Journal of Biogeography (J. Biogeogr.) (2007) 34, 1663–1678

ORIGINAL ARTICLE

A geometric morphometric approach to the study of ecogeographical and clinal variation in vervet monkeys Andrea Cardini1,2*, Anna-Ulla Jansson2 and Sarah Elton2

1

Dipartimento del Museo di Paleobiologia e dell’Orto Botanico, Universita´ di Modena e Reggio Emilia, via Universita` 4, Modena 41100, Italy, 2Hull York Medical School, The University of Hull, Cottingham Road, Hull HU6 7RX, UK

ABSTRACT

Aim To examine and visualize clines in size and shape of Cercopithecus aethiops Linneus, 1758 (Primate, Cercopithecidae) skulls, and to investigate environmental factors which might best explain the observed variation. Location Sub-Saharan Africa. Methods Eighty-six three-dimensional anatomical landmarks were used to describe 306 skulls of adult C. aethiops sampled over its entire distribution. Geometric morphometric methods for the quantitative analysis of form variation were applied. Size and shape variables were computed and regressed onto geographical coordinates and environmental variables (elevation, temperature, rainfall, moisture and Shannon rainfall diversity index) using both linear and curvilinear models. Components (geographical, environmental, spatially structured environmental and residual) of ecogeographical variation in skull form were partitioned using partial regression. A novel approach for summarizing and visualizing nonlinear patterns of clinal variation using surface rendering of three-dimensional shapes is presented. Results Clinal variation in size and shape was highly significant, and was best described by curvilinear models. There were strong similarities between females and males. The cline in size was especially pronounced, explaining up to about 40% of observed variation, and was mainly longitudinal rather than latitudinal. A major trend of clinal shape variation also occurred from west to east, and corresponded to an expansion of the face relative to the neurocranium in the west. In the east, skulls also tended to be deeper and with narrower zygomatic arches. Geography and the spatially structured environmental component were the major contributors to the explained variance in size in both sexes, but the proportion of variance explained by the latter was smaller in females. In contrast, geography and environment explained similar amounts of variation in shape and their contribution was about twice that of the spatially structured environmental component. About 60–80% of variation in skull form was not explained by any variable in the analysis. The main factors influencing skull size differed in females and males, with rainfall being very influential in males. Both female and male skull shapes were strongly affected by average annual rainfall.

*Correspondence: Andrea Cardini, Dipartimento del Museo di Paleobiologia e dell’Orto Botanico, Universita´ di Modena e Reggio Emilia, via Universita` 4, 41100, Modena, Italy. E-mail: [email protected], [email protected]

Main conclusions A strong spatial and environmental basis to variations in African vervet monkey skull form was evident. However, the observed pattern did not conform to predictions based on Bergmann’s rule. Rainfall consistently emerged as an important predictor, which may contribute to intraspecific variation in the size and shape of vervet monkey skulls through its effect on habitat productivity. Keywords Africa, Bergmann’s rule, Cercopithecus aethiops, clinal variation, curvilinear models, geometric morphometrics, rainfall and productivity, skull size/shape, surface rendering, vervet monkeys.

ª 2007 The Authors Journal compilation ª 2007 Blackwell Publishing Ltd

www.blackwellpublishing.com/jbi doi:10.1111/j.1365-2699.2007.01731.x

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A. Cardini, A.-U. Jansson and S. Elton INTRODUCTION The impact of geography and the environment on phenotypic variability in animals has long been recognized, and the relevance of an ecogeographical perspective on inter- and intraspecific morphological differences, particularly size and relative body proportions, has been highlighted by several recent studies and reviews (e.g. Ashton et al., 2000; Meiri & Dayan, 2003; Lomolino et al., 2006; Millien et al., 2006). There is also increasing interest in examining clinal variation in shape through the use of advanced morphometric techniques (Viguier, 2002; Frost et al., 2003; Monteiro et al., 2003; Marroig et al., 2004; Santos et al., 2004). Ecogeographical variation has traditionally been studied with respect to spatial descriptors such as latitude but the increased availability of environmental data facilitates more complex multifactorial analyses (Millien et al., 2006). The covariance of spatial and environmental data is also being considered in much greater detail (Legendre & Legendre, 1998; Ruggiero & Kitzberger, 2004; Botes et al., 2006). Recent reviews of research into ecogeographical variation have indicated that although temperature can have an influential role in determining body size – a common interpretation of Bergmann’s rule – other climatic variables, such as rainfall and moisture, are often better correlates (Ashton et al., 2000; Millien et al., 2006). There is thus mounting evidence that traditional interpretations of the mechanisms behind ecogeographical rules are an oversimplification of a complex set of environmental and spatial interactions (Ashton et al., 2000; Millien et al., 2006). Several species of primate have featured in the vast literature on clinal and ecogeographical size variation, with size gradients identified in Brazilian tufted-eared marmosets (Albrecht, 1982), Malagasy sifakas (Lehman et al., 2005), Kenyan vervets (Turner et al., 1997) and pig-tailed and crab-eating macaques in south-east Asia (Albrecht, 1980; Fooden & Albrecht, 1993). There is also a cline in the maxillary sinus volumes of Japanese macaques (Rae et al., 2003). Temperature is highlighted as being an important contributory factor in many of these studies (e.g. Albrecht, 1982; Fooden & Albrecht, 1993; Rae et al., 2003) although a strict Bergmannian relationship is not always evident. For example, at latitudes between 8 S and 13 N, skull length in the crab-eating macaque increases with latitude and decreasing temperature on both sides of the Equator, but above 13 N, skull length decreases, a phenomenon attributed to past evolutionary processes (Fooden & Albrecht, 1993). When parts of the body other than the skull are considered, patterns remain indistinct, with tail length in the rhesus macaque following Allen’s rule above 26 N but not further south (Fooden & Albrecht, 1999). In living Kenyan vervets from four sites at different latitudes and elevations, clines in tail length and female body mass are detected, but other morphological traits, including male body mass, male and female body length, lower and upper leg lengths and lower and upper arm lengths, do not follow the pattern predicted by ecogeographical rules (Turner et al., 1997). Instead, increased access to human food may have a significant impact upon 1664

