Under restrictive conditions, can the widths of linear enamel hypoplasias be used as relative indicators of stress episode duration?

Under Restrictive Conditions, Can the Widths of LinearEnamel Hypoplasias Be Used as Relative Indicators ofStress Episode Duration?

Amelia Hubbard,* Debbie Guatelli-Steinberg, and Paul W. Sciulli

Department of Anthropology, The Ohio State University, Columbus, OH 43210-1364

KEY WORDS bioarchaeology; Irene Mound; physiological stress; enamel defects

ABSTRACT Linear enamel hypoplasia (LEH), a typeof enamel defect reflecting nonspecific physiologicalstress, has traditionally been used by bioarchaeologiststo assess human health. Initially, measurements ofdefect width were used to estimate the duration of stressepisodes. More recently, methods of counting within-defect perikymata (enamel growth increments) weredeveloped to more accurately assess duration. Becauseperikymata are often not continuously visible withindefects, while widths can usually be measured, the pri-mary purpose of this article was to determine if, underrestrictive conditions, the widths of LEH defects mightbe used as relative indicators of stress episode duration.Using a set of dental replicas from the prehistoric IreneMound (1150–1400 A.D.), this study also investigatedpotential sources of variation in defect widths and how

often defect widths could be measured and within-defect perikymata counted. Of 120 defects, only 47 con-tained both measurable defect widths and total within-defect perikymata, while 79 had measurable defectwidths. Regression analysis revealed that, for these 47defects, defect widths were more strongly related to thetotal number of within-defect perikymata than theywere to crown region or tooth type. Although wide pre-diction intervals indicated that a defect’s width couldnot be used to predict the number of within-defect peri-kymata for an individual, narrower confidence intervalsassociated with hypothetical mean population widthssuggested that mean defect widths might be used torank populations in terms of relative average stress epi-sode duration. Am J Phys Anthropol 138:177–189,2009. VVC 2008 Wiley-Liss, Inc.

Enamel hypoplasia has been defined as a deficiency‘‘. . . in enamel thickness resulting from physiological per-turbations (stress) during the secretory phase of amelo-genesis [the process of enamel formation]’’ (Goodmanand Rose, 1990: p 59). Since enamel does not remodel,enamel hypoplasias provide indelible markers of physio-logical stress episodes severe enough to disrupt enamelformation (Hillson, 1996). Although the exact etiology ofvarious forms of enamel hypoplasias is unknown, themajority appears to be caused by malnutrition and/orchildhood febrile disease (Ten Cate, 1994; Hillson, 1996).Because teeth are the most often preserved skeletal ele-ments (Hillson, 1996), enamel hypoplasias have becomean important source of information for bioarchaeologistsas permanent, retrospective indicators of nonspecificphysiological stress.Linear enamel hypoplasia (LEH), the most commonly

occurring type of enamel hypoplasia (Hillson and Bond,1997), appears as a horizontal line or groove on a tooth’senamel surface. These defects have also been describedas having a ‘‘furrow form’’ (Hillson and Bond, 1997), inwhich occlusal and cervical ‘‘walls’’ of the furrow can beidentified (see Fig. 1). Because LEH defects can often beclearly associated with enamel growth increments, it ispossible to determine the approximate age at which anLEH defect formed (Reid and Dean, 2000) as well as toestimate the duration of the stress episode that an LEHdefect represents (Hillson and Bond, 1997). The presentstudy focuses specifically on methods for estimating theduration of physiological stress episodes represented byLEH defects.Bioarchaeologists first attempted to assess the dura-

tion of these stress episodes by measuring the widths of

LEH defects. Blakey and Armelagos (1985) proposed thismethod, applying it to a sample of deciduous teeth fromthe Dickson Mounds. It was assumed that wider defectsresult from longer periods of stress, while narrowerdefects reflect shorter stress episodes. Blakey and Arme-lagos’ method was subsequently adopted or modified byother researchers (Hutchinson and Larsen, 1988; Good-man and Rose, 1990; Ensor and Irish, 1995).Based on an understanding of how LEH defects form

with respect to enamel growth increments, Hillson andBond (1997) questioned the practice of inferring the du-ration of stress episodes from defect widths. Duringenamel formation, incremental growth layers are depos-ited from tooth cusp to cervix, manifesting themselveson the enamel surface as structures known as periky-mata (Ten Cate, 1994; Hillson, 1996). In modernhumans, each perikyma (sing.) represents between 6 and12 days of enamel growth, with an average of 9 days inmost populations (Reid and Dean, 2006). Through theirdetailed microscopic observations, Hillson and Bond

Grant sponsor: Department of Anthropology, The Ohio State Uni-versity.

*Correspondence to: Amelia Hubbard, Department of Anthropol-ogy, The Ohio State University, 124 West 17th Avenue, Columbus,OH 43210-1364, USA. E-mail: [email protected]

Received 17 December 2007; accepted 25 June 2008

DOI 10.1002/ajpa.20917Published online 18 August 2008 in Wiley InterScience

(www.interscience.wiley.com).

VVC 2008 WILEY-LISS, INC.

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 138:177–189 (2009)

(1997) demonstrated that an increase in the spacing ofperikymata, relative to what is normal for a particularcrown region, occurs within LEH defects, specifically inthe occlusal wall of the furrow (see Fig. 1). In the cervi-cal wall of the furrow, perikymata spacing returns towhat is normal for the crown region (see Fig. 1). It isspecifically the occlusal wall perikymata that representthe period of enamel growth disruption, while periky-mata in the cervical wall represent a return to regularenamel formation (Hillson and Bond, 1997). Hillson andBond (1997) explained that the total width of a defectincorporates both occlusal and cervical wall perikymataand therefore ‘‘overestimates’’ the duration of the stressepisode. Guatelli-Steinberg (2008) found that the num-ber of occlusal wall perikymata is usually approximatelyhalf of the total number of perikymata within defects (asimilar finding is reported in this study in the ‘‘Results’’section). Thus, the entire defect width might still be use-ful as a relative indicator of stress episode duration, if itis not possible to clearly identify the defect’s occlusalwall, as is often the case (Guatelli-Steinberg, 2003, 2004;Guatelli-Steinberg et al., 2004; this study).More importantly, however, Hillson and Bond (1997)

pointed out that since perikymata spacing varies overthe tooth crown, the number of perikymata withindefects will not be consistently associated with particulardefect widths. In human enamel, perikymata on anteriorteeth are spaced �100 lm apart in the incisal third ofthe crown, 70 lm apart in the mid-crown region, and50 lm (or less) apart in the cervical third of the crown(Hillson and Bond, 1997) (see Fig. 2); therefore, Hillsonand Bond (1997) explained, defects with the same num-ber of perikymata within them can have very differentwidths depending on perikymata spacing in the crownregion in which defects are located. Individual defectwidths, in their view, cannot then be used as accurateindicators even of relative stress episode duration.As Hillson and Bond (1997) explained, perikymata

within defects are more directly reflective of stress epi-sode duration than defect widths. Yet, even on unworntooth surfaces, perikymata may be difficult to clearly dis-

