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Multivariate analyses of the hominid ulna from Klasies River Mouth
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Multivariate analyses of the hominid ulna from Klasies River Mouth
Osbjorn M. Pearson
Department of Anthropology, Rutgers University, 131 George Street, New Brunswick, NJ 08901-1414, U.S.A. E-mail: [email protected]
Steven E. Churchill
Department of Biological Anthropology & Anatomy, Box 3170, Duke University Medical Center, Durham, NC 27710, U.S.A. E-mail: [email protected]
Frederick E. Grine
Departments of Anthropology & Anatomical Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-4364, U.S.A. E-mail: [email protected]
Erik Trinkaus
Department of Anthropology, Campus Box 1114, Washington University, St Louis, MO 63141, U.S.A. & Unité de Recherche Associée, 376 du Centre National de la Recherche Scientifique, Laboratoire d’Anthropologie, Université de Bordeaux I, 33405 Talence, France. E-mail: [email protected]
Trenton W. Holliday
Department of Sociology and Anthropology, University of Central Florida, P.O. Box 25000, Orlando, FL 32816-1360, U.S.A. E-mail: [email protected] Journal of Human Evolution (1998) 34, 653–656 Article No. hu980227
Groves (1998) has taken issue with the analysis of the morphological affinities of the human ulna from the Middle Stone Age levels at Klasies River Mouth (KRM) presented by Churchill et al. (1996). Groves’ main disagreement with our analysis lies in the fact that in order to determine the affinities of the KRM hominid, we performed a canonical variates analysis (CVA) on only three groups: (1) archaic humans (Neandertals and the Baringo Kapthurin specimen), (2) a large sample of fossil and recent modern humans, and (3) the KRM ulna (treated as a sample mean). We noted that the KRM specimen most closely matched the morphology of the ulnae of archaic humans and fell far away from the modern human centroid in discriminant space. We also noted that a few recent humans—primarily individuals from the prehistoric Khoisan and Jebel Sahaba samples—occupied positions 0047–2484/98/060653+04$25.00/0
in discriminant space that made them look as ‘‘archaic’’ (or more so) than the KRM specimen. Groves (1988, p. 119) found this analysis dubious, and stated that our ‘‘interpretation goes beyond the evidence’’. Instead of the CVA, Groves (1998) presented a principal components analysis (PCA) on the group means for the variables we used in our analysis (listed in Churchill et al., 1996, Table 4). Although our CVA and Groves’ PCA agree in emphasizing the similarity between the KRM ulna and archaic hominids, other aspects of the PCA appear to differ from our analysis, a fact which provides the basis for some statements that Groves makes about the relationships among the groups. This apparent disagreement probably stems from two sources; misinterpretation of our original statements, and the problem of which analysis is more appropriate for the question at 1998 Academic Press Limited
. .
654
hand, since differences in the computational mechanics of the methods lead to different results. The misinterpretations are easily clarified; the issue of which sort of analysis to use and why is more interesting and has applications in paleoanthropology beyond the analysis of the KRM ulna. Misunderstandings Groves disagrees with our statement that the Khoisan and Jebel Sahaba samples are the recent groups that have the most individuals on the ‘‘archaic’’ pole of the discriminant axis. He uses group means (and resultant group positions on principal axes) rather than individual measurements and positions to support his statement. Statistically, our observations that several individuals from the Khoisan (not the San or Bushman as Groves claims) and Jebel Sahaba groups fall farther to the ‘‘archaic’’ side of the CVA is a very different statement from the claim that the centroid (i.e., the mean) of either of these two groups is closer than other recent groups to the archaic centroid. In fact, we stated quite explicitly that the position of the Khoisan centroid ‘‘is not far from the pooled modern mean and is significantly different from the value for the Klasies ulna (2·672 standard deviations away)’’ (Churchill et al., 1996, p. 226). Rather than the sample centroids, it is a few outlying individuals within the Khoisan (again, not the San/Bushman) and Jebel Sahaba samples that approach the morphology of the KRM and archaic ulnae. In many cases, statements about the positions in multivariate space of individuals within samples cannot be tested by the positions of group means. CVA vs. PCA PCA and CVA are two of the most common multivariate techniques for exploring the magnitude and causes of variation within large data sets (Corruccini, 1978). The two
ET AL.