growth and development in these monkeys (Turner et al., 1997). The above examples hint at the probable complexity of environmental and spatial variation in primate morphology. When seeking to explain ecogeographical patterns, however, the main drawback of much work on primates is that spatial information, usually latitude, is used as a proxy for climatic and environmental variables. The actual relationships between climate and primate morphology are thus often unknown. Nonetheless, a limited number of studies examining body sizes in the context of measured climatic variables, including temperature, rainfall and elevation, have been undertaken on baboons. These indicate that rainfall, possibly acting via habitat productivity, is a major contributor to variation (Popp, 1983; Dunbar, 1990; Barrett & Henzi, 1997), although altitude (Anderson, 1982) and temperature (Dunbar, 1990) might influence body size in some individuals. More recently, in a study using direct climatic data, mean body size in sifakas has been positively correlated with annual rainfall and negatively correlated with seasonality, with body size differences attributed to variations in habitat productivity and resource seasonality (Lehman et al., 2005). Clinal variations in primate skull shapes have received less attention than size differences, but have been found to date in African baboons (Frost et al., 2003), Malagasy lemurs (Viguier, 2002) and Amazonian spider monkeys (Froehlich et al., 1991). In contrast, discontinuous rather than gradual change is observed in studies of several widely dispersed Neotropical marmoset and tamarin species (Moore & Cheverud, 1992; Marroig et al., 2004). Most studies examining variation in primate skull shape (or size and shape) across a wide geographical area have done so in order to test hypotheses relating to speciation events and taxonomy (Froehlich et al., 1991; Moore & Cheverud, 1992; Frost et al., 2003; Marroig et al., 2004). There has been less work on how skull shape responds to external environmental pressures. In one of the few such investigations, skull shape in the ring-tailed lemur and five Eulemur species appeared to be independent of phylogeny and constrained by environmental factors, including relative humidity (Viguier, 2002), although once again geographical distribution is used as a proxy for climate. Only a small number of studies have focused on clinal variation in African monkey species (Turner et al., 1997; Frost et al., 2003) and, although beyond the scope of the work we report here, a thorough examination of primate morphology across Africa with respect to ecogeographical rules is overdue. Additionally, little research has been undertaken on the relationships between environmental and spatial variables and primate skull morphology. The extensive geographical distribution of the African vervet monkey, Cercopithecus aethiops, makes it an ideal candidate for investigation of intraspecific ecogeographical and clinal variation, and provenanced skulls are well-represented in museum collections, enabling cross-referencing with geographical information system (GIS) and climatic data bases.

Journal of Biogeography 34, 1663–1678 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd

Ecogeographical variation in vervet monkeys The vervet is one of the most common and abundant African primates (Struhsaker, 1967), and is found in much of sub-Saharan Africa, from Senegal in the west to Somalia in the east and from the southern edge of the Sahara to coastal South Africa, although it is generally absent from the deserts of southern Africa and much of the Congo Basin (Fedigan & Fedigan, 1988). Vervet populations have been recorded from sea level up to 4500 m (Cawthon Lang, 2006) and exploit a range of habitats, including savanna grassland, cultivated land, many types of woodland, forest fringe areas and riverine forest (Kingdon, 1997). Environmental factors often exert a strong influence on their ecology and behaviour, and significant intraspecific variability in diet, foraging behaviour and sociality is evident (Fedigan & Fedigan, 1988). Vervets also show intraspecific variations in morphology. Pelage, external facial features such as whiskers and, in males, scrotal colour vary considerably between different C. aethiops groups (Groves, 2001). These have led to numerous taxonomic classifications (see Grubb et al., 2003, for a full review). One of the most well known is that of Kingdon (1997), who argues that C. aethiops is a superspecies subdivided into five largely geographically distinct species (C. aethiops, Cercopithecus tantalus, Cercopithecus sabaeus, Cercopithecus pygerythrus, Cercopithecus djamdjamensis). A sixth species, Cercopithecus cynosuros, is also recognized by some workers (Groves, 2001). In this paper, however, we follow Grubb et al. (2003) in treating all the variants as one species, and since there are many local names for C. aethiops across its distribution, including ‘vervet’, ‘grivet’ and ‘green monkey’, we also echo Fedigan & Fedigan (1988) and use the single common term ‘vervet’. Given the marked intraspecific differences in the external soft tissue morphology and coloration of vervets, variation in hard tissue might be expected. Interpopulation differences in body proportions are observed in living Kenyan vervets (Turner et al., 1997), but surprisingly, despite an extensive history of study, very little work has been undertaken on how vervet bone morphology, including skull form, varies in relation to geography and the environment. In this study we examine clinal variation in size and shape using linear and curvilinear regression models. In doing so, we present a novel approach for visualizing nonlinear patterns of clinal variation using surface rendering of three-dimensional shapes. In addition, the effects of environmental and climatic variables on skull form are explored using partial regression to partition the proportion of variance explained by the model into three sets of components: non-environmental spatial (in which the variance is explained exclusively by longitude and latitude), non-spatial environmental (explained exclusively by the environmental variables temperature, elevation, rainfall and moisture), and spatially structured environmental (longitude and latitude in combination with the environmental variables). Full regression models, in which size or shape are regressed onto both geography and environment, are used to investigate which environmental variables are likely to be related to changes in form once the effect of spatial distribution has been taken into account. Specifically, as primary habitat productivity

