Modeling the pre-industrial roots of modern super-exponential population growth.

To Malthus, rapid human population growth-so evident in 18th Century Europe-was obviously unsustainable. In his Essay on the Principle of Population, Malthus cogently argued that environmental and socioeconomic constraints on population rise were inevitable. Yet, he penned his essay on the eve of the global census size reaching one billion, as nearly two centuries of super-exponential increase were taking off. Introducing a novel extension of J. E. Cohen's hallmark coupled difference equation model of human population dynamics and carrying capacity, this article examines just how elastic population growth limits may be in response to demographic change. The revised model involves a simple formalization of how consumption costs influence carrying capacity elasticity over time. Recognizing that complex social resource-extraction networks support ongoing consumption-based investment in family formation and intergenerational resource transfers, it is important to consider how consumption has impacted the human environment and demography--especially as global population has become very large. Sensitivity analysis of the consumption-cost model's fit to historical population estimates, modern census data, and 21st Century demographic projections supports a critical conclusion. The recent population explosion was systemically determined by long-term, distinctly pre-industrial cultural evolution. It is suggested that modern globalizing transitions in technology, susceptibility to infectious disease, information flows and accumulation, and economic complexity were endogenous products of much earlier biocultural evolution of family formation's embeddedness in larger, hierarchically self-organizing cultural systems, which could potentially support high population elasticity of carrying capacity. Modern super-exponential population growth cannot be considered separately from long-term change in the multi-scalar political economy that connects family formation and intergenerational resource transfers to wider institutions and social networks

Pursuing past seasons: A re-evaluation of cementum increment analysis of Paleolithic archaeology.

In archaeological research on the Paleolithic period, it has been argued that certain developments in hominid biological and cultural evolution involved key changes in the organizational and technological strategies for exploiting seasonal resources. Among the archaeological methods suggested to address the shifts in seasonal activity patterns in the context of Pleistocene cultural and biological evolution, cementum increment analysis stands out. Initially, the analysis of microscopic seasonal growth increments in the cementum tissue of archaeologically common mammal teeth appeared to offer the recovery of valuable season-of-death and age-at-death data from Paleolithic sites. With the perspective of nearly 35 years of experience and hindsight since cementum increment analysis was first suggested as an archaeological technique (Saxon and Higham 1968) and over 20 years since the method was introduced to the study of seasonality in Paleolithic contexts (Spiess 1979), this dissertation presents a critical reassessment of cementum as an archaeological source of seasonality and mortality information. The following assertion is thoroughly examined: the commonly employed cementum increment methods are adequate to recover true season-of-death distributions from Paleolithic deposits. It is argued that previous studies of microscopic increments in archaeological teeth have not fully addressed three key issues that fundamentally bear on whether cementum increment analysis can meet its methodological promise---that is, to yield reliable information about the seasonal activity patterns of hunter-gatherers in the Paleolithic. First, the correlation between time of the year and the state of incremental cementum growth varies between animal populations and over time---from year to year---within the same population. Second, researchers have entirely ignored the potential for common biogeochemical processes to create false seasonal bands in archaeological cementum. Third, workers have not considered the potential for the sample preparation and microscopy techniques themselves to create the appearance of seasonal bands in cementum. Basically, in the analysis of a given set of archaeological tooth assemblages, we may need to develop further the means to measure the accuracy of the equation, Cementum equals Season-of-Death. The chapters that follow are presented in order to offer a guide to the key methodological facets for understanding this equation, so that Paleolithic researchers can better judge how cementum increment analysis might be employed in advancing our knowledge of seasonal activity patterns in the course of human biological and cultural evolution in the Pleistocene.Ph.D.ArchaeologySocial SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/132938/2/3058052.pd

Population elasticity of carrying capacity <i>c_t</i> (see equation 1) and % annual population change over time, for the case ; ; ; <i>b</i> = 2.20; ; and .

<p>As population change reaches an equilibrium steady state, population elasticity of carrying capacity is substantially negative, revealing the equilibrium to be weak.</p

Sensitivity analysis results for the extended consumption cost (CC) model.

1<p>The values in this column are calculated as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone-0105291-t002" target="_blank">Table 2</a>.</p>2<p>The values of <i>P_{2100 CE}</i> yielded by all six sensitivity cases examined for the CC model fall within the upper range of UN demographic projections for global human census size in the year 2100 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-United1" target="_blank">[4]</a>.</p>3<p>The values in this column are the % deviation of the CC model value of <i>P_{2012 CE}</i> from the observed value of .</p

Scatterplot of modeled population in 2012 CE (<i>P_{2012 CE}</i>) and the overall modeled trajectory's deviation from historical estimates and census data (here labeled <i>dev[1750</i>–<i>2012]</i> for clarity, defined as in the text and Tables 2 and 3) versus the cultural systemic limit to achieving economies of scale, <i>L</i>.

<p>The case shown involves ; ; and .</p

Typical preindustrial flow of transfers in human groups among all life history stages, emphasizing the importance of giving and receiving for prime adults engaged in family formation, investment in fertility, and offspring and descendant care.

<p>Transferred resources include consumable calories, material capital, social capital, technological know-how and environmental knowledge, and cultural competence. The preponderance of exchange within life history stages occurs among married adults and involves a combination of material and social capital, including reputation. In turn, married adults account for the bulk of transfers to other life stages. Modified after ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Stutz1" target="_blank">[37]</a>.</p

Semilog plot of historical estimates from 1–1950 CE (various shapes), recent UN census data for 1955–2012 (red asterisks with gray background), and 21^st Century projections based on variation in fertility and mortality rate trends (solid red line bounded by upper and lower range blue dotted lines), showing a logistic growth pattern with a remarkably rapid acceleration during the 19^th and 20^th centuries.

<p>Although uncertainty in the 21^st Century UN population projects encompasses continued growth to ca. 17 billion, as well as imminent decline toward ca. 6–7 billion, it is clear that demographic growth began decelerating over the past 20 years, and that deceleration is continuing. Data from refs. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-United1" target="_blank">[4]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Cohen2" target="_blank">[23]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-US1" target="_blank">[24]</a>.</p

Global human population (millions of people), 1–2012 CE.¹

1<p>Historical estimates for 1–1950 CE are from refs. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Biraben1" target="_blank">[72]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Blaxter1" target="_blank">[78]</a>. The UN global census data for 1955–2012 is from ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-US1" target="_blank">[24]</a>, which provides an open-access web-based summary of these data. The historical world population estimates are also summarized by Cohen <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Cohen2" target="_blank">[23]</a> in his Appendix 2. Note that the average population values—which are used to calculate (the distance for a given model population trajectory from the average population estimate/census value for the 1750–2012 data)—exclude duplicate estimates, in which a later study relies on an earlier study's result (e.g., Kremer's extensive use of the earlier estimates from McEvedy & Jones <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-McEvedy1" target="_blank">[74]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Kremer1" target="_blank">[77]</a>).</p

Consumption-cost (CC) model trajectory for the case involving ; ; ; ; and .

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone-0105291-t003" target="_blank">Table 3</a> for additional details.</p

Northern Hemisphere temperature variation, 700 AD – 1900 AD, juxtaposed with historical population estimates.

<p>Historical climate proxy data are from the supplementary materials in ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone.0105291-Moberg1" target="_blank">[35]</a>. Historical population data as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105291#pone-0105291-g001" target="_blank">Fig. 1</a>.</p