Introduction

Dental microwear analysis is used to test hypotheses about diets and feeding behaviors of ancient vertebrates1,2. Most dental microwear studies are based on replications of tooth surfaces, generally made from clear epoxy casts taken from polyvinylsiloxane impressions (molds) of tooth surfaces. Replications are widely used for pragmatic reasons. For example, skulls and mandibles are too large to fit under a microscope. It is comparatively easy to create a series of smaller and more manageable molds and casts. Replications are inevitably imperfect and will result in changes to the surface on some scale. Several studies evaluate impression materials for replicating dental surfaces3,4,5,6 and other kinds of surfaces7,8,9,10,11,12. However, few studies examine the surface impression materials in the context of dental microwear13,14,15. While the potential for error in dental microwear analysis has been widely recognized16,17,18,19,20, most dental microwear studies make no mention of the potential for error due to the replication materials used.

Despite the inevitability of information loss, researchers abundantly find ecologically correlated patterns in microwear data derived from epoxy casts of the teeth of extant species. It is therefore obvious that replications retain valuable information, but we do not yet understand the extent of information loss, or the sensitivities of different methods, magnifications, and resolutions to those sources of error.

In this paper, we compare the dental microwear of original tooth surfaces and clear epoxy casts made from polyvinlysiloxane impression material (Fig. 1). Prior studies attempted to compare epoxy casts generated from multiple impression materials including the Colténe Whaledent President Jet Product line, a product commonly used to generate molds for dental microwear research. Based on visual inspection of SEM micrographs, Galbany et al.13,14 concluded low and mid viscosity polyvinylsiloxane impression media produced the highest fidelity replications. A comparison of seven types of silicone-based impression media to real tooth surfaces using areal surface texture analysis (ISO 25178-2 and SSFA) resulted in similar conclusions15. The positive casting media used in these studies was not investigated and included an unspecified polyurethane14, and epoxy resins, Epotek 30113 and Epotek 320LV15. In all three of these studies, Colténe Whaledent President Jet Regular Body, an impression medium frequently used in dental microwear research, was the best or among the best performing among the tested impression media.

Figure 1
figure 1

(a) Area of analysis on right upper M1s of original teeth and casts; (b) color topography map with color Z scale; (c) grayscale photosimulation of the same surface.

Full size image

Here we attempt to provide additional analysis of tooth replications made from Colténe Whaledent President Jet Regular Body impression material (for molds) and Epokwick Epoxy Resin (20-8136-128) and hardener (20-20-8138-032) (for casts) using upper first molars (M1) of Rattus norvegicus that were divided into eight treatments based on different experimental diets (Mihlbachler et al. in review). Dental casts were compared to the original tooth surfaces using both a traditional dental microwear method (TM) and dental microwear texture analysis (DMTA) using international standards relating to the analysis of 3D areal surface texture (ISO 25178-2).

At some scale, microwear features on surface replications will have softened edges and rounder peaks and valleys due to the viscosity of the molding and casting compounds. The effects that these changes have on resulting microwear data may depend on the method of microwear analysis. TM involves a human observer who counts discrete abrasion scars (microwear features) and groups them according to size and shape21,22,23,24,25,26. Most prior concerns about TM involve its proneness to high rates of observer error16,17,18. However, TM studies use low magnification and/or low digital resolutions18,22 and although individual microwear features may loose some amount of depth and clarity due to viscosity of molding and casting compounds, they will retain their overall sizes, shapes, positions and orientations and are therefore likely to be categorized in the same way on casts (as scratches and pits) as they would be on the original specimens. We hypothesize that TM data will be minimally effected on reproduced surfaces than DMTA because the categorization of abrasion scars according to size and shape (as scratches and pits) does not rely on absolute relief or angularity of surface edges.