is increasingly identified as an important predictor of mammalian body size (Dunbar, 1990; Ashton et al., 2000; Lehman et al., 2005; Millien et al., 2006), we investigate whether rainfall, as a proxy for habitat productivity (Chapman & Chapman, 1990; Dunbar, 1990; Chapman & Balcomb, 1998), is a better predictor of morphology (both size and shape) than are temperature (Bergmann’s rule) and other environmental variables. MATERIAL AND METHODS Data collection The sample comprised 306 adult specimens of C. aethiops Linnaeus, 1758, of which 131 were female and 175 were male. The maturity of each specimen was judged on the basis of full eruption of canines and third molars. Specimens came from the collections of the National Museum of Natural History (Washington, USA), the American Museum of Natural History (New York, USA), the Museum of Comparative Zoology of Harvard University (Cambridge, USA), the Field Museum of Natural History (Chicago, USA), the Museum fu¨r Naturkunde of the Humboldt University (Berlin, Germany), the Zoologische Sammlung des Bayerischen Staates (Munich, Germany), the Royal Museum for Central Africa (Tervuren, Belgium), the British Museum of Natural History (London, UK) and the Powell-Cotton Museum (Birchington, UK). The list of museum specimens used in this study is available from the authors upon request. Three-dimensional coordinates of anatomical landmarks were directly collected by the same person on crania and mandibles using a three-dimensional digitizer (MicroScribe 3DX; Immersion Corporation, San Jose, CA, USA). Landmarks were digitized only on the left side to avoid redundant information in symmetric structures. The set (configuration) of 86 landmarks used for the analysis is shown in Fig. 1. Definitions of landmarks can be found in Table 1. Landmarks on crania and mandibles were digitized separately. Data collected on the mandible were then aligned onto the same coordinate system as those collected on the cranium by applying a least-squares superimposition (see below) of three registration points digitized twice (first with mandibles articulated on the cranium and then with disarticulated mandibles) on pieces of Plasticine affixed to the mandible. The three landmarks used for matching the cranium and mandible configurations were eventually discarded and only the 86 anatomical landmarks were used in the analyses. Measurement error and estimates of a small number of missing landmarks (1–2 in 13.7% and 3–6 in 3.1% of specimens), had negligible effects on the analysis. Data analysis Split-sex samples were used in all analyses due to the large degree of sexual dimorphism observed in C. aethiops and common to the guenons as a whole. All analyses were repeated after excluding potential outliers for size, shape, geography and

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A. Cardini, A.-U. Jansson and S. Elton

Figure 1 Landmark configuration including all landmarks (definitions in Table 1).

environmental variables. Outliers were identified using variable scatterplots (including those of residuals in regressions) and by looking for extreme cases in variable box-plots. The SAM 1.1. (Rangel et al., 2006) procedure for detection of spatial outliers was also used. Differences in findings with or without potential outliers are shown when significant. Geometric morphometric analyses (Rohlf & Marcus, 1993; Adams et al., 2004) were performed in the following computer programs: Morpheus (Slice, 1999), Morphologika (O’Higgins & Jones, 2006), TPSSmall 1.20 (Rohlf, 2003), NTSYS-pc 2.2c (Rohlf, 2005a). Statistical analyses were performed using SPSS 11.5.0 (SPSS Inc., 2004), NTSYS-pc 2.2d (Rohlf, 2005a) and SAM 1.1 (Rangel et al., 2006). A geometric morphometric analysis involves a series of main steps which are briefly described here. The form of an organism (or its organs) is first captured by the Cartesian coordinates of a three-dimensional configuration of anatomical landmarks. Differences in landmark coordinates, due to the position of the specimens during the digitization process, are then removed, and size is standardized. This was achieved in our study by optimally superimposing landmark configurations using a process called generalized Procrustes analysis (GPA), which is based on a least-squares algorithm (Rohlf & Slice, 1990). Centroid size (henceforth simply called ‘size’ for brevity) is a measure of the dispersion of landmarks around their centroid and is computed as the square root of the sum of 1666

squared distances of all landmarks from the centroid. The new Cartesian coordinates obtained after the superimposition are the shape coordinates used for statistical comparisons of individuals. The shape differences between landmark configurations of two individuals can be summarized by their Procrustes distance, which is the square root of the sum of squared distances between pairs of corresponding landmarks. An extensive introduction to applications of geometric morphometrics in biology is provided by Zelditch et al. (2004) (see also Rohlf, 2005b). Detailed mathematical descriptions of geometric morphometric methods are available in Bookstein (1991) and Dryden & Mardia (1998). Guidelines on how to implement linear statistical models in geometric morphometrics can be found in Rohlf (1998) and Klingenberg & Monteiro (2005). Shape coordinates are redundant (seven degrees of freedom are lost in the GPA of three-dimensional data) and often highly correlated. Thus, the number of variables used for the analysis of shape was reduced by including only the first principal components of the 258 shape coordinates. The number of principal components to be analysed was selected by measuring the correlation between the matrix of Procrustes shape distances in the full shape space and pairwise Euclidean distances in the reduced shape space (5, 10, 15 principal components, and so on). Plots of correlation coefficients onto the number of components can be used in a similar way to

Journal of Biogeography 34, 1663–1678 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd

Ecogeographical variation in vervet monkeys Table 1 Definition and numbering of landmarks (L). The terms ‘anterior’ and ‘posterior’ are used with reference to Fig. 1. Landmarks 65–86 are on the mandible. L

Definition

1 2 3 4 5 6–9 10 11–14 15 16 17 18 19 20 21 22 23 24 25–26 27 28,30 29 31 32, 35 33 34 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

