Paleodemography: Age Distributions from Skeletal Samples (Cambridge Studies in Biological and Evolutionary Anthropology, Series Number 31)

Paleodemography is the field of enquiry that attempts to identify demographic parameters from past populations (usually skeletal samples) derived from archaeological contexts, and then to make interpretations regarding the health and well-being of those populations. However, paleodemographic theory relies on several assumptions that cannot easily be validated by the researcher, and if incorrect, can lead to large errors or biases. In this book, physical anthropologists, mathematical demographers and statisticians tackle these methodological issues for reconstructing demographic structure for skeletal samples. Topics discussed include how skeletal morphology is linked to chronological age, assessment of age from the skeleton, demographic models of mortality and their interpretation, and biostatistical approaches to age structure estimation from archaeological samples. This work will be of immense importance to anyone interested in paleodemography, including biological and physical anthropologists, demographers, geographers, evolutionary biologists and statisticians. Half-title......Page 3 Series-title......Page 5 Title......Page 7 Copyright......Page 8 Dedication......Page 9 Contents......Page 11 Contributors......Page 13 Acknowledgments......Page 15 Introduction......Page 17 The need for better osteological methods......Page 19 The need for better reference samples......Page 20 The need to use Bayes’ theorem......Page 21 The need to assess the distribution of lifespans in the target population......Page 22 References......Page 23 Historical perspectives......Page 25 Ethnographic analogy for prehistoric demography......Page 29 The great debate: paleodemography on trial......Page 31 Answering Petersen’s challenge......Page 33 References......Page 34 Introduction......Page 45 Methods......Page 46 Discussion......Page 47 Acknowledgments......Page 57 References......Page 58 Introduction......Page 64 The evolution of age markers: single-trait systems......Page 66 Issues of validity and reliability......Page 69 Validity......Page 71 Phases or components? The case of the pubic symphysis......Page 73 From single-to multiple-trait techniques......Page 74 Complex method......Page 75 Multifactorial summary age......Page 76 Transition analysis method......Page 77 Interpersonal differences–young versus old individuals......Page 78 Interpopulation differences–horizontal considerations......Page 79 Interpopulation differences–vertical considerations......Page 80 Conclusions......Page 81 References......Page 82 Introduction......Page 89 Problems of adult age estimation......Page 91 Skeletal sample......Page 95 Transition analysis for a single trait......Page 97 Transition analysis for multiple traits......Page 101 Approximate confidence intervals......Page 102 Using an "external" f (a )......Page 103 Transition analysis for a single trait......Page 104 Discussion......Page 109 Appendix 5.1......Page 112 Symphyseal relief......Page 113 Superior apex......Page 114 Ventral symphyseal margin......Page 115 Iliac portion of the sacroiliac joint......Page 116 Superior, apical, and inferior surface morphology......Page 117 Superior and inferior posterior iliac exostoses......Page 118 Coronal pterica, sagittal obelica, lambdoidal asterica, zygomaticomaxillary, interpalatine (median palatine, posterior.........Page 119 References......Page 120 Morphological age estimation methods versus tooth cementum annulation......Page 123 Development of tooth cementum annulation age estimation......Page 124 Biological basis of tooth cementum annulation......Page 128 The concept of the current validation study......Page 129 The sample in the validation study......Page 131 Methods......Page 132 Preliminary results......Page 136 Perspectives......Page 140 Acknowledgments......Page 141 References......Page 142 Introduction......Page 145 What exactly do we need to model?......Page 149 What does the human mortality curve look like?......Page 153 Ways of modeling mortality......Page 156 Relational models......Page 157 Weibull, Rayleigh, and bi-Weibull models......Page 159 The Gompertz model......Page 161 The Gompertz–Makeham model......Page 162 The Siler model......Page 163 Interpreting competing hazards models when mortality is heterogeneous......Page 166 The mixed-Makeham model......Page 167 A more general approach to modeling heterogeneity......Page 169 Capturing the sex differential......Page 171 Discussion......Page 174 The age-at-death distribution for skeletons deposited over time......Page 175 The implications of heterogeneity for competing hazards models......Page 177 References......Page 178 Introduction......Page 185 The Historical Perspectives on Human Demography Database......Page 186 Modeling procedure......Page 188 Epidemic cycles......Page 190 Results......Page 191 Discussion......Page 192 References......Page 194 The problem......Page 197 Some theory behind the weight functions......Page 200 Estimating the weight functions......Page 202 Solution: Part II Estimating the mortality schedule......Page 203 Solution: Part III Estimating age-at-death for an individual......Page 205 Solution: Part IV Goodness-of-fit......Page 206 An application of our solution......Page 207 References......Page 208 Introduction......Page 209 Estimation of an age-at-death distribution......Page 210 Missing skeletal observations......Page 212 The life table method......Page 215 Estimating the target age-at-death distribution......Page 216 Estimating parameters for the reference distribution......Page 217 Parametric methods with ‘‘stage’’ or ‘‘phase’’ data......Page 219 Estimating parameters of the reference distribution using staged indicators......Page 222 Estimating parameters of the reference distribution......Page 223 Estimating parameters of the reference distribution......Page 224 Estimating the target age-at-death distribution......Page 225 Nonindependent indicators: the latent-trait method......Page 226 Estimating the target age-at-death distribution......Page 229 Application......Page 230 Conclusions......Page 233 References......Page 234 Introduction......Page 238 Modeling the dependence of a discontinuous ‘‘age indicator’’ on age......Page 239 Modeling survivorship......Page 241 Example of estimating Gompertz–Makeham parameters from pubic symphyseal data......Page 242 Gibbs sampling......Page 244 Simulating transition ages......Page 246 Simulating the Weibull parameters......Page 247 Example of estimating Weibull parameters from pubic symphyseal data......Page 249 Multivariate age estimation......Page 251 Mixed and missing data......Page 252 Whither now?......Page 253 Acknowledgments......Page 255 References......Page 256 Indian Knoll history......Page 259 Mathematical approach......Page 262 Comparative samples......Page 265 Standard error estimation......Page 266 Results......Page 267 Discussion......Page 269 Acknowledgments......Page 271 References......Page 272 Index......Page 274 Many methodological and theoretical problems exist in reconstructing demographic structure from archaeological populations. Paleodemography is an innovative volume which tackles these issues to reach a consensus on the way forward. It will be the benchmark for all those interested in skeletal samples and their implications for analysing past populations