Dental microwear texture analysis (DMTA) involves quantitative analysis of surface textures using confocal and/or focus variation microscopy27,28,29,30,31,32. DMTA largely eliminates human subjectivity but has generated new concerns about instrument inconsistency33. Additionally, DMTA more wholly measures surface texture and at finer scales than TM and error associated with replication could be a more significant problem than for traditional microwear (TM). Surface texture variables that are sensitive to the degree of relief and angularity of indentation features seem most likely to be sensitive to surface distortions due to replication. Aspects of surface texture strongly effected by surface replication could include the depths of microwear features, the sharpness of their edges, and the slopes of their walls. On the other hand, surface replications are expected to more accurately preserve other aspects of surface texture such as those related to the orientations, sizes, and overall shapes of indentation scars, and distances between indentations. We hypothesize that ISO texture parameters most strongly associated with relief and angularity will be most strongly affected while parameters more closely related to orientation, size, overall shapes of indentation scars, and distances between them will be less affected (Fig. 2).

Figure 2
figure 2

(a) Relative mean shifts (grey bars) normalized for the magnitude of the standard deviation of the original data and coefficient of variation shifts (black bars) of ISO parameters in the dental casts compared to the original surfaces. Positive values are instances where the casts generate greater values (either in the mean or standard deviation) and negative values are instances where casts of surfaces generate lesser values. The symbols to the right are predictions of whether the values produced by the casts should be greater (+), lesser (−) or unchanged (nc) from the values produced by the original surfaces. These symbols are enclosed in a square when the findings did not meet expectations, according to both the polarity (either positive or negative) and the significance of the mean shift (based on paired-T test results shown in Table 1). See Table 1 for abbreviations.

Full size image

Generally speaking, for both TM and DMTA, if cast surfaces are significantly homogenized compare to the original specimens, microwear analysis based on data collected from the casts should produce fewer significant differences between the feeding treatments than analysis of the original teeth. We refer to this type of erroneous result as type II “replication error” because they produce the same type of erroneous conclusion as a classic type II statistical error where a null hypothesis (of undifferentiated microwear) is erroneously accepted. Because information loss should lead to greater homogenization of microwear, the opposite type of error, where significantly different microwear textures emerge on the casts when none exist on the original specimens, is much less likely to occur. This type of error can be called a type I “replication error” due to its analogous relationship to type I statistical error where the null hypothesis (of undifferentiated microwear) is erroneously rejected. We hypothesize that if the surfaces on the casts are significantly homogenized, type II replication error will be the dominant type of disagreement between analysis of the casts and original teeth. Finally, if aspects of dental microwear are significantly degraded, the replications will be less successful to some degree than the original surfaces in correct post hoc classifications of the specimens to their respective feeding treatments based on discriminant function analyses.

Results

Combined group results

Descriptive statistics for the ISO and TM variables are in Supplementary Tables 1 and 2. Significant correlations (P > 0.001) between original and cast data were found in all variables, except one, ISO parameter 2D (P = 0.342). For ISO parameters, Pearson correlation coefficients (PCC) are as high as 0.919 (Sq) with an average PCC of 0.66 (Table 1). The narrow scratch (NS) data are the most highly correlated among the TM variables (PCC = 0.455) (Table 2) and the average PCC for TM variables is 0.410.

Table 1 ISO surface metrology parameters and statistical results for tooth and cast surfaces.
Full size table
Table 2 Traditional microwear variables and statistical results of tooth and cast surfaces.
Full size table

With the feeding groups combined, most (26 of 34) ISO parameters differed significantly between original and cast surfaces according to paired T-tests (Table 1). On average, the absolute value of the relative mean shift was 0.24 (Fig. 2). Sp, the most strongly effected parameter, had a relative mean shift of 0.99, indicating the mean of the cast data was shifted from the original data to a degree that is nearly equal in magnitude to the standard deviation of the original data. In most cases, the changes to the mean values associated with analysis of casts were positive with higher values. A strong mean shift was negative in only a small number of cases (Smr, Sal).

