Cyclical Metapopulation Mechanism Hypothesis:

Animal population cycles are created & driven by a population-wide hormone cycle

Janne Miettinen | ResearchGate: project page

Last update: May 20, 2022 | Work-in-progress


What creates and drives the multiannual animal population cycles is still a mystery to biologists even though the phenomenon has been studied for a century. No hypothesis thus far has been able to explain what creates the cycles or determines the variation in amplitude and periodicity of population cycles, but the high degree of consistency often observed no matter the species or location of the population strongly suggests that the cycles are not created through environmental interactions.

The cycles can be in phase-dependent synchrony inside metapopulations spanning thousands of square miles, and may continue for over a 1000 years with a highly consistent average periodicity. Presented below is a small portion of such a 1000+ year long Larch budmoth population cycle that is a prime example of a highly consistent cycle regarding its amplitude and periodicity.

A multiannual population cycle of Larch budmoth that continues for over a thousand years and has an average length of 9.3 years.

Even though the average length of a population cycle varies between species and populations, the animal population cycles exhibit highly similar phase-dependent oscillations to the average physiological (e.g. size) and behavioral (e.g. migration) attributes of individual animals. Because these phase-dependent variations always manifest in the same order during a population cycle, the average physical and behavioral attributes of succeeding birth cohorts are essentially predetermined once a population cycle has initiated.

No previously presented hypothesis has succeeded in accurately explaining 1) any of the phase-dependent oscillation phenomena, 2) the synchronization of of the populations cycles in a metapopulation, or 3) any other of the various similarities shared by the population cycles of different species. Several attempts to solve these mysteries have been made by presenting a combination of environmental and/or epigenetic factors that could produce the cycles, but no hypothesis has succeeded in accurately explaining the population cycles of even one species.

Because all of the phase-dependently oscillating physiological and behavioral attributes are modulated by hypothalamic hormone levels, it is presented here that the animal population cycles are a manifestation of a multiannual metapopulation-wide hormone cycle. The multiannual hormone cycle consist of generationally oscillating levels of sex hormones (affects e.g. fecundity), growth hormone (e.g. size), cortisol (e.g. immune response), and dopamine (e.g. migration) as is modeled below according to the average measurements and observations from studies of cyclical animal populations.

The average hormone levels in a cyclical population, indicated by ‘high’ and ‘low’, can decrease by over 50% from their peaks during a population cycle, causing significant variation to the developmental paths of birth cohorts / generations. The population cycle is divided into four phases in the model, which is a common practice among the studies of population cycles.

The multiannual hormone cycle defines the average hypothalamic hormone levels of birth cohorts and therefore the average developmental paths of birth cohorts, creating the phenomena of oscillating physiological and behavioral traits during the population cycles. The multiannual hormone cycle mechanism is parallel to the olfactory system’s circadian clock mechanism, that synchronizes the circadian rhythm between individual animals in a population via proximity, thus creating a common daily timekeeping rhythm between all animals in a population, resulting in synchronized circadian hormone level oscillations between all animals in a population.

Since the mid-cycle phase of increased migration has already been verified to significantly increase gene flow inside a metapopulation, the multiannual hormone cycle mechanism driving the population cycles is therefore presented to be an evolutionary metapopulation mechanism that catalyzes both microevolutionary and macroevolutionary processes of cyclical populations compared to non-cyclical populations. The mechanism can also synchronize nearby populations’ cycles during the mid-cycle migration phase, which explains why populations of the same species often oscillate in synchrony in a metapopulation.

In addition, this hypothesis presents that human populations in the US and several other countries are presented to be currently undergoing the same multiannual generational hormone cycle with a length of approximately 80 years. Statistics from these countries are presented to support the premise of generationally oscillating hormone levels. The Strauss-Howe generational theory, a generational cycle theory by historians William Strauss & Neil Howe, details an 80 year long generational cycle in the US population consisting of four succeeding 20 year long generations, with each of the four generations having their own typical physiological and behavioral traits; exactly as is with the generations in the cyclical animal populations. This hypothesis presents statistical and historical evidence that the Strauss-Howe generational theory is in fact a depiction of the cyclical animal populations’ generational hormone cycle being active in human populations.

1 Hormones and cycles

1.1 Animal population cycles

Many social animal species undergo multiannual population cycles. The documented average length of animal population cycles is from e.g. 4 years for small mammals like lemming and vole, 10 years for snowshoe hare, and 38 years for moose. (Myers, 2018​1​, Wang et al., 2009​2​, Krebs, 2010​3​, Krebs et al., 2014​4​, Hansson & Henttonen, 1985​5​) [Many fish species undergo biannual population cycles that are not yet accounted for in this hypothesis, but will be included in future updates. (Marjomäki et al., 2021​6​)]

Below are examples of multiannual population cycles that exhibit large oscillations to population size alongside a strikingly coherent periodicity regarding the total length of each cycle, and some cycles can go virtually uninterrupted for over a millennium: “Our report here that these regular cycles persisted without significant interruption for more than 1000 years portrays these larch budmoth cycles as even more remarkable.” (Esper et al., 2006​7​)

Larch budmoth population cycles. (Esper et al., 2006​7​)
Sockeye salmon population cycles indicated by escapement. (White et al., 2011​8​)
Snowshoe hare population cycles indicated by number of pelts. (S)

Cyclical populations are very common in nature: “…we analyzed nearly 700 long (25+ years) time series of animal field populations, looking for large-scale patterns in cycles. Nearly 30% of the time series were cyclic.” … “The incidence of cycles varied among taxonomic classes, being most common in fish and mammal populations. Fully 70% of the fish and mammal species comprised at least one cyclic population…” (Kendall et al., 1998/2002​9​) A study reviewing longer time series (over 50 years in length) included populations of 7 bird species, 10 mammal species, and 17 insect species concluded that “In every species we found at least the likelihood of cycles.” (Witteman et al., 1990​10​)

Environmental factors including predators, pathogens and limited food supply have often been suggested to drive the multiannual population cycles, but because the cycles do manifest even when these factors are not present, it is highly likely that something else is creating and driving the cycles: “…lemmings on islands are known to be without predators and yet still undergo a 4 year population cycle.” (Ginzburg & Colyvan​11​, p. 79) “Numerous experiments have been done in attempts to delay the decline or stop the population cycles of lemmings and voles by feeding or excluding predators. These have had mixed results and Krebs concluded that predators can ‘modify’ population cycles, but that predator removal cannot stop cyclic dynamics. Similarly, food addition experiments can modify vole densities but not drive cycles.”“Overall, experimentally stopping or starting population cycles has proven to be largely impossible.” (Myers, 2018​1​)

Increasing stress levels through increasing population density has also been used as a theory to explain the cycles, but this idea too has been disproven: “In 1967, Dennis Chitty proposed that larger and more aggressive voles would be selected for in increasing and high densities, and smaller voles with delayed reproductive maturity in low densities. The ‘Chitty Hypothesis’ predicted that variable selection would lead to a genetic shift over the 3 to 4 year cycle of voles. However, the genetic shifts predicted by this hypothesis have not been observed and the levels of heritability of traits required for the shift were unrealistically high.” (Myers, 2018​1​) No hypothesis exists that uses an epigenetic model to successfully explain the cycles’ different phases, and such a hypothesis is unlikely to succeed due to the high consistency of the cycle’s different phases in different environments.

The end result is that after over a century of intense research into animal population cycles, not even one hypothesis exists that explains even one species’ population cycles while taking into account the findings made by Krebs and others about the environmental factors not starting or stopping the cycles, resulting in a situation where all explanations and theoretical models are severely lacking in evidence and/or repeatability. (Andreassen et al., 2020​12​, Oli, 2019​13​, Myers, 2018​1​, Oli, 2003​14​, Martínez-Padilla et al., 2013​15​)

There are several mathematical models that claim that density dependence would explain some species’ cycles, but none of these mathematical models explain the phase-dependent oscillations to the physical and behavioral traits of the animals, and many mathematical models have problems with possibly false detection of density dependence. (Freckleton et al., 2006​16​) Since density dependency or its variations, or other previously presented hypotheses cannot explain the cycles, what can explain the recurring appearance and the phase-dependent traits of animals during the cycles regardless of the species or the environmental factors?

It is presented here that the true explanation for the cycles lies within the endocrine system that controls animal hormone levels and thus directly modulates both behavior and biological traits throughout a lifecycle: the animal population cycles are caused by generationally oscillating hormone levels, that oscillate phase-dependently in synchrony inside a population from the beginning to the end of a population cycle. Only an innate biological mechanism that controls the generational hormone levels can explain the animal population cycles in a way that is not dependent on environmental factors like predators, pathogens, or food supply, while simultaneously giving an answer to all of the previously unexplained phase-dependent phenomena.

It is important to note that the term ‘hormone levels‘ used throughout this text means that the hypothalamic neurons that secrete hormones are either small and inefficient or large and more efficient, like is presented in the lemming and vole studies below. In addition, the term ‘hormone’ will be used throughout the text, even though some of the molecules also work as neurotransmitters or neuromodulators in the brain, where the levels of these molecules modulate behavior. (S)

The mechanism creating the multiannual hormone level oscillations in cyclical populations is presumably presides in the olfactory system that modulates the hormone levels, and the olfactory system has its independent timekeeping oscillators that are synchronized in close proximity between individual animals inside a population, likely via pheromonal communication. (Siehler et al., 2021​17​, Baghel & Pati, 2015​18​, Groot et al., 2014​19​, Fuchikawa et al., 2016​20​, Thomas et al., 2016​21​) Even though not all social animals have a hypothalamus or the same hormones, the effects to animal fecundity, size, immune system, and migration are controlled by similar endocrinological mechanisms that are under the control of the olfactory system.

Although the olfactory system’s timekeeping and synchronization functions are still under research, and a mechanism for a multiannual hormone cycle has yet been verified to exist, the suggested multiannual mechanism relies on two verified functions of the olfactory system: 1) the olfactory system has its independent timekeeping oscillators that can control the rhythms of individual animals in a population, and 2) the olfactory system directly modulates the time-dependent hormone level oscillations of the hypothalamus.

Electron microscopy studies have documented the multiannual oscillations to the endocrine system of voles and lemmings during a population cycle: there are significant multiannual oscillations to average hormone levels, because the hypothalamic neurosecretory cells vary in size and efficiency depending on the phase of the cycle. (Arshavskaya et al., 1989​22​/PDF, Vladimirova et al., 2006​23​) The picture below largely represent the study’s key findings regarding generational hormone levels during a lemming population cycle, where the neurosecretory cells in the hypothalamus change in size and efficiency according to the phase of the cycle, resulting in animals born during different phases of the cycle having differing (average) hormone levels.

Generational variance in the lemming endocrine system during different phases of the cycle, where the size of hypothalamic neurons secreting hormones, neurotransmitters, and neuromodulators affects the sizes of organs that secrete hormones. Phases II and III represent the cycle’s decline and low phases. (Arshavskaya et al., 1989​22​)

Other studies of cyclical animal populations have made similar findings directly related to the endocrine system by measuring the weight and size of hormone glands, with observations made such as larger testicles during the increase phase and higher cortisol levels during the decline phase, as is the case with cyclical snowshoe hare population studies. (Sheriff et al., 2011​24​) “Testicular development was correlated with pituitary FSH. The seasonal variation in gonadotropin of females parallels seasonal changes in litter sizes… There was a sharp decrease in pituitary FSH of females [during phase of peak population size]… which coincides with a substantial decline in reproductive rate. It is suggested that changes in pituitary gonadotropins may affect reproduction in the [cyclical] snowshoe hare population…” (Davis & Meyer, 1973)

1.2 Modeling the multiannual hormone level oscillations of cyclical animal populations

To demonstrate the high consistency of the phase-dependent oscillations between cyclical populations of different species, this chapter lists and presents models of the multiannual oscillations to the key physiological and behavioral traits that oscillate phase-dependently during an animal population cycle. The traits are followed by the hypothalamic hormone that (primarily) modulates the specified trait in animals. The phases when the prevalence of these traits is highest are also listed.

To point out the high degree of similarity of these phase-dependent oscillations between different species, examples are presented from some of the most studied cyclical mammal and forest Lepidoptera populations. [Lists to be expanded during Q2/22.]

Sex hormone levels:

Increased fecundity when sex hormone (GnRH) levels are high: phases 1 -> 2.
– Lemming (Arshavskaya et al., 1989​22​)
– Snowshoe hare (Cary & Keith, 1979​25​, Boonstra et al., 1998​26​)
– Forest Lepidoptera, Snowshoe hare, vole, lemming (Myers, 2018​1​)

Growth hormone levels:

Increased physical size when growth hormone (GHRH) levels are high: phases 2 -> 3.
– Lemming and vole (Oli, 1999​27​, Fauteux et al., 2015​28​, Kshnyasev & Davydova, 2021​29​, Boonstra & Boag, 1987​30​, Yakushow & Sheftel, 2020​31​)
– Snowshoe hare (Cary & Keith, 1979​25​, Boonstra et al., 1998​26​)
– Forest Lepidoptera (Rhainds, 2019​32​)

It is important to note here that there are exceptions to the main rule of physical size peaking close to mid-cycle, aka. “the Chitty effect” that states that the average size of animals is largest during mid-cycle in most of the species. (Oli, 1999​27​) For example, cyclical forest Lepidoptera populations exhibit an inverted curve compared to the model above, as their body size is smaller during mid-cycle. (Klemola et al., 2008​33​) And in some species the body size doesn’t change virtually at all during a population cycle, making the growth hormone cycle the least common between different species.

Cortisol levels:

Increased stress symptoms and immune response when cortisol (CRH) levels are high: phases 3 -> 4.
– Lemming and vole (Arshavskaya et al., 1989​22​, Voutilainen et al., 2016​34​)
– Snowshoe hare (Boonstra et al., 1998​26​)
– Forest Lepidoptera (Myers, 2018​1​)

Dopamine levels:

When dopamine levels are high: increased social dominance. (Yamaguchi et al., 2017​35​, Nader et al., 2012​36​) When dopamine levels are low: increased migration (concurrent high population size can also increase emigration) and physical aggression (depending on species): phases 2 -> 3. (Marzecova et al., 2021​37​, Matthews et al., 2016​38​, Mahabir et al., 2013​39​)
– Snowshoe hare (Myers, 2018​1​)(Migration.)
– Forest Lepidoptera, Snowshoe hare, vole, lemming (Myers, 2018​1​)(Migration.)

The complete generational hormone cycle:

The model below is a combination of the previously presented generational oscillations of average hormone levels in cyclical animal populations.

To visually present how the multiannual hormone level oscillations drive the animal population cycles, below is the larch budmoth population cycle that can go on virtually uninterrupted for over a thousand years.

Larch budmoth population cycles. (Esper et al., 2006​7​)

And below is the generational hormone cycle inserted into the larch budmoth cycle.

Another example is provided below by inserting the generational hormone cycle into a snowshoe hare population cycle spanning over three decades.

Snowshoe hare density estimates combined with the generational hormone cycle. (Krebs et al., 2014​40​) Red oscillation is for sex hormones, green oscillation for growth hormone, yellow oscillation for cortisol, and blue oscillation for dopamine.

1.3 The cyclical metapopulation mechanism as an evolutionary catalyst

1.3.1 Hormone levels and development

[Chapter 1.3 currently presents the theoretical evolutionary benefits of the cyclical metapopulation mechanism in a highly compressed manner, but chapters will be expanded in future updates.]

The endocrine system modulates the development of an animal via hormone levels from the embryonic stages of an organism by affecting gene expression, including the expression of HOX genes. (Daftary & Taylor, 2006​41​, Lutchmaya et al., 2004​42​) Since different (sub)species have their unique developmental paths, the generational hormone cycle affects each species’ cyclical population differently: when a birth cohort is in different phases of development – especially the prenatal, infancy and puberty phases of development – the phase of the generational hormone cycle determines the average hormone levels that determine a birth cohort’s average physiological and behavioral traits. [Examples of developmental paths for different species will be added during Q2/2022.]

This means that the temporal location in the cycle when a cohort is born sets the predetermined average developmental path for the cohort. Therefore, the generational hormone cycle produces larger numbers of physiological and behavioral extremes in a population compared to non-cyclical populations, that have less variation to average traits between generations.

1.3.2 The Hardy-Weinberg Equilibrium

In animal ecology, the Hardy-Weinberg Equilibrium presents a state of stalled evolution in a population: when there are 1) no mutations, 2) mating is random, 3) natural selection is not a factor, 4) population size is large, and 5) there is no gene flow, the equilibrium causes a population’s allele and genotype frequencies to stay constant. However, according to the Red Queen hypothesis, a population that does not evolve faces defeat compared to a population that does evolve. (Brochurst et al., 2014​43​, Strotz et al., 2018​44​)

Since the multiannual cycles of cyclical populations cause 1) more mutations due to a phase of large population numbers, 2) annual changes to mating behavior due to varying levels of sex hormones, 3) natural selection being a factor due to increased emigration and immigration, 4) annually varying population sizes, and 5) increased migration between populations in a metapopulation, thus increasing gene flow between populations, the cycle essentially accelerates evolution by removing the elements described in the Hardy-Weinberg Equilibrium that de-accelerate evolution. Therefore according to the Red Queen hypothesis, the cyclical evolutionary metapopulation mechanism would give an advantage for a cyclical metapopulation when compared to a non-cyclical metapopulation.

1.3.3 Migration and gene flow

The mid-cycle migration phase, which consists of the 2nd and 3rd phases of a four phase cycle, causes animals to move into new habitats and areas. The migration phase therefore increases the geographic size of a cyclical population at regular intervals, thus increasing the probability of gene flow between conspecific populations inside a metapopulation (or, more rarely, between metapopulations).

Gene flow between cyclical animal populations increases during the high population numbers, since the animals move beyond the habitats they occupy during the years of low population numbers, resulting in increased gene flow between populations in a metapopulation through migration. “During the peaks, the accumulation of new alleles (i.e., alleles not discovered before within the population) and the appearance of a homogenous population structure suggest higher migration rates and, consequently, increased gene flow within the population compared to the crash periods.” (Rikalainen et al., 2012​45​) Another study based on data from seven cyclical lemming population states: “High genetic variability thus implies high gene flow over a considerable area for lemmings… Examination of empirical data suggests that high genetic diversity may be the rule rather than the exception in cyclic populations.” (Ehrich & Jorde, 2005​46​)

The migration phase has already been documented to cause large evolutionary effects, which are more significant than the other microevolutionary processes in cyclical populations: “In their review of population cycles, Norén and Angerbjörn (2014) concluded that the signatures of genetic drift and selection on population genetic diversity are weaker and obscured by density-dependent dispersal. The present study adds to the growing body of evidence that dispersal usually overshadows the impact of other microevolutionary processes in cyclic populations.” (Ishibashi & Takahashi, 2021​47​, Lidicker, 2015​48​, Norén & Angerbjörn, 2014​49​)

The gene flow effect in a metapopulation is enhanced by the fact that the cycles are often in sync between nearby cyclical populations. (Krebs et al., 2017​50​)(Tähkä et al., Endocrine aspects of population regulation in the genus Clethrionymus, Mem. Soc. fauna Flora Fenn., 1984) Or the cycles can alternatively travel as a “wave” in the terrain: “Then, the observed higher scale of gene flow in the direction parallel to the wave front may result from the recurrent redistribution of the genetic diversity during each outbreak between populations fluctuating in synchrony.” (Berthier et al., 2013​51​, Sherratt & Smith, 2008​52​, Jepsen et al. 2016​53​, Krebs et al., 2017)

Populations A, B, and C of a theoretical metapopulation exhibit higher rates of gene flow if they are cyclical, since there is a higher chance that the populations overlap through increased population size and migration.

