Evolutionary Mismatch and How To Evaluate It: A Basic Tutorial

[Download PDF]

Abstract

Evolutionary mismatch is a state of disequilibrium whereby an organism that evolved in one environment develops a phenotype that is harmful to its fitness or well-being in another environment. Mismatch is an integral part of evolution in changing environments and is becoming increasingly common for all species living in human-altered environments. It is especially important to understand mismatch in relation to our own species since humans have so radically altered their own environment and mismatches can occur for cultural evolution in addition to genetic evolution.  The first part of the article provides a basic tutorial on evolutionary mismatch by clarifying central concepts in mismatch: that of ancestral environment (E1), the novel environment (E2), and the trait under study (T). Our explanation of the concept of ‘evolutionary mismatch’ necessitates not only theoretical but also practical elaboration and exploration of the following list of properties and assumptions in order to confirm the application of a mismatch model in real life cases – identifying the population, the traits, inheritance mechanism, selection pressures and fitness consequences of associated traits in E1 and E2, and lastly integrating proximate and ultimate explanations for the study of the traits in E1 and E2.  The final section presents a list of practical questions for evaluating evolutionary mismatch hypotheses. We expect that even professional evolutionists can benefit from our “back to basics” approach to one of the central concepts of evolutionary theory.

Preface

This article was originally written in 2012 as part of a collaborative project between the National Evolutionary Synthesis Center (NESCent) and the Evolution Institute. The project brought together over 40 scientists to consider the problem of evolutionary mismatch in all its diversity and complexity. It was organized under NESCent’s “working group” rubric, which included three workshops. A subset of the 40 participants attended these workshops, with the full group functioning as a virtual community. The first workshop was held between May 1-4, 2011, the second between Nov 17-20, 2011, and the third between October 12-13, 2012.

The article was first made available as a white paper on the Evolution Institute website. The intention was for it to become the lead article in a special issue of an appropriate journal, but those plans did not materialize. Despite not being published in a peer-reviewed journal, the white paper did gain an audience and has been cited 47 times as of this writing. One reason for its popularity—and a reason to finally publish this article in an academic journal—is because it was written as a ‘back to basics’ tutorial: To provide a clear definition of mismatch; to clarify how to test mismatch models; and to identify what evidence is required for demonstrating an evolutionary mismatch. The utility of this ‘back to basics’ approach is evident in how the paper was later cited. The citing authors used the mismatch concept developed in the white paper in the following fields:    

Economics (Witt, 2014), public policy (Wilson & Gowdy, 2013), governance (Cox & Schoon, 2019), quality of life (Wilson, 2016), urban design (Weaver, 2016), neighborhood decline (Weaver, 2014; Weaver & Bagchi-Sen, 2014), prosociality (Szabo, 2021), privacy (Shariff et al., 2021), biophilia (Ostner, 2021), depression (Giosan, 2020; Malouchou Kanellopoulou, 2021), circadian rhythm (Gasperoni, 2022), education (Araya Schulz, 2021; Eirdosh & Hanisch, 2019), fake news (Deepak, 2021), water-strider ecology (Castillo & De León, 2021) and foraging in an obesogenic environment (de Vries et al., 2022).

The white paper has also generated vibrant philosophical enquiries to further clarify the definition of evolutionary mismatch (Bourrat & Griffiths, 2021; Cofnas, 2016; Morris, 2019, 2020; Sarto-Jackson, 2018) including a highly relevant critique of adaptationist logic (Lloyd, 2015).

Finally, the white paper has fostered early career research in the form of masters and doctoral dissertations in the areas of obesity (Rao, 2019), polycystic ovary syndrome (Pierce, 2021), cultural mismatch (Espericueta, 2021), privacy (Cester, 2021), foraging in an obesogenic environment (de Vries, 2021), and philosophical enquiries into subjectivity (Cuthbertson, 2019). These authors used the white paper to form the central thesis of their research, to provide a definition of mismatch, or to directly engage with the arguments developed in the whitepaper. In addition to the white paper, a video introduction to the NESCent funded project can also be found as a collection of 9 YouTube interviews with workshop participants, which has received a combined 7,923 views.

Given the influence of this collaborative project between NESCent and the Evolution Institute, despite the handicap of not being published in an academic journal, the whitepaper is as timely in 2024 as it would have been in 2012—perhaps even more so, given the mismatches that nearly every species on earth is experiencing during the Anthropocene. We have left the main body of the article in its original form, adding this preface, one table citing (Table 1) the more recent literature, and an additional section called ‘Remaining Questions’ that brings the article up to date.

It was a pleasure to reconnect with the members of the workshop in the process of updating our article, to learn how they have developed the concept of evolutionary mismatch.

Introduction

Evolutionary mismatch is a state of disequilibrium between an organism and its environment. It occurs when a trait evolves in one environment and then the environment changes, with the results that the trait has a detrimental effect in the latter environment that it did not have in the earlier one. (Related concepts are discussed in the literature as “adaptive lags” and “adaptive traps” (Schlaepfer et al., 2002); these are actually subtypes of evolutionary mismatches, as they assume that the past trait was an adaptation. In a sense, mismatch is very common, due to a lag between the adaptation of species to their environments; genetic evolution is not instantaneous.  

In addition, different types of “environment” are pertinent in different cases; sometimes this means an abiotic environment, but sometimes biotic or cultural environments, which can make a big difference to the speed of the adaptation of the organism to these aspects of its environment. In a classic case of mismatch, adaptations that contributed to survival and reproduction in previous environments become relatively maladaptive in a changed environment, a situation that can only be addressed by a behavioral accommodation, subsequent evolution, or another environmental change.  

The concept of mismatch is so central to evolutionary theory that a basic tutorial might seem unnecessary. On the contrary, a “back to basics” treatment is warranted for four reasons. First, human activities are changing the environments of other species at an unprecedented scale (Grimm et al., 2008; Smith & Bernatchez, 2008; Tseng, 2007). Many species are declining in large part because they are coping badly with novel environments, while others are doing so well that we call them pests (Hendry et al., 2011; Sih et al., 2011). Understanding evolutionary mismatch is thus of critical importance for environmental policy and management. A basic tutorial on mismatch is needed for the general public and sectors of the academic and public policy communities that are not already well schooled in evolutionary science.

Second, mismatch is an exceptionally relevant concept for our own species. The modern human environment is radically different from the environments we experienced as hunter-gatherers in small-scale societies only ten to fifteen thousand years ago (Barkow et al., 1992). Some genetic evolution has taken place during this period (Cochran & Harpending, 2009) and cultural change and niche construction are themselves evolutionary processes that adapt us to our environments (Cavalli-Sforza, & Feldman, 1981; Jablonka & Lamb, 2014; Laland & Brown, 2002; Richerson & Boyd, 2008). But cultural evolution and niche construction also take time, so cultural mismatch needs to be considered along with genetic mismatch. Moreover, cultural evolution is not necessarily evolutionarily adaptive (Toyne, 1999).

Third, even professional evolutionists can benefit from a “back to basics” approach to fundamental concepts. Consider the landmark book Natural Selection in the Wild, published by John Endler in 1986. Despite the fact that natural selection is the centerpiece of Darwin’s theory, evolutionists had not developed a clear set of guidelines for operationally defining it and standards of evidence for documenting it in natural populations. Endler’s book performed this “back to basics” service for the concept of natural selection, and this article aims to perform the same service for the concept of mismatch.

Fourth, existing concepts of mismatch—John Bowlby’s (1969) environment of evolutionary adaptedness (EEA), William Irons’ (1998) adaptively relevant environment (ARE) or the concepts of ‘adaptive lags’ and ‘adaptive traps’ (discussed by Crespi, 2000; Schlaepfer et al., 2002) —are limited in their application because they only consider past adaptations that become maladaptive in a novel environment. Our mismatch concept is defined in terms of traits that were neutral or maladaptive in the ancestral environment (e.g., neutral such as byproducts, or “spandrels”, as termed by Gould & Lewontin, 1979 and become maladaptive in a different way in a novel environment).

Providing standards of evidence for cases of mismatch in our own species is especially important because some putative examples have acquired the reputation of “just-so stories” (Coyne, 2010; Gould & Lewontin, 1979). Skeptical discussions often make it appear as if mismatch will forever remain a topic of idle speculation that is beyond the reach of serious scientific inquiry. This assessment is too pessimistic. As Endler and others have shown, hypotheses about adaptation and natural selection can be tested as thoroughly as other scientific hypotheses, and the same is true for hypotheses about mismatch. It is true that information about past environments can be difficult to obtain, but this is a problem for any historical science, and it is surprising how often hypotheses about the past can be strongly supported with enough ingenuity. Especially unhelpful is the suggestion that just because a mismatch hypothesis is difficult to test, it isn’t worth the effort. A substantial proportion of human misery is probably due to genetic and cultural mismatch with our current environments. If mismatch hypotheses are difficult to test, then that’s all the more reason to invest in the effort.

Finally, there is the question of what to do about cases of mismatch after they have been documented (Burnham & Phelan, 2012). An evolutionary mismatch is a particular type of detriment that results from evolution in changing environments. Evolution can result in dysfunctions for many other reasons. Consider atherosclerosis, the leading cause of cardiovascular disease in modern human populations (Lindeberg, 2009). Is it caused by an infectious agent? Is it an inevitable consequence of aging that has become more common because we are living longer? Or is it caused by a mismatch between our evolved dietary adaptations and our current food environment? In the last case, what are the details of the mismatch? Is it an imbalance of macronutrients, such as too much fat and carbohydrates? Or is it a more subtle aspect of the modern dietary environment, such as particular compounds in grains or dairy products that are not digested and trigger an inappropriate response of the immune system? Or does it relate to the cultivation of a different intestinal biome, a difference in our coordinating microbial ecology, in response to our contemporary modern diets? Clearly framing and testing mismatch hypotheses, along with other hypotheses informed by evolution, organizes the search for relevant information and possible solutions in ways that might not have occurred otherwise. As for atherosclerosis, so also for the myriad other maladies afflicting humans and other species in rapidly changing environments.

