Geographical and Taxonomic Influences on Cranial

Home , Colobinae, Red colobus, Western red colobus, Zanzibar red colobus

Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2009) 18, 248–263

Blackwell Publishing Ltd RESEARCH Geographical and taxonomic influences PAPER on cranial variation in red colobus monkeys (Primates, Colobinae): introducing a new approach to ‘morph’ monkeys Andrea Cardini1,2* and Sarah Elton2

1Museo di Paleobiologia e dell’Orto Botanico, ABSTRACT Universitá di Modena e Reggio Emilia, via Aim To provide accurate but parsimonious quantitative descriptions of clines in Università 4, 41100, Modena, Italy, 2Hull York cranial form of red colobus, to partition morphological variance into geographical, Medical School, The University of Hull, Cottingham Road, Hull HU6 7RX, UK taxonomic and structured taxonomic components, and to visually summarize clines in multivariate shape data using a method which produces results directly comparable to both univariate studies of geographical variation and standard geometric morphometric visualization of shape differences along vectors. Location Equatorial Africa. Methods Sixty-four three-dimensional cranial landmarks were measured on 276 adult red colobus monkeys sampled over their entire distribution. Geometric morphometric methods were applied, and size and shape variables regressed onto geographical coordinates using linear and curvilinear models. Model selection was done using the second-order Akaike information criterion. Components of variation related to geography, taxon or their combined effect were partitioned using partial regresssion. Multivariate trends in clinal shape were summarized using principal components of predictions from regressions, plotting vector scores on maps as for univariate size, and visualizing differences along main axes of clinal shape variation using surface rendering. Results Significant clinal variation was found in size and shape. Clines were similar in females and males. Trend surface analysis tended to be more accurate and parsimonious than alternative models in predicting morphology based on geography. Cranial form was relatively paedomorphic in East Africa and peramorphic in central Africa. Most taxonomic variation was geographically structured. However, taxonomic differences alone accounted for a larger proportion of total explained variance in shape (up to 40%) than in size (≤ 20%). Main conclusions A strong cline explained most of the observed size variation and a significant part of the shape differences of red colobus crania. The pattern of geographical variation was largely similar to that previously reported in vervets, despite different habitat preferences (arboreal versus terrestrial) and a long period since divergence (c. 14–15 Myr). This suggests that some aspects of morphological divergence in both groups may have been influenced by similar environmental, geographical and historical factors. Cranial size is likely to be evolutionarily more labile and thus better reflects the influence of recent environmental changes. Cranial shape could be more resilient to change and thus better reflects phylogenetically informative differences. Keywords Akaike information criterion, Central Africa, clinal variation, cranial shape, cranial *Correspondence: Andrea Cardini. size, curvilinear models, geometric morphometrics, Piliocolobus, partial regression, E-mail: [email protected] surface rendering.

‘Morphing’ red colobus cranial variation

(1988) argued that aridity during glacial maxima caused tropical INTRODUCTION forest contractions and confined forest fauna to refugia. These Understanding and appreciating clinal variation in size and potentially included highland areas (Mayr & O’Hara, 1986) like shape is a key aspect of biogeographical research. Recent studies the Fouta Djallon highlands (Guinea, West Africa), the Adamawa have demonstrated the utility of advanced morphometric Plateau (Cameroon, western equatorial Africa) and the moun- techniques in the study of clinal variation (Fadda & Corti, 2001; tains between the Rift Valley and the Lualaba River (central and dos Reis et al., 2002; Frost et al., 2003; Monteiro et al., 2003; eastern equatorial Africa). From there, according to this Santos et al., 2004; Cardini et al., 2007). However, although size hypothetical scenario, lowlands were recolonized during inter- differences are relatively easy to illustrate, it is much more glacials, when forests expanded. Indeed, ranges of assemblages of challenging to accurately display spatial variation in shape. red colobus (Grubb et al., 2003) are more or less centred around Visualizing differences is considered to be one of the strengths of those mountain refugia. Forest expansion interrupted the geometric morphometrics, so developing efficient methods for process of divergence and created contact areas like those found illustrating multivariate predictions of clinal shape is crucial to today in some regions of Central Africa (see map in Gautier-Hion the development of morphometric research within biogeography. et al., 1999, p. 81). A fundamental aim of this paper is therefore to report a new Patches of lowland forests along major rivers could also have method that uses a principal components analysis of clinal allowed the survival of small populations of forest animals shapes to produce a visual summary that is directly comparable (Colyn, 1991; Colyn et al., 1991). River barriers were and still are with results from classical biogeographical studies of single crucial to the evolution of red colobus and other Central African variables (e.g. size). By building on previous work (Cardini et al., primates (Colyn, 1991). The existence of several differentiated 2007), this method takes full advantage of powerful visualization taxa within interfluvial blocks of central equatorial Africa suggest tools like surface rendering to illustrate three-dimensional shape that during the last dry climatic period, populations may have variation along vectors. We apply this new method to a geometric survived in isolated patches of lowland forest within the Congo– morphometric study of clinal variation in a group of African Lualaba river basin (Colyn, 1991). Thus, in this region, primate primates, the red colobus monkeys, and examine the effects of diversity might not only be due simply to emigration during geography alongside taxonomy to investigate the factors that times of forest expansion from a major mountain refugium in might influence their cranial form. the Rift Valley, but also to the survival of islands of lowland forests Red colobus [Procolobus (Piliocolobus) de Rochebrune 1887 – during the arid interglacials. Phylogenetic reconstructions based Primates, Cercopithecidae] are medium-sized colobine monkeys on vocalizations (Struhsaker, 1981) give tentative support to the that are patchily distributed within the moist lowland forests of idea that refugia, particularly mountain regions, acted as major west, central and east equatorial and tropical Africa (Davies & originating centres for modern red colobus populations. Oates, 1994). They are currently considered to show more Interestingly, the sole published molecular phylogenetic analysis biological variability than is found in a single species and the (Ting, 2008) is fairly congruent with this earlier phylogeny, sup- prevailing trend is to treat them as a superspecies divided into porting an initial Late Pliocene split of the red colobus clade into assemblages (Grubb et al., 2003; Grubb, 2006). Grubb et al. (2003) three main lineages, which approximately correspond to the western, recognized four main Piliocolobus groups, which largely reflect western equatorial and central equatorial/eastern assemblages, the allopatric or parapatric distribution of their members followed by Pleistocene radiations within the three clades. (Fig. 1a): (1) Piliocolobus badius, consisting of three populations Quantifying clinal variation in cranial form is an important living in West Africa from Senegal to Ghana; (2) Piliocolobus element in understanding Piliocolobus population history and pennantii, including four populations from western equatorial biogeography. Although studies of cranial variability within red Africa; (3) several populations found to the east of the range colobus were conducted in the past (Verheyen, 1957; Colyn, 1991), of P. pennatii, which together form the ‘central equatorial African their main purpose was to assess the validity of taxa proposed on assemblage’ (of these, Piliocolobus sp. ellioti, Piliocolobus sp. foai, the basis of pelage colour rather than examining broader spatial Piliocolobus sp. oustaleti, Piliocolobus sp. tephrosceles and patterns. As briefly reviewed in Cardini et al. (2007), several Piliocolobus sp. tholloni were included in the study reported primate species, including Brazilian tufted-eared marmosets, here); (4) an eastern assemblage comprising three small and Malagasy sifakas, Kenyan vervets, pig-tailed and crab-eating isolated populations in Kenya and Tanzania (Piliocolobus macaques in Southeast Asia, Japanese macaques, African baboons gordonorum, Piliocolobus kirkii and Piliocolobus rufomitratus). and vervets are featured in the vast literature on clinal and Geographical factors probably played a major role in producing ecogeographical size variation. However, only a few of these the morphological variation and taxonomic complexity evident studies investigated African monkeys and none of them con- in red colobus today. Within West and Central Africa, red cerned strictly arboreal species. Therefore, investigation of clinal colobus populations tend to live in reasonably close proximity variation will not only shed light on red colobus morphological and are separated to a variable extent by rivers or mountains. variation but will also help to determine whether any general These often contiguous populations presumably diverged patterns exist within the African primates studied to date. because of reductions in gene flux during Pleistocene glacials Although temperature is highlighted as being a contributor (Verheyen, 1957, 1962; Rodgers et al., 1982; Colyn, 1991; to size in higher-latitude Asian monkeys (e.g. Albrecht, 1982; Gautier-Hion et al., 1999). Mayr & O’Hara (1986) and Hamilton Fooden & Albrecht, 1993; Rae et al., 2003), within African