By having a migration phase simultaneously increases gene flow between populations, since during the migration phase both immigration and emigration are occurring. If the cycles are not in phase, the cyclical populations outside their migration phase wouldn’t be as receptive regarding the immigrants, nor would they send emigrants into nearby populations in migration phase and accepting immigrants.

As for the macroevolutionary effects of the dispersal phase, it increases population emigration from cyclical populations into new areas, thus producing new populations and expanding a metapopulation faster compared to non-cyclical populations. The fact that the animals during the migration phase are, on average, 1) physically larger, 2) more aggressive, and 3) emigrate in larger numbers compared to a non-cyclical population, may give an advantage to a cyclical population in acquiring new habitats for the metapopulation.

Vole populations for example are known to be often cyclical with large fluctuations to population size, which may be the reason for their fast evolution: “The study focuses on 60 species within the vole genus Microtus, which has evolved in the last 500,000 to 2 million years. This means voles are evolving 60-100 times faster than the average vertebrate in terms of creating different species.” (Fletcher et al., 2019​54​/S)

The high population number phase of cyclical populations can also theoretically accelerate evolution through higher rates of beneficial mutations when compared to a non-cyclical population with less variance to population size, since larger populations are more likely to find beneficial mutations. (Vahdati et al., 2017​55​)

1.3.4 Predator avoidance

Predator avoidance can be more efficient in a cyclical population compared to a non-cyclical population, since both the average physical and behavioral attributes of the prey population change with every new birth cohort in a cyclical population, resulting in the predators always lagging in adaptation to maximize predator efficiency. For example, dopamine levels affect how far away the animals forage from their habitat, growth hormone levels affect how bold or fearful the animals are, and sex hormone levels affect mating behavior. In comparison, a non-cyclical population has less variance to average traits year-to-year, making it easier for the predator to adapt to the prey population’s average behavioral and physiological traits.

The annually varying average size of a a cyclical population is a good example: if a prey population’s each birth cohort’s average size is always different from the previous cohort, it becomes more difficult for the predator to catch the prey, since body size plays a crucial role in predator-prey interactions. (Lundvall et al., 1999​56​) During a population cycle, the predator’s jaws and other physical properties cannot adapt to the changing of their prey animal’s size in time, (unless the predator species would have a much shorter lifespan, so it could quickly adjust to the changes), resulting in the predator population lagging the prey population in adaptation. However, if the predator lives as long or longer than what the average population cycle length is for the prey population, the predator can adapt to the entire cycle’s length.

1.4 Cyclical human populations

Human populations are presented to undergo the same multiannual hormone cycle as other cyclical animal populations. Humans are mammals, and mammal populations are among the more frequently cyclic animal populations. (Kendall et al., 1998​9​) A historical cycle theory, the Strauss-Howe generational theory, describes an approximately 80 year long generational cycle repeating for centuries in the US and many other countries. (Karashchuk et al., 2020​57​) The Strauss-Howe generational theory’s cycle consist of four repeating phases and generations that are highly similar to the four repeating phases and generations in the animal population cycles, as that they always appear in the same sequence: “As we examine these pendular movements [of American generational history], a startling pattern emerges: a recurring cycle of four distinct types of peer personalities, arriving in the same repeating sequence.” (Generations, Strauss & Howe, 1991​58​)

Strauss & Howe are not the only ones to have noticed a repeating generational pattern in history. For example, sociologist and historian Jack Goldstone has observed a very similar centuries long generational pattern manifesting in Eurasia: “Goldstone was also encouraged by the publication in 1978 of Colin McEvedy and Richard Jones’s Atlas of World Population History, in which they highlighted an “astonishing synchronicity” in population booms and busts across Eurasia over millennia. A few months into his number-crunching, he had his eureka moment: “It was astounding: there really was a three-generation surge in population growth before every major revolution or rebellion in history.”” (S)

Other historians (listed by Strauss & Howe in their books Generations and The Fourth Turning), including Arnold Toynbee and Quincy Wrigth, have also located similar cyclical generational patterns in many historical cultures. Strauss & Howe note that even the renowned political philosopher Polybius discovered a similar generational cycle during the second century B.C. when he studied the histories of Greco-Roman city-states.

This hypothesis presents a generational hormone theory, that lays out a theoretical base and also presents evidence from many countries outside the tropic that they are currently undergoing similar generational hormone level oscillations as the cyclical animal populations do. The presented generational hormone theory concentrates on the Western nations, since the Strauss-Howe generational theory covers mainly the US generations, but also makes references to other Western countries. Below is the model for the generational hormone cycle in the US and other Western countries.

Data points are not available from all centuries before the 20th century, but are assumed to be similar.

All of these generational hormone level oscillations have been documented in the Strauss-Howe generational theory, with possibly the exception being cortisol levels, since cortisol levels do not modulate human behavior detectable as easily as the other hormones included in the model. In addition, only the human generational hormone cycle includes oxytocin for now, but it will be implemented into other animal population cycles (where present) in future updates.

The generational hormone theory presents that hypothalamic hormone levels undergo large variances throughout an 80 year cycle, creating behavioral trait differences between generations, and that the oscillating dopamine levels create eras of increased polarization and nationalism through social dominance and in-group cohesion; a phenomenon that seems to occur every 80 years in many Western countries. In history this era is usually preceded by roughly four decades of globalism, liberalism, and relatively peaceful times.

All of these different eras and generations will be reviewed, but as they have already been presented extensively by historians William Strauss and Neil Howe in their books Generations and The Fourth Turning, the larger goal is to review how hormones modulate social behavior and how these hormones are linked to the Strauss-Howe generational theory of 4 x 20 year generations.

It is important to note that this hypothesis does not suggest that any singular historical events have happened because of certain average hormone levels. Instead what this hypothesis suggests is that generational hypothalamic hormone levels may have modulated the average behavioral traits of generations that affect their average actions and reactions, and also the social mood during different eras.

To better explain the generational hormone cycle’s behavioral effects, an example of oscillating hormone levels creating oscillating behavior is the menstrual cycle: women in their reproductive years tend to have mood swings that are caused by varying hormone levels during their menstrual cycle, and the women react to their environment according to their current mood, like with increased anxiety, depending on the phase of the cycle. The menstrual cycle also has an effect on memory and spatial skills for instance, demonstrating that changing hormone levels impact both behavior and cognitive skills, and also the structure of the brain according to the phase of the cycle. (Pletzer et al. 2019​59​)(S)

Women’s mood and cognitive skills vary during the menstrual cycle due to fluctuating hormone levels. (S)

Even though menstrual synchrony among women is still a debated subject (S), if the synchrony was occurring, it would mean that there would be also behavioral and physiological trait synchrony among those women during different phases of the month. This is essentially the same thing that this the generational hormone theory is suggesting to be occurring in an 80 year long cycle when the generational hormone cycle is active in human populations: birth cohort specific average hormone levels affect each birth cohorts development and average behavioral & physiological traits, and many of these generationally oscillating traits are listed in the Strauss-Howe generational theory. And like the menstrual cycle, that is a multi-oscillatory circadian system, the generational hormone cycle is presumed to have different phases with different hormone level configurations. (Simonneaux & Bahougne, 2015​60​)

In Western human populations the cycles are presumably in sync within the countries, but between the countries it looks like the wave is starting from the US, then traveling north to Canada, east to Western Europe, and from there further east and also to the south, like Spain, that lags the Western Europe’s phase by a few years in statistics, while many Eastern European countries are lagging roughly 5-10 years. Russia lags about 15 years behind the US in the presented statistics. Most of the cyclical animal populations are located between northern latitudes 30 and 70, and those latitudes also include all the Western nations on the Northern Hemisphere and Russia. (Kendall et al., 1998/2002​9​)

1.5 Review of contents

After chapter 1, chapter 2.1 presents the generational hormone theory’s premises; 2.2 presents the Strauss-Howe generational theory’s generational cycle in more detail; 2.3.1 reviews statistical evidence of generationally oscillating sex hormone levels and links them to fertility rates; 2.3.2 links generationally oscillating sex hormone levels to childlessness rates; 2.4 reviews statistical evidence of generationally oscillating growth hormone levels; 2.5 reviews statistical evidence of generationally oscillating cortisol levels and links them to type 2 diabetes incidence rates; 2.6.1 reviews how time spent with children and breastfeeding rates are linked to the parent’s oxytocin levels; 2.6.2 reviews changing alcohol consumption rates in history; 2.7.1 reviews proxy statistics to find generationally oscillating dopamine levels; 2.7.2 ties eras of high/low social dominance and in-group cohesion to generationally oscillating dopamine levels; 2.7.3 reviews how generational dopamine and oxytocin levels possibly modulate the average voting behavior of birth cohorts.

Chapter 3.1 lays out the current societal trends of increasing intergroup cleavages and tensions and the failures of the sociological explanatory models to explain the intergroup cleavages; 3.2 is a short introduction to how hormone levels are related to individual and group behavior; 3.3 connects the effects of increasing dopamine levels to increasing “in-group vs. out-group” tensions on a societal scale; 3.4 presents the universally common paths that small and large groups typically undergo when social dominance increases and in-group cohesion tightens; 3.5 ties the biological roots of scapegoating to increasing in-group empathy.

Chapter 4.1 presents initial conclusions based on the findings made throughout this hypothesis; 4.2 includes some currently open questions.

Genetic factors of individuals are confined out of this hypothesis, since on a level of a population the genetic differences between individuals and generations are evened out. Hormone receptor SNPs are negated at this point for the same reason. There are differences in the mean genetic and SNP distribution between nations and continents, but for the moment, this fact is left aside, although it is very relevant in the context of population-scale behavioral differences between different geographic locations. (Allocco et al., 2007​61​)

Transgenerational epigenetic effects are left aside for now, because they are more case sensitive, but could theoretically contribute to the generational traits in some ways, since stress receptivity of generations presumably changes during the cycle. And since hormones act as epigenetic signals in development, epigenetics are taken into account, but not in a transgenerational way for now. [In 2019 this hypothesis included a theoretical framework on how the fluctuations in hormone levels might be due to epigenetic effects between generations, but this framework was abandoned due to several reasons, one of them being that it would be highly unrealistic for such a epigenetic cycle – for all hypothalamic hormones – to manifest as coherently as what the cycles have been documented to be between different species and also different environments.]

Differences in hormone effects between sexes are currently mostly negated, but will be implemented later on. Biologically the proposed human generational hormone cycle is very similar to the documented lemming and vole generational hormone cycles. Because functions of the hypothalamus have been tightly conserved through the mammalian evolution, this makes findings from rodents largely translatable to humans. (Caldwell & Albers, 2015​62​)(S) Hypothalamic hormones have a multitude of physiological and behavioral effects in different species, but only those hormone effects are accounted for that have 1) behavioral effects relevant to this hypothesis and 2) physiological functions that are used to find historical hormone levels by using proxy statistics (like breastfeeding statistics for oxytocin).

This hypothesis is a personal project and a byproduct of research done while planning for a master’s thesis in 2018. The planning included reviewing how hormone levels are connected to empathy and aggression, while simultaneously reading about the Strauss-Howe generational theory, and then evaluating if generational hormone levels could explain the generational differences in typical behavior depicted by the Strauss-Howe generational theory. The writing of this hypothesis begun without any other specific aims than to find out if there could be generationally oscillating hormone levels in the Western human populations, but a 2019 discovery of there being highly similar generational hormone level oscillations in cyclical animal populations to what was already modeled for human populations based on the Strauss-Howe generational theory essentially transformed this hypothesis into an evolutionary biology hypothesis.

Sources used are mainly from the fields of neurobiology, neuropsychology, animal ecology, chronobiology, and also history of human populations. Quotes are used to underscore some of the most important aspects of the biological and historical evidence central to this hypothesis. Because the text is a work-in-progress, at times it may have some incomplete paragraphs or sentences.

2 Generational history and hormone levels

2.1 Generational hormone theory

To shortly characterize the premises of the presented generational hormone theory: most Western countries have generationally oscillating hormone levels that modulate the average behavioral and physiological traits of Western generations, and that these are the same generational traits that the Strauss-Howe generational theory presents from the Anglo-Saxon generational history.

The graph below presents the average hormone levels in the Western countries of dopamine, cortisol, oxytocin, sex hormones, and growth hormone.

Theoretical average hormone levels in the Western countries, backed up by statistics presented in chapter 2. ‘Sex hormones’ represent presumed gonadotropin-releasing hormone levels. The ‘growth hormone’ curve represents the presumed levels of the growth-hormone-releasing hormone. The percentages are merely directional. [NOTE: currently the cycle for human populations includes oxytocin alongside cortisol. This is because oxytocin hasn’t been implemented to the cyclical animal population model(s), but will be implemented later.]

The generation hormone theory aims at providing a biological basis for the Strauss-Howe generational theory and uses it as a framework along with historical proxy statistics and the animal population cycle studies to establish a generational hormone cycle for human populations. Especially dopamine and oxytocin are at the center of the generational hormone theory, since both modulate group behavior: oxytocin modulates cohesion between e.g. family, friends and other close social connections, while dopamine modulates large-scale group cohesion. (Pearce et al., 2017​63​, Matthews et al., 2016​38​)

Generationally oscillating dopamine levels are presented to modulate group cohesion and the ‘in-group vs. out-group‘ setting in human populations, and these oscillations to group cohesion follow the Strauss-Howe generational theory’s 80 year cycle of oscillating group cohesion. (Lewis et al., 2010​64​, Lewis & Bates, 2017​65​)(S)(S) Sex hormone levels modulate fertility and infertility rates, and also sexual behavior on general, while growth hormone levels modulate feelings of confidence and paranoia for example. The table below details how each hormone level oscillation modulates each of the social mood oscillations depicted in the Strauss-Howe generational theory.

The list of social moods during the four turnings is from Strauss & Howe’s book The Fourth Turning.

It is important to note that while there are many other hormones that affect behavior, they are not taken into account in this hypothesis, as they are more “downstream” compared to the hormones secreted from the hypothalamus that is called the master gland, because it controls the other hormone producing and secreting glands.

The generational hormone theory aims at binding these hormone’s behavioral effects into societal phenomena, especially large-scale in-group vs. out-group behavior, and presents that historical eras of heightened nationalism are possibly linked into generationally oscillating hormone levels that modulate nationalistic behavior. The historical observations made throughout the generational hormone hypothesis are made only to create a link between 1) presumed changes in generational hormone levels in human populations that are similar to the animal population cycles and 2) the Strauss-Howe generational theory, but not to suggest that hormone levels in some way have created historical events. From the viewpoint of sociology, the generational hormone theory takes both micro and macro levels of societal change into account by looking separately at individual and group behavior, both of which are modulated by hormone levels.

2.2 The Strauss-Howe generational theory

During the 1980s, historians William Strauss and Neil Howe begun their research for a book about the history of the US generations labeled Generations. (1991 C-SPAN interview) In 1997 they released The Fourth Turning book, that went even further in describing the dynamics of their theoretical generational cycle. (1997 C-SPAN interview) According to the interviews of these generational historians, before their collaboration begun, both had independently come across the repeating generational cycle in their own research, and only after this they met and combined their research towards a common goal of presenting the history of the US generations utilizing the generational cycle they had discovered. (S)

The Strauss-Howe generational theory details a four generation cycle that goes on roughly at intervals of 2030-2010 | 2010-1990 | 1990-1970 | 1970-1950, etc., with each of the repeating four generations having distinct individual and group behavioral traits. (Karashchuk et al., 2020​57​) (The years are approximations and vary slightly between the books and the presented generational hormone theory, since the books are based on historical observations while this theory is based on a biological cycle.) From the standpoint of this hypothesis, the biggest flaw the Strauss-Howe generational theory has is that the authors explain the generational traits mainly through intergenerational social dynamics and societal events that would define the repeating four generations. (This is somewhat similar to the previous hypotheses of animal population cycles being supposedly caused by environmental factors.)

The cyclical four year generations are introduced next, and then it is explained how they form a repeating cycle of 80 years in total like William Strauss & Neil Howe have documented in their books including Generations (1991) and The Fourth Turning (1997). (S) One full 80 year cycle is called an ‘Anglo-American saeculum’, and the repeating four generations span all the way back to the 15th century England. Even though this chapter presents a lot of historical claims, the sources and quotes are not presented to be indisputable facts, but instead are used to construct a “bridge” between the Strauss-Howe generational theory’s cycle and the presented generational hormone cycle in the Western nations, which in part is similar to the generationally oscillating metapopulation-wide hormone levels of cyclical animal populations.

Strauss & Howe use the word ‘turning’ to describe a roughly 20-year long phase. A 1st generation is born during a 1st turning, and that generation is the Boomers in the current generational cycle. The generations and their traits were easily observable during the 20th century (Baby Boomers, Generation-X, Millennials) in most Western nations, especially in the U.S., due to the increasing societal freedoms and consumerism of the past century. Changes to youth culture mostly generated in the U.S. and spread on from there to other Western nations that were receptive to these movements, presumably by them having similar cyclical hormone levels (as is presented later on).

The most volatile point in the 80 year Strauss & Howe generational cycle has historically been the last (4th) turning, when a 1st generation, the so called Prophet/revolutionary generation (Baby Boomers in the current cycle), is largely holding the places of maximum civic and economic power. This has historically been a time of societal turmoil: old beliefs are challenged, group cohesion tightens, institutions are reshaped to serve the new goals, and the status quo is changing rapidly towards the end of a 4th turning.

There obviously are differences between hormone levels (and DNA) of individuals, but analyzing a generation as a whole averages out the individual differences and revels their average behavioral traits. As most people are married to and have a majority of their friends from their own generation, this increases the effects of generational hormone levels to individual and group behavior through behavioral synchrony. (Sources to be added…) Below are short descriptions of the four ‘turnings’ accompanied by short quotes from the book The Fourth Turning with approximate years of birth from the current cycle.

1st turning (1943-1960): “An upbeat era of strengthening institutions and weakening individualism, when a new civic order implants and the old values regime decays.” The society is unified and there is optimism about the future, institutions are trusted. The society eventually starts a movement towards globalism and liberalism, but nationalistic pride is still strong. A 1st generation is born into an era of tight group cohesion, and is a more optimistic, daring and selfish generation than the previous 4th generation. This sense of optimism has been observed even in the offspring of holocaust survivors. (S) A 1st generation is historically the populist generation once they gain major political power during the 4th turning.