tinguish or may not be continuously visible withindefects (this study; Guatelli-Steinberg et al., 2004; Gua-telli-Steinberg, 2008). Figure 3 depicts two differentdefects on dental replicas from an Inuit archaeologicalsample imaged under a scanning electron microscope. Totake these images, combinations of different magnifica-tions, tilts, rotations, accelerating voltages, and spotsizes were employed to obtain the clearest picture ofperikymata within each defect. Note that in defect A,perikymata are more clearly visible than they are indefect B. Of 44 defects (from a combined Neandertal andInuit sample) imaged in this same way, Guatelli-Stein-berg et al. (2004) found that it was possible to countperikymata continuously within defects for only half ofthe sample (22 defects). Part of the problem with visibil-ity may have to do with the fact that these are images ofdental replicas, not real teeth, albeit made from high-re-solution materials (in the case of these two images, fromStruers’ ‘‘RepliSet’’ impression system and ‘‘Epofix’’ ep-oxy). Yet, it is rarely possible to coat real teeth fromarchaeological samples with gold-palladium (as was donein these images) or carbon (as was done in the presentstudy) for microscopic viewing. Most of the time, micro-scopic LEH studies must rely on dental replicas ratherthan on real teeth.In contrast to the low percentage of visible within-

defect perikymata in the Guatelli-Steinberg et al. (2004)study, widths were measurable for 40 of the 44 defects,or 90% of the sample. In that study, defect width meas-urements were based on the ability to identify where thedefect walls first began to slope inwards from the enamelsurface. (In the present study, a slightly different setof criteria was used to identify defect borders: seeMaterials and Methods section). Bioarchaeologists arefaced with the choice of using a more accurate method(perikymata counting) on a small sample (given thesmall number of defects with continuously visible periky-mata within them), or using a potentially less accuratemethod (measuring defect widths) on a larger sample.

Fig. 1. Illustration of the occlusal and cervical walls withina linear (furrow-form) defect, and of the different spacing ofperikymata within these walls (adapted from Guatelli-Steinberget al., 2004).

Fig. 2. Illustration of the variable perikymata spacing ofperikymata within the three crown regions of a permanent, an-terior tooth. Note the buried increments in the appositionalenamel (gray shading) as compared to the exposed increments(perikymata) in the lateral enamel (not shaded) (modified fromHillson and Bond, 1997).

178 A. HUBBARD ET AL.

American Journal of Physical Anthropology

Because perikymata are often not clearly visible acrossthe entirety of a linear defect, but it is often possible tomeasure defect widths for a relatively greater number ofdefects, we wondered if there might be a condition or setof conditions under which defect width measurementscould yet provide useful information in the assessmentof relative stress episode duration. Specifically, wehypothesized that the widths of defects might provideuseful estimates of the number of perikymata withinthem, when there is limited variation in the spacing ofperikymata surrounding these defects. Thus, we won-dered if there might be a third option available to bio-archaeologists. This option would be for researchers todetermine if there is a linear relationship between thewidth of defects and the number of perikymata withinthem, when there is limited variation in perikymataspacing surrounding the defects in their sample. A linearrelationship, if found, might then be used to rank defectsfor which it is not possible to obtain complete periky-mata counts with respect to the predicted number oftotal perikymata these defect widths represent.Hillson and Bond’s (1997) paper first outlined the the-

oretical reasons why defect width might not correspondto stress episode duration. The present study furtherexplores these theoretical connections between defectwidths and stress episode duration and investigatesthem using empirical evidence. Figure 4 depicts howdefect widths are theoretically expected to change as afunction of within-defect perikymata number and periky-mata spacing. The bracketed area illustrates how defectwidths become more dependent on the number of periky-mata within them than on perikymata spacing whenvariation in perikymata spacing is constrained. One wayin which variation in perikymata spacing can be limitedis through an analysis of defects confined to particularregions of the crown (e.g., incisal vs. mid-crown vs. cervi-cal regions) for each tooth type. Thus, in the first part ofour analysis, we examine the relationship betweenwithin-defect perikymata counts and defect widths bycrown region and tooth type. Because linear enamelhypoplasias are most often found in the mid-crown andcervical regions (Goodman and Rose, 1990), we also sus-pected that variability in the spacing of perikymata forall of the defects in our sample might be small enoughthat it would only minimally affect the relationship

between defect widths and within-defect perikymatacounts. Note how in Figure 4, variation in perikymataspacing in the mid-crown and cervical regions has less ofan effect on defect width than it does in the incisalregion, where perikymata are spaced more widely. Thus,in the second part of our analysis we assess perikymatavariability in the entire sample (from all available toothregions and tooth types) and analyze the relationshipbetween defect width and within-defect perikymata forthe combined sample. Finally, we analyze to what extentdefect width is predictive of the number of perikymatawithin an individual defect and for the mean of a sampleof defects. In our discussion, we evaluate the utility ofthese predictive relationships for the potential use ofdefect widths as indicators of relative stress episode du-ration first in the comparison of individual defects, and

Fig. 3. Two defects on dental replicas from an Inuit archaeological sample imaged under a scanning electron microscope. Theseimages were collected using a combination of different magnifications, tilts, rotations, accelerating voltages, and spot sizes to obtainthe clearest picture of perikymata within each defect. In defect A, perikymata are more clearly visible than they are in defect B.

Fig. 4. Defect widths as a function of total perikymatawithin defects and the spacing of perikymata per mm. Inhuman anterior teeth perikymata spacing in the incisal third ofthe crown is generally less than 12 pk per mm, usually 12–20pk per mm in the middle third of the crown, and often greaterthan 20 pk per mm in the cervical third (with the exception ofthe last decile) (Guatelli-Steinberg et al., 2007). Bracketed lineshighlight how defect widths become more dependent on thenumber of perikymata within them than on perikymata spacingwhen variation in perikymata spacing is constrained, as it iswithin a single crown region.

179LEH WIDTHS AND RELATIVE STRESS EPISODE DURATION


second, in the comparison of means for different samplesof defects. These analyses were conducted on a sample ofLEH defects in replicas of the dental remains from theIrene Mound, a prehistoric site (1150–1400 A.D.) nearthe Georgia coast.

MATERIALS AND METHODS

The sample

The Irene Mound site is located in coastal Georgiaalong the Savannah River (Larsen, 1982). These burialsare associated with one of the last truly prehistoric pop-ulations in La Florida, unaffected by the arrival of Span-ish missionaries (Jesuit priests) in the mid-1500s(Larsen, 1982, 2001; Griffin et al., 2001). The Irene skel-etal collection contains close to 300 individuals rangingfrom 8- to 50-years old and is housed at the Smithso-nian’s National Museum of Natural History, WashingtonD.C. 300 dental replicas representing 120 linear enameldefects and 51 individuals were examined; of these 120identified defects, only 47 (from 33 individuals) wereused to statistically test our hypothesis. The IreneMound collection was selected because it is a representa-tive archaeological sample.

Sample selection

Dental casts were made of selected teeth from theIrene Mound, to microscopically measure defect widthsand count within-defect perikymata. Teeth were chosenfor casting based on four criteria, as follows:

Permanent, anterior teeth. Deciduous teeth were notincluded because, unlike permanent teeth, they rarelyexhibit perikymata (Boyde, 1976) or LEH (Hillson,1996), owing to their short enamel formation times andfast enamel extension rates. Posterior teeth wereexcluded because they are less frequently affected byLEH than anterior teeth (Goodman and Rose, 1990).