Defining Evolutionary Mismatch

An evolutionary mismatch can be defined as a negative consequence that results from a trait that evolved in one environment being placed in another environment. The typical example of mismatch was an adaptation in the prior environment, but traits that were originally neutral or even deleterious can potentially acquire new harmful consequences in an altered environment (see Crespi, 2000 on maladaptation). This is why the concept of mismatch is more general than related concepts such as “adaptive lag” and “adaptive trap (Schlaepfer et al., 2002).”

The most straightforward examples of mismatch have a clear temporal component. For example, species of birds on remote islands often evolve to become fearless of mammalian predators (Blondel, 2000). Their fearlessness might have been adaptive, insofar as it allowed them to better conduct other activities. Alternatively, their fearlessness might have been a neutral trait, caused by the accumulation of mutations that are not removed by natural selection, similar to the loss of eyesight that evolves in cave-dwelling species (Romero & Green, 2005). Either way, fearlessness becomes highly disadvantageous when the environment changes with the arrival of mammalian predators. In many cases, the native bird species cannot be trained to be fearful and the only way to prevent their extinction is to restore the earlier environment by removing the introduced mammalian predators, or by genetically selecting for more fearful strains of the native species (Milberg & Tyrberg, 1993; Zavaleta et al., 2001).

There is a spatial form of evolutionary mismatch that is closely related to the temporal form. Consider a species of aquatic insect that has always lived in a heterogeneous environment; sometimes the insects live in ponds that have fish in them (which is disastrous for the insects) while at other times they live in ponds that lack fish (in which case the insects thrive).  Every generation, adults disperse from their natal pond and lay eggs in new ponds. These insects evolved adaptations that allow them to preferentially choose ponds without fish, but the adaptation is far from perfectly reliable. As a result, in every generation, some larvae are deposited in bodies of water with fish and they are decimated. These individuals are as mismatched to their environments as the island birds encountering mammalian predators in the previous example. However, the species as a whole never experiences a change in environment, in that both kinds of pond are present at the same frequencies throughout the process (McPeek, 2008). This may be an example of mismatch at the individual organism level, and may or may not be one at the level of the population or species, since the distribution of insects may represent a risk-constraining strategy. We shall focus on the population level mismatches for the rest of this article, but sometimes it may be important to consider this type of environmental misfit at the organismal or even genetic level.  

Another type of mismatch involves a change in the developmental environment of the organism. In such a case, the organism develops in a novel environment that is outside the range of environments its ancestors encountered over its evolutionary history, and as a result, it develops a phenotype that does not function well later in life. For example, abandoned children in Romanian orphanages experienced highly-regimented collective child-rearing by paid, non-custodial care-givers, amounting to impoverished cognitive and emotional environments. These children developed myriad disorders as a result of exposure to this new developmental environment, including reduced brain activity, lower IQ, and lower attachment, language, and social-emotional development (Nelson et al., 2007; Ellis et al., 2012).

Traits that evolved in one environment need not be detrimental in a second environment; they can be neutral or fortuitously beneficial, but these cases are excluded by the term “mismatch”, which restricts our attention to the detrimental cases.

Evidence Required to Demonstrate a Case of Evolutionary Mismatch

In principle, the evidence required to demonstrate a case of evolutionary mismatch is straightforward. A given trait (T) must be shown to be adaptive, neutral, or deleterious in relation to its ancestral environment (E1), that is, the environment in which it evolved, and relatively detrimental in relation to another environment (E2). “Detrimental” will usually be defined in terms of evolutionary fitness, but it can also refer to welfare in more general terms. For example, consider a genetically influenced human disease that does not manifest itself until late in life. The disease might have a negligible effect on evolutionary fitness, but still be well worth curing to reduce human suffering, and would count for us as an evolutionary mismatch.

Before dealing with problematic cases of mismatch that have earned the reputation of “just-so stories”, it is important to stress that some cases of mismatch can be documented as thoroughly as any scientific fact. As one example, adult aquatic insects that fly in search of bodies of water are frequently attracted to artificial reflective surfaces such as solar panels or buildings with glass surfaces. Their attraction terminates the lineage, and is therefore obviously dysfunctional in terms evolutionary fitness. Why does it occur? A plausible hypothesis is that the insects have evolved to rely on certain cues such as polarized light to find bodies of water and that reflective artificial surfaces provide the same cues (Horváth et al., 2010). Horváth and colleagues tested this hypothesis by measuring the reflection-polarization characteristics of solar panels in relation to water, along with choice experiments by various species of aquatic insects. The authors showed that the solar panels were even more reflective than water and acted as super-stimuli for the insects, which not only preferred the panels but even completed their oviposition behavior.

Armed with this understanding of the proximate mechanism that causes the mismatch, Horváth et al., (2010) were able to solve the problem by adding white non-polarizing borders to the solar panels. This simple and inexpensive modification resulted in a 10- to 26-fold decrease in the attractiveness of the solar panels to the insects. Evidently, their perceptual machinery had evolved to find relatively large bodies of water and was not triggered by reflective surfaces enclosed by small non-reflective boundaries.

To formalize this example, the trait (T) is attraction to polarized reflective surfaces, (E1) are the surfaces of water that were present ancestrally, and to which the (T) is adapted, and the different environment (E2) is man-made polarized reflective surfaces. The adaptedness of (T) in relation to (E1) can be documented as well as any scientific fact and need not remain in the category of “just-so story”. The detrimental consequences of (T) in relation to (E2) are also easy to document. Finally, understanding the nature of the adaptation and the mismatch in mechanistic detail leads to an elegant and simple solution that might never have been discovered otherwise. Other cases of mismatch might be more difficult to document and solve than this case, but the same degree of understanding might be attainable with enough hard work. Crespi, (2000) offered a very nice analysis of the types of evidence that are needed to document a “maladaptation” (which in our definition of mismatch is not necessary, but is one important type of mismatch). This analysis reviews the evidence from developmental mechanisms, genetics and phenotypic selection that are needed to rigorously support the claim that a current trait is negatively correlated with some aspect of the fitness of the organism, a useful list for us to apply.

What makes a putative case of mismatch such as atherosclerosis in humans more difficult to study than the fatal attraction of aquatic insects to reflective surfaces? To begin, the trait (T) is harder to define. If we define the trait as “the disease atherosclerosis”, then it might never have existed in the human ancestral environment. Instead, the disease should be regarded as a detrimental consequence of an interaction between a set of traits (T) that evolved in an earlier environment (E1) and elements of the current environment (E2). The set of traits, the earlier environment, the elements of the current environment, and their interactions all must be determined by scientific research.

The prospects for working out such a complex story might seem daunting, but consider the plight of biomedical scientists trying to understand the causes of atherosclerosis without the help of evolutionary theory. They are faced with the same problem of understanding a complex interaction between a set of traits in the organism and a set of traits in the current environment, sometimes leading to pathological consequences. Is their plight more or less daunting than the plight of the evolutionist?

We think that the problem faced by the evolutionist is more tractable, because evolutionary theory offers an abundance of testable hypotheses that might not occur otherwise. This raises an important point about the “just-so story” accusation that merits discussion in general terms, before returning to the subject of mismatch. “Just-so story” is just another term for “untested hypothesis.” The purpose of any theory is to generate hypotheses, which always start out as untested. If hypotheses motivated by evolutionary theory were somehow less testable than other hypotheses, then they could be regarded as deficient, but there is no warrant for making this claim. Evolutionary theory’s fecundity as a generator of testable hypotheses should therefore be regarded as an asset rather than a liability.

To see how this plays out for a disease such as atherosclerosis, an evolutionist would immediately think of reasons why a maladaptive outcome might exist for a given organism in a given environment. One possibility is that the outcome is caused by an infectious agent, initiating one line of inquiry (Ewald, 2002). Another possibility is that the outcome is a negative byproduct of traits that are beneficial in other contexts, initiating another line of inquiry. A third possibility is that the outcome reflects a mismatch situation, initiating a third line of inquiry. A fourth possibility involves the evolutionary pressure of aging (Medawar, 1952). As we stated earlier, a mismatch is a particular type of detriment that results from evolution in changing environments, and the utility of a mismatch hypothesis is the specific testable predictions that it makes.

In the case of a disease such as atherosclerosis, the mismatch hypothesis immediately directs attention to foods that are prominent in modern diets (E2) but rare or absent from ancestral diets (E1). It encourages cross-cultural research and directs attention toward the few remaining human populations that still subsist on a pre-agricultural diet (E1). It raises the possibility of genetic evolution adapting different human populations to different diets over a range of time scales. It organizes the comparative study of nonhuman species. For example, what is the digestive physiology of grain-eating vs. non-grain-eating rodent species, and what happens when non-grain-eating species are fed grain? It suggests new ideas for human dietary trials that might not have been envisioned otherwise. All of these predictions start out untested, but they are just as testable as predictions that are formulated without the help of the mismatch hypothesis (Lindeberg, 2009).

Since the plight of the evolutionist is still daunting, even if less daunting than the plight of the non-evolutionist, it is important to be as clear as possible about the information that is required to test a mismatch hypothesis and how it can be obtained. In the next section, we enumerate the types of information that are required to fully test a mismatch hypothesis. Optimally, when scientists offer evidence for a case of evolutionary mismatch, they should include all of these components in their case study (Lloyd, 1987). At the beginning of an inquiry, a suggestive case of evolutionary mismatch would specify both a likely population and a current trait or traits (T), while also specifying and offering some evidence that the current trait(s) seems to be detrimental or badly fit to its present environment (E2). Some evidence may also be given that the trait existed in the past evolutionary environment (E1) and was also likely adaptive or neutral in that environment. These are the basics that are required to claim that a trait is an evolutionary mismatch. More well-established cases for mismatch require providing evidence for several of these components, and the more of them that receive empirical support, the better confirmed the mismatch hypothesis is (Lloyd, 1987). The confirmation of any explanation is always a matter of degree; this is not a black and white issue.