For TM variables, significant differences between original and cast surfaces were found for NS (narrow scratches) and SP (small pits) (Table 2). The relative mean shifts for NS (1.42) and SP (1.13) were the highest encountered in this study. The relative mean shifts of these variables were highly positive.

Although casting altered the absolute magnitude of the values for ISO parameters and TM variables, it did not lead to changes in the amount of variation in the data. The coefficient of variation shift was small on average (0.07) and only two ISO parameters had unusually high changes in coefficients of variation in either positive (Smr) or negative directions (Sdv). Differences in coefficients of variation among the TM series of variables were similarly small (Fig. 2).

ANOVA

ANOVAs tested for differences in microwear between the feeding treatments. ANOVA of the original data found significant differences between feeding trials in 23 out of 34 ISO parameters (Table 1) and in three of four TM variables (Table 2). ANOVA of the cast data found an identical set of significant results with the exceptions of two ISO parameters (Sha and Sku) for which the significant results in the original data were not replicated with the cast data (type II replication errors).

In Tukey’s Post hoc pairwise comparisons made of the pellet-fed control group to the remaining treatments, the original data found 18 significant pairwise differences among the ISO parameters involving feeding treatments Pde, Dcc, Dde, and Dqs (Table 3). The cast data failed to find half (9) of these differences (type II replication errors) and only produced significant results involving one treatment (Dqs). One type I replication error, a falsely significant result, was produced where ISO parameter Sdq was found to significantly differ between treatments P and Dqs in the cast data but not in the original data.

Table 3 Tukey’s test P-values for pairwise comparisons of ISO parameters for which significant differences were found between the pellet diet control group and other diet groups for tooth surfaces.
Full size table

For TM variables, Tukey’s Post hoc pairwise comparisons of the control (P) with the other diet treatments found four significant differences. The cast data failed to replicate two of these results (type II replication errors) and produced one erroneously significant difference (type replication I error) (Table 4).

Table 4 Tukey’s test P-values for pairwise comparisons of TM variables between the pellet diet control group and other diet groups for tooth surfaces.
Full size table

Discriminant Function Analysis

Comparisons of the DFA of the original and cast data reveal the abilities of casts to preserve discriminatory aspects of surface texture. All DFAs were significant (P < 0.001) (Table 5), however, for both TM and ISO, casts were 9.2 and 10.6 percentage points less successful than original data at post hoc classifications of specimens to their respective feeding treatments resulting in more group overlap in plots of the first and second discriminant functions for casts (Fig. 3a,b). The total evidence DFA performed better overall with the highest rates of correct post hoc classification, and the cast data produced a rate of correct rate of post hoc classification (68.9%) that is only 3.4 percentage points less than that of the original data (72.3%) (Table 5, Fig. 3c).

Table 5 Results of discriminant function analyses on data from original tooth surfaces and cast surfaces.
Full size table
Figure 3
figure 3

Outlines of areas occupied by the eight feeding treatments on discriminant functions one (x-axis) and two (y-axis) of original tooth surfaces and casts of ISO data, TM data, and total evidence. See Table 3 for diet group abbreviations.

Full size image

Canonical loadings (Supplementary Tables S8 and S9) are the correlations of the individual variables with the discriminant function. Ideally, perfect replications would produce a canonical structure identical to the original data, however the resulting canonical structures of the original and cast data are different. There is only a low degree of correlation between the canonical loadings of the first DFs of cast and original data (PCC = −0.443; P = 0.005) and there is no significant correlation between the second DFs (PCC = 0.034; P = 0.839). The canonical structures of the two datasets appear to be inverted in the sense that the first DF of the cast data is much more highly correlated to the second DF of the original data (PCC = 0.922: P < 0.001), and vice versa (PCC = 0.774; P < 0.001), therefore, similar canonical axes were produced by these datasets, albeit in different orders of significance (Supplementary Fig. S1).

