Electronic Supplementary...

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ELECTRONIC SUPPLEMENTARY INFORMATION ADDITIONAL DETAILS OF METHODS Studied specimens of Haplochromis purple-yellow (Hpy) and Neochromis gigas (Ng) were all males of similar standard length, and were selected blind with respect to tooth wear. Dentaries and premaxillae were dissected from preserved individuals, skin and muscle were carefully peeled away using tweezers, and jaws were then boiled in individual vials of distilled water for 4 hours to further clean tooth surfaces. Care was taken to avoid contact between instruments and teeth, but in order to further minimise the possibility of inadvertently recording preparation artefacts as microwear, the tooth closest to the symphysis, where right dentary and premaxilla were physically separated from their neighbours, was not analysed.

For scanning electron micrography and data acquisition jaws were sputter coated with gold (2 minutes). Both 2D and 3D data were collected from original gold-coated tooth surfaces. SEM images were acquired using a Jeol 6400 Scanning Electron microscope (Secondary electron images). Some teeth were subsequently reimaged using a Hitachi S-3600N. In 2D microwear analysis, most statistical testing was based on the summary outputs from Microware for each tooth (feature mean length, feature density, preferred orientation, and R (providing a measure of angular dispersion [1])). For a few tests, how microwear varies within individuals for example, raw data were extracted from Microware 4.02 output as x/y coordinates and processed by using simple trigonometric functions in MS Excel to derive the length, width, and long-axis orientation for every feature in a sample site.

The Alicona Infinite Focus microscope G4b has a CCD of 1624 x 1232 pixels. In theory, for a field of view of 145 µm, this equates to a lateral sampling distance of 0.09 µm, but the limits imposed by the wavelength of white light mean that lateral optical resolution is actually about 0.35 - 0.4 µm. For all samples, vertical resolution was set at 20 nm, and the lateral resolution factor for the IFM was set at 0.3. Exposure and contrast (gamma) settings were adjusted to maximise data quality in terms of measurement repeatability (this is estimated automatically by the IFM software during data capture) for each sample. Adjusting exposure and contrast do not affect the values for 3D measurements. All data were acquired using polarized co-axial incident illumination.

All 3D data were processed using the Alicona IFM software (version 2.1.2) as follows: 1, data editing (to delete dirt and dust particles from the surface); 2, data levelling and filtering (all points levelling; application of Gaussian wavelength filter to remove long wavelength features of the tooth surface (gross tooth form)); 3, generation of standard parameters from analysis of the resulting roughness surface. Most roughness parameters conform to the current draft ISO 25178-2 standards [2], and include: Height parameters (quantifying the distribution of height values along the z-axis), Spatial parameters (quantifying direction and spatial periodicity of the surface), Hybrid parameters (combining the information present on the x, y and z axes of the surface, quantifying aspects of the spatial shape of the data), and Functional parameters related to measures of volumes, such as peak material, calculated from the areal bearing ratio curve. All parameters analysed and brief definitions are provided in Table S1.

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Table S1. Short definitions and categorization of 3D areal surface texture parameters

parameter definition Sa Average height of surface height Sq Root-Mean-Square height of surface height Sp Maximum peak height of surface height Sv Maximum valley depth of surface height Sz Maximum height of surface height S10z Ten point height of surface segmentation Ssk Skewness of height distribution of surface height Sku Kurtosis of height distribution of surface height Sdq Root mean square gradient of the surface hybrid Sdr Developed interfacial area ratio hybrid Sk Core roughness depth, Height of the core material functional Spk Mean height of the peaks above the core material functional Svk Mean depth of the valleys below the core material functional Smr1 Surface bearing area ratio (the proportion of the surface which consists of peaks above the

core material) functional

Smr2 Surface bearing area ratio (the proportion of the surface which would carry the load) functional Vmp Material volume of the peaks of the surface (ml/m²) functional Vmc Material volume of the core of the surface (ml/m²) functional Vvc Void volume of the core of the surface (ml/m²) functional Vvv Void volume of the valleys of the surface (ml/m²) functional Vvc/Vmc Ratio of Vvc parameter to Vmc parameter functional Sal Auto correlation length. Horizontal distance of the auto correlation function (ACF) which

has the fastest decay to the value 0.2. Large value: surface dominated by low frequencies. Small value: surface dominated by high frequencies.