Information That Is Required To Support A Mismatch Hypothesis

In this section, we describe the components of a well-supported mismatch model, each of which can be supported with evidence by a variety of methods or techniques. We start with the population under consideration, and then identify the trait and its heritability, followed by the selection pressures and fitness consequences of the trait in its ancestral environment (E1). Then we examine and integrate both ultimate and proximate explanations for the trait in relation to this ancestral environment (E1), and compare this to the selection pressures and fitness consequences of the trait in (E2). Finally, we consider the integration of ultimate and proximate explanations for the trait in relation to the newer environment, (E2).

The population(s). Any evolutionary scenario must specify the population that is evolving in relation to its old environment, (E1), and its new environment, (E2). Evolution involves changes in a population distribution of traits over time, and we are seeking to define that population here. One reason that our aquatic insect example seemed simple is because we assumed that the population is the entire species, which has been uniformly selected to be attracted to polarized reflective surfaces. In reality, most species are divided into subpopulations that experience different selection pressures and are connected by varying degrees of gene flow, so identifying the relevant population for a given case of mismatch is not necessarily straightforward. Clear membership criteria should be given in cases where the population is subdivided.  Identifying the appropriate population for a case such as atherosclerosis in humans can be especially difficult. Is it the original small population that spread out of Africa approximately 60,000 years ago? Is it the population in a particular geographical area that has experienced a particular diet for 3,000, 20,000, or 40,000 years? These questions can only be answered by empirical research. Once again, if this kind of research appears daunting, consider the alternative. As a hypothetical example, suppose that atherosclerosis is caused in part by a substance in cow milk that is not digested by the adult human digestive system and triggers a maladaptive response of the immune system. Suppose that genetic evolution has taken place in human populations that have subsisted on cow milk during the last few thousand years, causing them to be less susceptible to this cause of atherosclerosis than other populations. How many decades would be required for biomedical researchers to come to this conclusion without the guidance provided by the concept of evolutionary mismatch? To summarize, it is essential to determine the relevant population for a given case of mismatch, no matter how easy or hard it might be.

The phenotypic trait(s). Evolutionists define phenotypic traits with great flexibility, as virtually any measurable property of an individual organism. Phenotypic traits that are subject to evolution must be heritable (see below), which means that they exhibit a correlation between parents and offspring.

Identifying the relevant phenotypic trait(s) (T) in a mismatch scenario might seem straightforward, but it can become fraught with difficulties. We will describe two relatively straightforward cases before turning to more problematic cases. These examples will also introduce the method of phylogenetic inference, or “tree thinking”, as a useful tool for studying evolutionary mismatch.

RuBisCO (for Ribulose-1,5-bisphosphate carboxylase oxygenase) is an enzyme that is responsible for the vast amount of carbon that is fixed into organic form. It is present in virtually all organisms (with the exception of some viruses that exploit the enzyme in their hosts), including the three major domains of Bacteria, Archaea, and Eucaryota, as shown by the phylogenetic tree in figure 1. Although it is theoretically possible that RuBisCO evolved independently in each domain, it is more plausible to hypothesize that it evolved very early in the history of life, before the domains branched off from each other, as indicated in the figure.

There is abundant evidence that the earth’s atmosphere was originally anaerobic and that the accumulation of oxygen was a toxic byproduct of anaerobic metabolic activities (E1). Aerobic metabolism evolved as a response to the new oxygenated environment (E2). It is also well known that oxygen reacts with RuBisCO in a way that is deleterious to organisms. Other adaptations are required to minimize the exposure of the enzyme to oxygen (e.g., the creation of a locally anaerobic environment) and to minimize damage when it occurs (e.g., by mopping up free radicals (Nisbet et al., 2007; Tabita et al., 2007).

This example contains several important lessons for the general study of evolutionary mismatch. First, it shows that a trait (T) can be identified that remains stable across species, environments, and time periods. The fitness associated with the trait varies, but the trait itself does not. Second, it shows that an earlier environment (E1), and the adaptedness of the trait to the earlier environment, can be ascertained with confidence, even when it occurred in the far distant past. Third, it illustrates the utility of “tree thinking” for drawing some of these inferences.  When we are studying a particular case of mismatch in a particular species, it is helpful to broaden the analysis to include other species and their historical relationships with each other. Fourth, it shows that evolutionary mismatches are not necessarily transient, but can become permanent features of life. RuBisCO is a phenotypic trait (T) that did not change when the environment changed, but rather remained static while other traits evolved to ameliorate the negative effects of the new environment (E2). Human efforts to ameliorate mismatches to our current environment sometimes bear an intriguing resemblance to the traits that evolved to ameliorate the effects of oxygen on RuBisCO.

Our second relatively straightforward example of the role of phenotypic traits in a mismatch scenario is the genetic evolution of lactose tolerance in human adults in some populations but not others (Cochran & Harpending, 2009; Swallow, 2003). Most mammals are lactose tolerant as infants but lose the ability to digest lactose as adults. It is not obvious that this should be the case from an evolutionary perspective. It is perfectly possible for a trait that is useful early in life to be retained later in life, but natural selection has been sufficiently economical in this case to “turn off” the phenotypic trait of the ability to digest lactose after it is no longer useful (see Crespi, (2000) for some useful tools to approach cases like this).

Humans are an exception, but only in some geographical regions and not others, at least prior to recent worldwide dispersal, as shown by the tree diagram in Figure 2. Unlike the tree for RuBisCO, where the trait is clearly ancestral, the trait of lactose tolerance in adult humans is clearly derived. Enough research has been conducted to be confident that the phenotypic trait evolved not once but at least twice, in Europe and Africa, and that the genetic basis of the phenotypic trait is different in these two cases (Cochran & Harpending, 2009; Ingram et al., 2007).  What appears to be a single trait in functional terms is at least two traits in mechanistic and historical terms. The mechanistic (or genetical) difference is likely to be important when it comes to the exact physiological response to lactose, and therefore of relevance to the question of “what to do about it” in a practical sense, and on an organismic level.

This example is one of the best documented cases of recent genetic evolution in humans—and an evolutionary mismatch for adult humans who are lactose-intolerant (T) in a world where milk products are a common part of the dietary environment (E2). It also contains some complexities that are instructive for the study of evolutionary mismatch as a whole. First, what seems like a straightforward phenotypic trait, lactose tolerance, becomes two traits when characterized genetically. This is likely to be common, because whenever the same phenotypic trait evolves independently in different populations, the exact mutations that arise and are selected typically are often not the same. Second, the fitness consequences of an environmental change (E2) on a given trait (T), the inability of adults to digest lactose, are likely to be complex and need to be considered on a component-by-component basis. When dairy practices first evolved by cultural evolution, the net benefit was positive despite the fact that most adults lacked the genetic ability to digest lactose.  Milk products might still have deleterious health consequences in some respects, even in populations that have genetically adapted in other respects. In general, many more generations are required to adapt to deleterious consequences that are expressed late in life, compared to those that are expressed during the peak reproductive years of the life cycle. Thus, milk products are implicated in late-onset diseases such as atherosclerosis, even in populations that have genetically evolved to digest lactose (Lindeberg, 2009).

The fact that a disease such as atherosclerosis might have been virtually nonexistent in humans prior to the advent of agriculture (Lindeberg, 2009) challenges conventional thinking on the stability of phenotypic traits. In the classic mismatch scenario, a new environment (E2) changes the fitness consequences of a trait (T) but does not change the trait. This can be true for traits with a strong genetic basis, such as RuBisCO or a particular genetic change that enables adults to digest lactose, but other phenotypic traits are caused by a more complex gene-environment interaction. For these traits, a change in the environment can change the trait, not just the fitness consequences associated with the trait. The hardening of the arteries associated with atherosclerosis provides an example. It is a genetically heritable phenotypic trait in most modern human populations, but it may have come into being not by genetic mutation, but by a change in the gene-environment interactions caused by a change in the environment. Similar examples are likely to be common whenever phenotypic traits are caused by gene-environment interactions. In the atherosclerosis example, the phenotypic trait is absent in (E1) and suddenly appears in (E2). It is also possible for a phenotypic trait to be present in (E1) and (E2) but to have a completely different mechanistic basis, in which case the phenotypic continuity is misleading.

In problematic cases such as these, the phenotypic traits are a composite of lower-level traits that are interacting with each other and the environment. By focusing on the component traits, we can potentially identify cases where the trait remains constant and only the fitness consequences are altered by the environmental change—more like RuBisCO than like atherosclerosis.

Sometimes, normally adaptive developmental plasticity can lead to a mismatch between environment and organism. This can occur when the organism evolved with a developmentally plastic trait adapted to respond to specific environmental cues, such as the long-term availability of good food, but later encounters a different environment from the one it responded to when fixing its traits in the womb or early childhood development. A classic case occurred during the Nazi occupation of the Netherlands during WWII, in which the Dutch experienced a seven-month famine. (We will discuss this case in more detail in the section, “Selection pressures and fitness consequences of the traits in (E2)”.) Pregnant Dutch women produced children during that period who became fetally programmed for a particular type of metabolism involving very scarce food, and later in life, experienced a whole set of eating and metabolic disorders associated with abundant food (Gluckman et al., 2009; Gluckman & Hanson, 2004; Ravelli et al., 1976). In the evolutionary past, food levels early in life were good predictors of food levels later in life, but not in this historical episode. Thus, the plastic trait, (T), of fetal programming according to food availability, a trait adaptive in the evolutionary context, (E1), became harmful in (E2), in which the cycle of food availability was much shorter than in the evolutionary past.