Although the above results indicate the canonical structure has been distorted, the discriminatory power of each ISO and TM variable was largely preserved. Calculations of the total discriminatory power of each ISO parameter and TM variable produced correlated results between original surfaces and casts (Pearson Correlation coefficient = 0.922; P < 0.001) (Fig. 4). In both sets of analyses, the hybrid parameters (Sdq and Sdr) had the highest overall discriminatory power among the ISO parameters. Among the TM variables, narrow scratches (NS) had the highest discriminatory power in the original data, but the influence of this variable was reduced in the cast data.

Figure 4
figure 4

Total discriminatory power of ISO parameters and TM variables as a function of the scaled sums of the canonical loadings of all 7 discriminant functions based on the total evidence analysis. See Tables 1 and 2 for abbreviations.

Full size image

Discussion

Research questions that are concerned with understanding contact mechanics and wear may require accurate measurement of surface texture. The casts produced data that were significantly different from the original surfaces. Goodall et al.15 found very few significant differences between tooth surfaces and replicated surfaces based on the same Colténe Whaledent President Jet Regular Body impression material. The discrepancy between our results (many differences) and those of Goodall et al.15 (few differences) may be related to the specific type of surface textures studied, poor quality control of the impression material itself, different epoxy resins, instrumentation differences, or differences in magnification. The Goodall et al.15 study was performed at a lower magnification (100x) than our study (150x). It is likely that magnification had some effect on these different results if the scale of the imperfections in the replications was similar in the two studies. Some researchers analyze the negative impressions34,35, which, when digitally scanned, can readily be inverted into the positive surface. Analysis of molds removes one step from the reproduction process and future tests on the efficacy of molds will review where and to what degree information is lost in the replication process.

Most dental microwear analyses test hypotheses by associating different microwear patterns with different diets or other aspects of feeding ecology. This objective doesn’t require accurate replication of true surface textures as long as unique aspects of texture are maintained in the replications. In this study, ANOVAs of both original and cast data found similar sets of ISO parameters and identical sets of TM variables for which significant differences between the diets occurred (Tables 1 and 2). However, casts performed poorly in the posthoc pairwise comparisons of the control (P) to the remaining feeding groups and found fewer significant pairwise differences than the original surfaces (Tables 3 and 4). We correctly hypothesized that cast data would produce more instances of type II replication error where microwear differences found on the original surfaces would not be found on the casts. Therefore, there is evidence for considerable information loss in the casts.

We surveyed published microwear studies that use ISO 25178-2 (Table 1). The five parameters found to most frequently produce significant findings in the literature survey (Sq, Sa, Vmc, Vvc and Vvv) also performed very well in our comparison of original surfaces and replications. The means and standard deviations of these five parameters were not strongly altered in the cast data. Significant differences in these parameters were found in both the original and cast data and they produced relatively high discriminatory power (Fig. 5). The two hybrid parameters, Sdq and Sdr, are examples of parameters that were not accurately replicated in the casts but nonetheless were the most discriminating ISO parameters in both the casts and original dental surfaces (Fig. 5).

Figure 5
figure 5

Discriminatory power (x-axis) versus relative mean shift (Y axis) of ISO parameters and TM variables. Variables appearing in the lower right quadrant have both ideal characteristics: low sensitivity to replication and high discriminatory power. See Tables 1 and 2 for abbreviations.

Full size image

Although the retention of discriminatory power in the replications is promising for dental microwear research, other inexplicable results emerged from the comparison of original and cast data. Figure 2 includes predictions for change for each parameter in the average value across the total sample for each variable based on definitions of ISO parameters36 and on the overall hypothesis that microwear features on cast surfaces should have lower relief, less angularity, and as a result, less clarity than the original features. Those features related to relief and angularity we predicted to undergo decreases in average values. For other parameters we predicted no (or minimal) change. Perplexingly the casts produced a large number of unusual results primarily in the form of higher values of surface texture parameters and TM variables.