Prosthion: antero-inferior point on projection of pre-maxilla between central incisors Prosthion2: antero-inferior-most point on pre-maxilla, equivalent to prosthion but between central and lateral incisors Posterior-most point of lateral incisor alveolus Anterior-most point of canine alveolus Mesial P3: most mesial point on P3 alveolus, projected onto alveolar margin Contact points between adjacent pre-molars/molars, projected labially onto alveolar margin Posterior midpoint onto alveolar margin of M3 Contact points between adjacent pre-molars/molars, projected lingually onto alveolar margin Posterior-most point of incisive foramen Meeting point of maxilla and palatine along midline Greater palatine foramen Point of maximum curvature on the posterior edge of the palatine Tip of posterior nasal spine Meeting point between the basisphenoid and basioccipital along midline Meeting point between the basisphenoid, basioccipital and petrous part of temporal bone Most medial point on the petrous part of temporal bone Most medial point of the foramen lacerum Meeting point of petrous part of temporal bone, alisphenoid and base of zygomatic process of temporal bone Anterior and posterior tip of the external auditory meatus Stylomastoid foramen Distal and medial extremities of jugular foramen Carotid foramen Basion: anterior-most point of foramen magnum Anterior and posterior extremities of occipital condyle along margin of foramen magnum Hypoglossal canal Centre of condylar fossa Opisthion: posterior-most point of foramen magnum Inion: most posterior point of the cranium Most lateral meeting point of mastoid part of temporal bone and supraoccipital Nasospinale: inferior-most midline point of piriform aperture Point corresponding to largest width of piriform aperture Meeting point of nasal and pre-maxilla on margin of piriform aperture Rhinion: most anterior midline point on nasals Nasion: midline point on fronto-nasal suture Glabella: most forward projecting midline point of frontals at the level of the supraorbital ridges Supraorbital notch Frontomalare orbitale: where frontozygomatic suture crosses inner orbital rim Zygo-max superior: antero-superior point of zygomaticomaxillary suture taken at orbit rim Centre of nasolacrimal foramen (fossa for lacrimal duct) Centre of optic foramen Uppermost posterior point of maxilla (visible through pterygomaxillary fissure) Frontomalare temporale: where frontozygomatic suture crosses lateral edge of zygoma Maximum curvature of anterior upper margin of zygomatic arch Zygo-max inferior: antero-inferior point of zygomaticomaxillary suture Zygo-temp superior: superior point of zygomaticotemporal suture on lateral face of zygomatic arch Zygo-temp inferior: infero-lateral point of zygomaticotemporal suture on lateral face of zygomatic arch Posterior-most point on curvature of anterior margin of zygomatic process of temporal bone Articular tubercule Distal-most point on post-glenoid process Posterior-most point of zygomatic process of temporal bone Foramen ovale (posterior inferior margin of pterygoid plate) Meeting point of zygomatic arch and alisphenoid on superior margin of pterygomaxillary fissure Meeting point of zygomatic arch, alisphenoid and frontal bone Bregma: junction of coronal and sagittal sutures Lambda: junction of sagittal and lamboid sutures

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A. Cardini, A.-U. Jansson and S. Elton Table 1 continued. L

Definition

65 66 67 68 69 70–73 74 75–78 79 80–81 82 83 84 85 86

Antero-superior point of mandible between central incisors Antero-superior point of mandible between lateral incisors Posterior-most point of lateral incisor alveolus Anterior-most point of canine alveolus Mesial P3: most mesial point on P3 alveolus, projected onto alveolar margin Contact points between adjacent pre-molars/molars, projected labially onto alveolar margin Posterior midpoint onto alveolar margin of M3 Contact points between adjacent pre-molars/molars, projected lingually onto alveolar margin Superior tip of coronoid process Most lateral and most medial points on mandible condylar surfaces Anterior-most point on roughening for attachment of masseter on inferior margin of the angle of mandible Mandibular foramen Posterior-most point on superior area of insertion of medial pterygoid Region of insertion of genioglossus muscles (midline posterior-most point on upper ‘ridge behind incisors’) Region of insertion of geniohyoid muscles (midline posterior-most point on lower ‘ridge behind incisors’)

scree plots to select how many variables summarize most shape variation. Depending on the data set used, 30–50 principal components provided a very good summary of total shape variation (79–86% of total variance; correlation with distances in the full shape space ‡ 0.99). Trend surface analysis (Legendre & Legendre, 1998; Ruggiero & Kitzberger, 2004; Botes et al., 2006) 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 the trend surface analysis were compared with those of simple linear models. Thus, size or shape variables were regressed onto latitude and longitude, and the percentage of variance explained was used to compare the goodness of fit of the models. Specimens were plotted according to geographical coordinates on a map of Africa using ArcView GIS 3.2 (1999). Clinal variation predicted by the best fit trend surface was illustrated with grey-scale colour symbols on the map. This can be easily done for size, which is described by a single variable. Thus, grey symbols of a tone proportional to the size of the skull predicted by geography were used. The visualization of nonlinear clines is less straightforward for shape, which is multivariate. Thus, a method was developed that summarized the main trends of clinal variation and allowed their visualization using surface rendering of skull shapes. As for size, clinal variation in shape predicted by the best fit trend surface was first computed and scores of predictions saved. The variables describing the predicted cline in shape were then subjected to a k-means cluster analysis. This was done in order to discriminate homogeneous groups of shapes predicted by geography (‘geographical shapes’, for brevity) to be used in the next step. The number of a priori clusters (six for females and seven for males) was arbitrarily chosen to achieve a compromise between accuracy of visualization and simplicity of interpretation. 1668

Groups obtained in the k-means cluster analysis were plotted on a map of Africa using different symbols. Eventually, variation within each group of ‘geographical shapes’ identified in the previous steps was summarized by using its mean, which was visualized using surface rendering. Minimum spanning trees (Rohlf, 1970) superimposed on scatterplots (> 80% of shape variance) of mean ‘geographical shapes’ were computed to aid the identification of similarity relationships among geographical clusters. The environmental variables used in the analysis were elevation, temperature, rainfall and moisture (annual averages and monthly standard deviations) and the Shannon rainfall diversity index, a measure of the differences in mean monthly rainfall over a 12-month period, with less seasonal environments represented by higher index values (Hill & Dunbar, 2002; Korstjens & Dunbar, in press). Elevation data were extracted from the SRTM 30 digital elevation model of Africa (data available from USGS/EROS, Sioux Falls, SD, USA) using the ‘extract values to points’ procedure in ArcGIS 9.0 (2004) Spatial Analyst. Climatic variables were taken from the Willmott and Matsuura data base (Legates & Willmott, 1990a,b; Willmott & Feddema, 1992; Willmott & Matsuura, 2001) which provides data at 0.5 grids. Partial linear regression was used to assess the effects of spatial structuring of variables and estimate the amount of variation of skull size or shape that could be attributed to one set of factors (environment) once the effects of the other (geography) had been taken into account (Legendre & Legendre, 1998; Ruggiero & Kitzberger, 2004; Botes et al., 2006). Thus, terms of the bestfit trend surface (spatial component) and environmental variables were combined and morphological variation was partitioned into four components: (1) non-environmental spatial (proportion of variance exclusively explained by geography); (2) spatially structured environmental (proportion of variance explained by both geography and environment); (3) non-spatial environmental (proportion of variance explained exclusively by environment); (4) unexplained