2nd turning (1961-1981): “A passionate era of spiritual upheaval, when the civic order comes under attack from a new values regime.” In a second turning the 1st generation leads the youth revolution and the more rigid nationalistic culture of a 1st turning makes way for a strong culture of liberalism, much through 1st generation young adults. A 2nd generation is born and raised very loosely, becoming a generation of independent individuals, driving the rise of individualism in their young adulthood during the next (3rd) turning.

3rd turning (1982-2004): “A downcast era of strengthening individualism and weakening institutions, when the old civic order decays and the new values regime implants.” When a 3rd turning begins, the family structures are weak, individualism is strong, and anti-social behavior like crime is common. A 3rd generation is born into a relatively peaceful liberal world, and they become more cosmopolitan in their ideological views than the previous two generations. General trust towards institutions begins to decay, and this accelerates during the next (4th) turning.

4th turning (2005-2027): “A decisive era of secular upheaval, when the values regime propels the replacement of the old civic order with a new one.” In a 4th turning the 1st generation enters positions of most political power and nationalistic cohesion (or divide) tends to reach its peak towards the end of the turning (different paths are assessed in chapter 3.4). (S) The 1st generation with their confidence and capital power tend to drive the economic bubbles to new heights before a bust, like in 1929 and 2007. (S) Group cohesion increases, ideological sides are being chosen at an accelerating pace, and cooperation among (possible) opposing factions of society deteriorates. This crisis era will basically either unify or divide a society even further. Information outlets and individuals with large audiences, like celebrities, begin to weigh in their opinions more and more in the increasingly polarized public ideological debate. A 4th generation is born, they are sensitive, and their view of the future is not very bright, just like today’s youth is often referred to as “snowflakes” by the older (and also bolder) generations. “The social mood changes” Strauss & Howe often remind their readers of what happens in a 4th turning once the so called crisis era begins.

It should be noted that terms that describe human ideology and behavior, like nationalism and globalism are only social constructions that are always tied to their historical context. Nationalism and globalism could be replaced with terms of tribalism and cosmopolitanism or some other way to convey the idea of a society’s social cohesion and a society being more open/closed to outside groups. The effects of hormones on ideology cannot therefore be bound too much to any terms of language, since generationally oscillating hormone levels manifest in different ways depending on the point in history, be it a feudal kingdom in 12th century England, a Germanic tribe in 15th century, or a nation belonging to the EU in the 2020s. But since the phenomena of increasing nationalism or cosmopolitanism share common elements throughout history, the terms nationalism and globalism will be used to differentiate historical eras of either low or high group cohesion in cyclical human populations.

Since hormones act as epigenetic signals in development, the presumed hormone cycle presumably modulates the average phenotypes of generations, resulting in synchronized average generational behavioral and physiological traits. (Fowden & Forhead, 2009​66​) This would result in generations to have their unique generational behavioral phenotypes when comparing different generations inside one full cycle. (Crawley, 2008​67​)(S) (The term ‘behavioral phenotypes’ has sometimes been used to describe mental illnesses, but here it is used to describe the presumed average behavioral traits of generations.) To establish the premise that succeeding generations in Western countries have different hormone levels at different stages of their development, chapter 2 presents statistical evidence of generationally oscillating hormone levels in the US and other Western countries, and also from Russia is lagging the Western countries by roughly 15 years in the statistics.

Although only proxy statistics are available for oxytocin, dopamine, and growth hormone levels regarding human populations, societal proxy statistics may be more accurate indicators of societal hormone levels than direct measurements from blood samples. If the hypothalamic neurosecretory neurons are small and ineffective, the effects, like breastfeeding initiation, do not manifest like they would if the hypothalamic neurons were large and effective. For example, oxytocin is released when a baby sucks on the mothers nipple, but if not enough of oxytocin is not released due to low levels of oxytocin secretion from the hypothalamus, there will be no milk ejection, which will show in the statistics. Proxy statistics can therefore possibly provide a much more accurate picture of a country’s average hormone levels than biological samples, that are typically collected from a limited geographical area and the studies rarely last for decades.

2.3 Generational sex hormone levels

2.3.1 Maternal and paternal age

This chapter reviews the effects of the 80 year generational hormone cycle to the human reproductive system. Most Western nations have seen low birth rates during the 2010s and infertility is more common than before, and poor sperm quality is the cause in most cases. (S) Sperm counts have also decreased significantly during the past decades with drops of 50% in many countries during the past 40 years. (Levine et al., 2017​68​) This has been accompanied by falling testosterone levels and the younger generations having less sex than the Baby Boom and Gen X generations. (Travison et al., 2007​69​)(S)(S)

Phase II generation below, roughly representing human birth cohorts who are in their reproductive ages during the first half of a 4th turning, shows greatly diminished size in the lemming gonadotropic cells, which has resulted in less sex hormones and therefore much smaller testes compared to the phase I and phase III cohorts.

Lemming generational endocrine system
Phase II in the lemming cycle represents roughly the first half of a 4th turning, and phase III represents roughly the latter half of a 4th turning. (S)

The same increases in both sex hormone levels and testicular size has been documented in the cyclical snowshoe hare populations, and the population decline phase is associated with lower levels of sex hormones. (Davis & Meyer, 1973​70​) Human male testicular size is therefore presumably lower for the age cohorts in reproductive ages during a 4th turning, which would have a negative effect on sperm quality (Condorelli et al., 2013​71​) and testosterone levels, which is in line with the current situation in the Western nations. Therefore, the sex hormone oscillation is adopted as is from the model of the generational hormone cycle for the cyclical animal population, and the cycle begins from the year 1950 according to the Strauss-Howe generational theory (in reality a few years earlier, but the years are approximations of a 80-82 year cycle).

Presuming that the sex hormone levels have peaked in the mid-1960s, causing faster sexual maturation and thus lowest maternal and paternal age some 5-10 years later, there seems to be a high degree of correlation between several Western countries of the maternal age reaching a low point during the 1970s: US (S), UK (S), Canada (S), Australia (S), Denmark (S), Austria (S), Netherlands (S), Norway (S), and France (S) all show very similar statistics with low points occurring close to the year 1970. (In the US the lowest point was closer to 1965, slightly earlier than in other countries.) Japan is showing a similar but less pronounced effect with only a minor decrease in maternal age (S), which is similar to the breastfeeding statistics from Japan that are also less varied when comparing to the other countries presented.

Age of mothers
Age of mothers                  in Australia
Average                        age of mother
Average age of                  mothers in France
Age of mother at childbirth in the Netherlands
Age of                        mothers in Canada

The similarity between these statistics is very apparent, and they also share a high degree of similarity with the breastfeeding charts.

Previous statistics have taken only mothers into account, but the statistic below of average paternal age show yet again a very similar curve when compared to the maternal age and breastfeeding statistics. (S) Men become fathers being a few years older than women on average, which is why the stats lag a few years from the statistics of maternal age. (S) The graph is very similar to the maternal ages presented before with the lowest point being during the 1970s, with only a few European countries lagging behind: Russia, Poland, Hungary, Estonia, Czech Republic from the east and Spain from the south. (S)

History of                  paternal age in europe
Mean paternal age in European countries. (S)

Having children later in life is very similar to the lemming cycle, since they mature sexually later after the peak phase of their cycle. (Erlinge et al., 2010​72​)

As for other possible explanations why the maternal age has risen from the 1970s, a commonly held belief by many in the field of sociology is that labor force participation percentage of females would somehow affect the age when women have children. But looking at longitudinal statistics from the US, Canada, and the UK, it is quite evident that these statistics have little in common with the maternal age statistics presented in this chapter. This implicates that the commonly held belief of female labor force participation affecting maternal age (on a scale previously suggested) is likely a false belief.

Long-run perspective on female labor force participation rates, 1890 to 2016. (S)

2.3.2 Childlessness rates

For women, low estrogen levels can produce many of the same symptoms that low testosterone levels cause for men, including infertility. (Weiss & Clapauch, 2014​73​)(S) Lower amounts of estrogen could be the cause for why many women are childless during this 4th turning, just like 80 years ago in the 1930s and 40s when many women born in the early 20th century were childless, which the statistic below of childlessness by year of birth illustrates.

Percentage of childless women by year of birth shows a high degree of synchrony across many Western nations. (S)

It should be noted that the curves between the countries correlate quite tightly, with Spain lagging by a few years with its low point, just like with Spain’s paternal age statistics (chapter 2.3.1) were lagging by about 10 years when compared to statistics from the US and the UK.

The statistic below shows childlessness levels being in relative synchrony among large geographical areas in Europe, and once again the central and eastern parts of Europe are lagging the US by about 5-10 years. New Zealand shares a highly similar curve regarding infertility by birth cohort. (Boddington & Didham, 2009​74​)

Putting together the proxy statistics of presumed sex hormone levels produces the model below, which presents the final hormone levels birth cohorts have received during their development of gonads, which occurs primarily between ages 10 and 20.

Birth cohorts with high fertility/infertility indicated by their cohort specific sex hormone levels. The percentages are merely directional.

The effects of low/high sex hormone levels manifest as fertility/infertility roughly 20-30 years later when the cohorts are at reproductive ages (which occurs earlier for the cohorts with higher levels of sex hormones).

Strauss & Howe note that over five centuries, every 4th turning has been marked by relatively low birthrates, which correlates with the presented statistics. (Generations, Strauss & Howe, 1991) The statistic below illustrates that the trend of childlessness by birth cohort does follow the presumed hormone levels in the US and Australia, at least to a degree. (Gobbi, 2012​75​) The red line indicates the presumed childlessness levels by birth cohort, as it is an inverted curve compared to the ‘sex hormone levels by birth cohort’ curve.

Childlessness by birth cohort in 45 to 49-year-old women.

In addition to infertility rates, when looking at birth cohort fertility rates, there is a striking synchronicity in timing between the Western countries presented below. Although fertility trends in the Western countries have descended for over two centuries in the Western countries, there is a clear deviation from this trend: birth cohorts from roughly 1910 to 1950 display a peak in fertility rates.

Birth cohort total fertility rates for women in Australia, Canada, New Zealand, and the US show very similar curves. (S) The peak fertility rate is close to the 1935 birth cohort in the US.

The birth cohorts with the highest levels of sex hormones were presumably born in the 1930s and 40s, which would help explain the significant baby boom of the 1950s and 60s that is displayed in the graph below. Spain is once again lagging by about 10 years behind others in this statistic, just like with the infertility rates and also oxytocin proxy statistics.

Fertility rates in the US, the UK, France (FR), Germany (DE), Switzerland (CH), Sweden (SE), and Spain (ES). (S) The peak is close to 1965.

As for the previous century, the changes in fertility rates are less pronounced in the US and the UK, that are presumably the two most likely cyclical countries, but the decades around 1880s are clearly above the trend line in the UK, and in the US, the source statistic does not show much variance in the 19th century. (S)(S) Many Western nations show a similar trend with the UK, as the decades after 1880s are generally decades of steep decline in fertility rates, while the previous decades have been more of a plateau (as a rough generalization). (S)

Below is the presumed sex hormone level curve inserted into the generational hormone cycle. [Because Strauss & Howe have estimated the current cycle to have begun after mid-1940s in the US, the sex hormone curve is presumed to have peaked slightly earlier in the current cycle, close to 1965 on average in the Western countries.]

If the sex hormone level curve is even loosely accurate, fertility rates will increase and childlessness rates will decrease during the 2020s, and this trend will continue throughout the 2030s.

As to why there are no clear population cycles in the Western countries like in the cyclical animal populations if they share a similar generational hormone cycle? On average, humans today live to be much older than before (Gurven & Kaplan, 2007​76​), and if humans still lived shorter lives like human populations who lived in the wild/nature, this would be more easily visible in population numbers. But since most of the Boomers are still alive in 2020, the population numbers have not plummeted very much. In addition, populations of larger mammals exhibit less variation to population size than populations of smaller mammals. (Erb et al., 2003​77​)

As for the societal effects of sex hormone levels, the curve in the graph above is roughly in line with the Strauss-Howe generational theory’s generationally oscillating ‘gender gap’, that reaches its maximum width during a 1st turning, meaning that the gonadotropic cells are presumably more active and secreting more sex hormones during that period of time. Increased levels of sex hormones would explain the sexual revolution of the late 1960s, an era when the sex hormone levels were presumably high, which would result in more sexual activity especially among the cohorts in their fertile age. (S)(S)(S)

Apart from issues related to sexual behavior and reproduction, low testosterone levels have other significant impacts to male health, including a heightened risk for developing metabolic syndrome, type 2 diabetes, and coronary artery disease. (Goodale et al., 2017​78​) Low testosterone levels have also been linked to depressive symptoms in men, and according to a meta-analysis, testosterone treatments have resulted in significant reductions in depressive symptoms. (Walther et al., 2019​79​)

2.4 Generational growth hormone levels

Growth-hormone-releasing hormone, that stimulates growth hormone secretion, is secreted from the hypothalamus, and higher levels of growth hormone secretion leads to larger animals and higher body mass on average. (Furigo et al., 2019​80​/S) This is why growth hormone levels are presumably highest during the mid-phase of the animal population cycles: “An important biological feature of cyclic populations of voles and lemmings is phase-related changes in average body mass, with adults in high-density phases being 20–30% heavier than those in low-density phases of a cycle. This observation, called the Chitty effect”, is considered to be a ubiquitous feature of cyclic populations… The Chitty effect is predicted to be most pronounced at the late increase or peak phase of a population cycle.” (Oli, 1999​27​)

Below is the presumed growth hormone level graph for Western human populations, adopted from the generational hormone cycle of cyclical animal populations.

Presumed growth hormone levels in Western countries.

Since most growth occurs during puberty, and on average the final height of humans is reached by the age of 20, birth cohorts born during early 1980s could be the tallest, after which there should be shortening of cohorts. Statistical evidence is available to support the claim that the birth cohorts in Western countries have in fact grown shorter than the birth cohorts going through puberty during the 1980s and 90s.

For 18 year old men and women in the US, women’s height peaked in 1988 and men’s height peaked in 1996. (NCD-RisC​81​) Below are recent birth cohort studies from the Netherlands and Switzerland. (S)(Vinci et al., 2019​82​)

Average height by birth cohort in the Netherlands.
Average height by birth cohort in Switzerland.

These statistics give more support to the presumption that cohorts from the early 1980s have had the highest levels of growth hormone during their youth.

In addition to physical size, growth hormone levels also modulate behavioral traits. Listed below are a few key behavioral effects commonly found in people with very low levels of growth hormone. (Maric et al., 2010​83​, Akaltun et al., 2018​84​)

  1. Interpersonal sensitivity
  2. Hostility
  3. Paranoid ideation
  4. Anxiety

It could be said that the recent years in the Western countries have seen increases to all of the listed behavioral traits, especially towards out-group members: 1 & 2) interpersonal sensitivity and hostility have manifested as increasing confrontational social behavior, 3) paranoia has manifested for example as increasing vaccine hesitancy, fears regarding the 5G technology, and all kinds of conspiracy theories, of which many have unified under the Qanon conspiracy theory, and 4) anxiety levels have been high compared to the 1970s and 80s.

High levels of growth hormone can make an individual feel overly confident, and inversely low levels can make an individual feel insecure. The Strauss-Howe generational theory presents that the general views of the future in the US, whether they are overly positive or negative, oscillate throughout the phases of the generational cycle: on average, the most positive views of the future occur at the end of a 2nd turning, and the most pessimistic views of the future occur at the end of a 4th turning. Below is the generational growth hormone curve along with these presented views of the future from the Strauss-Howe generational theory.

Growth hormone levels with the Strauss-Howe generational theory’s social mood regarding positive/negative views of the future. The percentages are merely directional.

The curve illustrates that the theoretical growth hormone cycle from the animal population cycles and human populations shows not only a correlation between height statistics from the past decades, but also a correlation with the Strauss-Howe generational theory regarding the views of the future. And in cyclical animal populations the behavioral traits are the same: animals are more bold during mid-cycle phases, and more timid at the end (and beginning) of the cycle.

2.5 Generational cortisol levels

As presented in chapter 1, cortisol (CRH/ACTH) levels peak in cyclical animal populations at the end of the 3rd phase of the cycle. In addition, abnormally high glucose levels and high rates of diabetes have been found at this phase of the cycle in a study conducted on cyclical lemming and vole populations. (Niklasson et al., 2006​85​)

Because higher cortisol levels cause increased inflammation, and inflammation is a precursor for type 2 diabetes, statistics of type 2 diabetes incidence may reveal if cortisol levels have been higher close to the year 2010 as is indicated by the generational hormone cycle model below. (Joseph & Golden, 2016​86​, Tsalamandris et al., 2019​87​, Hackett et al., 2016​88​)

The US statistic for type 2 diabetes incidence (below) correlates very closely with model of the generational hormone cycle, as the year 2009 had the highest incidence rate, after which the incidence rate has decreased by over 30%. (Benoit et al., 2019​89​)

Incidence of type 2 diabetes in the US.

Many other Western countries have witnessed a similar trend of declining type 2 diabetes incidence rates during the 2010s, including Canada and the UK. (Magliano et al., 2021​90​, Johansson & Norhammar, 2016​91​) As is with the other statistics presented in chapter 2, the turning of the trend among Western countries has not been explained in any study.

The downward turn in the incidence rates of type to 2 diabetes is especially perplexing to scientists since 1) obesity is the largest single contributing health factor in the development of type 2 diabetes (S), and 2) obesity rates increased in the US (statistic below), Canada (S), and the UK (S) during the 2010s.

US obesity prevalence 1999-2018. (CDC)

2.6 Generational oxytocin levels

2.6.1 Parenting intensity and breastfeeding rates

Higher oxytocin levels in parents leads to more time spent with children. (Gordon et al., 2010​92​) The Strauss-Howe generational theory states the following about child nurture (S) intensity: 1st turning nurture is relaxing, 2nd turning nurture is underprotective, 3rd turning nurture is tightening, and 4th turning nurture is overprotective, after which the next 1st turning child nurture is once again loosening. The graphic below, that is taken from the book Generations (1991), illustrates the 80 year cycle in nurture intensity (where the 2nd turning is roughly from 1965 to 1985 in the current cycle).

Type of nurture during turnings
‘Turnings’ and ‘generations’ (red text) have been later added for clarity. Social ‘eras’ are explained in detail in Strauss & Howe’s books Generations (1991) and The Fourth Turning (1997).

Child nurture in the 1970s was much more carefree on average than in the 2000s (S), and according to the statistic below, it seems that the 1970s were also the time when American parents spent the least time with their children compared to the other decades in the statistic.

Time spent                  with children
Weekly hours spent with children under age 5, the low peak being in the 1970s, and the high peak being in the 2000s. (S) The statistic correlates with the Strauss-Howe generational theory’s prediction regarding child nurture intensity.

The bottom year is close to 1975 and the top year seems to be close to 2005-2010 until the trend begins to go down, repeating the same years as with the breastfeeding rate and paternal age statistics. There is also a steeper climb from 1975 to 1985 like with the breastfeeding rate and maternal/paternal age statistics (reviewed next). Other Western nations also show large increases in parenting time since the 1970s. (Sani & Treas, 2016​93​) In addition, the statistic shows a pattern very similar to the breastfeeding initiation statistics and maternal & paternal age statistics presented next.

