Demographic uniformitarianism: the theoretical basis of prehistoric demographic research and its cross-disciplinary challenges

A principle of demographic uniformitarianism underpins all research into prehistoric demography (palaeodemography). This principle—which argues for continuity in the evolved mechanisms underlying modern human demographic processes and their response to environmental stimuli between past and present—provides the cross-disciplinary basis for palaeodemographic reconstruction and analysis. Prompted by the recent growth and interest in the field of prehistoric demography, this paper reviews the principle of demographic uniformitarianism, evaluates how it relates to two key debates in palaeodemographic research and seeks to delimit its range of applicability to past human and hominin populations.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia

Successive generations of hunter–gatherers of the Late Glacial and Early Holocene in Iberia had to contend with rapidly changing environments and climatic conditions. This constrained their economic resources and capacity for demographic growth. The Atlantic façade of Iberia was occupied throughout these times and witnessed very significant environmental transformations. Archaeology offers a perspective on how past human population ecologies changed in response to this scenario. Archaeological radiocarbon data are used here to reconstruct demographics of the region over the long term. We introduce various quantitative methods that allow us to develop palaeodemographic and spatio-temporal models of population growth and density, and compare our results to independent records of palaeoenvironmental and palaeodietary change, and growth rates derived from skeletal data. Our results demonstrate that late glacial population growth was stifled by the Younger Dryas stadial, but populations grew in size and density during the Early to Middle Holocene transition. This growth was fuelled in part by an increased dependence on marine and estuarine food sources, demonstrating how the environment was linked to demographic change via the resource base, and ultimately the carrying capacity of the environment.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Sometimes hidden but always there: the assumptions underlying genetic inference of demographic histories

Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Archaeology,demography and life history theory together can help us explain past and present population patterns

Population matters. Demographic patterns are both a cause and a consequence of human behaviour in other important domains, such as subsistence, cooperation, politics and culture. Demographers interested in contemporary and recent historical populations have rich data at their fingertips; the importance of demography means many interested parties have gathered demographic data, much of which is now readily available for all to explore. Those interested in the demography of the distant past are not so fortunate, given the lack of written records. Nevertheless, the emergence in recent years of a new interest in the demography of ancient populations has seen the development of a range of new methods for piecing together archaeological, skeletal and DNA evidence to reconstruct past population patterns. These efforts have found evidence in support of the view that the relatively low long-term population growth rates of prehistoric human populations, albeit ultimately conditioned by carrying capacities, may have been owing to ‘boom–bust’ cycles at the regional level; rapid population growth, followed by population decline. In fact, this archaeological research may have come to the same conclusion as some contemporary demographers: that demography can be remarkably hard to predict, at least in the short term. It also fits with evidence from biology that primates, and particularly humans, may be adapted to environmental variability, leading to associated demographic stochasticity. This evidence of the fluctuating nature of human demographic patterns may be of considerable significance in understanding our species'' evolution, and of understanding what our species future demographic trajectories might be.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

The MAD legacy: How meaningful is mean age-at-death in skeletal samples

This study examines the representativeness of palaeodemographic reconstructions from human skeletal remains. Mean age-at-death (MAD) is the primary statistic used in interpretations of changing patterns of health and well being from palaeodemographic analyses. A series of sampling experiments were conducted on three documented 19th century samples representing the total cemetery population from which skeletal samples could be drawn. Comparisons of the age-at-death distributions of simulated skeletal samples to the parent population were made to assess the relative magnitude of deviation associated with different types of bias (age, sex, temporal). From the examples presented, variability in age-at-death distribution is high in samples of less than 100, suggesting that for samples of less than 100analyzable individuals, it is probable that the mortality profiles constructed are not an accurate reflection of the cemetery. It is proposed that whateverprocess mean age-at-death reflects for past populations (fertility or mortality), is irrelevant if the sample on which the statistic is calculated is not representative of the population. Given that most cemetery samples will be subject, differentially, to biases at a variety of levels, comparative studies based on palaeodemographic data cannot be considered reliablewithout careful control for those biases. It is suggested that representativeness is the primary theoretical obstacle for researches to overcome, and that it is necessary to shift our focus to rigorously exploring those factors that bias our samples. Without some direct quantification of the representativeness of a sample, palaeodemographic estimators such as mean age-at-death are meaningless and any subsequent interpretations regarding the past, dubious at best.     