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Ecogeographical variation in vervet monkeys variation (proportion of variance explained by the effect of other factors). The full model, in which size or shape was regressed onto both geography and environment, was examined in order to investigate which environmental variables may be related to changes in form once the effect of spatial distribution had been taken into account. A backward elimination procedure was also employed to aid the interpretation of results from the full model. RESULTS Clinal variation Significant clinal variation in vervet monkey skull size was found both in males and in females (Table 2). The inclusion of curvilinear terms increased the proportion of size variance explained by geography from about 20% or less in the linear model to around 30–40%. Cubic terms of longitude were significant in both sexes, as were quadratic terms of latitude. The exclusion of potential outliers did not appreciably change the outcome of the analysis, with results showing similar percentages of explained variance and almost identical predictions. Patterns of clinal variation in size are visualized in Figs 2 and 3 using predictions of the trend surface analysis. A very similar trend was suggested in males and females. Skull size tended to be larger in West and Central Africa. Smaller skulls were generally found in East Africa. The most pronounced reduction in size was found in individuals from the Horn of Africa. Clinal variation was also highly significant for shape (Table 3). As was the case for size, the inclusion of curvilinear terms almost doubled the amount of shape variance explained by geography (from about 5% to 10% or more), and the exclusion of potential outliers had a negligible impact on the analysis. A visual summary of the pattern of clinal shape variation according to predictions of the trend surface analysis is shown in Figs 4 and 5. Again, like size, patterns of female and male ‘geographical shape’ variation were largely congruent. A major trend occurred from west (1) to east (3), corresponding to an expansion of the face relative to the neurocranium in the west. The eastern cluster also tended to

Table 2 Clinal variation in skull size: goodness of fit (R2) and significance of trend surface analysis and linear regression onto geographical coordinates (x, longitude; y, latitude). P values which are significant are in italics in this and other tables.

Sex

Predictors

R2 (%)

Female

x2, x3, y2, xy2 x, y x3, y, y2 x, y

38.6 16.4 28.6 21.1

Male

Adj. R2 (%)

d.f.1

d.f.2

F

P<

36.6 15.1 27.4 20.1

4 2 3 2

126 128 171 172

19.793 12.595 22.869 22.932

0.0001 0.0001 0.0001 0.0001

have deeper skulls and narrower zygomatic arches. Individuals inhabiting Central Africa and regions to the north of South Africa (4, 5, 7) had less distinctive skull shapes, somewhat intermediate between those from West and East Africa. South African (6) specimens had more distinctive shapes, with differences between females and males. The former had an enlarged neurocranium, the latter a relatively compressed skull. South African male skull shape was reminiscent of the forms found in the West African group, albeit with a face that was less elongated. Individuals from Sierra Leone and adjacent countries to the east (2) were also fairly distinctive but shared more similarities with the Central and East African groups than with the western one. Geographical, spatially structured environmental and non-spatial environmental component of variation Results of partial linear regressions of size or shape onto geography and environment are shown in Table 4. The proportions of variance explained by different components suggested similar patterns in females and males, except for female size when all specimens were included. However, when potential outliers were removed and analyses repeated, the results of the partial regressions of female size were comparable to those of males. Thus, the spatially structured environmental component tends to explain most variation in size (about 10– 30%). Geography (non-environmental spatial component) also explained a large proportion of size variation (10–20%), while environment alone (non-spatial environmental component) explained < 5%. Residual variation which cannot be related to any variable in the analysis accounted for about 60– 70% of size variance. Contrary to size, the spatially structured environmental component explained less shape variance (3– 4%) than geography (6–8%) or environment (7–8%) alone. Unexplained variance was about 80%. Ecological variation In the full model, regressions of size onto geography and environment were highly significant (females, F12,118 ¼ 7.802, P < 0.001; males, F11,163 ¼ 8.355, P < 0.001). Multivariate regressions of shape onto geography and environment were also highly significant (females, Wilks’ k ¼ 5 · 10)5, F585,965.1 ¼ 1.993, P < 0.001; males, Wilks’ k ¼ 3.6 · 10)5, F750,1653.7 ¼ 2.285, P < 0.001). Coefficients of variables in the full model (geography and environment together), with corresponding significance values, are shown in Table 5 (size) and Table 6 (shape). Significant variables often differed between sexes and also within sex if analyses were performed with or without potential outliers. For instance, average annual temperature was significant in the regression of size in females but not in that of males or in the regression of female size after removing potential outliers. Thus, a clear indication of the environmental factors that influenced the size and shape of vervet monkey skulls did not emerge. Backward elimination regressions of size and shape onto geography and environment

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Figure 2 Patterns of clinal variation in female size: predictions of the trend surface analysis. The grey scale of symbols is according to increasing size. The Hammer– Aitoff equal-area map projection is used in this and the following figures.

Figure 3 Patterns of clinal variation in male size: predictions of the trend surface analysis. See Fig. 2 for legend.

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Ecogeographical variation in vervet monkeys Table 3 Clinal variation in skull shape: goodness of fit (percentage of explained variance) and significance of trend surface analysis and linear regression* onto geographical coordinates (x, longitude; y, latitude). Sex

Predictors

Female

x, x, x, x,

Male

x2, y2, xy, x2y y x2, x3, y3, xy, x2y, xy2 y

Explained (%)

Wilks’ k

F

d.f.1

d.f.2

P<

9.6 5.3 11.8 6.3

0.00680 0.109 0.00283 0.106

3.135 3.781 3.159 5.093

225 90 350 100

408.4 168.0 832.2 246.0

0.0001 0.0001 0.0001 0.0001

*Multivariate multiple regressions were performed using the first 45 principal components of female shape variables and the first 50 of the males.

