaphysical status of higher-level phenomena and higher-level causes, see Baumgartner 2010; Glennan 2010 a, b; Hoffman-Kolss 2014, as well as the entries on causation and manipulability , physicalism , and scientific reduction .

As noted above, the fact that phenomena at higher levels of mechanisms depend upon the organization of component parts entails that the properties/activities of wholes are not simple sums of the properties/activities of the parts. Levels of mechanisms can thus be contrasted with levels of mere aggregation. Because the whole is greater (in this sense) than the sum of the parts, some (such as Wimsatt) have found it appropriate to describe this as a kind of emergence. Mechanistic (or organizational) emergence thus understood is ubiquitous and banal but extremely important for understanding how scientists explain things.

Also familiar is epistemic emergence , the inability to predict the properties or behaviors of wholes from properties and behaviors of the parts. Epistemic emergence can arise as a result of ignorance, such as failing to recognize a relevant variable, or from failing to know how different variables interact in complex networks. It might also result from limitations on human cognitive abilities or in current-generation representational tools (Bedau 1997; Boogerd 2005; Richardson and Stephan 2007). The practical necessity of studying mechanisms by decomposing them into component parts raises the epistemic challenge of putting the parts back together again in a way that actually works (Bechtel 2013a).

The mechanists’ emphasis on mechanistic/organizational and epistemic emergence contrasts with their desire to distance themselves from spooky emergence (Richardson and Stephan 2007). Spooky emergence would involve the appearance of new properties with no sufficient basis in mechanisms. It is not clear that emergent properties are properly said to be properties of the necessary mechanisms; and it is not clear in what since the emergent property is “emergent” rather than a fundamental feature of the causal structure of the world.

In short, such forms of emergence are altogether distinct from, and so gain no plausibility from, verbal association with organizational/mechanistic and epistemic emergence.

Because the framework concept of a mechanism is so useful for thinking about levels and explanation in the sciences, some scholars have sought in the notion of mechanism a way of fleshing out the ontological relationship of realization.

According to the “flat view” (Gillett 2002) realization is a relationship between different properties of one and the same thing (Kim 1998; Shapiro 2000; Shoemaker 2003, 2007; Polger 2007). The subset view, which holds that a property P1 (e.g., mean kinetic energy of the gas) realizes property P2 (e.g., temperature of the gas) when the causal powers distinctive of P2 (temperature) are a subset of the causal powers distinctive of P2 (mean kinetic energy), is an example of the flat view. P1 and P2 are both attributed to the same thing, the gas (Gillett 2002, 2003). The dimensioned view describes realization as a relationship holding between the properties of wholes and the properties of the parts and their organization. This view of realization comports with the explanatory aims of the special sciences and fits nicely with the evidential base on which interlevel claims are grounded (see Aizawa and Gillett 2011). Gillett has since expanded this notion to handle the realization of objects, properties, and processes (Gillett 2013); for criticism and alternatives, see Polger 2010; Melnyk 2003; Melnyk 2010)

Mechanistic theories of natural kinds develop out of Boyd’s homeostatic property cluster (HPC) view. The HPC view is a theory of natural kinds designed to work in domains with high individual variability. The HPC view is offered as a third way between essentialism and nominalism about kinds in the special sciences (Boyd 1991, 1997, 1999; Kornblith 1993; Wilson 1999, 2005; see also the entry on natural kinds ).

According to this view, a natural kind is characterized by i) a cluster of properties that regularly co-occur, and ii) a similarity generating mechanism that explains why the properties in (i) tend to co-occur. In short, kinds are property clusters explained by mechanisms.

This view of natural kinds has been deployed to argue for taxonomic revision in, for example, the biology of human emotion (Griffiths 1997), the structure of concepts (Machery 2009), and the taxonomy of psychiatry (Kendler, Zachar, and Craver 2010; see Craver 2009): a putatively single kind is split into multiple kinds because it is discovered that distinct properties in a property cluster are explained by distinct mechanisms. This view of kinds can also be used to make sense of kinds that are historically transient and, in some ways, the product of human attitudes and so socially constructed in this straightforward sense; perhaps race is like this (see Kuorikoski and Pöyhönin 2012; Khalidi 2013).

Emphasis on the importance of mechanisms is historically associated with the rejection of teleology and formal causes (e.g., Westfall 1971). Yet contemporary biology and many other special sciences, despite the widespread acceptance of the mechanism framework concept, continue to make use of the concept of function, a teleological notion (see the entry on teleological notions in biology ). How is the notion of function at play in contemporary science related to the concept of mechanism? Craver (2001a), following Cummins (1975), argues that functional description is a perspectival means of situating some part within a higher-level mechanism. According to this view, teleology is not a feature of the world so much as it is imposed upon it by an intentional describer (see also Machamer 1977). Garson (2011, 2012), following Wimsatt (1972b), Wright (1973) and Neander (1991a,b), argues that functions are effects of an item that are part of the etiological explanation (through selection, learning, or reinforcement) for why the item is present; as such, functions are reduced to causal histories. In a third view, Maley and Piccinini (forthcoming) argue that the (teleological) function of an item is its contribution to the goals of organisms, which may be objective or subjective goals. Like Garson, Maley and Piccinini hold that functions are objective (i.e., not observer relative). Unlike Garson, however, they are not grounded in the etiology of the item but in their current contribution to survival or reproduction (the objective goals of organisms) or to what the organism itself desires (the subjective goals of organisms).

What must the world be like for this mechanistic perspective to be accurate? Clearly, there are many ways of answering this question from different metaphysical starting assumptions. And clearly, many metaphysical starting assumptions rule this world picture out as illegitimate. The clearest path forward, it would seem, is to work out precisely what one must be committed to in holding that the world is composed of a hierarchy of mechanisms and precisely what of that can be recovered on the basis of different starting assumptions.

That said, not all applications of the mechanism framework require a fully articulated metaphysics. Work on discovery and explanation might proceed perfectly well without embracing any particular metaphysical world picture. Philosophers with different interests (discovery, explanation, testing, reduction, emergence, and so) are likely to elaborate the concept in different ways. There is every reason to doubt, that the idea of mechanism can be given a one-size fits all metaphysical analysis that will adequately address the diverse philosophical ends to which the concept is being deployed.

5. Relations between Scientific Disciplines: From Theory Reduction to Mechanism Integration

According to Nagel (1961), reduction is a species of covering-law explanation: one theory is reduced to another when it is possible to identify the theoretical terms of the first with those of the second and to literally derive the first from the second. On the assumption that scientific disciplines and theories correspond to one another, reduction serves as a model of interdisciplinary integration as well. Mechanist’s objections to the covering-law model of constitutive explanation (i.e., micro-reduction) are discussed above (see Section 3.1 ); here the focus is rather on how distinct disciplines of science are integrated.

On the Nagel view, reduction is an interlevel relationship. It is also a relationship between theories. Theories about phenomena at a higher level (e.g., gases, lightning, and life) are reduced to (i.e., derived from) theories about phenomena at lower levels (e.g., molecules, electrons, and physiological systems). Finally, the relationship is formally specified and has little to do with either the content of the theories or the material structures those theories describe. From the mechanistic perspective, each of these features of the Nagel model is problematic.

5.2 Mechanism Integration

First, mechanists criticize the idea that reduction should be understood primarily as a relationship between theories . In integrating their results, scientists are not simply building theories simpliciter; they are building theories about mechanisms. Mechanisms can perhaps be described using formal accounts of theories—perhaps they can be axiomatized in predicate logic or reconstructed as set theoretic predicates. But such formal accounts of the structures of scientific theories gloss over the mechanistic structures crucial for understanding how these theories are constructed and evaluated (Craver 2001b).

Mechanists also challenge the idea that disciplines are related by way of the relationship between their theories. Darden and Maull (1977) argued that disciplinary fields often integrate their findings through the construction of interfield theories, appealing to diverse material relationships between items in the different fields’ domains: relations such as cause and effect, part and whole, or structure and function. Darden and Maull did not offer a general account of interfield theories, but Bechtel presciently suggested that such theories often take the form of descriptions of mechanisms (Bechtel 1988: 101–102).

The mechanistic approach also has been claimed to have many advantages over reduction for thinking about interlevel forms of interdisciplinary integration. First, it provides a straightforward way to interpret the talk of levels (see Sections 2.4.5 and 4.2 ). Second, it offers significantly more insight into what interlevel integration is, into the evidential constraints by which interlevel bridges are evaluated, and into the forces driving the co-evolution of work at different levels. Constraints on the parts, their causal interactions, and their spatial, temporal, and hierarchical organization all help to flesh out an interlevel integration. Finally, mechanists repeatedly recognize the need to not only look down to the constitutive mechanisms responsible for a given phenomenon (emphasized by classical reduction models), but also to look up and around to the context within which the phenomenon is embedded: interlevel integration is an effort to see how phenomena at many different levels are related to one another (Bechtel 2009a; Craver 2007).

Mechanists have developed several extended examples of the many forms of mechanism integration pursued in mechanistic research programs. Darden (2005), for example, suggests that philosophers in the grip of classical reduction fundamentally misunderstood the relationship between Mendelian and molecular genetics. While reductionists see it as an instance of interlevel explanation, she argues, it is in fact a case in which different scientists worked on different parts of a mechanism that are etiologically (not constitutively) related to one another. Mendelian genetics did not reduce to molecular biology; rather, classical geneticists and molecular biologists integrated their work by focusing on different working entities in the sequentially operating chromosomal and molecular hereditary mechanisms. Examples have also been drawn from the discovery of the mechanisms of protein synthesis (Darden 2006) and cell biology (Bechtel 2006). Craver (2007) uses examples from the neuroscience of memory to explore how multilevel integration does and ought to proceed. In each case, the search for mechanisms serves as an abstract scaffold onto and around which the findings of diverse scientists converge.

The mechanistic perspective tends to emphasize integrative pluralism in scientific research (Mitchell 2003, 2009). The goal is not to explain the less fundamental in terms of the more fundamental in a step-wise relating of monolithic theories at one level to monolithic theories at another. Rather, such scientific achievements are collaborative and piecemeal, adding incremental constraints to an emerging picture of how a mechanism works both at a level and across levels. The many scientific disciplines that investigate a phenomenon pluralistically co-exist and co-inform one another by integratively contributing to the etiological, constitutive, and contextual mechanistic explanations of that phenomenon (Bechtel 2009a; Tabery 2014a).

The Nagel model of theory reduction offers a clear vision of the “unity of science” (see the entry on unity of science ). According to the model, the unity among scientific disciplines is achieved by reducing theories of higher-level disciplines to the theories of lower-level disciplines. Integration, on that vision, is understood as progress toward a grand, unified body of scientific knowledge. For mechanists, in contrast, integration is piecemeal, local, and pluralistic. What sort of unity could such an “integration” sustain? This question plays out in a back-and-forth between Longino and Tabery concerning disciplinary relationships in the behavioral sciences. Tabery argues that disciplines as disparate as neurobiology and quantitative genetics could pluralistically co-exist and co-inform one another’s causal explanations of complex behaviors by way of mechanism integration. Longino counters that Tabery’s pluralism is only a “moderate” sort because the push for integration ultimately is a push for unification (Longino 2013, 2014; Tabery 2014a,b). Sullivan (2009) also challenges the push for mechanism integration; she argues that there are significant barriers to the kind of integration mechanists envision. Different laboratories use different experimental protocols to study what they assume to be the same phenomenon; however, these different protocols often in fact target different phenomena, so the integration achieved by combining results is only illusory. These discussions are symptomatic of more general philosophical questions faced by mechanists: How are mechanism integrations actually achieved (as opposed to just asserted)? And what is the relationship between mechanism integration and unification? The new mechanical philosophy stands to benefit from future efforts to situate mechanistic integration into more general philosophical views of integration and pluralism.

6. Discovery: From A-ha Moments to Discovery Strategies

What can philosophers say about scientific discovery? Many logical empiricists had a simple answer: Nothing. According to Popper, for example, philosophers can illuminate the epistemology of testing, but they can say nothing of substance about how scientists generate the ideas to be tested (Popper 1959). Such “A-ha!” moments of creativity are in the province of psychology, not philosophy. Reichenbach distinguished the context of discovery from the context of justification (the “context distinction”) (Reichenbach 1938; but see the entry on Hans Reichenbach for an alternate interpretation of this distinction). The process of scientific discovery was thus largely off limits to philosophers.

Not all philosophers of science agreed. Hanson, for example, articulated a logic of discovery involving abductive inferences from anomalous data to new hypotheses designed to account for them (Hanson 1958). Others focused on methodologies of discovery that could either allow one to rationally reconstruct why something was discoverable at a given time (Nickels 1985) or to explain why a new hypothesis is considered promising and worthy of further investigation (Schaffner 1993). Early contributions to the new mechanical philosophy followed this path and characterized investigative strategies scientists use to discover mechanisms (see the entry on scientific discovery ).

Bechtel and Richardson’s Discovering Complexity (2010 [1993]) is organized around a flowchart representing choice-points in the discovery of a mechanism. The process of searching for mechanisms begins with a provisional characterization of the phenomenon. Then follow strategies of localizing the mechanism within the system, and decomposing the phenomenon into distinct sub-functions. Localization of function involves determining which of these sub-functions of the system is performed by which parts. Bechtel and Richardson further characterize the use of excitatory and inhibitory experiments to obtain these kinds of information. Bechtel and Abrahamsen (2013) add a subsequent stage, in which scientists recompose what they have learned about the functional parts by putting them back together to produce the phenomenon in question (perhaps using simulations).

Darden also emphasized mechanisms as an important framework concept in scientific discovery (Darden 1980, 1982, 1986, 1991). In the discovery of protein synthesis (jointly investigated by molecular biologists and biochemists in the 1950s and 1960s), scientists didn’t simply have an “A-ha” moment. Rather, they deployed strategies for revealing how a mechanism works (Darden 2006; Craver and Darden 2013). Darden characterizes the process of mechanism discovery as an “extended, piecemeal process with hypotheses undergoing iterative refinement”; that process occurs via the construction, evaluation, and revision of mechanism schemas in light of observational and experimental constraints (Darden 2006: 272).

Darden’s construction strategies are strategies for generating new hypotheses about a mechanism. In addition to decomposition and localization, Darden shows that scientists often borrow a schema type from another area of science, as when selection-type mechanisms were borrowed to understand how the immune system works, or assemble a mechanism from known modules of functional activity ( modular sub-assembly ), as is common in biochemistry and molecular biology. Sometimes, scientists know one part of the mechanism and attempt to work forward or backward through to the other parts and activities. In the discovery of the mechanism of protein synthesis, for example, molecular biologists worked forward from the structure of DNA to figure out what molecules could interact with it ( forward chaining ), and biochemists worked backward from proteins to figure out what chemical reactions would be necessary to create them ( backward chaining ). They met in the middle at RNA. Protein synthesis is now understood to involve transcribing DNA into RNA and then translating RNA into proteins. Far from being philosophically inscrutable, Darden points out that scientists used what they knew about the working entities and activities in the mechanism to infer what could come next or before in the mechanism of protein synthesis (Darden 2006; see also the entry on molecular biology ).

Evaluation strategies, for Darden, involve constraint-based reasoning to limn the contours of the space of possible mechanisms for a given phenomenon. Often scientists reason about how a mechanism works by building off basic findings concerning the spatial and temporal organization of its parts. Harvey, for example, reasoned his way to the circulation of the blood by considering the locations of the valves of the veins and their orientation with respect to the heart. These organizational constraints, and many others, combined to narrow the space of possible mechanisms to a small region containing a model in which the blood completes a circuit of the body (Craver and Darden 2013).

Darden and Craver also discuss experimental strategies for learning how a mechanism works. These strategies reveal how different entities and activities in a mechanism act, interact, and are organized together. For example, one might intervene to remove a putative component to see if and how the mechanism functions in its absence ( inhibitory experiments). Or one might stimulate that component to see if it can drive the mechanism or modulate its behavior. Or one might activate a mechanism by placing it in the precipitating conditions for the phenomenon and observe how the entity or activity changes as the mechanism works. Craver (2002) discusses these under the heading of “interlevel experiments” (see also Harinen forthcoming). Craver and Darden (2013) also discuss more complex kinds of experiments for learning what sort of entity or activity contributes to a process and for learning more complex features of a mechanism’s organization.