It is possible that additional variables, such as the differential reflectivity of enamel and the casting material (clear epoxy) were additional variables that influenced the results11. In light microscopy, clear epoxy casts are preferable to the original teeth due to their greater reflectance. Sputter coating may enhance reflectance. We suggest sputter coating both original specimens and epoxy casts would be a means of standardizing the reflectance of the surfaces and may offer a more controlled means of measuring the texture differences between original dental specimens and surface casts.

The hypothesis that TM is robust to replication is not supported. TM analysis occurs at the scale of individual microwear features. While the faintest of microwear features might be lost in the casts, changes to the depths and sharpness of microwear features have little bearing on their classification as pits or scratches. However, TM was found to be scale sensitive to replication. While large microwear features, WS (wide scratches) and LP (large pits), were not significantly affected, narrow scratches and small pits were the most altered variables in the entire study. Inexplicably, significantly higher numbers of small pits and narrow scratches were identified in the casts. The proportionality of pits and scratches was also distorted. The average ratio of total numbers of pits to total numbers of scratches in the original data (3.59) is greater than that of the cast data (3.08). Bivariate plots of pits and scratches from the cast data produce a similar, albeit displaced and somewhat distorted scatter pattern when compared to the original (Supplementary Fig. S2). It is noteworthy that NS had the highest discriminatory value in the total evidence DFA on original teeth, and this variable clearly has importance in dental microwear research for discriminating major dietary categories37. However, NS was also the most sensitive to replication, as the discriminatory power of NS was the most strongly diminished variable in the entire study (Fig. 5).

The results reported here demonstrate that the accurate characterization of specific microwear textures on worn tooth surfaces is significantly diminished on casts manufactured from molding and casting materials commonly used by dental microwear researchers. However, if one’s research objective is primarily to discriminate strongly differentiated textures rather than accurate measurement of texture, surface replications seem to be reasonably efficacious methodology. However, the weaker nature of the cast results suggests that subtle differences in microwear texture may be lost in replicates. Although the data produced by the replications seemed adequate for discriminating different microwear textures, their performance was most similar to the original surfaces when ISO and TM data were combined. Microwear researchers tend to favor one method over another, but they may generate better results by combining them.

Materials and Methods

Experimental animals and specimen preparation

All methods were performed in accordance with the relevant guidelines and regulations of the New York Institute of Technology Institutional Animal Care and Use Committee following the approved protocol 2014-BB-01. This study utilized the teeth of 138 rats fed experimental diets (Mihlbachler et al., in review). Controlled feeding experiments were conducted at NYIT-College of Osteopathic Medicine. The experimental rats were divided into eight varying diet categories. Rats were first designated to either a chow (pellet form) diet or bacon dough (soft form) diet. Within each food type, rat diets were further subdivided into four groups: (1) no added abrasives, (2) calcium carbonate, (3) diatomaceous earth, and (4) quartz sand. Rats were exposed to these special diets for 15 days and were then sacrificed. The control treatment was one in which rats were continued on the same pellet diet with no added abrasives that they had been fed prior to the experiment.

Replication methods

Following extraction and cleaning (Mihlbachler et al. review), Colténe Whaledent President Jet Regular Body was applied to the occlusal surfaces of the right upper molar rows using the applicator gun. The impression materials were allowed a minimum time of 45 minutes to harden. The molds were trimmed with a #22 scalpel blade to remove extraneous material. For purposes of pouring liquid epoxy with the molded dental surfaces facing up, circular retaining walls were hand sculpted around each trimmed mold with Colténe Lab-Putty, a product designed specifically to bond with the impression material. Epokwick Epoxy Resin (20-8136-128) and hardener (20-20-8138-032) were mixed (5:1 by weight) according to manufacturer instructions, degassed in a vacuum chamber at −25 < −30 inHg for five minutes, and poured over the molds. After pouring, each specimen was centrifuged for 1 minute with a hand-cranked centrifuge and left to harden without disturbance for 48 hours. The epoxy casts and molds were left together, placed in plastic bags and separated as needed for microscopy.