Figure 4 Patterns of clinal variation in female shape: predictions of the trend surface analysis. Predicted shapes are grouped in six homogeneous clusters (see Material and methods). Surface rendering for cluster mean shapes is used to summarize main patterns in clinal shape variation. A minimum spanning tree is used to emphasize similarity relationships of cluster mean shapes.

were therefore performed to see if the picture could be clarified. The analyses confirmed that the main factors influencing skull size might differ in females and males. The former were mostly affected by elevation (b ¼ 0.349, P ¼ 0.004) and average annual temperature (b ¼ 0.323, P ¼ 0.006), the latter by rainfall (annual average, b ¼ 0.816, P < 0.001; monthly standard deviation, b ¼ 0.595, P ¼ 0.003). However, after excluding outliers, average annual rainfall was the only significant environmental variable for females (b ¼ 0.315, P < 0.001). Backward elimination regressions of shape suggested some similarities between females and males, with average annual rainfall and moisture having a strong influence on skull shape in both sexes (P < 0.01 in all analyses). The average annual temperature and the standard deviation of monthly moisture might also be important, being highly significant (P < 0.01) in all regressions except one (female sample including potential outliers).

DISCUSSION Size variation Skull size is used as a proxy for body size in this study, in line with other similar work on primates (Fooden & Albrecht, 1993). The major size trend in vervet skulls was longitudinal rather than latitudinal, although latitude did contribute to some of the variance explained by both the linear and curvilinear models. Many studies testing Bergmann’s rule have done so on the basis of latitudinal gradients (Angilletta et al., 2004), and it is apparent that size variations in vervet skulls do not conform to expectations based simply on latitude. Thermoregulation is often cited as the mechanism underlying Bergmannian clines. Temperature varies with elevation as well as latitude, and in a landmass as large and topographically varied as Africa, elevation and

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Figure 5 Patterns of clinal variation in male shape: predictions of the trend surface analysis. Predicted shapes are grouped in seven homogeneous clusters and their mean shapes are used as in Fig. 4 to summarize main patterns in clinal variation. Table 4 Partial regressions of size and shape onto geography and environment: percentages of variance explained by different components. Size

Shape*

Components

Females 

Males

Females

Males

(1) (2) (3) (4)

25.7 11.0 1.9 61.4

9.6 17.8 4.4 68.3

6.4 3.2 8.6 81.8

7.8 4.0 7.3 80.9

Geography only Common Environment only Unexplained

*Multivariate multiple regressions were performed using the first 45 principal components of female shape variables and the first 50 of the males.  The partial regression of female size is the only case where results are appreciably different if potential outliers are excluded [adjusted R2 without potential outliers: (1) 19.8%, (2) 27.5%, (3) 3.7%, (4) 49.1%].

temperature could contribute alongside spatial factors to differences in skull size. It has been argued that altitude affects body size in baboons (Anderson, 1982), another widely dispersed and eurytopic African Old World monkey, although this has not been supported in all subsequent work (Dunbar, 1990). In our study, elevation was only significant in one case (backward elimination regression of size including all females). Temperature was significant only in females when all specimens were included, whereas standard deviation of monthly temperature was significant only in males excluding outliers. These observations do not provide strong support for Bergmann’s rule. 1672

Thermoregulatory demands contribute to differing body masses in some macaques (Albrecht, 1980; Fooden & Albrecht, 1993), and the plasticity of body size in response to differing climates in at least some cercopithecids is clear from studies of translocated macaques reared in different parts of the United States (Paterson, 1996; Clarke & O’Neil, 1999). Evidence for differences in body size because of temperature in other Old World monkeys is often patchy (see, for example, Dunbar, 1990; Barrett & Henzi, 1997; Turner et al., 1997), and our results suggest that large skull size, and by extension body size, may not simply be an adaptation in vervets to relatively cold environments. In the Drakensbergs, South Africa, it is likely that high thermoregulatory costs in a relatively resource-poor area impact upon reproduction in female baboons and the growth rates of their offspring, leading to smaller body sizes (Barrett & Henzi, 1997). This contradicts expectations based on Bergmann’s rule, and shows the importance of resource availability in primate growth and development, with the synergy between the energetic expense of keeping warm and finding adequate food in a hostile environment likely to be a crucial causal factor in the low adult body masses of the Drakensberg population. We found no convincing link between low temperatures and very small body masses, and concluded that temperature alone is unlikely to be a major selective pressure for either small or large body mass in vervet monkeys. However, further investigations into whether the interaction between thermoregulatory costs and resource availability affects vervet body size might be valuable. Rainfall consistently emerges as an important predictor variable in studies of Old World monkey body size (Popp,

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Ecogeographical variation in vervet monkeys Table 5 Regression of size onto geography and environment (full model): raw (b) and standardized (b) coefficients, standard errors of raw coefficients (SEb), and t-tests of significance of predictors. Females Variables Geography x2 x3 y y2 xy2 Environment Elevation Annual temperature Temperature month SD Annual rainfall Rainfall month SD Annual moisture Moisture month SD Shannon rainfall index

b

Males SEb

b

t

P 

b

SEb

b

t

P 

0.056 )0.002 – – 0.000

0.016 0.000 – 0.019 0.001

2.313 )3.118 – )0.372 0.207

3.627 )4.705 – )1.209 0.782

0.000* 0.000* – 0.229 0.436

– 0.000 )0.593 )0.003 –

– 0.000 0.168 0.010 –

– )0.480 )0.492 )0.055 –

– )4.085 )3.533 )0.340 –

– 0.000* 0.001* 0.734 –

0.005 1.524 2.255 )0.012 0.151 9.338 )9.414 56.304

0.003 0.724 1.987 0.010 0.092 11.759 12.477 23.411

0.270 0.436 0.179 )0.507 0.483 0.288 )0.117 0.496

1.780 2.104 1.135 )1.238 1.641 0.794 )0.754 2.405

0.078 0.038 0.259 0.218 0.103 0.429 0.452 0.018

0.003 )0.110 )4.603 0.040 )0.254 )24.329 )8.019 )81.745

0.003 1.103 2.462 0.016 0.118 18.031 13.868 28.864

0.136 )0.025 )0.309 1.175 )0.600 )0.548 )0.079 )0.597

1.010 )0.100 )1.870 2.515 )2.156 )1.349 )0.578 )2.832

0.314 0.920 0.063* 0.013* 0.033* 0.179* 0.564 0.005*

 Asterisks indicate significant predictors after excluding potential outliers.

