Biodemography is an omnibus term for the numerous connections between demography and biology.
Demography has multiple points of contact with biology, as well as with mathematics, statistics, the social sciences, and policy analysis. The biology–demography interface was central to the research of two distinguished demographers, Alfred J. Lotka (1880–1949) and Raymond Pearl (1879–1940), in the early decades of the twentieth century. Lotka developed concepts and methods that are still of fundamental importance in biodemography; his two most significant books are Elements of Physical Biology (1925) and Théorie Analytique des Associations Biologiques (1934–1939). Pearl pioneered biodemo-graphic research on several species, including flatworms, the aquatic plant Ceratophyllum demersum, Drosophila, and humans. He founded two major journals, the Quarterly Journal of Biology and Human Biology and helped found both the International Union for the Scientific Investigation of Population Problems (which later became the International Union for the Scientific Study of Population) and the Population Association of America.
At the beginning of the twenty-first century biodemography is reemerging as a locus of cutting edge demographic research. It is clearly accepted that fertility, mortality, morbidity, and other processes of profound interest to demographers have a basic biological component. Moreover, biology is fundamentally a population science and there is growing recognition that biological studies can benefit greatly from demographic concepts and methods. From a biologist's perspective, biodemography envelops demography because it embraces research pertaining to: any nonhuman species; populations of cells or molecules within an individual; populations of genotypes; and biological measurements related to age, health, physical functioning, and fertility. Within this vast territory, several research foci are noteworthy and are briefly described below.
Nothing in biology, the eminent biologist Theodosius Dobzhansky (1900–1975) asserted, makes sense except in light of evolution. It is equally valid to say that nothing in evolution can be understood except in light of demography. Evolution is driven by population dynamics governed by age schedules of fertility and survival. Lotka emphasized this. Following his groundbreaking research, models of the evolution of fertility, mortality, and other life-history patterns have been based on stable population theory. Lotka's equation
specifies the intrinsic growth rate, r, of a closed population, typically of females, as a function of the proportion, l(a), of newborns surviving to age a and the age-specific probability of maternity (or fertility), m(a). If a new subspecies emerges as a result of mutation, the subspecies is assumed to have an evolutionary advantage if its intrinsic growth rate is greater than that of other subspecies.
William D. Hamilton (1936–2000) used this perspective to model the evolution of senescence: In an influential article published in 1966 he argued that evolutionary pressures would inevitably result in a rising trajectory of age-specific death rates. Hamilton was a biologist who studied stable population theory and other aspects of demography at the London School of Economics. Recently the demographer Ronald D. Lee reconsidered the evolution of senescence and concluded that age-specific death rates can increase, decrease, or remain constant over age in various periods of life depending on the nature of intergenerational transfers of resources from parents to children, for example. Other demographers, including Shripad Tuljapurkar and Kenneth W. Wachter, have also explored this issue, from different perspectives.
Much can be learned by comparing mortality and fertility patterns across species. Relying on Gompertz's Law (developed by English actuary Benjamin Gompertz [1779–1865]), it had been thought that death rates rose exponentially at adult ages for almost all species. A dozen or so species, from yeast, worms, and insects to humans, have now been studied in sufficiently large numbers for the age trajectory of mortality at advanced ages to be reliably under-stood. For all these species, including humans, death rates either level off or decline at sufficiently advanced ages. Explaining this surprising deceleration of mortality is an active area of research in biodemography. One of the leaders in this field is James R. Carey, who has also done important comparative biodemographic research on the role of the elderly in nature and on the duration of life in thousands of species.
Lotka was deeply interested in the dynamics of inter-acting species. This interest is commemorated in the Lotka-Volterra equation (the model was developed independently by Lotka in 1925, and Italian mathematician Vito Volterra in 1926), which describes cycles in the populations of predators and prey. The yet to be solved two-sex problem in demography (i.e., how to satisfactorily incorporate both males and females in models of fertility and population growth) demonstrates how difficult it is to study interacting populations. Research in this area is crucial to understanding environmental stability and population–environment interactions.
Demography of a Species
Humans are but one of millions of species of living organisms. Life tables and many of the other basic concepts and methods of demography can be applied to any species. Life tables, including age-specific maternity rates, have been estimated for hundreds of species. Research has also been done on populations of populations of a species, such as honeybee hives.