spatial

Str Texture aspect ratio (values range 0-1). Ratio from the distance with the fastest to the distance with the slowest decay of the ACF to the value. 0.2-0.3: surface has a strong directional structure. > 0.5: surface has rather uniform texture.

spatial

Std Texture direction (°). Derived from the maximum of the angular power spectrum. Std = 90° means a dominant lay parallel to the y-axis.

spatial

Stdi Texture direction index (values range 0-1). Average value of the angular power spectrum divided by the value of the dominating direction. Smaller values correspond to stronger directional structures.

spatial

In order to test comparability and relative discriminatory power of the different approaches,

3D data from the cichlid oral tooth surfaces were acquired from the same dentary teeth as had been subjected to 2D analysis. The only exception to this was specimen HpyR118, the dentary tooth of which was accidentally destroyed subsequent to SEM analysis. 3D data from the premaxilla of this specimen were substituted (at 100µm scale sampling 2D analysis results indicate that scratch length on dentary and premaxilla teeth in this specimen do not differ). It became apparent during acquisition of 3D data, as a result of our increasing experience, that two of the teeth in the original data set were less worn that others because they were only recently erupted at the time the fish were caught. In order to test whether a modified sampling strategy was more effective in dietary discrimination, we conducted a second analysis in which data from the 3rd tooth from the symphysis was substituted in cases where the second tooth was only recently erupted.

PCA and LDA of oral teeth were based on data acquired from dentaries only. For LPJ Spearman Rank correlation was used to test relationships between fish size, diet and microtexture (in the form of results from PCA and LDA). For 2D microwear data, where the results of Shapiro-Wilks tests indicated that variables were non-normally distributed (p > 0.05), non parametric (Wilcoxon/Kruskal Wallis tests, 2-sample Z tests), and Chi2 tests were employed. Tests for differences within individual fish (e.g. dentary-premaxilla comparisons) and within individual teeth (300 µm – 100 µm sample area comparisons) employed matched pairs t-tests. For 2D orientation

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data, Watson-Williams multisample tests were used: samples where the null hypothesis that the orientation has a uniform (non-preferential) distribution could not be rejected (Rayleigh test, p = 0.05) were excluded from such analyses (because where distributions are uniform mean orientation data and tests based on non-uniform distributions are meaningless). These tests of orientation data were carried out using Oriana 2.02e [3]. All other statistical analysis was carried out using JMP 8.

For 3D microtextural datasets, a few variables were found to deviate from normality, but for most of these we were unable to reject the null hypothesis of normality (Shapiro-Wilks) for log-transformed data, and the latter were used for analysis. Where homogeneity of variance tests revealed evidence of unequal variances, Welch ANOVA was used.

Analysis of correlations between 2D and 3D data for oral teeth was based on the 100 µm data set (the 300 µm dataset is unsuitable for analysis because of the differences in scale between the 3D and 2D data). Analysis of correlations of the LPJ teeth was more difficult because they are not amenable to collection of 2D data. In this case we tested for rank correlations between 3D parameters and a qualitative scale of relative roughness generated by seven people independently ranking 2D images of the surfaces from which 3D data were acquired, from smoothest to roughest. Although no two individuals produced the same ranking, pairwise correlations between rankings were all significant (rs range from 0.67 to 0.98, p < 0.05). The mode of rank position for each sample was taken as a proxy for qualitative roughness and used for testing for correlation with quantitative roughness parameters. This ranking must be used with caution: although there was a high degree of agreement in recognizing the 2 or 3 specimens corresponding to the qualitatively most and least worn, there is less agreement about the specimens between the extremes. Indeed, if the two least and the three most worn specimens are excluded from analysis only 3 of 21 pairwise correlations are significant (specimens selected as most/least worn based on mode of rank position; 3 most worn specimens had equal rank).

For the LPJ data, where longer wavelength features of the surface might reflect tooth surface wear rather than aspects of original tooth form and might thus be informative with respect to diet, analysis was carried out on both levelled non-filtered 3D data, and levelled and Gaussian-filtered data. ANOVA, PCA and LDA was applied to four different datasets: one comprising data from only the most worn tooth from each individual (based on visual inspection; non-filtered and filtered), one comprising data from only the second most worn tooth of each (non-filtered and filtered), one comprising the average of the two teeth from each individual (non-filtered and filtered), and one comprising all the teeth sampled (non-filtered and filtered).