Breastfeeding statistics should correlate with the presumed generational oxytocin levels, as breastfeeding requires oxytocin to enable the milk let-down reflex. (Augustine et al., 2017​94​)(S)(S) “Circulating oxytocin is critical for normal birth and lactation. Oxytocin is synthesized by hypothalamic supraoptic and paraventricular neurons and is released from the posterior pituitary gland into the circulation… While it might be controversial as to whether oxytocin plays an indispensable role in parturition, the critical role that oxytocin plays in milk let-down during lactation is not disputed. The release of milk is mediated by secretion of oxytocin from the posterior pituitary gland, and oxytocin’s action at OTR [oxytocin receptor] in the mammary gland induces a rise in intra-mammary pressure and release of milk: an oxytocin-mediated reflex upon suckling.” (Scott & Brown, 2013​95​)

Available breastfeeding statistics from Western countries indicate that there is a low point in breastfeeding rates close to 1965-1975: U.S. (Albanesi & Olivetti, 2009​96​, Ryan, 1997​97​)(S), Australia (S), Norway (S), Sweden (S), New Zealand, and England & Wales. Even Japan shows a similar curve. (Inoue et al., 2012​98​)

US breastfeeding statistics.
Breastfeeding rates in Australia
Breastfeeding in Sweden
Breastfeeding rates in New Zealand
Breastfeeding rates in Japan and Norway
Breastfeeding in the UK

A verbal statement about the US rates reinforces the statistics that the rates were higher in the 1930s than in the 1970s: “Seventy-seven percent of the infants born between 1936 and 1940 were breastfed; the incidence declined during the subsequent decades to about 25% by 1970.” (Institute of Medicine, 1991​99​)

When looking at earlier centuries for similar patterns of low points in breastfeeding in the US, there unfortunately are no breastfeeding statistics from the 19th century, but here is a quote from the year 1887 that provides answers: “Then, bizarrely, American women ran out of milk. “Every physician is becoming convinced that the number of mothers able to nurse their own children is decreasing.” Another reported that there was “something wrong with the mammary glands of the mothers in this country.”…In the United States, nineteenth- and early-twentieth-century physicians, far from pressing formula on their patients, told women that they ought to breast-feed. Many women, however, refused. They insisted that they lacked for milk, mammals no more.” (S) The inability to breastfeed is a probable indicator of low oxytocin levels, and like the Strauss-Howe generational theory states, nurture intensity was also low during this period.

And from 80 years earlier, this text may provide answers: “As with so many popular trends, there came a backlash against the use of wet nurses. Come the late 1700s/early 1800s—as part of the reform movements that swept across the social landscape of Europe and the United States—many women and men were calling for a return to in-home breastfeeding of babies by their own mothers.” (S) If there were calls to return to breastfeeding, that could indicate that the breastfeeding rates by biological mothers quite possibly have been low during that point in history. (And as a side note, the quote also mentions the social reform movements happening at the same time, just like during the 1960s and 70s, decades of large social reforms in Western nations.)

Historical UK breastfeeding rates closely echo the findings from the U.S. The following quote enforces this presumption: “The interwar [between WW1 and WW2] years saw the start of a long, steady decline in breastfeeding… breastfeeding rates in mothers leaving the postnatal wards dropped to below 20 per cent around 1970… there is no doubt that the 1970s represent a nadir in breastfeeding rates… But, for whatever reason, it is a fact that breastfeeding rates were much lower in the 1970s than in the decades before, and lower than they are now [2007].” (S)

In addition, since oxytocin is also required for parturition, and there is a negative correlation between caesarean section and breastfeeding rates, this enforces the presumption that breastfeeding statistics would correlate with the mother’s oxytocin levels. (Blanks & Thornton, 2003​100​, Hobbs et al. 2016​101​, Smith, 2007​102​)

As for the connection between child the child nurture intensity, Generation X, a 2nd generation, was born during 1965-1983 (estimations slightly differ between Western nations and but also between generational historians) and would, according to the Strauss-Howe generational theory, be a 2nd generation that received underprotective nurture. (S) Parents’ high oxytocin levels lead to more intense nurture, and low oxytocin levels lead to less intense nurture. (Gordon et al., 2010​92​) Especially the Australian breastfeeding statistics, which are probably more accurate than the US statistic (comprised of three different sources), illustrates that rates are at the lowest point during the birth of Generation X, which correlates with low nurture intensity, and this is presumably due to low societal oxytocin levels. (Gordon et al., 2010​92​)(S)

Breastfeeding                  rates and nurture
The Strauss-Howe generational theory’s nurture intensity is lowest for the 2nd generation, and the Generation X in Australia was born in 1965-1983.

The statistical evidence therefore supports the presumption that the Strauss-Howe generational theory’s 80 year nurturing intensity cycle is a generationally oscillating oxytocin cycle with a span of roughly 80 years.

2.6.2 Alcohol consumption

Alcohol consumption statistics should reveal changes in historically varying oxytocin levels because alcohol and oxytocin have similar effects (Mitchell et al., 2015​103​), and oxytocin has been shown to inhibit the effects of and lessen the cravings for alcohol. (Bowen et al., 2015​104​, King & Becker, 2019​105​, King et al., 2021​106​) If these studies are accurate, then low oxytocin levels could lead to higher alcohol consumption and higher oxytocin levels could lead to lower alcohol consumption in the population. The Fourth Turning book states the following about per capita alcohol consumption rates following the 80 year generational cycle: “They begin rising late in a 1st turning, peak near the end of the 2nd turning, and then begin a decline during the 3rd turning amid growing public disapproval.” The graph below shows that the 3rd turnings in recent centuries (1830-1850 and 1910-1930) indeed saw large sudden decreases in per capita alcohol consumption.

Historical                  consumtion of alcohol per capita in the U.S.
Per capita alcohol consumption in the US form 1710 to 1970. (S)

Two more statistics are available from the 19th and 20th century, and both show similar behavior in alcohol consumption: low points of alcohol consumption are in the 1840s and 1920s when compared to the previous decades in the UK (S)(S) and the Netherlands (S).

There was once again a similar drop in the alcohol consumption 80 years after the low point of 1920s, as roughly the year 2000 was a low point in the U.S. alcohol consumption, preceded by a high point in the 1980s. (S) Most Western nations show this same pattern of year 2000s having lower alcohol consumption rates compared to the 1980s. (S) Especially adolescence alcohol consumption has greatly gone down for the last two decades in the Western nations (S)(S)(S), while Boomers’ consumption has gone up in several nations. (S)(S) These statistics are directly in sync with the previously made presumption that the younger generations (Millennials, Gen Z) have higher oxytocin levels than the older generations (Boomers, Gen X) in their youth. But the trend for adolescents should be turning around 2020-2025, and first signs of this are possibly already showing for instance in Sweden. (S)

Dopamine levels should be also taken into account when looking at alcohol consumption rates, and this is reviewed in chapter 2.7.1. It is presumed that low dopamine levels drive up the alcohol consumption, which peaks close to the beginning of the 3rd turning (close to the year 1990 in this cycle), and then starts a sharp decline that ends before the 3rd turning comes to a close.

Below are the presumed oxytocin levels on a societal level compiled to a model.

Presumed generational oxytocin levels
The percentages are merely directional.

If the generational oxytocin level curve is accurate, oxytocin levels will continue to decrease during the 2020s, leading to decreasing trends in breastfeeding rates and less intensive childcare.

Although in the 21st century oxytocin can be given as a nasal spray at hospitals for mothers that are unable to breastfeed after giving birth, which may skew some statistics from the 21st century, statistics show that breastfeeding rates in the US and the UK have started to decrease after mid 2010s. (S)(S)

In addition to low sex hormones leading to higher maternal age, higher oxytocin levels have also been linked to higher maternal age. (Erickson et al., 2019​107​) In human studies higher baseline levels of oxytocin has been attributed to higher prevalence of infertility, which may also have attributed to the high infertility rates of the cohorts born during the 1980s and 90s. (Lui et al., 2010​108​)

2.7 Generational dopamine levels

2.7.1 Crime rates

While oxytocin levels modulate social cohesion in the context of family, friends, and other small-scale social connections, dopamine modulates group cohesion in large-scale social networks. (Pearce et al., 2017​63​) Dopamine modulates an individual’s feelings of being a part of a large-scale social network like a political party, religious group, or a nation. The feeling of belonging to a large in-group is stronger for an individual that has higher levels of dopamine, and the feeling is weaker for an individual that has lower levels of dopamine. Therefore a society experiencing high levels of dopamine would exhibit high levels of in-group cohesion, leading to higher levels of in-group favoritism and out-group derogation.

Low dopamine levels predispose an individual to having learning problems, impulsive and aggressive behavior, and proneness to substance abuse and addiction. (Chester et al., 2015​109​, Leyton & Vezina, 2014​110​, Gold et al., 2014​111​)(S)(S)(S) Therefore birth cohorts exhibiting these behavioral traits should indicate low levels of dopamine. Strauss & Howe have noted that birth cohorts from 1961 to 1964 display the kind of behavioral traits mentioned above, even though they have not been able to connect the traits to low dopamine levels in the 1980s or 90s, because back then the effects of dopamine were not understood as well as they are today.

The chart from Generations (1991) illustrates how the birth cohorts from the early 1960s fare poorly at school, show high levels of addition to substance use, and exhibit various impulsive behavioral traits.

Individuals who commit crimes have lower levels of dopamine production and secretion than the population on average, and therefore crime rates could indicate oscillating dopamine levels in the US and other Western countries. (Schlüter et al., 2013​112​) Since the Strauss-Howe generational theory states that a noticeable increase in crime occurs during a 2rd turning and that the crime rates start to drop during the 3rd turning, crime rates should peak somewhere close to the year 1990. The statistics below from the US, Canada and the UK confirm these premises.

Rate of crime in the US peaked in 1991. (S)
Rate of crime in Canada peaked in 1991. (S)
Rate of crime in the UK peaked in 1995. (S)

The reasons for these large shifts in crime rates still remain ‘a mystery’ to researchers who have been studying the closely concurrent phenomenon in different countries. (Tonry, 2014​113​, Lappi-Seppälä & Lehti, 2014​114​)(S)(S) No sociological or criminological hypothesis exists that can explain the phenomenon in even one country, nor the high degree of statistical synchrony between countries. (Farrell et al., 2014)

Since most of crime is committed by individuals in their twenties and crime peaked in most Western countries in the early 90s, this would mean that the highest levels of crime are committed by the birth cohorts from the 1960s and 70s, which has been confirmed in a recent study. (Spelman, 2021​115​)

And similarly to the other proxy statistics presented, Russia lags roughly 15 years behind the US in the crime rate statistics with a clear peak in the mid 2000s. (Chistic, 2019​116​)

Rate of crime in Russia. (S)

To make more connections between low dopamine levels and crime, impulsive behavior is connected to a low resting heart rate (Portnoy et al., 2014​117​, Baker et al., 2009​118​), and it is considered to be the best-replicated biological correlate of antisocial behavior. Violent and non-violent criminals have been found to have a lower heart resting rate and lower blood pressure compared to the population as a whole. (Latvala et al., 2015​119​, Murray et al., 2016​120​, Culpepper & Froom, 1980​121​) Since low dopamine levels result in lower resting heart rate and lower blood pressure, the connection between dopamine levels and the tendency to take criminal actions is quite apparent. (Ziegler et al., 1985​122​)

The connection between high crime rates and low dopamine levels (thus low blood pressure) is also reflected in the annual seasonal changes: 1) crime rates are highest during late summer, which is also when blood pressure levels are at their lowest levels. (Tiihonen et al., 2017​123​, Fares, 2013​124​)(S) Dopamine levels have also been directly observed to be lower during the summer, which even further establishes the link between low dopamine levels and high crime rates. (Eisenberg et al., 2010​125​)

As for dopamine’s effect on alcohol consumption, chapter 2.3.4 already established the fact that the Baby Boomers (a 1st generation) have the highest rates of alcohol consumption among current generations, and, in addition, the statistic below from the UK shows how the birth cohorts from the early 1960s also have the lowest levels of abstinence.

Birth cohort percentage of men (cubes) and women (triangles) abstainers in the UK. (Meng et al., 2013​126​)

The listed proxy statistics for presumed dopamine levels regarding criminal behavior, alcohol consumption, and educational performance are put together in the graph below.

Presumed generational vasopressin levels
The percentages are merely directional. (Note: The curve should be slightly sharper at the bottom to better represent the sharp reversal in crime rates after the peak of 1990s.)

The Strauss-Howe generational theory states that anti-social selfish behavior is common and better tolerated during the 2nd and 3rd turnings, and that such behavior is less common and tolerated during the 4th and 1st turnings, which is in line with the presented hormone levels, since low dopamine levels promote anti-social behavior.

Below is the dopamine curve inserted in to the generational hormone cycle.

These statistics of mid-cycle aggressive behavior are in line with the observations regarding cyclical animal populations during the mid-cycle (2nd and 3rd phases): “…animals in high-density phases are much more aggressive than those in low-density phases.” (Oli, 2019​13​, Matthiopoulos et al., 2002​127​, Piertney et al, 2008​128​)

2.7.2 Group cohesion and territoriality

This chapter presents how varying dopamine levels affect the strength of group cohesion. Placing dopamine in the center of human social evolution is not a novel idea, as it has been suggested before. (Previc, 2009​129​) A recent study enforces this idea, because what sets humans apart from other apes is abnormally high levels of dopamine: “However, in line with another recent study on gene expression, humans had dramatically more dopamine in their striatum than apes… Humans also had less acetylcholine, a neurochemical linked to dominant and territorial behavior, than gorillas or chimpanzees. The combination “is a key difference that sets apart humans from all other species”… Those differences in neurochemistry may have set in motion other evolutionary changes, such as the development of monogamy and language in humansAs human ancestors got better at cooperating, they shared the know-how for making tools and eventually developed language—all in a feedback loop fueled by surging levels of dopamine.” (Raghanti et al., 2018​130​/S/S)

During vole population peak years, that being the 2nd and 3rd phases, immigration increases, territory boundaries are reduced, and co-existence with other species of voles increases. (Johnsen et al., 2019​131​)(Tähkä et al., Endocrine aspects of population regulation in the genus Clethrionymus, Mem. Soc. fauna Flora Fenn., 1984) During the last (4th) phase of the vole cycle, group cohesion and territoriality increase and co-existence with other vole species decreases. Similar phase-dependent changes have been observed in other cyclical animal populations as well.

In human populations this kind of behavior would mean that the 2nd and 3rd turnings are more cosmopolitan eras of low nationalism and low group cohesion, which would be roughly the years 1970-2010 in the current cycle. Group cohesion, territoriality, and nationalism have risen in many Western nations during the past 4th turnings: 1930-1950, 1850-1870, and 1770-1790 have witnessed it, and in 2010-2030 the same thing is very evidently happening again. Going further in history, during the 4th turning of 1690-1710 there was a movement towards increased centralized power of kings and the Pope, even though the concept of a nation was not yet largely adopted in Europe. Also the 4th turning years of 1610-1630 were “nationalistic” and had populistic tendencies. These event are taken into account in the graph below, which represents the presumed dopamine levels affecting group cohesion and territoriality.

To further elaborate the effects of dopamine, the graph below illustrates the effect to group cohesion in a society when the levels of dopamine are either high or low. The circles represent figurative outer limits of social, ideological and cultural norms. The out-groups left outside have historically often been immigrants, Jews, and other cultural and ideological minorities, suspect of ending up as scapegoats for many societal problems when demands for tighter social cohesion increase.

Social circles and social coherence
The spots can represent either individuals or groups (depending on the scale).

Strauss & Howe write about tightening social (group) cohesion being a part of a 4th turning and that keeping good social relations within one’s existing social circles is important, because those individuals who are shunned outside social circles will end up without much social support as the social cohesion tightens. (More on group cohesion and scapegoating in chapter 3.) Examples of increasing group cohesion in civic life during the current 4th turning are the PC culture / cancel culture and MeToo-movement. These movements have been about enforcing tightening unwritten in-group rules, but the new tighter rules have also been enforced through the justice system, which represents the written rules. Together these written and unwritten rules form an in-group’s behavioral and ideological boundaries, and these boundaries are tighter in a 4th turning due to increased group cohesion.

The PC culture became mainstream during the 2000s and has become stronger ever since, evolving into an outrage culture (aka. cancel culture). The MeToo-movement was founded in 2006, became mainstream in 2017, and spawned a multitude of other call-out movements aimed at reducing many old behavioral traits that are seen as derogatory toward other in-group members, even though many of these traits were more tolerated during the 2nd and 3rd turnings. The call-out culture has been spreading through the Western countries quickly, and those who have not complied with the new social rules have been shunned out of social groups, just like Strauss & Howe predicted roughly three decades ago.

Another example of tightening in-group cohesion in a society is the destruction of statues that represent figures from the past that, in their time, have had opinions that are no longer tolerated at all, like statesmen who were proponents of slavery. Books and other platforms presenting the opposing side’s ideals may be banned or destroyed. This is the same behavior that is often witnessed after a war has ended: the winners write the history, and the losing side’s institutions are molded to represent the winning side’s ideology, often resulting in the suppression of the culture of the losing side.

The two graphs below represent a simplified expression of higher and lower group cohesion (dopamine) effects in the history of the Western cultures, especially in the U.S., and how it affects group cohesion. The idea is not to suggest that hormone levels have caused any of the historical events mentioned, but if dopamine levels have been higher during certain eras, it may have facilitated eras of low and high degree of group cohesion during which such events may have become more probable. (The most significant 4th turning events regarding the US are marked with the color red.)

The blue line represents high/low dopamine levels in the Western countries.
The blue line represents high/low dopamine levels in the Western countries.

Nationalism tends to be higher during the 4th turnings. ‘Group cohesion and territoriality’ largely reflects the political/ideological uprisings/revolutions, which have often occurred during or close to the 4th turnings.

When group cohesion increases, the demand for institutions to take sides grows, since institutions have control over the individuals in a society. Institutions are basically “stable, valued, recurring patterns of behavior”. (S) Institutions on general can be divided into public (government and laws), private (companies and organizations), and cultural institutions (common traditions and language). Cultural advances largely rely on functional institutions.

Institutions represent feelings of individuals as moral values in their rules and aims. (More on this in this video from a MIT professor in psychology.) The Strauss-Howe generational theory states that institutional power peaks in during the 1st turning, which is when the social cohesion is also the strongest before they both begin to decline again. Institutions enforce the common rules through the rule of law, but also through other means of influence. If the institutions do not represent the general will, aka ‘volonté générale‘ like Jean-Jacques Rousseau called it (S), there may be a revolt to overturn those institutions, something that has happened relatively often during the past few 4th turnings in history. (Chapter 3.4 presents the three basic paths societies usually take during eras of increasing group cohesion.)