types of analyses have important differences. CVA requires that OTUs (operational taxonomic units) be assembled a priori into groups, which then become the subject of the analysis. CVA finds major axes of between-group differences, but it first scales these differences by the inverse of the average within-group variance–covariance matrix (Krzanowski, 1988). Thus CVA uses within-group variation to assess the importance of between-group variation. Within numerical taxonomy, it is a wise practice to ensure that the ‘‘groups’’ entered into a CVA are of comparable taxonomic rank. For example, as Groves would undoubtedly agree (although others would disagree), two OTUs comprising recent humans (as a group) and Neandertals could be considered to be of an equal taxonomic level, but a set of three OTUs consisting of Neandertals, Aboriginal Australians, and Europeans would not because it would include two groups from one species (Australians and Europeans) and a third group (Neandertals) that may constitute a separate species. Given a sample of OTUs, a PCA finds major, orthogonal axes of variance in the sample in multivariate space as well as the vectors (eigenvectors) that define these axes. PCA treats each OTU equally so that each has the potential to contribute an equal share to the total variance within the sample. PCA ignores any subdivisions within a data set, which is undesirable if these subdivisions are really the subject of interest. For example, in Groves’ PCA, each group mean is considered equally important. Groves’ (1998) PCA uses a total of 14 OTUs. Supposing that the variance within the sample is distributed equally among the OTUs (which it almost certainly is not), each OTU would account for 7·14% of the total variance—close to a non-significant amount. Thus, if the OTUs should be grouped into larger sets, the equal weighting of OTUs in PCA can place unwanted emphasis upon the variance present in only a
few of these larger sets. For example, again assuming that each OTU accounts for 7·14% of the variance, in the PCA, the OTUs in Churchill et al.’s (1996) ‘‘KRM group’’ would account for 7·14% of the variance, the ‘‘archaic group’’ would account for 14·28%, and the ‘‘modern humans’’ would account for 78·5%. In such a case, most of the findings from a PCA would be devoted to description of axes of variation that define differences among the recent samples and would de-emphasize the uniqueness (if any) of the fossils. Moreover, because such an approach may emphasize (by default) differences within the group that contains the most OTUs (the ‘‘modern humans’’ in this case), it can scatter the differences between fossil and recent OTUs over several different axes, complicating interpretation of what distinguishes a fossil OTU from one or more recent OTU. Similar problems would arise (and did, in preliminary analyses for Churchill et al., 1996) if one were to perform a CVA on the full data set divided into groups corresponding to Groves OTUs. Interpreting PCA loadings and OTU positions PCA loadings (or more exactly, eigenvectors) highlight the variables responsible for the most variance in a cloud of OTUs such as the sample means for the ulnar measurements. Eigenvectors sum the effects of many variables (eight in the present case) to produce positions for each observation (group means, in this case) on a principal axis (Krzanowski, 1988). It is important to emphasize, however, that Groves’ (1998, p. 120) statement that the loadings and group positions can be superimposed ‘‘to show which variables contribute most to the position of which OTU’’ is inaccurate. Such a procedure does not necessarily provide that information about any specific OTU. OTUs can end up in similar positions on a
655
principal axis for different reasons; only the combination of the eigenvector loadings and the OTU’s deviations from the grand mean for all measurements will show which variables are most responsible for its position on an axis. This is a technical point; Groves’ statement is correct for the entire sample of points although it may not necessarily be true for particular points within the sample. Interpretation of PCA results Plots of results from PCA and CVA demand interpretation, and it is incumbent upon the researcher to provide a description that accurately summarizes the results. Groves noted a variety of features in the results from his PCA. He emphasizes the geographical clustering in group positions on axes 2 and 3, including ‘‘European, Khoisan and Bushman . . ., and Zulu and Black American’’ (Groves, 1998, p. 120). Unfortunately, this appears to be a selective list of near-neighbors on PCA axes 2 and 3. A more complete list would have to include Eskimo (Inuit) and Jebel Sahaba; Australian and Amerindian; Khoisan and Upper Paleolithic; and Khoisan and SkhulQafzeh. These groupings do not conform to close geographical linkages. In the PCA plot, the evidence for geographical structure is weak, and if one considers individual scores, the overlap between groups (including those from opposite sides of the globe) is considerable. One more aspect of Groves’ analytical procedure deserves comment. Following a common practice in morphometric studies, Groves (1998, p. 119) discards the first principal component, remarking that it ‘‘represents size: all the loadings are positive, though of varying weights’’. If a researcher wishes to investigate between-group differences, failing to consider the first principal component may be inadvisable. In fact, the first component usually captures size as well
656
. .