The significance of linear discriminant analyses reported in the main text was assessed using Wilks’ Lamda, and robustness was tested through cross validation using a jack knife approach: for both the oral and LPJ 3D datasets one specimen was randomly deleted from the dataset, the LDA was rerun, and the resulting discriminant function was then applied to the complete dataset with the deleted specimen reinstated. Three iterations of this process for each dataset failed to produce a LDA that resulted in specimens being misassigned, indicating that the LDAs are robust.

Stomach and gut content data were also collected for most of the fish (see ESM Table S2).

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Table S2: Stomach content data for Haplochromis p-y, Neochromis gigas and Astatoreochromis alluaudi. Stomach contents data for Hpy and NgR was collected by OS. All stomachs except NgR104 were full to very full; for the empty stomach, gut contents were used for analysis. For Haplochromis and Neochromis gut contents were quantified in terms of estimated volume percentages. Dietary data for lab raised A. alluaudi are based on what they were fed. For five of the six wild fish gut contents data were recorded in the field (by Hoogerhoud). Data for the sixth fish was unavailable to us. Melanoides and Bellamya are gastropods; Spaerium and Caeltura are bivalves. Taxon specimen museum

number ecology Stomach contents

Neochromis gigas NgR92 RMNH.PISC 37855

Rock scraper

5% cladophora, 95% insect larvae


Rock scraper

90% cladophora, 10% insect larvae


Rock scraper

100% cladophora

Haplochromis purple-yellow

HpyR118 RMNH.PISC 37858

Rock scraper

5% cladophora, 30% macrophyte chunks, 32.5% filamentous blue greens, 32.5% diatoms



Rock scraper

14.3% macrophyte chunks, 31% filamentous blue greens, 38.1% brown detritus, 9.5% zooplankton



Rock scraper

31.6% cladophora, 10.5% insect larvae, 21.1% macrophyte chunks, 6.3% filamentous blue greens, 12.6% brown detritus, 12.6% diatoms, 5.3% sand


HpyV01 RMNH.PISC 37861

Veg. scraper 100% insect larvae



Veg. scraper 100% macrophyte fragments



Veg. scraper 5% insect larvae, 75% macrophyte fragments, 10% filamentous blue greens, 10% diatoms

Astatoreochromis alluaudi

37864 RMNH.PISC 37864

Lab-raised minced heart and liver, with vitamins and Tetramin flakes


37865 RMNH.PISC 37865



37866 RMNH.PISC 37866



37867 RMNH.PISC 37867

Smaller-wild

9.4 mg Bellamya; 4.46 mg Melanoides; 1 mg ostracods


37868 RMNH.PISC 37868

Smaller-wild

Bellamya, Melanoides, ostracods, cladocera, chironomids present


37869 RMNH.PISC 37869

Smaller-wild

7.1 mg Bellamya; 3.4 mg Melanoides; ostracods, cladocera, chironomids present


37870 RMNH.PISC 37870

Size-equiv. wild

14.5 mg Bellamya; 49 mg Melanoides; 2.6 mg Spaerium; 5 mg Caeltura; 2.2 mg ostracods