2.7.3 Group hormones and political ideology

This chapter presents a possible close connection between the levels of dopamine and oxytocin and political ideology, since there seems to be a theoretical and also statistical connection between voting behavior and group hormone levels. (It should be stated that linking political ideology to hormone levels was never a goal for this hypothesis, but the possible connection has arisen by chance when comparing generational voting data to the theoretical generational hormone levels.) The left vs. right ideological spectrum is universal like height, weight, and blood pressure, and it forms a bell-shaped curve of normal distribution in a country like the other mentioned physical properties. (Cochrane C., Left and Right, McGill-Queen’s University Press, 2015​132​)

The left vs. right spectrum is especially visible in the US, where two major parties present the left vs. right political divide in all major societal issues. (S) Most of the central arguments on important issues like tax rates, welfare, gun rights, etc. can be drawn from the basic attitude towards the society: on average, individual rights and responsibilities (aka. self reliance) are more important for the right-wing voter, and shared rights and responsibilities (aka. the common good) are more important for the left-wing voter.

It could also be said that even as all humans are capable of empathy, the circle of empathy is different between the left-wing and right-wing voters: left-wing voters tend to have a circle of empathy that usually covers minority groups such as immigrants and the least wealthy, and they’re seen as preferring cosmopolitanism, while in comparison the right-wing voters have a slightly tighter circle of empathy that is usually more centered on those who are the most beneficial to the society and thus may increase the society’s success when competing against other societies, and they usually are more nationalistic than cosmopolitan in their world views.

Therefore, it could be theoreticized that higher levels of group hormones dopamine and oxytocin manifest as more left-leaning ideological behavior, since higher levels of these hormones leads to increased prosocial behavior. Inversely, lower levels of dopamine leads to less social behavior and increased self reliance, thus the presumed inclination to lean right.

A clear connection between oxytocin and ideological behavior seems to be that oxytocin increases openness to experiences in individuals (Cardoso et al., 2011​133​)(Pearce et al., 2019​134​), and simultaneously openness to experience has been found the be the most consistent indicator of ideology: “Our findings show that, in line with the congruency model of personality, Openness to Experience is the best and most consistent correlate of political ideology, with politicians high on Openness to Experience being more likely to be found among the more progressive left-wing political parties.” (Joly et al., 2018​135​) This indicates that higher oxytocin levels may lead to a tendency towards a left-wing ideology.

Another connection between oxytocin and political ideology is that oxytocin increases gaze cues (for emotional faces) (Tollenaar et al., 2013​136​), and since individuals leaning left have been found to follow gaze cues more than individuals leaning right (Dodd et al., 2010​137​/S), this adds to the evidence of high oxytocin levels being possibly connected to a left-wing ideology. The dopamine receptor system has been found to affect political preferences (through the receptor alleles), and twin studies show genetics affect voting behavior, which enforce the premise that dopamine levels also modulate ideological preferences. (Hatemi et al., 2014​138​, Settle et al., 2010​139​)

Political ideology has been found to form largely during ages 14-24, after which the effects become smaller, especially after reaching the age 40. (S)

The most formative years regarding political ideology are ages 14 to 24. (S)

Since the formation of political ideology continues (to a lesser extent) during adulthood, roughly the ages of 20-25 years is the average age of ideological preferences solidifying inside a birth cohort. Therefore a birth cohort being 20-25 years old in 1990, when dopamine levels were presumably at their lowest point, should lean right on the political spectrum.

Placing a divider so 50% of the cohorts with low or high levels of dopamine and oxytocin are on both sides produces the chart below. The birth cohorts presumably leaning right leaning were born approximately 1930-1970, but there should be a small trend towards left leaning ideology for cohorts born close to 1950.

Presumed average oxytocin and dopamine levels 25 years after birth. Each ideological side is allocated 40 years of the 80 year cycle by the dotted line. (Percentages are merely directional.)

As examples of presumably mostly right-leaning cohorts, the cohorts born close to 1945 experience low oxytocin levels during their most formative years considering ideology, and the cohorts born close to 1965 experience low dopamine levels during their most formative years considering ideology.

By adding a marker (blocks) to whenever either the oxytocin or the dopamine oscillation is at its lowest value produces the graph below.

A divider (blocks) has been added to show what birth cohorts have the lowest oxytocin or the lowest dopamine levels. The percentages are merely directional.

The birth cohort voting preference curve (above) shares a strikingly high level of similarity with the curve of US voting trend by birth cohort (below), which is largely synchronized inside birth cohorts: “The peaks and valleys occur in almost identical locations, strongly suggesting a generational trend.” (S)

The y-axis represents political alignment on the left vs. right ideological axis, meaning that the higher the line goes, the more likely individuals in those birth cohorts are to vote left. The graph has been turned upside down from the source material in order to graphically align it with the generational hormone cycle’s model. (S)

These voting statistics are very much in line with the presumption that generations born roughly from 1930 to 1970 tend to vote right, with a small leftist peak close to 1950, and the highest leftist peak is close to 1990 in both graphs. This implies that political ideology is modulated by hormone levels present during maternal and paternal age. According to the charts, it would also seem that low dopamine levels may have a stronger effect on the cohort leaning right than low oxytocin levels.

In addition to birth cohorts displaying ideological preferences, a well known dividing factor in the left vs. right political spectrum is the “urban vs. rural” divide, since generally urban voters lean left and rural voters lean right. (S)(S)(S) This implicates that breastfeeding rates are therefore lower in rural areas, which is confirmed by studies. (S)(S) (Since population density is lower in rural areas, this leads to speculate if individuals with less efficient oxytocin systems prefer a rural environment due to them being less socially active compared to individuals with highly functional oxytocin systems, who are living in urban environments.)

But can the endocrine system directly modulate ideological preferences? The following study implicates that this is entirely possible.

“The phylogenetically ancient neuropeptide oxytocin has been linked to a plethora of social behaviors. Here, we argue that the action of oxytocin is not restricted to the downstream level of emotional responses, but substantially alters higher representations of attitudes and values by exerting a distant modulatory influence on cortical areas and their reciprocal interplay with subcortical regions and hormonal systems… Notably, a recent longitudinal epigenetic study detected a positive link between methylation of the OXT [oxytocin] receptor gene at birth and callous-unemotional traits at age 13, which corroborates the hypothesis of abnormalities in the oxytocin system as a core element of developmental pathways to callous-unemotional traits. These findings, together with the relationship between variations in common polymorphisms of the OXT receptor gene and antisocial behavior, and high callous-unemotional traits, all point to an involvement of the OXT system in upstream attitudinal representations… OXT also influences, and interacts with, representations of attitudes and values in more recently developed cortical regions…” (Hurlemann et al., 2017​140​)

What is important to note is that the study’s result “…corroborates the hypothesis of abnormalities in the oxytocin system as a core element of developmental pathways to callous-unemotional traits”, which are detectable by a persistent pattern of behavior that reflects a disregard for others, and also a lack of empathy and generally deficient affect. (S) These characterizations best fit the 1st and 2nd generations in the Strauss-Howe generational theory, and these generations lean ideologically mostly to right.

According to a recent study, voting is much less rational and more based on emotions than what has been previously thought to be the case.

“But how should we treat questions of control, free will, and responsibility, given the growing body of findings about the dubious value of conscious control? An interesting possibility is that humans possess some automatic control processes for socially relevant thinking and behavior, just as we have automatic control processes for autonomic regulation. Violent behavior, for example, is likely inhibited (for most people) through automatic control mechanisms that do not require one to stop and think about consequences. Emotions may play key roles in such automatic regulation of behavior. Moreover, the very associative memory processes that sometimes promote bias can work to prevent bias. Changes in attitudes and associations can be learned through classical or instrumental conditioning, just as prejudices and bad habits can be unlearned. Attitudes toward same-sex marriage, for example, have undergone rapid change over the past several decades, and it seems clear this cannot be fully explained through generational replacement. New beliefs and feelings have been widely adopted, and we believe it is unlikely this was the result of careful reconsideration of priors. It is much more likely, we think, that these new attitudes and considerations have formed unconsciously through direct and indirect experience and an increasingly consistent societal message of support for marriage equality. The most interesting and important questions about human behavior concern cause, responsibility, and control, but we do not yet have a satisfactory understanding of the basic underlying mechanisms that give meaning to these questions. Our research exploring automaticity in political-information processing and our dual-process theory that roots feeling, thinking, and doing in the associative architecture of memory is a valuable early step toward a process-valid model of political behavior… As it stands, JQP [John Q. Public Model of Political Information Processing] paints a very pessimistic view of human possibilities. We fear this portrait of “the cognitive monster” may be accurate, but we think control processes deserve more study. Our gut tells us this last optimism may be rationalization.” (Taber & Lodge, 2016​141​)

It should be noted that individual voting behavior is affected by a myriad of different factors such as who is leading in the polls (S), what images are seen shortly before the voting, etc. (S) And like in all decision making, cognitive biases come into play. (S) Things like the ideological affiliations of college friends can also have an effect on voting behavior, but the effect is quite small, possibly due to the brain being already quite mature in young adulthood. (S) But even as these random environmental factors do play a role in voting behavior, they are not that significant when looking at population-wide statistics over a period of several decades.

3 Hormones and group behavior

This chapter further presents how the generationally oscillating hormone levels can have population-wide effects in human societies; to both individual and group behavior. The idea of hormone levels having effects on a societal level is relatively novel, but neuroscience can be used in this manner not only to explain individual behavior, but also to explain formation and behavior of 1) groups and 2) institutions. (Stanley & Adolphs, 2013​142​) (On how feelings shape institutions and organizations: a short video from a MIT professor in psychology, Stephen Chorover.) [NOTE: Growth hormone isn’t fully incorporated into this chapter yet, but will be incorporated during Q4 2021.]

When looking at the current political and ideological trends, it is clear that populist nationalism and xenophobia have been on the rise in the Western countries during the 2010s and 20s. Both cultural globalism and multilateralism are increasingly rejected by many political parties. (S) Ideological polarizations are increasing and centrist parties are losing support.(Zur, 2019​143​)(S)(S)(S) Increasing social divisions based on ideological identity can be clearly seen in basically all Western countires today, as two or more sides are separating from each other and creating ideological rifts. (S)(S) Hate crimes against minorities have become more common (S)(S), including anti-Semitism. (S)(S)(S) Many scholars have compared the 2010s to the 1930s, since many of the same societal effects can be observed to have become more prevalent during those decades that are 80 years apart.

Continuing listing the current societal trends: the status quo of globalistic politics is being challenged at an accelerating pace (S), press freedom is increasingly being restricted by state actors (S)(S), free speech is being restricted by non-state actors (S), and religious freedoms are increasingly suppressed. (S) News outlets are increasingly ideologically divided (S), fake news are more frequent, and lying on behalf of one’s own ideological beliefs (S) is becoming more common. Not even the scientific community is safe from the effects of the so called post-truth era. (S)(S)(S)

Most studies and pundits explain the movement towards populist nationalism, and more generally the polarization of opinions in political and civic life, by explaining that they happen because of different societal phenomena like mass immigration, economic inequality and social media platforms that are polarizing opinions by forming echo chambers and allowing the distribution of fake news. (S)(S) These phenomena are assumed to create anxieties for the voters of populist nationalists. But none of the explanations or explanatory models built on these observations can predict or explain the rise of populist nationalism or the other mentioned phenomena with satisfactory accuracy, and the accuracy gets even worse when trying to apply these explanatory models to several or all of the Western countries, or to similar societal situations throughout the history.

Tendencies towards populist nationalism are growing basically in every democratic country in Europe, and this movement started well before any big immigration or economic crisis of the 2010s. Nationalism grew as the economic situation in Europe was good and it has kept rising through the worse economic times. Financial crises do raise populist support for a while, but in a historical perspective populist support has generally leveled out in about 4 years after a crisis. (S) It is the same thing with immigration crises: the pace of the rise of nationalism and support for populist parties has not changed much through the years, not on the far-right or the far-left end. Nationalism and populism are not the same thing, but nationalistic parties are more often populist than not, especially on the far-right of the political spectrum.

Populist vote share            in Europe 1998-2018
Populist vote share in Europe from 1998 to 2018. (S)

The overall increasing support for populist nationalism in Europe has been very clear and steady over the past 20 years. The incremental maps below from 1998 to 2018 illustrate how each European country has followed its path to the current situation of higher than average support for populist parties.

Rise of populism in            Europe, 1998-2018
Populist vote share in Europe from 1998 to 2018. (S) After these maps were published, nationalists made their way to the parliaments of Latvia in October 2018 (S), Estonia in March 2019 (S), and Portugal in October 2019 (S). This means that there is not even one European nation without nationalists in their parliament in 2020.

The statistic below further illustrates how the 1980s and 90s were decades of low populism, after which especially right wing populism has increased.

The 1990s were an era of relatively low support for populism on both sides of the ideological spectrum. (S)

According to Gini statistics below, economic inequality cannot explain the rise of nationalism and populism, as income inequality has not changed much in the EU, and has actually gone down for most countries during the last 20 years. In the U.S. income inequality has risen, but attempts to raise taxes on the top earners or otherwise level out economic inequality have been pretty rare even on the left, highlights being the candidacy of Bernie Sanders and movements against the big banks (and neither can be said to be very much associated with populist nationalism).

Income inequality in            EU and USA
Gini income inequality index from 1988 to 2016. (S)

Gini is not the only indicator that should be looked at, as low interest rates and quantitative easing by the worlds central banks have had a lifting effect on rent prices in Europe and the U.S., which has affected mostly low-income workers and may contribute to the dissent against the so called “elites”. But still, the economic conditions and employment numbers have generally gotten better during the last five years, and yet populism has increased at the same time. (S) Poland is a prime example of a country that has had record economic success during recent years, but in spite of this, populist nationalism and xenophobia have risen sharply:

“This is not 1937. Nevertheless, a parallel transformation is taking place in my own time, in the Europe that I inhabit and in Poland, a country whose citizenship I have acquired. And it is taking place without the excuse of an economic crisis of the kind Europe suffered in the 1930s. Poland’s economy has been the most consistently successful in Europe over the past quarter century. Even after the global financial collapse in 2008, the country saw no recession. What’s more, the refugee wave that has hit other European countries has not been felt here at all. There are no migrant camps, and there is no Islamist terrorism, or terrorism of any kind.” (S) But if Poland was undergoing an economic depression today, the situation would very likely be used as a reason for the current increases in nationalism and xenophobia. Outside of Europe, Australia is not much different from Poland, as their uninterrupted economic boom has been going on for almost three decades, and nationalism is on the rise like it is in Europe and the U.S. (S)(S)

Social media platforms on the other hand seem to accelerate the divisions between individuals with diverging ideological stances: “Social media use tends to diversify communication within social networks by making people aware of what others think and feel about political and social issues. Social media enhance the perception of difference, and interpersonal contacts in these environments are typically rated less positively than interpersonal contacts in face-to-face communication.” (S) Moral-emotional posts tend to spread more effectively on social media platforms (S), and the platforms do play a role in the increasing polarization, but mainly as a catalyst: “Despite these limitations, this study has provided evidence that social media contribute to the growth of negative affect in political communication. Moreover, this negative affect is related to the comparatively high degree of perceived political disagreement that people encounter in social media settings. Thus, to a certain extent, perceived disagreement in social media settings has its roots in affective communication processes.” (S) So if social media is mainly intensifying the current societal trends, whatever they may be at a certain point in time, what could be the root cause for the increases in populist nationalism among Western nations?

The generational hormone theory presented here suggests that the studies and pundits blaming the economic disparities, immigration, social media, and other “usual suspects” for the rise of populist nationalism are probably correct in their observations of these phenomena occurring at the same time or before the rise of populist nationalism, but at the same time are fundamentally incorrect in how much these societal phenomena affect societal change and the general social mood, and thus cannot build working models to explain the rise of populist nationalism in detail. This is because these mentioned societal phenomena are expected to mainly work as catalysts for a generations long trend, a generational cycle of dopamine levels to be more precise, which is modulating the rise (and fall) of nationalism in Western nations at intervals of roughly 80 years.

It is suggested that these catalytic events essentially hide a significant reason for the rise of xenophobia and populist nationalism, just like the Great Depression of 1930s has often been claimed to be the main reason for the rise of nationalism and anti-Semitism in pre-WW2 Germany and elsewhere. (S) The goal is to demonstrate that generationally varying levels of dopamine largely modulate the historical increases and decreases of societal phenomena like populist nationalism and xenophobia by oscillating group cohesion levels of cyclical societies.

3.2 Societal group behavior

The goal of chapter 3 is to point out that many of the current societal trends presented in the previous chapter, especially the rise of populist nationalism and its related phenomena, are presumably created by a combination of high levels of dopamine in human populations, and how stress hormones oxytocin (primary female stress hormone) and vasopressin (primary male stress hormone) increase the intergroup divisions. Even though the human endocrine system and its effects on behavior are still not fully understood, the suggestions made here are aimed at building a bridge between hormonal activity and societal phenomena based on latest research.

As a broad generalization of human social behavior, humans are highly social animals that live and act in groups. (Young, 2008​144​)(S) Humans have created extensive tools to enhance communication, science, and culture, but these are basically extensions of animal behavior, since other species too can communicate and learn new languages and dialects, build tools and do math, and pass on their culture to their offspring. (Whitehead et al., 2019​145​) And similarly to humans, animals have been found to have the “more complex” feelings that humans have, e.g. empathy, altruism, grief, contempt, and jealousy. Hormone levels modulate these feelings in humans and other animal species, thus modulating group behavior in human and other animal species.

The standard viewpoints to history concentrates on the societal events and actions taken by individuals, but the generational hormone theory adds a layer of internal biological responsiveness in the form of (presumed) generational hormone levels. Hormone levels modulate individual and group behavior, and also group formation and group cohesion; the larger the hypothalamic neurons that secrete hormones are, the stronger the (hormone specific) reaction to others and the environment is.

Human behavior essentially consist of the responses of individuals and groups to external stimuli, and hormone levels modulate these responses because they modulate feelings. Average hormone levels affect how individuals and groups react to:

1) individuals;
2) groups;
3) their environment.

History will thus be mostly viewed here as group and intergroup behavior being further divided into in-group and out-group behavior. Typical in-groups are family, friends, gender, nation, culture, ethnicity, religion, political ideology, etc. Out-groups are basically comprised of people belonging to other in-groups than one’s own in-groups. Dopamine modulates large-scale social network group cohesion, whereas oxytocin modulates social cohesion in the context of family, friends, and other small-scale social connections. (Pearce et al., 2017​63​)

Viewing history as group behavior is a divergence from the more traditional setting of mainly analyzing individual leaders and their associates, and how their actions and reactions have impacted history. The intent is not to disregard historical individuals who, for example, want to advance their own cause and rise to power by using populist messaging, but instead explain when and why individuals are open, or possibly even inclined, to receive populist messaging. This kind of approach largely negates the belief held in sociology that the rise of populism and nationalism happen (virtually) solely due to societal issues.