A. Cardini and S. Elton

Figure 1 (a) Distribution of red colobus taxa (modified from Colyn, 1991). (I) Piliocolobus badius (western Tropical Africa): 1, Piliocolobus badius temminckii; 2, Piliocolobus badius badius; 3, Piliocolobus badius waldroni; (II) Piliocolobus pennatii (western equatorial Africa): 4, Piliocolobus pennatii epieni; 5, Piliocolobus pennatii pennantii; 6, Piliocolobus pennatii preussi; 7, Piliocolobus pennatii bouveri; (III) central African assemblage: 8, Piliocolobus sp. tholloni; 9, Piliocolobus sp. oustaleti; 10, Piliocolobus sp. parmentieri; 11, Piliocolobus sp. lulindicus-foai; 12, Piliocolobus sp. langi-ellioti; 13, Piliocolobus sp. tephrosceles; (IV) eastern African species: 14, Piliocolobus gordonorum; 15, Piliocolobus rufomitratus; 16, Piliocolobus kirkii. Grey areas are putative Pleistocene mountain refugia from Mayr & O’Hara (1986). (b) Geographical distribution of Piliocobus female and male specimens with different symbols according to taxonomy. Hammer–Aitoff equal area map projection is used in this and the following figures. monkeys, temperature appears to influence size and/or shape less differing relationships between primate morphology and than other environmental factors, including increased access to environment in Africa and Asia are logical, given that precipi- food (Turner et al., 1997) and rainfall as a proxy of habitat tation is a more important component of climatic variation productivity (see Cardini et al., 2007, and references therein than temperature at low latitudes (deMenocal & Bloemendal, for a short review on this subject). Elton (2008) argued that the 1995).

‘Morphing’ red colobus cranial variation

The extensive geographical distribution of the African red Table 1 Taxa used in the analysis and sample sizes. colobus from southern Senegal in the west to Kenya and Tanzania in the east makes this taxon an ideal candidate for the n investigation of clinal variation in a tropical forest monkey. Thus, three-dimensional coordinates of anatomical landmarks were Genus/subgenus Species Subspecies Females Males measured on crania of a large sample of red colobus including most populations, all endangered East African taxa and at least Procolobus Piliocolobus badius badius 36 23 temminckii 10 4 one representative for each of the main assemblages. Geometric waldroni 15 6 morphometrics methods were then applied alongside a new gordonorum –4– method of visualization to investigate: whether any signiﬁcant kirkii – 33 10 cline in size (1) or shape (2) occurred, using linear and curvilinear pennantii bouveri 1– regression models and applying information criteria for model epieni –1 selection; (3) whether components related to taxon, geography preussi 31 10 or their combined effect could be partitioned using partial rufomitratus – 51 regression to better understand the relationship between sp.* ellioti 16 18 geographical and taxonomic differences. foai 36 oustaleti 95 tephrosceles 716 MATERIAL AND METHODS tholloni 42

