Desktop version

Home arrow History

  • Increase font
  • Decrease font

<<   CONTENTS   >>

How Do Life Histories Change in Persons and Populations?

The answer is complex. In abstract, it is this: The environment, most specifically its mortality regime, changes as a function of space, time, and structure,5 to which humans react facultatively, developmentally, and genetically.

The ratio of extrinsic to intrinsic mortality can vary in space: First, there is physical geographic variation via clinal change in latitude and longitude, altitude and aridity, and seasonality and severity. Second, environments change with time. Eccentricity (Shackleton 2000), axial tilt (Rubincam 1995), precession of the equinox (Kutzbach and Otto-Bliesner 1982), glaciation (Clark et al. 2001), wind systems (Trauth et al. 2000), Milankovitch cycles (Bennett 1990), Heinrich events (Jennerjahn et al.

2004), and oceanic currents (Stouffer et al. 2006) are among the many variables that can fluctuate temporally (Marsh and Kaufman 2013). Geographic and temporal environmental variation directly alters the prevailing mortality regime under which humans operate, but geographic and temporal environmental variation also changes the flora and fauna which we alternately struggle with, and benefit from (Sherrat and Wilkinson 2009). As temperature, moisture, insolation, and seasonality vary, so too will predators (Levy et al. 2016; Upadhyay and Iyengar 2005; Alebraheem and Hasan 2014), parasites (Nealis et al. 1984; Fels and Kaltz 2006; Blanford et al. 2013), pathogens (Gill and Reichel 1989; Cooper et al. 2007; Paul 2012), and prey (Pike-Tay and Cosgrove 2002; Hamback 1998; Keuroghlian and Passos 2001). Consequent effects on mortality can be extreme (Hertler 2016; Low 1988; Connah 2001; Landes 1998). By way of example, hold time constant and ignore all other variables, to consider only latitudinal changes in malarial infection. The moist warmth of tropical habitats like those found in West Africa are conducive to two mosquito species carrying deadly malaria (Bush et al. 2001, page 87, table 3.14) with the result that nine of ten childhood deaths from malaria occur in Africa (Wertheim et al. 2012, p. 15; Snow et al. 2001). Adult mortality is also significant. Malaria requires programmatic intervention for the commonplace hazard it presents to pregnant mothers and their developing young in Zambia (Chaponda et al. 2015), Malawi (Boudova et al. 2015), Uganda (Mbonye et al. 2016), Sudan (Sharief et al. 2011), Benin (Alvarez 2015), Nigeria (Uzoh et al. 2015), and other portions of Africa contained within, or approaching, 160 north and 200 south latitude with sufficient precipitation to support tropical and subtropical conditions (Bush et al. 2001). Additional correlates of malaria, for instance, miscarriage (Stivala 2015) and mental illness (Idro et al. 2016), though not factored into mortality estimates, are indirectly relevant to them.

As Quinlan (2007) stated, it is difficult to imagine how assiduous parental effort could meaningfully reduce “vector-borne pathogens like malaria.” This source of mortality is highly extrinsic. It can be mitigated against only by early reproduction that engenders large, diverse broods (Fincher and Thornhill 2012). Alternatively, late Medieval and Early Modern Europe was threatened by exposure and starvation and responded by dramatically delaying marriage and childbirth; a trend that continued with the result that, according to some estimates, only 55 % of fertile women were bearing children by the seventeenth century (Huppert 1998). As Huppert (1998, p. xi) describes, “we will find the demographic pattern whose most obvious component is the voluntary limitation of births among the mass of population by means of delayed marriage.” This voluntary limitation is only voluntary in that it was chosen by necessity as the lesser of two evils. As Huppert (1998, p. xii) makes clear, active demographic restriction is increasingly,

Thought to have been conditioned by the catastrophes of the 14th century and later recurrences of famine and disease which struck whenever the population reached the level of density and compatible with Food Supplies of nourishment that be drawn from some from the available land for the 14th century there was still some room for population growth after the 18th century but the food supply and employment opportunities were to become more flexible as a result of industrialization and global trade. It was only in the early modern period that precarious balance between resources and consumption could not be upset without the gravest consequences ...

While it can fluctuate annually, dearth is largely a product of seasonality.

This source of mortality is then highly intrinsic (Griskevicius et al. 2011).

It can be predicted and controlled; it responds to delayed reproduction, stable marital unions, smaller brood size, future-oriented laboring, and other markers of the slow life history (Brumbach et al. 2009; Ellis etal. 2009).

The question then becomes, how are life histories adjusted to environmental mortality regimes? The answer is that life histories change facultatively, developmentally, and evolutionarily. First, people facultatively make life history relevant trade-offs to some degree, as illustrated by a series of experiments that “unconsciously primed people’s attitudes towards safety and mortality and thereafter asked them questions about reproduction including if and when they would have children” (Griskevicius et al. 2011). So life history relevant decisions can, to some extent, be altered cognitively in real time. Second, early in development there is a sort of sensitive period, a developmental window wherein a slow or fast life history trajectory is taken (Ellis and Essex 2007). As acacia tree stems regrow with formidable thorns after being browsed upon (Milewski and Madden 2006), or water fleas develop a protective carapace in the presence of predatory fly larva (Travis 2009), human children may demonstrate a binary form of irreversible phenotypic plasticity,6 compressing life history when imprinted by proxies of extrinsic mortality, such as father absence, abuse, neglect, and violent crime (Griskevicius et al. 2011). It is akin to using tonight’s weather forecast to choose tomorrow’s attire. The plant’s thorn, the insect’s carapace, and the human’s compressed life history represent a simple form of developmental learning; they are investments in future survival undertaken in reaction to past threats. In ancestral environments and in modern environments with low social mobility, one’s childhood environment is a useful heuristic predictor of one’s adult environment. From hence came a degree of phenotypic plasticity (Stearns and Koella 1986; Stearns 1992) in the form of a crude binary switch, shunting one toward the fast or slow end of the life history spectrum (Del Giudice etal. 2009).

Third, there is a substantial genetic component to life history, upon which evolution acts. As Figueredo et al. (2006) report, “sexual behavior, marriage and divorce, fertility desires, fertility ideals and expectations, age of first explicit attempt to get pregnant, completed family size, and parenting behavior” all show substantial genetic influence and at least moderate heritability estimates. The same holds for intelligence and executive function (Wenner et al. 2013) as well as conscientiousness and its behavioral correlates (MacDonald 1997). So it follows that the meta-trait of life history also has a substantial genetic component (Figueredo et al. 2004; Sherman et al. 2013). Specifically, life history heritability estimates often range between .60 and .70 (Figueredo et al. 2004; Figueredo and Rushton 2009), though as discussed thoroughly by Figueredo et al. (2006), exact estimates vary by method and measure: When using the K-Factor, Covitality, Genetic Super-K Factor, or General Factor of Personality, heritability estimates were respectively reported to be .65, .52, .68, and .59. “This finding,” Figueredo et al. (2006) write, “supports the hypothesis that life-history strategy is predominantly under the control of regulatory genes that coordinate the expression of an entire array of life-history traits.” A meta-analysis conducted by van der Linden et al. (2010) suggests more modest estimates of approximately .50 (Figueredo et al. 2013). A review by Figueredo et al. (2004), though it reports some estimates extending to .90, is generally consistent with the above mentioned meta-analysis, placing most estimates between .40 and .60. Clearly then, mortality regimes shape lineages as much as persons. Facultative and developmental shifts take place across a narrow range of genetically defined values to determine the ultimate phenotypic expression of the adult life history.

<<   CONTENTS   >>

Related topics