Although populist nationalism has had various ways of manifesting, as structures of countries and also types of available ideologies have varied through the centuries, the manifestations of populist nationalism share many easily detectable similarities across millennia, which strongly indicates that the phenomenon has its roots directly in biology.

3.3 Hormones and populist nationalism

Below is a short list of expected changes in individual and group behavior when dopamine levels increase. [Sources to be added regarding oxytocin and vasopressin during Q4 2021]

1. More nationalism, xenophobia and demands for in-group cohesion.
2. Negative emotions towards in-group non-cooperators.
3. Perceptions of trust and fairness are altered as group-serving lying is promoted.
4. Infra-humanization of out-groups and willingness to fight out-groups.

Points 1 and 2 are often a big part of populist ideology, point 3 explains the post-truth (aka. fake news) era. Points 1-3 of these group behavior changes have been on the rise for the last 20 years or so in the Western nations when compared to the 1990s. The number of hate groups has also increased in the US and the EU. (S)(S)

Oxytocin is women’s main stress hormone, while oxytocin in men’s main stress hormone. While oxytocin does increase empathy (Geng et al., 2018​146​), the separation between in-groups and out-groups increases at the same time, and this combination can lead to a “wrong kind” of empathy, as the rising feelings of empathy is directed towards the in-group, and coupled with vasopressin this empathy can even lead to aggressive behavior towards in-group non-cooperators and out-groups, especially if the in-group target of an aggressor is perceived to be under stress. (Buffone & Poulin, 2014​147​) Advocates for populist nationalism use the feelings of empathy constantly to their advantage in their messaging by pitting the in-group against out-groups, and by including possible threats from out-groups to their messaging they can increase their supporter’s stress levels and the secretion of stress hormones oxytocin and vasopressin.

Since higher levels of oxytocin and vasopressin increase anxiety towards unpredictable threats (Grillon et al., 2013​148​, Kawada et al., 2019​149​), populist nationalist leaders can gain support by 1) messaging about perceived out-group threats towards the in-group and then 2) presenting solutions on how to address the threats posed by out-groups and keep the in-group safe. Such anxieties are created through emotions – especially fear:

“Populism peddles a politics of fear—of crime, terrorism, unemployment, economic decline, the loss of national values and tradition—and asserts that other parties are leading their countries to disaster. Surveys make clear that populist voters are extremely pessimistic: they believe the past was better than the present and are extremely anxious about the future. But pessimism has infected Western societies more generally. A recent PEW survey for example revealed that even though growing percentages of European citizens view their country’s economic situation as dramatically better than a decade ago, this has not translated into greater optimism about the future. Indeed, in many European countries the “experience-expectation” differential has grown: in the Netherlands, Sweden and Germany, for example, approximately 80 percent or more say the economy is doing well, but less than 40 percent believe the next generation will be better off than their parents. These views reflect a troubling reality: particularly in times of change and uncertainty [note: low growth hormone levels promote loss of confidence], people’s views are shaped more by emotions than rationality.” (S)

Populist leaders can therefore gain support through creating/compounding economic anxieties. Anxieties related to xenophobia seem to most often be an efficient way of gaining populist votes, but a combination of anxieties towards immigrants and economic worries seems to be an even more efficient combination for populists in many instances: “illegal immigrants stealing the jobs and committing crimes” or “selfish Jews hoarding capital at the top” for example are commonly used. These kinds of combinations of oxytocin and vasopressin enhanced anxieties have been used in order to make scapegoats and gain popularity through thousands of years of political history. (S)(S)(S) It should be noted that scapegoating is a mechanism that is common among social mammal species. (S)

Although populist nationalism has had various ways of manifesting, as structures of countries and also types of available ideologies have varied through the centuries, the manifestations of populist nationalism share many easily detectable similarities across millennia, which strongly indicates that the phenomenon has its roots directly in biology. Based on the presented studies, it can be stated that increased levels of dopamine may manifest as stronger levels of documented group behavior like in-group favoritism, out-group homogeneity, and group conformity (i.e. large-scale behavioral synchrony); trends that seem to be on the rise in the Western countries. (Everett et al., 2015​150​, Huang et al., 2019​151​)(S)(S)

3.4 Paths of tightening group cohesion

This [unfinished] chapter puts the proposed generational hormone theory to test by looking at an earlier peaks of nationalism, as the early 17th century contains clear signs of populism and rising feelings of early nationalism. 4th turnings are estimated to have happened roughly every 80 years: 1530-1550 | 1610-1630 | 1690-1710 | 1770-1790 | 1850-70 | 1930-1950 and currently 2010-2030.

The graphs in this chapter coarsely illustrate how tightening group cohesion during a 4th turning leads to different end results depending on the starting point that exists at the ending of a 3rd turning. This is in line with the Strauss-Howe generational theory: a society’s direction in a 4th turning is largely dependent on the situation it is in when a 4th turning begins, and that the beginning is marked by a catalytic event that starts the process towards a new order in the civic life as well.

3rd turning to 4th turning paths

Path 1 is the most common path and was explained earlier (chapter 2.3.1). Path 2 presents a situation where the ideological cleavages are already strong when entering a 4th turning, thus there is a risk of the divisions eventually splitting the main group into two (or possibly more) separate in-groups. The division into separate in-groups may eventually escalate into a conflict, but this would depend entirely on the contentious societal issues at hand. In this situation virtually everyone is situated on one side or another, thus scapegoating is rare because both sides try to garner as much supporters as possible, and the opposing group is blamed/scapegoated for at least the most pressing societal issues/problems.

But an external threat may also accomplish an unification during a 4th turning even if it is difficult otherwise, just like the rise of the Nazi Germany mostly suppressed the populist movements (but not nationalism) in many of the war waging countries during WW2, because the war efforts required cooperation basically at every level of a society. So path 2 can change into path 1 if the external threat is strong enough to unify the society.

Path 3 presented below is typical for dictatorships, kingdoms, and monarchies that have accumulated most if not all of the power to a small elite at the top.

Social coherence power center

Below are examples of the paths taken regarding the US during the past three 4th turnings.

1) WW2 was preceded by path 1, while communists and fascists were the out-groups and scapegoats, and this way of thinking continued after the war.

2) The American Civil War was preceded by path 2. There were two distinct in-groups, the North and the South, and there were virtually no scapegoats apart from the other side.

3) The American Revolutionary War was preceded by path 3. The British were the out-group (although one third of the American colonists initially fought on the side of Britain).

In 2020 it would seem that the current 4th turning path in the US is closest to path 2, while most of the Western nations seem to be undergoing path 1, largely because their multi-party system lower the probability of nations splitting into two opposing camps.

When group cohesion tightens in a society, institutions will be used (or at least tried to be used) to advance the benefit of the in-group and simultaneously often to derogate an out-group. The usual targets are the justice system, army leadership, state media, and the education system, which are increasingly subjugated under the ruling party. Such mobilization of institutions is what has happened during the 4th turnings in history according to Strauss & Howe. People leading these institutions are increasingly expected to advance their own in-group’s agendas or the face being replaced by individuals who are more willing to advance the in-group’s causes.

If no ideological group can control all of the media, the media tends to polarize into two opposing camps, and this has manifested especially in the US during this 4th turning, where both ideological sides have their own news outlets, which only increases the cleavages between the two sides.

3.5 In-group empathy and scapegoating of the out-group

When nationalism rises and the unification is successful (or the society was already unified), there most often is a minority group or groups that the nationalists target, and the ones who belong to that minority group become scapegoats who will be blamed for many of the most pressing problems (real or made up problems) a nation faces: economic depression or economic inequality, crimes, disease, famine, or any other problem that affects the community negatively. Because scapegoating has been around for thousands of years and since scapegoating is found also in other animal species, it seems that it is an innate biological mechanism in humans, which utilizes the neural in-group vs. out-group setting. (S)

History shows that it really does not matter who the scapegoats are, basically any minority group can be used as a scapegoat. Since there must be someone to blame, suggests that if a community is very homogeneous in culture and ethnicity, like the Western European countries very much were during the 16th and 17th centuries, there can be imaginary scapegoats of groups/individuals like witches, who were blamed for things like disease or a village losing crops. (On a sidenote, witch hunts are still happening in modern times for instance in India and Tanzania.)

[Chapter to be expanded.]

4 Conclusions

4.1 Initial conclusions

This hypothesis has presented both a theoretical model and also statistical evidence of an evolutionary metapopulation mechanism, that creates and maintains the multiannual hormone cycle of cyclical animal populations. The mechanism creates the phase-dependent oscillations to the average physiological and behavioral traits of individual animals, and even though this hypothesis isn’t finished yet, the evolutionary advantages of the mechanism are already quite evident.

Although a few open questions regarding the phenomena of the multiannual population cycles remain for now, including the question of why populations of the same species can undergo cycles of different lengths at different geographic locations, the generational hormone cycle provides an answer to all of the previously unanswered questions regarding the phase-dependent physiological and behavioral phenomena, that always manifest in the same order during a multiannual animal population cycle. In addition, this hypothesis explains why the cycles can be in synchrony over vast geographical areas.

[The rest of the chapter currently concentrates on summing up the societal effects of the generational oscillating hormone levels in human populations.]

This hypothesis has also presented a theoretical model and evidence from many human populations to support the premise of a generational hormone cycle being active in several countries. Although more statistical evidence needs to be gathered to verify the generational oscillations to hormone levels, no other established explanations are available for the phenomena of the large societal shifts to rates of breastfeeding, crime or childlessness for example. This is especially evident when looking at the high degree of statistical synchrony between different countries, which is another phenomenon that the generational hormone theory explains.

It cannot be emphasized enough that hormone levels modulate behavior on an unconscious level, thus modulating the actions and reactions of individuals and groups on an unconscious level. The presented generational oscillations to hormone levels can create the generational traits listed by the Strauss-Howe generational theory.

The strength of the generational hormone theory is in the relative simplicity which allows overlooking individual differences received from genetics and environments by reviewing entire generations’ traits and actions. When looking at the current societal developments, the presented generational hormone theory is the only theory that addresses basically all of the central phenomena related to the rise of populist nationalism like rising feelings of nationalism, xenophobia (anti-Semitism, anti-Muslim sentiments, etc.), ideological and political polarization, and group serving lying (aka. post-truth era).

The generational hormone theory also addresses questions like why these phenomena are occurring today and why they are happening all around the Western nations – even in those nations that have not experienced major economic or civic difficulties for over two decades. The theory gives ground to the current slow abandonment of the “liberal status quo” inside many Western liberal democracies.

The more conventional and traditional viewpoints on history can be seen as “actions leading to consequences”, and then succeeding actions taken upon these consequences. The example used in the introductory chapter was the often repeated claim that the Great Depression of the 1930s largely caused the Nazi party to get to power in the pre-WW2 Germany. But such suggestions do not explain why the 1970s oil crisis did not cause any nationalistic movements. Obviously there were other reasons as well for the rise of the Nazi’s to power, such as the Treaty of Versailles, but such treaties have not commonly led to the rise of nationalism. So the explanation is a soup of consequences and actions which rarely solely cause large increases in nationalism, but together they somehow did. But even this does not explain the simultaneous rise of nationalism and anti-Semitism during the 1930s in other European nations.

Presuming that the model for the generational hormone cycle in human populations is at least somewhat accurate, predictions could be made about the upcoming years in general. But these predictions would obviously be only like weather forecasts that combine historical models of past 4th turnings with the current societal trends and events. The Fourth Turning book links the turnings to seasons of nature, as the exact conditions of seasons in nature cannot be known beforehand, but the seasons do run their course with very familiar and predictable patterns. This is also true regarding the animal population cycles, especially when looking at a metapopulation, whereas individual populations can more easily drop out of being cyclical for a period of multiple years.

‘Predicting the future’ of human societies has a slightly dubious ring to it, but is it predicting the future that a pre-pubertal individual will soon undergo years of increased irritability, mood swings, and changing sexual behavior? Or is it predicting the future to say that a woman close to her period may have increased irritability, mood swings, and changing sexual behavior? Or that a cyclical lemming population at the beginning of the cycle will first become less territorial and socially coherent for the next two years, and after that more territorial and socially coherent for the next two years. Presumably, all of these are predetermined hormonal events, where the timing is executed by biological clocks.

History cannot predict the future, but taking (the presumed) varying hormone levels among human populations into account could provide more background to why historical events have happened the way they have happened, and also to what direction the current societal issues are pivoting towards. That being said, if the predictions about the hormone levels are correct, for the remainder of this 4th turning, the trends listed below will presumably spread and gain popularity in the Western nations.

1) More populist nationalism and political polarization, and centrist parties will continue to lose support. The U.S. will keep turning into a propaganda democracy with two diverging partisan ideological realities with different sets of facts.
2) Ideologically influenced news and also “fake news” will become increasingly efficient as receptivity for them increases. Conspiracy theories built around opposing in-group’s activities become more wide-spread.
3) More xenophobia and hate crimes in different forms towards out-groups, including hate speech, as acts of violence are increasingly based on intergroup issues. Armed militias and gangs will become more common.
4) Institutions and organizations (including companies), both public and private, will be increasingly demanded to take sides on societal issues or face the threat of boycotts.

Many European countries will presumably stay on the path on becoming more nationalistic and the EU may start to malfunction as individual countries oppose EU regulations regarding their internal agendas. More problems for example with Poland and Hungary are to be expected during 2020s. As populist parties grow in size, they may be increasingly accepted into governments. A lengthened economic recession or depression would very likely increase the “us vs. them” tensions greatly, since “they” (out-groups) are most often blamed for causing the economic downturn and/or making things worse during it.

[April 2020 update to recent events: All of the predictions above have come true on average in the Western countries, with notable increases in support for populist nationalists, anti-Semitism, and other societal movements that indicate increasing intergroup cleavages. The current coronavirus crisis environment will presumably increase stress levels across the Western countries, which presumably leads to more nationalism and support for populism. International institutions and organizations will presumably continue to derogate as political support and funding becomes increasingly hard to find from inside the member countries due to increasing nationalism and economic turmoil.]

[May 2021 update to recent events: As predicted in the previous April 2020 update, since the pandemic started last year, nationalism has stayed at a high level and intergroup cleavages have increased. (S) The US and German parliaments were attacked by groups consisting (mostly) of far-right nationalists. (S)(S) BLM protests and riots were also signs of in-group tightening especially on the left-wing side of the political spectrum, and there were BLM protests around the Western countries. (S) Interest and support for Qanon increased noticeably once the pandemic started (S), and this trend has continued in 2021. (S) Anti-Semitism has stayed at high levels, breaking records in some countries. (S)(S) Cancel culture has remained strong in the Western countries. (S)(S)]

[April 2022 update to recent events: Russia has begun invading Ukraine in the biggest land war in Europe after WWII (1939-45). Since Russia is the biggest perceived threat for the Western countries at the moment, the Western countries have unified towards a common goal of deterring Russia from continuing its invasion, which has resulted in the easing of intergroup tensions between and inside the Western countries. The length and severity of the crisis will largely define what the intergroup relations inside and between the Western nations are once the crisis is over.

Russia has received an unprecedented amount of sanctions and boycotts from the Western countries. Even countries like Germany are sending military and other aid to Ukraine, which would have been unthinkable before the invasion, and Poland is taking in hundreds of thousands of refugees even though they have taken in very few immigrants during the past decades. It could be said that the status quo of previous political alignments inside and between countries in the West and the East is currently heavily in flux.]

4.2 Further discussion

1) The hypothesis could be easily verified by looking at the size of hypothalamic neurons in the cyclical species’ individuals at the different parts of the cycle by using electron microscopes. The same tests could be done in the animal populations that experience population explosions. As for humans, the current imaging devices (MRI) are not accurate enough to see individual neuron size in the hypothalamus.

2) The remaining hypothalamic hormones that are yet to be included in this hypothesis are corticotropin-releasing hormone and somatostatin.

3) What could be controlling a generational oscillation of the endocrine system? In theory, the presumed infradian clock could be counting years from the seasonal amounts of light, seasonal temperatures, and/or some other annual external variable.

The most logical answer would be the suprachiasmic nucleus (SCN) that controls the body’s biological clocks: “Outside of the tropics (where day length remains relatively consistent throughout the year), the changing photoperiod is a reliable and predictable seasonal signal that presents an opportunity for organisms to adapt to seasonal changes in factors such as temperature and resource availability in an anticipatory fashion… The SCN receives input from the retina directed towards its ventrolateral subregion, while its dorsomedial region features numerous direct and indirect projections to other hypothalamic nuclei controlling homeostatic function and to the rest of the brain.” (Tackenberg & McMahon, 2018​152​) “…the SCN perceives and encodes changes in day length and drives seasonal changes in downstream pathways and structures…” (Coomans et al., 2015​153​)

The vast majority of cyclical animal populations exist in the area between 30 to 70 degrees northern latitude, which could be due to these latitudes having strong annual rhythms of nature. (Sinclair, 2003​154​, Kendall et al., 1998​9​) The occurrence and amplitude of the cycles is strongest on the northern latitude 55, which very closely corresponds with the minimum latitude in which nautical twilight can last all night near the summer solstice. During nautical twilight the illumination is such that the horizon is still visible even on a moonless night, meaning that it gets very dark even in the summer (compared to more northern latitudes), but the length of day is still very long (compared to more southern latitudes). (S) This latitude is where the changes in the length of day are the most pronounced, while still having a dark period every day of the year, which could be the explanation why this latitude sees the highest occurrence and amplitude regarding the cycles.

The tropic (between the Tropic of Cancer and the Tropic of Capricorn), that has very small annual changes to light periods, has fewer regular population cycles, but instead more population outbreaks, like huge swarms of grasshoppers. The smaller amount of cyclical populations could therefore be because there is only little variation in the annual light periods. This presumption is enhanced by the fact that there apparently are no (primary) population cycles in populations of nocturnal species. (Sources to be added…) In addition to the changing photoperiod, light’s wavelength also has an effect on the SCN. (Wahl et al., 2019​155​, Duffy & Czeisler, 2009​156​)

5) Assuming that the premises presented in this hypothesis are correct, a question remains that Strauss & Howe pondered in their books: what would the modern history of the Western cultures look like without the generational hormone cycle? What if there weren’t eras of low group cohesion that presumably create high levels of individualism and crime, or eras of high group cohesion that create high rates of in-group favoritism and scapegoating of other groups?

The early 2000s in the Western nations could be a relatively good reference point for such an era: no ideological battles that consume everything in politics, low levels of populism, relatively good relations between most nations, no large-scale wars between coalitions, no significant culture of over-selfishness nor ideological groupthink, and individuals are relatively stress free and reasonably optimistic about the future.