Teeth exhibiting LEH defects. The permanent anteriorteeth of each individual were observed with a 103 mag-nification hand-lens for the presence of LEH. BecauseLEH defects can result from either localized traumasaffecting a single tooth or systemic stressors affectingthe entire dental arcade (Hillson, 1996), it was necessaryto ensure that each defect included in the analysis repre-sented a disruption due to systemic stress rather thanlocalized trauma. Therefore, at least two teeth, eachexhibiting defects in overlapping regions of enamel for-mation, were cast for each individual. Hillson’s (1996)developmental charts were used to determine if an indi-vidual’s LEH defects were located in corresponding de-velopmental regions. When defects could be matched inregions of developmental overlap on two or more teeth,all anterior teeth were cast. Though both antimereswere cast, only the antimere with the best preservationof perikymata was used when measuring defect widthsand counting within-defect perikymata.

Teeth with well-preserved and minimally obscuredenamel. In archaeological samples, preservation ofenamel can be poor due to taphonomic processes thatwear or chip away at the tooth surface. Therefore, teethwere not cast if they exhibited extensively cracked ormissing enamel that masked or obliterated more thanhalf of a defect or teeth that appeared to be heavilyabraded. In some cases, enamel surfaces were alsoobscured by dental calculus, museum glue, and clear

lacquer, though LEH defects were still present and visi-ble through the obstructing material. Because the muse-um’s conservation guidelines prohibited the cleaning ofenamel surfaces with either acetone or isopropyl alcohol,teeth exhibiting dental calculus or lacquer/glue that cov-ered more than half of any defect were excluded. Thereason for this restriction was that, although the LEHdefect was visible, the obstructing material would pre-vent perikymata within the defect from making animpression on the molding material (see below).

Teeth retaining at least 80% of their estimated orig-inal crown height. Prehistoric dental samples, such asIrene Mound, commonly exhibit high levels of attritiondue to use and gritty diets (Cohen and Armelagos, 1984).To maximize the sample size from worn teeth only thoseteeth retaining at least 80% of their estimated originalcrown height were cast. While restricting the sample toteeth that retained at least 90% or more of their originalcrown height would have been ideal, few teeth were suffi-ciently complete. Using Mitutuyo sliding calipers, two ormore unworn teeth for each tooth type (except mandibu-lar canines) were measured from the cemento-enameljunction (CEJ) of the labial surface to the incisal edge/cusp tip and an average crown height was calculated foreach tooth type (Table 1). For mandibular canines, therewere no complete cusp tips; in this case, the least wornteeth were measured to obtain the average crown height.The minimum crown height required for a tooth to be80% or more complete was calculated (Table 1).Dental impressions of teeth meeting all four criteria

were made using Coltene-Whaledent’s President’s JetRegular Body system (a polyvinyl siloxane compound),which has been used in numerous microwear (El-Zaatariet al., 2005) and LEH studies (Hillson, 1992; Guatelli-Steinberg et al., 2004) because of its ability to capture agreat level of detail in enamel microstructures. Castswere made using Struers’ Epofix epoxy and hardenerunder a vacuum. To make casts conspicuous under themicroscope, these translucent replicas were coated using20 nm of carbon. Though gold palladium is often used inscanning electron microscopy of dental replicas, thisstudy employed a binocular microscope, and carbon pro-vided a suitable alternative.

Preliminary data analysis

Three hundred replicas were directly examinedthrough the lens of a standard, binocular microscope(Nikon SMZ 1500) and a secondary, fiberoptic lighting

TABLE 1. Crown height estimates from unworn anterior teethin the Irene Mound

Tooth type

Averagecrown

height (mm)a

80%crown

height (mm)b

Maxillary central incisors (n 5 7) 11.6 �9.3Maxillary lateral incisors (n 5 2) 11.3 �9.0Maxillary canines (n 5 7) 12.9 �9.6Mandibular canines (n 5 6) 11.3 �9.0

a Average crown heights of each tooth were estimated for wornteeth using measurements of total height from the CEJ to toothapex on unworn teeth (for canines, these teeth represent theleast worn).b Eighty percent of the average crown height for each tooth wascalculated to standardize sample selection of teeth exhibitingminimal attrition (i.e., 20% or less).



source by the first author. Prior to microscopic observa-tion, LEH defects were examined using a 103 magnifica-tion hand lens, and labeled on the tooth surface so thatdefects could be easily identified under the microscope.Each dental cast was then tilted, rotated, and examinedat 203 and 503 magnification because perikymata werebest viewed in both the occlusal and cervical walls of thedefect at these magnifications. The borders of each defectwere initially defined as occurring between the pointswhere the occlusal and cervical walls initially slopedinward from the enamel surface (see Fig. 1). However,because the occlusal border of each defect has the addi-tional defining characteristic of being marked by the be-ginning of irregularly spaced perikymata (Hillson andBond, 1997), this criterion was also used to indicate thestart of the defect. Using these two criteria for establish-ing defect borders, within-defect perikymata could becounted and defect widths could be measured in a stand-ardized fashion.Once each defect was located and its borders defined,

the number of within-defect perikymata was counted.When perikymata were continuously visible throughoutthe defect (Fig. 5a), total perikymata were counted and,

if possible, separate counts of cervical and occlusal wallperikymata were collected. After returning each speci-men to its horizontal position, a measurement of defectwidth was taken in micrometers using a reticule cali-brated to each magnification. When perikymata were notcontinuously or uniformly visible within a defect, periky-mata were not counted. However, if the perikymata priorto and within the start of these same defects were visible(Fig. 5b), defect width could still be measured. Finally,when perikymata were neither continuously visiblewithin a defect nor clearly visible prior to the start ofthe defect (Fig. 5c), no measurements or counts were col-lected because the borders of the defect were not clearlydistinguishable. Using these data, four preliminary ques-tions were asked: 1) In what proportion of the samplewere the occlusal and cervical borders of defects clearlydefined (i.e., were defect widths measurable)?, 2) Inwhat proportion of the sample could within-defect peri-kymata be counted (i.e., were perikymata both presentand clearly visible)?, 3) Is the ratio of occlusal wall peri-kymata to total within-defect perikymata 2:1 in our sam-ple (i.e., as found by Guatelli-Steinberg, 2008)?, and 4)How variable is perikymata spacing among the crownregions of different tooth types?To assign defects to regions, the average estimated

crown heights for each tooth type were subdivided intothree equal parts representing the incisal, mid-crown,and cervical regions of the tooth (Table 2). Defects onreplicas were then assigned to one of the three regionsusing measurements from the CEJ of the labial surfaceto the occlusal and cervical borders of each defect.Defects that fell between two regions were not includedin the by-region statistical analysis (see below). Defectslocated on boundaries between regions (e.g., located inthe incisal region but with one border lying on theincisal/mid-crown tooth boundary) were excludedbecause crown region boundaries were based on crownheight averages, and individuals vary in their crownheights.To determine the variability of perikymata spacing

prior to the beginning of defects from different crownregions and tooth types, perikymata were counted within0.5 mm increments preceding the beginning of eachdefect’s occlusal wall. This procedure follows Guatelli-Steinberg’s (2003) method of assessing perikymata spacing

Fig. 5. Illustrates three hypothetical scenarios and methodsfor defining borders, counting within-defect perikymata, andmeasuring defect width in each. Where present, occlusal wallperikymata are shaded in dark gray and cervical wall periky-mata are shaded in light gray. In scenario ‘‘A,’’ perikymata arecontinuously visible throughout the defect so that defect borderscan be defined (indicated by the arrows) and data collected fortotal perikymata number and defect width. In scenario ‘‘B,’’perikymata are not continuously visible throughout the defect,so that total within-defect perikymata cannot be counted. How-ever, because perikymata are present at the occlusal border ofthe defect (i.e., ‘‘start’’) of the defect, a width measurement canbe made. Finally, in scenario ‘‘C,’’ perikymata are neither con-tinuously visible nor present at the occlusal border of the defect.In such cases, no data can be collected.