37871 RMNH.PISC 37871

Size-equiv. wild

3.18 Bellamya; 21.4 mg Melanoides; 12.86 mg Spaerium; 6.5 mg ostracods


37872 RMNH.PISC 37872

Size-equiv. wild

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ADDITIONAL DETAILS OF ANALYSIS OF ORAL TEETH Table S3: Results of statistical hypothesis testing to determine whether 2D microwear sampled on dentary and premaxilla differs (300µm and 100µm fields of view; matched pairs t-test, dentary and premaxilla pair within fish matched. Orientation analysis based on Watson-Williams multisample test). 100µm, pairwise dentary - premaxilla comparisons Reject? t p Feature length does not differ, dent-pmax no 1.95 0.09 Feature R does not differ, dent-pmax No 0.34 0.74 Feature density does not differ, dent-pmax No -0.47 0.65 F df p Feature orientation does not differ, dent-pmax No 2.00 1,13 0.18 300µm, pairwise dentary - premaxilla comparisons Reject? t p Feature length does not differ, dent-pmax Yes -3.23 8 0.012 Feature R does not differ, dent-pmax No 1.62 8 0.14 Feature density does not differ, dent-pmax No -0.11 8 0.91 F df p Feature orientation does not differ, dent-pmax No 4.31 1,13 0.06 Table S4. Results of statistical hypothesis testing to determine whether microwear sampled at different scales differs within fish (300 µm and 100 µm fields of view; matched pairs t-test. Orientation analysis based on Watson-Williams multisample test). Dentary, pairwise 100-300 comparisons Reject? t p Feature length does not differ, 100-300 Yes 8.83 <0.0001 Feature R does not differ, 100-300 No 0.97 0.36 Feature density does not differ, 100-300 Yes -5.57 0.0005 F df p Feature orientation does not differ, 100-300 Yes 5.68 1,13 0.03

As reported in the main text, exploratory ANOVA of the first 3D data set from the oral teeth (same teeth sampled as 2D analysis) revealed significant differences between Sa, Sk, Spk, Vmp, Vmc, Vvc, and Sal. A Tukey HSD procedure revealed significant pairwise differences for most of these variables between NgR and both Hpy populations. ANOVA of the data set with two substitutions of 3rd teeth from the symphysis revealed similar results: significant differences between Sa, Sk, Spk, Vmp, Vmc, Vvc, and Sal (Welch ANOVA). Tukey pairwise comparisons however, were a little different: most variables (Sa, Sk, Vmc, Vvc) differed between HpyV and NgR, indicating that these should be assigned to different groups, with HpyR not differing significantly from either of the other populations. Spk and Vmp differentiated NgR from both Hpy populations. Analysis of correlations between 2D data and 3D parameters revealed that in oral teeth there are no significant correlations between any 3D parameters and feature length, feature width, feature mean orientation, or R. Fifteen parameters are correlated with density, most of which are height and functional parameters (Sa, r = 0.79, p = 0.01; Sq, r = 0.87, p = 0.002; Sp (log), r = 0.75, p = 0.02; Sv (log), r = 0.90, p = 0.001; Sz (log), r = 0.88, p = 0.002; S10z (log), r = 0.73, p = 0.02; Sdr, r = 0.81, p = 0.01; Sk, r = 0.73, p = 0.02; Spk, r = 0.68, p = 0.04; Svk, r = 0.92, p = 0.00; Vmp, r = 0.67, p = 0.05; Vmc, r = 0.75, p = 0.02; Vvc, r = 0.71, p = 0.03; Vvv, r = 0.92, p = <0.001; Sal, r =, 0.71, p = 0.03). In all cases the correlations are positive: as feature density increases, so do the roughness parameters. For the LPJ teeth, qualitative ranking of surface roughness was correlated with twelve quantitative roughness parameters (Sa, rs = 0.7227, p = 0.0278; Sq, rs = 0.6975, p = 0.0367; Sp , rs = 0.9412, p = 0.0002; Sz, rs = 0.8404, p = 0.0046; S10z, rs = 0.874, p = 0.0021; Sdq, rs = 0.832, p = 0.0054; Sdr, rs = 0.832, p = 0.0054; Sk, rs = 0.8152, p = 0.0074; Spk, rs = 0.7899, p = 0.0113; Vmp , rs = 0.7899, p = 0.0113; Vmc, rs = 0.7227, p = 0.0278; Vvc, rs = 0.8152, p = 0.0074). These are a mixture of functional, height and hybrid parameters (see Table S1). All correlations are positive: as qualitative roughness increases, so do the quantitative roughness parameters. Figure S2 provides a

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degree of visualization, showing renderings of unfiltered and filtered 3D surface data for the qualitatively most worn (specimen 37872) and least worn of the LPJ teeth (specimen 37864. It is worth noting that unlike the results of quantitative analysis, the qualitative ranking of roughness is not correlated with diet (rs = 0.49, p = 0.27).