  1. 1.
    Myers JH. Population cycles: generalities, exceptions and remaining mysteries. Proc R Soc B. Published online March 21, 2018:20172841. doi:10.1098/rspb.2017.2841
  2. 2.
    Wang H, Nagy JD, Gilg O, Kuang Y. The roles of predator maturation delay and functional response in determining the periodicity of predator–prey cycles. Mathematical Biosciences. Published online September 2009:1-10. doi:10.1016/j.mbs.2009.06.004
  3. 3.
    Krebs CJ. Of lemmings and snowshoe hares: the ecology of northern Canada. Proc R Soc B. Published online October 27, 2010:481-489. doi:10.1098/rspb.2010.1992
  4. 4.
    Krebs CJ, Bryant J, Kielland K, et al. What factors determine cyclic amplitude in the snowshoe hare (Lepus americanus) cycle? Can J Zool. Published online December 2014:1039-1048. doi:10.1139/cjz-2014-0159
  5. 5.
    Hansson L, Henttonen H. Gradients in density variations of small rodents: the importance of latitude and snow cover. Oecologia. Published online October 1985:394-402. doi:10.1007/bf00384946
  6. 6.
    Marjomäki TJ, Auvinen H, Helminen H, et al. Occurrence of Two-Year Cyclicity, “Saw-Blade Fluctuation”, in Vendace Populations in Finland. Annales Zoologici Fennici. Published online September 6, 2021. doi:10.5735/086.058.0408
  7. 7.
    Esper J, Büntgen U, Frank DC, Nievergelt D, Liebhold A. 1200 years of regular outbreaks in alpine insects. Proc R Soc B. Published online December 12, 2006:671-679. doi:10.1098/rspb.2006.0191
  8. 8.
    Wilson White J, Botsford LW, Hastings A, Holland MD. Stochastic models reveal conditions for cyclic dominance in sockeye salmon populations. Ecological Monographs. Published online February 2014:69-90. doi:10.1890/12-1796.1
  9. 9.
    Kendall, Prendergast, Bjornstad. The macroecology of population dynamics: taxonomic and biogeographic patterns in population cycles. Ecol Letters. Published online November 1998:160-164. doi:10.1046/j.1461-0248.1998.00037.x
  10. 10.
    Witteman GJ, Redfearn A, Pimm SL. The extent of complex population changes in nature. Evol Ecol. Published online April 1990:173-183. doi:10.1007/bf02270914
  11. 11.
    Ecological orbits: how planets move and populations grow. Choice Reviews Online. Published online February 1, 2005:42-3404-42-3404. doi:10.5860/choice.42-3404
  12. 12.
    Andreassen HP, Sundell J, Ecke F, et al. Population cycles and outbreaks of small rodents: ten essential questions we still need to solve. Oecologia. Published online December 28, 2020:601-622. doi:10.1007/s00442-020-04810-w
  13. 13.
    Oli MK. Population cycles in voles and lemmings: state of the science and future directions. Mam Rev. Published online May 10, 2019:226-239. doi:10.1111/mam.12156
  14. 14.
    Oli MK. Population cycles of small rodents are caused by specialist predators: or are they? Trends in Ecology & Evolution. Published online March 2003:105-107. doi:10.1016/s0169-5347(03)00005-3
  15. 15.
    Martínez-Padilla J, Redpath SM, Zeineddine M, Mougeot F. Insights into population ecology from long-term studies of red grouseLagopus lagopus scoticus. Wilson K, ed. J Anim Ecol. Published online June 25, 2013:85-98. doi:10.1111/1365-2656.12098
  16. 16.
    FRECKLETON RP, WATKINSON AR, GREEN RE, SUTHERLAND WJ. Census error and the detection of density dependence. Journal of Animal Ecology. Published online June 23, 2006:837-851. doi:10.1111/j.1365-2656.2006.01121.x
  17. 17.
    Siehler O, Wang S, Bloch G. Social synchronization of circadian rhythms with a focus on honeybees. Phil Trans R Soc B. Published online August 23, 2021. doi:10.1098/rstb.2020.0342
  18. 18.
    Baghel KK, Pati AK. Pheromones as time cues for circadian rhythms in fish. Biological Rhythm Research. Published online May 26, 2015:659-669. doi:10.1080/09291016.2015.1046246
  19. 19.
    Groot AT. Circadian rhythms of sexual activities in moths: a review. Front Ecol Evol. Published online August 7, 2014. doi:10.3389/fevo.2014.00043
  20. 20.
    Fuchikawa T, Eban-Rothschild A, Nagari M, Shemesh Y, Bloch G. Potent social synchronization can override photic entrainment of circadian rhythms. Nat Commun. Published online May 23, 2016. doi:10.1038/ncomms11662
  21. 21.
    Thomas KA, Burr RL, Spieker S. Light and maternal influence in the entrainment of activity circadian rhythm in infants 4–12 weeks of age. Sleep Biol Rhythms. Published online January 5, 2016:249-255. doi:10.1007/s41105-015-0046-2
  22. 22.
    Arshavskaya T, Polenov A, Tkachev A. The hypothalamo-hypophysial system of the lemming, Dicrostonyx torquatus Pallas. III. Population aspects of neuroendocrine regulation. Z Mikrosk Anat Forsch. 1989;103(4):627-647.
  23. 23.
    Vladimirova EG, Chernigovskaya EV, Danilova OA. Hypothalamo-pituitary neurosecretory system of the Northern redbacked vole Clethrionomys rutilus in the course of population cycle. J Evol Biochem Phys. Published online March 2006:208-216. doi:10.1134/s002209300602013x
  24. 24.
    Sheriff MJ, Krebs CJ, Boonstra R. From process to pattern: how fluctuating predation risk impacts the stress axis of snowshoe hares during the 10-year cycle. Oecologia. Published online January 19, 2011:593-605. doi:10.1007/s00442-011-1907-2
  25. 25.
    Cary JR, Keith LB. Reproductive change in the 10-year cycle of snowshoe hares. Can J Zool. Published online February 1, 1979:375-390. doi:10.1139/z79-044
  26. 26.
    Boonstra R, Hik D, Singleton GR, Tinnikov A. THE IMPACT OF PREDATOR-INDUCED STRESS ON THE SNOWSHOE HARE CYCLE. Ecological Monographs. Published online August 1998:371-394. doi:10.1890/0012-9615(1998)068[0371:tiopis];2
  27. 27.
    Oli MK. The Chitty Effect: A Consequence of Dynamic Energy Allocation in a Fluctuating Environment. Theoretical Population Biology. Published online December 1999:293-300. doi:10.1006/tpbi.1999.1427
  28. 28.
    Fauteux D, Gauthier G, Berteaux D. Seasonal demography of a cyclic lemming population in the Canadian Arctic. Ims R, ed. J Anim Ecol. Published online July 15, 2015:1412-1422. doi:10.1111/1365-2656.12385
  29. 29.
    Kshnyasev IA, Davydova YuA. Population Cycles and the Chitty Syndrome. Russ J Ecol. Published online January 2021:70-75. doi:10.1134/s1067413621010082
  30. 30.
    Boonstra R, Boag PT. A TEST OF THE CHITTY HYPOTHESIS: INHERITANCE OF LIFE-HISTORY TRAITS IN MEADOW VOLES MICROTUS PENNSYLVANICUS. Evolution. Published online September 1987:929-947. doi:10.1111/j.1558-5646.1987.tb05868.x
  31. 31.
    Yakushov VD, Sheftel BI. Is There a Relationship between the Chitty Effect and the Types of Population Dynamics? Dokl Biol Sci. Published online May 2020:89-92. doi:10.1134/s0012496620030084
  32. 32.
    Rhainds M. Variation in Wing Load of Female Spruce Budworms (Lepidoptera: Tortricidae) During the Course of an Outbreak: Evidence for Phenotypic Response to Habitat Deterioration in Collapsing Populations. Sword G, ed. Environmental Entomology. Published online December 20, 2019:238-245. doi:10.1093/ee/nvz144
  33. 33.
    Klemola T, Andersson T, Ruohomäki K. Fecundity of the autumnal moth depends on pooled geometrid abundance without a time lag: implications for cyclic population dynamics. J Anim Ecology. Published online May 2008:597-604. doi:10.1111/j.1365-2656.2008.01369.x
  34. 34.
    Voutilainen L, Kallio ER, Niemimaa J, Vapalahti O, Henttonen H. Temporal dynamics of Puumala hantavirus infection in cyclic populations of bank voles. Sci Rep. Published online February 18, 2016. doi:10.1038/srep21323
  35. 35.
    Yamaguchi Y, Lee YA, Kato A, Jas E, Goto Y. The Roles of Dopamine D2 Receptor in the Social Hierarchy of Rodents and Primates. Sci Rep. Published online February 24, 2017. doi:10.1038/srep43348
  36. 36.
    Nader MA, Nader SH, Czoty PW, et al. Social Dominance in Female Monkeys: Dopamine Receptor Function and Cocaine Reinforcement. Biological Psychiatry. Published online September 2012:414-421. doi:10.1016/j.biopsych.2012.03.002
  37. 37.
    Marzecová A, Kaiser LF, Maddah A. Neuromodulation of Foraging Decisions: The Role of Dopamine. Front Behav Neurosci. Published online April 13, 2021. doi:10.3389/fnbeh.2021.660667
  38. 38.
    Matthews GA, Nieh EH, Vander Weele CM, et al. Dorsal Raphe Dopamine Neurons Represent the Experience of Social Isolation. Cell. Published online February 2016:617-631. doi:10.1016/j.cell.2015.12.040
  39. 39.
    Mahabir S, Chatterjee D, Buske C, Gerlai R. Maturation of shoaling in two zebrafish strains: A behavioral and neurochemical analysis. Behavioural Brain Research. Published online June 2013:1-8. doi:10.1016/j.bbr.2013.03.013
  40. 40.
    Krebs CJ, Boonstra R, Boutin S, et al. Trophic Dynamics of the Boreal Forests of the Kluane Region. ARCTIC. Published online January 13, 2014:71. doi:10.14430/arctic4350
  41. 41.
    Daftary GS, Taylor HS. Endocrine Regulation of HOX Genes. Endocrine Reviews. Published online April 21, 2006:331-355. doi:10.1210/er.2005-0018
  42. 42.
    Lutchmaya S, Baron-Cohen S, Raggatt P, Knickmeyer R, Manning JT. 2nd to 4th digit ratios, fetal testosterone and estradiol. Early Human Development. Published online April 2004:23-28. doi:10.1016/j.earlhumdev.2003.12.002
  43. 43.
    Brockhurst MA, Chapman T, King KC, Mank JE, Paterson S, Hurst GDD. Running with the Red Queen: the role of biotic conflicts in evolution. Proc R Soc B. Published online December 22, 2014:20141382. doi:10.1098/rspb.2014.1382
  44. 44.
    Strotz LC, Simões M, Girard MG, Breitkreuz L, Kimmig J, Lieberman BS. Getting somewhere with the Red Queen: chasing a biologically modern definition of the hypothesis. Biol Lett. Published online May 2018:20170734. doi:10.1098/rsbl.2017.0734
  45. 45.
    Rikalainen K, Aspi J, Galarza JA, Koskela E, Mappes T. Maintenance of genetic diversity in cyclic populations-a longitudinal analysis inMyodes glareolus. Ecology and Evolution. Published online June 11, 2012:1491-1502. doi:10.1002/ece3.277
  46. 46.
    Ehrich D, Jorde PE. HIGH GENETIC VARIABILITY DESPITE HIGH-AMPLITUDE POPULATION CYCLES IN LEMMINGS. Journal of Mammalogy. Published online April 2005:380-385. doi:10.1644/ber-126.1
  47. 47.
    Ishibashi Y, Takahashi K. Role of individual dispersal in genetic resilience in fluctuating populations of the gray‐sided vole            Myodes rufocanus. Ecol Evol. Published online February 21, 2021:3407-3421. doi:10.1002/ece3.7300
  48. 48.
    Lidicker WZ Jr. Genetic and spatial structuring of the California vole (Microtus californicus) through a multiannual density peak and decline. JMAMMAL. Published online August 18, 2015:1142-1151. doi:10.1093/jmammal/gyv122
  49. 49.
    Norén K, Angerbjörn A. Genetic perspectives on northern population cycles: bridging the gap between theory and empirical studies. Biol Rev. Published online November 4, 2013:493-510. doi:10.1111/brv.12070
  50. 50.
    Krebs CJ, Boonstra R, Boutin S. Using experimentation to understand the 10‐year snowshoe hare cycle in the boreal forest of North America. Wilson K, ed. J Anim Ecol. Published online July 24, 2017:87-100. doi:10.1111/1365-2656.12720
  51. 51.
    Berthier K, Piry S, Cosson JF, et al. Dispersal, landscape and travelling waves in cyclic vole populations. Liebhold A, ed. Ecol Lett. Published online November 17, 2013:53-64. doi:10.1111/ele.12207
  52. 52.
    Sherratt JA, Smith MJ. Periodic travelling waves in cyclic populations: field studies and reaction–diffusion models. J R Soc Interface. Published online January 22, 2008:483-505. doi:10.1098/rsif.2007.1327
  53. 53.
    Jepsen JU, Vindstad OPL, Barraquand F, Ims RA, Yoccoz NG. Continental-scale travelling waves in forest geometrids in Europe: an evaluation of the evidence. White A, ed. J Anim Ecol. Published online January 16, 2016:385-390. doi:10.1111/1365-2656.12444
  54. 54.
    Fletcher NK, Acevedo P, Herman JS, Paupério J, Alves PC, Searle JB. Glacial cycles drive rapid divergence of cryptic field vole species. Ecol Evol. Published online November 23, 2019:14101-14113. doi:10.1002/ece3.5846
  55. 55.
    Vahdati AR, Sprouffske K, Wagner A. Effect of Population Size and Mutation Rate on the Evolution of RNA Sequences on an Adaptive Landscape Determined by RNA Folding. Int J Biol Sci. Published online 2017:1138-1151. doi:10.7150/ijbs.19436
  56. 56.
    Lundvall D, Svanbäck R, Persson L, Byström P. Size-dependent predation in piscivores: interactions between predator foraging and prey avoidance abilities. Can J Fish Aquat Sci. Published online July 1, 1999:1285-1292. doi:10.1139/f99-058
  57. 57.
    Karashchuk OS, Mayorova EA, Nikishin AF, Kornilova OV. The Method for Determining Time-Generation Range. SAGE Open. Published online October 2020:215824402096808. doi:10.1177/2158244020968082
  58. 58.
    Strauss W, Howe N. Generations. William Morrow and Co; 1991.
  59. 59.
    Pletzer B, Harris TA, Scheuringer A, Hidalgo-Lopez E. The cycling brain: menstrual cycle related fluctuations in hippocampal and fronto-striatal activation and connectivity during cognitive tasks. Neuropsychopharmacol. Published online June 13, 2019:1867-1875. doi:10.1038/s41386-019-0435-3
  60. 60.
    Simonneaux V, Bahougne T. A Multi-Oscillatory Circadian System Times Female Reproduction. Front Endocrinol. Published online October 20, 2015. doi:10.3389/fendo.2015.00157
  61. 61.
    Allocco DJ, Song Q, Gibbons GH, Ramoni MF, Kohane IS. Geography and genography: prediction of continental origin using randomly selected single nucleotide polymorphisms. BMC Genomics. Published online 2007:68. doi:10.1186/1471-2164-8-68
  62. 62.
    Caldwell HK, Albers HE. Oxytocin, Vasopressin, and the Motivational Forces that Drive Social Behaviors. In: Behavioral Neuroscience of Motivation. Springer International Publishing; 2015:51-103. doi:10.1007/7854_2015_390
  63. 63.
    Pearce E, Wlodarski R, Machin A, Dunbar RIM. Variation in the β-endorphin, oxytocin, and dopamine receptor genes is associated with different dimensions of human sociality. Proc Natl Acad Sci USA. Published online May 1, 2017:5300-5305. doi:10.1073/pnas.1700712114
  64. 64.
    Lewis GJ, Bates TC. Genetic Evidence for Multiple Biological Mechanisms Underlying In-Group Favoritism. Psychol Sci. Published online October 25, 2010:1623-1628. doi:10.1177/0956797610387439
  65. 65.
    Lewis GJ, Bates TC. The Temporal Stability of In-Group Favoritism Is Mostly Attributable to Genetic Factors. Social Psychological and Personality Science. Published online June 7, 2017:897-903. doi:10.1177/1948550617699250
  66. 66.
    Fowden AL, Forhead AJ. Hormones as epigenetic signals in developmental programming. Experimental Physiology. Published online May 14, 2009:607-625. doi:10.1113/expphysiol.2008.046359
  67. 67.
    Crawley JN. Behavioral Phenotyping Strategies for Mutant Mice. Neuron. Published online March 2008:809-818. doi:10.1016/j.neuron.2008.03.001
  68. 68.
    Levine H, Jørgensen N, Martino-Andrade A, et al. Temporal trends in sperm count: a systematic review and meta-regression analysis. Human Reproduction Update. Published online July 25, 2017:646-659. doi:10.1093/humupd/dmx022
  69. 69.
    Travison TG, Araujo AB, O’Donnell AB, Kupelian V, McKinlay JB. A Population-Level Decline in Serum Testosterone Levels in American Men. The Journal of Clinical Endocrinology & Metabolism. Published online January 1, 2007:196-202. doi:10.1210/jc.2006-1375
  70. 70.
    Davis GJ, Meyer RK. FSH and LH in the snowshoe hare during the increasing phase of the 10-year cycle. General and Comparative Endocrinology. Published online February 1973:53-60. doi:10.1016/0016-6480(73)90129-9
  71. 71.
    Condorelli R, Calogero AE, La Vignera S. Relationship between Testicular Volume and Conventional or Nonconventional Sperm Parameters. International Journal of Endocrinology. Published online 2013:1-6. doi:10.1155/2013/145792
  72. 72.
    Erlinge S, Hasselquist D, Svensson M, Frodin P, Nilsson P. Reproductive behaviour of female Siberian lemmings during the increase and peak phase of the lemming cycle. Oecologia. Published online May 3, 2000:200-207. doi:10.1007/s004420051006
  73. 73.
    Weiss RV, Clapauch R. Female infertility of endocrine origin. Arq Bras Endocrinol Metab. Published online March 2014:144-152. doi:10.1590/0004-2730000003021
  74. 74.
    Boddington B, Didham R. Increases in childlessness in New Zealand. J Pop Research. Published online May 26, 2009:131-151. doi:10.1007/s12546-009-9008-3
  75. 75.
    Gobbi PE. A model of voluntary childlessness. J Popul Econ. Published online November 10, 2012:963-982. doi:10.1007/s00148-012-0457-1
  76. 76.
    Gurven M, Kaplan H. Longevity Among Hunter- Gatherers: A Cross-Cultural Examination. Population & Development Review. Published online June 2007:321-365. doi:10.1111/j.1728-4457.2007.00171.x
  77. 77.
    Erb J, Boyce MS, Stenseth NChr. Population dynamics of large and small mammals. Oikos. Published online January 2001:3-12. doi:10.1034/j.1600-0706.2001.920101.x
  78. 78.
    Goodale T, Sadhu A, Petak S, Robbins R. Testosterone and the Heart. Methodist DeBakey Cardiovascular Journal. Published online April 2017:68-72. doi:10.14797/mdcj-13-2-68
  79. 79.