An environmental change can even change the nature of phenotypic traits that are species-typical and have remained stable for eons. The vertebrate eye, for example, is a product of a complex gene-environment interaction during development (Lachke & Maas, 2010). The reason that eyes develop so reliably is because the elements of the environment involved in the gene-environment interaction are so reliably present. If these previously reliable elements of the ancestral environment (E1) change, then even adaptations that have been species-typical for eons can become maladaptive. In the case of vision, eye development has taken place outdoors with a wide range of focal distances for eons. In modern human populations, the large proportion of time spent indoors focusing at short distances has resulted in an epidemic of vision disorders requiring an environmental intervention (glasses) to fix (Long, 2018). Even more severe disorders result when the vertebrate immune system develops in modern environments (E2) lacking elements that were reliably present in the past (E1). The most important general point is that the trait (T) cannot necessarily be defined independently of the environments (E1 and E2) when gene-environment interactions in the development of traits are taken into account.

Inheritance. All types of traits must be heritable to evolve, but the raw fact of heritability says remarkably little about a trait in the absence of other information (Sesardic, 2005). For example, a trait that is under strong directional selection evolves until heritable variation is exhausted. The absence of heritability at this point says nothing about the presence of heritability during its evolution. Heritable individual differences can be either adaptive (e.g., personality differences maintained by balancing selection) or nonadaptive (e.g., products of recombination, or being in poor condition, or weak, which are heritable but not genetic traits). A given trait such as height might have high heritability in a uniform environment (such as when everyone is well fed), and low heritability in a variable environment (such as when some individuals have more access to food than others).

Increasingly, evolutionists are studying genetic polymorphisms as traits that can be measured directly as DNA sequences, such as the long and short repeat regions of the dopamine receptor gene DRD4 (Cochran & Harpending, 2009). It might seem that such traits can be studied more rigorously than phenotypic traits whose genetic basis is unknown, but this is not always the case. Very few genetic polymorphisms result in a single phenotypic trait whose fitness consequences can be measured. In most cases, a genetically polymorphic locus plays a role in the expression of many phenotypic traits through a web of gene-gene and gene-environment interactions. Studying a single genetic polymorphism that affects many phenotypic traits can be as difficult as studying a single phenotypic trait that is influenced by many genes.

Another important point is that there is more to inheritance than genetic inheritance. Epigenetic mechanisms, familial and social learning (in many species), and symbolic systems of thought (largely restricted to humans) also result in traits that are transmitted across generations with a correlation between parents and offspring or their equivalents (Jablonka & Lamb, 2014). A good example of cultural mismatch was documented by the political scientist Robert Putnam in his book Making Democracy Work (Putnam, 1992). When Italy decentralized its government during the 1970’s, the provinces varied greatly in how well they produced efficient governments of their own. The variation ran largely along a north-south axis and was due to differences in social organization that had been in place for a millennium. Northern provinces had a “horizontal” social organization, originally based upon trade guilds that encourage cooperation among peers. Southern provinces had a “vertical” social organization, originally based upon a military empire, that caused peers to compete with each other to curry the support of social superiors. These cultural adaptations to past environments had a large impact on the capacity for efficient governance in the current environment. Although Putnam did not explicitly frame his analysis in terms of cultural evolution and mismatch, he provided an excellent case study and future research can probably benefit from a more explicitly evolutionary approach.

To summarize, inheritance is required for evolution to occur, and the more information that can be obtained about inheritance the better, but it must also be appropriately interpreted. Evidential support of this type can be gained through breeding experiments (Reznick & Travis, 1996), heritability studies, analysis of consecutive generations, and pedigree analysis. Once again, if this seems dauntingly complex, consider the alternative of facing the same degree of complexity without the guidance provided by evolutionary theory. Moreover, a strong case for mismatch can be built without much detailed knowledge about heritability. In our aquatic insect example, the attraction of adults to reflective surfaces clearly counts as an adaptation because it is a complex trait essential for reproduction. This is the kind of evidence that enabled Darwin to build a compelling case for his theory of natural selection without any mechanistic knowledge about inheritance at the organismic level, which also can be used to produce a strong case for mismatch.

Selection pressures and fitness consequences of the trait(s) in the ancestral environment (E1). All adaptations evolve in the context of selective pressures that operated in the past, which can be called the “adaptively relevant past environment” or “environment of evolutionary adaptedness”, a term coined by the pioneering evolutionary psychologist John Bowlby, (1969). Not all mismatches were adaptations in their past, however; they could alternatively have been neutral or even detrimental. We will use the term “ancestral environment” (E1) for simplicity, knowing that it needs to be appropriately interpreted. Evidence for a description of the ancestral environment may include geological information (for example, about an ice age or aspects of the savannah environment), information about the availability of different foods or supplies, or evidence concerning aspects of the local fauna or human social groups that may be deemed relevant to fitness. Some ancestral environments are easy to infer, in part because they still exist in the present. In our aquatic insect example, the ancestral environment was, and remains, bodies of water with their polarized reflective surfaces. For the human eye, the ancestral environment was the outdoor environment with its range of focal distances.

Because the ancestral environment is defined mostly in terms of relevant selection pressures, it must be defined separately for each organismic trait. The appropriate spatial and temporal scale can be hugely variable, as can be seen from the examples described in previous sections--everything from RuBisCO, which became entrenched early in the history of life, to social organizations that became entrenched in current-day Italy a mere millennium ago. Some genes that affect human appetite evolved so long ago that they are also present in nematodes (Ashrafi et al., 2003). Some aspects of our dietary physiology might reflect the largely vegetarian diets of the primate order, the diets of our more recent chimp-like ancestors that might have included meat, the diets of our still more recent hominid ancestors that might have included more meat and foods made edible by cooking, the diets associated with different regions of the world inhabited during the last 50,000 years, or the diets associated with domestication of plants and animals during the last few thousand years. Research is required to determine the appropriate spatial and temporal scale for any particular case, and to verify that the ancestral environment really did display the hypothesized selective forces. Once again, evolutionary theory makes this formidable task easier by asking the appropriate questions.

The term “ancestral environment” does not imply that the relevant selection pressures were homogenous. A given trait could have been positively selected at some times or locations but not others, evolving on the strength of its net effect. Patterns of spatial and temporal heterogeneity in the ancestral environment are especially important for understanding patterns of phenotypic plasticity in modern environments. As a straightforward example, the trait of human skin pigmentation reflects patterns of spatial and temporal heterogeneity in the ancestral environment (Jablonski, 2004). Sunlight is important for the skin to manufacture vitamin D but also causes cancer. In sunny equatorial regions, constant exposure to the sun led to the evolution of relatively dark skin as a fixed trait. In the temperate zones, seasonal variation in exposure to the sun led to the evolution of skin pigmentation as a phenotypically plastic trait—sun tanning. Dark skinned people who move to temperate regions suffer from an inability to manufacture vitamin D, a clear case of mismatch that luckily can be easily corrected with dietary supplements. People capable of skin tanning can also experience mismatch in a variety of ways, because their phenotypically plastic adaptation is calibrated to the particular pattern of variation that existed in their ancestral environment. A Caucasian person from England who moves to Australia will never become as dark as the aborigines who have inhabited Australia for 40,000 years. Anyone capable of tanning who spends a lot of time indoors or covered with clothing will experience sunburn when their skin is suddenly exposed to the sun, a pattern of variation that seldom, if ever, occurred in their ancestral environment. In general, phenotypically plastic traits are as vulnerable to mismatch as phenotypically fixed traits, whenever the patterns of environmental variation of the new environment (E2) depart from those of the ancestral environment (E1).

Evidence for past selection pressures and fitness consequences can sometimes be obtained by experiments that attempt to reproduce the hypothesized adaptively relevant ancestral environment, (E1), while placing organisms that exhibit the relevant trait, (T), in that environment: the consequences for fitness are then obtained through the usual means (Endler, 1986; Reznick & Travis, 1996). In this fashion, the trait can be correlated with fitness gains in the hypothesized past environment, (E1), thus supporting the claim that in the ancestral condition, the trait was adaptive. Sometimes the experiment can’t be carried out (either because of biological or ethical considerations). For example, we can’t manufacture an organism that doesn’t use RuBISCo, or a human being who doesn’t like to eat sugar and fat.

Alternatively, there may be “natural experiments” available in living populations in various present-day environments. For example, current human hunter-gatherer societies have often provided information about the correlations between various traits and fitness values that are used in evolutionary inferences (e.g. Boehm, 1993; Lee & Daly, 1999). Such inferences must be made with due caution (Boehm, 1993), but the information is still highly useful. For example, a few human populations still subsist on diets that are largely pre-agricultural. Not only do these populations differ from populations subsisting on a modern western diet, but sometimes the frequency distributions barely overlap. Virtually everyone who subsists on a modern western diet has at least some fatty deposits in their veins, which is the cause of atherosclerosis. People at the extreme low end of the continuum for this trait are at the extreme high end of the continuum for the same trait on the Trobriand islands of Melanesia (Lindeberg, 2009). Making use of “natural experiments” such as these is an important precursor to conducting real experiments, such as dietary trials in which elements of the diet are systematically varied.

Integrating ultimate and proximate explanations for the study of the trait(s) in relation to E1. One of the most important distinctions in evolutionary theory is between ultimate and proximate causation. Ultimate causation explains why a given trait evolved, compared to many other traits that could have evolved, often (but not always) due to the winnowing action of selection. Proximate causation explains the ontogenetic mechanisms that cause the trait to develop in individuals. These explanations mutually inform each other and both are required to fully understand the evolution of any particular trait.

