Society for American Archaeology

Soil pH, Bone Preservation, and Sampling Bias at Mortuary SitesAuthor(s): Claire C. Gordon and Jane E. BuikstraSource: American Antiquity, Vol. 46, No. 3 (Jul., 1981), pp. 566-571Published by: Society for American ArchaeologyStable URL: http://www.jstor.org/stable/280601 .Accessed: 10/03/2011 05:46

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SOIL pH, BONE PRESERVATION, AND SAMPLING BIAS AT MORTUARY SITES

Claire C. Gordon and Jane E. Buikstra

Prediction of human skeletal preservation at mortuary sites is important in archaeological research and in cultural resources management. In this study, correlations between osseous deterioration and soil acidity, as measured by pH, were found to be significant. Age-associated preservation biases were also evident. The use of multiple regression is suggested as a technique for estimating recovery of human osteological remains in ar- chaeological context.

Wise management of archaeological resources frequently requires predictions of the quantity and quality of data which are recoverable from the archaeological record. This is true in the selection of sites suitable for specific research problems, and in long-term management of the cultural resource base. Because cemeteries are an important archaeological resource, decision- making in reference to them should include careful consideration of prospects for bone recovery. In regions such as eastern North America, where inhumation seems to have been the dominant terminal mortuary act, local soil conditions appear to be particularly important in skeletal preser- vation.

This research will focus upon one aspect of soil chemistry and its effects on bone preservation: soil acidity as measured by pH. Although an empirically demonstrable relationship between human bone preservation and pH should surprise no one, few if any researchers have attempted to quantify this for predictive purposes. The strength of the pH-preserv ation ship will be reported and regression equations, which can be used to predict preservational status and thereby the richness of the archaeologically recoverable data base, will be presented. Though the specific model developed here may not be directly applicable in regions remote from the study area, it has proved reliable in cultural resource management work at other locations in west- central Illinois. It is therefore likely that the technique can be useful, if properly adapted, in other contexts.

MATERIALS

The skeletons utilized for this study were excavated from seven Late Woodland burial mounds, under the direction of the junior author. Two of these mounds constitute the Ledders site, located on eastern bluffs overlooking the Mississippi River near Hamburg, Illinois. The remaining five mounds constitute a portion of the Helton site, which is located on eastern bluffs of the Illinois River Valley in Woodville Township, Illinois. The location of the sites is indicated in Figure 1. The Ledders site was excavated in 1970-1971 (Pickering and Buikstra 1974) and the Helton site is the subject of ongoing excavations.

Claire C Gordon and Jane E. Buikstra, Department of Anthropology, Northwestern University, Evanston, IL 60201

Copyright ? 1981 by the Society for American Archaeology 0002-7316/81/030566-06$1.10/1

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0 5 10 I I I

SCALE IN MILES

INDEX MAP

Figure 1. Mortuary sites in the Lower Illinois River Valley region.

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Radiocarbon dates for these mounds range between A.D. 830 and A.D. 1200 (Tainter 1975). Thus, the skeletons considered here have been buried no longer than 1,200 years and no less than 700 years. Three of the mounds lack radiocarbon dates (Hn20, Hn46, Hn47), but artifacts associated with the burial facilities suggest that they are contemporaneous with those for which C-14 dates exist.

All mounds are constructed of silty loam (loess) which has undergone varying degrees of soil weathering both predating and postdating mortuary events. Prehistoric mound construction generally began by preparing a surface, into which pits were dug and upon which bodies were placed and subsequently covered by earth. Intermediate continuous mound surfaces were con- structed of loaded earth, and the sequence was repeated. Moundfill in all cases appears to have been obtained from the local region, either from the mixing and reuse of soil from excavated and re-excavated pits or from shallow borrow areas near the mounds. Thus the soil chemistry of any particular mound is largely governed by the extent of local soil development and the degree of soil mixing attendant to the interment episodes.

Variations in weathering of the parent soil (loess) are assumed to be the results of differences in relief, vegetational cover, and water percolation (U.S. Bureau of Plant Industry, Soils, and Agricultural Engineering 1962; Basile 1971). The greater the intensity of mineral leaching, the more developed the profile. Extensive leaching of minerals can create B-horizons of clay, a soil much more acidic than the parent loess. Several of the mounds (Ld2, Hn22, Hn46) were built in areas of mature soil profiles which included well-developed B-clay horizons. The other mounds in this study were built largely from loess which had undergone minimal development. Bone associated with pits dug into and fill utilizing relatively unmodified soils are anticipated to be bet- ter preserved than those in features associated with soils where active mineral leaching had in- creased soil acidity (decreased pH).