TABLE 2. Estimation of crown regions

Tooth typeCrownregion

Crown regionmeasurements(mm from CEJ)a

Estimatedaverage crownheight (mm)

Maxillarycentralincisors

Incisal 7.74–11.60 11.6Mid-crown 3.88–7.74Cervical 0–3.87

Maxillarylateralincisors


Maxillarycanines


Mandibularcanines


a Average crown heights from Table 1 were divided into equalthirds. Each measurement was taken from the CEJ to theboundary of that region. For example, for the maxillary centralincisors, the lower boundary is the CEJ and the upper boundaryis 3.87 mm from the CEJ.



associated with LEH defects by counting perikymata im-mediately prior to the disruption of enamel formation.

Intra-observer error

Twenty-two defects were randomly selected for anintra-observer error test from the total number of defectsfor which it was possible to both count within-defectperikymata and measure defect width (n 5 47). Repli-cate measurements of within-defect perikymata countsand width measurements were conducted on separatedays by the first author. Paired t-tests were used to com-pare the first and second sets of defect width measure-ments and within-defect perikymata counts. For thecomparisons between the first and second counts of totalwithin-defect perikymata, the t statistic was 21.093 (P\ 0.29) and for the comparisons between the first andsecond measurements of defect width, the t statistic was1.501 (P \ 0.15). The mean difference between the firstand second counts of total within-defect perikymata was1.3 perikymata (95% confidence interval 60.4) and themean difference between the first and second defectwidth measures was 82.3 lm (95% confidence interval657.2 lm). Correlations between average defect widthand the difference between the first and second widthmeasurements (r 5 20.03; P \ 0.89) or total perikymatacounts (r 5 20.22; P \ 0.32) indicates that the error inmeasuring defect widths or counting perikymata did notincrease with increasing size of defects.

Nature of the relationship between defect widthsand within-defect perikymata

Regional assessment. One way in which the effects ofperikymata spacing can be limited is through the isola-tion of defects by crown region. In this analysis, wewanted to determine how closely correlated defect widthand within-defect perikymata were in each region. Itwas expected that, by controlling for the effects of variedperikymata spacing, there would be a close correlationbetween defect widths and total within-defect periky-mata. Therefore, a Pearson product moment correlationbetween defect width and total within-defect perikymatawas calculated for each available region and tooth type.

Combined sample: Assessing sources of variation indefect width. In this portion of the study, the strengthof the relationship between within-defect perikymataand defect width was examined when all defects for eachof the tooth types and regions were combined. Additionalsources of variation in defect width in this combinedsample were also investigated. Specifically, we wanted todetermine the extent to which, 1) defect width wasaffected by total within-defect perikymata, crown region,and tooth type, and 2) the relationship between defectwidths and total within-defect perikymata might beaffected by region and tooth type.First, a Pearson product moment correlation was cal-

culated for defect width, total perikymata, and the num-ber of perikymata within a 0.5-mm region prior to theocclusal border of each defect (n 5 29). A partial correla-tion, in which the spacing of perikymata prior to thestart of the defect was held constant, was also used todetermine the effects of perikymata spacing on the rela-tionship between defect width and total within-defectperikymata.Second, a general linear model was performed in

which the dependent variable was defect width and the

independent variables were crown region, tooth type,and total within-defect perikymata. This procedure inthe SAS statistical system provides results for both aType I sum-of-squares model and a Type III sum-of-squares model. The Type I model provided a means forassessing the separate effects of tooth type, region, andtotal within-defect perikymata on defect widths. TheType III model tested the effects of region and tooth typeon the relationship between total within-defect periky-mata and defect width.

Nature of the predictive relationship betweendefect widths and within-defect perikymata

Based on the results from the preceding analyses (seeResults section), it was determined that the combinedsample of defects could be used, in place of individualregressions by tooth type and region, to test the natureof the predictive relationship between defect widths andtotal within-defect perikymata. In this combined regres-sion analysis, total within-defect perikymata numberwas treated as the dependent variable and defect widthwas treated as the independent variable (the reverse ofthe model used to test region and tooth effects). The fol-lowing equation was used to model the number ofwithin-defect perikymata for a given width:

Total number of perikymata ¼Intercept þ Slope ðMeasured defect widthÞ

Next, associated prediction and confidence intervalswere calculated to determine the range of variation intotal perikymata counts around the regression line forindividual defect widths and the mean defect width ofthe Irene Mound.Prediction intervals are commonly used in linear

regression analyses to predict the outcome or range ofoutcomes of a particular variable by looking at the distri-bution of individual data points within a sample and fit-ting a particular data point of interest to that distribu-tion (Zar, 1999). In our sample, the prediction intervalswere used to determine how predictive individual defectwidths were of total within-defect perikymata. The pre-diction intervals allowed us to determine how great adifference must exist between two defect widths in orderto distinguish them as different in their predicted num-ber of perikymata.In contrast to prediction intervals, which investigate

the behavior of individual data points, confidence inter-vals are used to analyze the range of responses of a pop-ulation parameter (Zar, 1999). Therefore, the purpose ofcalculating confidence intervals was to determine, basedon our sample, how great the difference between twomean defect widths would have to be in order to distin-guish them as different in their predicted number ofwithin-defect perikymata.To make this determination, the mean of defect widths

in the Irene sample and the means of defect widths intwo hypothetical samples (n 5 25; n 5 100 defects) werecompared. The primary assumption in this comparisonwas that the Irene Mound and the hypothetical samplesshared similar, if not identical, average perikymata spac-ing and contained similar distributions of defects acrossthe tooth crown.Both the prediction and confidence intervals were

assessed using a range of defects from 400 to 2200 lm,



at 100 lm intervals. To calculate prediction intervals,the following equation was used:

Number of within-defect perikymata ðinþ=� perikymataÞ

¼p"

Mean Square Error

�1þ 1

n

þ ðdefect width�meanÞ2Px2

�#

where, n 5 47 defects, defect width 5 a given defectwidth, mean 5 mean defect width of Irene sample, andx2 5 Irene sample’s defect width variance; Mean squareerror 5 estimate of total error expected in a sample.To calculate confidence intervals, the following equa-

tion was used:

Number within-defect perikymata

¼p"

Mean Square Error

�1

n1þ 1

n2

þ ðdefect width1 � defect width2Þ2Px2

�#

where, n1 5 total number of defects in the combinedIrene sample and n2 5 hypothetical population’s samplesize; defect width1 5 Irene population’s mean defectwidth and defect width2 5 hypothetical population’smean defect width; x2 5 Irene sample defect width var-iance; Mean square error 5 estimate of total errorexpected in a sample.In both cases, when there was any overlap in the total

number of within-defect perikymata between two ormore intervals, the associated defects were considered tobe indistinguishable in terms of their predicted numberof within-defect perikymata.