Datteri (2009; Datteri and Tamburrini 2007), explores the use of robotic simulations for the purposes of testing mechanisms. They discuss both how assumptions are built into robotic models and how experiments can be designed to reveal how mechanisms work. This work extends the mechanistic framework into the area of bio-robotics and reveals a set of strategies distinct from those explored in Darden’s work.

Rather than focusing on the process by which mechanism schemas are constructed, evaluated, and revised, Steele focuses on the question of how one extrapolates from a sample population or a model organism to the structure of a mechanism in the target. Will a treatment proven to suppress tumors in mice (a model organism) also suppress tumors in humans (the target population)? After developing a probabilistic account of mechanisms, Steele considers how researchers get around what he calls the extrapolator’s circle: determining

how we could know that the model and the target are similar in causally relevant respects without already knowing the causal relationship in the target. (Steel 2008: 78)

Steel breaks the extrapolator’s circle by developing a mechanisms-based extrapolation strategy—the strategy of comparative process tracing . Once a mechanism for some phenomenon has been elucidated in a model (such as a particular process of carcinogenesis in rats), scientists (toxicologists in this case) then compare key stages (particularly downstream stages) of the model with the stages in the target, paying particular attention to points in the process where differences are most likely to arise. The greater the similarities of the entities, activities, and organization of the mechanisms in both populations, the stronger is the basis for extrapolation; the greater the differences, the weaker the basis (but see Howick et al. 2013; see also the sections on extrapolation in the entries on molecular biology and experiment in biology ).

Discovery in medicine is another domain where the mechanical philosophy has been applied. Thagard draws on the case of H. pylori as a cause of ulcers to provide an account of how investigating mechanisms contributes to scientific discovery.

Thagard draws attention to both statistical evidence that suggests ulcers are somehow associated with H. pylori , as well as mechanistic evidence that can explain how the agent of infection could persist in a hostile environment long enough to cause an ulcer. More recently, philosophers interested in evidence-based medicine have probed the relationship between these two types of evidence in the health sciences. Russo and Williamson argue that both types of evidence are necessary to justify causal inference; the correlational evidence establishes that there is a difference-making relation between some cause and some effect, while the mechanistic evidence establishes how exactly the cause produces its effect—the “Russo-Williamson Thesis” (Russo and Williamson 2007). Philosophers have since refined the Russo-Williamson Thesis, pointing out, for instance, that “type of evidence” could refer to different methodologies for gathering evidence or to different objects of evidence. Difference-making methodologies include observational studies and randomized controlled trials, while mechanistic methodologies include interventionist experiments such as those described above; likewise, the object of evidence could be the evidence of an associated difference or it could be the evidence concerning the mechanism linking the cause and effect (Illari 2011; see also Campaner 2011). Evidence-based medicine hierarchies, which rank different kinds of evidence in terms of its epistemic strength, tend to prioritize evidence from difference-making methodologies (such as randomized controlled trials and meta-analyses) over mechanistic evidence; in reply, these philosophers argue that the different types of evidence are on a par (each with its own strengths and weaknesses) and advocate for integrating difference-making and mechanistic evidence, a sentiment which aligns with the emphasis on mechanism integration discussed in Section 5.2 above (Clarke et al. 2013, 2014).

Many mechanists have explored the strategies that scientists use in discovery. Bechtel and Richardson attended to decomposition and localization; Darden and Craver highlighted forward and backward chaining; Russo and Williamson emphasized drawing on both difference-making and mechanistic evidence. These strategies were found in specific, experimental sciences, such as neuroscience and molecular biology. So one task for philosophers moving forward is to assess whether or not similar strategies exist in other sciences, especially those that operate outside the traditional laboratory, both in the human sciences (such as sociology and economics) and in the physical sciences (such as cosmology).

We also expect tremendous development to come from bridging the gap between the qualitative accounts of mechanisms and mechanistic explanation developed in the new mechanism and quantitative theories of discovery from the discipline of machine learning and causal modeling (Spirtes et al. 2000; Pearl 2009). The latter offer tools to mine correlational data for causal dependencies. Such tools might escape more qualitative, historical approaches and might, in fact, go beyond the common strategies that scientists traditionally use. Such tools also offer a means to assess discovery strategies by exploring the conditions under which they succeed and fail and the efficiency with which they deliver verdicts on causal hypotheses.

The new mechanical philosophy and, more generally, attention to the framework concept of “mechanism” has expanded rapidly over the last two decades bringing with it new orientations toward a wide range of issues in the philosophy of science. Yet it is clear that many of the major topics are only beginning to develop, leaving a lot of work for scholars to elaborate the basic commitments of this framework and to consider what it means to do science outside of that framework. The near future is likely to see continued discussion of the implications and limits of this framework for thinking about science and scientific practice.

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Anscombe, Gertrude Elizabeth Margaret | biology: experiment in | causation: and manipulability | causation: counterfactual theories of | causation: the metaphysics of | Descartes, René | empiricism: logical | Helmholtz, Hermann von | individualism, methodological | models in science | molecular biology | natural kinds | Newton, Isaac | physicalism | physics: intertheory relations in | reduction, scientific | reduction, scientific: in biology | Reichenbach, Hans | science: unity of | scientific discovery | scientific explanation | teleology: teleological notions in biology

Acknowledgments

We are grateful to the following individuals for very helpful feedback on earlier drafts of this entry: Lindley Darden, Melinda Fagan, Stuart Glennan, Phyllis Illari, Beate Krickel, Lucas Matthews, Irena Mikhalevich, Anya Plutynski, Gualtiero Piccinini, Mark Povich, and Katheryn Shrumm. We are also grateful to Pamela Speh for help with Figure 1.

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In This Article Expand or collapse the "in this article" section Mechanisms in Science

Introduction, central articles and books.

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• What Are Mechanisms?
• Discovery and Evidence
• Mechanisms, Reduction, and Interfield Integration
• Mechanistic Explanation (General Background)
• Mechanisms and Causation
• Mechanisms and Laws of Nature
• Mechanisms and Natural Kinds
• Functionalism, Abstraction, and Mechanisms
• Levels of Mechanisms and Interlevel Causation
• Dynamical Models, Minimal Models, and Network Models: Conflict or Cooperation?
• Objections from the Failure of Localization and Decomposition as a Strategy

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Mechanisms in Science by Carl Craver , Beate Krickel LAST REVIEWED: 30 October 2019 LAST MODIFIED: 30 October 2019 DOI: 10.1093/obo/9780195396577-0395

Many areas of science are animated by the search for mechanisms: experiments are designed to find them, explanations are built to reveal them, models are constructed to describe them, funding is disbursed to prioritize their discovery, and translational research is premised on their value for manipulation and control. Over the last twenty or thirty years, a number of philosophers of science, starting from the biological and neural sciences, have directed attention at the concept of mechanism. They have emphasized that mechanisms play central roles in discovery, explanation, experimentation, modeling, and reduction. At the same time, the idea of mechanism has served for many as a suggestive hint as to the metaphysics of the middle range: covering that domain of phenomena above the size scale of atomic physics and beneath that of planets, encompassing biology, physiology, psychology, and the human and so-called special sciences more generally. What must mechanisms be if they are to play these diverse roles? The term “new mechanism” (to distinguish the modern account from the classical mechanical worldview defended by philosophers such as Descartes or La Mettrie), introduced to describe this research area, runs the risk of homogenizing what has become a heterogeneous body of work, serving many masters and tugging the analysis in many directions at once. Here, we attempt to collect some points of consensus while highlighting areas of productive disagreement and criticism going forward. The authors thank Stuart Glennan and Lindley Darden for feedback on an earlier draft, Sue McKinney for assistance formatting references, and Paola Hernandez-Chavez for support in finding non-English texts.

This section contains central articles and books of notable historical significance within the new mechanism for understanding what the new mechanism sought to achieve and the diverse areas of the philosophy of science in which it was thought to do useful service. Bechtel and Richardson 1993 sketches a road map (or decision-tree) that researchers face in the search for mechanisms. The authors focus specifically on the effort to decompose complex functions into subfunctions, and to localize those subfunctions to different locations in a system, such as a cell or a body. These strategies, they point out, make sense against the background idea that the scientists aim, in their research, to discover mechanisms. Bechtel and Abrahamsen 2005 extends this work to offer a mechanistic theory of explanation opposed to the idea that explanations require knowing the laws of nature. Glennan 1996 also deploys the concept of mechanism, but in the service of a philosophical analysis of causation. For Glennan, mechanisms are the hidden connection Hume sought between a cause and its effect. He has subsequently refined and revised his views in light of criticism and theory development (see Glennan 2002 for contrasts with Salmon’s mechanistic view; see also Glennan 2017 for its latest development, emphasizing nominalism and an activities-based view of causation). Machamer, Darden, and Craver (commonly known as MDC) were less concerned with a particular set of research strategies and with Hume’s search for a hidden connection between cause and effect than they were with the suggestion that attention to mechanisms could revolutionize the philosophy of science, transforming discussions of causation, discovery, functions, laws, levels, and reduction. They characterize mechanisms as entities and activities organized in the production of regular changes from start or set-up conditions to finish and termination conditions, and they argue that the drive to satisfy this explanatory demand shapes the practice of the biological and neural sciences (see Machamer, et al. 2000 ). Following this publication, Darden pursued the issue of mechanism discovery, both alone and in cooperation with Craver and others. Key papers in this development are compiled in Darden 2006 . Darden’s view perhaps has its fullest development in Craver and Darden 2013 . Craver, in contrast, began to work on the relevance of mechanism to the topic of scientific explanation. In Craver 2001 he explores how Cummins’ idea of an analytic account of functional explanation applies to the multilevel mechanistic theories of the physiological sciences. His most systematic treatment of the topic can be found in Craver 2007 , which became a focus of much subsequent discussion.

Bechtel, William, and Adele Abrahamsen. “Explanation: A Mechanist Alternative.” In Special Issue: Mechanisms in Biology . Edited by Carl F. Craver and Lindley Darden. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 36.2 (2005): 421–441.

DOI: 10.1016/j.shpsc.2005.03.010

Bechtel and Abrahamsen set the grounds for the epistemic interpretation of mechanisms as explanatory models. They discuss the benefits of this view over law-based accounts of explanation.

Bechtel, William, and Robert C. Richardson. Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research . Princeton, NJ: Princeton University Press, 1993.

Analyzes the heuristics of decomposition and localization that are crucial for the discovery of mechanisms and in the development of mechanistic models in cell biology, cognitive neuroscience, and genetics.

Craver, Carl F. “Role Functions, Mechanisms and Hierarchy.” Philosophy of Science 68 (2001): 31–55.

DOI: 10.1086/392866

Craver unifies Cummins’ view of functions with his multilevel mechanistic picture of explanation ( Cummins 1975 ), arguing that contextual role functions constitute a third aspect of causal-mechanical explanation, beyond etiological and constitutive.

Craver, Carl F. Explaining the Brain: Mechanisms and the Mosaic Unity of Neuroscience . New York: Oxford University Press, 2007.

DOI: 10.1093/acprof:oso/9780199299317.001.0001

Discusses the nature of explanations in neuroscience and defends the view that good neuroscientific explanations are descriptions of multilevel mechanisms.

Craver, Carl F., and Lindley Darden. In Search of Mechanisms: Discoveries across the Life Sciences . Chicago: University of Chicago Press, 2013.

DOI: 10.7208/chicago/9780226039824.001.0001

Explains the relevance of mechanism to discovery, with detailed examples and case-studies from the history of biology and contemporary biology, including the neural sciences. Details stages of mechanism discovery and strategies for solving specific discovery problems.

Darden, Lindley. Reasoning in Biological Discoveries: Essays on Mechanism, Interfield Relations, and Anomaly Resolution . New York: Cambridge University Press, 2006.

DOI: 10.1017/CBO9780511498442

Contains many of Darden’s papers, alone and with coauthors, on how the search for mechanisms constrains scientific discovery, in addition to Darden’s earlier work on interfield theories and an anomaly-driven theory change.

Glennan, Stuart. “Mechanisms and the Nature of Causation.” Erkenntnis 44 (1996): 49–71.

DOI: 10.1007/BF00172853

Proposes a mechanistic theory of causation according to which A and B causally interact only if there is a mechanism between them.

Glennan, Stuart. “Rethinking Mechanistic Explanation.” Philosophy of Science 69.S3 (2002): S342–S353.

DOI: 10.1086/341857

Develops Glennan’s complex system view of mechanisms and contrasts it with Wesley Salmon’s and Peter Railton’s accounts.

Glennan, Stuart. The New Mechanical Philosophy . Oxford: Oxford University Press, 2017.

DOI: 10.1093/oso/9780198779711.001.0001

See especially Glennan’s discussion in chapter 2 of the many ways of solving problems of both defining and demarcating mechanisms.

Machamer, Peter, Lindley Darden, and Carl F. Craver. “Thinking About Mechanisms.” Philosophy of Science 67.1 (2000): 1–25.

DOI: 10.1086/392759

This is probably the most prominent article of the new mechanistic literature. It provides starting points for many discussions, such as how to characterize mechanisms and how to describe the relationship between mechanisms and mechanistic explanation and the role of mechanisms in discovery.

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11 Mechanistic versus Functional Understanding

• Published: September 2019
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Many natural and artificial entities can be predicted and explained both mechanistically, in term of parts and proximate causal processes, as well as functionally, in terms of functions and goals. Do these distinct “stances” or “modes of construal” support fundamentally different kinds of understanding? Based on recent work in epistemology and philosophy of science, as well as empirical evidence from cognitive and developmental psychology, this chapter argues for the “weak differentiation thesis”: the claim that mechanistic and functional understanding are distinct in that they involve importantly different objects. The chapter also considers more tentative arguments for the “strong differentiation thesis”: the claim that mechanistic and functional understanding involve different epistemic relationships between mind and world.

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Perspective

The Perspective section provides experts with a forum to comment on topical or controversial issues of broad interest.

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The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?

* E-mail: [email protected]

Affiliation Systems Biology Department, Centro Nacional de Biotecnología, CSIC, C/ Darwin, Madrid, Spain

• Victor de Lorenzo

Published: February 8, 2024

• https://doi.org/10.1371/journal.pbio.3002495
• Reader Comments

Molecular Biology has long tried to discover mechanisms, considering that unless we understand the principles, we cannot develop applications. Now machine learning and artificial intelligence enable direct leaps to application without understanding the principles. Will this herald a decline in mechanistic studies?

Citation: de Lorenzo V (2024) The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies? PLoS Biol 22(2): e3002495. https://doi.org/10.1371/journal.pbio.3002495

Copyright: © 2024 Victor de Lorenzo. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by European Union Horizon Programs (MIX-UP H2020-BIO-CN-2019-870294 and HORIZON-CL6-2021-UE 101060625 to VdL) and by European Structural and Investment Funds (BIOSINT-CM (Y2020/TCS- 6555-CAM to VdL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The author has declared that no competing interests exist.

Biology as a scientific and research domain seems to have undergone major breakthroughs and paradigm shifts every time external disciplines have intersected with it. Typically, after a given time of somewhat uneasy coexistence, the community embraces the new conceptual frame and its associated technologies as a lens through which biological phenomena can be (re)interpreted. The happy encounter between biology and chemistry gave birth to biochemistry, enzymology, and metabolism. Much later, the interest of post-war physicists for live systems brought about the onset of molecular biology, which reached its biggest milestones in the elucidation of the DNA double helix and the deciphering of the genetic code. During the many decades dominated by molecular biology and molecular genetics, the emphasis has been on mechanistic understanding of biological phenomena, enabled by rigorous hypothesis-driven approaches imported from physics and formal mathematical logic. These departed from mere trial-and-error approaches that prevailed in previous stages and have enabled all-rational understanding of many key biological processes on the same principles that govern the rest of the material world. This is, after all, the ultimate mission of science as a human endeavour: rational understanding of reality with universal laws and principles.

Yet, the notion that by knowing the functioning of specific biological components we can understand the functioning of whole live systems became insufficient in view of the avalanche of data later generated by the plethora of “omics” technologies. Molecular biology reduces the complexity of a given phenomenon to a point where rigorous logic can be applied, experimental results unambiguously interpreted, and conclusions fixed as permanent pieces of knowledge. Yet, for this to happen, there should be a limited number of actors in an experiment. It thus follows that molecular biology is not capable of handling systems with too many components. Another conceptual and technical framework was clearly required. And thus systems biology [ 1 ], which largely relies on network theory (ultimately a branch of physics [ 2 ]), came about with the motto that “for understanding the whole, one has to study the whole”.