As previously mentioned, the dominant mortuary features in this study are primary interments. However, cremation was a significant aspect of the burial program at three of the mounds. Cremated remains were eliminated from this study because of the special preservational proper- ties of burned bones (Merbs 1967; Buikstra and Goldstein 1973). Evidence of pre-interment body processing such as defleshing and desiccation was also recovered. Because extended pre-interment treatment may also affect preservational potential of the bone, these cases were also eliminated from our current study. If and when larger sample sizes of cremated and processed individuals become available, it would be most informative to assess pH-preservation relationships in these contexts.

METHODS OF DATA COLLECTION AND ANALYSIS

Soil samples were recovered from feature fill in direct association with bone for burials com- prising 63 adults and 32 children. Each sample was mixed as one part soil to two parts distilled water, and the pH of this solution was determined with a Heath-Schlumberger portable pH meter. Each determination carried a ? .04 error factor, computed from the following additive sources of error: (1) inherent meter error; (2) variability of the buffer solution used to standardize the meter; and (3) within-sample variation and investigator error in reading the meter scale.

Skeletal remains of each burial were then scored for preservation according to the following six categories: Category 1, Strong Complete Bone: Skeletal elements are whole and undamaged. There is no evidence of postmortem destruction of osseous material which is not directly referable to local root, microorganism, or burrowing mammal activity. For immature individuals, ossification centers are present and recoverable. All classes of skeletal data may be collected (n = 30 for this study). Category 2, Fragile Bone: Bony elements may be fragmented, but they are completely reconstructible. External surfaces may show some etching. Articular surfaces of long bones and surfaces of sternum, vertebrae, and other cubical bones show superficial destruction. Essentially all classes of standard osteological descriptive data can be collected; however, microstructure studies could be severely limited. In immature individuals, epiphyseal ossification centers are eroded, but diaphyses are reconstructible (n = 29 for this study). Category 3, Fragmented Bone: Skeletal elements are generally cracked and fragmented. Most units are iden- tifiable and reconstructible with copious labor and skill. Bone surfaces are heavily etched and

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cracked. Articular ends of long bones, vertebrae, and other trabecular bone may not be reconstructible. The skull is reconstructible to the point that most standard descriptive measures of the vault are possible; however, the face may not be observable. Data classes such as the length of long bones and many forms of pathology (e.g., degenerative joint disease) are severely limited (n = 21 for this study). Category 4, Extremely Fragmented Bone: Skeletal elements are severely fragmented and many may not be recognizable. One cannot consistently collect any osteometric data or observe pathological changes. Nonmetric variants may be scored, but the battery of observations is frequently incomplete. Determinations of age at death and sex of adult skeletons may not always be possible (n = 9 for this study). Category 5, Bone Meal/Ghost: Bones are re- duced to a powdery substance which will not hold shape without support from the soil or chemical preservatives. Fragmentary tooth crowns may still be recoverable; however, even these are fragile. Bone outlines may be present as stains only. No forms of osteological data can be con- sistently collected (n = 6 for this study).

Preservational scoring, as described above, ignored localized effects of roots and rodents. In addition, although preservation had been scored at convenient intervals, there is an underlying continuity to "preservation," and this variable will be treated as continuous for analytical pur- poses (Sokal and Rohlf 1969:12). Finally, these skeletal remains were also aged and scored in five- year interval age classes.

Because previous experience had suggested that immature bone is more susceptible to decay than mature bone, age of the remains at death was initially treated as a noise factor in the pH- preservation relationship. A two-way Analysis of Variance (Sokal and Rohlf 1969:299; Nie et al. 1975:405) was used to to test for the significance of age-pH interactive effects on preservation. As expected, the interactive sum of squares was significant: F = 5.542, p < .001. This suggested that in order to assess the true strength of the pH factor in determining bone preservation, mature and immature bone must be analyzed separately.

The strength of the pH-preservation relationship was then tested using the SPSS subprogram "Scattergram" (Nie et al. 1975:293), which generates both correlation coefficients and regression equations. Pearson product-moment correlation coefficients were tested for significance as were the slopes and intercepts of the regression equations.

RESULTS AND DISCUSSION

The correlation between soil pH and bone preservation was significant for the mature sample: r = -.92, p < .00001, n = 63. Thus, as soil pH decreases, the destruction of osseous materials in- creases. This relationship explains 84% (r2) of the variation in mature bone preservation for this sample. The regressions equation (see Table 1) was also significant; both slope and intercept had p < .00001.