RESULTS

Preliminary data analysis

One hundred and twenty linear enamel defects wereidentified on 300 replicas from the Irene Mound sample.

Of these 120 defects, 41 (34%) contained indistinct defectborders and perikymata that were not clearly visible. Inthe remaining 79 defects (66%), defect borders were welldefined, with measurable defect widths. Of these 79defects, 47 (59%) also exhibited visible total perikymataand 32 (42%) contained distinguishable occlusal and cer-vical wall perikymata. Finally, 23 (29%) of these 79defects contained at least some visible perikymata.Three defects fell between the cervical and mid-crownregions, so that a positive region assignment could notbe made. Table 3 gives the average number of within-defect occlusal wall perikymata, average number of totalwithin-defect total, and the ratio between the two. Onaverage, the total number of perikymata in a defect wasapproximately equal to twice the number of perikymatain the occlusal wall; though, there was variation in thisratio by individual defect.Finally, Table 4 lists the results of the test of variabili-

ty in perikymata spacing within 0.5 mm increments justprior to the start of each defect. Average spacing bytooth type and region are listed. Although there wasonly limited variation in perikymata spacing among themaxillary central incisors (mid-crown and cervicalregions), lateral incisors (both mid-crown and cervicalregions), and maxillary canines (mid-crown only), thereis a marked difference in the spacing of perikymata inthe mandibular canines (mid-crown only). It appearsthat perikymata are more tightly packed in the toothcrown of the mandibular canines (mid-crown region)than in any other region or tooth.

Nature of the relationship between defect widthsand within-defect perikymata

Before undertaking any of the statistical testsdescribed below, all data were tested for normality; onlythe raw data for width was normally distributed. Thesquare root of total within-defect perikymata and ofnumber of perikymata prior to the start of each defectwas taken to normalize these data. Table 5 lists theteeth and regions included in these analyses; tests werenot run in crown regions where there were fewer thansix data points.

Regional assessment. Table 6 lists the results of thePearson correlation between the square root of totalwithin-defect perikymata and defect width by tooth type

TABLE 3. Averages of within-defect perikymata (total and occlusal wall perikymata)

Tooth typeAveragetotal pk

Average occlusalwall pk

Ratio of average occlusalwall pk to average total pk

Maxillary central incisor (n 5 19) 14.32 6.79 0.46Maxillary lateral incisor (n 5 8) 15.5 8.5 0.58Maxillary canine (n 5 3) 15 6 0.45Mandibular canine (n 5 2) 13 7 0.54

TABLE 4. Average number of perikymata per 0.5 mm immediately prior to the ooclusal wall of a defect (by tooth type and region)

Tooth type Crown regionAverage pk spacing incisalto the defect (# pk/0.5 mm)

Standarddeviation

Maxillary central incisors Mid-crown (n 5 8) 8.0 0.9Cervical (n 5 6) 7.8 2.4

Maxillary lateral incisors Mid-crown (n 5 5) 7.7 1.8Cervical (n 5 3) 8.8 2.2

Maxillary canines Mid-crown only (n 5 4) 7.8 0.5Mandibular canines Mid-crown only (n 5 3) 10.3 2.1



and region. For the maxillary central incisors correla-tions of 0.92 and 0.89 were for the mid-crown and cervi-cal regions, respectively. A similar correlation was foundfor the maxillary lateral incisors in the mid-crownregion, with a correlation of 0.93. The combined maxil-lary and mandibular canines (mid-crown region only)had the highest correlation of 0.94. Finally, disregardingthe effects of tooth type and region, the correlation forthe combined sample was 0.86.

Combined sample: Assessing sources of variation indefect width. The results of the second Pearson correla-tion for defect width, the square root of total within-defect perikymata, and the square root of the number ofperikymata prior to the start of each defect are describedbelow. The strongest correlation was found between thetotal number of within-defect perikymata and defectwidth (0.83), while weak correlations were foundbetween the number of perikymata prior to the start ofeach defect and defect width (0.05) and between thenumber of perikymata prior to the start of each defectand the total number of within-defect perikymata (0.09).In the partial correlation among these three variables,holding the number of perikymata prior to the start ofeach defect constant did not affect the correlationbetween the total number of within-defect perikymataand defect width (i.e., it remained at 0.83).Table 7 lists the results of the Type I and Type III

sum-of-squares regression models. In the Type I model,region appears to have no significant effect on defectwidth (F 5 0.3, P \ 0.59). However, both tooth type andtotal within-defect perikymata had significant effects(Tooth type: F 5 15.51, P \ 0.0001; Total perikymata: F5 119.78, P \ 0.0001). Figure 6 plots the total numberof within-defect perikymata and defect width separatedby tooth type. Note how the best-fit line for the caninesdiverges from the best-fit line for the incisors. However,in the Type III model, when the number of within-defectperikymata was held constant, the effect of tooth type

was not significant (F 5 3.05, P \ 0.06) nor was theregion (F 5 2.59, P\ 0.12).

Nature of the predictive relationship betweendefect widths and within-defect perikymata

Given the possibility that tooth type may have a mini-mal influence on the relationship between within-defectperikymata and defect width, we calculated regressionequations for both the combined sample (i.e., incisorsand canines) and separately for the incisors. The result-ing equations were nearly identical:

Combined sample: Total number of perikymata 5 2.281 0.0016 (Measured defect width)

Incisors alone: Total number of perikymata 5 2.281 0.0017 (Measured defect width)

Therefore, in the calculation of prediction and confi-dence intervals, only the combined regression was used.

Fig. 6. Defect width vs. total within-defect perikymata bytooth type. Notice how the canine line diverges from the incisorlines in a manner consistent with Figure 4. In our sample, can-ines had more closely spaced perikymata than did incisors. Con-sistent with Figure 4, this difference in perikymata spacing hasa greater effect on defect width as the number of within-defectperikymata increases.

TABLE 6. Correlations between square root of total perikymataand defect width by region and tooth type

Tooth type Region Correlation

Central Incisorsa Cervical 0.89Mid-crown 0.92

Lateral Incisorsa Cervical 0.80Caninesb Mid-crown 0.94Combined All regions 0.86

a Maxillary teeth.b Maxillary and mandibular teeth.

TABLE 5. Composition of the combined sample used in theregression analysis

Tooth Region # Defects

Central Incisorsa

Mid-Crown 17Cervical 9

Lateral Incisorsa

Mid-Crown 7Cervical 5

Caninesb Mid-crown 9Combined Mid-crown 1 Cervical 47 total

a Maxillary teeth.b Maxillary and mandibular teeth.

TABLE 7. Results of SAS regression Type I sum-of-squares andType III sum-of-squares

F-statistic P-value

Type I (Model: width 5 region, tooth type, sqrt total pk)a

Region 0.30 P 5 0.59Tooth type 15.51 P\ 0.0001Square root of total pk 119.78 P\ 0.0001

Type III (Model: width 5 tooth type, sqrt total pk)b

Region 2.59 P 5 0.115Tooth type 3.05 P 5 0.058Square root of total pk 119.78 P\ 0.0001

a The Type I model tests the effects of region, tooth type, andtotal within-defect perikymata on defect width.b The Type III model tests the effects of region and tooth typeon the relationship between defect width and the square root ofthe total number of pk within a defect.