Confocal microscopy

TM and DMTA are based on identical sets of 3D surface scans acquired from a Sensofar Plu NEOX optical profiler. We scanned the mesial enamel ridge of each specimen’s second maxillary molar (M2) at 150x (Fig. 1a). All scans were done with the same orientation with respect to the mesiodistal axis of the tooth. All scans were done with white light with a 150x objective (NA = 0.90, WD = 1.50 mm) with step sizes of 0.10 µm. Each initial scan, prior to extraction of a smaller area, was 1360 × 1024 pixels (each pixel is 0.09 µm). ISO parameters were derived with the default threshold settings in place (SMr c = 1 µm under the highest peak; Smc p = 10%; Sxp p = 50% and q = 97.5%; Sal s = 0.2; Str s = 0.2; Std reference angle is 0°; Vm p = 10%; Vv p = 10%; Vmp p = 10% and q = 80%; Vmc p = 10% and q = 80%; Vvc p = 10% and q = 80%; Vvv p = 80%; Spd pruning = 5%; Spc pruning = 5%; S10z pruning = 5%; S5p pruning = 5%; S5v pruning = 5%; Sda pruning = 5%; Sha pruning = 5%; Sdv pruning = 5%; Shv pruning = 5%).

The same areas on both the casts and original teeth were identified and scanned using individual microwear features that could be identified on both teeth and casts as landmarks to frame the same areas (Fig. 1b). Specimens were excluded when the surfaces had visible contaminants after several cleaning attempts. Each scan resulted in a “.plu” file that was loaded into SensoMAP Premium software (version 7.2.7368). An area of 80 × 100 micrometers was extracted, then leveled using the least square planes method. Leveling was accomplished by subtraction to a least squares plane. The curvatures of the surfaces were removed to reduce the effect of the gross contours of the tooth on the data, using a polynomial of degree 3. A 3D view was produced so that it could be manipulated and viewed in various orientations to visualize problem areas of abnormal peaks and valleys. These problem areas were retouched using the “retouch surface points” operator. Missing data points were filled in by a smooth shape calculated from the nearest neighbors. The parameters table and texture direction studies were then obtained (Table 1). Further details on handling of specimens, positioning, scanning protocols, and processing protocols are provided elsewhere.

TM analysis

The data files described above were converted to 268 × 214 pixel images with a pixel density of 7.18 pixels per μm2 (Fig. 1c) in Adobe Photoshop CC using the Bicubic Sharper interpolation method. The grayscale images derived from the confocal data superficially resemble photomicrographs of clear epoxy casts taken under a light microscope, and these images were analyzed with methods derived from light microscopy methods18,20. The confocal scans were converted to 1338 pixel x 1070 pixel images covering the same tooth surface areas of 100 μm × 80 μm from which the ISO data were derived. The digital density of the images was reduced by resampling the images in Adobe Photoshop, resulting in 268 × 214 pixels images with a pixel density of 7.18 pixels per μm2. Reducing the digital resolution of high-resolution microwear photomicrographs reduces the potential for observer error without significantly diminishing the differences in dental microwear patterns between samples18.

Microwear features were assigned to one of four primary categories as defined in Table 3. In addition to these four primary variables, two secondary variables were calculated: TS (total number of scratches) and TP (total number of pits). Microwear features smaller than the above criteria were not counted because observer repeatability for small poorly resolved microwear features was found to be low with similar methods (Mihlbachler and Beatty 2012).

Microwear features were assigned to four categories as defined in Table 2. Smaller microwear features were excluded. Observer blindness was achieved by randomly ordering and assigning arbitrary numbers to the image files. Microwear features were traced directly on the images by superimposing standardized circles (for pits) and lines (for scratches) in Adobe Illustrator software. The images were examined in random order three times by a single observer. The superimosed tracings were saved after each pass, preserving the observer’s interpretation. Multiple passes allowed the images to be more comprehensively sampled for discrete microwear features by eliminating the diminishing effects of observer fatigue. During each pass, additional features that had been missed in earlier passes were identified. By the third pass, very few additional microwear features were recognized (one or two per image) so additional passes were not made.

