Paula Sheppard & Zachary Van Winkle
In our recent article, Paula Sheppard and I tested the assumptions of life history theory, which is commonly used in evolutionary anthropology and related disciplines. Evolutionary anthropologists are interested in how human physiology and behaviour have developed across our species’ history. In contrast to most sociologists, evolutionary anthropologists often incorporate theories from biology to understand society.
What is Life History Theory?
One of these theories is called life history theory. In mainstream biology, life history theory is concerned with trade-offs between growth, maintenance and reproduction in different environments. The idea is relatively simple: Species that live in ecosystems where early death is likely have less time to pass on their genes. The best strategy for these species is to invest only enough to maintain a small body size, move through juvenile period quickly to maturity, and then produce many offspring. Flies are good examples here. Species that live in ecosystems where early death is unlikely can invest more in body size, mature slower, and have fewer children. Elephants are good examples here.
There are basically two life strategies: The fast strategy – grow up fast, have many offspring, and die early – and the slow strategy – grow up slow, have few offspring, and die late. The purpose of our article was to assess whether this logic also applies to humans. Do humans exhibit coherent life history strategies? And do those strategies fit on a fast-slow continuum?
Life history theory can also be used to understand differences within species. For example, biologists studying cichlids in Lake Tanganyika have found that fish living in harsher areas of the lake are smaller, grow up faster, and have more offspring before dying. In the more lush parts of the lake, the same species of fish are a bit bigger, take more time growing up, and have fewer offspring before dying somewhat later than their unluckier peers.
We also wanted to know whether this logic applies to humans. As we know, some children grow up in rich settings, while others grow up in poverty. So is low socioeconomic status during childhood associated with faster life strategies and high childhood socioeconomic status associated with slow life strategies?
How We Tested Life History Theory
Here are some boring details on how Paula and I answered our questions. Of course, all the boring details can be found in the article. We used the Wisconsin Longitudinal Study (WLS), which is an ongoing collection of data from a randomly-selected sample of more than 10,000 school children who graduated from schools in Wisconsin, USA, in 1957. Participants have been followed up five times to date and selected siblings of the original respondents were contacted in 1977 and have been interviewed three times since then. These data are ideal for our purposes for two reasons: first, the long-term nature of the study (the participants are now eighty) allows us to examine full lives, and second there are data on all the key life history events we are interested in: timing of puberty, childbirths, menopause, and mortality (around 1,300 of the original graduates have died). It is exceptionally rare to have these variables as well as socioeconomic variables in a study that has run for 60 years. We restrict our sample to female graduates as well as the sisters of graduates born in 1940 or earlier to increase our sample size. We exclude men because women’s life events are more discrete than men’s and data tend to be more reliable (e.g. women know exactly when they gave birth).
We use sequence analysis to test if human life histories are coherent strategies and also to examine exactly how they are patterned. Sequence analysis can be traced back to Vladimir Levenstein in 1965 as a means to enumerate the similarity of character strings and was initially used in the natural sciences to identify similarities and regularities in DNA strings. The goal of sequence analysis in the social sciences is to analyse regularities in sequences.
However, sequence analysis has never been applied to the biological components in human life history research. Here we use sequence analysis to establish whether there are coherent patterns in life histories and to identify those patterns. We constructed sequences with annual states from age five to 77 years. At any given age, an individual can be in childhood (C), in adulthood (A), in maternity (M), in post-menopausal adulthood (P), or dead (D). We additionally differentiate between being the 1st, 2nd, 3rd, and 4th-or-more maternity (M1, M2, M3, M4+).
To answer our questions – whether humans exhibit coherent life strategies and whether those fall into a fast-slow continuum – we will use a clustering method to group persons that have similar sequences. We can then visualize those groups and see whether some represent a fast cluster – enter puberty fast, have many children, and die early – while other represent a slow cluster – enter puberty slowly, have few children, and die late. To answer our third question – whether childhood socioeconomic status is associated with individuals’ strategies – we will use a method called multinomial logistic regression to see whether women who were poorer during childhood were more likely to exhibit fast life strategies.
Do Humans Exhibit Life Strategies?
To answer our first question, whether humans exhibit coherent life strategies, we took a look at a few statistics to see how many groups are in the data and whether these are coherent groups. Those statistics can be seen in Figure 1.
Figure 1: Average Silhouette Width Cluster Solution Quality Criteria
Basically, we conclude that there are likely five groups in the data. According to standard interpretation of the ASW statistic, which is 0.48, our five cluster solution indicates a weak structure, but verges on their criteria for a reasonable structure (0.5). Therefore, based on a relatively small number of clusters and the reasonably high ASW value, we find weak support for the existence of coherent life history strategies for humans.