    Walther A, Breidenstein J, Miller R. Association of Testosterone Treatment With Alleviation of Depressive Symptoms in Men. JAMA Psychiatry. Published online January 1, 2019:31. doi:10.1001/jamapsychiatry.2018.2734
  80. 80.
    Furigo IC, Teixeira PDS, de Souza GO, et al. Growth hormone regulates neuroendocrine responses to weight loss via AgRP neurons. Nat Commun. Published online February 8, 2019. doi:10.1038/s41467-019-08607-1
  81. 81.
    A century of trends in adult human height. eLife. Published online July 26, 2016. doi:10.7554/elife.13410
  82. 82.
    Vinci L, Floris J, Koepke N, et al. Have Swiss adult males and females stopped growing taller? Evidence from the population-based nutrition survey menuCH, 2014/2015. Economics & Human Biology. Published online May 2019:201-210. doi:10.1016/j.ehb.2019.03.009
  83. 83.
    Maric NP, Doknic M, Pavlovic D, et al. Psychiatric and neuropsychological changes in growth hormone-deficient patients after traumatic brain injury in response to growth hormone therapy. J Endocrinol Invest. Published online December 2010:770-775. doi:10.1007/bf03350340
  84. 84.
    Akaltun İ, Çayır A, Kara T, Ayaydın H. Is growth hormone deficiency associated with anxiety disorder and depressive symptoms in children and adolescents?: A case-control study. Growth Hormone & IGF Research. Published online August 2018:23-27. doi:10.1016/j.ghir.2018.06.001
  85. 85.
    Niklasson B, Nyholm E, Feinstein RE, Samsioe A, Hörnfeldt B. Diabetes and myocarditis in voles and lemmings at cyclic peak densities—induced by Ljungan virus? Oecologia. Published online July 26, 2006:1-7. doi:10.1007/s00442-006-0493-1
  86. 86.
    Joseph JJ, Golden SH. Cortisol dysregulation: the bidirectional link between stress, depression, and type 2 diabetes mellitus. Ann NY Acad Sci. Published online October 17, 2016:20-34. doi:10.1111/nyas.13217
  87. 87.
    Tsalamandris S, Antonopoulos AS, Oikonomou E, et al. The Role of Inflammation in Diabetes: Current Concepts and Future Perspectives. Eur Cardiol. Published online April 30, 2019:50-59. doi:10.15420/ecr.2018.33.1
  88. 88.
    Hackett RA, Kivimäki M, Kumari M, Steptoe A. Diurnal Cortisol Patterns, Future Diabetes, and Impaired Glucose Metabolism in the Whitehall II Cohort Study. The Journal of Clinical Endocrinology & Metabolism. Published online February 2016:619-625. doi:10.1210/jc.2015-2853
  89. 89.
    Benoit SR, Hora I, Albright AL, Gregg EW. New directions in incidence and prevalence of diagnosed diabetes in the USA. BMJ Open Diab Res Care. Published online May 2019:e000657. doi:10.1136/bmjdrc-2019-000657
  90. 90.
    Magliano DJ, Chen L, Islam RM, et al. Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings. The Lancet Diabetes & Endocrinology. Published online April 2021:203-211. doi:10.1016/s2213-8587(20)30402-2
  91. 91.
    Johansson I, Norhammar A. Diabetes and heart failure notions from epidemiology including patterns in low-, middle- and high-income countries. Diabetes Research and Clinical Practice. Published online July 2021:108822. doi:10.1016/j.diabres.2021.108822
  92. 92.
    Gordon I, Zagoory-Sharon O, Leckman JF, Feldman R. Oxytocin and the Development of Parenting in Humans. Biological Psychiatry. Published online August 2010:377-382. doi:10.1016/j.biopsych.2010.02.005
  93. 93.
    Dotti Sani GM, Treas J. Educational Gradients in Parents’ Child-Care Time Across Countries, 1965-2012. Fam Relat. Published online April 19, 2016:1083-1096. doi:10.1111/jomf.12305
  94. 94.
    Augustine RA, Ladyman SR, Bouwer GT, et al. Prolactin regulation of oxytocin neurone activity in pregnancy and lactation. J Physiol. Published online March 23, 2017:3591-3605. doi:10.1113/jp273712
  95. 95.
    Scott V, Brown CH. Beyond the GnRH Axis: Kisspeptin Regulation of the Oxytocin System in Pregnancy and Lactation. In: Advances in Experimental Medicine and Biology. Springer New York; 2013:201-218. doi:10.1007/978-1-4614-6199-9_10
  96. 96.
    Albanesi S, Olivetti C. Gender Roles and Medical Progress. Journal of Political Economy. Published online June 2016:650-695. doi:10.1086/686035
  97. 97.
    Ryan AS. The Resurgence of Breastfeeding in the United States. PEDIATRICS. Published online April 1, 1997:e12-e12. doi:10.1542/peds.99.4.e12
  98. 98.
    Inoue M, Binns CW, Otsuka K, Jimba M, Matsubara M. Infant feeding practices and breastfeeding duration in Japan: A review. Int Breastfeed J. Published online 2012:15. doi:10.1186/1746-4358-7-15
  99. 99.
    Nutrition During Lactation. National Academies Press; 1991. doi:10.17226/1577
  100. 100.
    BLANKS A. The role of oxytocin in parturition. BJOG: An International Journal of Obstetrics and Gynaecology. Published online April 2003:46-51. doi:10.1016/s1470-0328(03)00024-7
  101. 101.
    Hobbs AJ, Mannion CA, McDonald SW, Brockway M, Tough SC. The impact of caesarean section on breastfeeding initiation, duration and difficulties in the first four months postpartum. BMC Pregnancy Childbirth. Published online April 26, 2016. doi:10.1186/s12884-016-0876-1
  102. 102.
    Smith LJ. Impact of Birthing Practices on the Breastfeeding Dyad. Journal of Midwifery & Women’s Health. Published online November 12, 2007:621-630. doi:10.1016/j.jmwh.2007.07.019
  103. 103.
    Mitchell IJ, Gillespie SM, Abu-Akel A. Similar effects of intranasal oxytocin administration and acute alcohol consumption on socio-cognitions, emotions and behaviour: Implications for the mechanisms of action. Neuroscience & Biobehavioral Reviews. Published online August 2015:98-106. doi:10.1016/j.neubiorev.2015.04.018
  104. 104.
    Bowen MT, Peters ST, Absalom N, Chebib M, Neumann ID, McGregor IS. Oxytocin prevents ethanol actions at δ subunit-containing GABAA receptors and attenuates ethanol-induced motor impairment in rats. Proc Natl Acad Sci USA. Published online February 23, 2015:3104-3109. doi:10.1073/pnas.1416900112
  105. 105.
    King CE, Becker HC. Oxytocin attenuates stress-induced reinstatement of alcohol seeking behavior in male and female mice. Psychopharmacology. Published online March 28, 2019:2613-2622. doi:10.1007/s00213-019-05233-z
  106. 106.
    King CE, Griffin WC, Lopez MF, Becker HC. Activation of hypothalamic oxytocin neurons reduces binge-like alcohol drinking through signaling at central oxytocin receptors. Neuropsychopharmacol. Published online June 14, 2021:1950-1957. doi:10.1038/s41386-021-01046-x
  107. 107.
    Erickson EN, Carter CS, Emeis CL. Oxytocin, Vasopressin and Prolactin in New Breastfeeding Mothers: Relationship to Clinical Characteristics and Infant Weight Loss. J Hum Lact. Published online April 29, 2019:136-145. doi:10.1177/0890334419838225
  108. 108.
    Lui C, Cui X gang, Wang Y xin, You Z dong, Xu D feng. Association between neuropeptide oxytocin and male infertility. J Assist Reprod Genet. Published online August 14, 2010:525-531. doi:10.1007/s10815-010-9451-2
  109. 109.
    Chester DS, DeWall CN, Derefinko KJ, et al. Looking for reward in all the wrong places: dopamine receptor gene polymorphisms indirectly affect aggression through sensation-seeking. Social Neuroscience. Published online December 7, 2015:487-494. doi:10.1080/17470919.2015.1119191
  110. 110.
    Leyton M, Vezina P. Dopamine ups and downs in vulnerability to addictions: a neurodevelopmental model. Trends in Pharmacological Sciences. Published online June 2014:268-276. doi:10.1016/
  111. 111.
    Gold MS, Blum K, Oscar–Berman M, Braverman ER. Low Dopamine Function in Attention Deficit/Hyperactivity Disorder: Should Genotyping Signify Early Diagnosis in Children? Postgraduate Medicine. Published online January 2014:153-177. doi:10.3810/pgm.2014.01.2735
  112. 112.
    Schlüter T, Winz O, Henkel K, et al. The Impact of Dopamine on Aggression: An [18F]-FDOPA PET Study in Healthy Males. J Neurosci. Published online October 23, 2013:16889-16896. doi:10.1523/jneurosci.1398-13.2013
  113. 113.
    Tonry M. Why Crime Rates Are Falling throughout the Western World. Crime and Justice. Published online September 2014:1-63. doi:10.1086/678181
  114. 114.
    Lappi-Seppälä T, Lehti M. Cross-Comparative Perspectives on Global Homicide Trends. Crime and Justice. Published online September 2014:135-230. doi:10.1086/677979
  115. 115.
    Spelman W. Why birth cohorts commit crime at different rates. Social Science Research. Published online August 2021:102628. doi:10.1016/j.ssresearch.2021.102628
  116. 116.
    Chistik OF. Statistical Approach To The Research Of Crime In The Russian Federation. Published online March 20, 2019. doi:10.15405/epsbs.2019.03.121
  117. 117.
  118. 118.
    Baker LA, Tuvblad C, Reynolds C, Zheng M, Lozano DI, Raine A. Resting heart rate and the development of antisocial behavior from age 9 to 14: Genetic and environmental influences. Dev Psychopathol. Published online July 7, 2009:939-960. doi:10.1017/s0954579409000509
  119. 119.
    Latvala A, Kuja-Halkola R, Almqvist C, Larsson H, Lichtenstein P. A Longitudinal Study of Resting Heart Rate and Violent Criminality in More Than 700 000 Men. JAMA Psychiatry. Published online October 1, 2015:971. doi:10.1001/jamapsychiatry.2015.1165
  120. 120.
    Murray J, Hallal PC, Mielke GI, et al. Low resting heart rate is associated with violence in late adolescence: a prospective birth cohort study in Brazil. Int J Epidemiol. Published online January 28, 2016:491-500. doi:10.1093/ije/dyv340
  121. 121.
    Culpepper L, Froom J. Incarceration and blood pressure. Social Science & Medicine Part A: Medical Psychology & Medical Sociology. Published online December 1980:571-574. doi:10.1016/s0271-7123(80)80064-2
  122. 122.
    Ziegler M, Kennedy B, Holland O, Murphy D, Lake C. The effects of dopamine agonists on human cardiovascular and sympathetic nervous systems. Int J Clin Pharmacol Ther Toxicol. 1985;23(4):175-179.
  123. 123.
    Tiihonen J, Halonen P, Tiihonen L, Kautiainen H, Storvik M, Callaway J. The Association of Ambient Temperature and Violent Crime. Sci Rep. Published online July 28, 2017. doi:10.1038/s41598-017-06720-z
  124. 124.
    Fares A. Winter Hypertension : Potential Mechanisms. IJHS. Published online June 2013:210-219. doi:10.12816/0006044
  125. 125.
    Eisenberg DP, Kohn PD, Baller EB, Bronstein JA, Masdeu JC, Berman KF. Seasonal Effects on Human Striatal Presynaptic Dopamine Synthesis. Journal of Neuroscience. Published online November 3, 2010:14691-14694. doi:10.1523/jneurosci.1953-10.2010
  126. 126.
    Meng Y, Holmes J, Hill-McManus D, Brennan A, Meier PS. Trend analysis and modelling of gender-specific age, period and birth cohort effects on alcohol abstention and consumption level for drinkers in Great Britain using the General Lifestyle Survey 1984-2009. Addiction. Published online September 13, 2013:206-215. doi:10.1111/add.12330
  127. 127.
    Matthiopoulos J, Moss R, Lambin X. The kin facilitation hypothesis for red grouse population cycles: territorial dynamics of the family cluster. Ecological Modelling. Published online January 2002:291-307. doi:10.1016/s0304-3800(01)00420-3
  128. 128.
    PIERTNEY SB, LAMBIN X, MACCOLL ADC, et al. Temporal changes in kin structure through a population cycle in a territorial bird, the red grouse Lagopus lagopus scoticus. Molecular Ecology. Published online April 22, 2008:2544-2551. doi:10.1111/j.1365-294x.2008.03778.x
  129. 129.
    Previc FH. The Dopaminergic Mind in Human Evolution and History. Published online 2009. doi:10.1017/cbo9780511581366
  130. 130.
    Raghanti MA, Edler MK, Stephenson AR, et al. A neurochemical hypothesis for the origin of hominids. Proc Natl Acad Sci USA. Published online January 22, 2018:E1108-E1116. doi:10.1073/pnas.1719666115
  131. 131.
    Johnsen K, Devineau O, Andreassen HP. Phase- and season-dependent changes in social behaviour in cyclic vole populations. BMC Ecol. Published online January 25, 2019. doi:10.1186/s12898-019-0222-3
  132. 132.
    COCHRANE C. Left and Right. Published online October 1, 2015. doi:10.2307/j.ctvnjbgwt
  133. 133.
    Cardoso C, Ellenbogen MA, Linnen AM. Acute intranasal oxytocin improves positive self-perceptions of personality. Psychopharmacology. Published online October 20, 2011:741-749. doi:10.1007/s00213-011-2527-6
  134. 134.
    Pearce E, Wlodarski R, Machin A, Dunbar RIM. Genetic Influences on Social Relationships: Sex Differences in the Mediating Role of Personality and Social Cognition. Adaptive Human Behavior and Physiology. Published online November 26, 2019:331-351. doi:10.1007/s40750-019-00120-5
  135. 135.
    Joly JK, Hofmans J, Loewen P. Personality and Party Ideology Among Politicians. A Closer Look at Political Elites From Canada and Belgium. Front Psychol. Published online April 17, 2018. doi:10.3389/fpsyg.2018.00552
  136. 136.
    Tollenaar MS, Chatzimanoli M, van der Wee NJA, Putman P. Enhanced orienting of attention in response to emotional gaze cues after oxytocin administration in healthy young men. Psychoneuroendocrinology. Published online September 2013:1797-1802. doi:10.1016/j.psyneuen.2013.02.018
  137. 137.
    Dodd MD, Hibbing JR, Smith KB. The politics of attention: gaze-cuing effects are moderated by political temperament. Atten Percept Psychophys. Published online November 4, 2010:24-29. doi:10.3758/s13414-010-0001-x
  138. 138.
    Hatemi PK, Medland SE, Klemmensen R, et al. Genetic Influences on Political Ideologies: Twin Analyses of 19 Measures of Political Ideologies from Five Democracies and Genome-Wide Findings from Three Populations. Behav Genet. Published online February 26, 2014:282-294. doi:10.1007/s10519-014-9648-8
  139. 139.
    Settle JE, Dawes CT, Christakis NA, Fowler JH. Friendships Moderate an Association between a Dopamine Gene Variant and Political Ideology. The Journal of Politics. Published online October 2010:1189-1198. doi:10.1017/s0022381610000617
  140. 140.
    Hurlemann R, Marsh N, Schultz J, Scheele D. Oxytocin shapes the priorities and neural representations of attitudes and values. Behav Brain Sci. Published online 2017. doi:10.1017/s0140525x16000807
  141. 141.
    Taber CS, Lodge M. The Illusion of Choice in Democratic Politics: The Unconscious Impact of Motivated Political Reasoning. Political Psychology. Published online January 22, 2016:61-85. doi:10.1111/pops.12321
  142. 142.
    Stanley DA, Adolphs R. Toward a Neural Basis for Social Behavior. Neuron. Published online October 2013:816-826. doi:10.1016/j.neuron.2013.10.038
  143. 143.
    Zur R. Stuck in the middle: Ideology, valence and the electoral failures of centrist parties. Brit J Polit Sci. Published online September 25, 2019:706-723. doi:10.1017/s0007123419000231
  144. 144.
    Young S. The neurobiology of human social behaviour: an important but neglected topic. J Psychiatry Neurosci. 2008;33(5):391-392.
  145. 145.
    Whitehead H, Laland KN, Rendell L, Thorogood R, Whiten A. The reach of gene–culture coevolution in animals. Nat Commun. Published online June 3, 2019. doi:10.1038/s41467-019-10293-y
  146. 146.
    Geng Y, Zhao W, Zhou F, et al. Oxytocin Enhancement of Emotional Empathy: Generalization Across Cultures and Effects on Amygdala Activity. Front Neurosci. Published online July 31, 2018. doi:10.3389/fnins.2018.00512
  147. 147.
    Buffone AEK, Poulin MJ. Empathy, Target Distress, and Neurohormone Genes Interact to Predict Aggression for Others–Even Without Provocation. Pers Soc Psychol Bull. Published online September 10, 2014:1406-1422. doi:10.1177/0146167214549320
  148. 148.
    Grillon C, Krimsky M, Charney DR, Vytal K, Ernst M, Cornwell B. Oxytocin increases anxiety to unpredictable threat. Mol Psychiatry. Published online November 13, 2012:958-960. doi:10.1038/mp.2012.156
  149. 149.
    Kawada A, Nagasawa M, Murata A, et al. Vasopressin enhances human preemptive strike in both males and females. Sci Rep. Published online July 4, 2019. doi:10.1038/s41598-019-45953-y
  150. 150.
    Everett JAC, Faber NS, Crockett M. Preferences and beliefs in ingroup favoritism. Front Behav Neurosci. Published online February 13, 2015. doi:10.3389/fnbeh.2015.00015
  151. 151.
    Huang Y, Zhen S, Yu R. Distinct neural patterns underlying ingroup and outgroup conformity. Proc Natl Acad Sci USA. Published online February 19, 2019:4758-4759. doi:10.1073/pnas.1819421116
  152. 152.
    Tackenberg MC, McMahon DG. Photoperiodic Programming of the SCN and Its Role in Photoperiodic Output. Neural Plasticity. Published online 2018:1-9. doi:10.1155/2018/8217345
  153. 153.
    Coomans CP, Ramkisoensing A, Meijer JH. The suprachiasmatic nuclei as a seasonal clock. Frontiers in Neuroendocrinology. Published online April 2015:29-42. doi:10.1016/j.yfrne.2014.11.002
  154. 154.
    Sinclair ARE. Mammal population regulation, keystone processes and ecosystem dynamics. Phil Trans R Soc Lond B. Published online August 28, 2003:1729-1740. doi:10.1098/rstb.2003.1359
  155. 155.
    Wahl S, Engelhardt M, Schaupp P, Lappe C, Ivanov IV. The inner clock—Blue light sets the human rhythm. J Biophotonics. Published online September 2, 2019. doi:10.1002/jbio.201900102
  156. 156.
    Duffy JF, Czeisler CA. Effect of Light on Human Circadian Physiology. Sleep Medicine Clinics. Published online June 2009:165-177. doi:10.1016/j.jsmc.2009.01.004