Statistical methods

All statistical analyses were performed on SPSS ver. 24. Of the 138 rat specimens, some were excluded from analysis because we could not get good scans for a variety of reasons (e.g. cracked and damaged enamel, specimen damaged during molding). Some additional specimens were rejected from TM because it was difficult for the observer to differentiate large overlapping wear features. Statistical analyses were run only on specimens for which complete data could be obtained for the group of variables in question (ISO or TM). Analyses of DMTA data included 123 specimens. Analyses of TM data included 120 specimens. Discriminant function analyses included only specimens for which complete data had been recovered. Keeping in mind the large number of statistical tests reported above, we caution that the distributions of significant results are more meaningful than the results of individual tests. Here, we report the results of parametric analyses on the raw data. Some of the data had distributions that deviated significantly from normality (Shapiro-Wilk P ≤ 0.05). Therefore, we ran a parallel series of analyses using rank-transformed data and other nonparametric methods to ensure that erroneous conclusions were not made due to assumption violations. These analyses (Supplementary text and Tables S3–S7) were not identical but similar enough to warrant the same conclusions.

Combined analyses

With the eight feeding treatments combined, we ran Pearson Correlation coefficients (PCC) and paired-T tests to compare the original and cast data (Table 1 for ISO, Table 2 for TM).

To summarize the degree to which the mean values of the data were shifted in the replicated surfaces, a ratio, termed the “relative mean shift” (RMS) (Fig. 2) was calculated:

$$RMS=\frac{Mr-Mo}{So}$$

This ratio normalizes the magnitude of the individual parameters according to the standard deviation of the original data.

To summarize the degree to which surface replication homogenized the data, the difference between the coefficient of variation of the cast data and the coefficient of variation of the original data was calculated. This value was termed the “coefficient of variation shift” (CVS) (Fig. 2):

$$CVS=\frac{Sr}{Mr}-\frac{So}{Mo}$$

In the above equations, Mr is the mean value of the replicated surface data, Mo the mean value of the original data, So the standard deviation of the original data, and Sr the standard deviation of the replicated surface data.

Replication of ANOVA results

ANOVAs were run on original data and then again on the cast data. ANOVAS test for differences between the microwear of the eight feeding treatments (Table 3 for ISO, Table 4 for TM). ANOVA is robust to violations of the assumption of normal distribution38,39. Levene’s test was used to test for unequal variances within each dataset. For the majority of data, homogeneity of variance between the feeding treatments could not be falsified. Therefore, Tukey’s Post Hoc tests determined which of the numerous two-way comparisons of the analyses of raw data significantly differ. In cases where ISO parameters were found to have unequal variances between the feeding treatments (Sdr, Spd, Spc) we used Dunnett’s T3, which is a more appropriate test for unequal variances. We report only the two-way comparisons between the control diet (pellets) with the other diet treatments (Table 3 for ISO, Table 4 for TM).

Replication of discriminant function analysis results

Two sets of discriminant function analysis (DFAs) were run on the original and cast data to determine the success rate of cast data at predicting diet compared to the original data. DFAs were run using the ISO data alone and the TM data alone (Table). Thirdly, a “total evidence” analysis in which ISO and TM data were combined was run. To examine similarities in the canonical structure of original specimens and casts, we calculated Pearson correlation coefficients on the total evidence canonical loadings of the first and second discriminant function axes.

A final calculation summarized the total influence each ISO parameter and TM variable in the discriminant function analyses. The following calculation considers the diminishing amount of variance explained by each subsequent DF, where c = canonical loading, v equals the percentage of variance explained by each DF, and N is the number of discriminant functions (Supplementary Tables S8 and S9).

$$\sum _{i=1}^{N}{c}^{i}{v}^{i}$$