Using a systems biology approach, motifs, patterns, and correlations can be identified that in turn raise questions and testable hypothesis. However, the scientific agenda up to this point remained mechanistic comprehension of biological phenomena from first principles. The merging of molecular biology with systems biology has produced, for example, a comprehensive theory of gene expression regulation that captures the response mechanisms of live systems to changing scenarios in space and time in a simplified manner. This understanding has enabled the development of many heterologous expression systems and transcriptional circuits that can be parametrised and connected among them, and their behaviour predicted with high accuracy. Adding complexity, regulatory devices can be abstracted as connectable Boolean gates, thereby paving the way to biological computation based on logic circuits, not unlike their silicon-based counterparts [ 3 ]. Alas, the underlying assumption that biological systems can be understood as finite state machines (as typical computers are) breaks down when we face biological questions involving a very high number of variables that themselves vary on the fly and thus scape rigorous computation or simple relational logic [ 4 ].

There are at least 3 intertwined qualities of life that can make full comprehension of living systems ultimately unreachable. First, they grow, mutate, and evolve. This means that one live object we inspect at a given moment in space and time will not be identical to the same a moment later, let alone if we perturb it to sample or measure specific properties. Second, parameters associated to specific biological devices are context-dependent. One promoter in a location of the genome will show different input–output transfer functions when placed on another site or when cells are grown under different nutritional or environmental conditions. Since the number of such possible conditions is virtually infinite, so is the variability of the parameters at stake. Finally, biological matter is often soft, i.e., not hard in a physical sense. Instead, biology is about flexible materials, plastic shapes, glues, etc. Such components also change their form, which adds another degree of difficulty to making predictions with an accuracy remotely comparable to other branches of science and technology. This does not mean that Biology escapes the laws of physics, but that its ever-growing complexity makes its complete comprehension with standard approaches unreachable—even theoretically. This is the point where machine learning and artificial intelligence (ML/AI) come to the rescue.

ML is about adopting learning algorithms whose objective is to obtain a result (e.g., patterns, rules, correlations) dependent on the input variables (data) but without assuming any prefixed interplay among them. Through the use of computational and statistical methods, such algorithms are trained to make classifications and scores with an explicit degree of reliability. On this basis, it cannot come as a surprise that the large volume of data generated by omics technologies become an excellent input for training ML platforms. In turn, they can deliver dependable predictions on specific, yet complex, biological questions that are not yet amenable to mechanistic understanding.

One revealing case is the prediction of protein folding, a long-standing issue in biology that is now solved to a large extent by the ML/AI-run AlphaFold (AF) [ 5 ] and other AI-driven platforms. AF predictions, accurate as they may be, are not built on first principles but largely out of experience and statistical correlations among diverse data on the matter. AF is, in turn, the basis of a whole collection of remarkable platforms for protein engineering, including altogether new-to-nature structures and activities (see, e.g., https://loschmidt.chemi.muni.cz/portal/ ). To an extent, ML/AI is a return to the experience-based, trial-and-error, black-box approach, which was the source of knowledge in the prescientific era: We know how things behave and how to make them work, but we do not know ultimately why. But … does it matter? ( Box 1 ).

Box 1. A biological principle of uncertainty?

Mechanistic understanding from first principles anticipates solar eclipses and the successful test of the first prototype of an atomic bomb with 100% accuracy. This is hardly attainable in Biology if the phenomenon at stake has many components. On the other hand, ML-based Alpha Fold enables prediction of complex protein structures with an average accuracy of >80%. True, it is not 100% but it is good enough for moving ahead in most cases. If we accept that we cannot follow all variables of a system—in particular if they change through time and space—resorting to patterns and probabilities might enable us to nevertheless solve practical problems without knowing the underlying basis.

This raises a deeper question about how we study the composition and evolution of biological systems. As physicists gave up on describing the exact position of particles beyond a certain subatomic scale, biologists may also have to give up mechanistic understanding of live systems beyond a given level of complexity in favour of a probabilistic, experience-based approach. In specific scales, mechanistic comprehension—and thus complete predictability—could be inherently impossible. Thus, efforts in that direction are likely to be ultimately futile. Yet, we can still handle description and even understanding of biological systems in terms of chances, scores, and so on.

This should not lead us to disappointment, but rather should be celebrated, as it may open new conceptual frameworks for the understanding of biological phenomena as open-ended occurrences [ 6 ] that evolve through trial-and-error progressions, in a way not dissimilar to the processes that ML/AI tries to somehow capture.

Things in biology can become way more complicated than protein folding. Let us take a quite extreme example: the gut microbiome. We know it is an essential partner of the human body that interplays with the immune system, determining health and even psychological well-being through the gut–brain axis. The microbiome encompasses a hypercomplex association of microorganisms of all types undergoing continuous genetic exchange as well as massive molecular trade with the human host. Numerous microbiomes of individuals of diverse age, lifestyles, diets, locations, etc. have been characterised in detail in the last decades, associating distinct patterns of species to either healthy conditions or disease [ 7 ] (what has been called dysbiosis). But the enormity of the factors at play makes mechanistic studies virtually impossible. Is that a problem? It could well happen that AI-enabled developments will allow, e.g., for translational development of microbiome science without a deep understanding of how or why. Same in other areas of biology or biotechnology where the need of solutions is more pressing than the importance to understand their basis.

The emphasis on pursuing the laws for understanding the material world could be seen as one of the noblest human undertakings, but it has always been accompanied by the expectation of enabling accurate predictions for practical purposes. The more predictive a branch of science is, the more credible and appealing it is. Biology can make good predictions in systems or subsystems with a limited level of complexity, beyond which it relies on experience-based probabilities. Whether the results of ML/AI are just information or authentic scientific knowledge, as Sydney Brenner would put it [ 8 ], remains a legitimate question. A second question is whether there will be a decreasing interest in mechanistic understanding of biological phenomena in favour of using AI to obtain useful rules to make things happen in living systems with a high probability, even if we do not know why they do. Remarkably, even synthetic biology, which has the explicit aim of rational engineering of biology, increasingly relies on ML for adjusting parameters [ 9 ], dealing with context sensitivity [ 10 ], and guiding the artificial assembly of complex pathways [ 11 ]. We should celebrate this latest intersection of biology with a somewhat alien discipline—surely to be followed by other creative encounters in the future.

Acknowledgments

VdL is indebted to various participants of a Twitter/X discussion on the subject of this Perspective during 7–14 October 2023 ( https://shorturl.at/imnFM ).

• 1. Alon U. An introduction to systems biology: design principles of biological circuits. CRC Press; 2019.
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The concept of mechanism in biology

Affiliation.

• 1 Konrad Lorenz Institute for Evolution and Cognition Research, Adolf Lorenz Gasse, 2, Altenberg A-3422, Austria. [email protected]
• PMID: 22326084
• DOI: 10.1016/j.shpsc.2011.05.014

The concept of mechanism in biology has three distinct meanings. It may refer to a philosophical thesis about the nature of life and biology ('mechanicism'), to the internal workings of a machine-like structure ('machine mechanism'), or to the causal explanation of a particular phenomenon ('causal mechanism'). In this paper I trace the conceptual evolution of 'mechanism' in the history of biology, and I examine how the three meanings of this term have come to be featured in the philosophy of biology, situating the new 'mechanismic program' in this context. I argue that the leading advocates of the mechanismic program (i.e., Craver, Darden, Bechtel, etc.) inadvertently conflate the different senses of 'mechanism'. Specifically, they all inappropriately endow causal mechanisms with the ontic status of machine mechanisms, and this invariably results in problematic accounts of the role played by mechanism-talk in scientific practice. I suggest that for effective analyses of the concept of mechanism, causal mechanisms need to be distinguished from machine mechanisms, and the new mechanismic program in the philosophy of biology needs to be demarcated from the traditional concerns of mechanistic biology.

Copyright © 2011 Elsevier Ltd. All rights reserved.

Publication types

• Historical Article
• Research Support, Non-U.S. Gov't
• Biology / history*
• Concept Formation*
• History, 17th Century
• History, 18th Century
• History, 19th Century
• History, 20th Century
• Models, Biological*
• Philosophy / history*
• Science / history*

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Mechanistic studies, explanation and examples

Published: 06 June 2019

Version: 1.3 March 2024

EME encourages hypothesis-testing mechanistic studies. These studies can explore the mechanisms of action of the intervention, the causes of differing responses, or promote an understanding of any potential adverse effects and how these could be reduced. They could also contribute to understanding of the disease process.

We will fund hypothesis driven studies that can be tested using sample analysis, images, or other data. Commonly these mechanistic studies use the difference between treatment groups in a randomised clinical trial to explore the mechanism of action of the treatment, but we welcome other designs involving interventions used with therapeutic intent in patients.

It is important that applicants have a clear hypothesis about the mechanisms under study, and can demonstrate how the proposed tests or data analysis will confirm or refute the hypothesis. The rationale for the hypothesis will need to be justified to persuade the Funding Committee that there is sufficient grounds to fund the research.

In addition, the application must evidence how:

• The proposed study would add to the existing body of knowledge
• The proposed costs of the research are reasonable and commensurate with the proposed work involved. It is expected that applications to the Mechanisms of Action of Health Intervention call would typically be valued up to £500,000   
• The study design will answer the research question proposed
• The proposed study is feasible and deliverable
• The team has the necessary skill mix and experience and project management of the study is sound
• The proposed mechanistic evaluation will not undermine the delivery of the underlying efficacy or effectiveness trial (if it is still ongoing)

In general, where the investigation primarily aims to identify mechanisms of pathophysiology or disease, or to demonstrate proof-of-concept evidence of the validity and importance of new discoveries or treatments this would fall under the remit of the Medical Research Council (MRC) schemes for funding. Where the investigation aims to identify mechanisms of an intervention for which there are existing proof of concept or efficacy data, this would fall under the remit of the EME scheme for funding.

We will not fund exploratory analyses of samples or data to seek out previously unknown analytes or unexpected associations with outcomes. Examples of exploratory analyses that EME would be unlikely to fund are “biomarker discovery”, “proteomics”, “peptidomics”, “genome-wide association studies”, and similar strategies. EME may fund collection, processing and storage of samples from patients for the duration of an RCT or other NIHR-funded study testing a therapeutic intervention in patients, for future hypothesis driven analyses where these samples represent a unique collection that would otherwise not be available. In keeping with principles of open access, we would expect samples to be made available for use for other investigators, and would expect to see an initial plan for sample sharing to be submitted alongside any proposal.

We do not fund any research involving animals or animal tissues.

How we support mechanistic studies

We support mechanistic studies in three ways:

• We fund mechanistic studies integrated within a main efficacy study (see studies EME 08/43/03 ,  EME 13/50/17 and others as examples).
• We also support mechanistic studies that use samples or data from eligible on-going or completed RCTs or other studies that are funded by the NIHR testing a therapeutic intervention in patients (see study  EME 14/205/01 below for an example)
• In addition, the remit has expanded to also include mechanistic studies using patients and samples collected from non-NIHR funded projects, for example, from MRC, Wellcome Trust, equivalent funding from the Devolved Administrations, and major charities that include external review processes if recruitment has been completed.

Applications for stand-alone mechanistic studies using samples or data are submitted in a two-stage application process. Please see the relevant commissioning brief for further information.

We will potentially fund mechanistic studies regardless of whether the result from the completed clinical study has shown benefit, no difference or harm.

Examples of EME funded studies with mechanistic components

Studies where determinants of the treatment response are investigated.

An investigator studied the clinical effectiveness of intermittent parent-determined montelukast as a treatment in pre-school wheezing children. The amount of enzyme that determines the production of the target molecule for montelukast varies with different genotypes of the promoter gene. The investigator designed a study that investigated the effect of intermittent parent-determined montelukast treatment on pre-school wheeze, whilst simultaneously determining if the treatment effect was modified by the child’s genotype. ( EME 08/43/03 )

A team of researchers are studying the efficacy of an IL-1 receptor antagonist in pustular psoriasis. It is possible that patients with pustular psoriasis have a deficiency in interleukin-36 receptor antagonist (IL36RA) leading to unrestrained IL-36 signalling and increased IL-1 production. The investigators will investigate the abnormal IL-1 signalling caused by IL-36 and other gene mutations to determine if they predict response to IL-1 receptor blockade. ( EME 13/50/17 )

Studies where the mechanism by which a treatment works is investigated

A study is using standard clinical pain assessment tools to test whether morphine provides analgesia and increased physiological stability during retinopathy of prematurity screening in neonates. In addition to standard clinical pain assessment the investigators are using recently developed electrophysiological techniques to examine the effects of morphine on the underlying brain and spinal cord activity evoked by the painful procedures, to determine how nociceptive information is transmitted to and processed by the cortex and where morphine interacts with this pathway. ( EME 14/187/01 )

In a study of ulipristal acetate against a levonorgestrel-releasing intra-uterine system (Mirena) for the long-term treatment of heavy menstrual bleeding a sub-set of patients will undergo imaging of the uterus using high resolution MRI of the uterine matrix and fibroids and Dynamic Contrast Enhanced MRI to measure uterine perfusion. This will determine if ulipristal influences menstrual bleeding via a reduction in uterine blood flow. ( EME 12/206/52 )

Studies using data or samples from other NIHR-funded trials

Intravenous tranexamic acid may reduce the severity of traumatic brain injury by reducing the severity of intracranial bleeding. It is also possible that tranexamic acid has a direct anti-inflammatory action on the brain which may also be beneficial in patients with a brain injury. An investigator is using brain microdialysis techniques in a sub-set of patients recruited to the HTA-funded CRASH-3 study to determine whether markers of brain inflammation are changed by tranexamic acid and whether this is related to outcome. ( EME 14/205/01 )

An EME-funded study aims to understand the mechanisms of change in group-based weight loss and related behaviour change interventions. The investigators are using recordings of group sessions from two randomised controlled trials and a feasibility study, all NIHR funded. Qualitative research methods are being used to extract target features at group level and then determine if they are associated with successful weight loss. ( EME 14/202/03 )

Examples of mechanistic components that would be outside EME’s remit

An investigator proposes testing a new intervention to treat exacerbations of refractory asthma with a control group treated according to standard BTS guidelines. She proposes collecting urine specimens at randomisation on all patients. Using physicochemical protein capture and high-performance liquid chromatography with tandem mass spectrometry she proposes to characterise the urinary proteome and then look for as yet unknown differences between “responders” and “non-responders” to the new treatment. Any identified biomarkers might provide the basis for future stratified care.

Comment: there is no proposed mechanism or identified analyte. This is a “biomarker discovery” project. EME would consider funding the sample collection and storage but would not fund the analysis.

A researcher proposes a study of an on-line, self-administered cognitive behavioural therapy (CBT) programme against an attention control for patients with obsessional-compulsive disorder. He requests funding for functional MRI studies in all patients to confirm previous findings that, in patients with obsessional-compulsive disorder, there is an abnormality of fMRI-determined amygdala activation to human face images.

Comment: there is no hypothesis that relates to the intervention under study, the researcher is requesting funding to study the whole cohort to confirm previous studies in this disease.

A very large effectiveness study funded by the NIHR is looking at an agent that may reduce the number of patients who develop metastatic deposits of colonic adenocarcinoma after primary resection. The agent may work via a mechanism involving endothelial cell surface antigens. A researcher proposes an add-on mechanistic study. This involves measuring the expression of these markers in venous biopsies in both groups to see how the treatment changes the surface markers of interest, and parallel work involving in vitro studies on endothelial cell cultures from an immortalised cell line.

Comment: the venous biopsy component would be within EME’s remit as it examines one possible mechanism of action of the treatment. Parallel laboratory studies not involving patient material or data are out of remit.

An investigator proposed a study of a structured exercise regimen for rehabilitation of patients with lung cancer treated with pulmonary resection. The control group was “care as usual”. She proposed a mechanistic study that included measures of muscle strength, maximal oxygen consumption during exercise (VO2max) and ultrasound-determined quadriceps mass.

Comment: the mechanistic component in this study would simply confirm the intervention had the expected effect on measures of physical fitness and does not explore novel mechanisms.

MECHANISTIC THEORY

is one of many theories which attempt to explain human behaviours. Mechanistic theory implies that human behaviours can be explained in the exact same way that mechanical and physiological processes are explained and understood.