Soil pH was also significantly correlated with bone preservation in children: r = -.48, p < .005, n = 32. The pH alone, however, explained only 23% of the preservational variation in this im- mature bone sample. The relatively large amount of residual variation is not unexpected. It is probably the result of the large range of bone densities expressed in the age interval (0-14.99 years), which would also have a large range of preservational potentials at any given pH. Never- theless, the simple regression equation for immature bone (see Table 1) was also significant, p < .005 for the slope, and p < .002 for the intercept. It is evident from the steeper slope and larger in- tercept of the regression equation characterizing immature remains that preservation declines more rapidly with decreasing pH in juveniles than is the case for adults.

It is clear from this analysis that soil pH can be a very strong predictor of preservational state. Bone maturity, however, is also an important factor. The differences in preservational potential by age present a particular problem for the paleodemographer: at marginal pH ranges all or most of the infants and children may be systematically eliminated from the mortuary sample by preser- vational bias. Thus if pH is to be maximally useful in anticipating recovery potentials and sam- pling biases at mortuary sites, a regression formula must be generated which includes age.

The SPSS subprogram "Multiple Regression" (Nie et al. 1975:328) was utilized for this purpose, with age coded as shown in Table 2. As expected, the multiple correlation coefficient was high, r = .87, with probability approaching .00000. The joint effects of soil pH and age explained 76%

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Table 1. Regression Equations and Their Significances.

Adult's Simple Regression PRESERVATION = -1.3pH + 12.5 Slope p < .00001, Intercept p < .00001

Children's Simple Regression PRESERVATION = - 1.5 pH + 14.9 Slope p < .005, Intercept p < .002

Multiple Regression PRESERVATION = -1.3pH - .14AGE + 13.2 pH slope p - .00000, Age slope p - .00000, Intercept p - .00000

of the total preservational variation in this sample. The regression equation (see Table 1) was highly significant, with the probabilities of both slopes and the intercept approaching .00000. If the younger age intervals were made even smaller, it is probable that much of the 24% residual variation could also be explained by the joint effects of age and soil pH.

Regionally appropriate regression equations such as this should be useful in several aspects of archaeological decision-making. By systematically testing mortuary sites for soil pH, expected skeletal preservation can be calculated for any age group. These estimates could then be used in determining the excavation strategy which would allow recovery of the maximum amount of osteological data. In addition, researchers should be able to estimate the degree of bias in their samples due to imperfect preservation, which should be a useful complement to statistical tech- niques for evaluating bias offered by paleodemographers such as Weiss (1973).

The pH-preservation regression may also be used to make informed site choices when complete skeletal data are not of primary concern. Archaeologists who study the social dimensions of mor- tuary behavior, for example, may require only age and sex estimates for the skeletons, while desiring detailed information concerning feature construction and seasonality inferences based upon pollen diagrams. For this research design, the archaeologist would select sites with relative- ly acidic soils, where soil features and pollen grains are better preserved, while limiting his levels of acceptable pH to those where age and sex of remains can still be estimated. This regression technique should also be useful in the context of phased contracts which require budget estimates for data analysis before extensive excavation has been conducted. Estimation of the preserva- tional status of human remains at the site could aid the archaeologist in projecting the relative amount of bone recoverable and the types of analysis possibilities for those remains.

SUMMARY AND CONCLUSIONS

In an era of problem-oriented research and the selection of sites for protection due to their potential in future archaeological study, an ability to predict recovery of significant data sets is of crucial importance. The present study has presented evidence that the preservation of one promi- nent class of archaeologically recoverable materials-inhumed human bone-is quite strongly related to soil pH. It is suggested that regression equations such as those we have calculated

Table 2. Age Classes Used in Multiple Regression Coding (in years).

(1) 0-4.99 (2) 5-9.99 (3) 10-14.99 (4) 15-19.99 (5) 20 +

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would be useful in other regions, and that the refinement of this technique should prove important in multiple aspects of archaeological decision-making.

Acknowledgments. This research has received the support of the National Science Foundation, Grant No. GS-41242; the Northwestern University Research Committee; the University of Chicago Committee of Evolu- tionary Biology; and the Northwestern University Archaeological Program.

REFERENCES CITED

Basile, Robert M. 1971 A geography of soils. Wm. C. Brown, Dubuque, Iowa.

Buikstra, Jane E., and Lynne Goldstein 1973 The Perrins Ledge crematory. Illinois State Museum, Reports of Investigations No. 28. Springfield,

Illinois. Merbs, C. F. M.