Figure 7 illustrates the regression line and 95% confi-dence interval for the square root of total perikymataand defect width. For the sample of incisors, the r-squared value was 0.77 (P \ 0.0001); in the combinedsample the r-squared value was 0.74 (P\ 0.0001).Using the combined regression equation, predicted

perikymata numbers for individual hypothetical defectwidths were calculated in 100 lm increments from 400to 2200 lm, representing the range of defect widths thatwere observed in the Irene Mound. Because of substan-tial overlap in prediction intervals for perikymata countsassociated with these different hypothetical widths, Ta-ble 8 lists the prediction intervals for just four cases inequal intervals. A difference of five perikymata existsbetween the prediction intervals for defect widths of 400and 2200 lm while there is an overlap of three periky-mata between prediction intervals for defect widths of400 and 1600 lm. A difference of �1800 lm between twodefect widths is therefore required before defects can beconsidered to differ in their number of predicted totalperikymata. This extensive overlap in prediction inter-vals indicates that most defect widths cannot be differen-tiated from each other in terms of the total number ofperikymata that could be encompassed within them.As noted previously, bioarchaeologists are often inter-

ested not only in individual reactions to stress, but alsoin population-wide stress experiences. We thereforeinvestigated the extent to which confidence intervals forthe defect width means of two hypothetical populationscould be differentiated from the Irene Mound sample,based on the combined regression equation. Confidenceintervals were compared between the Irene Mound popu-

lation and two hypothetical defect populations using thefollowing equation:

Total Number of perikymata ðinþ=� perikymataÞ

¼p"

22:266

�1=47þ 1=nþ ðdefect width1 � 919:79Þ2

49231700

�#

Sample sizes for the hypothetical populations (n 5 25;n 5 100 defects) were chosen based on the minimumand maximum number of defects the authors believedmight be present in a given archaeological sample. Hy-pothetical mean widths were selected using the samedefect width range (400–2200 lm) used in the calcula-tion of prediction intervals, again in 100 lm increments.Table 9 provides a condensed version of the results,showing only those defect widths that had nonoverlap-ping confidence intervals for their mean number of peri-kymata. Mean defect widths can be inferred to representdefects with a distinctly different number of perikymatawithin them where there is no overlap in these confi-dence intervals. This analysis suggests that two groupscan be distinguished in terms of the mean number ofwithin-defect perikymata that their linear enamel hypo-plasias represent if there is a difference of at least600 lm between their mean defect widths, one-third ofthe difference (1800 lm) needed in the comparison ofindividual widths.

Fig. 7. Regression plot for the combined sample with associated regression equation and 95% confidence interval.

TABLE 8. Prediction intervals for individual defect widths inthe combined Irene sample

Na Widthb Pkc Prediction intervals

47 400 9 9 6 10 or {0–19}47 1000 17 17 6 10 or {7–27}47 1600 26 26 6 10 or {16–36}47 2200 34 34 6 10 or {24–44}

a Total defects from Irene population.b Hypothetical defect widths for an individual.c Number of perikymata calculated for a given width.

TABLE 9. Confidence intervals for mean defect widths in thecombined Irene sample

Na Widthb Pkc Confidence intervals

25 400 9 9 6 3 or {6–12}25 1000 17 17 6 2 or {15–19}25 1600 26 26 6 3 or {23–29}25 2200 34 34 6 3 or {31–37}

100 400 9 9 6 2 or {7–11}100 1000 17 17 6 2 or {15–19}100 1600 26 26 6 2 or {24–28}100 2200 34 34 6 2 or {32–36}

a Total defects from hypothetical population.b Hypothetical mean defect widths for a population.c Number of perikymata calculated for a given width.



DISCUSSION

The goals of the present study were to use an archaeo-logical dental sample to: 1) investigate how often thewidths of defects can be measured and perikymatacounted within them, 2) explore the sources of variationin defect widths, and 3) test the hypothesis that thewidths of defects can be used to statistically predict thetotal number of perikymata within them. The first goalof this study is important because perikymata are oftendifficult to clearly distinguish or may not be continuouslyvisible within defects, even with SEM imaging (Guatelli-Steinberg, 2003, 2004; Guatelli-Steinberg et al., 2004;Guatelli-Steinberg, 2008). However, because defect bor-ders can be identified for a greater proportion of defects,it is more often possible to measure the widths of defectsthan to count perikymata within them (Guatelli-Stein-berg, 2003, 2004; Guatelli-Steinberg et al., 2004; Gua-telli-Steinberg, 2008). The preliminary data analysis ofthe present study strongly supports these previous find-ings. In 300 dental replicas (which were examined forobscuring materials such as dirt, calculus, or lacquerprior to casting) and 120 LEH defects, only 47 defectscontained clearly and continuously visible perikymatawithin them. This number represents �40% of the origi-nal defect sample. By contrast, 79 defects had measura-ble defect widths, representing 66% of the total sample.These findings underscore the justification for this study:that because there are more data available for thewidths of defects than for the number of perikymatawithin them, it is worth investigating if the former canbe used to statistically predict the latter.Using an SEM, which has a greater depth of field than

the light microscope used in the present study, Guatelli-Steinberg et al. (2004) reported that only 50% of thedefects in their sample had clearly visible perikymatawithin them. This percentage is similar to the 40% foundin the present study, despite the fact that Guatelli-Stein-berg et al. (2004) used a superior imaging tool. This sim-ilarity of the two studies suggest that even with the bestimaging techniques, it may not be possible to see periky-mata clearly and continuously throughout the entirewidth of an LEH defect. Guatelli-Steinberg et al. (2004)also reported that 90% of their sample had measurabledefect widths, a much higher percentage than that foundhere. This difference between the two studies probablyreflects the different criteria that were used to identifydefect borders. In the present study, an occlusal borderwas not identified as such unless a change in periky-mata spacing between the enamel just before (i.e., imme-diately incisal to) the defect and at the edge of thedefect’s occlusal wall could be observed. In the Guatelli-Steinberg et al. (2004) study, occlusal borders were iden-tified solely on the basis of whether the point at whichthe occlusal wall first began to slope inward from theenamel surface could be distinguished.Also in the preliminary data analysis of the present

study, the total number of perikymata within defects wasfound to be generally twice the number of occlusal wallperikymata. Thus, on average, the total number of peri-kymata within defects reflects the number of perikymatainvolved in the growth disruption, i.e., those in the occlu-sal wall. However, individual defects vary, such that forsome defects the total number of perikymata is somewhatmore or less than twice the number of occlusal wall peri-kymata. This variation suggests that for a single defect,one cannot assume that the total number of perikymata

within it is directly related to the number of perikymatain its occlusal wall. That assumption appears to be onlyvalid for the average of a sample of defects.In the second part of this study, sources of variation in

defect widths were explored. Within the maxillary cen-tral incisors (cervical region), maxillary central incisors(mid-crown region), maxillary lateral incisors (mid-crownregion), and combined maxillary and mandibular canines(mid-crown region), the number of perikymata withindefects was highly correlated with defect widths (correla-tion coefficients ranged from 0.89 to 0.93). As explainedearlier (see Introduction), this result was expected: whenthe spacing of perikymata is constrained, as it is withina particular region of the crown, the total number ofperikymata within the defect becomes the primary deter-minant of defect width (see Fig. 4). The fact that the cor-relations are not perfect (i.e., not correlations of 1) islikely a result of measurement error and variation inperikymata spacing within each crown region.Sources of variation in defect widths were then