Table 6 Regression of shape onto geography and environment: multivariate tests of significance of predictors. Females Variables Geography x x2 x3 y y2 y3 xy x2y xy2 Environment Elevation Annual temperature Temperature month SD Annual rainfall Rainfall month SD Annual moisture Moisture month SD Shannon rainfall index

Males

Wilks’ k

F

d.f.1

d.f.2

P 

Wilks’ k

F

d.f.1

d.f.2

P 

0.422 0.420 – – 0.415 – 0.426 0.459 –

2.225 2.238 – – 2.288 – 2.187 1.915 –

45 45 – – 45 – 45 45 –

73 73 – – 73 – 73 73 –

0.001* 0.001* – – 0.001* – 0.001 0.007 –

– 0.509 0.461 0.389 – 0.581 0.390 0.528 0.457

– 2.119 2.568 3.453 – 1.584 3.444 1.965 2.617

– 50 50 50 – 50 50 50 50

– 110 110 110 – 110 110 110 110

– 0.001* 0.000* 0.000* –* 0.024 0.000 0.002 0.000*

0.524 0.562 0.634 0.503 0.515 0.508 0.540 0.626

1.476 1.265 0.937 1.601 1.528 1.571 1.380 0.969

45 45 45 45 45 45 45 45

73 73 73 73 73 73 73 73

0.069 0.183* 0.586 0.036* 0.053* 0.042 0.109* 0.538

0.602 0.528 0.680 0.562 0.560 0.503 0.611 0.626

1.452 1.963 1.038 1.715 1.727 2.178 1.398 1.313

50 50 50 50 50 50 50 50

110 110 110 110 110 110 110 110

0.054* 0.002* 0.427 0.010* 0.009* 0.000* 0.075* 0.120*

 Asterisks indicate significant predictors after excluding potential outliers.

1983; Dunbar, 1990; Barrett & Henzi, 1997; Clarke & O’Neil, 1999) as well as behaviour (Bronikowski & Altmann, 1996). It has also been shown to influence size in several other mammals, including Malagasy sifakas (Lehman et al., 2005) and the southern African pouched mouse (Ellison et al., 1993). Rainfall can be used as a proxy for habitat productivity, which influences body mass via food availability (Chapman &

Chapman, 1990; Dunbar, 1990; Barrett & Henzi, 1997; Chapman & Balcomb, 1998). In our study, annual rainfall was a significant predictor in most analyses of males and in one analysis of females, and higher rainfall, probably acting via greater habitat productivity, appears to be related to larger male skull size in vervets. This is consistent with the positive correlations between rainfall and skull size in both females and

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A. Cardini, A.-U. Jansson and S. Elton males. Interestingly, a similar trend in body size was not found in wild-living Kenyan vervets at a high-rainfall site with good habitat productivity (Turner et al., 1997), which points to a degree of variation in the association across Africa. Seasonality, viewed as a key determinant of body mass in a number of animals and shown to affect Old World monkey foraging behaviour (Hill & Dunbar, 2002; Hill, 2005), could also contribute to differences in size: larger body sizes are often observed in more seasonal environments as a result of slower and more prolonged growth (Vrba, 1995) or because a bigger body can provide a buffer in times of nutritional stress (Boyce, 1978; Macho et al., 1996). Rainfall seasonality (the Shannon rainfall diversity index) was influential in our model, in both male and female analyses. Thus, it might also contribute to differences in vervet skull size, with smaller skulls generally being found in less seasonal habitats, the pattern that is predicted based on past work (Boyce, 1978). Differences between males and females were evident in the environmental analyses of vervet skull size. The proportion of variance explained by the environment was smaller in females, suggesting contrasting external influences on growth and development between the sexes. These findings resemble those from research on common baboons, in which body mass in females appeared less responsive than that of males to environmental pressures (Dunbar, 1990). Contrasting lifehistory strategies are seen as crucial to understanding sexual differences in size in baboons and vervets (Dunbar, 1990; Turner et al., 1997). Female vervets become reproductively active earlier than males, and consequently mature faster (Turner et al., 1997), a process that is evident in the skeleton of C. aethiops (Bolter & Zihlman, 2003) and which also occurs in baboons (Dunbar, 1990). Female growth is therefore truncated by the energetic expense of reproduction, with the result that proximate environmental factors such as habitat productivity tend to have a reduced effect on a final post-menarchical growth spurt, since energy is diverted into gestation and lactation (Dunbar, 1990). There are no such constraints on male post-pubertal development, leading to greater responsiveness of body size to proximate environmental influences (Dunbar, 1990). The sex-specific pattern we identified, however, needs to be confirmed on a larger sample of female vervets since potential outliers had a strong influence on results of the partial regression. Notwithstanding the observed influence of environment and geography on vervet skull size, a relatively large proportion of size variance was unexplained. Ecological pressures, such as predation rate, can affect body size (Isbell, 1994; Hill & Dunbar, 1998), as can increased food availability through exploitation of human settlements and cultivated areas (Strum, 1991; Turner et al., 1997). Neither of these have been accounted for in our analyses, but they are highly likely to have contributed to the intraspecific variation seen in the vervet sample. Identifying causal factors for differences in form is made more problematic because primates adapt behaviourally as well as morphologically and physiologically to their environments. Variations in primate time budgets, activity 1674