Our aquatic insect example nicely illustrates how ultimate and proximate explanations both play an essential role in documenting a case of mismatch. The example begins with the phenomenon of aquatic insects attracted to artificial polarized reflective surfaces. Ultimate causation plays a role at the beginning of the inquiry by providing a hypothetical functional explanation: these insects evolved to be attracted to water, which is mimicked by artificial polarized reflective surfaces. Proximate causation plays an increasingly large role as the inquiry continues: not only are the insects attracted to shiny surfaces, but the particular cue is polarized light unbounded by non-reflective borders. Knowledge about proximate causation is especially important for devising a solution to the problem: adding non-reflective borders to artificial polarized reflective surfaces. Most cases of mismatch can benefit from a similar interplay of ultimate and proximate explanation.

In an influential paper titled “The Methods and Aims of Ethology”, Nobel laureate Nikko Tinbergen (Tinbergen, 1963) stressed the need for four separate explanations for any particular trait, including 1) its functional basis; 2) its mechanistic basis; 3) how it develops during the lifetime of the organism; and 4) how it evolved during the history of the lineage. “Tinbergen’s four questions” as they are often called, are an elaboration of the ultimate/proximate distinction and serve as well for the study of mismatch as for the study of ethology or any other topic that is approached from an evolutionary perspective. We have already emphasized the importance of understanding development, which reveals the interdependence of the foundational concepts “trait” and “environment.”

Selection pressures and fitness consequences of the  trait(s) in E2. Cases of mismatch often present themselves in the form of obvious detriments either deleterious to fitness or well-being that demand an explanation, such as insects attracted to solar panels or humans dying of heart disease. As the inquiry continues, it is necessary to identify the relevant aspects of (E2) that cause the detriment, just as it is necessary to explain why this trait did not have this negative consequence in (E1). In our aquatic insect example, it is the polarized light reflected by artificial surfaces in (E2), and not any other aspect. In the case of human eye development, it is the contracted range of focal distances experienced in indoor environments in (E2).

There are a variety of ways to measure the detriment occurring in cases of mismatch. In cases involving diminishment of actual evolutionary fitness, there are surrogate measures or performance measures for fitness, in addition to standard reproductive success. In general, there is some negative consequence associated with the trait(s), whether it is fitness, well-being, or other measures of desirability or health. This is the point at which the notion of evolutionary mismatch offers real insight: this negative outcome is interpreted as the result of an old, adaptive or neutral trait thrust into a new environment, rather than on the intrinsic pathology of the trait itself. Evidence of a present selection pressure (E2), can be found in various cases; for example, including key elements of the modern western diet, such as foods rich in fats and sugars. In this case, the presence of an environment with large quantities and easy access to high fat and high sugar foods creates a selection pressure new to our species, and one, combined with our previously evolved preferences for such foods, detrimental to our health.

In cases of mismatch involving phenotypic plasticity, the relevant aspects of (E2) that cause the detriment can be subtle and seemingly distantly related to the detriment itself, introduced in the previous section, “Traits.” Toward the end of World War II, the Germans imposed a famine upon the Dutch that lasted for seven months. Decades later, adults who were fetuses during this period developed diet-related diseases at a greater rate than those born earlier or later. Until then, no one had imagined that adult-onset diseases could be influenced by events taking place so early in life. Although the effect could have been due to a disruption of development, subsequent research has implicated a form of phenotypic plasticity called Predictive Adaptive Response (PAR), whereby environmental cues experienced early in life direct pathways of development that are manifested throughout life (Gluckman & Hanson, 2004). Evidently, food-poor and food-rich environments require different metabolic adaptations, just as sun-rich and sun-poor environments require different degrees of skin pigmentation. In the ancestral environment of humans and many other mammals (E1), the amount of available food varied, but the pattern of spatial and temporal variation was such that the amount of food experienced early in life reliably predicted the amount of food available throughout life. This particular pattern of environmental variation resulted in a particular pattern of phenotypic plasticity in which the environmental cue determining the adult phenotype is experienced during an early stage of development, even before birth. In the case of the Dutch famine, the pattern of environmental variation in the modern environment (E2) was altered, such that individuals experiencing a food-poor environment as fetuses were born into a food-rich environment, resulting in a host of diet-related diseases later in life.

This example nicely illustrates some general themes about mismatch, in addition to the particular points that we are making about discovering the relevant aspects of (E2). As we stated earlier, mismatch is a particular type of detriment that results from evolution, which needs to be distinguished from other types of detriment. In the case of the Dutch famine, the adult-onset diseases resulting from food deprivation during gestation could potentially be explained in two very different ways: 1) as a disruption of normal development; and 2) as the normal operation of a phenotypically plastic mechanism that is mismatched to the current environment. These two hypotheses make very different predictions that can be used to guide empirical research. It is extremely unlikely that the kind of research motivated by the mismatch hypothesis would be conducted by biomedical researchers who do not have the possibility of mismatch in mind.

Many other examples can be cited in which obvious physiological, behavioral, and social dysfunctions are interpreted as disruptions of normal development, ignoring the possibility of adaptive mechanisms of phenotypic plasticity that have become mismatched to the current environment (Ellis et al., 2012 for risky adolescent behavior).

Integrating ultimate and proximate explanations for the study of the trait(s) in relation to (E2). Taking both ultimate and proximate causation into account is as important for studying the trait in relation to (E2) as to (E1). In cases where the trait was an adaptation in (E1), its functional design in relation to (E1) needs to be understood to appreciate how it is misfiring in (E2). Optimality models can be used to formulate testable hypotheses, and we can do experiments on mechanisms in present-day organisms to explore the present-day effects of traits evolved in (E1) on fitness, well-being, or other measures of desirability. Continuing the example of the previous section, a PAR (Predictive Adaptive Response) is a strategy in terms of ultimate causation, requiring the fetus to make a decision on the basis of available information. The fact that the fetus might still lack a brain is irrelevant. Bacteria and plants also lack brains, but they also make many “decisions” (i.e., adaptively changing their behavior on the basis of environmental information) as far as ultimate causation is concerned.

Of course, to make further progress, we must know the proximate mechanisms whereby the decision is made. In the case of human fetuses “deciding” which metabolic strategy to employ, the information takes the form of certain substances derived from the environment that trigger different patterns of gene expression, and so on. The more the mismatch is understood from both an ultimate and proximate evolutionary perspective, the more likely solutions will be found to prevent detriment from taking place in the current environment. For these purposes, we need information about actual mechanisms linking the trait(s) and fitness or other measures arising out of the experiments mentioned in the above section, as well as the studies of past and present selection pressures.

Conclusion: Evidential requirements for evolutionary mismatch

The purpose of this article is to provide a “back to basics” account of evolutionary mismatch, which can be as useful for the professional evolutionist as for readers encountering the concept for the first time. The classic case of mismatch involves a trait (T) that can be clearly measured, is clearly adapted to an earlier environment (E1) and clearly maladapted to a current environment (E2). We have shown that a more thorough analysis reveals many subtleties, such as when the trait itself changes with the environmental change, based on gene-environment interactions, or when the earlier trait is neutral or disadvantageous, while the later trait may not necessarily be maladaptive, but merely damaging to health or wellbeing.

A central objective of this article is to outline the evidence required to support a mismatch hypothesis. In sum, a well-established case of evolutionary mismatch would specify both a population and a current trait or traits, (T), while also specifying and offering evidence that the current trait(s) is correlated with detrimental outcomes or evidence that it is badly fit to its present environment (E2). Some evidence supporting the inheritance or genetic basis of the trait should also ideally be offered. A mechanism linking this trait(s) to the detrimental outcome should also be provided. Evidence should also be given that the trait existed in the ancestral environment (E1). If the claim is not that the trait was neutral in the evolutionary past, but was, in fact, adaptive, then evidence needs to be offered that in the past environment of evolutionary adaptation the trait promoted fitness. In addition, evidence concerning a mechanism linking the trait to evolutionary fitness in the past also needs to be provided.

The amount of effort required to support a mismatch hypothesis is highly variable. In some cases, such as aquatic insects attracted to reflective surfaces or baby sea turtles heading inland toward artificial lights, the mismatch is glaringly obvious. Other cases are more subtle, requiring information that is exceedingly difficult to obtain. Even for the difficult cases, however, a clear understanding of mismatch as one kind of detriment that can result from evolution makes the inquiry easier than it would be otherwise. A summary list of desirable types of evidence for a well-supported case is provided in Table 2.  

Remaining Questions

Why do cases of mismatch persist? Mismatch is typically regarded as a transient condition, in which the trait that has become detrimental in (E2) is gradually modified by natural selection, or the organism modifies its environment through niche construction, or both. The amount of time required depends upon factors such as the intensity of selection, the heritability of the trait, and the availability of more adaptive variants. Deleterious effects expressed toward the end of the life cycle will be removed more slowly than those that are exhibited during peak years of reproductive value, as we have seen in the case of dietary mismatch. In addition, some cases of mismatch can become permanently entrenched, as we saw in the case of RuBisCO, in which the cost of the trait (T) in the new environment is ameliorated by the evolution of other traits, rather than via a modification of the original trait.

Even strong selection on the trait (T) in the new environment (E2) can require too many generations to solve problems of mismatch in a practical sense. In one classic example, baby sea turtles that hatch on the beach have evolved to make their way toward the sea by being attracted to light reflected on the water surface (E1). They become disoriented by lights from beach houses (E2), causing them to head inland, which is fatal. Very strong selection is operating on the phenotypic trait of orienting toward the sea (T), but there is no guarantee that the population will respond to selection before going extinct. An environmental intervention is required, such as shielding the lights from beach houses or collecting the turtles and carrying them to the sea (Schlaepfer et al., 2002).

It is important to remember that some cases of mismatch involve traits that are nearly neutral with respect to genetic fitness but nevertheless important for human or animal welfare, such as diseases expressed very late in life. These cases of mismatch will persist for a very long time unless an environmental intervention is found.

