The origins and evolution of modern humans has been the dominant interest in palaeoanthropology for the last decade, and much archaeological interpretation has been structured around the various issues associated with whether humans have a recent African origin or a more ancient one. While the archaeological record has been used to support or refute various aspects of the theories, and to provide a behavioural framework for different biological models, there has been little attempt to employ the evidence of stone tool technology to unravel phylogenetic relationships. Here we examine the evidence that the evolution of modern humans is integrally related to the development of the Upper Palaeolithic and similar technologies, and conclude that there is only a weak relationship. In contrast there is a strong association between the evolution and spread of modern humans and Grahame Clark's Mode 3 technologies (the Middle Stone Age/Palaeolithic). The implications of this for the evolution of Neanderthals, the multiple pattern of human dispersals, and the nature of cognitive evolution, are considered.

Mode 3 Technologies and the Evolution of Modern Humans

THE French site of Arcy-sur-Cure is a key locality in documenting the Middle–Upper Palaeolithic transition in Europe. Reliable attribution of the fragmentary hominid fossils associated with its early Upper Palaeolithic Châtelperronian industry has not been possible. Here we report the first conclusive identification of one of these fossils as Neanderthal on the basis of newly discovered derived features of the bony labyrinth. Dated at about thirty-four thousand years (34 kyr) ago, the fossil is representative of the youngest known Neanderthal populations, and its archaeological context indicates that these hominids used a rich bone industry as well as personal ornaments. The evidence supports the hypothesis of a long term coexistence with technocultural interactions between the first modern humans and the last Neanderthals in Europe. However, the complete absence of the derived Neanderthal traits in labyrinths of modern Upper Palaeolithic specimens from western Europe argues against phylogenetic continuity between the two populations in this region.

A late Neanderthal associated with Upper Palaeolithic artefacts

Fuzzy modelling of vegetation from remotely sensed imagery

Linear discriminant analysis (LDA) is frequently used for classification/prediction problems in physical anthropology, but it is unusual to find examples where researchers consider the statistical limitations and assumptions required for this technique. In these instances, it is difficult to know whether the predictions are reliable. This paper considers a nonparametric alternative to predictive LDA: binary, recursive (or classification) trees. This approach has the advantage that data transformation is unnecessary, cases with missing predictor variables do not require special treatment, prediction success is not dependent on data meeting normality conditions or covariance homogeneity, and variable selection is intrinsic to the methodology. Here I compare the efficacy of classification trees with LDA, using typical morphometric data. With data from modern hominoids, the results show that both techniques perform nearly equally. With complete data sets, LDA may be a better choice, as is shown in this example, but with missing observations, classification trees perform outstandingly well, whereas commercial discriminant analysis programs do not predict classifications for cases with incompletely measured predictor variables and generally are not designed to address the problem of missing data. Testing of data prior to analysis is necessary, and classification trees are recommended either as a replacement for LDA or as a supplement whenever data do not meet relevant assumptions. It is highly recommended as an alternative to LDA whenever the data set contains important cases with missing predictor variables.

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Classification Trees as an Alternative to Linear Discriminant Analysis

A total of 11 340 Cartesian coordinates of 42 homologous landmarks on five excavated human crania were recorded by three observers using a three-dimensional (3D) digitiser and computer models created with a 3D laser scanner The aim was to compare the errors of the coordinate data of landmarks of different types recorded with these two techniques The results showed that digitiser-based and 3D model-based coordinate measurements had overall standard deviations of, respectively, ±079 and ±105 mm However, the 3D digitiser yielded the most precise coordinate data for landmarks defined primarily by biological criteria (Type I landmarks), while the 3D laser scanner models yielded the most precise coordinate data for landmarks defined primarily by geometric criteria (Type III landmarks) These findings are likely to influence the research design of future craniometric studies, as they indicate that the suitability of certain landmark types as reference points for geometric operations, such as partial Procrustes analysis, depends on the method by which they are measured This information is particularly important for retrospective research or for combined databases such as FORDISC or CRANID, which may integrate different types of landmarks recorded by different researchers and/or instruments Crania displaying poor preservation and surface discoloration yielded larger measurement errors, especially for the 3D model measurements This is not surprising given that landmarks on 3D models cannot be located using tactile means, but have to be located solely on a visual basis Nonetheless, even though the digitiser measurements exhibit an overall precision slightly greater than the 3D model measurements, both techniques yield coordinate data with a precision sufficient for most craniometric research Copyright © 2010 John Wiley & Sons, Ltd

Comparison of coordinate measurement precision of different landmark types on human crania using a 3D laser scanner and a 3D digitiser: Implications for applications of digital morphometrics

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W. W. Howells

Papers

Multivariate Statistical Methods in Physical Anthropology