Sample *The following populations were considered of uncertain taxonomic status by Grubb et al. (2003). The sample, described in Nowak et al. (2008), comprised 276 adult specimens (of which 174 were female and 102 male) derived from museum and field collections in Zanzibar. Following with within-sex species means), alongside negligible measure- the classification scheme of Grubb et al. (2003), taxa were identi- ment error, did not introduce any appreciable error in either size fied on the basis of geographical distribution (taken from the or shape. distribution maps in Colyn, 1991) and the published taxonomy Geometric morphometric analyses were performed using of particular specimens (Colyn, 1991). These assignments were Morpheus (Slice, 1999), TPSSmall 1.20 (Rohlf, 2006a), NTSYS- generally congruent with information from museum catalogues. pc 2.2L (Rohlf, 2006b) and Morphologika (O’Higgins & Jones, Any specimens that could not be taxonomically assigned with 2006). Variations in the form of the landmark configurations confidence were excluded from the sample, and the utmost care were examined using Procrustes-based geometric morphometrics, was taken to correctly identify species and define a priori groups, rather than angle- or distance-based approaches, because this a necessary step given the unstable and unclear taxonomy of red method has desirable statistical properties as detailed in Franklin colobus monkeys. As detailed in Table 1 and shown in Fig. 1(b), et al. (2008) and provides an efficient separation of size and most groups of Piliocolobus monkeys were included in the shape components of form differences (Adams et al., 2004). sample. These groups were then analysed as if all populations Further, by applying appropriate mathematical functions (e.g. within Piliocolobus were of equal taxonomic rank (i.e. without the thin plate spline) to warping of images or grids, they allow discriminating between putative species versus subspecies – see localization of shape differences between pairs of landmark below). configurations. An extensive introduction to the theory of geometric morphometrics and its applications in biology is provided in Cardini et al. (2007) and Cardini & Elton (2008a,c). Data collection and geometric morphometrics

One of us (A.C.) collected three-dimensional coordinates of Shape variables anatomical landmarks on crania and mandibles using a 3D digitizer (MicroScribe 3DX, Immersion Corporation). Land- Due to the large degree of sexual dimorphism evident in colobines marks were digitized only on the left side to avoid redundancy of (Cardini & Elton, in press), all analyses were performed using information in symmetric structures and increase the number of split-sex samples. Using separate sexes may result in a loss of measured specimens. The set (configuration) of 64 landmarks statistical power within smaller samples but it has at least two used for the analysis is shown in Fig. 2; landmarks are described advantages. First, it avoids the use of corrections for sexual in Nowak et al. (2008). These correspond to a subset of cranial dimorphism, which often makes results less easy to interpret. landmarks used in a series of studies on skull variation in Old Second, it allows the verification of observed patterns through World monkeys (Cardini et al., 2007; Cardini & Elton, 2008a,b,c) the comparison of results from females and males. Whenever and already employed to analyse variation in red colobus congruencies are found, findings are corroborated and confidence (Nowak et al., 2008). is subsequently increased. As discussed in Nowak et al. (2008), a very small percentage A principal components analysis (PCA) of shape variables was of specimens with one to four missing landmarks (substituted used, which identifies the axes of greatest variation in a sample,

A. Cardini and S. Elton

Statistical analyses: geographical variation in size and shape

Trend surface analysis (TSA; Legendre & Legendre, 1998; Ruggiero & Kitzberger, 2004; Botes et al., 2006; Cardini et al., 2007) was used for fitting geographical coordinates to variation in skull size and shape, taking into account nonlinearities. Thus, size or shape variables were regressed onto a third-order polynomial of latitude and longitude and non-significant terms were removed one by one until all terms of the multiple regression were significant. Results of TSA were compared with those of both simple linear models and the full third-order polynomial expansion of geographical coordinates to decide whether TSA was both parsimonious and informative compared with alternative models. Thus, size or shape variables were regressed onto geographical coordinates, and the second-order Akaike information criterion (AICc; Burnham & Anderson, 1998; Mazerolle, 2004) was used to compare the goodness of fit of the models. AICc is a measure based on information theory and derived from the Figure 2 Landmark configuration (modified from Cardini et al., concept of entropy in physics. Briefly, it measures the lack of fit of 2007). the data (sum of squared residuals in a regression) to a given model, where the model is penalized in proportion to the to reduce dimensionality in the analysis of shape coordinates. number of parameters it employs. Thus, the best model com- Thus, the number of variables was reduced by including only the pared with available alternatives is the one with the lowest AICc

first several principal components of the 192 shape coordinates (AICcmin). For multivariate data, Burnham & Anderson (1998) from the Procrustes analysis of the raw data. The number of suggested that AICc = AIC + 2k(k + v)/(N × p – k – v), where principal components to be analysed was selected by measuring AIC = N × log(residual sum of squares/N) + 2k, k is the number the correlation between the matrix of Procrustes shape distances of parameters in the model, N is sample size and 1 ≤ v ≤ p(p + 1)/ in the full shape space and pairwise Euclidean distances in the 2, with p the number of dependent variables. In this study, AICc reduced shape space (5, 10, 15 principal components, and so on). values were always calculated using v = 1, as in the univariate Plots of correlation coefficients onto the number of components case. This was done for simplicity as AICc values (not shown) can be used in a similar way to scree plots to select how many obtained with v = p(p + 1)/2 were virtually identical to those variables summarize most shape variation (Fadda & Corti, 2000; with v = 1. Finally, the relative level of support of different Δ Cardini et al., 2007). Depending on the data set used, 30–35 models was evaluated by AICc = AICc– AICcmin and Akaike principal components provided an accurate but reasonably weights (Burnham & Anderson, 1998). Akaike weights provide parsimonious summary of total shape variation (> 80% of total another measure of the strength of evidence (likelihood) for each Δ variance; correlation with distances in the full shape space model and approximately represent the ratio of AICc values for ≥ 0.995). each model relative to the whole set of candidate models. Burn- Δ Issues with non-homogeneous samples due to sexual dimor- ham & Anderson (1998) suggest that models with AICc values of Δ phism or an exceedingly large number of shape variables in 0–2 provide similar support, whereas AICc > 2 indicate substan- statistical analyses are relatively easy to address (as shown above). tially less support than the best model. Taking into account the non-independence of samples due to Specimens were plotted according to geographical coordinates phylogeny in statistical tests, in contrast, is less straightforward. on a map of Africa using Arcview GIS 3.2 (ESRI, Redlands, CA). Rohlf (2006c) recently reviewed phylogenetic comparative Clinal variation predicted by the selected model was illustrated methods and suggested that some problems could be addressed with grey-scale symbols on the map. Size, which is univariate, by using more complex models. None of these, however, could can be easily described by a single variable. Thus, grey symbols of have been fruitfully applied in our analysis, because a phylogeny a tone proportional to the size of the skull predicted by geography including all study taxa is not yet available. The very recent were used. publication of Ting’s (2008) molecular analysis of phylogenetic The visualization of nonlinear clines is less straightforward relationships among African colobines represents a big step for shape, which is multivariate. Thus, we developed a modified forward in this respect, but does not include about a third of the version of the method described by Cardini et al. (2007) to more populations analysed in our study. Thus, at present, benefits of a effectively summarize the main trends of clinal variation and better taxonomic coverage and avoidance of unnecessary loss of allow their visualization using surface rendering of shapes information (with concomitant big gaps in the spatial distribution described by three-dimensional anatomical landmarks. As for of our data) outweigh the potential advantage of applying phylo- size, clinal variation in shape predicted by the selected model was genetic comparative methods to a small set of taxa. first computed and scores of predictions were saved. The variables