Responses to mismatch. It is beyond the scope of this paper to go into any depth on this topic, but we do want to mention a few thoughts, elaborated by Terry Burnham and Jay Phelan in their book, Mean Genes (2001), on general strategies to respond to evolutionary and cultural mismatch. Let’s take a case in which we view the negative impact of mismatch in which an animal chooses an option A over a feasible option B, and we have a view that B is better. “Better” can have many meanings including surviving instead of dying or being healthier; it can also be defined in terms of some larger group of organisms such as an organization, a group, a country, a species, or an ecosystem.  What sorts of strategies can we employ to alter the negative outcome? Burnham and Phelan explored various strategies in their book, and we’ll simply mention them here.

    1. Add options. We can create option C where the organism prefers C to the ‘bad’ outcome of A. Some examples of C in human areas might be, a roller coaster instead of driving a car fast, methadone instead of heroin, NutraSweet instead of sugar.  In all of these cases, the new option might be worse than the old option, but they are illustrative.

    2. Remove options. If we can remove the bad option A we may be able to improve the outcome. Horvath and co-authors removed the bad version of the solar panel by adding the small non-reflective boundaries. In human behavior, people have been particularly interested in the notion of mast-strapping (from The Odyssey) where people commit to certain paths. This removal of choices can be a powerful method of favoring constructive actions. For example, people can legally bind themselves to payroll deductions to increase their savings. Similarly, they can limit food options brought into the house, or not take a credit card to a casino.

    3. Alter the organism so that it no longer prefers the bad options. The compound antabuse is designed to make alcohol unpleasant. There are nicotine vaccines that aim to remove the pleasant feeling from the drug (Hatsukami et al., 2005). A less technological version of this strategy is to eat before going to the grocery store. A satiated shopper may make better purchases than one who is hungry.

    4. Will power. For humans, we might be able to train people to make better choices. At one level they still prefer the bad option A, but manage to choose B. This might work in some cases for some humans, but in many cases people have already tried to use will power and failed. Thus, strategies 1-3 are more likely to work in many circumstances.

Overview of current research on mismatch. Below is a brief overview of the mismatch-related research since the last NESCent workshop in 2012 by the authors in the acknowledgement section to bring this article up to date with recent research. An additional table (Table 1) provides a brief survey of current mismatch research not already included in this section.

Darwinian Economics. Neoclassical economic thinking rests on the assumptions that preferences a) remain stable despite circumstances and irrespective of age, and b) are guided through unconscious rational choice processes leading to maximization. Behavioral economics’ critique of neoclassical economics recognizes deviations from rational choice as anomalies, however, fails to explain the origin of anomalous behavior. Through a series of publications called Ordinaries Columns in the Journal of Bioeconomics, Burnham and colleagues address the behavioral critique and provide a root cause analysis of anomalous behaviors by bringing economics in contact with modern evolutionary theory and especially evolutionary mismatch (Burnham, 2016; Burnham & Phelan, 2021; for a classic source see Frank, 1988).

Causes of maladaptations. Human induced rapid environmental change has resulted in plant and animal communities being maladapted to their environment. A body of work has arisen in recent years exploring the root causes of this maladaptation – ecological, evolutionary, developmental, and behavioral causes have been identified. This suggests an intellectual turn from studying design and adaptations to studying maladaptations due to climate change with implications for policy and conservation (Brady, Bolnick, Angert, et al., 2019; Brady, Bolnick, Barrett, et al., 2019). See especially the introductory article by Brady, Bolnick, Barrett, et al., (2019) to a Special Feature on Maladaptation which was a product of a working group which met between December 2015 and 2016 at McGill University’s Gault Preserve and of a symposium held at the 2018 meeting of the American Society of Naturalists in Asilomar, California. These theoretical advances have been accompanied with an empirically oriented metanalysis of the utility of conservation strategies (Derry et al., 2019), and a database of rates of phenotypic change of microevolutionary processes associated with human disturbances (Sanderson et al., 2022).

Ecological and evolutionary traps. A related body of work has developed under the rubric of ‘traps’ (Robertson et al., 2013; Robertson & Chalfoun, 2016). Organisms rely on environmental cues to determine current and future environmental states –rapid environmental change creates a mismatch such that the organism is unable to accurately assess fitness relevant properties of the environment. A trapped organism prefers a novel environmental cue that mimics the properties of the environment it originally evolved in without receiving the expected fitness benefits (aquatic insect and polarized example elaborated above).

Behavioral plasticity. Novel environmental cues also provide a learning opportunity. Species which show reliance on learning could, if appropriate priming at the developmental stage is present, show behavioral adaptations which surmount the effects of a novel environment. This was originally shown and confirmed in Shah et al., (2010) and Urszán et al., (2018).

On the other hand, Barrett et al., (2019) show how cultural transmission and social learning could constrain the acquisition of adaptive behavior in a trapped organism if the organism continues to learn outdated information about the environment. Moreover, properties of a novel environment itself could alter the cultural transmission process by disturbing the signal between sender and receiver.

Sih, (2013) provides a conceptual overview to understand this variation in behavioral responses in trapped organisms by drawing on research from signal detection theory, extended reaction norms, cost-benefit theory, and adaptive plasticity theory. Based on this pluralistic perspective Sih, (2013) suggests that behavioral variation in responses to human induced rapid change arises due to the following factors; a) existing behavioral ‘rules of thumb’, b) accuracy of available of environmental information, c) organism’s behavioral flexibility, d) past evolution and developmental experience, and e) the degree of match/mismatch between past and current environments.

Restoration ecology and community interactions. Smith et al., (2015) flip the conversation around novelty by focusing on the effect of introduced novel species on the environment. Novel species could lead to community wide changes in the local environment altering the local diversity and creating a cascade of novel environments for all species linked in the community web. Downstream implications of community restoration projects which focus only on the foundation species while ignoring the global consequences are explored in Whitham et al., (2020). Evans et al., (2016), Gehring et al., (2014), and Stone et al., (2018) show how cascading effects of plant stress affects whole communities from microbes to vertebrates.

Maternal constraints and developmental mismatch. Cues received in early development influence the development of phenotypes in later life. In an unchanging environment these cues are reliable predictors of the future state resulting in an adapted phenotype, however, when the predicted and actual environment differs, a developmental mismatch could lead to maladaptive fitness outcomes and/or poor health and well-being. For more, see Gluckman et al., (2019) and the other 17 articles in the associated theme issue on “Developing differences; early life effects and evolutionary medicine.”

Senescence and life-history strategy. Ratikainen & Kokko, (2019) propose that organisms need not be stuck along a given maladaptive developmental response. Their models show that reversible phenotypic plasticity – ability to change phenotype and lifespan – may coevolve and be causally linked to slow/fast changes in environmental fluctuations. For instance, in slow changing environments benefits of reversible plasticity are small because they are never realized over the life span of the organism. In such an environment high reproductive effort and shorter lifespans with a fast life history strategy is predicted to be favored by their model.

Literature Cited:

Araya Schulz, R. (2021). Enriching elementary school mathematical learning with the steepest descent algorithm. Mathematics, 9(11), 1197. 

Ashrafi, K., Chang, F. Y., Watts, J. L., Fraser, A. G., Kamath, R. S., Ahringer, J., & Ruvkun, G. (2003). Genome-wide RNAi analysis of Caenorhabditis elegans fat regulatory genes. Nature, 421(6920), 268–272. 

Barkow, J. H., Cosmides, Leda., & Tooby, John. (1992). The Adapted mind: Evolutionary psychology and the generation of culture. Oxford: Oxford University Press.

Barrett, B., Zepeda, E., Pollack, L., Munson, A., & Sih, A. (2019). Counter-culture: Does social learning help or hinder adaptive response to human-induced rapid environmental change? Frontiers in Ecology and Evolution, 7, 183. 

Biglan, A., Ryzin, M. J. V., Moore, K. J., Mauricci, M., & Mannan, I. (2019). The socialization of boys and men in the modern era: An evolutionary mismatch. Development and Psychopathology, 31(5), 1789–1799. 

Blondel, J. (2000). Evolution and ecology of birds on islands: Trends and prospects. Vie et Milieu, 50(4), 205–220.

Boehm, C. (1993). Egalitarian Behavior and Reverse Dominance Hierarchy. Current Anthropology, 34(3), 227–254. 

Boonstra, W. J., Björkvik, E., Haider, L. J., & Masterson, V. (2016). Human responses to social-ecological traps. Sustainability Science, 11(6), 877–889. 

Bourrat, P., & Griffiths, P. E. (2021). The idea of mismatch in evolutionary medicine. The British Journal for the Philosophy of Science [Forthcoming].

Bowlby, J. (1969). Attachment and loss: Attachment. United Kingdom: Basic Books.  

Brady, S. P., Bolnick, D. I., Angert, A. L., Gonzalez, A., Barrett, R. D. H., Crispo, E., Derry, A. M., Eckert, C. G., Fraser, D. J., Fussmann, G. F., Guichard, F., Lamy, T., McAdam, A. G., Newman, A. E. M., Paccard, A., Rolshausen, G., Simons, A. M., & Hendry, A. P. (2019). Causes of maladaptation. Evolutionary Applications, 12(7), 1229–1242. 

Brady, S. P., Bolnick, D. I., Barrett, R. D. H., Chapman, L., Crispo, E., Derry, A. M., Eckert, C. G., Fraser, D. J., Fussmann, G. F., Gonzalez, A., Guichard, F., Lamy, T., Lane, J., McAdam, A. G., Newman, A. E. M., Paccard, A., Robertson, B., Rolshausen, G., Schulte, P. M., … Hendry, A. (2019). Understanding maladaptation by uniting ecological and evolutionary perspectives. The American Naturalist, 194(4), 495–515. 

Burnham, T. C. (2016). Economics and evolutionary mismatch: Humans in novel settings do not maximize. Journal of Bioeconomics, 18(3), 195–209. 

Burnham, T. C., & Phelan, J. (2021). Ordinaries. Journal of Bioeconomics, 23(3), 225–236.

Burnham, T., & Phelan, J. (2012). Mean genes: From sex to money to food: Taming our primal instincts. London, UK: Penguin. 