as between-group differences that mark some of the largest or smallest-sized OTUs, i.e., shape that is correlated with size (Jungers et al., 1995). These aspects of between-group differences are thus correlated with differences in size, but they may not necessarily result from allometry. Arguably, such differences should not be discarded after being summarily ascribed to ‘‘size’’. In morphometric studies, it is essential to tailor the analysis performed to answer specific questions. The CVAs in Churchill et al. (1996) were intended to determine whether the primary morphological affinities of the KRM ulna lay with archaic or recent humans. The KRM’s morphological affinities clearly lay with archaic humans. We then examined the positions of individual ulnae on the canonical axis to ask whether any recent ulnae had similarly ‘‘archaic’’ morphology in the set of proportions that had proved useful in distinguishing modern from archaic ulnae. The answer was clearly ‘‘yes’’, although the frequency of such modern ulnae with ‘‘archaic’’ morphology is low. Based on the analyses we presented, any explanation for the occurrence of such morphology in recent people is, of course, speculative. We are heartened by the fact that Groves’ re-analysis via PCA of some of our data corroborated the major point of our paper—that the morphological affinities of the KRM ulna lie with archaic humans rather than with the vast majority of modern humans (Churchill et al., 1996). We agree that the variability present in fossil and modern human postcranial bones warrants more investigation, and have made this the subject of considerable research (Trinkaus, 1981, 1983; Churchill, 1994, 1996; Churchill & Formicola, 1997; Holliday, 1995; Holliday, 1997a, 1997b; Pearson, 1997). However,
ET AL.
we add a note of caution that multivariate techniques provide answers to specific questions (Corruccini, 1978); in our analysis of the morphological affinities of the KRM ulna, we consider CVA to be more appropriate than PCA to answer the questions of interest. References Churchill, S. E. (1994). Human upper body evolution in the Eurasian later Pleistocene. Ph.D. Dissertation, University of New Mexico, Albuquerque. Churchill, S. E. (1996). Particulate versus integrated evolution in the upper body in Late Pleistocene humans: a test of two models. Am. J. phys. Anthrop. 100, 559–583. Churchill, S. E. & Formicola, V. (1997). A case of marked bilateral asymmetry in the upper limbs of an Upper Palaeolithic male from Barma Grande (Liguria), Italy. Int. J. Osteoarchaeol. 7, 18–38. Churchill, S. E., Pearson, O. M., Grine, F. E., Trinkaus, E. & Holliday, T. W. (1996). Morphological affinities of the proximal ulna from Klasies River main site: archaic or modern? J. hum. Evol. 31, 213–237. Corruccini, R. S. (1978). Morphometric analysis: uses and abuses. Yearb. phys. Anthrop. 21, 134–150. Groves, C. P. (1998). The proximal ulna from Klasies River. J. hum. Evol. 34, 119–121. Holliday, T. W. (1995). Body size and proportions in the Late Pleistocene western Old World and the origins of modern humans. Ph.D. Dissertation, University of New Mexico, Albuquerque. Holliday, T. W. (1997a). Body proportions in Late Pleistocene Europe and modern human origins. J. hum. Evol. 32, 423–447. Holliday, T. W. (1997b). Postcranial evidence of cold adaptation in European Neandertals. Am. J. phys. Anthrop. 104, 245–258. Jungers, W. L., Falsetti, A. B. & Wall, C. E. (1995). Shape, relative size, and size-adjustments in morphometrics. Yearb. phys. Anthrop. 38, 137–161. Krzanowski, W. J. (1988). Principles of Multivariate Analysis. Oxford: Oxford University Press. Pearson, O. M. (1997). Postcranial morphology and the origin of modern humans. Ph.D. Dissertation, State University of New York at Stony Brook. Trinkaus, E. (1981). Neandertal limb proportions and cold adaptation. In (C. B. Stringer, Ed.) Aspects of Human Evolution, pp. 187–224. London: Taylor & Francis. Trinkaus, E. (1983). The Shanidar Neandertals. New York: Academic Press.