explored for the combined sample of defects from all toothtypes and crown regions. Again, the total number ofwithin-defect perikymata was highly correlated withdefect width (correlation coefficient of 0.83), but some-what less so than it was in the analysis of separate crownregions. The reason for the diminished correlation seemsto be unrelated to perikymata spacing, which was notstrongly correlated with defect width (correlation coeffi-cient of 0.05). Furthermore, holding perikymata spacingconstant through a partial correlation between defectwidth and within-defect perikymata had no effect on thecorrelation coefficient between these two variables, whichremained at 0.83. The lack of a strong correlationbetween defect width and perikymata spacing is a resultof the nature of the sample: defects were located only inthe mid-crown and cervical regions, with a narrow rangeof variation in perikymata spacing. In fact, as Table 3illustrates, the average spacing of perikymata was simi-lar across all crown regions from the different toothtypes, with the exception of the mandibular canines.In the mandibular canines (data only from mid-crown

region), perikymata were spaced more closely than theywere in the other crown regions (see Table 4). It is thiscloser spacing which likely accounts for the results of theType I sum-of-squares model. In the Type I model, defectwidth was regressed on region, tooth type, and the totalnumber of within-defect perikymata. As shown in Table6, region was not a significant source of variation indefect width (F 5 0.3, P \ 0.59), but tooth type (F 515.51, P\ 0.001) and the number of within-defect periky-mata (F 5 119.78, P \ 0.001) were. The graph of defectwidth versus the number of within-defect perikymata(see Fig. 6) broken out by maxillary central incisors, max-illary lateral incisors, and canines (maxillary and man-dibular combined) is consistent with this interpretation:note how, in Figure 6, when the number of within-defectperikymata is large, canine defect widths are narrowerthan incisor defect widths. A best-fit line through the ca-nine data points diverges from best-fit lines through theincisor data points as within-defect perikymata numbersbecome greater. Given the closer spacing of perikymataon canines, this is exactly what would be expected. Thepattern of divergence of the incisor and canine linesmatches the theoretical data pattern depicted in Figure4, in which, as within-defect perikymata numbers becomegreater, so does the effect of perikymata spacing on varia-tion in defect width. Thus, with different tooth types



included in the combined sample, there is more variationin perikymata spacing than there is within each toothregion. This variation makes the correlation betweendefect width and the number of perikymata withindefects lower in the combined sample than it is for eachseparate crown region.However, the Type III sum-of-squares model reveals

that neither region nor tooth type has a significant effecton the relationship between defect width and the num-ber of within-defect perikymata (Table 6). In the Type IIImodel, the number of within-defect perikymata is heldconstant: region remains insignificant (F 5 2.59, P \0.12), and tooth type became insignificant (F 5 3.05, P\0.06). Thus, even when defects from all crown regionsand tooth types are combined, perikymata spacing varia-tion is constrained enough in our sample to maintain avery strong relationship between defect width andwithin-defect perikymata. If our sample had includeddefects from the incisal region, then the relationshipbetween defect width and within-defect perikymatawould likely have been weaker. In part, then, the natureof our sample is responsible for the strong relationshipwe found between defect width and within-defect periky-mata in the combined sample. We believe, however, thatthe distribution of defects across crown regions in oursample is representative of many other dental samples.As Goodman and Rose (1990) note, LEH defects occurmore frequently in the cervical and mid-crown regionsthan they do in the incisal region, even on unworn teeth.The reason that few LEH defects form in the incisalregion of a tooth may be that Retzius planes form moreshallow angles with the enamel surface in this regionthan they do in other regions. Many of the defects form-ing in the incisal region might therefore be so shallow asto be imperceptible as distinct furrows.The third part of this study followed from the preced-

ing exploration of sources of variation in defect width.Given the strong relationship in the combined samplebetween defect width and the number of within-defectperikymata, we investigated whether the former couldbe used to statistically predict the latter. We thereforecalculated regression equations for total within-defectperikymata (now the dependent variable) on defectwidths (independent variable). Based on our precedinganalysis, we thought it might be important to determineif tooth type had some effect on this regression. Thus,we calculated the regression equation for the combinedsample, and for just the incisors (excluding canines).Finding the regression equations to be nearly identical,we then used the regression for the combined sample tocalculate prediction intervals associated with individualdata points. By doing so, we found that only defectsexhibiting a difference of at least 1800 lm could be dis-tinguished as different in their total number of periky-mata. This finding provides compelling empirical evi-dence in support of Hillson and Bond’s (1997) theoreticalargument that the width of a defect cannot be used asan indicator of the number of perikymata within it.Moreover, when one asks the question of whether the

width of an individual defect reflects stress episode dura-tion, there are additional reasons why the answer mustbe ‘‘no.’’ For an individual defect, one cannot assumethat the total number of perikymata is twice the numberof occlusal wall perikymata; while our data suggest thatthis assumption is reasonable for the average of a sam-ple of defects, individual defects are clearly more varied.In addition, perikymata periodicities vary from 6 to 12

days in the teeth of individuals (Reid and Dean, 2006),compounding the uncertainty of stress episode durationrepresented by individual defects. Finally, perikymataspacing varies among individuals as a function of theirperiodicities (Reid and Ferrell, 2006), adding yet onemore complication in any attempt to use regression topredict the number of perikymata within a defect on thebasis of its width.Because bioarchaeologists are often more interested in

means for populations than in individual values, weused our combined regression model to determine if hy-pothetical populations could be distinguished from theIrene sample in their mean number of within-defectperikymata as predicted by their mean defect widths. Inboth hypothetical population comparisons (n 5 25; n 5100 defects), a difference in defect width means of only600 lm was needed to distinguish between two groupsin their mean number of predicted within-defect periky-mata. The question that now arises is whether a differ-ence in 600 lm or more in the average defect width oftwo populations reflects the average duration of stressepisodes. Two additional pieces of information are neces-sary to answer this question. First, as noted above, thedata from our preliminary analysis suggested that on av-erage, the number of within-defect perikymata repre-sents the number of perikymata in the occlusal wall.Second, Reid and Dean (2006) found that average popu-lation periodicities are not nearly as variable as theperiodicities of individuals within populations. Thesefacts, taken together with our hypothetical populationanalysis, suggest that it may be possible to rank popula-tions in terms of relative average stress episode durationbased on their mean defect widths.However, it is imperative that these findings not be mis-

interpreted. We are not recommending a return to themeasurement of defect widths to assess stress episode du-ration. Instead, we are suggesting that when researcherscount perikymata within defects to compare populationsin terms of their relative average stress episode durations,they might be able to use defect width measurements toaugment their sample sizes under restrictive conditions.Researchers would first need to determine whether thereis a strong relationship between defect widths and within-defect perikymata in their entire sample. If a strong rela-tionship exists, it would then be necessary to develop sam-ple-specific regressions of within-defect perikymata ondefect width. Finally, it would be necessary to calculateconfidence intervals for perikymata counts associatedwith different defect widths to determine if populationsdiffer in terms of relative average stress episode duration.Only under these specific conditions would it be possibleto make use of defect width data.There are several additional issues that warrant con-