Approaching prehistoric demography: proxies,scales and scope of the Cologne Protocol in European contexts

In many theories on the social and cultural evolution of human societies, the number and density of people living together in a given time and region is a crucial factor. Because direct data on past demographic developments are lacking, and reliability and validity of demographic proxies require careful evaluation, the topic has been approached from several different directions. This paper provides an introduction to a geostatistical approach for estimating prehistoric population size and density, the so-called Cologne Protocol and discusses underlying theoretical assumptions and upscaling transfer-functions between different spatial scale levels. We describe and compare the specifics for farming and for foraging societies and, using examples, discuss a diachronic series of estimates, covering the population dynamics of roughly 40 kyr of European prehistory. Ethnohistoric accounts, results from other approaches—including absolute (ethno-environmental models) and relative estimates (site-numbers, dates as data, etc.) allow a first positioning of the estimates within this field of research. Future enhancements, applications and testing of the Cologne Protocol are outlined and positioned within the general theoretical and methodological avenues of palaeodemographic research. In addition, we provide manuals for modelling Core Areas in MapInfo, ArcGIS, QGIS/Saga and R.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Amplicon sequencing of 42 nuclear loci supports directional gene flow between South Pacific populations of a hydrothermal vent limpet

In the past few decades, population genetics and phylogeographic studies have improved our knowledge of connectivity and population demography in marine environments. Studies of deep‐sea hydrothermal vent populations have identified barriers to gene flow, hybrid zones, and demographic events, such as historical population expansions and contractions. These deep‐sea studies, however, used few loci, which limit the amount of information they provided for coalescent analysis and thus our ability to confidently test complex population dynamics scenarios. In this study, we investigated population structure, demographic history, and gene flow directionality among four Western Pacific hydrothermal vent populations of the vent limpet Lepetodrilus aff. schrolli. These vent sites are located in the Manus and Lau back‐arc basins, currently of great interest for deep‐sea mineral extraction. A total of 42 loci were sequenced from each individual using high‐throughput amplicon sequencing. Amplicon sequences were analyzed using both genetic variant clustering methods and evolutionary coalescent approaches. Like most previously investigated vent species in the South Pacific, L. aff. schrolli showed no genetic structure within basins but significant differentiation between basins. We inferred significant directional gene flow from Manus Basin to Lau Basin, with low to no gene flow in the opposite direction. This study is one of the very few marine population studies using >10 loci for coalescent analysis and serves as a guide for future marine population studies.     

Dendrochronological dates confirm a Late Prehistoric population decline in the American Southwest derived from radiocarbon dates

The northern American Southwest provides one of the most well-documented cases of human population growth and decline in the world. The geographic extent of this decline in North America is unknown owing to the lack of high-resolution palaeodemographic data from regions across and beyond the greater Southwest, where archaeological radiocarbon data are often the only available proxy for investigating these palaeodemographic processes. Radiocarbon time series across and beyond the greater Southwest suggest widespread population collapses from AD 1300 to 1600. However, radiocarbon data have potential biases caused by variable radiocarbon sample preservation, sample collection and the nonlinearity of the radiocarbon calibration curve. In order to be confident in the wider trends seen in radiocarbon time series across and beyond the greater Southwest, here we focus on regions that have multiple palaeodemographic proxies and compare those proxies to radiocarbon time series. We develop a new method for time series analysis and comparison between dendrochronological data and radiocarbon data. Results confirm a multiple proxy decline in human populations across the Upland US Southwest, Central Mesa Verde and Northern Rio Grande from AD 1300 to 1600. These results lend confidence to single proxy radiocarbon-based reconstructions of palaeodemography outside the Southwest that suggest post-AD 1300 population declines in many parts of North America.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Inference for nonlinear epidemiological models using genealogies and time series

Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.     