Principal components analysis of all 2D variables is discussed in the main text. For the 100µm data, reducing the number of variables analysed does not result in ordinations with axes that correspond closely to ecological differences between samples, but they are informative nonetheless. PCA based on ‘standard’ variables of feature mean length, preferred orientation, R and feature density, for example, gives the following results. PC1 accounts for 39% of the variance, and reflects positive loadings of feature mean length and R, with negative loadings from preferred orientation and feature density. There is no separation of populations long this axis. PC2 accounts for 31% of the variance, feature mean length and preferrred orientation loading positively, R and feature density loading negatively. Along this axis, HpyR are separated from the other populations. PC3 also accounts for a significant amount of variance (20%) and reflects positive loading of feature mean length and feature density, and negative loading of preferred orientation and R. N. gigas and HpyV are separated along this axis. Plotting PC2 against PC3 thus results in non-overlapping distribution of the three populations. For the 300µm 2D data, analyses based on ‘standard’ variables of feature mean length, preferred orientation, R and feature density give similar results to analyses based on all 2D variables. Preferred orientation and feature density load positively on PC1, with feature mean length loading negatively. This axis accounts for 54% of the variance. PC2 (27% of variance) reflects strong positive loading of R (0.92) and negative loading of feature mean length. Compared to the seven variable analysis, separation of populations in PCA space is reduced, and separation of HpyV from rock scrapers along axis two is incomplete, with specimen NgR92 plotting among HpyV specimens. This highlights a problem with 2D scoring approaches to microwear: where surfaces are very rough, individual features are more difficult to recognise and score, and the resulting microwear data may be misleading.

Stepwise LDA of the 2D 100µm data reveals that 7 variables are required for correct assignment of all specimens to their populations, but that correct assignment of all but one specimen (NgR101, misclassified as HpyV) is achieved with only 4 variables (feature mean length, SD of length, preferred orientation, SD of orientation). Omitting orientation data, values for which, as noted above, are not meaningful for some populations, 6 variables are required to assign all specimens to their correct groups (feature mean length, SD of length, feature mean width, SD of width, R and feature density). Canonical axis 1 explains 93% of variance, axis 2 the remaining 7%. The significance of the LDA is doubtful (Wilks' Lambda, 0.007, p = 0.42), and although the probabilities of correct assignment to groups for all teeth except one are are either 99% or 100%, the least worn NgR specimen (NgR101) has a 49.9% probability of being assigned to the HpyV population. Scores for canonical axis 1 are correlated with feature length (r = -0.80, p = 0.01) and the SD of length (r = -0.74, p = 0.02). No other correlations between variables and canonical scores are significant. Analysis including 5 of these variables (all but feature mean width) correctly assigns all specimens except NgR101, but the significance of the LDA is again doubtful (Wilks’ Lamda 0.015, p = 0.16).

Analysis of the 300µm data produces a more informative LDA. Five variables are required to assign all specimens to their correct groups (SD of length, feature mean width, SD of width, preferred orientation, R). Canonical axis 1 explains 99.7% of variance, axis 2 the remaining 0.3%. Wilks' Lambda is significant (0.00004, p = 0.0006) and the probability of correct assignment to populations is 100% for all specimens. Scores for canonical axis 1 are correlated with feature width SD (r = -0.7051, p = 0.034); scores for canonical axis 2 are correlated with feature orientation (r = 0.83, p = 0.005). No other correlations between variables and canonical scores are significant. Three variables (feature mean width, preferred orientation, R) correctly assign all but one specimen (HpyV01 misclassified as HpyR; Wilks’ Lamda = 0.04, p = 0.02. If orientation is excluded, correct assignment of all specimens cannot be achieved.