Table 2 Clinal variation in cranial size: comparison of different models using percentages of size variance explained (R2, squared correlation coefficient; adjusted R2, squared correlation coefficient adjusted for the number of predictors) by geography, significance of tests, second order Δ Akaike information criterion (AICc), delta AICc ( AICc = AICc – AICcmin) and Akaike weights (Wi).

2 2 Δ Sex Predictors* R (%)R adj. (%) d.f.1 d.f.2 FP AICc AICc Wi

Females x 2, x 3, y 2, xy, x 2y 60.4 59.2 5 168 51.293 4.3 × 10–32 340.9 0.0 0.959 Full polynomial 61.8 59.7 9 164 29.463 4.9 × 10–30 347.2 6.3 0.041 x, y 12.1 11.1 2 171 11.751 0.00002 394.4 53.5 0.000 Males x, x 2, y 2, xy, x 2y, xy 2 71.2 69.3 6 95 39.049 1.4 × 10–23 200.5 0.0 0.975 Full polynomial 71.2 68.4 9 92 25.251 2.9 × 10–21 207.9 7.4 0.025 x, y 11.0 9.2 2 99 6.088 0.0032 240.8 40.3 0.000

*In this and the next table, signiﬁcant terms in the trend surface are shown using x for longitude and y for latitude.

describing the predicted cline in shape were then subjected to a taxon); (4) unexplained variation (proportion of variance PCA (‘geo-shape PCA’ or gsPCA) in order to summarize most of explained by the effect of other factors). the variation predicted by the model with a few variables. Even- tually, variation along gsPCA axes (gsPC1 and gsPC2), which RESULTS together accounted for most of the clinal variation in shape, was illustrated using both (a) grey-scale colour symbols of a tone Clinal variation in size proportional to the score of gsPC1 (or gsPC2) for individuals plotted on a map of Africa and (b) surface renderings of shapes Significant clinal variation in cranial size was found both in corresponding to individuals at opposite extremes of gsPC1 (or females and males (Table 2). The inclusion of curvilinear terms gsPC2). To aid interpretation of (b) relative to (a), shapes were increased the proportion of variance in size explained by geo- shown using the same grey tone as for symbols on the map. Thus, graphy from about 10% to about 60–70%. TSA had the lowest AICc, Δ for instance, if lowest scores on gsPC1 were shown using light and AICc was close to seven for polynomial models and larger grey symbols on the map, light grey was also used to for surface than 40 for the simple linear regressions on longitude and rendering of the shape predicted for the negative extreme of latitude. Thus, these models were either weakly supported (range gsPC1. This allowed: (1) mapping of clinal shape in a fashion 3–7) or very unlikely (> 10) according to the guidelines sug- similar to clinal size, and (2) visualization of geographical shape gested by Burnham & Anderson (1998). Akaike weights of TSA variation as is commonly done in geometric morphometrics by models were larger than 0.95. This indicates that, given the data, using predictions for shapes along a vector (e.g. for PCA axes they had a more than 95% chance of being the best models Milne & O’Higgins, 2002; Franklin et al., 2007a; Wroe & Milne, among those considered in the analysis. Thus, only results from 2007; Cardini & Elton, 2008a,c; and for ontogenetic vectors TSA were considered. O’Higgins & Jones, 1998; Collard & O’Higgins, 2001; Cardini & Patterns of clinal variation in size predicted by TSA are shown Thorington, 2006; Cobb & O’Higgins, 2007; Franklin et al., 2007b). in Fig. 3. Visual inspection of plots (Fig. 3a,b) and also the high correlation (r = 0.753, P = 0.002) between female and male predictions of clinal size for geographical coordinates corresponding Statistical analyses: taxonomic and geographical to population centroids (i.e. averages of geographical coordinates components of size and shape variation within each population) suggested a very similar trend in both Partial linear regression was used to assess the effects of spatial sexes. Cranial size was intermediate in West Africa, larger in structuring of variables and estimate the amount of skull size or central equatorial Africa and smaller in East Africa. However, in shape variation attributable to one set of factors (taxon) once the males there was a progressive increase in size from west to central effects of the other factors (geography) were taken into account. equatorial Africa, whereas in females, size increased from Senegal This method is commonly used in biogeographical studies to to Cameroon, decreased slightly in western Congo and finally partition the effects of geography and environment (Legendre & increased again in eastern Congo and Uganda. The most con- Legendre, 1998; Ruggiero & Kitzberger, 2004; Botes et al., 2006; spicuous reduction in size in both sexes occurred in East Africa, Cardini et al., 2007). Thus, geographical predictors selected in and this finding was supported even after excluding the two steps 1–2 (spatial component) and grouping variables for smallest eastern populations (P. kirkii and P. rufomitratus) from populations were combined. Morphological variation was the analysis. partitioned into four components: (1) non-taxonomic spatial (proportion of variance exclusively explained by geography); Clinal variation in shape (2) spatially structured taxonomic (proportion of variance explained by both geography and taxon); (3) non-spatial Clinal variation in shape was highly significant in both sexes taxonomic (proportion of variance explained exclusively by (Table 3). As for size, the inclusion of curvilinear terms led to an

Figure 3 Patterns of clinal variation in female (a) and male (b) size of Piliocobus: predictions of trend surface analysis (TSA). Grey scale of symbols is according to increasing size.

Table 3 Clinal variation in skull shape: comparison of different models* using percentages of shape variance explained (% ex.), signiﬁcance in multivariate tests, second-order Akaike information criterion, delta AICc and Akaike weights. See Table 2 for abbreviations.

λ Δ Sex Predictors % ex. Wilks F d.f.1 d.f.2 P AICc AICc Wi

Females x, x 2, x 3, y, y 2, xy, x 2y, xy 2 26.7 2.3 × 10–4 7.120 280 1040.9 8.7 × 10−122 –444.2 0.0 0.581 Full polynomial 27.2 1.8 × 10–4 6.188 315 1157.8 1.3 × 10−116 –442.3 1.8 0.232 x, y 10.2 6.5 × 10–2 11.426 70 274.0 2.2 × 10−50 –441.9 2.3 0.187 Males x, x 2, y 2, xy, x 2y, xy 2 25.1 3.4 × 10–4 6.340 180 397.7 4.8 × 10−53 –250.3 0.5 0.402 Full polynomial 32.0 7.6 × 10–5 4.185 270 566.0 8.6 × 10−47 –247.1 3.7 0.082 x, y 8.8 5.9 × 10–2 7.306 60 140.0 2.5 × 10−22 –250.8 0.0 0.516

*Multivariate multiple regressions were performed using the ﬁrst 35 principal components of shape variables for females and the ﬁrst 30 for males. The same number of principal components was used in the partial regression (Table 4). evident increase in the amount of shape variance explained by models. In males, however, the linear model had the lowest AICc Δ geography (from 10% or less to more than 25%). However, AICc but was about as good as TSA ( AICc < 2) whereas the full polynomial was fairly similar across models. TSA was the best model in model was substantially less supported than the other two Δ Δ females and AICc was about 2 for linear and full polynomial ( AICc > 2). Thus for shape, information criteria were not as

© 2009 The Authors 254 Global Ecology and Biogeography, 18, 248–263, Journal compilation © 2009 Blackwell Publishing Ltd ‘Morphing’ red colobus cranial variation conclusive as for size in model selection. However, TSA was Table 4 Partial regressions of size and shape onto geography and almost three times more likely than alternative models in females taxon: percentages of variance explained by different components. and about as likely as the linear regression in males. Thus, as TSA produced a much better fit to the data and was either more likely Size Shape or as likely as alternative models, this model was adopted in all analyses. Components Females Males Females Males A visual summary of the pattern of clinal shape variation (1) Geography only 2.2 2.0 4.2 5.6 according to predictions of the trend surface analysis is shown in (2) Common 58.2 69.2 22.6 19.6 Figs 4 and 5 using the first two gsPCs which together explained (3) Taxon only 12.7 2.8 9.0 14.5 about 65% of clinal shape variation. As for size, patterns of (4) Unexplained 26.9 26.0 64.2 60.4 female and male ‘geographical shape’ variation were largely congruent. This was suggested by the visual inspection of plots (Figs 4 & 5) and also by the high correlation (r = 0.827, P = 0.0001) between matrices of shape distance based on female (c. 2%). In contrast, non-spatial taxonomic variation in size was and male predictions of clinal shape for geographical coordinates fairly large in females (c. 13%) but small in males (c. 3%). corresponding to the population centroids from our sample. Indeed, in the full model (spatial and taxonomic predictors all In both sexes clinal variation summarized by gsPC1 was included), the effect of taxon was significant in females but not in × –9 largely congruent with the cline in size. This was suggested again males (females, F12,156 = 6.117, P = 9.7 10 ; males, F11,84 = 0.831, by evident similarities in patterns (Figs 3 and 4), and by a very P = 0.609). high correlation between gsPC1 and predictions of TSA for size For shape, results for females and males were almost identical.

(rfemales = 0.932, P < 0.00001; rmales = 0.981, P < 0.00001). Consist- Most of the variance remained unexplained (c. 60%). As for size, ently, populations with the smallest and largest size tended to spatially structured taxonomic variation accounted for more score lowest and highest on gsPC1. This means that in both sexes, variation (c. 20%) than either non-taxonomic spatial (c. 5%) or the small P. kirkii was on one extreme of gsPC1 and either the non-spatial taxonomic (c. 9–15%) variation. However, for shape large P. p. preussi (females) or specimens from eastern Congo and the non-spatial taxonomic component accounted for up to half Uganda (males) were on the opposite extreme. Clinal variation of the total amount of explained variance, whereas for size it was visualized by morphing shapes along gsPC1. Thus, East was only up to one-sixth. In the full model, the effect of taxon African populations tended to have a short orthognathous face on shape was highly significant in both sexes (females, Wilks λ × –21 with large orbits and a relatively small palate. The neurocranium, = 0.00476, F420,1394.5 = 1.992, P = 9.9 10 ; males, Wilks λ × –16 in contrast, was long and wide. Specimens from central equatorial = 0.000302, F330,602.1 = 2.167, P = 1.2 10 ). Africa were prognathous with a deep upper jaw and fairly short nasals, and had a small neurocranium with a pronounced DISCUSSION narrowing of the anterior temporal fossa. Individuals from West Africa were somewhat intermediate between east and central ‘Geo-shape PCA’ and the visualization of clinal shape equatorial populations. Clinal variation summarized by gsPC2 also followed a direction The increasing use of complex, multivariate morphological data common to both sexes. This time the vector mostly picked up in biogeographical studies (e.g. Frost et al., 2003; Cardini et al., differences between East and West African populations, and 2007) has led to the need for efficient methods for illustrating mainly concerned facial depth (more pronounced in the west) multivariate predictions of clinal shape. We suggest that the and nasal length (longer in the east). In the west this trend was ‘geo-shape PCA’ proposed in this paper is an effective way to most pronounced in P. badius badius but was also evident in summarize and visualize clinal shape variation in geometric P. badius temminckii. Pilocolobus badius waldroni, in contrast, morphometric studies. Geometric morphometrics is extensively scored next to zero on gsPC2 and was thus intermediate between used and has a number of advantages over traditional morpho- the other two western taxa and the eastern populations. metrics. It is powerful in statistical tests and efficient in separat- ing size and shape components of form, and also allows the use of various kinds of diagrams for an effective visualization of Taxonomic and geographical components of size and shape variation in terms of its geometry (Adams et al., 2004). shape variation Many classical applications of statistical analyses in biology Results of partial regressions used to partition components of were developed in order to predict a single variable using one or size and shape variation are shown in Table 4. With the exception more explanatory predictors. Often, these methods can be easily of the non-spatial taxonomic component of size, patterns were extended to multivariate shape data. However, the last step of the very similar in females and males. analysis, where shape differences are summarized and findings Spatially structured taxonomic variation accounted for most visualized back in the space of the landmark configuration of the variation in size (c. 60–70%). Less than 30% of total (Bookstein, 2000), is not necessarily straightforward, as exempli- variance remained unexplained. Non-taxonomic spatial fied by studies of nonlinear clinal variation. Predictions from variation, which only relates to geography, was very small clines need to be plotted on a geographical map to show where