Estimating diaphyseal length from fragmentary subadult skeletal remains: Implications for palaeodemographic reconstructions of a southern Ontario ossuary

Fragmentary skeletal remains are a significant problem for osteologists attempting to reconstruct individuals or populations. This problem is further aggravated by sites yielding commingled remains, such as are recovered from the large protohistoric and historic ossuaries from southern Ontario, for which individual methods of age estimation and sex determination cannot be used concurrently. While some attention has been given to the estimation of long bone length from fragmentary, adult remains, little attention has been given to the equally important problem of fragmentary long bones in subadult assemblages. Analysis of data on diaphyseal length is a crucial aspect of reconstructing subadult palaeodemographic profiles, particularly for ossuary collections where dental remains are not associated with individuals and are often less represented than long bones. Such analysis also aids in the assessment of conditions of past population health. This study reports the results of several regression techniques used to estimate diaphyseal length from shaft-end breadths. Data collected from two southern Ontario ossuary samples were compiled to calculate the regression equations. Reliability of these equations and implications for palaeodemographic profiles are discussed. © 1996 Wiley-Liss, Inc.     

Using stage‐based system dynamics modeling for demographic management of captive populations

Management of captive populations relies on a complex synthesis of genetic and demographic analyses to guide populations toward sustainability. Demographic analyses of captive populations currently utilize age‐based matrix projections to predict a population's trajectory. An alternate approach is to use a stage‐based, system dynamics model for captive systems. Such models can more easily incorporate complex captive systems in which population dynamics are dependent on a combination of management and a species' biology. By linking these two areas, population managers can gain a more accurate understanding of how management decisions impact captive populations and which aspects of a species' demography should be of special concern in the future. We present a general stage‐based system dynamics model that has been developed for use with captive populations. The utility of the model is then illustrated by applying it to three captive bear populations: spectacled bears (Tremarctos ornatus), sloth bears (Melursus ursinus), and sun bears (Helarctos malayanus). Zoo Biol 22:45–64, 2003. © 2003 Wiley‐Liss, Inc.     

Spatiotemporal landscape genetics: Investigating ecology and evolution through space and time

Genetic time‐series data from historical samples greatly facilitate inference of past population dynamics and species evolution. Yet, although climate and landscape change are often touted as post‐hoc explanations of biological change, our understanding of past climate and landscape change influences on evolutionary processes is severely hindered by the limited application of methods that directly relate environmental change to species dynamics through time. Increased integration of spatiotemporal environmental and genetic data will revolutionize the interpretation of environmental influences on past population processes and the quantification of recent anthropogenic impacts on species, and vastly improve prediction of species responses under future climate change scenarios, yielding widespread revelations across evolutionary biology, landscape ecology and conservation genetics. This review encourages greater use of spatiotemporal landscape genetic analyses that explicitly link landscape, climate and genetic data through time by providing an overview of analytical approaches for integrating historical genetic and environmental data in five key research areas: population genetic structure, demography, phylogeography, metapopulation connectivity and adaptation. We also include a tabular summary of key methodological information, suggest approaches for mitigating the particular difficulties in applying these techniques to ancient DNA and palaeoclimate data, and highlight areas for future methodological development.     

New methodologies for studying lipid synthesis and turnover: Looking backwards to enable moving forwards

Our ability to understand the pathogenesis of problems surrounding lipid accretion requires attention towards quantifying lipid kinetics. In addition, studies of metabolic flux should also help unravel mechanisms that lead to imbalances in inter-organ lipid trafficking which contribute to dyslipidemia and/or peripheral lipid accumulation (e.g. hepatic fat deposits). This review aims to outline the development and use of novel methods for studying lipid kinetics in vivo. Although our focus is directed towards some of the approaches that are currently reported in the literature, we include a discussion of the older literature in order to put “new” methods in better perspective and inform readers of valuable historical research. Presumably, future advances in understanding lipid dynamics will benefit from a careful consideration of the past efforts, where possible we have tried to identify seminal papers or those that provide clear data to emphasize essential points. This article is part of a Special Issue entitled: Modulation of Adipose Tissue in Health and Disease.     