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Results of PCA analysis of the 3D dataset where recently erupted teeth were substituted with data from the 3rd tooth from the symphysis (specimens HpyR163 and HpyR205) were as follows: Results from analysis of the six parameters that differ significantly produces similar results to the non-substituted dataset, but with better separation of populations. All six parameters load positively and approximately equally (eigenvectors 0.38 – 0.42) on PC1, which accounts for 93% of the variance. NgR specimens are clearly separated from Hpy populations on this axis. PC2 (4% variance) reflects heavy positive loading of Vvv (0.82), and negative loading of Spk and Vmp (0.36, 0.4). With the exception of HpyV01, HpyV and HpyR are separated along this axis (HpyV positive values, HpyR negative), and the three populations have non-overlapping distributions in the space defined by PC1 and PC2. Analysis of all 3D roughness parameters produces very similar results to the non-substituted dataset. The same parameters load on PC1 (59% of variance), with similar eigenvectors; NgR and Hpy populations are separated along this axis, with the exception of one specimen (HpyV mp03). PC2 (23% of variance) reflects positive loadings (2.3 – 3.2) of Smr2, Sal and Stdi, and Vvc (functional and spatial parameters), and negative loadings (-2.4 - -3.5) of Ssk, Sku, Sp, and Sdq (height and hybrid parameters). This axis provides good ecological separation of populations, slightly better than the non-substituted dataset, with non-overlapping distribution in the space defined by PC1 and PC2. HpyV range from negative to near zero values, HpyR are around zero, and NgR have positive values.

Results of stepwise LDA of the oral tooth 3D roughness data are reported in the main text and in Fig. 3. Three parameters (Spk, Svk, Smr2) are enough to assign all specimens to their correct populations (canonical axis 1 explains 99.94% of variance, axis 2 the remaining 0.06%; Wilks' Lambda, = 0.002, p = 0.0001; probability of correct assignment to groups is 100% for all specimens; scores for canonical axis 1 are correlated with Spk (r = 0.8729, p = 0.002); scores for canonical axis 2 are correlated with Svk (r = 0.80, p = 0.01) and Smr2 (r = -0.97, p < 0.0001). No other correlations between parameters and canonical scores are significant.

LDA of the 3D dataset with substitution of less worn teeth has similar discriminatory power to the unsubstituted dataset, but some of the parameters that discriminate differ. When parameters that differ significantly are analysed, 4 variables are required to assign specimens to their correct groups (Sk, Spk,Vmp, Vmc), but when all variables are analysed only three are required (Vmp, Std and Stdi). In both cases, the three populations are separated along the first canonical axis, and this axis corresponds to the HpyV, HpyR, NgR ecological spectrum discussed in the main text.

As noted in the main text, stomach contents vary considerably from individual to individual: two of the rock scraping NgR contained large volumes of green algae (Cladophora; 90% and 100%) and two of the HpyV had large volumes of macrophyte chunks (100% and 75%). The third specimen from each of these populations contained large volumes of insect larvae. HpyR stomach contents are more varied. PCA results show separation of HpyR from the other two populations along PC1, but HpyV and NgR overlap one another. This axis accounts for only 43% of the variance, and reflects equal positive loadings (4.0 - 4.4) of filamentous blue-green algae, brown detritus, zooplankton, and sand. PC2 accounts for 23% of the variance, and reflects heavy loading of diatoms and thus separates members of the HpyR population from one another, rather than separating any of the populations. PC3 accounts for 15% of the variance, and reflects heavy positive loading of small macrophyte fragments, and heavy negative loading of cladophora. This axis thus goes some way towards separation of HpyV and NgR populations, but the two specimens with high volumes of insect larvae are, unsurprisingly not separated.

The results of stepwise LDA are comparable. Five variables are required for all specimens to be assigned to their correct groups, but the confidence with which two specimens (NgR92 and HpyV01 – the two with large volumes of insect larvae) are assigned to their groups is low, and does not increase with addition of more variables. Results of stepwise LDA of stomach contents are less clear than analyses of microwear data: HpyR are separated from other populations along canonical axis 1 (strongly loaded by macrophyte chunks), while HpyV and NgR fishes are distributed along axis 2, reflecting weighting of cladophora and insect larvae.

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ADDITIONAL DETAILS OF ANALYSIS OF LPJ TEETH The main text reports the results of ANOVA of the filtered 3D parameters derived from the

most worn tooth in each individual. ANOVA of the filtered 3D parameters derived from the second teeth sampled in each individual found significant differences only in Svk, Sal and Vvv (Welch ANOVA). Analysis based on mean values derived from filtered parameter values for both teeth sampled in each individual yielded similar results to the analysis of the most worn teeth. Significant Differences were found between a similar but not identical list of height, functional and spatial parameters (Sa, Sq, Sk, Spk, Svk, Vmc, Vvc, Vvv, Vvv/Vmc, Sal). A Tukey HSD procedure revealed that for all but one of these, the significant differences are between the lab-raised (soft-diet) and the standard-length-equivalent-wild fish, not between lab-raised and smaller-wild fish. For Vvc/Vmc, lab-raised fish differ from the smaller-wild fish.