Figure 4 (a, b) Patterns of clinal variation in female (a) and male (b) shape of Piliocobus: predictions of TSA summarized by ‘geo-shape principal components’ (gsPCs). The ﬁrst major axis (gsPC1) is shown which summarizes 42.9% (females)/41.5% (males) of clinal variation in shape predicted by the best ﬁt trend surface. Grey scale of symbols on the map is according to increasing gsPC score. Shape changes at extremes of the axis (maximum above and minimum below) are shown with surface renderings using three views (lateral, frontal and ventral). Consistent with the grey scale of symbols on the map, dark and light grey tones are used for shapes corresponding, respectively, to the positive (largest gsPC score – females and males, respectively, 0.0258, 0.0357) and negative (smallest gsPC score – females and males, respectively, −0.0271, −0.0373) extremes of the axis.

Figure 5 (a, b) Patterns of clinal variation in female (a) and male (b) shape of Piliocobus: predictions of TSA. The second major axis (gsPC2) is shown which summarizes 23.7% (females)/23.5% (males) of clinal variation in shape predicted by the best ﬁt trend surface. Grey scale of symbols on the map is according to increasing gsPC score. Shape changes at extremes of the axis (maximum above and minimum below) are shown with surface renderings using three views (lateral, frontal and ventral). Consistent with the grey scale of symbols on the map, dark and light grey tones are used for shapes corresponding respectively to the positive (largest gsPC score – females and males, respectively, 0.0161, 0.0238) and negative (smallest gsPC score – females and males, respectively, –0.0232, –0.0220) extremes of the axis.

© 2009 The Authors Global Ecology and Biogeography, 18, 248–263, Journal compilation © 2009 Blackwell Publishing Ltd 257 A. Cardini and S. Elton and how the dependent variable changes as a function of commonly done in standard software for the analysis of spatial geographical coordinates. This is very easy when there is only one data (e.g. sam, Rangel et al., 2006). dependent variable, like size, which can be plotted using different Further components (gsPC2, gsPC3 etc.) are similarly symbols/colours for different scores of predictions. In contrast, illustrated in separate plots. Correlations between gsPCs and because each shape is described by many variables, shapes clinal size can be used to quickly summarize the degree of predicted by locality cannot be illustrated simply by varying congruence of size and shape clines. Finally, shapes which symbols on a map, even if there is only one shape for each locality. correspond to opposite extremes of clinal variation summarized One way to visualize clinal shape would be to directly plot by gsPC1 (gsPC2, gsPC3 and so on) are computed and plotted shapes (instead of symbols) on the map. This would be ana- next to the map with, for instance, dark grey symbols corre- logous to plotting scores (i.e. numbers instead of grey-scale sponding to the positive extreme and light grey to the negative symbols) for size predictions. However, this would lead to figures one. This last operation strictly mirrors the ‘standard’ way of overcrowded with shape diagrams or numbers which would be visualizing shape variation along a vector (as found in PCs, difficult or even impossible to interpret. An interactive visualization canonical variates, partial least squares and multivariate regression may provide a much better alternative, where one can simply vectors) and achieves the last goal of geometric morphometric drag a pointer on a digital map and display clinal shape using analyses: ‘Shape differences are summarized by multivariate diagrams that change as a function of geographical coordinates. analysis of shape coordinates, and findings are visualized by This is similar to visualization tools already available in standard quantitative diagrams back in the plane or space where the statistical software like Morphologika (O’Higgins & Jones, 2006) landmark data originated’ (Bookstein, 2000). or TPSRelw (Rohlf, 2007) to show shapes in any point of the In conclusion, gsPCA provides a simple way for summarizing two-dimensional space implied by any pair of PCs from a PCA of and visualizing clinal shape variation, produces results directly the original superimposed shape coordinates. However, for comparable to those of classical biogeographical studies of single publications, this method requires an electronic format with variables (e.g. size) but takes full advantage of powerful visualiza- either specific software or digital animations, and is not applicable tion tools like surface rendering by using the same underlying to standard articles printed on paper or made available as pdf techniques commonly employed for visualization of shape files. differences in geometric morphometric analyses (i.e. diagrams of To circumvent this problem, Cardini et al. (2007) developed a shapes predicted for opposite extremes of a vector). method which employs a k-means cluster analysis of predicted shapes and surface rendering diagrams for cluster means to Clinal size and shape, and geographical and respectively summarize and visualize the main trends of clinal taxonomic components of variation shape variation. Thus, they used a k-means cluster analysis on variables which described the predicted cline in shape in order to Significant clinal variation was found in red colobus crania. discriminate homogeneous groups of shapes predicted by geog- The cline in size was particularly pronounced and explained raphy. Groups obtained in the k-means cluster analysis were then most of the variance in the sample. The cline in shape was also plotted on a map using different symbols, and variation within significant but accounted for a smaller proportion of shape vari- each group of ‘geographical shapes’ was summarized by using its ance. For both size and shape, the major trend was longitudinal mean, which was then visualized using surface rendering. rather than latitudinal, although latitude did contribute to some This method can be seen simply as a shortcut to produce a of the variance explained by both the linear and curvilinear summary of a continuous pattern of multivariate clinal shape models. The dominance of longitude in clines may be partly variation on paper. However, the effectiveness of this visualiza- explained by the fact that the major axis of the distribution range tion will partly depend on the arbitrary choice of an appropriate is also longitudinal (about three times longer than latitude). number of a priori groups for the k-means cluster analysis, which However, Cardini et al. (2007) also reported a prevalence of cannot be too large (accurate but not easy to interpret) or too longitude in determining a morphological cline in vervet skulls. small (inaccurate but easier to interpret). Also, the outcome will Vervet populations are found over most of sub-Saharan Africa, in not be directly comparable to either the output of the analysis of contrast to red colobus which are confined to tropical forests in clinal variation in size or to any standard visualization of shape the equatorial belt. Thus, in vervets, the longitudinal extension of differences using predictions of shapes for extreme points along the range is about the same as the latitudinal one and a larger main axes of variation. This way of displaying shape differences contribution of longitude to the vervet cline cannot simply be along vectors is standard in geometric morphometrics and cus- explained by the length of the main geographical distribution axis. tomary in studies like those involving ordinations or regressions The fact that, despite ecological differences, most geographical (see Methods). The ‘geo-shape PCA’ method proposed in this variation occurred both in vervets and red colobus on a west to study overcomes some of these difficulties by taking clinal shape east axis is especially interesting if one considers that, generally, predictions, doing a second PCA on these scores and using the largest differences in temperature are latitudinal. Bergmann’s resulting PCs (gsPCs) for both plots on maps and surface rule predicts that size increases with latitude (as a surrogate for rendering along vectors. Thus, the main component of clinal temperature) but, if cranial size is taken as a proxy for body size, shape can be shown on a map using grey-scale symbols of a tone neither of these primate taxa appear to follow this rule. In vervets proportional to gsPC1 scores, as was done for clinal size and as is there was only a weak trend towards size increase in populations