Introduction

The mechanistic theory is an important topic in the biological and social sciences since it allows us to understand complex systems. It adopts an original approach by viewing all aspects of the natural world and social processes as interrelated pieces of a vast machine. It may unearth the rich historical context in which mechanistic theory was established, investigate the main principles that define it, and expose its practical applicability in diverse disciplines of study by delving deeply into it.

Mechanistic theory is a point of view that looks both natural and social phenomena as systems made up of interconnected components that work together in a machine-like fashion. According to this theory, it may successfully predict and explain the behavior and results of the entire system by developing a comprehensive understanding of how different pieces interact and work together.

Background and Context

Historical perspective.

The mechanistic viewpoint can trace its origins back to the Scientific Revolution :

• The mechanistic theory originated during the Scientific Revolution of the 16th and 17th centuries.
• René Descartes and Isaac Newton played significant roles in developing the mechanistic perspective.
• The theory was further refined during the industrial revolution when parallels were drawn between machines and natural systems.

Current State of Research

Modern breakthroughs in mechanistic theory have progressed beyond simple mechanical analogies, incorporating insights from domains as diverse as systems biology, cognitive science, and artificial intelligence research.:

• Modern developments in mechanistic theory incorporate insights from various fields.
• The current focus is on understanding complex, dynamic systems rather than purely deterministic models.

The mechanistic theory is particularly useful in scientific research, where it guides the formation of hypotheses and the design of experiments.:

• Mechanistic theory provides a valuable framework for understanding complex systems.
• It plays a critical role in scientific research, guiding hypothesis formulation and experimental design.

Key Concepts and Terminologies

Two essential terms in the mechanistic theory are :

• System: In the mechanistic theory refers to an assembly of components interacting in a coordinated manner.
• Mechanism : refers to the processes through which these components interact to produce system behavior.

Practical Applications and Implications

The mechanistic hypothesis underpins a wide range of disciplines, including biology and sociology. It aids in the understanding of processes at the cellular level in biology, and it aids in the explanation of social structures and events in sociology. The theory can also be used to develop artificial systems like computers and robotics by borrowing inspiration from natural mechanisms.

FAQ: Frequently Asked Questions

What is the mechanistic theory, and why is it important.

The mechanistic theory is a point of view that sees natural and social processes as systems made up of interconnected pieces, similar to a machine. The significance of this theory arises from its ability to provide a thorough understanding, prediction, and control over the behavior of complex systems.

How do scientists conduct research based on the mechanistic theory?

In their study, scientists frequently use a combination of observation, experimentation , and mathematical modeling. This hybrid approach assists them in identifying and comprehending the fundamental mechanisms that govern the behavior of distinct systems.

What are the different branches of the mechanistic theory?

The mechanistic theory is adaptable and finds applications in a variety of domains, each with its own adaptations . Mechanistic biology investigates biological systems, mechanistic psychology investigates mental and behavioral processes, and mechanistic sociology investigates social structures and events.

How do concepts from the mechanistic theory apply to daily life?

The principles of the mechanistic theory can be employed to understand a wide range of everyday phenomena. For instance, the functioning of a car engine, which involves various parts working together, or the dynamics of a sports team, where individual roles and interactions lead to collective performance, can be understood using the mechanistic theory.

How does the mechanistic theory influence the design of artificial systems?

 The mechanistic theory plays a significant role in the design of artificial systems li ke computers and robots. By understanding natural mechanisms, designers can mimic these processes to create efficient, sophisticated systems that accomplish specific tasks.

How has the mechanistic theory evolved over time?

The mechanistic theory has its roots in the 16th and 17th centuries' Scientific Revolution. It has evolved over time to incorporate findings from many domains like as systems biology, cognitive science, and artificial intelligence. The emphasis has also changed from solely deterministic models to comprehending complex, dynamic systems.

What limitations does the mechanistic theory have?

While the mechanistic paradigm is effective, it has limits. It has the potential to oversimplify complicated systems by disregarding characteristics such as randomness and unpredictability. Furthermore, not all systems, particularly those with non-linear or emergent features , may fit cleanly into a mechanistic framework.

How does the mechanistic theory relate to other scientific theories?

Other scientific theories are frequently supplemented by mechanistic theory. In biology, for example, it works alongside evolutionary theory to explain organismal complexity. It can be used in conjunction with theories of social behavior to explain social structures and occurrences in the social sciences.

 References

Bechtel, W. (2005).  Discovering Cell Mechanisms: The Creation of Modern Cell Biology  (Cambridge Studies in Philosophy and Biology). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139164962

Bechtel, William, and Robert C. Richardson. “Front Matter.” Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research , The MIT Press, 2010, pp. i–vi. JSTOR , http://www.jstor.org/stable/j.ctt5hhhpx.1. Accessed 16 May 2023.

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The Origin of Life: Concept, Approaches and Theories

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In this article we will discuss about:- 1. Concept of the Origin of Life 2. Approaches of the Origin of Life 3. Theories.

Concept of the Origin of Life:

The origin of life on earth is one of the most elusive and oldest problems of bio­logy. For about one half of the long span of earth’s history, the earth was barren and lifeless. But at some indeterminate time the entity called life came into exis­tence.

What form it took and what circum­stances brought it into being remain ever a problematic issue. The attention of the keenest minds of science and philosophy tried to answer this question. From time immemorial several explanations have been put forward to explain this phenomenon. But recent researches and experimenta­tions have abandoned most of the expla­nations.

Approaches of the Origin of Life :

By the turn of this century several main approaches have been made on the origin of life. These approaches are the vitalism, special creation, panspermia, mechanistic theory and materialism.

1. Vitalism:

This concept attributes the distinctive properties of the living things to a supernatural life force.

2. Special Creation:

This concept is nothing but the literal interpretation of the Biblical aspects of genesis.

3. Panspermia:

By assuming the eternal and universal existence of life, this concept avoids the question of the origin of life on this earth.

These three ideas regarding the origin of life, viz., vitalism, special creation and panspermia, need no detailed considera­tion, because they are based on assump­tion without having any scientific back­ground. But the other two theories, the mechanistic theory and the materialistic, doctrine need tremendous consideration.

Theories of the Origin of Life:

1. Mechanistic Theories:

These theories are based on the assumption that (i) the origin and activities of living things are derived from natural laws and (ii) that the living things are derived from inorga­nic matters in accordance with those natural laws. But these ideas were con­fronted with serious difficulties.

The real potent question is how can a living thing be formed from inorganic matters? These theories believed that the earth was total­ly devoid of organic compounds in pre-biological days arid that only living organisms can produce organic com­pounds.

The organic compounds cons­titute the most vital part of the living or­ganisms and as such their origin in the absence of organic compounds remains a formidable problem for ever.

The mechanistic theories resolved that the first living thing was a macromolecule or “living molecule” that was originated by the accidental coming together of the components that composed the living molecule in appropriate and proper arrangements and proportions.

It is also believed that the living molecule was probably a molecule of protein—the most important part of the living things. The mechanistic concepts advocate the origin of first living things by the chance combi­nation of these elements. This has come about by trail and error through an enor­mously long span of time.

2. Materialistic Theories:

These theories hold a different view by applying the natural laws to explain the origin of life and reject the contention of the mechanistic theories that only living things can synthesise organic compounds. On the contrary these theories propose that the organic compounds were formed abiogenecally, (i.e. without the intervention of living things) prior to the living organisms came into existence.

Instead of an acci­dental get-together of the elements to produce living things all at once as advo­cated in mechanistic doctrine, materialis­tic theories advocate the origin of life as result of series of steps towards increas­ing complexities and eventually leading up to the living state.

One of the most important steps is the abiogenic synthesis of the organic ‘compounds. Every step leads into a higher grade of organisation of matter that did not exist at the lower level. With the gradual formation of molecules, new physical as well as chemi­cal properties appeared in the scene which were non-existing in the realm of uncombined elements.

By this way and with the advent of living organisms, biological laws came into operation which did not exist before. The materialistic theories hold that the origin of life was not an accident, but it was the end result of matters evolving into higher and higher forms step by step.

Evolution of living things from non-living matters was the speculation of the thinkers of the past. To name a few, they were F. Engels, H. Spencer, T. H. Huxley, C. Mitchell and E. A. Schafer. F. Engels in his “Dialectics of Nature” condemned the concept of the spontaneous generation and vitalism and maintained that the living things have originated from a continuous evolution of matters.

A Russian Biochemist, A. I. Oparin in 1922, holds that the organic compounds had existed on the earth before the origin of living things and the living things have evolved from these pre-existing compounds.

He views the evolutionary sequences (such as the formation of inorganic compounds, organic compounds, primitive and higher living things) not as separate events, but as merely the steps in the process of un­folding of the same biological process.

J. B. S. Haldane in 1928 was also of the opinion of the abiogenic formation of organic substances in the prebiological history of the earth. He postulated that the organic compounds must have accu­mulated before the origin of life.

He assumed that the primitive atmos­phere contained only carbon dioxide, ammonia and water vapour. There was no oxygen. Haldane was of the opinion that such a combination, exposed to ultra­violet light, can evolve into a large variety of organic compounds.

Oparin’s Hypothesis:

In his attempt to solve the problem of the origin of organic compounds on this earth, Oparin based his concept on the then accepted theory on the origin of earth proposed by Sir James Jeans. Although Sir Jean’s theory of the fiery origin of the planets is no longer acceptable, it provided a basis for the formulation of Oparin’s theory of the origin of organic compounds on the earth.

Oparin assumes that the first carbon compounds were the hydrocarbons and not the carbon dioxide. To put the question in other way, he assumes the first appearance of carbon in the reduced state and not in the oxidised state because the pri­mordial atmosphere was devoid of oxygen.

With the cooling down of the atmosphere to 1000°C. and lower, the highly reactive free radicals (viz. CH and CH 2 ) combined to produce a variety of simple saturated and unsaturated hydrocarbons.

The crust of the earth had begun to form but the temperature was still too hot to allow condensation of steam to water. But the atmosphere above the crust contained carbon as well as nitrogen in reduced state. The reduced state of carbon was the hydrocarbons while that of nitrogen was ammonia. Such reducing atmosphere forms the basis of Oparin’s theory of the origin of life.

The simplest substances, such as hydro­gen, steam, methane, hydrogen sulfide, ammonia and nitrogen are continuously producing simple hydrocarbons and their derivatives—the alcohols, aldehydes, sugars, ketones and acids.

Of the deriva­tives there are the amino-acids and the polymerisation of the amino-acids results in the formation of protein. The mole­cules of protein have the capability of coming together and form large complexes. By this way colloidal hydrophilic com­plexes are produced and these complexes play the active role in the transformation of inanimate things into living systems.

Related Articles:

• Origin of Life on Planet Earth!
• Modern Concept on the Origin of Life (With Diagram)

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Ecological Rants

Ecological opinions of charley krebs and judy myers, tag archives: mechanistic hypotheses, have we moved on from hypotheses into the new age of ecology.

For the last 60 years a group of Stone Age scientists like myself have preached to ecology students that one needs hypotheses to do proper science. Now it has always been clear that not all ecologists followed this precept, and a recent review hammers this point home (Betts et al. 2021). I have always asked my students to read the papers from the Stone Age about scientific progress – Popper (1959), Platt (1964), Peters (1991) and even back to the Pre-Stone Age, Chamberlin (1897). There has been much said about this issue, and the recent Betts et al. (2021) paper pulls much of it together by reviewing papers from 1991 to 2015. Their conclusion is dismal if you think ecological science should make progress in gathering evidence. No change from 1990 to 2015. Multiple alternative hypotheses = 6% of papers, Mechanistic hypotheses = 25% of papers, Descriptive hypotheses = 12%, No hypotheses = 75% of papers. Why should this be after years of recommending the gold standard of multiple alternative hypotheses? Can we call ecology a science with these kinds of scores? 

The simplest reason is that in the era of Big Data we do not need any hypotheses to understand populations, communities, and ecosystems. We have computers, that is enough. I think this is a rather silly view, but one would have to interview believers to find out what they view as progress from big data in the absence of hypotheses. The second excuse might be that we cannot be bothered with hypotheses until we have a complete description of life on earth, food webs, interaction webs, diets, competitors, etc. Once we achieve that we will be able to put together mechanistic hypotheses rapidly. An alternative statement of this view is that we need very much natural history to make any progress in ecology, and this is the era of descriptive natural history and that is why 75% of papers do not list the word hypothesis .

But this is all nonsense of course, and try this view on a medical scientist, a physicist, an aeronautical engineer, or a farmer. The fundamental principle of science is cause-and-effect or the simple view that we would like to see how things work and why often they do not work. Have your students read Romesburg (1981) for an easy introduction and then the much more analytical book by Pearl and Mackenzie (2018) to gain an understanding of the complexity of the simple view that there is a cause and it produces an effect . Hone et al. (2023) discuss these specific problems with respect to improving our approach to wildlife management

What can be done about the dismal situation described by Betts et al. (2021)? One useful recommendation for editors and reviewers would be to request for every submitted paper for a clear statement of the hypothesis they are testing, and hopefully for alternative hypotheses. There should be ecology journals specifically for natural history where the opposite gateway is set: no use of ‘ hypothesis ’ in this journal. This would not solve all the Betts et al. problems because some ecology papers are based on the experimental design of ‘do something’ and then later ‘try to invent some way to support a hypotheses’, after the fact science. One problem with this type of literature survey is, as Betts et al. recognized, is that papers could be testing hypotheses without using this exact word. So words like ‘proposition’, ‘thesis’, ‘conjectures’ could camouflage thinking about alternative explanations without the actual word ‘hypothesis’.

One other suggestion to deal with this situation might be for journal editors to disallow all papers with hypotheses that are completely untestable. This type of rejection could be instructive to authors to assist rewriting your paper to be more specific about alternative hypotheses. If you can make a clear causal set of predictions that a particular species will go extinct in 100 years, this could be described as a ‘possible future scenario’ that could be guided by some mechanisms that are specified. Or if you have a hypothesis that ‘climate change will affect species geographical ranges, you are providing  a very vague inference that is difficult to test without being more specific about mechanisms, particularly if the species involved is rare.

There is a general problem with null hypotheses which state there is “no effect”. In some few cases these null hypotheses are useful but for the most part they are very weak and should indicate that you have not thought enough about alternative hypotheses.

So read Platt (1964) or at least the first page of it, the first chapter of Popper (1959), and Betts et al. (2021) paper and in your research try to avoid the dilemmas they discuss, and thus help to move our science forward lest it become a repository of ‘stamp collecting’.

Betts, M.G., Hadley, A.S., Frey, D.W., Frey, S.J.K., Gannon, D., Harris, S.H., et al. (2021) When are hypotheses useful in ecology and evolution? Ecology and Evolution, 11, 5762-5776. doi: 10.1002/ece3.7365.

Chamberlin, T.C. (1897) The method of multiple working hypotheses. Journal of Geology, 5, 837-848 (reprinted in Science 148: 754-759 in 1965). doi. 10.1126/science.148.3671.754.

Hone, J., Drake, A. & Krebs, C.J. (2023) Evaluation options for wildlife management and strengthening of causal inference BioScience, 73, 48-58.doi: 10.1093/biosci/biac105.

Pearl, J., and Mackenzie, D. 2018. The Book of Why. The New Science of Cause and Effect . Penguin, London, U.K. 432 pp. ISBN: 978-1541698963.

Peters, R.H. (1991) A Critique for Ecology . Cambridge University Press, Cambridge, England. ISBN: 0521400171.

Platt, J.R. (1964) Strong inference. Science , 146, 347-353.doi: 10.1126/science.146.3642.347.

Popper, K.R. (1959) The Logic of Scientific Discovery . Hutchinson & Co., London. ISBN: 978-041-5278-447.

Romesburg, H.C. (1981) Wildlife science: gaining reliable knowledge. Journal of Wildlife Management, 45, 293-313. doi:10.2307/3807913.

On Assumptions in Ecology Papers

What can we do as ecologists to improve the publishing standards of ecology papers? I suggest one simple but bold request. We should require at the end of every published paper a annotated list of the assumptions made in providing the analysis reported in the paper. A tabular format could be devised with columns for the assumption, the perceived support of and tests for the assumption, and references for this support or lack thereof. I can hear the screaming already, so this table could be put in the Supplementary Material which most people do not read. We could add to each paper in the final material where there are statements of who did the writing, who provided the money, and add a reference to this assumptions table in the Supplementary Material or a statement that no assumptions about anything were made to reach these conclusions.

The first response I can detect to this recommendation is that many ecologists will differ in what they state are assumptions to their analysis and conclusions. As an example, in wildlife studies, we commonly make the assumption that an individual animal having a radio collar will behave and survive just like another animal with no collar. In analyses of avian population dynamics, we might commonly assume that our visiting nests does not affect their survival probability. We make many such assumptions about random or non-random sampling. My question then is whether or not there is any value in listing these kinds of assumptions. My response is that this approach of listing what the authors think they are assuming should alert the reviewers to the elephants in the room that have not been listed.

My attention was called to this general issue by the recent paper of Ginzburg and Damuth (2022) in which they contrasted the assumptions of two general theories of functional responses of predators to prey – “prey dependence” versus “ratio dependence”. We have in ecology many such either-or discussions that never seem to end. Consider the long-standing discussion of whether populations can be regulated by factors that are “density dependent” or “density independent”, a much-debated issue that is still with us even though it was incisively analyzed many years ago.  

Experimental ecology is not exempt from assumptions, as outlined in Kimmel et al. (2021) who provide an incisive review of cause and effect in ecological experiments. Pringle and Hutchinson (2020) discuss the failure of assumptions in food web analysis and how these might be resolved with new techniques of analysis. Drake et al. (2021) consider the role of connectivity in arriving at conservation evaluations of patch dynamics, and the importance of demographic contributions to connectivity via dispersal. The key point is that, as ecology progresses, the role of assumptions must be continually questioned in relation to our conclusions about population and community dynamics in relation to conservation and landscape management.

Long ago Peters (1991) wrote an extended critique of how ecology should operate to avoid some of these issues, but his 1991 book is not easily available to students (currently available on Amazon for about $90). To encourage more discussion of these questions from the older to the more current literature, I have copied Peters Chapter 4 to the bottom of my web page at https://www.zoology.ubc.ca/~krebs/books.html for students to download if they wish to discuss these issues in more detail.

Perhaps a possible message in all this has been that ecology has always wished to be “physics-in-miniature” with grand generalizations like the laws we teach in the physical sciences. Over the last 60 years the battle in the ecology literature has been between this model of physics and the view that every population and community differ, and everything is continuing to change under the climate emergency so that we can have little general theory in ecology. There are certainly many current generalizations, but they are relatively useless for a transition from the general to the particular for the development of a predictive science. The consequence is that we now bounce from individual study to individual study, typically starting from different assumptions, with very limited predictability that is empirically testable. And the central issue for ecological science is how can we move from the present fragmentation in our knowledge to a more unified science. Perhaps starting to examine the assumptions of our current publications would be a start in this direction.  

Drake, J., Lambin, X., and Sutherland, C. (2021). The value of considering demographic contributions to connectivity: a review. Ecography 44 , 1-18. doi: 10.1111/ecog.05552.

Ginzburg, L.R. and Damuth, J. (2022). The Issue Isn’t Which Model of Consumer Interference Is Right, but Which One Is Least Wrong. Frontiers in Ecology and Evolution 10 , 860542. doi: 10.3389/fevo.2022.860542.

Kimmel, K., Dee, L.E., Avolio, M.L., and Ferraro, P.J. (2021). Causal assumptions and causal inference in ecological experiments. Trends in Ecology & Evolution 36 , 1141-1152. doi: 10.1016/j.tree.2021.08.008.

Peters, R.H. (1991) ‘ A Critique for Ecology .’ (Cambridge University Press: Cambridge, England.) ISBN:0521400171 (Chapter 4 pdf available at https://www.zoology.ubc.ca/~krebs/books.html )

Pringle, R.M. and Hutchinson, M.C. (2020). Resolving Food-Web Structure. Annual Review of Ecology, Evolution, and Systematics 51 , 55-80. doi: 10.1146/annurev-ecolsys-110218-024908.

On Evolution and Ecology and Climate Change

If ecology can team up with evolution to become a predictive science, we can all profit greatly since it will make us more like physics and the hard sciences. It is highly desirable to have a grand vision of accomplishing this, but there could be a few roadblocks on the way. A recent paper by Bay et al. (2018) illustrates some of the difficulties we face.

The yellow warbler ( Setophaga petechia ) has a broad breeding range across the United States and Canada, and could therefore be a good species to survey because it inhabits widely different climatic zones. Bay et al. (2018) identified genomic variation associated with climate across the breeding range of this migratory songbird, and concluded that populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected population abundance. This study by Bay et al. (2018) sampled 229 yellow warblers from 21 locations across North America, with an average of 10 birds per sample area (range n = 6 to 21). They examined 104,711 single-nucleotide polymorphisms. They correlated genetic structure to 19 climate variables and 3 vegetation indices, a measure of surface moisture, and average elevation. This is an important study claiming to support an important conclusion, and consequently it is also important to break it down into the three major assumptions on which it rests.

First, this study is a space for time analysis, a subject of much discussion already in plant ecology (e.g. Pickett 1989, Blois et al. 2013). It is an untested assumption that you can substitute space for time in analyzing for future evolutionary changes.

Second, the conclusions of the Bay et al. paper rest on an assumption that you have adequate data on the genetics involved in change and on the demography of the species. A clear understanding of the ecology of the species and what limits its distribution and abundance would seem to be prerequisites for understanding the mechanisms of how evolutionary changes might occur.

The third assumption is that, if there is a correlation between the genetic measures and the climate or vegetation indices, one can identify the precise ‘genomic vulnerability’ of the local population. Genomic variation was most closely related to precipitation variables at each site. The geographic area with one of the highest scores in genomic vulnerability was in the desert area of the intermountain west (USA). As far as I can determine from their Figure 1, there was only one sampling site in this whole area of the intermountain west. Finally Bay et al. (2018) compared the genomic vulnerability data to the population changes reported for each site. Population changes for each sampled site were obtained from the North American Breeding Bird Survey data from 1996 to 2012.

The genetic data and its analysis are more impressive, and since I am not a genetics expert I will simply give it a A grade for genetics. It is the ecology that worries me. I doubt that the North American Breeding Bird Survey is a very precise measure of population changes in any particular area. But following the Bay et al. paper, assume that it is a good measure of changing abundance for the yellow warbler. From the Bay et al. paper abstract we see this prediction:

“Populations requiring the greatest shifts in allele frequencies to keep pace with future climate change have experienced the largest population declines, suggesting that failure to adapt may have already negatively affected populations.”

The prediction is illustrated in Figure 1 below from the Bay et al. paper.

Consider a single case, the Great Basin, area S09 of the Sauer et al. (2017) breeding bird surveys. From the map in Bay et al. (2018) Figure 2 we get the prediction of a very high genomic vulnerability (above 0.06, approximate red dot in Figure 1 above) for the Great Basin, and thus a strongly declining population trend. But if we go to the Sauer et al. (2017) database, we get this result for the Great Basin (Figure 2 here), a completely stable yellow warbler population for the last 45 years.

Figure 2. Data for the Great Basin populations of the Yellow Warbler from the North American Breeding Bird Survey, 1967 to 2015 (area S09). (From Sauer et al. 2017)

One clue to this discrepancy is shown in Figure 1 above where R 2 = 0.10, which is to say the predictability of this genomic model is near zero.

So where does this leave us? We have what appears to be an A grade genetic analysis coupled with a D- grade ecological model in which explanations are not based on any mechanism of population dynamics, so that the model presented is useless for any predictions that can be tested in the next 10-20 years. I am far from convinced that this is a useful exercise. It would be a good paper for a graduate seminar discussion. Marvelous genetics, very poor ecology.

And as a footnote I note that mammalian ecologists have already taken a different but more insightful approach to this whole problem of climate-driven adaptation (Boutin and Lane 2014).

Bay, R.A., Harrigan, R.J., Underwood, V.L., Gibbs, H.L., Smith, T.B., and Ruegg, K. 2018. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359 (6371): 83-86. doi: 10.1126/science.aan4380.

Blois, J.L., Williams, J.W., Fitzpatrick, M.C., Jackson, S.T., and Ferrier, S. 2013. Space can substitute for time in predicting climate-change effects on biodiversity. Proceedings of the National Academy of Sciences 110 (23): 9374-9379. doi: 10.1073/pnas.1220228110.

Boutin, S., and Lane, J.E. 2014. Climate change and mammals: evolutionary versus plastic responses. Evolutionary Applications 7 (1): 29-41. doi: 10.1111/eva.12121.

Pickett, S.T.A. 1989. Space-for-Time substitution as an alternative to long-term studies. In Long-Term Studies in Ecology: Approaches and Alternatives. Edited by G.E. Likens. Springer New York, New York, NY. pp. 110-135.

Sauer, J.R., Niven, D.K., Hines, J.E., D. J. Ziolkowski, J., Pardieck, K.L., and Fallon, J.E. 2017. The North American Breeding Bird Survey, Results and Analysis 1966 – 2015. USGS Patuxent Wildlife Research Center, Laurel, MD. https://www.mbr-pwrc.usgs.gov/bbs/

On Post-hoc Ecology

Back in the Stone Age when science students took philosophy courses, a logic course was a common choice for students majoring in science. Among the many logical fallacies one of the most common was the Post Hoc Fallacy, or in full “Post hoc, ergo propter hoc”, “After this, therefore because of this.” The Post Hoc Fallacy has the following general form:

• A occurs before B .
• Therefore A is the cause of B .

Many examples of this fallacy are given in the newspapers every day. “I lost my pencil this morning and an earthquake occurred in California this afternoon.” Therefore….. Of course, we are certain that this sort of error could never occur in the 21 st century, but I would like to suggest to the contrary that its frequency is probably on the rise in ecology and evolutionary biology, and the culprit ( A ) is most often climate change.

Hilborn and Stearns (1982) pointed out many years ago that most ecological and evolutionary changes have multiple causes, and thus we must learn to deal with multiple causation in which a variety of factors combine and interact to produce an observed outcome. This point of view places an immediate dichotomy between the two extremes of ecological thinking – single factor experiments to determine causation cleanly versus the “many factors are involved” world view. There are a variety of intermediate views of ecological causality between these two extremes, leading in part to the flow chart syndrome of boxes and arrows aptly described by my CSIRO colleague Kent Williams as “horrendograms”. If you are a natural resource manager you will prefer the simple end of the spectrum to answer the management question of ‘what can I possibly manipulate to change an undesirable outcome for this population or community?’

Many ecological changes are going on today in the world, populations are declining or increasing, species are disappearing, geographical distributions are moving toward the poles or to higher altitudes, and novel diseases are appearing in populations of plants and animals. The simplest explanation of all these changes is that climate change is the major cause because in every part of the Earth some aspect of winter or summer climate is changing. This might be correct, or it might be an example of the Post Hoc Fallacy. How can we determine which explanation is correct?

First, for any ecological change it is important to identify a mechanism of change. Climate, or more properly weather, is itself a complex factor of temperature, humidity, and rainfall, and for climate to be considered a proper cause you must advance some information on physiology or behaviour or genetics that would link some specific climate parameter to the changes observed. Information on possible mechanisms makes the potential explanation more feasible. A second step is to make some specific predictions that can be tested either by experiments or by further observational data. Berteaux et al. (2006) provided a careful list of suggestions on how to proceed in this manner, and Tavecchia et al. (2016) have illustrated how one traditional approach to studying the impact of climate change on population dynamics could lead to forecasting errors.

A second critical focus must be on long-term studies of the population or community of interest. In particular, 3-4 year studies common in Ph.D. theses must make the assumption that the results are a random sample of annual ecological changes. Often this is not the case and this can be recognized when longer term studies are completed or more easily if an experimental manipulation can be carried out on the mechanisms involved.

The retort to these complaints about ecological and evolutionary inference is that all investigated problems are complex and multifactorial, so that after much investigation one can conclude only that “many factors are involved”. The application of AIC analysis attempts to blunt this criticism by taking the approach that, given the data (the evidence), what hypothesis is best supported? Hobbs and Hilborn (2006) provide a guide to the different methods of inference that can improve on the standard statistical approach. The AIC approach has always carried with it the awareness of the possibility that the correct hypothesis is not present in the list being evaluated, or that some combination of relevant factors cannot be tested because the available data does not cover a wide enough range of variation. Burnham et al. (2011) provide an excellent checklist for the use of AIC measures to discriminate among hypotheses. Guthery et al. (2005) and Stephens et al. (2005) carry the discussion in interesting ways. Cade (2015) discusses an interesting case in which inappropriate AIC methods lead to questionable conclusions about habitat distribution preferences and use by sage-grouse in Colorado.

If there is a simple message in all this it is to think very carefully about what the problem is in any investigation, what the possible solutions or hypotheses are that could explain the problem, and then utilize the best statistical methods to answer that question. Older statistical methods are not necessarily bad, and newer statistical methods not automatically better for solving problems. The key lies in good data, relevant to the problem being investigated. And if you are a beginning investigator, read some of these papers.

Berteaux, D., et al. 2006. Constraints to projecting the effects of climate change on mammals. Climate Research 32 (2): 151-158. doi: 10.3354/cr032151.

Burnham, K.P., Anderson, D.R., and Huyvaert, K.P. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology and Sociobiology 65 (1): 23-35. doi: 10.1007/s00265-010-1029-6.

Guthery, F.S., Brennan, L.A., Peterson, M.J., and Lusk, J.J. 2005. Information theory in wildlife science: Critique and viewpoint. Journal of Wildlife Management 69 (2): 457-465. doi: 10.1890/04-0645.

Hilborn, R., and Stearns, S.C. 1982. On inference in ecology and evolutionary biology: the problem of multiple causes. Acta Biotheoretica 31 : 145-164. doi: 10.1007/BF01857238

Hobbs, N.T., and Hilborn, R. 2006. Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. Ecological Applications 16 (1): 5-19. doi: 10.1890/04-0645

Stephens, P.A., Buskirk, S.W., Hayward, G.D., and Del Rio, C.M. 2005. Information theory and hypothesis testing: a call for pluralism. Journal of Applied Ecology 42 (1): 4-12. doi: 10.1111/j.1365-2664.2005.01002.x

Tavecchia, G., et al. 2016. Climate-driven vital rates do not always mean climate-driven population. Global Change Biology 22 (12): 3960-3966. doi: 10.1111/gcb.13330.

Climate Change and Ecological Science

One dominant paradigm of the ecological literature at the present time is what I would like to call the Climate Change Paradigm . Stated in its clearest form, it states that all temporal ecological changes now observed are explicable by climate change . The test of this hypothesis is typically a correlation between some event like a population decline, an invasion of a new species into a community, or the outbreak of a pest species and some measure of climate. Given clever statistics and sufficient searching of many climatic measurements with and without time lags, these correlations are often sanctified by p< 0.05. Should we consider this progress in ecological understanding?

An early confusion in relating climate fluctuations to population changes was begun by labelling climate as a density independent factor within the density-dependent model of population dynamics. Fortunately, this massive confusion was sorted out by Enright (1976) but alas I still see this error repeated in recent papers about population changes. I think that much of the early confusion of climatic impacts on populations was due to this classifying all climatic impacts as density-independent factors.

One’s first response perhaps might be that indeed many of the changes we see in populations and communities are indeed related to climate change. But the key here is to validate this conclusion, and to do this we need to talk about the mechanisms by which climate change is acting on our particular species or species group. The search for these mechanisms is much more difficult than the demonstration of a correlation. To become more convincing one might predict that the observed correlation will continue for the next 5 (10, 20?) years and then gather the data to validate the correlation. Many of these published correlations are so weak as to preclude any possibility of validation in the lifetime of a research scientist. So the gold standard must be the deciphering of the mechanisms involved.

And a major concern is that many of the validations of the climate change paradigm on short time scales are likely to be spurious correlations. Those who need a good laugh over the issue of spurious correlation should look at Vigen (2015), a book which illustrates all too well the fun of looking for silly correlations. Climate is a very complex variable and a nearly infinite number of measurements can be concocted with temperature (mean, minimum, maximum), rainfall, snowfall, or wind, analyzed over any number of time periods throughout the year. We are always warned about data dredging, but it is often difficult to know exactly what authors of any particular paper have done. The most extreme examples are possible to spot, and my favorite is this quotation from a paper a few years ago:

“A total of 864 correlations in 72 calendar weather periods were examined; 71 (eight percent) were significant at the p< 0.05 level. …There were 12 negative correlations, p< 0.05, between the number of days with (precipitation) and (a demographic measure). A total of 45- positive correlations, p<0.05, between temperatures and (the same demographic measure) were disclosed…..”