Recent progress in understanding larval dispersal: new directions and digressions

Larvae have been difficult to study because their small sizelimits our ability to understand their behavior and the conditionsthey experience. Questions about larval transport focus largelyon (a) where they go [dispersal] and (b) where they come from[connectivity]. Mechanisms of transport have been intensivelystudied in recent decades. As our ability to identify larvalsources develops, the consequences of connectivity are garneringmore consideration. Attention to transport and connectivityissues has increased dramatically in the past decade, fueledby changing motivations that now include management of fisheriesresources, understanding of the spread of invasive species,conservation through the design of marine reserves, and predictionof climate-change effects. Current progress involves both technologicaladvances and the integration of disciplines and approaches.This review focuses on insights gained from physical modeling,chemical tracking, and genetic approaches. I consider how newfindings are motivating paradigm shifts concerning (1) life-historyconsequences; (2) the openness of marine populations, self-recruitment,and population connectivity; (3) the role of behavior; and (4)the significance of variability in space and time. A challengefor the future will be to integrate methods that address dispersalon short (intragenerational) timescales such as elemental fingerprintingand numerical simulations with those that reflect longer timescalessuch as gene flow estimates and demographic modeling. Recognitionand treatment of the continuum between ecological and evolutionarytimescales will be necessary to advance the mechanistic understandingof larval and population dynamics.     

Phylodynamic Inference for Structured Epidemiological Models

Coalescent theory is routinely used to estimate past population dynamics and demographic parameters from genealogies. While early work in coalescent theory only considered simple demographic models, advances in theory have allowed for increasingly complex demographic scenarios to be considered. The success of this approach has lead to coalescent-based inference methods being applied to populations with rapidly changing population dynamics, including pathogens like RNA viruses. However, fitting epidemiological models to genealogies via coalescent models remains a challenging task, because pathogen populations often exhibit complex, nonlinear dynamics and are structured by multiple factors. Moreover, it often becomes necessary to consider stochastic variation in population dynamics when fitting such complex models to real data. Using recently developed structured coalescent models that accommodate complex population dynamics and population structure, we develop a statistical framework for fitting stochastic epidemiological models to genealogies. By combining particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a wide class of stochastic, nonlinear epidemiological models with different forms of population structure to genealogies. We demonstrate our framework using two structured epidemiological models: a model with disease progression between multiple stages of infection and a two-population model reflecting spatial structure. We apply the multi-stage model to HIV genealogies and show that the proposed method can be used to estimate the stage-specific transmission rates and prevalence of HIV. Finally, using the two-population model we explore how much information about population structure is contained in genealogies and what sample sizes are necessary to reliably infer parameters like migration rates.     

Identifying the critical climatic time window that affects trait expression

Identifying the critical time window during which climatic drivers affect the expression of phenological, behavioral, and demographic traits is crucial for predicting the impact of climate change on trait and population dynamics. Two widely used associative methods exist to identify critical climatic periods: sliding-window models and recursive operators in which the memory of past weather fades over time. Both approaches have different strong points, which we combine here into a single method. Our method uses flexible functions to differentially weight past weather, which can reflect competing hypotheses about time lags and the relative importance of recent and past weather for trait expression. Using a 22-year data set, we illustrate that the climatic window identified by our new method explains more of the phenological variation in a sexually selected trait than existing approaches. Our new method thus helps to better identify the critical time window and the causes of trait response to environmental variability.     

Complex population dynamics and complex causation: devils, details and demography

Population dynamics result from the interplay of density-independent and density-dependent processes. Understanding this interplay is important, especially for being able to predict near-term population trajectories for management. In recent years, the study of model systems-experimental, observational and theoretical-has shed considerable light on the way that the both density-dependent and -independent aspects of the environment affect population dynamics via impacting on the organism's life history and therefore demography. These model-based approaches suggest that (i) individuals in different states differ in their demographic performance, (ii) these differences generate structure that can fluctuate independently of current total population size and so can influence the dynamics in important ways, (iii) individuals are strongly affected by both current and past environments, even when the past environments may be in previous generations and (iv) dynamics are typically complex and transient due to environmental noise perturbing complex population structures. For understanding population dynamics of any given system, we suggest that 'the devil is in the detail'. Experimental dissection of empirical systems is providing important insights into the details of the drivers of demographic responses and therefore dynamics and should also stimulate theory that incorporates relevant biological mechanism.     