Multivariate analyses of the hominid ulna from Klasies River Mouth
Osbjorn M. Pearson
Department of Anthropology, Rutgers University, 131 George Street, New Brunswick, NJ 08901-1414, U.S.A. E-mail: [email protected]
Steven E. Churchill
Department of Biological Anthropology & Anatomy, Box 3170, Duke University Medical Center, Durham, NC 27710, U.S.A. E-mail: [email protected]
Frederick E. Grine
Departments of Anthropology & Anatomical Sciences, State University of New York at Stony Brook, Stony Brook, NY 11794-4364, U.S.A. E-mail: [email protected]
Erik Trinkaus
Department of Anthropology, Campus Box 1114, Washington University, St Louis, MO 63141, U.S.A. & Unité de Recherche Associée, 376 du Centre National de la Recherche Scientifique, Laboratoire d’Anthropologie, Université de Bordeaux I, 33405 Talence, France. E-mail: [email protected]
Trenton W. Holliday
Department of Sociology and Anthropology, University of Central Florida, P.O. Box 25000, Orlando, FL 32816-1360, U.S.A. E-mail: [email protected] Journal of Human Evolution (1998) 34, 653–656 Article No. hu980227
Groves (1998) has taken issue with the analysis of the morphological affinities of the human ulna from the Middle Stone Age levels at Klasies River Mouth (KRM) presented by Churchill et al. (1996). Groves’ main disagreement with our analysis lies in the fact that in order to determine the affinities of the KRM hominid, we performed a canonical variates analysis (CVA) on only three groups: (1) archaic humans (Neandertals and the Baringo Kapthurin specimen), (2) a large sample of fossil and recent modern humans, and (3) the KRM ulna (treated as a sample mean). We noted that the KRM specimen most closely matched the morphology of the ulnae of archaic humans and fell far away from the modern human centroid in discriminant space. We also noted that a few recent humans—primarily individuals from the prehistoric Khoisan and Jebel Sahaba samples—occupied positions 0047–2484/98/060653+04$25.00/0
in discriminant space that made them look as ‘‘archaic’’ (or more so) than the KRM specimen. Groves (1988, p. 119) found this analysis dubious, and stated that our ‘‘interpretation goes beyond the evidence’’. Instead of the CVA, Groves (1998) presented a principal components analysis (PCA) on the group means for the variables we used in our analysis (listed in Churchill et al., 1996, Table 4). Although our CVA and Groves’ PCA agree in emphasizing the similarity between the KRM ulna and archaic hominids, other aspects of the PCA appear to differ from our analysis, a fact which provides the basis for some statements that Groves makes about the relationships among the groups. This apparent disagreement probably stems from two sources; misinterpretation of our original statements, and the problem of which analysis is more appropriate for the question at 1998 Academic Press Limited
. .
654
hand, since differences in the computational mechanics of the methods lead to different results. The misinterpretations are easily clarified; the issue of which sort of analysis to use and why is more interesting and has applications in paleoanthropology beyond the analysis of the KRM ulna. Misunderstandings Groves disagrees with our statement that the Khoisan and Jebel Sahaba samples are the recent groups that have the most individuals on the ‘‘archaic’’ pole of the discriminant axis. He uses group means (and resultant group positions on principal axes) rather than individual measurements and positions to support his statement. Statistically, our observations that several individuals from the Khoisan (not the San or Bushman as Groves claims) and Jebel Sahaba groups fall farther to the ‘‘archaic’’ side of the CVA is a very different statement from the claim that the centroid (i.e., the mean) of either of these two groups is closer than other recent groups to the archaic centroid. In fact, we stated quite explicitly that the position of the Khoisan centroid ‘‘is not far from the pooled modern mean and is significantly different from the value for the Klasies ulna (2·672 standard deviations away)’’ (Churchill et al., 1996, p. 226). Rather than the sample centroids, it is a few outlying individuals within the Khoisan (again, not the San/Bushman) and Jebel Sahaba samples that approach the morphology of the KRM and archaic ulnae. In many cases, statements about the positions in multivariate space of individuals within samples cannot be tested by the positions of group means. CVA vs. PCA PCA and CVA are two of the most common multivariate techniques for exploring the magnitude and causes of variation within large data sets (Corruccini, 1978). The two
ET AL.