Are Those Five Life Strategies Fast or Slow?
To answer our second question, whether our life history clusters represent fast or slow individual strategies, we visualised the clusters. Figure 2 shows the five life strategy groups we found in our data. The left panels show 100 representative life history sequences for each cluster. The box plots in the right panels of Figure 3 indicate the representativeness of each sequence, i.e. how closely that particular woman’s life history resembles the average life history of her cluster.
We found that our clusters entirely reflect completed fertility, with clusters for “Childless”, “1 Child”, “2 Children”, “3 Children”, and “4 or more Children”. The “1 Child” and “Childless” clusters are the smallest, consisting of only 149 (4.7%) and 217 (6.9%) women, respectively. Our high fertility cluster “4 or more Children” has 1,208 (38.7%) women, and is by far the largest cluster. The “2 Children” and “3 Children” clusters are each comprised of 751 (24%) and 795 (25.4%) women. By design, women’s life history sequences across all clusters begin at age five in the state of pre-pubescent childhood (yellow), and transition into the state of maturity (green) following the onset of menarche. Women within the “Childless” cluster remain within the state of maturity until the onset of menopause (purple) and/or death (black). Women within the other clusters transition into parenthood (light blue) and have additional children (darker blue shades).
For these clusters to reflect fast or slow life history strategies, we would expect there to be differences in the age of menarche, first birth, menopause, and death across clusters. Specifically, these ages should be lowest for women in the “4 or more Children” cluster and highest for the women in the “Childless” cluster. However, there are few differences across clusters for all for life history events, although there is a trend towards earlier first births for women with higher fertility. In sum, we find little support that life history strategies are on a fast-slow continuum.
Does Socioeconomic Status Associated with the Five Life History Strategies?
As we did not find any evidence for fast and slow life history strategies, the question as to whether childhood socioeconomic status predicts the pace of strategies is redundant. Nonetheless, we took a look at whether childhood socioeconomic status is related to any of our life history groups. Figure 3 shows how the probability being in a life history group changes with increasing childhood socioeconomic status. Our results indicate that socioeconomic status has little impact on the probability of being in any one group. We found that women in the most affluent childhood homes have a higher probability of being in the “2 Child” cluster and have a lower probability of being in the “4 or more Children” cluster.
Figure 3: Results from multinomial logistic regression of life history cluster membership on childhood socioeconomic status.
What Did We Learn
We set out to empirically test the assumption in evolutionary anthropology and other disciplines that human life histories are coherent strategies and that individuals fall on somewhere on a fast-slow continuum. Using powerful statistical techniques to describe the data from a sample of women from Wisconsin, we found very little evidence in support of this assumption. Our analysis revealed that women do cluster into groups, but that these are wholly defined by the numbers of children they had. The timing of life history events, especially age of puberty and death, did not change across groups.
A secondary aim of this paper was to test if early life adversity, measured by low childhood socioeconomic status, predicted a fast life history strategy. Given that our ‘strategies’ (i.e. the clusters) were simply indicators of fertility, our models no longer test this hypothesis. Nevertheless, they do test if childhood conditions are associated with fertility. However, our models showed no convincing support for this either as there were few statistically significant findings predicting cluster membership, or age at first birth.
Does this mean evolutionary anthropologists and other disciplines should scrap life history theory altogether? We don’t think so. It is important to remember the “ecosystem” of the women in our sample: a fairly well-off agricultural community in Wisconsin. In 1940s Wisconsin, poorer childhood socioeconomic conditions would not likely translate into severely undernourished children who delay maturation. We think that the ‘poor childhood-faster strategy’ argument may not apply to populations living in non-nutritionally-stressed environments. Therefore, our work should be replicated in other settings, especially less WEIRD ones, which might reveal more coherent life-history patterns that we do not find in Wisconsin.
In all, our findings tell a cautionary tale to researchers of human life histories. We need to think hard about the interpretation of our empirical findings and resist the temptation to deduce that associations with one or two life history events are indicative of anything more than just that. It is intuitively appealing to apply species-level phenomena to human individuals, but perhaps it is prudent to step back and return to first principles. Humans are flexible and long-lived organisms, and have cultural values and institutions that impact on fertility norms, and buffer us from death. It is entirely plausible that individuals adopt mixed strategies which they can, at least to some extent, alter during their lives depending on current circumstances.
Note: This study was pre-registered on Open Science Framework. The original proposal and replication package can be found at https://osf.io/zx6w3/.