The climate change paradigm is well established in biogeography and the major shifts in vegetation that have occurred in geological time are well correlated with climatic changes. But it is a large leap of faith to scale this well established framework down to the local scale of space and a short-term time scale. There is no question that local short term climate changes can explain many changes in populations and communities, but any analysis of these kinds of effects must consider alternative hypotheses and mechanisms of change. Berteaux et al. (2006) pointed out the differences between forecasting and prediction in climate models. We desire predictive models if we are to improve ecological understanding, and Berteaux et al. (2006) suggested that predictive models are successful if they follow three rules:

(1) Initial conditions of the system are well described (inherent noise is small);

(2) No important variable is excluded from the model (boundary conditions are defined adequately);

(3) Variables used to build the model are related to each other in the proper way (aggregation/representation is adequate).

Like most rules for models, whether these conditions are met is rarely known when the model is published, and we need subsequent data from the real world to see if the predictions are correct.

I am much less convinced that forecasting models are useful in climate research. Forecasting models describe an ecological situation based on correlations among the measurements available with no clear mechanistic model of the ecological interactions involved. My concern was highlighted in a paper by Myers (1998) who investigated for fish populations the success of published juvenile recruitment-environmental factor (typically temperature) correlations and found that very few forecasting models were reliable when tested against additional data obtained after publication. It would be useful for someone to carry out a similar analysis for bird and mammal population models.

Small mammals show some promise for predictive models in some ecosystems. The analysis by Kausrud et al. (2008) illustrates a good approach to incorporating climate into predictive explanations of population change in Norwegian lemmings that involve interactions between climate and predation. The best approach in developing these kinds of explanations and formulating them into models is to determine how the model performs when additional data are obtained in the years to follow publication.

The bottom line is to avoid spurious climatic correlations by describing and evaluating mechanistic models that are based on observable biological factors. And then make predictions that can be tested in a realistic time frame. If we cannot do this, we risk publishing fairy tales rather than science.

Berteaux, D., et al. (2006) Constraints to projecting the effects of climate change on mammals. Climate Research, 32, 151-158. doi: 10.3354/cr032151

Enright, J. T. (1976) Climate and population regulation: the biogeographer’s dilemma. Oecologia, 24, 295-310.

Kausrud, K. L., et al. (2008) Linking climate change to lemming cycles. Nature, 456, 93-97. doi: 10.1038/nature07442

Myers, R. A. (1998) When do environment-recruitment correlations work? Reviews in Fish Biology and Fisheries, 8, 285-305. doi: 10.1023/A:1008828730759

Vigen, T. (2015) Spurious Correlations, Hyperion, New York City. ISBN: 978-031-633-9438

Was the Chitty Hypothesis of Population Regulation a ‘Big Idea’ in Ecology and was it successful?

Jeremy Fox in his ‘Dynamic Ecology’ Blog has raised the eternal question of what have been the big ideas in ecology and were they successful, and this has stimulated me to write about the Chitty Hypothesis and its history since 1952. I will write this from my personal observations which can be faulty, and I will not bother to put in many references since this is a blog and not a formal paper.

In 1952 when Dennis Chitty at Oxford finished his thesis on vole cycles in Wales, he was considered a relatively young heretic because he did not see any evidence in favour of the two dominant paradigms of population dynamics – that populations rose and fell because of food shortage or predation. David Lack vetoed the publication of his Ph.D. paper because he did not agree with Chitty’s findings (Lack believed that food supplies explained all population changes). His 1952 thesis paper was published only because of the intervention of Peter Medawar. Chitty could see no evidence of these two factors in his vole populations and he began to suspect that social factors were involved in population cycles. He tested Jack Christian’s ideas that social stress was a possible cause, since it was well known that some rodents were territorial and highly aggressive, but stress as measured by adrenal gland size did not fit the population trends very well. He then began to suspect that there might be genetic changes in fluctuating vole populations, and that population processes that occurred in voles and lemmings may occur in a wide variety of species, not just in the relatively small group of rodent species, which everyone could ignore as a special case of no generality. This culminated in his 1960 paper in the Canadian Journal of Zoology. This paper stimulated many field ecologists to begin experiments on population regulation in small mammals.

Chitty’s early work contained a ‘big idea’ that population dynamics and population genetics might have something to contribute to each other, and that one could not assume that every individual had equal properties. These ideas of course were not just his, and Bill Wellington had many of the same ideas in studying tent caterpillar population fluctuations. When Chitty suggested these ideas during the late 1950s he was told by several eminent geneticists who must remain nameless that his ideas were impossible, and that ecologists should stay out of genetics because the speed of natural selection was so slow that nothing could be achieved in ecological time. Clearly thinking has now changed on this general idea.

So if one could recognize these early beginnings as a ‘big idea’ it might be stated simply as ‘study individual behaviour, physiology, and genetics to understand population changes’, and it was instrumental in adding another page to the many discussions of population changes that had previously mostly included only predators, food supplies, and potentially disease. All this happened before the rise of behavioural ecology in the 1970s.

I leave others to judge the longer term effects of Chitty’s early suggestions. At present the evidence is largely against any rapid genetic changes in fluctuating populations of mammals and birds, and maternal effects now seem a strong candidate for non-genetic inheritance of traits that affect fitness in a variety of vertebrate species. And in a turn of fate, stress seems to be a strong candidate for at least some maternal effects, and we are back to the early ideas of Jack Christian and Hans Selye of the 1940s, but with greatly improved techniques of measurement of stress in field populations.

Dennis Chitty was a stickler for field experiments in ecology, a trend now long established, and he made many predictions from his ideas, often rejected later but always leading to more insights of what might be happening in field populations. He was a champion of discussing mechanisms of population change, and found little use for the dominant paradigm of the density dependent regulation of populations. Was he successful? I think so, from my biased viewpoint. I note he had less recognition in his lifetime than he deserved because he offended the powers that be. For example, he was never elected to the Royal Society, a victim of the insularity and politics of British science. But that is another story.

Chitty, D. (1952) Mortality among voles ( Microtus agrestis ) at Lake Vyrnwy, Montgomeryshire in 1936-9. Philosophical Transactions of the Royal Society of London, 236, 505-552.

Chitty, D. (1960) Population processes in the vole and their relevance to general theory. Canadian Journal of Zoology, 38, 99-113.

In Defence of Hypothesis Testing in Ecology

In two recent scientific meetings I have attended (which must remain nameless to protect the innocent), I have found myself wondering about the state of hypothesis testing in ecological science. I have always assumed that science consists of testing hypotheses, yet I would estimate roughly that 75% of the talks I have been able to attend showed no sign of any hypothesis. I need to qualify that. Some of these studies are completely descriptive – what species of ferns occur in national park X? Much effort now is devoted to sequencing genomes, the ultimate in descriptive biology. This kind of research work can be classified as alpha-biology, basic description which is necessary before any problems can be formulated. In my particular specialty of population cycles in mammals, much descriptive work had to be carried out to recognize the phenomenon of “cycles”. But then the question arises – at what point should we stop simple descriptions of mammal populations rising and falling? Do we need to study the dynamics of every rodent species that exists? Or in genetics, is our objective to sequence the genome of every species on earth? My point is that after we have enough basic description, we should move into hypothesis testing, or asking why some phenomenon occurs, the mechanisms behind the simple observations. The important point here is that we should not have a single hypothesis or explanation for any set of observations but rather several alternative hypotheses. As a simple example, if we find our favourite plant species is declining in abundance, we should not simply try to connect this decline with climatic warming without having a series of alternative explanations with the emphasis that our observations or experiments should be capable of distinguishing among the alternative hypotheses.

The alternative argument is that we do not know enough about ecological systems to set up a series of credible alternative hypotheses. It is quite possible to go on describing events endlessly in science in the hope that some wisdom will emerge. I do not think this is a profitable use of time or money in science. In ecology in particular I would argue that there is not a single question one can ask that cannot be answered by at least 2 or 3 different mechanistic hypotheses. Our job is to articulate these alternatives and to do whatever studies or experiments are needed to distinguish among them. Of course it is always possible that the correct answer is not among the 2 or 3 hypotheses we suggest at the start of an investigation, and this is often why one study leads to a further one. Consequently we cannot accept statements like “I have no idea why this observation has occurred”. Such a statement means you have not thought deeply enough about what you are studying. Ecological surprises certainly occur while we study any particular community or ecosystem, but we know enough now to suggest several possible mechanisms by which any ecological surprise might be generated.

So I think it incumbent on every ecologist to ask (1) what is the problem or question my research is addressing? And (2) what probable mechanisms can be invoked as the cause of this problem or the answer to this question. Vagueness may be a virtue in politics but it is not a virtue in science. And I look forward to future conferences in which every paper specifies a precise hypothesis and alternative hypotheses. Chamberlin (1897) stated the case for multiple hypotheses, Karl Popper (1963) asked very specifically what your hypothesis forbids from happening, and John Platt (1964) pulled it together in a critical paper. There was important work done before the Iphone was invented. Good reading.

Chamberlin, T. C. 1897. The method of multiple working hypotheses. Journal of Geology 5 :837-848 (reprinted in Science 148: 754-759 in 1965).

Platt, J. R. 1964. Strong inference. Science 146 :347-353.

Popper, K. R. 1963. Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge and Kegan Paul, London.

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Mechanistic microclimate models and plant pest risk modelling

• Open access
• Published: 10 May 2024

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• Jonathan R. Mosedale   ORCID: orcid.org/0000-0001-9008-5439 1 ,
• Dominic Eyre 2 ,
• Anastasia Korycinska 2 ,
• Matthew Everatt 2 ,
• Sam Grant 2 ,
• Brittany Trew   ORCID: orcid.org/0000-0002-0649-828X 1 ,
• Neil Kaye 3 ,
• Deborah Hemming   ORCID: orcid.org/0000-0001-5245-0546 3 , 4 &
• Ilya M. D. Maclean   ORCID: orcid.org/0000-0001-8030-9136 1  

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Climatic conditions are key determining factors of whether plant pests flourish. Models of pest response to temperature are integral to pest risk assessment and management, helping to inform surveillance and control measures. The widespread use of meteorological data as predictors in these models compromises their reliability as these measurements are not thermally coupled to the conditions experienced by pest organisms or their body temperatures. Here, we present how mechanistic microclimate models can be used to estimate the conditions experienced by pest organisms to provide significant benefits to pest risk modelling. These well-established physical models capture how landscape, vegetation and climate interact to determine the conditions to which pests are exposed. Assessments of pest risk derived from microclimate conditions are likely to significantly diverge from those derived from weather station measurements. The magnitude of this divergence will vary across a landscape, over time and according to pest habitats and behaviour due to the complex mechanisms that determine microclimate conditions and their effect on pest biology. Whereas the application of microclimate models was once restricted to relatively homogeneous habitats, these models can now be applied readily to generate hourly time series across extensive and varied landscapes. We outline the benefits and challenges of more routine application of microclimate models to pest risk modelling. Mechanistic microclimate models provide a heuristic tool that helps discriminate between physical, mathematical and biological causes of model failure. Their use can also help understand how pest ecology, behaviour and physiology mediate the relationship between climate and pest response.

Avoid common mistakes on your manuscript.

Key messages

The use of meteorological data is a common source of errors in pest risk modelling.

We propose the more frequent use of mechanistic microclimate models.

These models estimate the actual conditions to which pest organisms are exposed.

They help us understand pest responses to climate and identify causes of pest model failure.

Routine integration of microclimate modelling into pest risk modelling systems is now feasible

Introduction

Plant pests reduce agricultural productivity and food security with annual losses reaching c.18% of global crop production (Oerke 2006 ; Savary et al. 2019 ; Fones et al. 2020 ). Significant economic costs are also imposed on the forestry sector where pest outbreaks reduce ecosystem function, resilience and biodiversity (Godfray et al. 2010 ; Bradshaw et al. 2016 ). The routine application of pesticides and other measures to control pests incurs additional economic, environmental and human health costs (Pretty et al. 2000 ; Pimentel and Burgess 2014 ; Bourguet and Guillemaud 2016 ). These impacts of plant pests are forecast to change as the distributions of many pest species shift poleward, and invasive species more readily establish, under warmer climatic conditions (Bebber et al. 2013 , 2014 ).

Quantitative pest risk models form a key component of pest management; the ability to forecast and map the risk of pest outbreaks is essential for the deployment of effective monitoring and control methods (Rock and Shaffer 1983 ; Tonnang et al. 2017 ; Coop et al. 2020 ; Hemming and Macneill 2020 ). Various indicators of pest risk can be modelled, informed by pest biology and the mitigation options available. Quantitative measures of pest risk may be derived from models of pest survival, growth or development. For an insect pest, model outputs may be a predicted date of completing a key developmental stage, or indicators of population growth such as voltinism or the likely exposure to causes of pest mortality. For a fungal pest or pathogen growth, model outputs may describe risk in terms of the favourability of conditions for pest growth, reproduction or dispersal. Models may express the response of a ‘typical’ or ‘average’ individual, a set of individuals or that of a pest population.

Climate is fundamental for determining the conditions in which ectothermic pest species flourish, imposing limits on their distribution and governing the biological processes that determine individual fitness and population dynamics (Uvarov 1931 ; Taylor 1981 ; Gillooly et al. 2001 ; Damos and Savopoulou-Soultani 2011 ; Clarke 2017 ). Climate-related variables are therefore key predictors in nearly all pest risk modelling. Long-term pest risk projections are commonly based on current or future climate suitability to endemic or invasive pests (Meyerson and Reaser 2002 ; Venette et al. 2010 ; Magarey et al. 2011 ; Hlásny et al. 2021 ), whereas short-term projections, that improve the efficacy and lower the economic and environmental costs of pest control measures, are typically driven by seasonal weather and short-range forecasts (Brunner et al. 1982 ; Damos and Savopoulou-Soultani 2010 ; Crimmins et al. 2020 ; Barker et al. 2020 ). Models used to inform the management of insect pests are often driven solely by temperature (Taylor 1981 ; Gillooly et al. 2001 ; Damos and Savopoulou-Soultani 2011 ), while models of slug pests (Choi et al. 2004 ; Wilson et al. 2015 ) or of fungal pathogens and oomycetes, are commonly expressed as functions of temperature and moisture (Jacome et al. 1991 ; Huber and Gillespie 1992 ; Magarey and Sutton 2007 ).

Although statistical models have been employed in pest risk analyses, for example to extrapolate from existing distributions of pest species to predict their potential establishment and range in new regions or under different climate scenarios (Lantschner et al. 2019 ), mechanistic or semi-mechanistic approaches are generally preferred (Sutherst and Maywald 1985 ; Savary et al. 2019 ) due to the acknowledged limitations of purely statistical approaches (Sutherst 2014 ; Lantschner et al. 2019 ; Srivastava et al. 2021 ). The use of mechanistic models facilitates a ‘biophysical’ approach to pest risk modelling which has particular benefit when forecasting how pests respond to novel conditions, whether as a result of climate change, pest or host migration. Under such conditions, the relationships between variables described by purely statistical models are less likely to endure than those derived from explicit biophysical processes and interactions.

A key aspect of a biophysical approach is an ability to reliably measure or estimate the abiotic conditions under which organisms live. Unfortunately, the importance of climate-derived variables in pest risk modelling does not denote the suitability of standard meteorological data as model inputs. Indeed, the use of meteorological data has been identified as one of the most common causes of failure in pest forecasting (Magarey and Isard 2017 ) whether in the form of data from individual weather stations (Trnka et al. 2007 ; Elliott et al. 2011 ; Jacquemin et al. 2014 ; Babu et al. 2014 ), in-field weather loggers (Chuang et al. 2014 ) or as gridded climate datasets derived from interpolated meteorological station data (Jarvis and Baker 2001 ; Hijmans et al. 2005 ; Magarey et al. 2011 ; Kriticos et al. 2012 ; Fick and Hijmans 2017 ; Lantschner et al. 2019 ; Crimmins et al. 2020 ; Barker et al. 2020 ).