Castillo, A. M., & De León, L. F. (2021). Evolutionary mismatch along salinity gradients in a Neotropical water strider. Ecology and Evolution, 11(10), 5121–5134.

Cavalli-Sforza, Luigi Luca, & Feldman, M. W. (1981). Cultural transmission and evolution (MPB-16), Volume 16. Princeton, NJ: Princeton University Press. 

Cester, V. P. (2021). Towards a hyperconnected civilisation: From social to connected beings [PhD Thesis]. University of Essex.

Charifson, M. A., & Trumble, B. C. (2019). Evolutionary origins of polycystic ovary syndrome: An environmental mismatch disorder. Evolution, Medicine, and Public Health, 2019(1), 50–63. 

Cochran, G., & Harpending, H. (2009). The 10,000 year explosion: How civilization accelerated human evolution. New York: Basic Books.

Cofnas, N. (2016). A teleofunctional account of evolutionary mismatch. Biology & Philosophy, 31(4), 507–525. 

Cox, M., & Schoon, M. (2019). Adaptive governance from an evolutionary perspective. In Global challenges, governance, and complexity. Edward Elgar Publishing.

Coyne, J. A. (2010). Why evolution is true. Oxford: Oxford University Press. 

Crespi, B. (2020). Evolutionary medical insights into the SARS-CoV-2 pandemic. Evolution, Medicine, and Public Health, 2020(1), 314–322. 

Crespi, B. J. (2000). The evolution of maladaptation. Heredity, 84(6), 623–629. 

Cuthbertson, T. (2019). The poietics of "en-owning" (Ereignis): Ecstatic subjectivity in Heidegger and British Romanticism [PhD Thesis]. State University of New York at Binghamton.

de Vries, R. (2021). Foraging minds in modern food environments: A high-calorie bias in human spatial memory and its implications for eating behavior [PhD Thesis]. Wageningen University and Research.

de Vries, R., Boesveldt, S., Sainz, A. S., Copier, J., & de Vet, E. (2022). Wired for harsh food environments: Human spatial memory favours the effortless location and consumption of high-calorie foods. Food Quality and Preference, 97, 104478.

Deepak, P. (2021). Ethical Considerations in Data-Driven Fake News Detection. Data Science for Fake News. The Information Retrieval Series, 205-232

Derry, A. M., Fraser, D. J., Brady, S. P., Astorg, L., Lawrence, E. R., Martin, G. K., Matte, J.-M., Negrín Dastis, J. O., Paccard, A., Barrett, R. D. H., Chapman, L. J., Lane, J. E., Ballas, C. G., Close, M., & Crispo, E. (2019). Conservation through the lens of (mal)adaptation: Concepts and meta-analysis. Evolutionary Applications, 12(7), 1287–1304. 

Eirdosh, D., & Hanisch, S. (2019). The role of evolutionary studies in education for sustainable development. In: D. S. Wilson, G. Geher, H. Mativetsky, & A. C. Gallup (Eds.) Darwin’s roadmap to the curriculum: Evolutionary studies in higher education (pp. 249–271). Oxford, UK: Oxford University Press. 

Ellis, B. J., Del Giudice, M., Dishion, T. J., Figueredo, A. J., Gray, P., Griskevicius, V., Hawley, P. H., Jacobs, W. J., James, J., Volk, A. a, & Wilson, D. S. (2012). The evolutionary basis of risky adolescent behavior: Implications for science, policy, and practice. Developmental Psychology, 48(3), 598–623. 

Endler, J. A. (1986). Natural selection in the wild. Princeton, NJ: Princeton University Press.

Espericueta, L. (2021). Effects of cultural mismatch on the age at first reproduction in hispanic women and the cooperative help received from grandmothers-to-be. [Master Thesis]. University of California, Los Angeles.

Evans, L. M., Kaluthota, S., Pearce, D. W., Allan, G. J., Floate, K., Rood, S. B., & Whitham, T. G. (2016). Bud phenology and growth are subject to divergent selection across a latitudinal gradient in Populus angustifolia and impact adaptation across the distributional range and associated arthropods. Ecology and Evolution, 6(13), 4565–4581. 

Ewald, P. W. (2002). Plague time: The new germ theory of disease. Norwell, MA: Anchor.

Frank, R. H. (1988). Passions within reason: The strategic role of the emotions. New York, NY: W W Norton & Co.

Gasperoni, F. (2022). The Innate chronotoxicity hypothesis: An ubiquitous physiological flaw both unnoticed and inevitable. Could circadian rhythm be an evolutionary mismatch? La Clinica Terapeutica, 173(1), 67–78. 

Gehring, C., Flores-Rentería, D., Sthultz, C. M., Leonard, T. M., Flores-Rentería, L., Whipple, A. V., & Whitham, T. G. (2014). Plant genetics and interspecific competitive interactions determine ectomycorrhizal fungal community responses to climate change. Molecular Ecology, 23(6), 1379–1391. 

Gelfand, M. J. (2021). Cultural evolutionary mismatches in response to collective threat. Current Directions in Psychological Science, 30(5), 401–409. 

Giosan, C. (2020). Evolutionary psychopathology and depression. In C. Giosan (Ed.), Cognitive evolutionary therapy for depression: Therapy manual (pp. 11–16). Cham, Switzerland: Springer International Publishing. 

Gluckman, P. D., & Hanson, M. A. (2004). The fetal matrix: Evolution, development and disease. Cambridge, UK: Cambridge University Press. 

Gluckman, P. D., Hanson, M. A., Buklijas, T., Low, F. M., & Beedle, A. S. (2009). Epigenetic mechanisms that underpin metabolic and cardiovascular diseases. Nature Reviews Endocrinology, 5(7), 401–408. 

Gluckman, P. D., Hanson, M. A., & Low, F. M. (2019). Evolutionary and developmental mismatches are consequences of adaptive developmental plasticity in humans and have implications for later disease risk. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1770), 20180109. 

Gould, S. J., & Lewontin, R. C. (1979). The Spandrels of San Marco and the Panglossian Paradigm: A Critique of the Adaptationist Programme The spandrels of San Marco and the Panglossian paradigm: A critique of the adaptationist programme. Proceedings of the Royal Society of London. Series B. Biological Sciences, 205(1161), 581–598. 

Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global Change and the Ecology of Cities. Science, 319(5864), 756–760. 

Gurven, M. D., & Lieberman, D. E. (2020). WEIRD bodies: Mismatch, medicine and missing diversity. Evolution and Human Behavior, 41(5), 330–340. 

Hendry, A. P., Kinnison, M. T., Heino, M., Day, T., Smith, T. B., Fitt, G., Bergstrom, C. T., Oakeshott, J., Jørgensen, P. S., Zalucki, M. P., Gilchrist, G., Southerton, S., Sih, A., Strauss, S., Denison, R. F., & Carroll, S. P. (2011). Evolutionary principles and their practical application. Evolutionary Applications, 4(2), 159–183. 

Horváth, G., Blahó, M., Egri, Á., Kriska, G., Seres, I., & Robertson, B. (2010). Reducing the Maladaptive Attractiveness of Solar Panels to Polarotactic Insects. Conservation Biology, 24(6), 1644–1653. 

Ingram, C. J. E., Elamin, M. F., Mulcare, C. A., Weale, M. E., Tarekegn, A., Raga, T. O., Bekele, E., Elamin, F. M., Thomas, M. G., Bradman, N., & Swallow, D. M. (2007). A novel polymorphism associated with lactose tolerance in Africa: Multiple causes for lactase persistence? Human Genetics, 120(6), 779–788. 

Irons, W. (1998). Adaptively relevant environments versus the environment of evolutionary adaptedness. Evolutionary Anthropology: Issues, News, and Reviews, 6(6), 194–204. 

Jablonka, E., & Lamb, M. J. (2014). Evolution in four dimensions, revised edition: Genetic, epigenetic, behavioral, and symbolic variation in the history of life. Cambridge, MA: MIT Press.

Jablonski, N. G. (2004). The evolution of human skin and skin color. Annual Review of Anthropology, 33, 585–623.

Lachke, S. A., & Maas, R. L. (2010). Building the developmental oculome: Systems biology in vertebrate eye development and disease. WIREs Systems Biology and Medicine, 2(3), 305–323. 

Laland, K. N., & Brown, G. R. (2002). Sense and nonsense: Evolutionary perspectives on human behaviour (1st ed.). Oxford, UK: Oxford University Press. 

Lee, R., & Daly, R. (1999). The Cambridge encyclopedia of hunters and gatherers. Cambridge, UK: Cambridge University Press.

Li, N. P., van Vugt, M., & Colarelli, S. M. (2018). The evolutionary mismatch hypothesis: Implications for psychological science. Current Directions in Psychological Science, 27(1), 38–44. 

Lin, K. H., Rutter, J. C., Xie, A., Pardieu, B., Winn, E. T., Bello, R. D., Forget, A., Itzykson, R., Ahn, Y.-R., Dai, Z., Sobhan, R. T., Anderson, G. R., Singleton, K. R., Decker, A. E., Winter, P. S., Locasale, J. W., Crawford, L., Puissant, A., & Wood, K. C. (2020). Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer. Nature Genetics, 52(4), 408–417. 

Lindeberg, S. (2009). Food and western disease: Health and nutrition from an evolutionary perspective. Oxford, UK: Wiley-Blackwell

Lloyd, E. A. (1987). Confirmation of evolutionary and ecological models. Biology and Philosophy, 2, 277–293.

Lloyd, E. A. (2015). Adaptationism and the logic of research questions: How to think clearly about evolutionary causes. Biological Theory, 10(4), 343–362. 

Logan, A. C., & Jacka, F. N. (2014). Nutritional psychiatry research: An emerging discipline and its intersection with global urbanization, environmental challenges and the evolutionary mismatch. Journal of Physiological Anthropology, 33(1), 22. 