types of analyses have important differences. CVA requires that OTUs (operational taxonomic units) be assembled a priori into groups, which then become the subject of the analysis. CVA finds major axes of between-group differences, but it first scales these differences by the inverse of the average within-group variance–covariance matrix (Krzanowski, 1988). Thus CVA uses within-group variation to assess the importance of between-group variation. Within numerical taxonomy, it is a wise practice to ensure that the ‘‘groups’’ entered into a CVA are of comparable taxonomic rank. For example, as Groves would undoubtedly agree (although others would disagree), two OTUs comprising recent humans (as a group) and Neandertals could be considered to be of an equal taxonomic level, but a set of three OTUs consisting of Neandertals, Aboriginal Australians, and Europeans would not because it would include two groups from one species (Australians and Europeans) and a third group (Neandertals) that may constitute a separate species. Given a sample of OTUs, a PCA finds major, orthogonal axes of variance in the sample in multivariate space as well as the vectors (eigenvectors) that define these axes. PCA treats each OTU equally so that each has the potential to contribute an equal share to the total variance within the sample. PCA ignores any subdivisions within a data set, which is undesirable if these subdivisions are really the subject of interest. For example, in Groves’ PCA, each group mean is considered equally important. Groves’ (1998) PCA uses a total of 14 OTUs. Supposing that the variance within the sample is distributed equally among the OTUs (which it almost certainly is not), each OTU would account for 7·14% of the total variance—close to a non-significant amount. Thus, if the OTUs should be grouped into larger sets, the equal weighting of OTUs in PCA can place unwanted emphasis upon the variance present in only a
few of these larger sets. For example, again assuming that each OTU accounts for 7·14% of the variance, in the PCA, the OTUs in Churchill et al.’s (1996) ‘‘KRM group’’ would account for 7·14% of the variance, the ‘‘archaic group’’ would account for 14·28%, and the ‘‘modern humans’’ would account for 78·5%. In such a case, most of the findings from a PCA would be devoted to description of axes of variation that define differences among the recent samples and would de-emphasize the uniqueness (if any) of the fossils. Moreover, because such an approach may emphasize (by default) differences within the group that contains the most OTUs (the ‘‘modern humans’’ in this case), it can scatter the differences between fossil and recent OTUs over several different axes, complicating interpretation of what distinguishes a fossil OTU from one or more recent OTU. Similar problems would arise (and did, in preliminary analyses for Churchill et al., 1996) if one were to perform a CVA on the full data set divided into groups corresponding to Groves OTUs. Interpreting PCA loadings and OTU positions PCA loadings (or more exactly, eigenvectors) highlight the variables responsible for the most variance in a cloud of OTUs such as the sample means for the ulnar measurements. Eigenvectors sum the effects of many variables (eight in the present case) to produce positions for each observation (group means, in this case) on a principal axis (Krzanowski, 1988). It is important to emphasize, however, that Groves’ (1998, p. 120) statement that the loadings and group positions can be superimposed ‘‘to show which variables contribute most to the position of which OTU’’ is inaccurate. Such a procedure does not necessarily provide that information about any specific OTU. OTUs can end up in similar positions on a
655
principal axis for different reasons; only the combination of the eigenvector loadings and the OTU’s deviations from the grand mean for all measurements will show which variables are most responsible for its position on an axis. This is a technical point; Groves’ statement is correct for the entire sample of points although it may not necessarily be true for particular points within the sample. Interpretation of PCA results Plots of results from PCA and CVA demand interpretation, and it is incumbent upon the researcher to provide a description that accurately summarizes the results. Groves noted a variety of features in the results from his PCA. He emphasizes the geographical clustering in group positions on axes 2 and 3, including ‘‘European, Khoisan and Bushman . . ., and Zulu and Black American’’ (Groves, 1998, p. 120). Unfortunately, this appears to be a selective list of near-neighbors on PCA axes 2 and 3. A more complete list would have to include Eskimo (Inuit) and Jebel Sahaba; Australian and Amerindian; Khoisan and Upper Paleolithic; and Khoisan and SkhulQafzeh. These groupings do not conform to close geographical linkages. In the PCA plot, the evidence for geographical structure is weak, and if one considers individual scores, the overlap between groups (including those from opposite sides of the globe) is considerable. One more aspect of Groves’ analytical procedure deserves comment. Following a common practice in morphometric studies, Groves (1998, p. 119) discards the first principal component, remarking that it ‘‘represents size: all the loadings are positive, though of varying weights’’. If a researcher wishes to investigate between-group differences, failing to consider the first principal component may be inadvisable. In fact, the first component usually captures size as well
656
. .