The problem is that neither the microclimates experienced by pest organisms nor their body temperatures are thermally coupled to standard meteorological data derived from weather stations (Baker 1980 ; Ferro et al. 1979 ; Ruesink 1976 ; Samietz et al. 2007 ; Wellington 1950 ). This divergence between meteorological data and the conditions experienced by organisms is not simply due to a mismatch between the spatial resolution of meteorological data and the size of organisms (sensu Potter et al. 2013 ). Weather stations, measuring ambient air temperature at 2 m above ground, are designed and located to minimise the effects of local topography and vegetation on energy fluxes between the biosphere and atmosphere (WMO 2018 ). Yet it is these same energy fluxes that determine the microclimate conditions experienced by organisms (Daly 2006 ; WMO 2018 ; Bramer et al. 2018 ). Furthermore, the complex interactions mediating the relationship between meteorological and microclimate conditions cannot be reduced to a simple correlative relationship (Monteith and Unsworth 2013 ; Briscoe et al. 2023 ).

In this paper, we argue that the continued use of standard meteorological data in pest risk analyses ignores advances in mechanistic microclimate modelling over the past fifty years (Waggoner and Reifsnyder 1968 ; Goudriaan 1977 ; Deardorff 1978 ; Ogée et al. 2003 ; Kearney and Porter 2017 ; Maclean and Klinges 2021 ). These models are based on well-established physical processes (Richardson 1922 ; Penman 1948 ; Monteith and Szeicz 1962 ) and are today able to model microclimate conditions, at fine temporal and spatial resolutions, across entire landscapes (e.g. Trew et al. 2022 ). We propose their application can help resolve key shortcomings in existing biophysical approaches to pest risk modelling.

High-resolution microclimate and other environmental data forms a key part of new approaches to agricultural and forest management, such as ‘precision agriculture’ (Robert 2002 ). These approaches use data generated by ‘in-field’ sensor technologies and remote sensing to tailor management inputs and activities, including pest control, to conditions within individual fields or plots (Schultze et al. 2021 ). The deployment and maintenance of an extensive network of sensors, however, remains expensive and labour-intensive (Skocir et al. 2021 ), especially outside of highly controlled or homogeneous environments. The integration of mechanistic microclimate models has the potential of lowering the cost and extending the scope of precision agriculture approaches.

In the first part of this paper, we consider how pest thermal responses are measured and represented in pest risk models. In doing so, we revisit long-standing debates on how the type of mathematical model, conditions of fitting and use of meteorological data in model application can all introduce significant bias to model results. Secondly, we consider how microclimate conditions diverge from local weather conditions and the implications for pest risk modelling. Thirdly, we outline the theory of mechanistic microclimate modelling and how recent technological advances are transforming access to and use of these models. In the final part, we outline potential applications, and remaining challenges, of using the outputs of mechanistic microclimate models as inputs to pest risk models. We argue that the integration of microclimate models provides a powerful heuristic tool to improve our understanding of pest ecology and the reliability of pest risk modelling systems.

Although we focus primarily on modelling the effects of temperature on insect pests, our arguments apply to the modelling of other climate-related conditions relevant to pest and pathogen risk, such as incident radiation, humidity, soil moisture and leaf wetness.

Modelling thermal response

Thermal response (or ‘performance’) curves, ‘TPCs’, (Angilletta 2009 ) describe the relationship between temperature and a biological function or process. TPCs are typically derived from empirical observations made at different constant temperatures under controlled conditions (Quinn 2017 ; Shi et al. 2017 ). The concept commonly underlies models of pest life cycles and phenology, voltinism and population dynamics.

When measured over a sufficiently wide range of temperatures, ectothermic organisms typically display a skewed, unimodal response to temperature (Vasseur et al. 2014 ). The most likely cause of this response are the thermodynamics of enzyme reaction rates and protein stability. The response can be described by the fitting of various types of nonlinear statistical models to empirical observations (Kontodimas et al. 2004 ; Damos and Savopoulou-Soultani 2011 ; Rebaudo and Rabhi 2018 ). Pest modelling typically applies TPCs to actual field conditions using rate summation, where the response (such as developmental completion) is measured by accumulating response fractions per unit time (Liu et al. 1995 ). A key assumption of the approach is that the thermal response at any given instant is independent of the wider thermal regime.

Despite the empirical evidence and theory supporting a nonlinear response to temperature as the norm, in practice the majority of invertebrate pest risk models apply a simplified linear response (Quinn 2017 ; Shi et al. 2017 ; Rebaudo and Rabhi 2018 ). In part, this reflects the greater effort and resources required to fit a nonlinear response across a temperature range. The use of linear responses is also justified by assuming organisms are rarely exposed to extreme temperatures (Campbell et al. 1974 ). Among the most common insect pest risk models are those that use units of heat accumulation, such as degree days, to predict developmental rates. In such cases, the x-axis intercept of the thermal response curve defines a lower limit for the calculation of heat accumulation and the reciprocal of the slope defines a thermal constant that designates the amount of heat accumulation required, for example, to complete a developmental stage or life cycle (see Fig.  1 ).

Demonstrating the effects of different temperature data on linear and nonlinear thermal response models. In a, response curves of linear (green) and Brière 2 nonlinear (violet) models of codling moth ( Cydia pomonella ) larval development (Aghdam et al. 2009 ) that are applied to the frequency distributions of three contrasting hypothetical temperature datasets (derived from normal distributions with different means and standard deviations). The range of temperature values where both model responses are approximately linear is also shown. In b–d , we show model responses to each temperature dataset and indicate both the mean response to the temperature distribution (dashed lines) and the response to overall mean temperature of the distribution (dash-dot lines) with colours corresponding to the model type as in ( a ). In ( b ), temperatures exceed the shared zone of linearity in model response and hence for the nonlinear model the mean response differs from the response to mean temperature, whereas the corresponding values for the linear model are the same. In ( c ), temperatures fall below the shared zone of linearity and therefore the mean response differs from the response to mean temperature for both linear and nonlinear models. In ( d ), temperatures vary predominantly within the shared zone of linearity and hence mean response and response to mean temperature are very similar for both response models. The differences between mean response and response to mean temperature of the different models demonstrate how both the choice of model type and the use of aggregated temperature data can introduce bias in the predictions of thermal pest response. Note response and temperature scales are identical for figures ( b – d ) although minimum and maximum values vary

In principle, the type of model used to describe the thermal response, and the conditions under which it is fitted, should determine the temperature data used for model application. In practice, however, this logic is often inverted. Readily available weather station measurements, in the form of daily averages or degree days, have been widely used as inputs to pest response models (Babu et al. 2014 ; Magarey and Isard 2017 ; Crimmins et al. 2020 ; Barker et al. 2020 ). By using such data, the likelihood increases of values falling within the range of linear response (Fig.  1 ) and can suggest the suitability of a simplified, linear model of thermal response. However, the approach also introduces several well-documented sources of error that can affect the reliability of not only linear, but also nonlinear, response models.

Firstly, the statistical method used to calculate degree days (or daily averages) and the extrapolation of a linear response to higher temperatures, or use of subjective cut-offs, can all affect model predictions, as has been acknowledged and discussed for several decades (Allen 1976 ; Higley et al. 1986 ; McMaster and Wilhelm 1997 ; Bonhomme 2000 ).

Of greater significance is the implicit presumption when using spatially or temporally aggregated temperature data that pest response to mean temperature is the same as the mean pest response to fluctuating temperatures. It is a presumption that does not prevail when the underlying response is nonlinear and temperatures fluctuate significantly—irrespective of whether the TPC is simplified to a linear response or not. The implications of this mathematical property of nonlinear functions, referred to as Jensen’s inequality (Jensen 1906 ; Ruel and Ayres 1999 ), have been widely discussed in pest modelling literature by reference to rate summation (Tanigoshi et al. 1976 ) and the Kaufmann effect (Bryant et al. 1999 ; Ikemoto and Egami 2013 ). The key implication is that changes to temperature variance can be as important as changes to mean temperature in determining a biological response (Ruel and Ayres 1999 ; Vasseur et al. 2014 ; Bütikofer et al. 2020 ). Referring to Fig.  1 , temperature fluctuation around T low will result in a higher accumulated response than that predicted from the mean temperature, while variation around the optimal temperature will result in a lower response. The magnitude of the divergence will depend upon the degree of nonlinearity and skewness of the response curve, as well as temperature mean and variation.

Finally, and most importantly, when models are fitted to observations made under controlled and constant conditions, ambient air temperature corresponds to pest microclimate and body temperature. No such correspondence exists when applied to field conditions where the use of meteorological temperatures, measured within standardised environments (e.g. Stevenson screen at 2 m above ground level), exclude many of the factors, such as direct sunlight and wind, that affect pest microclimates and their body temperatures.

In summary, the application of models using rate summation from TPCs requires not only addressing the nonlinearity of thermal responses and how they are studied, but also the spatiotemporal variation of temperature to which organisms are exposed in the field (Maiorano et al. 2012 ; von Schmalensee et al. 2021 ). Recent studies (von Schmalensee et al. 2021 ) have demonstrated how the use of nonlinear TPCs driven by microclimate measurements can provide much more accurate predictions of pest biology than using linear degree day models or nonlinear TPCs driven by weather station data. These same studies suggest reliability is more affected by microclimate divergence than the parameterisation of TPCs under constant conditions. The divergence of pest microclimates from meteorological conditions is therefore a key element and challenge to the reliable application of pest modelling.

The importance of pest microclimates

The concept of microclimate does not simply refer to the spatial or temporal resolution of climate observations but incorporates the effects of topography and vegetation on energy fluxes and hence on local air and surface conditions (see Table  1 ). These effects vary ‘horizontally’ across a landscape and ‘vertically’ relative to a canopy, trunk, leaf or ground surface. A range of empirical and theoretical studies have shown how differences in microclimate conditions can affect the growth, development, mortality and diversity of pest organisms (Baker 1980 ; Pincebourde and Woods 2012 ; Saudreau et al. 2013 ; Faye et al. 2017 ; Pincebourde and Casas 2019 ).

The divergence between microclimate and meteorological conditions varies according to the type of microhabitat, prevailing weather conditions, the spatiotemporal scale and the type of metric. Locations near the top of vegetative canopies typically experience greater extremes than ambient air temperature during the day. Greater heat accumulation at the top of the canopy occurs because radiative heating of leaf surfaces generally exceeds cooling except under conditions of low sunlight. Locations lower within the canopy are buffered from air temperature extremes experienced above the canopy (De Frenne et al. 2019 , 2021 ; Haesen et al. 2021 ). This relationship between microclimate and ambient air temperatures varies under different conditions. Summer temperatures beneath a dense canopy are generally lower than above-canopy air temperatures due to canopy shading, but reduced air movement within the canopy can cause warmer relative temperatures under certain wind conditions.

The divergence between meteorological data and microclimate conditions varies not only relative to a canopy or ground surface, but also spatially across a landscape. Many of these landscape effects are often termed ‘mesoclimatic’ (Maclean et al. 2019 ) and include the effects of elevation, topographical wind sheltering, cold-air drainage and coastal exposure. At high spatial resolutions, the effects of slope and aspect on the exposure to direct solar radiation can significantly affect surface and beneath-canopy temperatures. Regions characterised by more variable topography will often result in more variable microclimate conditions. In alpine environments, for example, diurnal and seasonal averages may diverge from nearby weather station data by up to 20 °C (Scherrer and Körner 2011 ).

Microclimate conditions therefore not only play a role in determining current geographical ranges, but also mediate biotic responses to climate change and thereby the propensity of current ranges to enlarge or diminish under future conditions (Maclean and Early 2023 ). The importance of microclimate conditions may be most significant at the margins of a pest range where it is more subject to climate-related limiting factors. Microclimates can permit species to persist under otherwise unfavourable macroclimatic conditions, or conversely, cause local extinctions despite a favourable climate (Suggitt et al. 2018 ; Ma and Ma 2022 ). Depending on the type of habitat and weather, a microclimate may reduce or increase exposure to the extreme temperatures that can be a direct cause of pest mortality (Alford et al. 2018 ; Pincebourde and Casas 2019 ). Winter minima temperatures, for example, are often associated with cold-air pooling and temperature inversions (Cooke and Roland 2003 ) that are influenced by fine-scale topographical features and rarely captured by standard weather station measurements (Schultze et al. 2021 ). Conversely, under dense canopy, minimum temperatures are generally higher than in open areas due to the effect of vegetation (De Frenne et al. 2021 ).

The importance of microclimate conditions to the reliable application of pest risk and crop growth models is well-established (Cellier et al. 1993 ; Bonhomme 2000 ) but not often captured. Where pest risk modelling has explicitly addressed the importance of microclimate, it has often done so by estimating local mesoclimate or microclimates from meteorological measurements by statistical downscaling methods using covariates of those properties, such as elevation or aspect, thought to affect local conditions (Royer et al. 1989 ; Marques da Silva et al. 2015 ; Rebaudo et al. 2016 ; Zellweger et al. 2019 ; Ogris et al. 2019 ). These estimated local temperatures are then used to drive thermal response-type models parameterised under constant, controlled conditions. An alternative approach uses models parameterised from observed pest distributions or behaviour in the field and relates these directly to local meteorological measurements.

In certain cases, different suites of models develop according to whether they are derived primarily from controlled experiments or from field observations. For the bark beetle, Ips typographus , one set of models uses estimates of bark temperature to drive nonlinear response models parameterised under controlled conditions (Baier et al. 2007 ; Ogris et al. 2019 ). Another set of models uses meteorological data to drive a degree day response parameterised from field observations, to map pest ranges under current and future conditions (Jönsson et al. 2007 , 2011 ; Bentz et al. 2019 ).

Although the second approach does avoid discrepancy between the temperature data used in model parameterisation and model application, it does not address the issue of microclimates.

Whereas the first approach explicitly downscales meteorological data to local conditions, the second approach subsumes the processes relating weather to microclimate into the model of pest response to meteorological conditions. Both approaches suffer the same limitation; namely that the relationships they describe are valid within highly prescribed spatiotemporal limits. These limits are imposed by the many and complex physical processes that relate macroclimate to microclimate conditions. These processes vary in relative importance according to topographical, vegetative and weather conditions. Models depend on local parameterisation (where this is possible) and are unreliable when applied to novel combinations of geography and climate.

The adoption of a biophysical approach, that includes direct measurement and/or mechanistic modelling of the abiotic conditions to which pest organisms are exposed, offers a way to improve the reliability of pest risk modelling. Such an approach can explicitly address (i) the physical mechanisms determining the relationship between meteorological conditions and those experienced by pest organisms, (ii) how these processes vary spatially and temporally across the landscape, and (iii) how microclimate conditions relate to the body temperature of pest organisms. The development of more performative and accessible mechanistic microclimate models over recent years, offers the potential of their routine incorporation in integrated pest risk modelling and mapping.

Mechanistic microclimate modelling

Physical theory and model methods.

The mechanisms that determine microclimate conditions reflect well-understood physical processes (Bramer et al. 2018 ) expressed by equations for the exchange of energy and mass between different components of the environment and with organisms within that environment (Campbell and Norman 1998 ; Briscoe et al. 2023 ).

The mechanistic modelling of above-canopy microclimates has its origin in the work on weather forecasting by Richardson ( 1922 ), who demonstrated the basic laws of turbulent mixing in the surface layer of the atmosphere. Theory and methods were subsequently developed in the agricultural and forest sciences and have been well-established since the 1950s (Penman 1948 ; Monin and Obukhov 1954 ; MacHattie and McCormack 1961 ; Monteith and Szeicz 1962 ; Allen et al. 1976 ; Goudriaan 1977 ). Local conditions relative to a ground or vegetative surface are modelled using the physical processes that govern energy exchange (sensible, latent and radiative heat fluxes). By including a term for the rate of heat storage (by the ground or by vegetation), the energy fluxes can be assumed to reach steady state allowing equations to be rearranged to solve for temperature iteratively or using for example the Penman–Monteith equation (e.g. Jarvis & Stewart 1979 ).

The effects of terrain, vegetation and soil properties are incorporated by calculating their effects on components of the energy budget. The incorporation of mesoclimate processes, such as lapse rates, coastal effects and wind sheltering effects of terrain, must be informed by whether the weather data used as model inputs have partly or fully accounted for such effects. Local field measurements may capture many effects while the gridded products interpolated from weather stations often include correction for elevation, coastal and urban heat effects (Hollis et al. 2019 ) to the spatial resolution of the final product.