Long, E. (2018). Evolutionary medicine: Why does prevalence of myopia significantly increase? Evolution, Medicine, and Public Health, 2018(1), 151–152. 

Malouchou Kanellopoulou, C. (2021). What can an evolutionary explanation bring to the cemarcation of the normal from the pathological in psychiatry? Nesse’s case of depression. [Master Thesis]. Georgia State University 

McPeek, M. (2008). Dragonflies and damselflies: Model organisms for ecological and evolutionary research. Oxford, UK: Oxford University Press.

Medawar, P. B. (1952). An unsolved problem of biology. London, UK: H. K. Lewis

Milberg, P., & Tyrberg, T. (1993). Naïve birds and noble savages: A review of man-caused prehistoric extinctions of island birds. Ecography, 16(3), 229–250.

Morris, R. (2019). Be fruitful and multiply: Fitness and health in evolutionary mismatch and clinical research. [Forthcoming]

Morris, R. (2020). Stranger in a strange land: An optimal-environments account of evolutionary mismatch. Synthese, 197(9), 4021–4046.

Nelson, C. A., Zeanah, C. H., Fox, N. A., Marshall, P. J., Smyke, A. T., & Guthrie, D. (2007). Cognitive recovery in socially eeprived young children: The Bucharest early intervention project. Science, 318(5858), 1937–1940. 

Nisbet, E. G., Grassineau, N. V., Howe, C. J., Abell, P. I., Regelous, M., & Nisbet, R. E. R. (2007). The age of Rubisco: The evolution of oxygenic photosynthesis. Geobiology, 5(4), 311–335. https://doi.org/10.1111/j.1472-4669.2007.00127.x

Ostner, S. W. (2021). Biophilia hypothesis: The benefits of nature in the workplace. In: R. Appel-Meulenbroek and V. Danivska (Eds.) A handbook of theories on designing alignment between people and the office environment (pp. 169–180). Oxfordshire, UK: Routledge.

Pierce, C. (2021). Polycystic Ovarian Syndrome (Pcos) as an evolutionary mismatch disorder: An argument for the significance of hyperandrogenism on reproductive fitness in ancestral populations [PhD Thesis]. University of Colorado at Denver.

Pollack, L., Munson, A., Savoca, M. S., Trimmer, P. C., Ehlman, S. M., Gil, M. A., & Sih, A. (2022). Enhancing the ecological realism of evolutionary mismatch theory. Trends in Ecology & Evolution, 37(3), 233–245. 

Putnam, R. D. (1992). Making democracy work: Civic traditions in modern Italy. Princeton, NJ: Princeton University Press.

Rao, S. (2019). Evolutionary perspectives on obesity and associated metabolic donditions, and a test of the ancestral diet hypothesis [PhD Thesis] Binghamton University. 

Ratikainen, I. I., & Kokko, H. (2019). The coevolution of lifespan and reversible plasticity. Nature Communications, 10(1), 538. 

Ravelli, G.-P., Stein, Z. A., & Susser, M. W. (1976). Obesity in young men after famine exposure in utero and early infancy. New England Journal of Medicine, 295(7), 349–353. 

Reznick, D. N., & Travis, J. (1996). The empirical study of adaptations in natural populations. In Adaptation (pp. 243–289). San Diego, CA: Academic Press.

Richerson, P. J., & Boyd, R. (2008). Not by genes alone: How culture transformed human evolution. Chicago, IL: University of Chicago Press.

Robertson, B. A., & Chalfoun, A. D. (2016). Evolutionary traps as keys to understanding behavioral maladapation. Current Opinion in Behavioral Sciences, 12, 12–17. 

Robertson, B. A., Rehage, J. S., & Sih, A. (2013). Ecological novelty and the emergence of evolutionary traps. Trends in Ecology & Evolution, 28(9), 552–560. 

Romero, A., & Green, S. M. (2005). The end of regressive evolution: Examining and interpreting the evidence from cave fishes. Journal of Fish Biology, 67(1), 3–32. 

Sanderson, S., Beausoleil, M.-O., O’Dea, R. E., Wood, Z. T., Correa, C., Frankel, V., Gorné, L. D., Haines, G. E., Kinnison, M. T., Oke, K. B., Pelletier, F., Pérez-Jvostov, F., Reyes-Corral, W. D., Ritchot, Y., Sorbara, F., Gotanda, K. M., & Hendry, A. P. (2022). The pace of modern life, revisited. Molecular Ecology, 31(4), 1028–1043. 

Sarto-Jackson, I. (2018). Time for a change: Topical amendments to the medical model of disease. Biological Theory, 13(1), 29–38. 

Sbarra, D. A., Briskin, J. L., & Slatcher, R. B. (2019). Smartphones and close relationships: The case for an evolutionary mismatch. Perspectives on Psychological Science, 14(4), 596–618. 

Schlaepfer, M. A., Runge, M. C., & Sherman, P. W. (2002). Ecological and evolutionary traps. Trends in Ecology & Evolution, 17(10), 474–480. 

Sesardic, N. (2005). Making sense of heritability. Cambridge University Press.

Shah, A. A., Ryan, M. J., Bevilacqua, E., & Schlaepfer, M. A. (2010). Prior experience alters the behavioral response of prey to a nonnative predator. Journal of Herpetology, 44(2), 185–192. 

Shariff, A., Green, J., & Jettinghoff, W. (2021). The privacy mismatch: Evolved intuitions in a digital world. Current Directions in Psychological Science, 30(2), 159–166.

Sih, A. (2013). Understanding variation in behavioural responses to human-induced rapid environmental change: A conceptual overview. Animal Behaviour, 85(5), 1077–1088. 

Sih, A., Ferrari, M. C. O., & Harris, D. J. (2011). Evolution and behavioural responses to human-induced rapid environmental change. Evolutionary Applications, 4(2), 367–387. 

Smith, D. S., Lau, M. K., Jacobs, R., Monroy, J. A., Shuster, S. M., & Whitham, T. G. (2015). Rapid plant evolution in the presence of an introduced species alters community composition. Oecologia, 179(2), 563–572. 

Smith, T. B., & Bernatchez, L. (2008). Evolutionary change in human-altered environments. Molecular Ecology, 17(1), 1–8.

Stone, A. C., Gehring, C. A., Cobb, N. S., & Whitham, T. G. (2018). Genetic-based susceptibility of a foundation tree to herbivory interacts with climate to influence arthropod community composition, diversity, and resilience. Frontiers in Plant Science, 9. 

Swallow, D. M. (2003). Genetics of lactase persistence and lactose intolerance. Annual Review of Genetics, 37(1), 197–219. 

Szabo, T. G. (2021). Conclusion: Designing organizations with love: An ACT prosocial framework for social justice, diversity, and inclusion. In A scientific framework for compassion and social justice (pp. 315–325). Oxfordshire, UK: Routledge.

Tabita, F. R., Hanson, T. E., Li, H., Satagopan, S., Singh, J., & Chan, S. (2007). Function, structure, and evolution of the RubisCO-like proteins and their RubisCO homologs. Microbiology and Molecular Biology Reviews, 71(4), 576–599. 

Tinbergen, N. (1963). On aims and methods of Ethology. Zeitschrift Für Tierpsychologie, 20(4), 410–433.   

Toyne, J. M. (1999). The transformation of coca to cocaine: An overview of traditional drug use and modern drug use. The University of Western Ontario Journal of Anthropology, 7(1).

Tseng, M. (2007). Evolution in human-altered environments: A summit to translate science into policy. Molecular Ecology, 16(16), 3287–3288. 

Urszán, T. J., Garamszegi, L. Z., Nagy, G., Hettyey, A., Török, J., & Herczeg, G. (2018). Experience during development triggers between-individual variation in behavioural plasticity. Journal of Animal Ecology, 87(5), 1264–1273. 

Vugt, M. van, & Ronay, R. (2014). The evolutionary psychology of leadership: Theory, review, and roadmap. Organizational Psychology Review, 4(1), 74–95. 

Weaver, R. (2016). Evolutionary theory and neighborhood quality: A multilevel selection-inspired approach to studying urban property conditions. Applied Research in Quality of Life, 11(2), 369–386.

Weaver, R. C. (2014). Urban Geography Evolving: Toward an Evolutionary Urban Geography. Quaestiones Geographicae, 33(2), 7–18. 

Weaver, R. C., & Bagchi-Sen, S. (2014). Evolutionary analysis of neighborhood decline using multilevel selection theory. Annals of the Association of American Geographers, 104(4), 765–783.

Whitham, T. G., Gehring, C. A., Bothwell, H. M., Cooper, H. F., Hull, J. B., Allan, G. J., Grady, K. C., Markovchick, L., Shuster, S. M., Parker, J., Cadmus, A. E., Ikeda, D. H., Bangert, R. K., Hultine, K. R., & Blasini, D. E. (2020). Using the Southwest Experimental Garden Array to enhance riparian restoration in response to global environmental change: Identifying and deploying genotypes and populations for current and future environments. In: Carothers, Steven W.; Johnson, R. Roy; Finch, Deborah M.; Kingsley, Kenneth J.; Hamre, Robert H., Tech. Eds. Riparian research and management: Past, present, future. Volume 2. Gen. Tech. Rep. RMRS-GTR-411. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. (63–79).

Wilson, D. S. (2016). Quality of life from an evolutionary perspective. Applied Research in Quality of Life, 11(2), 331–342.

Wilson, D. S., & Gowdy, J. M. (2013). Evolution as a general theoretical framework for economics and public policy. Journal of Economic Behavior & Organization, 90, S3–S10.

Witt, U. (2014). The future of evolutionary economics: Why the modalities of explanation matter. Journal of Institutional Economics, 10(4), 645–664.

Zavaleta, E. S., Hobbs, R. J., & Mooney, H. A. (2001). Viewing invasive species removal in a whole-ecosystem context. Trends in Ecology & Evolution, 16(8), 454–459.