© 2009 The Authors 258 Global Ecology and Biogeography, 18, 248–263, Journal compilation © 2009 Blackwell Publishing Ltd ‘Morphing’ red colobus cranial variation further from the equator, whilst in red colobus the largest similar size reduction would not be expected for vervets, as their individuals were found around the equator with populations terrestriality would have enabled them to move between forest north and south of it generally being smaller. patches, and thus allow them to avoid strong competition within Closer inspection of the longitudinal cline in red colobus size restricted areas. reveals further similarities with vervets. In both groups, the Within red colobus crania there were clear geographical differ- largest individuals are mostly from central equatorial Africa in a ences. However, compared with size, much less cranial shape was range that goes from the west coast (c. 10° E) to the Rift Valley explained by geography. This is perhaps unsurprising as size (c. 30° E). East of the Rift Valley, red colobus size becomes much tends to be highly plastic and adaptive; this, again, is consistent smaller, again mirroring the vervet trend. This pattern was with findings from vervets (Cardini et al., 2007). Nevertheless, evident even when the insular (and small) P. kirkii and the small size and shape are seldom uncorrelated and clinal variation in mainland P. ruformitratus were excluded from the analysis. shape corresponded well to expectations based on size in the Cardini et al. (2007) found rainfall together with seasonality to presence of allometry. Thus, small red colobus from West and, be consistently important predictors of size in vervet skulls. especially, East Africa had short muzzles, large orbits and Rainfall, as a proxy for habitat productivity, might thus be central expanded cranial vaults relative to populations from central to intraspecific size variations in other primate groups, even equatorial Africa. This size-related aspect of shape variation those that are phylogenetically well separated or have significant follows a common trend in primates and other mammals, where ecological differences. To put it simply, animals may grow larger it is often observed that within a group of closely related popula- where productivity is higher, and this association is expected for tions, those smaller in size tend to be somewhat paedomorphic. both terrestrial and arboreal taxa. Since, the largest representa- This has been demonstrated in papionins (Collard & O’Higgins, tives of Piliocolobus inhabit central African regions with highest 2001; Singleton, 2002), guenons (Cardini & Elton, 2008a), ungu- average annual rainfall (Fig. 6b), the effects of productivity on lates (Emerson & Bramble, 1993) and sciurid rodents (Cardini & body size could help to explain some of the similarities in the red O’Higgins, 2004; Cardini et al., 2005). In the smallest guenon, colobus and vervet clines. Miopithecus, rate hypomorphosis – a decrease in growth rate Nonetheless, the general patterns observed in red colobus and over a given time – could account for the observed paedomorphy vervets are not identical, and the links with rainfall and hence (Shea, 1992). However, evolutionary explanations for paedo- productivity are not equally strong across the red colobus range. morphy are far from clear. Functional constraints and allometric In high-rainfall west tropical Africa, from Ghana to Senegal, red scaling might be involved if, for instance, a relatively smaller colobus are medium-small compared with other Piliocolobus, brain in larger animals allows better accommodation of their whereas vervets are comparatively large. It is possible that proportionally larger masticatory muscles (Emerson & Bramble, environmental or ecological variables other than rainfall and 1993). It might also be related to some fundamental and highly temperature (or indeed interaction effects between variables) conserved process of skull ontogeny, since ‘strong parallels influence red colobus size in West Africa. Consideration of between intraspecific ontogenetic allometry and interspecific additional variables in future work may help to fully understand scaling relationships have been documented in a number of similarities and incongruences between clines in cranial size of tetrapod groups ... as ... diverse as cichlid fishes, plethodontid red colobus and vervets. salamanders, iguanid lizards, anteaters, dogs and great apes’ It is also possible (indeed probable) that the patterns we (Emerson & Bramble, 1993, p. 399). observe today not only reflect adaptations to the environment in When variation in red colobus cranial form was partitioned, the present or in the recent past but also events that took place geographically structured taxonomic variation was the major earlier in the evolutionary history of Piliocolobus. The intense component influencing both size and shape. This observation climatic fluctuations of the Pleistocene and attendant changes to indicates that population differences are largely consistent with African habitats including tropical forest may have been an those expected based on geographical barriers like rivers and important factor in primate speciation and differentiation mountains, and suggests that in the past these same barriers may (Hamilton, 1988). Forest reduction in Africa during glaciations have had a crucial role in reducing gene fluxes (Rodgers et al., 1982; presumably confined tropical forest fauna to refugia (Mayr & Colyn, 1991; Colyn et al., 1991). The importance of taxonomy as an O’Hara, 1986). Thus, the taxa were probably split into isolated explanatory factor in shape is consistent with Ting’s (2008) populations in a fragmented habitat made up of forest patches hypothesis that the evolutionary history of red colobus may actually with high population densities. There, as often happens for large be longer than traditionally thought, with the clade originating in mammals on islands (Lomolino, 2005), a reduction in size might the late Miocene and radiating in the Plio-Pleistocene. Our have been advantageous for reducing competition for food. A confidence in the patterns observed in this study is further strength- reduction in size in small peripheral populations is likely to have ened by the congruence between proportions of variance explained occurred at least a couple of times in the evolutionary history of by different components in males and females. The only remark- red colobus (Rodgers et al., 1982; Colyn, 1991), since red colobus able difference (the taxonomic component of size, large in taxa with the smallest size are found in small and relict popula- females and small in males) might have occurred simply because tions, like those of the Tana River and Zanzibar Archipelago. If a of sampling error, likely to be greater in the smaller male sample. relatively small size in western red colobus is related to a history Interestingly, taxonomic differences alone accounted for a of geographical isolation within Pleistocene forest ‘islands’, a much larger proportion of total explained variance in shape