Phylogenomics at the tips: inferring lineages and their demographic history in a tropical lizard,Carlia amax

High‐throughput sequencing approaches offer opportunities to better understand the evolutionary processes driving diversification, particularly in nonmodel organisms. In particular, the 100–1000's of loci that can now be sequenced are providing unprecedented power in population, speciation and phylogenetic studies. Here, we apply an exon capture approach to generate >99% complete sequence and SNP data across >2000 loci from a tropical skink, Carlia amax, and exploit these data to identify divergent lineages and infer their relationships and demographic histories. This is especially relevant to low‐dispersal tropical taxa that often have cryptic diversity and spatially dynamic histories. For C. amax, clustering of nuclear SNPs and coalescent‐based species delimitation analyses identify four divergent lineages, one fewer than predicted based on geographically coherent mtDNA clades (>9.4% sequence divergence). Three of these lineages are widespread and parapatric on the mainland, whereas the most divergent is restricted to islands off the northeast Northern Territory. Tests for population expansion reject an equilibrium isolation‐by‐distance model for two of the three widespread lineages and infer refugial expansion sources in the relatively mesic northeast Top End and northwest Kimberley. The latter is already recognized as a hotspot of endemism, but our results also suggest that a stronger focus on the northeast Top End, and adjacent islands is warranted. More generally, our results show how genome‐reduction methods such as exon capture can yield insights into the pattern and dynamics of biodiversity across complex landscapes with as yet poorly understood biogeographic history and how exon data can link between population and phylogenetic questions.     

Past and potential future population dynamics of three grouse species using ecological and whole genome coalescent modeling

Studying demographic history of species provides insight into how the past has shaped the current levels of overall biodiversity and genetic composition of species, but also how these species may react to future perturbations. Here we investigated the demographic history of the willow grouse (Lagopus lagopus), rock ptarmigan (Lagopus muta), and black grouse (Tetrao tetrix) through the Late Pleistocene using two complementary methods and whole genome data. Species distribution modeling (SDM) allowed us to estimate the total range size during the Last Interglacial (LIG) and Last Glacial Maximum (LGM) as well as to indicate potential population subdivisions. Pairwise Sequentially Markovian Coalescent (PSMC) allowed us to assess fluctuations in effective population size across the same period. Additionally, we used SDM to forecast the effect of future climate change on the three species over the next 50 years. We found that SDM predicts the largest range size for the cold‐adapted willow grouse and rock ptarmigan during the LGM. PSMC captured intraspecific population dynamics within the last glacial period, such that the willow grouse and rock ptarmigan showed multiple bottlenecks signifying recolonization events following the termination of the LGM. We also see signals of population subdivision during the last glacial period in the black grouse, but more data are needed to strengthen this hypothesis. All three species are likely to experience range contractions under future warming, with the strongest effect on willow grouse and rock ptarmigan due to their limited potential for northward expansion. Overall, by combining these two modeling approaches, we have provided a multifaceted examination of the biogeography of these species and how they have responded to climate change in the past. These results help us understand how cold‐adapted species may respond to future climate changes.     

Statistical phylogeography

While studies of phylogeography and speciation in the past have largely focused on the documentation or detection of significant patterns of population genetic structure, the emerging field of statistical phylogeography aims to infer the history and processes underlying that structure, and to provide objective, rather than ad hoc explanations. Methods for parameter estimation are now commonly used to make inferences about demographic past. Although these approaches are well developed statistically, they typically pay little attention to geographical history. In contrast, methods that seek to reconstruct phylogeographic history are able to consider many alternative geographical scenarios, but are primarily nonstatistical, making inferences about particular biological processes without explicit reference to stochastically derived expectations. We advocate the merging of these two traditions so that statistical phylogeographic methods can provide an accurate representation of the past, consider a diverse array of processes, and yet yield a statistical estimate of that history. We discuss various conceptual issues associated with statistical phylogeographic inferences, considering especially the stochasticity of population genetic processes and assessing the confidence of phylogeographic conclusions. To this end, we present some empirical examples that utilize a statistical phylogeographic approach, and then by contrasting results from a coalescent-based approach to those from Templeton's nested cladistic analysis (NCA), we illustrate the importance of assessing error. Because NCA does not assess error in its inferences about historical processes or contemporary gene flow, we performed a small-scale study using simulated data to examine how our conclusions might be affected by such unconsidered errors. NCA did not identify the processes used to simulate the data, confusing among deterministic processes and the stochastic sorting of gene lineages. There is as yet insufficient justification of NCA's ability to accurately infer or distinguish among alternative processes. We close with a discussion of some unresolved problems of current statistical phylogeographic methods to propose areas in need of future development.