sideration when applying the method outlined above.First, a range of periodicities for population averagesshould be considered, given that there is some variabilityin average population periodicities (Reid and Dean,2006). Reid and Dean (2006) found mean populationperiodicities to vary between 7.8 and 9.1 days. Second,the overall sample distribution of defect widths shouldbe checked for strong multi-modal tendencies or skew-ness. Such deviations from normality would suggest thatthe mean is not an accurate reflection of central tend-ency. In such cases, we cannot advocate the use of themedian or the mode because these would violate the ba-sic assumptions of our argument. The argument we haveadvanced for the potential development of mean defect



widths as indicators of relative stress episode durationrests on: 1) the calculation of confidence intervals for themean based on the regression of total within-defect peri-kymata on defect widths, 2) the fact that for a sample ofdefects, the mean number of total within-defect periky-mata is about twice the number in the occlusal wall,and, 3) that average periodicities for different popula-tions tend to center around 8 or 9 days. These assump-tions invalidate the use of the median or the mode of asample of defect widths as indicators of the central tend-ency of stress episode duration.Finally, it is not entirely clear what mean stress epi-

sode duration indicates about the health of a population.As Guatelli-Steinberg et al. (2004) pointed out, LEHfrequencies are of limited value because similar LEH fre-quencies can mask potential differences among popula-tions in stress experience. For example, two populationsmight have a similar prevalence of LEH, but bioarch-aeologists would be mistaken to assume that the twopopulations experienced similar levels of stress if most ofthe defects in the first population consisted of a singleperikyma, while most of those in the second populationincluded 20 perikymata or more. Assessment of meanstress episode duration would make it possible to detectthis difference between the two populations.However, it would also be unwise to simply use mean

stress episode duration as the sole indicator of differen-ces in LEH between two populations. For example, it ispossible for one population to have many minor defects(consisting of one perikyma or two perikymata) and asecond population to have only a few defects consistingof 20 perikymata or more. In this case, which is themost ‘‘stressed’’ population-the one with a high LEH fre-quency consisting of brief growth disruptions or the onewith a low LEH frequency consisting of prolongedgrowth disruptions? This example illustrates thatattempts to assess the duration of stress episodes shouldbe coupled with assessments of LEH prevalence. In sucha case as described in this example, an alternativemethod might provide more useful information aboutpopulation stress. This is the method of King et al.(2005) who counted all of the perikymata within LEHdefects for each stress episode (matched across the dif-ferent teeth of an individual) in order to calculate thepercentage of enamel formation time that growth wasdisrupted. This method has the advantage of directlyreflecting for how much of the crown formation periodstressful periods were experienced. The disadvantage ofthis method, of course, is that since it relies solely oncounting perikymata within defects, there will be somedefects for which it is not possible to count perikymata.Thus, this method will not only entail sample size reduc-tions but also might underestimate the percentage oftime that enamel growth is disrupted. Finally, all ofthese methods for assessing the frequency and durationof enamel growth disruption can be biased by toothwear. In employing these methods, it will be importantto determine which areas of the tooth surface are wornand what percentage of the tooth surface the worn areasrepresent. Wear assessments such as these should betaken into account when two different populations arecompared for LEH frequency or duration.

CONCLUSIONS

Hillson and Bond (1997) explained that defect widthsshould not be used in the assessment of stress episode

duration for two reasons. First, because the entire widthof the defect incorporates both the period of disruption(occlusal wall) and recovery (cervical wall), defectswidths overestimate the actual period of disruption. Sec-ond, because perikymata spacing varies along the crownsurface, defects of similar widths can represent stressepisodes of different duration if the defects are located indifferent crown regions. The central purpose of the pres-ent study was to determine if, under certain conditions,the widths of linear enamel hypoplasias might serve asrelative indicators of stress episode duration. The impe-tus for this investigation was that previous researchers(Guatelli-Steinberg, 2003, 2004; Guatelli-Steinberg et al.,2004; Guatelli-Steinberg, 2008) found that within somedefects, it was either not possible to see perikymata orat least not possible to see them continuously (even withan SEM). However, in these same studies, defect widthswere measurable for a larger proportion of defects. Fur-thermore, Guatelli-Steinberg (2008) noted that the totalnumber of perikymata within a defect was about twicethe number of perikymata in a defect’s occlusal wall.Given that this ratio appeared to be fairly constant, itseemed that defect widths might be indicative of relativestress episode duration. Of course, this possibility couldonly be realized if defects were located in areas of thecrown with similar perikymata spacing.Based on these considerations, we gathered data on

perikymata spacing, within-defect perikymata, anddefect widths for the anterior teeth of a bioarchaeologi-cal sample from the Irene Mound, a prehistoric site(1150–1400 A.D.) near the Georgia coast. In our prelimi-nary analysis, we found that it was indeed possible tocollect more data for defect widths (40% of the defectsample) than for total within-defect perikymata (66% ofthe defect sample). We also found that the average ratioof total within-defect perikymata to occlusal wall periky-mata was 2:1, but that the ratio varied for individualdefects. In the next step of our data analysis, we foundthat the number of within-defect perikymata was closelycorrelated with defect widths when crown regions wereanalyzed separately. When data from all crown regionsand teeth were combined, the correlation was stillstrong, but slightly weaker than it was for individualcrown regions. We found evidence that the closer spac-ing of perikymata in the mid-crown region of the man-dibular canines, when combined with the wider spacingof perikymata of the other tooth types, was the primaryreason for the reduction of the correlation betweenwithin-defect perikymata and defect width in the com-bined sample.In the final step of our analysis, we tested the hypoth-

esis that defect widths can be used to statistically pre-dict the total number of perikymata within LEH defects.We found that the prediction intervals were too wide todistinguish defects of differing widths in terms of thenumber of perikymata encompassed within them. More-over, given that individual periodicities range from 6 to12 days and that individual defects do not always have aratio of total within-defect perikymata to occlusal wallperikymata of 2:1, the widths of individual defects arevirtually useless as indicators of relative stress episodeduration. However, we did find that the confidence inter-vals associated with mean defect widths were narrowenough that they might prove useful in the comparisonof defect width means for different populations. Yet evenin this case, a range of average population periodicitieswould need to be considered. Finally, to attempt to use



this method in a different sample would require thatresearchers perform all of the analytical steps employedhere in their own samples, because the relationship ofdefect-width to within-defect perikymata is likely to besample-specific, and the strength of that relationshipwill depend on how much variation in perikymata spac-ing exists in the sample.

ACKNOWLEDGMENTS

The authors like to thank the Editor-in-Chief, Associ-ate Editor, and reviewers for their helpful and insightfulcomments on this article. They are also grateful to Dr.Clark Spencer Larsen for providing sample recommenda-tions and for access to a collection of field notes from hisoriginal study of La Florida, to Dr. David Hunt of theSmithsonian’s National Museum of Natural History foraccess to the Irene collection, and the Oberlin CollegeDepartment of Geology and the Ohio State UniversityCenter for Electron Optics for allowing unfettered accessto their coating apparatus and materials.

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Under restrictive conditions, can the widths of linear enamel hypoplasias be used as relative indicators of stress episode duration?