To test demographic hypotheses, experiments can be conducted with nonhuman populations. Pearl pioneered this powerful form of research, focusing on whether population growth followed a logistic pattern. In the 1990s demographers, working with biologists, designed and carried out experiments to test whether there is a genetically determined maximum length of life for individuals in all or most species. No sign of a maximum was found in genetically identical populations of yeast, worms, and insects, casting doubt on whether humans were subject to such a limit. More recently, various biodemographic experiments have been conducted to explore the relationship between fertility and mortality. Among genetically identical individuals in controlled environments, reproduction decreases subsequent probabilities of survival. Other experiments have been conducted to determine the impact of lethal and sub-lethal stress on the subsequent mortality of a cohort. Mortality selection (the death of the frail) and hormesis (the increased resistance of individuals who survive) increase future survival chances whereas debilitation decreases them.
Some individuals die young; others live to an advanced age. Some individuals have no children; others have many. The genetic and common environment components of these variations–in lifespans, fertility, and other demographic characteristics–can can be analyzed in humans using data on twins, siblings, cousins, and other relatives of various degree. These data are available in genealogies and in twin, household, parish, and other population registries. In nonhuman species, inbred and crossbred lines can be studied. It is not necessary to have information from DNA about specific genes: it is necessary rather to have information about the proportion of genes shared by two individuals and about shared nongenetic influences. Analysis of variance methods, correlated frailty approaches, and nested event-history models have been applied by demographers. Hans-Peter Kohler has studied how much of the variation in number of children can be attributed to genetic variation in family size preferences among potential parents, and Anatoli Yashin has analyzed genetic variation as it relates to susceptibility to various diseases and to mortality in general.
Another topic in genetic demography concerns the genetic structure and dynamics of a population. Data about population size, migration flows, and inbreeding can lead to insights into the genetic heterogeneity of a population. Information from DNA about genetic polymorphisms (i.e., mutations) can be used to determine the genetic structure of a population and to make inferences about the influence of migration and inbreeding on the population.
A central goal of molecular demography is to identify genetic polymorphisms that affect mortality, morbidity, functioning, fecundity, and other sources of demographic change. Some of this research has focused on finding genetic variants that influence longevity. This relationship can be studied by analyzing changes with age in the proportion of survivors who have some specific allele (i.e., version of a gene). If in a given cohort the allele becomes more frequent with age, that allele may be associated with lower mortality.
Some research concerns how differences in the frequency of a particular allele among populations lead to differences in mortality patterns and life expectancy. Douglas Ewbank, for example, has studied the demographic impact of variants of the ApoE gene in various population distributions. Other research, by Richard Udry, has focused on how hormone levels influence behavior relevant to fertility and family dynamics, and in particular the differences in behavior between males and females.
Demography and epidemiology intersect and over-lap. Demographers frequently focus on how diseases and disabilities influence the structure and dynamics of a population, whereas epidemiologists are more typically concerned with how population patterns of a specific disease can shed light on the etiology, prevention, and cure of the disease. Many demographers, however, have acquired substantial knowledge of the biology of various diseases and disabilities and have developed models of morbidity and mortality. Some of these models relate disease and disability patterns and trends in a population to consequences for health-care systems. Kenneth G. Manton is a leading researcher in this field.
Demographers since the last half of the twentieth century have become increasingly involved with the design of surveys and the analysis of survey data, especially that pertaining to fertility or morbidity and mortality. Various kinds of physical measurements (such as height and weight), physiological measurements (for example, of blood pressure and cholesterol levels), nutritional status (as assessed by analysis of blood or urine and other methods), physical performance (for example, hand-grip strength or ability to pick a coin up from the floor), and genetic makeup (as determined by analysis of DNA) have been added to surveys, including those conducted by Kaare Christensen, Noreen Goldman, Maxine Weinstein, and Zeng Yi. These biological measurements (biomarkers) can be used as covariates in demographic analyses in much the same way that social and economic information is used.
Skeletal remains are the source of information about prehistoric populations regarding sex, age at death, lifetime morbidity and nutrition, as well as, for women, number of children born. Hence, a main focus of paleodemography is determining how to extract more information from bones. This requires a sophisticated understanding of biology as well as facility with methods of using physical indicators to determine sex and to estimate age at death and other variables. A promising recent advance has been the development, by Ursula Wittwer-Backofen and Jutta Gampe, of methods to count annual rings deposited in teeth as a way of determining age at death. (Roughly similar methods can be used to estimate the age of animals in the wild, with teeth used for mammals and otoliths, ear bones, for fish). Lesions in bones and minerals in teeth and bones can shed light on health and nutritional histories. Information about human population development for the long period during which written records were scarce or nonexistent thus hinges on biological information.
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James W. Vaupel