The results of analysis of filtered 3D parameters from all teeth (2 from each individual) is again similar. Significant differences were found between Sa, Sq, Sk, Spk, Svk, Vmp, Vmc, Vvc, Vvv, and Sal. However, a Tukey HSD procedure revealed more complex pairwise differences: for several parameters (Sa, Sq, Spk, Vmp, Vvc), lab raised fish differ from both wild groups; for Sk and Vmc, the significant differences are between the lab-raised and the size-equivalent wild fish; for Sal differences are between the size-equivalent-wild fish and the two other groups; while for Svk and Vvv all three groups differ from one another.

Analysis of the levelled but non-filtered 3D surface datasets, whether derived from most worn teeth, second teeth, means of the two, or all teeth, found no significant differences between the three groups of fish.

Results of stepwise LDA of the LPJ tooth 3D roughness data are reported in the main text and in Fig. 3. When all parameters are included, three (Svk, Smr1, Str) are enough to correctly discriminate between populations (Wilks' Lambda = 0.01, p = 0.0016). Canonical axis 1 explains 99.77% of variance, axis 2 the remaining 0.23%. The probability of correct assignment to populations is 99.9% or 100%, except for two specimens with probabilities of 98% and 91%, so the probability of any of the specimens being mis-assigned is low). Scores for canonical axis 1 are correlated with Svk (r = 0.90, p = 0.0009); scores for canonical axis 2 are correlated with Str (r = 0.89, p = 0.001). No other correlations between variables and conical scores are significant.

If stepwise LDA is limited to those parameters that differ (ANOVA) three are required to assign all samples to their correct trophic group (Sq, Svk, Vvv), but the significance of this LDA is doubtful (Wilks' Lambda, 0.09, p = 0.07). Canonical axis 1 accounts for 87.97% of variance, axis 2 the remaining 12.03%. The probability of correct assignment to groups is between 91.1% and 99.8% for most specimens, but two have lower probabilities: specimen 37865 has a 33% probability of misassignment to the smaller-wild group, and specimen 37869 has a 46% probability of being misassigned to the lab-raised soft diet group. Stepwise inclusion of two additional parameters (Spk and Vmc) produces a more informative LDA, also with correct assignment of all specimens (Wilks' Lambda = 0.0002, p = 0.0025; probability of correct assignment to populations = 100% for all specimens). Canononical axis 1 accounts for 99.96% of variance, axis 2 the remaining 0.04%. All 5 parameters are correlated with scores for canonical axis 1 (Sq, r = -0.8198, p = 0.007; Spk, r = 0.7092, p = 0.03; Svk, r = -0.8899, p = 0.001; Vmc, r = -0.7844, p = 0.01; Vvc, r = -0.7716P = 0.015). No parameters are correlated with scores for canonical axis 2.

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Table S5: Summary of results of stepwise LDA of LPJ teeth. For each dataset the parameters required to assign all samples to their correct trophic group are listed. For mean filtered data, only 2 variables are required, but addition of a third variable (in brackets) increases the separation between groups and the probability that samples are correctly assigned. Analysis results Parameters required, 100% discrimination Most worn tooth, raw data, all parameters Sk, Sa, Sp, Sdr, Str 2 teeth, raw data, all parameters Spk, Smr1, Vmp, Sq, Sp, Sv, S10z, Sdq Mean of 2 teeth, raw data, all parameters Vvc/Vmc, Ssk, Sdr Most worn tooth, filtered data (log transformed), all parameters

Svk, Smr1, Str

Most worn tooth, filtered data (log transformed), parameters that differ (ANOVA)

Sq, Svk, Vvv

2 teeth, filtered data (log transformed), all parameters Sz, Sdq, Svk, Smr2 2 teeth, filtered data (log transformed), parameters that differ (ANOVA)

Sk, Spk, Svk, Vmp, Vmc, Vvv

Mean filtered data from 2 teeth (log transformed), all parameters

Svk, Vvc/Vmc, (Sal)