1967 Cremated human remains from Point of Pines, Arizona. American Antiquity 32:498-506. Nie, Norman H., C. H. Hull, J. G. Jenkins, K. Steinbrenner, and D. H. Bent

1975 Statistical package for the social sciences. McGraw-Hill, New York. Pickering, Robert B., and Jane E. Buikstra

1974 The Ledders site report. Ms. on file, Department of Anthropology, Northwestern University. Sokal, Robert R., and F. James Rohlf

1969 Biometry. Freeman, San Francisco. Tainter, Joseph A.

1975 The archaeological study of social change: Woodland systems in west-central Illinois. Unpublished Ph.D. dissertation, Department of Anthropology, Northwestern University.

U.S. Bureau of Plant Industry, Soils, and Agricultural Engineering 1962 Soil survey manual. U.S. Government Printing Office, Washington, D.C.

Weiss, Kenneth M. 1973 Demographic models for anthropology. Memoirs of the Society for American Archaeology 27.

NEAREST NEIGHBORS, BOUNDARY EFFECT, AND THE OLD FLAG TRICK: A GENERAL SOLUTION

Charles H. McNutt

This paper presents a method for quantifying "boundary effect" in square, rectangular, triangular, and cir- cular study areas. It is based upon a very different, theoretically oriented modification of the classic computa- tion methods, is applicable to the variously shaped study areas described, and produces results which approx- imate simulation data quite closely.

In a recent issue of this journal, Pinder, Shimada, and Gregory (1979) raised certain questions regarding the nearest neighbor statistic developed by Clark and Evans (1954). Specifically, they were quite rightly concerned by an apparently predictable disparity between (1) values for the in- dex which were calculated by the formula developed on a purely theoretical basis by Clark and

would be useful in other regions, and that the refinement of this technique should prove important in multiple aspects of archaeological decision-making.

Acknowledgments. This research has received the support of the National Science Foundation, Grant No. GS-41242; the Northwestern University Research Committee; the University of Chicago Committee of Evolu- tionary Biology; and the Northwestern University Archaeological Program.

REFERENCES CITED

Basile, Robert M. 1971 A geography of soils. Wm. C. Brown, Dubuque, Iowa.

Buikstra, Jane E., and Lynne Goldstein 1973 The Perrins Ledge crematory. Illinois State Museum, Reports of Investigations No. 28. Springfield,

Illinois. Merbs, C. F. M.

1967 Cremated human remains from Point of Pines, Arizona. American Antiquity 32:498-506. Nie, Norman H., C. H. Hull, J. G. Jenkins, K. Steinbrenner, and D. H. Bent

1975 Statistical package for the social sciences. McGraw-Hill, New York. Pickering, Robert B., and Jane E. Buikstra

1974 The Ledders site report. Ms. on file, Department of Anthropology, Northwestern University. Sokal, Robert R., and F. James Rohlf

1969 Biometry. Freeman, San Francisco. Tainter, Joseph A.

1975 The archaeological study of social change: Woodland systems in west-central Illinois. Unpublished Ph.D. dissertation, Department of Anthropology, Northwestern University.

U.S. Bureau of Plant Industry, Soils, and Agricultural Engineering 1962 Soil survey manual. U.S. Government Printing Office, Washington, D.C.

Weiss, Kenneth M. 1973 Demographic models for anthropology. Memoirs of the Society for American Archaeology 27.

NEAREST NEIGHBORS, BOUNDARY EFFECT, AND THE OLD FLAG TRICK: A GENERAL SOLUTION

Charles H. McNutt

This paper presents a method for quantifying "boundary effect" in square, rectangular, triangular, and cir- cular study areas. It is based upon a very different, theoretically oriented modification of the classic computa- tion methods, is applicable to the variously shaped study areas described, and produces results which approx- imate simulation data quite closely.

In a recent issue of this journal, Pinder, Shimada, and Gregory (1979) raised certain questions regarding the nearest neighbor statistic developed by Clark and Evans (1954). Specifically, they were quite rightly concerned by an apparently predictable disparity between (1) values for the in- dex which were calculated by the formula developed on a purely theoretical basis by Clark and

Charles H. McNutt, Department of Anthropology, Memphis State University, Memphis, TN 38152 Charles H. McNutt, Department of Anthropology, Memphis State University, Memphis, TN 38152

Copyright ? 1981 by the Society for American Archaeology 0002-7316/81/030571-22$2.70/1

Copyright ? 1981 by the Society for American Archaeology 0002-7316/81/030571-22$2.70/1

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