as between-group differences that mark some of the largest or smallest-sized OTUs, i.e., shape that is correlated with size (Jungers et al., 1995). These aspects of between-group differences are thus correlated with differences in size, but they may not necessarily result from allometry. Arguably, such differences should not be discarded after being summarily ascribed to ‘‘size’’. In morphometric studies, it is essential to tailor the analysis performed to answer specific questions. The CVAs in Churchill et al. (1996) were intended to determine whether the primary morphological affinities of the KRM ulna lay with archaic or recent humans. The KRM’s morphological affinities clearly lay with archaic humans. We then examined the positions of individual ulnae on the canonical axis to ask whether any recent ulnae had similarly ‘‘archaic’’ morphology in the set of proportions that had proved useful in distinguishing modern from archaic ulnae. The answer was clearly ‘‘yes’’, although the frequency of such modern ulnae with ‘‘archaic’’ morphology is low. Based on the analyses we presented, any explanation for the occurrence of such morphology in recent people is, of course, speculative. We are heartened by the fact that Groves’ re-analysis via PCA of some of our data corroborated the major point of our paper—that the morphological affinities of the KRM ulna lie with archaic humans rather than with the vast majority of modern humans (Churchill et al., 1996). We agree that the variability present in fossil and modern human postcranial bones warrants more investigation, and have made this the subject of considerable research (Trinkaus, 1981, 1983; Churchill, 1994, 1996; Churchill & Formicola, 1997; Holliday, 1995; Holliday, 1997a, 1997b; Pearson, 1997). However,
ET AL.
we add a note of caution that multivariate techniques provide answers to specific questions (Corruccini, 1978); in our analysis of the morphological affinities of the KRM ulna, we consider CVA to be more appropriate than PCA to answer the questions of interest. References Churchill, S. E. (1994). Human upper body evolution in the Eurasian later Pleistocene. Ph.D. Dissertation, University of New Mexico, Albuquerque. Churchill, S. E. (1996). Particulate versus integrated evolution in the upper body in Late Pleistocene humans: a test of two models. Am. J. phys. Anthrop. 100, 559–583. Churchill, S. E. & Formicola, V. (1997). A case of marked bilateral asymmetry in the upper limbs of an Upper Palaeolithic male from Barma Grande (Liguria), Italy. Int. J. Osteoarchaeol. 7, 18–38. Churchill, S. E., Pearson, O. M., Grine, F. E., Trinkaus, E. & Holliday, T. W. (1996). Morphological affinities of the proximal ulna from Klasies River main site: archaic or modern? J. hum. Evol. 31, 213–237. Corruccini, R. S. (1978). Morphometric analysis: uses and abuses. Yearb. phys. Anthrop. 21, 134–150. Groves, C. P. (1998). The proximal ulna from Klasies River. J. hum. Evol. 34, 119–121. Holliday, T. W. (1995). Body size and proportions in the Late Pleistocene western Old World and the origins of modern humans. Ph.D. Dissertation, University of New Mexico, Albuquerque. Holliday, T. W. (1997a). Body proportions in Late Pleistocene Europe and modern human origins. J. hum. Evol. 32, 423–447. Holliday, T. W. (1997b). Postcranial evidence of cold adaptation in European Neandertals. Am. J. phys. Anthrop. 104, 245–258. Jungers, W. L., Falsetti, A. B. & Wall, C. E. (1995). Shape, relative size, and size-adjustments in morphometrics. Yearb. phys. Anthrop. 38, 137–161. Krzanowski, W. J. (1988). Principles of Multivariate Analysis. Oxford: Oxford University Press. Pearson, O. M. (1997). Postcranial morphology and the origin of modern humans. Ph.D. Dissertation, State University of New York at Stony Brook. Trinkaus, E. (1981). Neandertal limb proportions and cold adaptation. In (C. B. Stringer, Ed.) Aspects of Human Evolution, pp. 187–224. London: Taylor & Francis. Trinkaus, E. (1983). The Shanidar Neandertals. New York: Academic Press.