Figure 6 Average annual temperature (a) and average annual rainfall (b) from HADCRUT2V Data. Images provided by the NOAA-ESRL Physical Sciences Division, Boulder Colorado from their website at http://www.cdc.noaa.gov/: (a), http://www.cdc.noaa.gov/Data/ descriptions/HADCRUTEM2.html; (b) http://www.cdc.noaa.gov/cdc/ data.cmap.html.

(one-quarter to almost a half) than in size (less than one-sixth). elsewhere by cross-validated discriminant analyses of cranial shape, This may be due to the evolutionary lability of size, in that size in which a high percentage of specimens were correctly classified tends to reflect adaptations and differences from the recent past to population (> 84%) compared with cranial size (< 43%; and is therefore prone to convergence. Shape, in contrast, thanks Cardini & Elton, in press). Thus, size may be better in detecting to its inherently multivariate complexity, is less likely to be easily the effects of recent environmental changes on form, whereas and frequently modified and is thus more likely to retain infor- shape may better reflect the differences created by older evolu- mation useful for taxonomic identification and phylogenetic tionary events. If so, it is likely that the study of shape differences inference. The effectiveness of shape in discriminating among red colobus can be much more informative about the taxonomic differences in red colobus has been demonstrated evolutionary history of these endangered populations than

Struhsaker (1981) believed when he issued his warning that Botes, A.M.A., McGeoch, H., Robertson, G., van Niekerk, A., morphological comparisons of skulls in red colobus might be Davids, H.P. & Chown, S.L. (2006) Ants, altitude and change hampered by the remarkable variation of form within populations. in the northern Cape Floristic Region. Journal of Biogeography, 33, 71–90. Burnham, K.P. & Anderson, D.R. (1998) Model selection and ACKNOWLEDGEMENTS inference: a practical information-theoretic approach. Springer, This paper is dedicated to the memory of Marco Corti (1950– New York. 2007) in recognition of his great contribution to the development Cardini, A. & Elton, S. (2008a) Variation in guenon skulls I: and application of geometric morphometrics to the study of species divergence, ecological and genetic differences. Journal systematics and the mechanisms of speciation in mammals. He of Human Evolution, 54, 615–637. was a pioneer, clearing the path and serving as an inspiration for Cardini, A. & Elton, S. (2008b) Variation in guenon skulls II: sexual many mammalogists. His advice and example was invaluable for dimorphism. Journal of Human Evolution, 54, 638–647. us and for many young zoologists who were struggling to learn Cardini, A. & Elton, S. (2008c) Does the skull carry a phyloge- geometric morphometrics and multivariate statistics. netic signal? Evolution and modularity and in the guenons. We are deeply grateful to all museum curators and collection Biological Journal of the Linnean Society, 93, 813–834. managers who allowed and helped us to study their collections. Cardini, A. & Elton, S. (in press) The radiation of red colobus Among them, special thanks to Hans-Walter Mittmann (Staatliches monkeys (Primates, Colobinae): morphological evolution in a Museum für Naturkunde, Karlsruhe) for sending us specimens clade of endangered African primates. Zoological Journal of the on loan during our visit at the Museum für Naturkunde in Linnean Society. Berlin. Wim Wendelen (Royal Museum for Central Africa, Cardini, A. & O’Higgins, P. (2004) Patterns of morphological Tervuren) and Olav Olav Röhrer-Ertl (formerly at Staatliche evolution in Marmota (Rodentia, Sciuridae): geometric mor- Naturwissenschaftliche Sammlugen Bayerns, Munich) provided phometrics of the cranium in the context of marmot phylogeny, invaluable help with specimen identiﬁcation and advice on ecology and conservation. Biological Journal of the Linnean Procolobus collections, and Cristina Murari (University of Modena Society, 82, 385–407. and Reggio Emilia) provided crucial support for running Cardini, A. & Thorington, R.W. Jr (2006) Post-natal ontogeny of computer analyses on Linux workstations. We thank Kate Nowak the marmot (Rodentia, Sciuridae) cranium: allometric trajec- for her assistance and collaboration in the earlier part of this tories and species divergence. Journal of Mammalogy, 87, 201– study. Claudio Gentilini, Roberta Cantaroni, Costantino Cresci- 216. manno, Andrea Ghidoni and Maria Teresa Martinelli (all of them Cardini, A, Hoffmann, R.S. & Thorington, Jr R.W. (2005) at the University of Modena and Reggio Emilia) were also of Morphological evolution in marmots (Rodentia, Sciuridae): great help solving computer and network problems. Craig size and shape of the dorsal and lateral surfaces of the cranium. Ludwig (National Museum of Natural History, Washington), Journal of Zoological Systematics and Evolutionary Research, 43, Emiliano Brunner and Paolo Colangelo (University of Rome), 258–268. Damiano Preatoni and Adriano Martinoli (University of Insubria), Cardini, A., Jansson, A-U. & Elton, S. (2007) Ecomorphology of and Andrew Marshall (University of York) were all of great help vervet monkeys: a geometric morphometric approach to the during various stages of this study. We are also very grateful to study of clinal variation. 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