Mean filtered data from 2 teeth (log transformed), parameters that differ (ANOVA)

Svk, Vvc/Vmc, (S10z)

Table S6. Spearman’s rank correlations between results of multivariate analyses of LPJ 3D data and fish size and diet. PCA axis 1 and first canonical axis are derived from analysis of LPJ 3D data; fish size = standard length, diet = mg of hard shelled prey material in guts. N is low, so p values should be treated with caution. Fish size and diet are not correlated: rs = 0.2594, p = 0.5742). Standard length (n = 9) Hard shelled prey in gut (mg; n =7) Analysis result (PCA Axis 1) rs p rs p Most worn tooth, unfiltered data, all parameters

0.267 0.488 0.037 0.937

Most worn tooth, filtered data, all parameters 0.250 0.516 0.852 0.015 Most worn tooth, filtered data, parameters that differ (ANOVA)

0.233 0.546 0.927 0.003

Most worn tooth, filtered data (log transformed), all parameters

0.250 0.516 0.852 0.015


0.250 0.516 0.964 0.0005

Second tooth, filtered data (log transformed), all parameters

-0.083 0.831 0.704 0.077

Mean filtered data from 2 teeth (log transformed), all parameters

0.117 0.765 0.964 0.0005

Mean filtered data from 2 teeth (log transformed), parameters that differ (ANOVA)

0.100 0.798 0.927 0.003*

Analysis results (first canonical axis) Most worn tooth, unfiltered data, all parameters

-0.867 0.002 -0.371 0.413

Mean of 2 teeth, unfiltered data, all parameters -0.571 -0.717

0.180 0.03

-0.148 0.751

Most worn tooth, filtered data (log transformed), all parameters

-0.317 0.406 -0.927 0.003


0.317 0.406 0.927 0.003

Mean filtered data from 2 teeth (log transformed), stepwise, 2 parameters from all

-0.283 0.460 -0.889 0.007

Mean filtered data from 2 teeth (log transformed), stepwise, 3 parameters from those that differ

0.283 0.460 0.889 0.007

Mean filtered data from 2 teeth (log transformed), stepwise, 3 parameters from all

0.067 0.865 0.889 0.007

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Supplementary figures

Figure S1. Images showing the 100 µm areas sampled for 2D scoring, with the unfiltered and the filtered 3D surface data collected from the two samples of oral teeth exhibiting the highest (a-c, specimen NgR104) and lowest density of 2D microwear features (d-f, specimen HpyVMP01) (respectively epilithic and epiphytic scraping specialists). As noted in the text, several roughness parameters are positively correlated with the density of features (Sa, Sq, Sp (log), Sv (log), Sz (log), S10z (log), Sdr, Sk, Spk, Svk, Vmp, Vmc, Vvc, Vvv, Sal. (a, d) scanning electron micrographs, field of view 100 x 100 µm. (b, c, e, f) Alicona rendered 3D data, 60° tilt, field of view 145 µm wide; (b, e) raw, levelled 3D data, vertical colour scale from 10 to -24 µm, (c, f) filtered 3D data (roughness surface), vertical colour scale from 5 to -5 µm.

Figure S2. 3D surface data from the qualitatively most worn (a, b, specimen 37872 a size-equivalent-wild fish) and least worn of the LPJ teeth (c, d, specimen 37864; a lab-raised soft-food fish) As noted in the text, several roughness parameters are positively correlated with qualitative assessment of roughness. Alicona rendered 3D data, 60° tilt, field of view 145 µm wide; (a, c) raw, levelled 3D data, vertical colour scale from 6 to -13 µm, (b, d) filtered 3D data (roughness surface), vertical colour scale from 5 to -5 µm.

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REFERENCES 1. Zar J.H. 1999 Biostatistical analysis. fourth ed. New Jersey, Prentice Hall; pp. 663. 2. International Organization for Standardization. 2007 Geometrical product specifications (GPS) - Surface texture: Areal - Part 2: Terms, definitions and surface texture parameters (ISO 25178-2); pp. 42. 3. Kovach W. 2006 Oriana Version 2.02e. Anglesey, Wales, Kovach Computing Services.

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