Beneath a canopy, vegetative properties and canopy structure affect radiation transmission, vapour exchange and wind profiles, which in turn determine heat exchange, air and surface temperatures. Modelling microclimates beneath canopies, and within the soil, is typically undertaken by estimating heat and vapour fluxes between a series of homogeneous horizontal layers (Maclean and Klinges 2021 ). Establishing temperature profiles requires a theory that quantifies how heat is exchanged between these levels in the canopy. Doing so using the same principles of heat transfer that are used in above-canopy models would imply that vertical heat exchange occurs from warm to cool parts of the canopy in a predictable way determined by average wind speed (i.e. a gradient diffusion process). However, accumulated evidence (reviewed by Finnigan & Raupach 1987 , and Raupach 1988 ) shows that this is not a valid assumption as frequent and pronounced counter gradient fluxes can be observed (Denmead and Bradley 1985 , 1987 ). An alternative theory for modelling microclimate within canopies has been developed by adopting a Lagrangian (fluid-following) viewpoint (e.g. Raupach 1989 ). Here, the dispersion pattern of heat is assumed to be determined by the motion of the air that exchanges heat from multiple leaves within the canopy. Of dominant importance is the effect of the persistence of the turbulence relative to the travel time of the heat. At any given point in the canopy, heat arrives from multiple sources with varying travel times, but by assuming the canopy to be horizontally homogeneous, it is possible to find a computationally tractable solution to derive the temperature profile. These principles form the basis of several more recent below-canopy microclimate models (e.g. Bonan et al. 2021 ; Ogée et al. 2003 ) meaning that it is possible to estimate the microclimate temperature of almost any canopy environment.

Mechanistic microclimate models are typically used to return time series at specified heights or depths relative to the ground and/or canopy surface. Time increments can vary from seconds to several days, and outputs are usually for single locations or gridded points across a landscape. The outputs variables will typically include not only air or surface temperatures but also humidity, wind speeds and solar radiation which in turn allow integration with the biophysical modelling of pest organism body temperatures if required.

Model inputs and interfaces

Awareness of mechanistic microclimate models has permeated slowly across disciplines within the biological sciences. Early use was largely limited to the study of relatively homogeneous environments, such as crop fields, for which vegetation, topographical and other parameters could be measured in situ and/or constant values applied. Only in recent years have we begun to realise many potential applications of these models (Briscoe et al. 2023 ) .

Microclimate models are typically run at high temporal and spatial resolutions, making model execution computationally intensive. The array of data inputs (see Fig.  2 ) and significant processing resources required for microclimate modelling presented major barriers to the application of these models only a decade ago (Sutherst 2014 ). Today, many of the same technologies that are transforming agricultural practices—including numerical weather forecasting, geographical positioning systems, in-field sensors, remote sensing and unmanned aerial vehicles—also facilitate the routine and extensive use of microclimate models.

Key to microclimate modelling is the climate forcing data used to drive models. Model re-analyses data, such as ECMWF Re-Analysis 5 (Hersbach et al. 2020 ) and NCEP re-analysis dataset (Saha et al. 2014 ), provide a physically consistent, hourly description of global atmospheric characteristics, including cloud cover and incoming radiation. These datasets assimilate empirical weather station observations with short-range, weather forecasting models to generate low spatial, but high temporal, resolution climate data. Remote sensing products provide high spatial resolution topographical and land surface data, including properties such as albedo (Bonafoni and Sekertekin 2020 ).

An additional requirement of many microclimate models are descriptors of vegetation structure, such as plant area index and canopy height, to permit the quantification of microclimate processes such as radiation transfer through a canopy. For some applications, constant values may suffice, or dynamic canopy or crop development models can be applied to simulate seasonal changes in vegetative properties. Such models may be driven by the same climate inputs already described.

In other cases, the direct or remote measurement of actual canopy characteristics may be required, either as direct inputs into microclimate models or as drivers of crop growth or canopy development models (Toda and Richardson 2018 ; Kasampalis et al. 2018 ). Novel data products derived from sensors such as light detection and ranging (LIDAR), digital aerial photogrammetry, thermal radiometry and synthetic aperture radar (Jensen et al. 2021 ; Steele-Dunne et al. 2017 ), including those mounted on the International Space Station (Fisher et al. 2020 ; Dubayah et al. 2020 ), demonstrate the ability to quantify the vertical structure and properties of plant canopies across a landscape. The availability of global datasets of climate, land surface and increasingly canopy structure, combined with faster processing and programmatic access, today allows the near real-time modelling of microclimates across landscapes at high spatial and temporal resolution (Zellweger et al. 2019 ; Kearney et al. 2020 ; Duffy et al. 2021 ; Klinges et al. 2022 ).

Programmatic access to the inputs of microclimate models (Kemp et al. 2012 ; Kearney et al. 2020 ; Klinges et al. 2022 ) also facilitates the development of on-line portals that generate microclimate conditions (e.g. http://bioforecasts.science.unimelb.edu.au/app_direct/soil/ ), improving access to microclimate modelling for non-specialist users. Existing portals generate time series for single point locations, but could be extended to provide more extensive microclimate modelling for specific crop or forest systems. Faster, high-level programming languages (Bezanson et al. 2017 ), grid-modelling frameworks, access to cloud computing services (Yang et al. 2017 ) and statistical emulations of mechanistic microclimate models using Gaussian processes (Baker et al. 2022 ; Gómez-Dans et al. 2016 ) all offer the opportunity for more rapid modelling over larger geographical and temporal extents.

Recent years have also seen the publication of freely available regional, continental and global datasets of microclimate measurements. These include hourly gridded temperature estimates of above and below ground microclimates under different shade conditions or substrate types (Kearney et al. 2014 ; Kearney 2019 ) and a compilation of soil and near-surface temperature measurements from across the globe (Lembrechts et al. 2020 ).

The range of potential data inputs, algorithmic methods and precomputed microclimate datasets is such that guidance concerning their suitability for different end-uses is now required (Meyer et al. 2023 ). Technological advances have also greatly facilitated the validation of microclimate model outputs, whether by direct measurement of remotely sensed surface temperature (Marques da Silva et al. 2015 ; Zellweger et al. 2019 ) or the availability of reliable and cheap temperature and humidity micro-loggers (Lembrechts et al. 2020 ; Maclean et al. 2021 ).

Applying microclimate models to pest risk assessment and management

The greatest benefit of mechanistic microclimate models will be realised when they form part of a broader biophysical approach to pest risk modelling. Potential applications of the approach include the short-term forecasting of pest risk, typically to inform the timing and targeting of pest monitoring or treatments, as well as longer-term risk mapping of potential shifts in geographical range of invasive or migratory species and/or as a response to climate change (Kearney and Porter 2009 ). Microclimate conditions under future climate scenarios (Maclean 2020 ; Briscoe et al. 2023 ) can be modelled using probabilistic estimates of future projected climate from climate/earth system models (Smith et al. 2017 ) or by using the outputs of regional climate models. The outputs can be used to estimate pest risk under future climate scenarios in an analogous approach to that used with crop growth models to forecast the suitability of novel crops (Gardner et al. 2021 ).

There remain, however, many challenges to realising the potential of mechanistic microclimate models as a component of pest risk modelling and analysis. The integration of microclimate models in pest risk modelling requires a sufficient understanding of pest ecology to provide information on the microhabitats occupied by the organism at key stages in its life cycle. Existing microclimate models may need to be extended to capture the conditions of specific habitats. For example, modelling the conditions experienced by wood-boring beetles, a group of pests of significant ecological and economic concern, would need to capture heat fluxes within the host tree trunk. Field observations of whether pests display a preference between available microhabitats, such as height within a canopy, or the sunny or shady sides of a tree trunk (Gent et al. 2017 ), can also inform the choice of model inputs and parameters.

In general, the body temperature of small ectotherms with a high surface area to volume ratio remains closely coupled to surrounding air and/or surface temperatures, particularly where wind speeds are non-negligible. Pest physiology and behavioural responses will nonetheless mediate how microclimate conditions affect their body temperatures. Where necessary, heat fluxes between an organism and its surface and air microclimates can be integrated into microclimate models to capture, for example, convective and radiative heat transfers (Pincebourde and Woods 2020 ; Kearney et al. 2021 ).

Microclimate modelling also renders explicit the selection of biologically relevant spatial and temporal scales used to inform risk modelling. This choice of scale must be informed by the type of pest response and microhabitat that are modelled. Whereas models of insect development or phenology may be driven by daily data, measures of pest exposure to extreme temperatures may require modelling at higher temporal resolutions. Similarly, a lower temporal resolution may prove adequate for modelling buffered, soil microclimates, than for fluctuating microclimates like leaf surfaces. The choice of spatial resolution when modelling microclimates across a landscape will also need to consider variation in the processes and underlying properties determinant of microclimate conditions. A higher spatial resolution may be required to capture the effects of mountainous or hilly terrain.

The integration of mechanistic microclimate modelling must reflect the end-use of pest risk modelling, and tolerance to different kinds of error. If mapping a measure of pest risk across a landscape grid, then it may be appropriate to model the most and least favourable microclimates within individual grid cells. For example, where a pest can occupy habitats throughout a canopy, modelling microclimates at the top and bottom of the canopy can provide a plausible range of risk. Appropriate weighting might be applied to reflect the propensity of different habitats available to a pest or its typical distribution within a canopy. In cases where pest risk mapping does not need to reflect actual landcover, the adoption of canopy parameters typical of a habitat may be appropriate. Conversely, in cases where it is important to reflect actual landcover characteristics, remote sensing observations or in-field sensors can provide estimates of actual vegetative properties.

In all cases, the computational demands of microclimate modelling will also need to be weighed against potential benefits. Even with more efficient algorithms and exploitation of cloud computing, the mapping of multiple microclimates across extensive spatiotemporal extents requires significant resources in terms of time and computational resource.

Microclimate modelling as a heuristic tool

Progress in the development of more reliable pest risk models has been hampered by a dearth of published information about the occurrences and causes of model failure (Magarey and Isard 2017 ), augmented by the use of model systems which conflate different sources of error. This includes the use of meteorological data as a descriptor of pest abiotic conditions. As a result, it has proved difficult to resolve important questions about the constancy of thermal response models derived from controlled environment studies, or the relative importance of pest adaptation or acclimatisation to abiotic conditions (von Schmalensee et al. 2021 ).

We believe the use of mechanistic microclimate models offers a valuable heuristic tool to help discriminate between potential causes of model failure. The validity of thermal response curves continues to be debated when used to predict pest response under fluctuating field conditions (Niehaus et al. 2012 ; Colinet et al. 2015 ; Khelifa et al. 2019 ; Ma et al. 2021 ). It is known that the thermal response of ectotherms can vary between different organisms and populations of the same species, even if the extent of such variation remains uncertain (Chuine and Régnière 2017 ). The speed and duration of temperature fluctuations can affect insect response to high or low temperatures through thermal stress accumulation, temperature-induced or time-dependent acclimation (Sinclair et al. 2016 ; Sears et al. 2019 ). Emerald ash borer pre-pupae, for example, have greater heat tolerance when shifted slowly to high temperatures (Sobek et al. 2011 ). Under certain conditions, some invertebrates may even display semi-permanent endothermy, maintaining higher body temperature than their environment (Dinets 2022 ).

The use of meteorological data as a measure of the conditions to which pests are exposed conflates potential biological explanations of why a model may fail to describe responses in the field, with physical processes mediating effects on microclimate and with mathematical effects such as Jensen’s inequality. Mechanistic microclimate models provide a means to disentangle these different effects and assess different biological explanations of the discrepancies between observed and predicted pest response. Their use complements carefully designed empirical studies and re-analyses of published data which suggest thermal response models are reliable subject to consideration of nonlinearity and spatiotemporal heterogeneity in microclimate conditions (von Schmalensee et al. 2021 ).

A biophysical approach also permits better understanding of how behavioural and physiological adaptations mediate pest response to climate, whether by selecting from available microclimates, modifying their microclimate conditions or altering the effect of their microclimate on body temperatures (Table  2 ). In such cases, the thermal response model remains valid if driven by suitable data. With an adequate knowledge of pest biology, we can adapt mechanistic microclimate models to capture many of these effects.

For example, if it is known that a pest can actively move between different available microclimates, we might assume it will select the microclimate where conditions are closest to their optimal response. Modelling microclimate conditions at contrasting locations within a host plant canopy, at an appropriate temporal resolution that reflects the mobility of the organism and microclimate variability, can provide a range of available microclimate conditions from which the most optimal conditions could be selected. Adaptive responses are likely to reduce exposure to temperatures lying outside the zone of linear response (see Fig.  1 ), but it cannot be inferred that the modelling of microclimate conditions is less important as this also depends on the coupling between microclimate and meteorological conditions.

Modifications of pest microclimates through feeding and/or the creation of specialist structures such as leaf mines may require the integration of additional models (Pincebourde and Casas 2006 ). Integrated biophysical models offer a means of capturing how plant parameters are changed by pest feeding activity (Pincebourde and Casas 2019 ) and their resultant impact on microclimate conditions.

The use of microclimate modelling with dynamic plant canopy and pest developmental models also offers the possibility of better understanding how the correspondence between plant and pest phenology varies, an important aspect of pest risk, or of informing control methods based on the management of canopy structure (Barradas and Fanjul 1986 ; Huber and Gillespie 1992 ; Pangga et al. 2011 ; Caffarra et al. 2012 ; Saudreau et al. 2013 ). A better understanding of the spatiotemporal distribution of abiotic conditions to which pests are exposed can help improve our understanding of the limiting factors that act upon pest populations. Where available microclimate conditions approach optimal conditions, biological limiting factors may exert greater influence (Łaszczyca et al. 2021 ).

The integration of microclimate, plant canopy and pest body temperature models has been undertaken for over a decade (Saudreau et al. 2013 ) and such biophysical model systems have contributed to our understanding of pest biology and control. However, until recently it has been impractical to apply such an approach to risk modelling across entire landscapes and multi-year timeframes. We believe the new generation of microclimate models removes many of these barriers to their use and can make a significant contribution towards more reliable pest risk modelling.

Recent advances in remote sensing and computing resources have greatly expanded the capacity of microclimate modelling to estimate conditions across wide spatiotemporal extents at high resolutions. Although there remain challenges to the modelling of processes determining canopy microclimates, the fundamental principles of mechanistic microclimate modelling have been resolved for several decades.

Mechanistic microclimate models bring significant benefits to pest risk modelling where the widespread use of meteorological data as model inputs conflates various physical, experimental, mathematical and biological mechanisms that can undermine the reliability of risk forecasting. By approximating the conditions experienced by pest organisms and their body temperatures, microclimate estimates, (i) more closely correspond to the data used in the development and fitting of thermal response models under controlled environment conditions; (ii) provide a better estimate of pest exposure to extreme temperatures; (iii) capture spatiotemporal variations and interdependences of landscape, vegetative and climate characteristics that determine the conditions to which pests are exposed; and (iv) help discriminate between possible causes of model failure.

To fully exploit the benefits of microclimate modelling, nonlinear pest models that better reflect thermal responses at extreme temperature need to be adopted. Application to pest risk management also requires an adequate understanding of pest habitat and ecology, including how ecological, behavioural and physiological adaptations mediate the relationships between body temperatures and weather conditions. We believe the adoption of mechanistic microclimate modelling is a key element for building a more robust biophysical approach to pest risk management, informed by pest ecology, the physical determinants of microclimates and the practical requirements of end users.

The application of mechanistic microclimate models helps refocus attention on pest ecology, in particular the availability and occupancy of microhabitats that determine the abiotic conditions to which pests are exposed, and those behavioural or physiological adaptations that mediate the relationship with pest body temperature.

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The work was supported by funding from the Met Office Hadley Centre Climate Programme (HCCP) funded by UK government departments BEIS and Defra.

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Jonathan R. Mosedale, Brittany Trew & Ilya M. D. Maclean

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Dominic Eyre, Anastasia Korycinska, Matthew Everatt & Sam Grant

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Neil Kaye & Deborah Hemming

Birmingham Institute of Forest Research (BIFOR), Birmingham University, Birmingham, B15 2TT, UK

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JRM wrote the manuscript, with contributions from IMDM, DE, AK, SB, ME, NK and DH. The article was informed by explorative, integrated plant-pest-microclimate modelling performed by JRM and BT who also provided Fig.  1 . The opinion piece was conceived jointly by IMDM and D.H.

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Correspondence to Jonathan R. Mosedale .

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Mosedale, J.R., Eyre, D., Korycinska, A. et al. Mechanistic microclimate models and plant pest risk modelling. J Pest Sci (2024). https://doi.org/10.1007/s10340-024-01777-y

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Received : 24 July 2023

Revised : 29 January 2024

Accepted : 25 March 2024

Published : 10 May 2024

DOI : https://doi.org/10.1007/s10340-024-01777-y

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