patterns and group sizes can occur to maintain access to resources in different environments (Hill & Dunbar, 2002; Korstjens et al., 2006; Korstjens & Dunbar, in press), which might reduce the extent of morphological adaptation. Nonenvironmental factors can also affect morphology, and clines can be an indicator of admixture and gene flow (Storz, 2002). Addressing the contribution of dietary and genetic factors to geographical variation in vervet morphology will therefore require more detailed field and molecular data than are currently available. Shape variation Shape variation was evident in both male and female vervets across Africa, with the sexes displaying similar patterns. Despite the clear geographical differences, vervet skull shape apparently showed much less of a response than size to external climatic pressures. This is perhaps unsurprising, given the highly plastic and adaptive nature of size. However, previous studies correlating environment and skull morphology have come to a variety of conclusions with respect to the relative responses of size and shape. In the Brazilian punare´ rat, skull shape apparently followed an environmental gradient but size did not, in contrast to the results of at least two other mammalian studies (Monteiro et al., 2003) as well as those reported here. Such differences can possibly be attributed to the complexity of the interaction between body form and proximate and ultimate agents of change. Annual rainfall and moisture, standard deviation of monthly rainfall, monthly moisture and annual temperature all had an influence on skull shape in both males and females. However, annual rainfall was the only significant predictor of shape in all analyses for both sexes. This reinforces the notion that although rainfall has less striking links with shape than size, it still makes an important overall contribution to variation in vervet skull morphology. In addition to differences that relate to environmental adaptation, shape changes in the skull can occur as a result of population bottlenecks (Cardini, 2003) and other evolutionary processes such as gene flow between populations. Genetic factors might therefore account for some of the clinal variation. A more detailed interpretation of shape differences in this context is difficult, as much remains to be discovered about the origin and differentiation of vervets. Data from the X chromosome suggest that the terrestrial guenons (vervets, patas monkeys and L’Hoest’s monkeys) began to diversify around 4.8 Mya (Tosi et al., 2005) but to date no detailed studies of vervet phylogeography and dispersal have been undertaken. Kingdon (1971) argued, on the basis of external morphology, that vervets originated in southern and south-west Africa and moved north and east to exploit grasslands. However, there is no vervet fossil record in southern Africa until the Middle to Late Pleistocene, well after the splitting event within the terrestrial guenon clade, and it has instead been suggested that C. aethiops was a southern African immigrant (Elton, 2007). Pending suitably

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Ecogeographical variation in vervet monkeys detailed data, further work correlating genetic and geographical distances between populations of C. aethiops might shed light on the processes that contributed to geographical variation. Levels of significance for spatial and environmental predictors were very high, but approximately 80% of variance in shape in both male and female vervets was unexplained by the ecogeographical variables used in our analyses. Some interand intraspecific shape differences in mammalian skulls are linked to diet (e.g. Milne & O’Higgins, 2002; Goheen et al., 2003; Lieberman et al., 2004; Mavropoulos et al., 2004; Semprebon et al., 2004; Viguier, 2004; Wright, 2005; Taylor, 2006), reflecting selective pressures (Goheen et al., 2003) as well as individual ontogenetic plasticity and remodelling (Lieberman et al., 2004; Mavropoulos et al., 2004). Shifts in feeding behaviour and resource use can result in spatially based variation in skull morphology: mandibular morphology in geographically separated orangutan populations, for example, varies predictably with differences in the mechanical properties of fallback foods (Taylor, 2006). Vervets are dietary generalists that exploit a wide range of foods throughout the year, including fruits, seeds, gum, invertebrates and leaves (Fedigan & Fedigan, 1988). Food availability and choice is determined to some extent by the environment, including temperature and rainfall, so diet is partially but indirectly included in our analysis. Nonetheless, it is likely that differences in diet and feeding behaviour contribute directly to the morphological variation observed in the vervet sample, forming part of the unexplained proportion of variance. CONCLUSIONS It is clear that there is a strong spatial and environmental basis to variation in skull morphology in the African vervet monkey. Skulls decreased in size from West and Central Africa to East Africa, with the smallest individuals, both male and female, found in Ethiopia. However, the observed pattern did not conform to predictions based on Bergmann’s rule. There was also a statistically significant west to east cline in skull shape: vervets in the west (specifically Senegal, the Gambia and Guinea Bissau in females and those countries plus Sierra Leone in males) had an expanded face relative to the neurocranium, with vervets in the east, particularly in Ethiopia and eastern Sudan, showing the opposite pattern alongside deeper skulls and narrower zygomatic arches. Rainfall consistently emerged as an important predictor of shape but also contributed together with seasonality to explain variation in skull size. Rainfall, as a proxy for habitat productivity, might be central to intraspecific size variations in vervet monkeys, and possibly also influences skull shape, although the mechanisms by which this operates are less clear. Notwithstanding the observed influence of environment and geography on vervet skull morphology, a relatively large proportion of variance was unexplained, and this study has highlighted several areas in which further research could elucidate other contributory factors. Detailed data on intraspecific genetic differences and

population divergence dates, as well as a methodical comparison of dietary variations and the influence of social structure within C. aethiops, would benefit our understanding of morphological variation in this fascinating group of animals enormously.

ACKNOWLEDGEMENTS We thank J.A.F. Diniz-Filho, P.J. Halls, D. Fontaneto, and P.O’Higgins for help with various aspects of this study. We also thank R. Dunbar, J. Lehmann, M. Korstjens, R. Hill and E. Willems for stimulating discussions about the background to this work, and are exceedingly grateful to M. Korstjens for providing the climatic data and giving patient advice on its use. Finally, we would like to thank two anonymous referees for their helpful comments and suggestions, which greatly contributed to improving the manuscript. This study was funded by a grant from the Leverhulme Trust.

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BIOSKETCHES Andrea Cardini is a Lecturer in Biology at the University of Modena and Reggio Emilia. His main interest is the study of the phylogenetic signal in animal morphology and his work focuses on the ontogenetic, ecological, demographic and biogeographical factors which may have an influence on form and contribute to setting the tempo and mode of morphological evolution. Anna-Ulla Jansson is a research student at Hull York Medical School. Her research concerns locomotor and climatic variation in the guenon post-cranial skeleton. Sarah Elton is Senior Lecturer in Anatomy at the Hull York Medical School, UK. Her work focuses on ecological and morphological variation in modern and fossil monkeys.

Editor: Brett Riddle

Journal of Biogeography 34, 1663–1678 ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd