When size makes a difference: allometry, life-history and morphological evolution of capuchins (Cebus) and squirrels (Saimiri) monkeys (Cebinae, Platyrrhini)

BMC Evolutionary Biology20077:20

DOI: 10.1186/1471-2148-7-20

Received: 01 August 2006

Accepted: 14 February 2007

Published: 14 February 2007

Abstract

Background

How are morphological evolution and developmental changes related? This rather old and intriguing question had a substantial boost after the 70s within the framework of heterochrony (changes in rates or timing of development) and nowadays has the potential to make another major leap forward through the combination of approaches: molecular biology, developmental experimentation, comparative systematic studies, geometric morphometrics and quantitative genetics. Here I take an integrated approach combining life-history comparative analyses, classical and geometric morphometrics applied to ontogenetic series to understand changes in size and shape which happen during the evolution of two New World Monkeys (NWM) sister genera.

Results

Cebus and Saimiri share the same basic allometric patterns in skull traits, a result robust to sexual and ontogenetic variation. If adults of both genera are compared in the same scale (discounting size differences) most differences are small and not statistically significant. These results are consistent using both approaches, classical and geometric Morphometrics. Cebus is a genus characterized by a number of peramorphic traits (adult-like) while Saimiri is a genus with paedomorphic (child like) traits. Yet, the whole clade Cebinae is characterized by a unique combination of very high pre-natal growth rates and relatively slow post-natal growth rates when compared to the rest of the NWM. Morphologically Cebinae can be considered paedomorphic in relation to the other NWM. Geometric morphometrics allows the precise separation of absolute size, shape variation associated with size (allometry), and shape variation non-associated with size. Interestingly, and despite the fact that they were extracted as independent factors (principal components), evolutionary allometry (those differences in allometric shape associated with intergeneric differences) and ontogenetic allometry (differences in allometric shape associated with ontogenetic variation within genus) are correlated within these two genera. Furthermore, morphological differences produced along these two axes are quite similar. Cebus and Saimiri are aligned along the same evolutionary allometry and have parallel ontogenetic allometry trajectories.

Conclusion

The evolution of these two Platyrrhini monkeys is basically due to a size differentiation (and consequently to shape changes associated with size). Many life-history changes are correlated or may be the causal agents in such evolution, such as delayed on-set of reproduction in Cebus and larger neonates in Saimiri.

Background

Since Gould's publication of Ontogeny and Phylogeny [14] a wave of renewed interest in the role of development in generating evolutionary novelties spread through biology. Heterochrony, evolutionary modifications in the rates and/or the timing (onset and offset) of development [2], become widely recognized as an important agent of evolutionary change [26]. The study of heterochrony, while interesting per se, does not take us any closer to understanding the developmental, genetic, and physiological processes underlying evolutionary change [29]. Yet, the study of heterochrony, even when using size in place of time, may be quite helpful in understanding evolutionary diversification (see criticisms in [13]). Allometry, the differential and relative growth of organismal parts may be conceived as a size-based "heterochrony" [26]. Heterochrony addresses trait change relative to time and allometry examine trait change relative to others traits (usually size), the latter being a comparison of heterochronic results to one another [26].

Size and shape are important biological properties of organisms arising from their genetic basis in complex association and sometimes interaction with the external and internal environment. Usually, a large fraction of the variability in morphometric data is due to size variation among individuals. Scaling effects might result in shape changes associated with changing size due to allometric relationships among traits, unless all morphological components grow or scale at the same rates (isometry). A long tradition in morphometrics has been to regard size as a nuisance factor in comparisons of organisms with several methods being used to adjust size before comparisons (e.g. [3, 39, 32, 20]). The rationale behind this approach is to regard size as a plastic feature of organisms and shape changes, unassociated with size (non-allometric), as adaptive [40]. Another motivation for developing methods allowing the separation of size and shape was the need to compare forms with very different sizes [41]. Yet, size is as much a property of organisms as is shape, with important functional and ecological implications. For example, a simple increase in skull size (and concomitant allometric shape changes) might result in larger animals being able to handle larger and harder food items and therefore explore new resources or niches. Here I present a study of size and shape variation in two New World primates, the squirrel (Saimiri) monkeys and capuchin (Cebus) monkeys. The approach used here combines traditional and geometric morphometrics, comparative analyses of life-history data and statistical analyses of size and shape differences to understand the evolution of these two sister genera.

The subfamily Cebinae, as used here, refers to the two modern genera, Saimiri and Cebus, which are united on the basis of dental morphology and proportions, overall cranial morphology and others skeletal features [9]. There is now a consensus that these two genera are indeed living sister clades based on recent phylogenetic studies [34, 33, 35]. Adult squirrel monkeys weigh less than 1.0 kg, on average (males 858 g and females 715 g) while capuchins usually weigh 3 times more (males 2,912 g and females 2,042 kg). Capuchins occur throughout the Neotropical region occupying virtually all types of forested habitats, from mangroves and disturbed forests to well-preserved Amazonian and Atlantic forests. Squirrel monkeys occur throughout the Amazon region to Central America, but not in the Cerrado and Atlantic forests, also occupying a great variety of forested habitats. Group sizes usually range from 6 to 30 individuals in Cebus while Saimiri had group size ranging from 10 to up to 75 individuals, and sometimes both genera mix together in foraging parties. Besides, both genera share some unique life-history patterns in New World Monkeys (NWM), with relatively heavy brains for their body weight [15].

Here I present a study of the morphological variation in Cebus and Saimiri, focusing on the allometric patterns, differentiation and evolution of size and shape in Cebinae. Ontogenetic and static allometric patterns and shape (free of size) variation are compared in order to describe similarities and differences in skull variation between genera. Finally, these results are compared to life-history traits and ecology of NWM to understand the Cebinae morphological evolution. All these approaches converge to a simple picture: Cebus and Saimiri evolved from a common ancestor basically diverging in size. This divergence follows a common ontogenetic trajectory which is basically revealed by the fact that evolutionary allometry (those differences in shape associated with size differences among lineages) and ontogentic allometry (shape changes associated with size differences during the ontogeny on each lineage) are highly correlated and morphologically describe the same changes in the skull. This size evolution might be caused by life-history changes like a delayed on-set of reproduction in Cebus.

Results

Static and ontogenetic allometry

Table 1 shows the multivariate allometric coefficients (ACs), corresponding standard deviations obtained from the bootstrap, and the lower and upper 95% confidence limits for each genus. Results for each sex analyzed separately are nearly equal to those presented here pooling both sexes within each genus and for simplicity are not presented. Those ACs with confidence limits not encompassing one (isometry) were considered either negatively (below 1) or positively (above 1) allometric. Eleven of the 17 neural traits (65%) and 9 of the 23 facial traits (39%) are negatively allometric in Cebus and the same figures for Saimiri are 13 in 17 (76% neural) and 11 in 23 (48% facial). Conversely, 2 of the 17 neural traits (12%) and 6 of the 23 facial traits (26%) are positively allometric in Cebus and the same figures for Saimiri are 3 in 17 (18% neural) and 8 in 23 (35% facial). Allometric vector repeatabilities are 0.99 for both genera and therefore sampling error is negligible in judging vector correlations. Allometric vector repeatabilities were also quite high in the sub-adult sample (t = 0.98 for Saimiri and t = 0.99 for Cebus) and therefore sampling error should have a negligible impact upon the vector similarities. The following vector correlations were obtained: Saimiri adult × Cebus adult = 0.968, Cebus adult × Cebus young = 0.978, Saimiri adult × Cebus young = 0.963, Saimiri young × Saimiri adult = 0.951, Cebus young × Saimiri young = 0.981, Cebus adult × Saimiri young = 0.980. Furthermore, the following averages and confidence interval were observed in the correlation of each vector against its 100 random permutation sample: Saimiri young = 0.773 (0.707–0.84), Saimiri adult = 0.82 (0.759–0.88), Cebus young = 0.808 (0.736–0.88), Cebus adult = 0.769 (0.697–0.842). Therefore all allometric vector correlations are higher that expected by the correlation of any two size vectors. Additionally, table 1 also show the PC1total extracted from the V/CV of the natural log-transformed data used in the MASS correction. This PC1 accounts for 90% of the total variation in the data and is quite similar (r = 0.954) to an isometric vector (all elements equal to 1/390.5). Also, this PC1total is quite similar to the size vectors representing within genus variation (r = 0.936 with Saimiri and 0.912 with Cebus).
Table 1

Allometric coefficients

 

Saimiri

   

Cebus

    

PC1total

Traits

AC

SE AC

L1

L2

AC

SE AC

L1

L2

Skull Region

 

ISPM

1.22

0.07

1.09

1.35

0.77

0.07

0.63

0.90

Face

0.18

ISNSL

0.97

0.09

0.79

1.14

1.06

0.08

0.90

1.22

Face

0.16

ISPNS

0.80

0.08

0.64

0.95

0.78

0.07

0.65

0.91

Face

0.19

PMZS

0.85

0.09

0.67

1.03

1.06

0.08

0.91

1.21

Face

0.19

PMZI

0.72

0.10

0.53

0.90

0.95

0.10

0.76

1.14

Face

0.17

PMMT

0.63

0.05

0.54

0.73

0.71

0.06

0.60

0.82

Face

0.19

NSLNA

0.90

0.15

0.61

1.20

0.64

0.12

0.40

0.88

Face

0.20

NSLZS

0.89

0.08

0.73

1.04

0.78

0.06

0.66

0.89

Face

0.14

NSLZI

0.77

0.07

0.63

0.92

0.86

0.07

0.73

0.99

Face

0.14

NABR

0.62

0.06

0.50

0.74

0.57

0.07

0.43

0.70

Neurocranium

0.17

NAFM

0.62

0.05

0.53

0.72

0.65

0.05

0.55

0.74

Face

0.14

NAPNS

0.62

0.06

0.51

0.73

0.84

0.05

0.73

0.94

Face

0.16

BRPT

0.50

0.07

0.35

0.64

0.42

0.07

0.29

0.55

Neurocranium

0.16

BRAPET

0.63

0.05

0.53

0.74

0.47

0.04

0.38

0.56

Neurocranium

0.12

PTFM

0.61

0.18

0.25

0.96

0.90

0.33

0.26

1.55

Face

0.14

PTAPET

0.65

0.06

0.55

0.76

0.80

0.12

0.58

1.03

Neurocranium

0.14

PTBA

0.89

0.04

0.80

0.97

0.89

0.08

0.74

1.04

Neurocranium

0.15

PTEAM

1.12

0.07

0.98

1.26

1.12

0.11

0.90

1.34

Neurocranium

0.17

PTZYGO

1.45

0.09

1.27

1.64

1.56

0.14

1.29

1.84

Face

0.16

PTTSP

1.39

0.17

1.05

1.72

1.82

0.38

1.07

2.58

Neurocranium, face

0.13

FMZS

0.42

0.13

0.16

0.68

0.44

0.12

0.21

0.67

Face

0.11

FMMT

0.86

0.05

0.77

0.95

0.93

0.04

0.85

1.01

Face

0.17

ZSZI

0.56

0.10

0.37

0.76

0.85

0.12

0.61

1.08

Face

0.12

ZIMT

1.43

0.14

1.16

1.70

1.47

0.09

1.29

1.65

Face

0.25

ZIZYGO

1.97

0.15

1.67

2.27

2.26

0.11

2.04

2.48

Face

0.18

ZITSP

1.53

0.08

1.37

1.70

1.64

0.07

1.51

1.78

Face

0.15

MTPNS

0.63

0.06

0.51

0.74

0.88

0.07

0.74

1.02

Face

0.14

PNSAPET

1.65

0.14

1.37

1.93

1.45

0.09

1.26

1.63

Neurocranium

0.21

APETBA

1.17

0.08

1.01

1.34

1.08

0.05

0.98

1.18

Neurocranium

0.13

APETTS

0.65

0.08

0.50

0.81

0.60

0.07

0.45

0.74

Neurocranium

0.14

BAEAM

0.84

0.06

0.73

0.95

0.66

0.04

0.58

0.73

Neurocranium

0.16

EAMZYGO

1.58

0.16

1.28

1.89

0.90

0.10

0.70

1.10

Face

0.25

ZYGOTSP

1.84

0.10

1.65

2.03

1.43

0.07

1.29

1.57

Face

0.20

LDAS

0.42

0.09

0.23

0.60

-0.16

0.08

-0.31

0.00

Neurocranium

0.05

BRLD

0.27

0.08

0.13

0.42

0.19

0.18

-0.16

0.54

Neurocranium

-0.01

OPILD

0.64

0.17

0.30

0.98

0.07

0.11

-0.15

0.30

Neurocranium

0.06

PTAS

0.82

0.05

0.72

0.93

0.87

0.06

0.75

0.98

Neurocranium

0.17

JPAS

0.76

0.07

0.62

0.89

0.68

0.07

0.55

0.81

Neurocranium

0.13

BAOPI

0.29

0.11

0.07

0.50

0.19

0.07

0.05

0.34

Neurocranium

0.15

Multivariate allometry coefficients (AC), theirs standard errors (SE AC) and 95% confidence limits (L1 and L2) for both genera based on the first principal component extracted from each genus within-group V/CV matrix. PC1 vectors were normalized and each coefficient divided by (1/39)1/2 to obtain the AC. Standard deviation estimates obtained from bootstrap analysis. Allometric coefficients with L1 higher that one (isometry) were considered to be positively allometric with general size (shown in bold and underlined) and conversely, AC with L2 lower that one were considered to be negatively allometric (bold and italic) with size. ACs with confidence limits encompassing 1.0 were considered to be isometric with size (normal font). The last column show the first principal component extracted from the whole sample (Cebus + Saimiri) and used in the MASS transformation.

Differentiation with and without size

A MANOVA was performed on the 39 measurements using sex, genus, and sex by genus interaction as independent variables in order to determine whether sexual dimorphism needs to be accounted for in the analyses. Five hundred sixty-four individuals were analyzed and significant multivariate (Wilk's Λ = 0.016; df = 39, 522; P < 1.0 × 10-5) and univariate (all P < 1.0 × 10-4) differences between the genera were found. There was also significant multivariate differences in sex (Wilk's Λ = 0.462; df = 39, 522; P < 1.0 × 10-5). Thirty-five variables presented univariate differences in sex significant at P < 1.0 × 10-3, two were significant between 1% and 5% (BA-OPI and OPI-LD) and two were found non-significant (LD-AS and BR-LD). Moreover, there was also significant multivariate sex by genus interaction (Wilk's Λ = 0.741; df = 39, 522; P < 1.0 × 10-5) and 33 significant sex by genus interaction (31 with P < 0.001 and 2 with P < 0.05) in the univariate tests. There is strong evidence for differentiation between the two genera, the two sexes and for the interaction of sex with genus. Additionally, an ANOVA performed on size (first principal component extracted from the V/CV matrix of the LN transformed data accounting for 90% of the total variance) show significant differences between the two genera (MSgenus = 530.27, F = 14228.63, P > 10-5), between the two sexes (MSsex = 10.70, F = 287.21, P > 10-5) and also significant interaction between effects (MSgenus*sex = 0.39, F = 10.48, P = 0.001), all effects with 1 degree of freedom (error term with d.f = 560 and MSerror = 0.037). Therefore all analyses below were done independently for both sexes, except where specifically noted, allowing also to properly control for interespecific variation within each genus.

The MANOVA performed on the original unscaled variables using 309 complete male skulls with genera and species nested within genera as factors was highly significant (Wilk's Λ = 0.021; df = 39, 252; P < 0.0001) with the single canonical variate (Table 2) separating completely the two groups (Figure 1). Correlations between CV scores and skull measurements are also presented in Table 2. Based on the correlations between variables and function, the CV is a size factor because all the significant correlations are positive, except for BR-LD. CV has large contributions from both neurocranial and facial traits. The MANOVA performed on the 255 complete female skulls with genera and species nested within genera as factors was also highly significant (Wilk's Λ = 0.032; df = 39, 198; P < 0.0001) with the single CV (Table 2) also separating the two groups completely (Figure 1). Correlations between CV scores and skull measurements are also presented in Table 2. The two CV's (males and females) are very similar with a vector correlation between them of 0.90. The MANOVA results with species nested within genus indicate that only one trait (BR-LD) does not show significant differences between the two genera (using the conservative Bonferroni correction of the significance level P = 0.05/39) in the univariate F-tests for both, males and females (Table 3).
Table 2

Canonical variate functions and correlations of traits to function

 

Canonical Variate

 

Correlation between traits and function

  

Canonical Variate

 

Correlation between traits and function

 
 

Males

Females

Males

Females

 

Males

Females

Males

Females

Traits

CV1 original

CV1 original

CV1 original

CV1 original

Traits

CV1 MASS

CV1 MASS

CV1 MASS

CV1 MASS

ISPM

-0.154

0.023

0.963

0.968

MASS-ISPM

-0.225

-0.467

0.161

0.168

ISNSL

0.136

-0.085

0.936

0.930

MASS-ISNSL

0.059

-0.639

0.078

0.291

ISPNS

0.383

0.725

0.978

0.970

MASS-ISPNS

-0.469

-0.792

-0.209

-0.024

PMZS

-0.047

-0.067

0.954

0.951

MASS-PMZS

-0.967

0.549

0.282

0.488

PMZI

-0.159

-0.136

0.938

0.951

MASS-PMZI

0.585

-1.334

0.301

0.471

PMMT

0.577

0.384

0.986

0.979

MASS-PMMT

-0.252

-0.119

-0.104

0.323

NSLNA

-0.070

0.100

0.901

0.899

MASS-NSLNA

0.368

-0.781

0.138

0.109

NSLZS

0.461

0.668

0.949

0.947

MASS-NSLZS

0.397

-1.054

0.227

0.440

NSLZI

-0.854

-1.495

0.950

0.958

MASS-NSLZI

-0.581

1.321

0.251

0.418

NABR

-0.056

-0.136

0.972

0.959

MASS-NABR

0.534

-0.623

-0.211

-0.464

NAFM

0.314

0.483

0.959

0.952

MASS-NAFM

-0.442

-0.333

-0.345

-0.161

NAPNS

-0.036

-0.195

0.973

0.967

MASS-NAPNS

-0.581

-0.222

-0.140

0.135

BRPT

0.129

-0.365

0.973

0.956

MASS-BRPT

-1.026

0.010

-0.213

-0.404

BRAPET

0.197

0.639

0.971

0.955

MASS-BRAPET

0.095

-0.102

-0.152

-0.363

PTFM

0.664

0.553

0.765

0.649

MASS-PTFM

-1.705

-1.057

0.099

0.114

PTAPET

-1.479

-2.920

0.946

0.940

MASS-PTAPET

-2.141

-0.645

-0.468

-0.378

PTBA

2.578

3.363

0.973

0.978

MASS-PTBA

1.585

-0.118

-0.274

-0.284

PTEAM

-0.050

0.088

0.953

0.959

MASS-PTEAM

0.300

-0.205

-0.249

-0.281

PTZYGO

-0.438

0.921

0.897

0.904

MASS-PTZYGO

0.025

-0.899

-0.145

-0.295

PTTSP

-0.314

-1.039

0.714

0.648

MASS-PTTSP

-1.337

-0.494

-0.387

-0.389

FMZS

0.451

0.534

0.840

0.823

MASS-FMZS

-0.877

-0.546

-0.257

-0.271

FMMT

-0.050

-0.098

0.974

0.981

MASS-FMMT

0.295

-0.147

0.104

0.180

ZSZI

0.069

0.602

0.875

0.870

MASS-ZSZI

0.152

-1.053

0.192

0.190

ZIMT

0.176

0.350

0.946

0.964

MASS-ZIMT

-0.615

-0.586

0.353

0.452

ZIZYGO

-0.792

-0.960

0.816

0.807

MASS-ZIZYGO

0.250

-0.740

0.237

0.026

ZITSP

-0.063

-0.247

0.886

0.883

MASS-ZITSP

-0.202

-0.689

0.167

-0.107

MTPNS

0.131

-0.016

0.928

0.945

MASS-MTPNS

-0.472

-0.418

-0.087

-0.006

PNSAPET

-0.110

0.424

0.919

0.933

MASS-PNSAPET

-0.329

-0.586

0.338

0.218

APETBA

-0.854

-1.075

0.930

0.944

MASS-APETBA

-1.021

-0.512

0.273

0.106

APETTS

0.128

0.139

0.932

0.932

MASS-APETTS

-0.215

-0.387

-0.010

0.042

BAEAM

-0.247

-0.077

0.983

0.975

MASS-BAEAM

-0.107

-0.157

-0.100

-0.087

EAMZYGO

-0.247

-0.286

0.955

0.934

MASS-EAMZYGO

-0.130

-1.242

-0.263

-0.196

ZYGOTSP

-0.073

-0.360

0.931

0.955

MASS-ZYGOTSP

-0.698

-0.584

0.303

0.057

LDAS

-0.082

0.096

0.648

0.721

MASS-LDAS

-0.076

-0.539

-0.363

-0.617

BRLD

-0.125

-0.494

-0.105

-0.208

MASS-BRLD

0.159

0.221

0.130

0.360

OPILD

-0.088

-0.398

0.606

0.599

MASS-OPILD

-0.293

-0.402

-0.378

-0.617

PTAS

0.747

0.718

0.980

0.982

MASS-PTAS

-0.961

-0.702

-0.405

-0.250

JPAS

-0.036

-0.053

0.946

0.944

MASS-JPAS

-0.011

-0.129

0.053

0.208

BAOPI

0.162

0.102

0.934

0.923

MASS-BAOPI

-0.398

-0.460

-0.345

0.023

The canonical variate obtained for both males and females using either unscaled or MASS data are show. Also the correlation of each trait to each CV is also show, with significant (P < 0.05) correlations in bold.

Table 3

Differentiation analyses results from unscaled data

Males

     

Females

     

Source

SS

df

MS

F

P

Source

SS

df

MS

F

P

ISPM

624.03

1

624.03

1881.23

< 0.00001

ISPM

270.34

1

270.34

1589.58

< 0.00001

Error

96.20

290

0.33

  

Error

40.14

236

0.17

  

ISNSL

2208.91

1

2208.91

1094.03

< 0.00001

ISNSL

753.22

1

753.22

561.51

< 0.00001

Error

585.52

290

2.02

  

Error

316.57

236

1.34

  

ISPNS

8866.98

1

8866.98

3443.39

< 0.00001

ISPNS

3466.56

1

3466.56

1878.82

< 0.00001

Error

746.77

290

2.58

  

Error

435.44

236

1.85

  

PMZS

2771.68

1

2771.68

1988.35

< 0.00001

PMZS

904.62

1

904.62

799.20

< 0.00001

Error

404.25

290

1.39

  

Error

267.13

236

1.13

  

PMZI

4907.99

1

4907.99

1624.74

< 0.00001

PMZI

1768.39

1

1768.39

921.21

< 0.00001

Error

876.03

290

3.02

  

Error

453.03

236

1.92

  

PMMT

6091.46

1

6091.46

5727.18

< 0.00001

PMMT

2282.79

1

2282.79

2606.01

< 0.00001

Error

308.45

290

1.06

  

Error

206.73

236

0.88

  

NSLNA

1614.81

1

1614.81

653.76

< 0.00001

NSLNA

678.68

1

678.68

279.02

< 0.00001

Error

716.31

290

2.47

  

Error

574.05

236

2.43

  

NSLZS

1600.16

1

1600.16

1552.98

< 0.00001

NSLZS

566.57

1

566.57

650.30

< 0.00001

Error

298.81

290

1.03

  

Error

205.61

236

0.87

  

NSLZI

5429.01

1

5429.01

1596.46

< 0.00001

NSLZI

2000.24

1

2000.24

891.50

< 0.00001

Error

986.19

290

3.40

  

Error

529.51

236

2.24

  

NABR

19412.50

1

19412.50

2202.66

< 0.00001

NABR

7782.90

1

7782.90

875.87

< 0.00001

Error

2555.83

290

8.81

  

Error

2097.06

236

8.89

  

NAFM

2352.00

1

2352.00

2073.69

< 0.00001

NAFM

856.69

1

856.69

1166.02

< 0.00001

Error

328.92

290

1.13

  

Error

173.39

236

0.73

  

NAPNS

6079.51

1

6079.51

2707.97

< 0.00001

NAPNS

2159.91

1

2159.91

1270.51

< 0.00001

Error

651.06

290

2.25

  

Error

401.21

236

1.70

  

BRPT

11044.23

1

11044.23

2274.23

< 0.00001

BRPT

4500.39

1

4500.39

727.55

< 0.00001

Error

1408.31

290

4.86

  

Error

1459.82

236

6.19

  

BRAPET

6227.39

1

6227.39

2353.72

< 0.00001

BRAPET

2522.35

1

2522.35

916.62

< 0.00001

Error

767.27

290

2.65

  

Error

649.42

236

2.75

  

PTFM

848.11

1

848.11

293.76

< 0.00001

PTFM

259.66

1

259.66

101.66

< 0.00001

Error

837.26

290

2.89

  

Error

602.80

236

2.55

  

PTAPET

3520.35

1

3520.35

896.99

< 0.00001

PTAPET

1273.51

1

1273.51

363.74

< 0.00001

Error

1138.14

290

3.92

  

Error

826.28

236

3.50

  

PTBA

9388.69

1

9388.69

1681.36

< 0.00001

PTBA

3645.97

1

3645.97

751.50

< 0.00001

Error

1619.35

290

5.58

  

Error

1144.97

236

4.85

  

PTEAM

6012.35

1

6012.35

1120.82

< 0.00001

PTEAM

2257.54

1

2257.54

491.86

< 0.00001

Error

1555.63

290

5.36

  

Error

1083.19

236

4.59

  

PTZYGO

3409.62

1

3409.62

524.15

< 0.00001

PTZYGO

1225.50

1

1225.50

238.55

< 0.00001

Error

1886.46

290

6.51

  

Error

1212.40

236

5.14

  

PTTSP

570.13

1

570.13

122.77

< 0.00001

PTTSP

166.37

1

166.37

35.31

< 0.00001

Error

1346.77

290

4.64

  

Error

1111.99

236

4.71

  

FMZS

725.73

1

725.73

511.53

< 0.00001

FMZS

319.79

1

319.79

236.29

< 0.00001

Error

411.43

290

1.42

  

Error

319.40

236

1.35

  

FMMT

6504.36

1

6504.36

2604.76

< 0.00001

FMMT

2440.20

1

2440.20

1724.24

< 0.00001

Error

724.16

290

2.50

  

Error

334.00

236

1.42

  

ZSZI

1124.79

1

1124.79

578.97

< 0.00001

ZSZI

436.54

1

436.54

277.55

< 0.00001

Error

563.39

290

1.94

  

Error

371.19

236

1.57

  

ZIMT

3466.72

1

3466.72

1490.40

< 0.00001

ZIMT

1179.17

1

1179.17

1336.39

< 0.00001

Error

674.55

290

2.33

  

Error

208.23

236

0.88

  

ZIZYGO

1513.75

1

1513.75

275.46

< 0.00001

ZIZYGO

470.49

1

470.49

136.15

< 0.00001

Error

1593.66

290

5.50

  

Error

815.52

236

3.46

  

ZITSP

2221.52

1

2221.52

512.02

< 0.00001

ZITSP

826.68

1

826.68

363.06

< 0.00001

Error

1258.23

290

4.34

  

Error

537.36

236

2.28

  

MTPNS

599.94

1

599.94

1097.34

< 0.00001

MTPNS

207.63

1

207.63

722.52

< 0.00001

Error

158.55

290

0.55

  

Error

67.82

236

0.29

  

PNSAPET

1931.14

1

1931.14

763.05

< 0.00001

PNSAPET

701.46

1

701.46

431.80

< 0.00001

Error

733.94

290

2.53

  

Error

383.38

236

1.62

  

APETBA

1194.94

1

1194.94

816.34

< 0.00001

APETBA

472.17

1

472.17

551.26

< 0.00001

Error

424.50

290

1.46

  

Error

202.14

236

0.86

  

APETTS

843.27

1

843.27

1062.46

< 0.00001

APETTS

320.89

1

320.89

540.82

< 0.00001

Error

230.17

290

0.79

  

Error

140.03

236

0.59

  

BAEAM

3155.44

1

3155.44

3416.08

< 0.00001

BAEAM

1217.39

1

1217.39

1465.18

< 0.00001

Error

267.87

290

0.92

  

Error

196.09

236

0.83

  

EAMZYGO

4471.57

1

4471.57

1638.73

< 0.00001

EAMZYGO

1645.18

1

1645.18

696.06

< 0.00001

Error

791.32

290

2.73

  

Error

557.80

236

2.36

  

ZYGOTSP

1901.08

1

1901.08

851.07

< 0.00001

ZYGOTSP

739.99

1

739.99

722.40

< 0.00001

Error

647.79

290

2.23

  

Error

241.74

236

1.02

  

LDAS

491.17

1

491.17

229.61

< 0.00001

LDAS

308.24

1

308.24

153.01

< 0.00001

Error

620.34

290

2.14

  

Error

475.43

236

2.01

  

BRLD

2.03

1

2.03

0.16

0.69321

BRLD

27.08

1

27.08

2.54

0.11255

Error

3781.79

290

13.04

  

Error

2519.18

236

10.67

  

OPILD

650.73

1

650.73

149.32

< 0.00001

OPILD

454.45

1

454.45

97.59

< 0.00001

Error

1263.76

290

4.36

  

Error

1098.99

236

4.66

  

PTAS

13504.40

1

13504.40

2474.10

< 0.00001

PTAS

5011.51

1

5011.51

1031.10

< 0.00001

Error

1582.91

290

5.46

  

Error

1147.04

236

4.86

  

JPAS

1382.67

1

1382.67

1147.25

< 0.00001

JPAS

519.56

1

519.56

596.60

< 0.00001

Error

349.51

290

1.21

  

Error

205.52

236

0.87

  

BAOPI

907.95

1

907.95

1342.30

< 0.00001

BAOPI

316.07

1

316.07

474.52

< 0.00001

Error

196.16

290

0.68

  

Error

157.20

236

0.67

  

Results from the GLM analyses for each sex using unscaled data with "genus" as a factor and "species" nested within genus as independent factors. For each trait is presented the Sum of Squares (SS), degrees of freedom (df), Mean Square (MS), F value and the associated probability (P). Significant differences in bold.

http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-7-20/MediaObjects/12862_2006_Article_314_Fig1_HTML.jpg
Figure 1

Canonical variates. Canonical variate 1 (CV1) obtained separately for males and females with frequency distribution of CV1 scores on the margin. On the x-axis is shown the CV1 obtained from the unscaled data and on the y-axis the CV1 from the MASS corrected data.

Results from the MANOVA done upon the MASS corrected data are quite different from the analyses upon the original unscaled data. While the CV (Table 2) is also highly significant for males (Wilk's Λ = 0.413; df = 39, 252; P < 0.0001) the two genera are now widely overlapping (Figure 1). The same pattern holds for females, with the CV (Table 2) being also significant (Wilk's Λ = 0.642; df = 39, 198; P < 0.0001), the scores of the two groups widely overlapping (Figure 1). Moreover, correlations of the variables with the CV, for both males and females, are now very small with around half of them being significant (Table 2). The MANOVA results with species nested within genus shows only two traits (IS-PNS and PM-ZS) with significant difference for the females (again using the Bonferroni correction) and six traits with significant differences in the males (IS-PNS, NA-FM, NA-PNS, PT-FM, ZI-ZYGO, PNS-APET, Table 4).
Table 4

Differentiation analyses results from MASS data

Males

     

Females

     

Source

SS

df

MS

F

P

Source

SS

df

MS

F

P

MASS-ISPM

0.06

1

0.06

0.38

0.53703

MASS-ISPM

1.76

1

1.76

12.94

0.00039

Error

45.32

290

0.16

  

Error

32.07

236

0.14

  

MASS-ISNSL

7.30

1

7.30

6.06

0.01442

MASS-ISNSL

0.10

1

0.10

0.09

0.76821

Error

349.42

290

1.20

  

Error

272.50

236

1.15

  

MASS-ISPNS

47.66

1

47.66

33.05

<0.00001

MASS-ISPNS

31.42

1

31.42

19.92

0.00001

Error

418.21

290

1.44

  

Error

372.15

236

1.58

  

MASS-PMZS

0.37

1

0.37

0.46

0.49812

MASS-PMZS

16.80

1

16.80

18.23

0.00003

Error

232.91

290

0.80

  

Error

217.43

236

0.92

  

MASS-PMZI

2.87

1

2.87

1.63

0.20299

MASS-PMZI

0.13

1

0.13

0.09

0.76245

Error

511.86

290

1.77

  

Error

325.44

236

1.38

  

MASS-PMMT

6.26

1

6.26

5.20

0.02333

MASS-PMMT

1.45

1

1.45

1.73

0.19019

Error

349.16

290

1.20

  

Error

197.64

236

0.84

  

MASS-NSLNA

22.70

1

22.70

10.06

0.00168

MASS-NSLNA

9.87

1

9.87

4.43

0.03637

Error

654.58

290

2.26

  

Error

525.89

236

2.23

  

MASS-NSLZS

5.11

1

5.11

7.82

0.0055

MASS-NSLZS

7.14

1

7.14

9.10

0.00283

Error

189.32

290

0.65

  

Error

185.17

236

0.78

  

MASS-NSLZI

1.31

1

1.31

0.91

0.34022

MASS-NSLZI

0.00

1

0.00

0.00

0.94965

Error

415.99

290

1.43

  

Error

278.84

236

1.18

  

MASS-NABR

9.64

1

9.64

1.34

0.24798

MASS-NABR

3.59

1

3.59

0.44

0.50744

Error

2086.87

290

7.20

  

Error

1921.51

236

8.14

  

MASS-NAFM

13.38

1

13.38

18.94

0.00002

MASS-NAFM

1.39

1

1.39

2.16

0.14339

Error

204.90

290

0.71

  

Error

152.70

236

0.65

  

MASS-NAPNS

22.52

1

22.52

19.03

0.00002

MASS-NAPNS

1.43

1

1.43

1.39

0.239

Error

343.23

290

1.18

  

Error

241.62

236

1.02

  

MASS-BRPT

0.57

1

0.57

0.11

0.73811

MASS-BRPT

0.05

1

0.05

0.01

0.92523

Error

1472.11

290

5.08

  

Error

1303.78

236

5.52

  

MASS-BRAPET

4.26

1

4.26

2.44

0.11953

MASS-BRAPET

2.68

1

2.68

1.25

0.265

Error

506.40

290

1.75

  

Error

506.94

236

2.15

  

MASS-PTFM

50.68

1

50.68

22.99

<0.00001

MASS-PTFM

5.97

1

5.97

2.45

0.11876

Error

639.43

290

2.20

  

Error

575.23

236

2.44

  

MASS-PTAPET

0.04

1

0.04

0.02

0.90209

MASS-PTAPET

2.64

1

2.64

1.10

0.29604

Error

731.79

290

2.52

  

Error

567.86

236

2.41

  

MASS-PTBA

8.29

1

8.29

3.53

0.06136

MASS-PTBA

1.62

1

1.62

0.61

0.43607

Error

681.78

290

2.35

  

Error

627.62

236

2.66

  

MASS-PTEAM

22.87

1

22.87

9.07

0.00282

MASS-PTEAM

3.63

1

3.63

1.26

0.2624

Error

731.02

290

2.52

  

Error

679.63

236

2.88

  

MASS-PTZYGO

33.73

1

33.73

10.53

0.00132

MASS-PTZYGO

6.35

1

6.35

1.81

0.18004

Error

929.47

290

3.21

  

Error

828.24

236

3.51

  

MASS-PTTSP

13.38

1

13.38

3.84

0.05094

MASS-PTTSP

10.85

1

10.85

2.70

0.10192

Error

1009.69

290

3.48

  

Error

949.35

236

4.02

  

MASS-FMZS

7.68

1

7.68

6.71

0.01005

MASS-FMZS

6.68

1

6.68

5.29

0.02235

Error

331.62

290

1.14

  

Error

298.05

236

1.26

  

MASS-FMMT

4.20

1

4.20

7.69

0.0059

MASS-FMMT

1.64

1

1.64

2.72

0.10044

Error

158.15

290

0.55

  

Error

142.66

236

0.60

  

MASS-ZSZI

3.67

1

3.67

2.76

0.09755

MASS-ZSZI

1.79

1

1.79

1.41

0.23645

Error

384.99

290

1.33

  

Error

299.18

236

1.27

  

MASS-ZIMT

0.08

1

0.08

0.10

0.75682

MASS-ZIMT

0.17

1

0.17

0.28

0.59791

Error

247.42

290

0.85

  

Error

144.36

236

0.61

  

MASS-ZIZYGO

54.67

1

54.67

16.68

0.00006

MASS-ZIZYGO

24.54

1

24.54

8.44

0.00402

Error

950.39

290

3.28

  

Error

686.25

236

2.91

  

MASS-ZITSP

1.28

1

1.28

0.58

0.44756

MASS-ZITSP

0.45

1

0.45

0.28

0.5991

Error

640.34

290

2.21

  

Error

381.88

236

1.62

  

MASS-MTPNS

3.03

1

3.03

10.24

0.00153

MASS-MTPNS

0.03

1

0.03

0.14

0.70912

Error

85.76

290

0.30

  

Error

47.70

236

0.20

  

MASS-PNSAPET

49.00

1

49.00

40.10

<0.00001

MASS-PNSAPET

14.42

1

14.42

11.31

0.0009

Error

354.33

290

1.22

  

Error

300.94

236

1.28

  

MASS-APETBA

0.30

1

0.30

0.36

0.54778

MASS-APETBA

0.42

1

0.42

0.62

0.43181

Error

243.43

290

0.84

  

Error

159.95

236

0.68

  

MASS-APETTS

3.02

1

3.02

4.59

0.03291

MASS-APETTS

0.49

1

0.49

0.95

0.3315

Error

190.37

290

0.66

  

Error

121.68

236

0.52

  

MASS-BAEAM

0.73

1

0.73

1.61

0.20485

MASS-BAEAM

0.02

1

0.02

0.05

0.82879

Error

131.42

290

0.45

  

Error

109.71

236

0.46

  

MASS-EAMZYGO

13.68

1

13.68

8.35

0.00416

MASS-EAMZYGO

22.44

1

22.44

9.83

0.00194

Error

475.33

290

1.64

  

Error

539.08

236

2.28

  

MASS-ZYGOTSP

1.71

1

1.71

1.77

0.18456

MASS-ZYGOTSP

3.04

1

3.04

4.20

0.04151

Error

279.83

290

0.96

  

Error

170.80

236

0.72

  

MASS-LDAS

9.86

1

9.86

4.67

0.03149

MASS-LDAS

27.25

1

27.25

13.63

0.00028

Error

611.85

290

2.11

  

Error

472.02

236

2.00

  

MASS-BRLD

49.78

1

49.78

3.68

0.05594

MASS-BRLD

0.08

1

0.08

0.01

0.93036

Error

3919.26

290

13.51

  

Error

2551.68

236

10.81

  

MASS-OPILD

15.31

1

15.31

3.61

0.0583

MASS-OPILD

56.06

1

56.06

11.81

0.0007

Error

1228.41

290

4.24

  

Error

1120.04

236

4.75

  

MASS-PTAS

0.61

1

0.61

0.22

0.64043

MASS-PTAS

0.80

1

0.80

0.33

0.56576

Error

815.43

290

2.81

  

Error

574.13

236

2.43

  

MASS-JPAS

0.01

1

0.01

0.01

0.91656

MASS-JPAS

0.27

1

0.27

0.41

0.52287

Error

258.37

290

0.89

  

Error

154.58

236

0.65

  

MASS-BAOPI

5.46

1

5.46

6.39

0.01198

MASS-BAOPI

0.01

1

0.01

0.02

0.89451

Error

247.63

290

0.85

  

Error

182.77

236

0.77

  

Results from the GLM analyses for each sex using MASS data with "genus" as a factor and "species" nested within genus as independent factors. For each trait is presented the Sum of Squares (SS), degrees of freedom (df), Mean Square (MS), F value and the associated probability (P). Significant differences in bold.

Interestingly, the MANOVA performed upon the MASS corrected data to test for genus, sex and sex by genus effects shows only a small interaction of the factors, with only 3 traits (IS-PM, PM-ZS and LD-AS) deemed significant. Also, few traits are significant between genera (ZI-ZYGO, BR-PT, PM-MT). Conversely, 17 of the 39 traits show significance differences between the sexes using the conservative Bonferroni threshold.

Heterochrony and life-history

Figure 2 shows the regression between the ages of first reproduction against adult weights, after correcting for non-independence between points due to shared history (phylogeny). Notice that Cebus is the only genus deviating significantly from the regression line. This indicates that capuchins have a delayed on-set of reproduction in relation to the other genera given that its age of first reproduction is larger than expected for a NWM of its size. Likewise, Figure 3 shows the regression between the birth weights against body weight (the result is the same if skull size is used instead of body weight). Notice that squirrel monkeys deviate significantly from the regression line. This indicates that Saimiri babies are born heavier than expected for a NWM with its body size. Figure 4 shows the regression of the age at weaning against adult body weight. Squirrels monkeys seem to lie slightly below the 95% confidence interval of the regression line indicating that they are weaned earlier than expected for a NWM of its size. Conversely, capuchins seem to deviate from the regression line in the upper direction, suggesting that they are weaned later that expected for a NWM of its size. Figure 5 show the regression of the fetal growth rate (birth weight/gestation length) against adult body weight. Saimiri and Cebus lie slightly above the regression line. These patterns seem to be robust to within genus between species variation in life-history data. Unfortunately, complete information on life-history traits is not available for all species within each genus as well as solid phylogenetic hypotheses for all species within each of the two genera.
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Figure 2

Comparative phylogenetic regression of life-history traits. Plot of the age of first reproduction against adult weight in New World Monkeys controlling for shared history (phylogeny). The regression line and 95% confidence limits were obtained from the method described in Garland and Ives (2000) and implemented in package PDAP in MESQUITE.

http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-7-20/MediaObjects/12862_2006_Article_314_Fig3_HTML.jpg
Figure 3

Comparative phylogenetic regression of life-history traits. Plot of the birth weight against adult weight in New World Monkeys controlling for shared history (phylogeny). The regression line and 95% confidence limits were obtained from the method described in Garland and Ives (2000) and implemented in package PDAP in MESQUITE.

http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-7-20/MediaObjects/12862_2006_Article_314_Fig4_HTML.jpg
Figure 4

Comparative phylogenetic regression of life-history traits. Plot of the age at weaning against adult weight in New World Monkeys controlling for shared history (phylogeny). The regression line and 95% confidence limits were obtained from the method described in Garland and Ives (2000) and implemented in package PDAP in MESQUITE.

http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-7-20/MediaObjects/12862_2006_Article_314_Fig5_HTML.jpg
Figure 5

Comparative phylogenetic regression of life-history traits. Plot of the pre-natal growth rate against adult weight in New World Monkeys controlling for shared history (phylogeny). The regression line and 95% confidence limits were obtained from the method described in Garland and Ives (2000) and implemented in package PDAP in MESQUITE.

Growth trajectories

I tested the assumption of size as a proxy for age by regressing size (PC1) against developmental age codes ([30], ages 1 to 6) separately for males and females. Cebus data was used because the juveniles and sub-adult sampling is much more extensive in this genus and have sex information available. Both regressions were highly significant (P < 0.00001) and the multiple R was 0.80 for females and 0.82 for males. Similar analyses in Saimiri (but ignoring sex dimorphism due to lack of sex in most of the juvenile and sub-adult sample) show also a similar multiple R (0.81). Furthermore, for Cebus apella at least there is available information for absolute age (in months) for each age class based on dental eruption (see Table 2 in [30]). Therefore is possible to calculate the correspondence between absolute size, time and age classes. Age classes and the natural log of age (in months) present a correlation of 0.97 for males of Cebus apella. Absolute time and size are also highly correlated (0.82) again indicating that size is a reasonable proxy to time. Given that absolute age is not available for the genus Saimiri, I use dental age classes here in the paper as a "developmental marker" and absolute size as an estimate of time.

Comparison of the growth trajectories are summarized in Figure 6 and Table 5. Most traits present a linear trajectory in the ln-scale, but some exceptions occur. Two general patterns are evident: 1) simple extension/truncation of the growth trajectory (Fig. 7a, 7b) 2) a step along the size (time) axis causing a shift in the otherwise parallel trajectories with Saimiri above (Fig. 7c, d). Most traits conform to one of these two patterns with a few exceptions. A group of such exceptions correspond to those traits which present very low correlation with size (Table 5) including traits LD-AS, BR-LD, OPI-LD, BA-OPI (Fig. 7e,f). Twenty-six traits (67% of all traits) can be assigned to pattern 1 (simple extension) and nine traits (23%) to pattern 2 (step along the size axis) (see Table 5).
Table 5

Growth trajectories

Trait

Pattern 1

Pattern 2

Functional/developmental group

ISPM

hypermorphosis/progenesis

 

Oral

ISNSL

 

pre-/postdisplacement (Saimiri above)

Nasal

ISPNS

hypermorphosis/progenesis

 

Oral, nasal

PMZS

hypermorphosis/progenesis

 

Oral

PMZI

hypermorphosis/progenesis

 

Oral

PMMT

hypermorphosis/progenesis

 

Oral

NSLNA

hypermorphosis/progenesis

 

Nasal

NSLZS

hypermorphosis/progenesis

 

Nasal

NSLZI

hypermorphosis/progenesis

 

Oral, nasal

NABR

hypermorphosis/progenesis

 

Cranial vault

NAFM

hypermorphosis/progenesis

 

Orbit

NAPNS

hypermorphosis/progenesis

 

Nasal

BRPT

hypermorphosis/progenesis

 

Cranial vault

BRAPET

hypermorphosis/progenesis

 

Cranial vault

PTFM

 

pre-/postdisplacement (Saimiri above)

Orbit

PTAPET

hypermorphosis/progenesis

 

Cranial vault

PTBA

hypermorphosis/progenesis

 

Cranial vault

PTEAM

hypermorphosis/progenesis

 

Cranial vault

PTZYGO

 

pre-/postdisplacement (Saimiri above)

Zygomatic

PTTSP

 

pre-/postdisplacement (Saimiri above)

Cranial vault, zygomatic

FMZS

hypermorphosis/progenesis

 

Orbit

FMMT

hypermorphosis/progenesis

 

Zygomatic

ZSZI

 

pre-/postdisplacement (Saimiri above)

Oral

ZIMT

hypermorphosis/progenesis

 

Oral

ZIZYGO

 

pre-/postdisplacement (Saimiri above)

Zygomatic

ZITSP

 

pre-/postdisplacement (Saimiri above)

Zygomatic

MTPNS

hypermorphosis/progenesis

 

Oral

PNSAPET

hypermorphosis/progenesis

 

Cranial base

APETBA

 

pre-/postdisplacement (Saimiri above)

Cranial base

APETTS

hypermorphosis/progenesis

 

Cranial base

BAEAM

hypermorphosis/progenesis

 

Cranial base

EAMZYGO

hypermorphosis/progenesis

 

Zygomatic

ZYGOTSP

 

pre-/postdisplacement (Saimiri above)

Zygomatic

LDAS

low correlation with size

low correlation with size

Cranial vault

BRLD

low correlation with size

low correlation with size

Cranial vault

OPILD

low correlation with size

low correlation with size

Cranial vault

PTAS

hypermorphosis/progenesis

 

Cranial vault

JPAS

hypermorphosis/progenesis

 

Cranial base

BAOPI

low correlation with size

low correlation with size

Cranial base

Interpretation of the bivariate plots (each trait against centroid size) relative to heterochronic processes.

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Figure 6

Growth trajectories. Bivariate plots of 6 skull measurements against centroid size (both in ln). A and B correspond to simple extension pattern, C and D to the step pattern, E to the low correlation with size pattern and F to the only trait showing an apparent mix of extension/step patterns (see also Table 5). The fit correspond to the LOWESS function with Saimiri in blue and Cebus in red.

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Figure 7

Geometric Morphometrics – size and evolutionary shape allometry. Plot of the PC1-3D against centroid size (ln). Specimens with different ages are show in different colours and symbols and skull 3D reconstructions are show in oblique and dorsal views.

Geometric morphometrics

Centroid size is highly correlated with PC1 score derived from the linear distances (R = 0.999, P < 0.0001). Also, centroid size and PC1 score are linearly related when both are in ln scale. Therefore I use the natural log of the centroid size as a measure of absolute skull size. Figure 7 show the plot of the first PC-3D against centroid size [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9]. This PC1-3D basically represents allometric variation in shape associated with size, accounting for 51.5% of all variation in shape. Starting from the smaller values (Saimiri young's), morphologically PC1-3D represents a lowering of the cranial vault with a large dislocation of landmark BR to a more posterior position as well as a relatively smaller posterior cranial base with a large dislocation of landmark LD to a more anterior position. Therefore, a major change described by this PC1-3D is that involving the neurocranium, with changes in the height of the vault associated with the base/back of the skull, involving landmarks LD, BR and to a lesser extent AS. These landmarks are dislocated from a more posterior (LD) and lateral (AS) position in Saimiri babies to a more anterior (LD) and medial position (AS) in Cebus adults. Those changes in landmarks BR and LD dominates the PC1-3D which can be easily observed from a vector of changes in landmarks coordinates comparing the upper and higher limits of variation described by PC1-3D (Table 6). Another change in shape associated with PC1-3D is in the face, being more prognathic (landmarks IS and PM) in the upper end (Cebus), with a longer and slender palate (landmark MT) and the zygomatic arch (more robust and lateral – landmarks ZI and ZYGO). Also associated with this PC1-3D is the dislocation of landmarks PT and TSP to a more medial position resulting in a more slender skull in Cebus (Figure 7). Figure 8 present the plot of the second PC-3D against size. This PC2-3D is basically an ontogenetic vector accounting for 15.6% of all variation in shape. The PC2-3D represents (again starting from the smaller values – Cebus and Saimiri young's) a relative decrease in the neurocranium region with landmark BR once more involved but this time being dislocated to a forward and lower position. Also, an enhanced prognathism resulting from landmarks IS and PM being dislocated forward and upward. Another change involves landmarks MT and ZI being dislocated forward and to a lower position resulting in a palate region comparatively smaller, face more prognathic and with a more robust pre-zygomatic region. Also, the cranial base is to some extent relatively smaller with landmark TSP being dislocated to a more posterior position and closer to landmarks APET, BA, TS and JP. Contributing to PC2-3D is also, and again, a dislocation of landmarks LD to a more anterior position and AS to a more medial position, exactly the same change described in PC1-3D. So, to some extent changes in shape described by PC1-3D and PC2-3D are similar (Table 6).

Only the PC1-3D presents significant differences between Cebus and Saimiri (t = 53.97, df = 231.6, P < 10-5) and this difference holds for all age classes analyzed separately. All other PC's variables, that cumulatively account for 97% of all shape variation (from PC2 to PC 40) do not present any significant differences between the two genera.
Table 6

Landmarks change vectors

 

Saimiri

    

Cebus

      

Landmarks

Ages 1–2

Ages 2–3

Ages 3–4

Ages 4–5

Ages 5–6

Ages 1–2

Ages 2–3

Ages 3–4

Ages 4–5

Ages 5–6

PC1-3D

PC2-3D

IS

0.234

0.229

0.217

0.220

0.204

0.215

0.196

0.205

0.204

0.188

0.083

0.044

PM(E)

0.253

0.248

0.244

0.236

0.235

0.233

0.238

0.232

0.226

0.231

0.055

0.084

NSL

0.029

0.031

0.082

0.021

0.103

0.033

0.136

0.090

0.065

0.128

0.017

0.071

NA

0.026

0.027

0.048

0.031

0.057

0.034

0.070

0.052

0.041

0.067

0.006

0.013

BR

0.357

0.365

0.397

0.367

0.415

0.381

0.408

0.415

0.412

0.429

0.691

0.931

PT(E)

0.192

0.193

0.205

0.199

0.209

0.193

0.208

0.207

0.203

0.211

0.102

0.056

FM(E)

0.031

0.031

0.016

0.044

0.009

0.042

0.022

0.014

0.034

0.015

0.013

0.006

ZS(E)

0.080

0.074

0.074

0.075

0.067

0.073

0.076

0.062

0.066

0.059

0.011

0.012

ZI(E)

0.263

0.259

0.237

0.262

0.235

0.251

0.249

0.228

0.221

0.233

0.098

0.075

MT(E)

0.236

0.235

0.241

0.230

0.243

0.232

0.255

0.236

0.233

0.243

0.035

0.148

PNS

0.102

0.101

0.088

0.099

0.081

0.096

0.070

0.083

0.088

0.070

0.014

0.009

APET(E)

0.129

0.127

0.095

0.133

0.080

0.126

0.052

0.090

0.107

0.061

0.040

0.007

BA

0.190

0.186

0.204

0.172

0.204

0.175

0.217

0.198

0.181

0.204

0.008

0.101

OPI

0.086

0.084

0.083

0.082

0.080

0.081

0.077

0.080

0.079

0.076

0.006

0.006

EAM(E)

0.169

0.170

0.171

0.177

0.173

0.175

0.173

0.171

0.167

0.173

0.079

0.044

PEAM(E)

0.199

0.198

0.217

0.188

0.221

0.193

0.229

0.216

0.204

0.224

0.035

0.063

ZYGO(E)

0.284

0.280

0.316

0.258

0.322

0.265

0.346

0.310

0.283

0.328

0.039

0.163

TSP(E)

0.143

0.145

0.184

0.142

0.204

0.148

0.228

0.194

0.172

0.226

0.067

0.156

TS (E)

0.152

0.151

0.161

0.146

0.162

0.149

0.163

0.161

0.155

0.163

0.036

0.030

JP(E)

0.101

0.099

0.096

0.099

0.096

0.100

0.089

0.098

0.101

0.093

0.017

0.013

LD

0.464

0.471

0.409

0.498

0.389

0.492

0.345

0.418

0.469

0.374

0.681

0.120

AS (E)

0.309

0.308

0.296

0.307

0.288

0.306

0.266

0.293

0.300

0.274

0.097

0.076

For each consecutive age (age 1 and 2) and for each of the two genera the vector of absolute change in landmark position is show. All vectors were normalized to one in order to be directly comparable. Also, the magnitude of change in landmark position between the two extremes is each of the allometric vectors (PC1-3D and PC2-3D) is show.

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Figure 8

Geometric Morphometrics – size and ontogenetic shape allometry. Plot of the PC2-3D against centroid size (ln). Specimens with different ages are show in different colours and symbols and skull 3D reconstructions are show in oblique and dorsal views.

Both PC1-3D and PC2-3D are highly correlated with size variation within each genus (Table 7). Also, PC1-3D and PC2-3D are also highly correlated between them within each of the two genera. PC1-3D is also highly correlated with size among genera (R = 0.98, P < 10-5), while PC2-3D scores are uncorrelated (R close to zero) with both size and PC1-3D among genera (as expected because PC1 and PC2 are by definition extracted as orthogonal vectors). All other PC's variables are uncorrelated with size (from PC3 to PC 40).
Table 7

Correlation between absolute size, evolutionary and ontogenetic allometry

 

SIZE

PC1-3D

PC2-3D

SIZE

1

P < 10-5

0.320

PC1-3D

0.972

1

0.872

PC2-3D

-0.060

0.009

1

Cebus

SIZE

PC1-3D

PC2-3D

SIZE

1

P < 10-5

P < 10-5

PC1-3D

0.864

1

P < 10-5

PC2-3D

0.809

0.768

1

Saimiri

SIZE

PC1-3D

PC2-3D

SIZE

1

P < 10-5

P < 10-5

PC1-3D

0.723

1

P < 10-5

PC2-3D

0.699

0.868

1

The Pearson correlation and associated probability between absolute size, PC1-3D and PC2-3D are show for a conjoint analysis (both genera) and for each of the two genera.

Discussion

Sexual dimorphism in Cebus and Saimiri is well marked, either in the original traits or the MASS corrected data. Indeed, 33 of the unscaled traits show significant sexual dimorphism in Cebus and 30 in Saimiri, using the conservative Bonferroni threshold. After removing scale differences, MASS corrected data show 17 traits with significant sexual dimorphism in Cebus and 11 in Saimiri (again using the 0.05/39 threshold). Males in both genera are larger than females, but skull size dimorphism is more evident in Cebus (on average females are 66% of the males size) while in Saimiri females are on average 82% of the males. Besides, both sexes share a high similarity in their allometric vector correlation (0.948 in Cebus and 0.945 in Saimiri). Altogether these results suggests that sexual dimorphism in Cebinae is not simply a function of size related differences. In other words, if females were to grow to the same size as males in either Saimiri or Cebus, sexual dimorphism in shape would still be evident. Therefore nearly all analyses were performed separately for each sex.

Differences between the two genera are massive (Figure 1) considering the original data, with Mahalanobis D2 distances pointing out the complete separation of the two groups in both sexes (D2 males = 1299 and D2 females = 1374). Conversely, there is a wide overlap between both genera considering the MASS corrected data (Figure 1) with very low D2 distances (D2 males = 3.84 and D2 females = 1.74). Moreover, 38 of the 39 original traits show significant differences (P > 10-5) between the two genera in both sexes. Conversely, only two traits show significant differences between the two genera after correcting for scaling differences (MASS data) in females. Males present a slightly larger differentiation with 6 traits showing significant differences between the genera in the MASS corrected data. Taken together these results suggest that most of the differences between Cebus and Saimiri are related to size. Indeed, the only trait in the original scale not showing significant differences between the two genera (BR-LD) is the only one not influenced by size (Table 1 PC1total). This is an interesting result given that these two landmarks BR and LD are by far the most influential in the shape changes described by PC1-3D and PC2-3D. In fact, given that PC1-3D is the axis of major differentiation between Saimiri and Cebus, and that BR is dislocated to a more posterior and lower position while LD is dislocated forward that explain why the linear distance between the two landmarks is basically the same in both genera, despite the huge size difference between them. This can also be observed in the additional on-line material [see Additional files 1, 2, 3, 4, 5, 6, 7, 8, 9], particularly on the lateral view. After removing scale differences from the data (MASS correction) the large differences between the two genera nearly disappear, with only a small differentiation being observed. These results from the Euclidean distances analyses are totally consistent with the results obtained from the geometric morphometrics approach. Moreover, these results also show that males are somewhat more differentiated than females, after removing scale differences. In short, for the most part, squirrel monkeys are scaled down versions of capuchins, or vice versa.

Allometric vectors are much more similar than expected by chance in all comparisons as indicated by the comparisons of observed vectors correlations against its random permutations. This can be quantified by the angles formed between those allometric vectors, with observed angles ranging from 11.18° to 18.01°, well below the minimum expected angle of 28.36 ° from the random permutations. Given strong similarity in the allometric vectors of Cebus and Saimiri, and that most of the differences between them are size-related, it is seems clear that during the evolutionary diversification of these two sister genera size plays a major role. Conservation of allometric patterns in Cebus and Saimiri suggest that they simply follow the same growth patterns but evolved to attain different adult final sizes (Figure 6a, b). This conclusion is reinforced by the results of the geometric Morphometrics approach where the absolute magnitude of changes in landmarks position along PC1-3D and PC2-3D between consecutive age classes within Saimiri and Cebus are compared. All vectors of change are similar either within or between genus and present vectors correlation above 0.97, which again is well above the expected range from the random permutation tests (0.60–0.87).

In a size-based scheme for heterochrony (see page 42 in [26]) this would suggest that Saimiri evolved its small size by some sort of ontogenetic scaling or allometric progenesis or conversely, Cebus evolved its larger size by hypermorphosis, or both processes were involved since the genera diverged from their last common ancestor. At present is impossible to know the direction of change or in other words, which is the ancestral condition and which derived. Furthermore, both processes might have happen while both genera diverged from an ancestor of intermediate size.

Yet, despite conservation of allometric patterns, a broader, and perhaps more interesting, picture arises when we look at the comparison of growth trajectories (Table 5). Comparison of growth trajectories suggests two general and diverse underlying changes in development (Figure 6). Pattern one corresponds to an extension/truncation of the growth trajectory and occurs in 67% of all traits (Fig. 7a, b). Pattern two corresponds to a translation (see Fig. 4a in [31]) or height of otherwise parallel trajectories (Fig. 7c, d) and accounts for 23% of all traits. Therefore, developmental changes involved in the Cebus - Saimiri evolution seem to be to a larger part mix of two different heterochronic patterns: progenesis-hypermorphosis and pre-postdisplacement if we take a classic Morphometrics approach.

But, what light can be shed on this discussion by the results of the geometric morphometrics approach? First, is clear that the PC1-3D is an allometric size vector with a correlation of 0.97 with absolute size (centroid size). This is also the axis of major differentiation between the two genera and in fact the only one where they do not overlap and present a significant difference on a series of t-tests performed upon each of the first 40 PC's of the 3D analyses. These results are quite similar to the ones obtained with the canonical variate analyses done upon the original and the MASS data and basically reinforce the point that Cebus is a scaled-up version of Saimiri.

Also, the orientation of the PC1-3D is basically the same between the two genera (regression slope of PC1-3D against centroid size: k = 0.273, 95% CI 0.249–0.298 for Cebus and k = 0.265, 95% CI 0.217–0.314 for Saimiri). Second, PC2-3D is also an allometric vector with a high correlation with size if the focus is the within genus variation (r = 0.81 in Cebus and r = 0.70 in Saimiri). Indeed, PC2-3D is also highly correlated with PC1-3D (r = 0.77 in Cebus and r = 0.87 in Saimiri). Furthermore, the orientation of the PC2-3D is also basically the same between the two genera (regression slope of PC2-3D against size: k = 0.351, 95% CI 0.312–0.389 for Cebus and k = 0.293, 95% CI 0.231–0.356 for Saimiri). Considering all these results both PC's 3D can be interpreted as allometric vectors. In other words, PC2-3D represents ontogenetic allometry (Fig. 8) while PC1-3D represents evolutionary allometry (Fig. 7). In fact, changes in both PC's seem to some extent similar involving basically the neurocranium, face and zygomatic regions and it is not a surprise that both represent allometric variation.

What these results inform us about the evolution of Cebus and Saimiri ? First, almost all differentiation between these two genera is related to size/scaling. Second, these differences either in scale (absolute size) or shape associated with size (allometry) follow a common evolutionary trajectory (Fig. 7). This last point is also totally supported by the correlation between absolute size, the axis of differentiation between the two genera (CV1), and PC1-3D (all R's larger than 0.98). Thirdly, ontogenetic variation in shape in one genus is parallel to the ontogenetic variation in the other. In other words, ontogenetic allometry follows a common and parallel trajectory between Cebus and Saimiri (Fig. 8) while the latter genus obviously start from a different point due to the huge difference in absolute size among them (Saimiri newborns weight 109 g while Cebus newborns weight 235 g). Fourthly, ontogenetic and evolutionary allometries are correlated. Considering all these findings together it seem correct to conclude that the evolution of these two genera results from developmental changes tweaking with body size and that nearly all differences observed among adult morphologies are a consequence of this size scaling. It is impossible at this time to polarize this change and in fact, it might well be the case that after the split from their common ancestor Cebus and Saimiri both diverges in opposite directions, Cebus scaling up and Saimiri scaling down.

What role might life-history evolution play in triggering those morphological changes? Figure 2 show that Cebus has a delayed on-set of reproduction. This is consistent with 67% of the traits growth trajectories and with the hypermorphic condition of Cebus and suggests that the extension of the growth trajectory was attained by delaying the age of first reproduction. Conversely, figure 3 shows that Saimiri neonates are born heavier than expected for a NWM of its size and this suggests that the height observed in the trajectories, where Saimiri is translated above Cebus (23% of all traits) might be explained by this larger starting point for the post-uterine growth period. Figure 4 also add another piece in this puzzle, showing that Cebus infants are weaned later than expected for a NWM of its size while the reverse is true for squirrel monkeys. Delayed weaning and age of first reproduction suggests that Cebus has a very slow developmental pattern compared to the rest of the NWM. Early weaning in Saimiri would suggest at first the reverse, but others factors should be considered here in judging whether or not Saimiri present a "fast" or " slow" life-history pattern. Saimiri neonates are born relatively heavy and represent almost 14% of the total weight of the mother, representing the largest pre-natal investment in NWM [11] in a single newborn (tamarins and marmosets which usually have twins invest even more if we consider litter weight). Squirrel monkey mothers also usually do not have support from group members in raising their infants which should impose a heavy burden on them. Saimiri compensate for this burden by a prolonged interbirth interval [11]. Garber and Leigh also point out that in Saimiri "An ontogenetic trajectory associated with large neonatal body size and rapid neurological development may facilitate early foraging independence, thus shifting metabolic costs away from the mothers and to the developing individual". After weaning, developing young follow a long and slow growth trajectory [11], which in a way is similar to their sister clade, Cebus. Figure 5 sheds additional light on this point, because the Cebus/Saimiri clade is characterized by the highest pre-natal growth rates among NWM, after accounting for differences in adult body size (and historical relatedness). Because most of neurocranial growth occurs during the pre- and peri-natal period, this faster growth in capuchins and squirrel monkeys accounts for the largest encephalization index in this clade within NWM [15], which can also be observed in the very long neural region in Saimiri and Cebus young's (Figures 7 and 8). Conversely, Cebus and Saimiri post-natal growth rates are among the lowest among NWM (Table 8). Therefore, summarizing all these life-history changes, the whole clade of capuchin/squirrel monkeys might be characterized by fast pre-natal growth and very slow post-natal growth. This is an interesting conclusion, because while obviously Saimiri represents a paedomorphic (juvenilized) morphology and Cebus a peramorphic (adult like) morphology when compared to one another, the whole clade might be considered paedomorphic relative to NWM as a whole.
Table 8

Life-history data

Genus

age first rep (days)

Adult Weight

Gestation Lenght

Age of weaning

Birth Weight

Pre-natal Growth Rate (regression)

Post-natal growth rate

Alouatta

1460.00

6404.2

186

369

407.7

1.150

1.337

Ateles

1642.50

8276.3

229

653

482.0

1.137

1.443

Brachyteles

2737.50

8840.0

225

639

.

  

Lagothrix

2555.00

7150.0

218

340

450.0

1.135

1.124

Cacajao

1642.50

2893.8

180

547

.

  

Chiropotes

1460.00

2632.5

160

 

.

  

Pithecia

1125.42

2003.5

170

122

121.0

0.934

1.486

Callicebus

1350.50

997.3

160

192

100.0

0.907

1.078

Cebus

2007.50

2475.1

168

477

234.6

1.065

0.950

Saimiri

912.50

786.9

167

51

109.0

0.917

1.164

Aotus

730.00

1018.7

133

75

97.0

0.935

1.381

Leontopithec

638.75

471.4

133

91

50.0

0.800

1.430

Saguinus

699.58

444.4

145

79

43.0

0.756

1.482

Callimico

547.50

505.0

155

65

50.0

0.776

1.833

Callithrix

547.50

351.2

148

106

30.0

0.681

1.881

Cebuella

501.88

108.5

137

91

14.0

0.536

1.573

Age of first reproduction, Adult weight, gestation length, age of weaning, birth weight, and Pre- and Pos-natal growth rates are presented for NWM.

Conclusion

Saimiri and Cebus represent a unique radiation within the NWM in many aspects. The differentiation of these two genera from their common ancestor is, to a large extent, due to size evolution. Most morphological differences between these two genera are related to scaling. Furthermore, this scaling is to a large extent due to a simple extension/truncation of growth, but also includes pre- and post-displacement. Several life-history changes seem correlated to, or perhaps are even causal of the morphological diversification of Cebus and Saimiri; such as delayed on-set of reproduction in Cebus, faster pre-natal growth rates and delayed weaning in Cebus, and accelerated weaning in Saimiri. Post-natal life-history is also slow in both genera relative to other NWM.

Methods

Sample and measurements

A total number of 886 specimens were measured, with 30 specimens not included in the analyses due to missing values. The adult sample includes 605 specimens in 18 species for the two genera as follows: 11 species of the genus Cebus, including the following species: C. albifrons (N = 13), C. cesarae (N = 17), C. apella (N = 135), C. capucinus (N = 20), C. libidinosus (N = 38), C. macrocephalus (N = 11), C. nigritus (N = 78), C. nigrivitattus (N = 9), C. paraguayanus (N = 19), C. robustus (N = 35), C. xanthosternus (N = 4); 7 species of the genus Saimiri, including the following species: S. albigena (N = 5), S. boliviensis (N = 6), S. cassiquiarensis (N = 29), S. macrodon (N = 13), S. oerstedi (N = 32), S. sciureus (N = 114), S. ustus (N = 16), and S. vanzolinii (N = 11). Adult specimens were used in the morphological differentiation analyses, properly controlling for species and sexual variation. Additional 41 specimens were discarded because they lost sex information (NM = 309 and NF = 255 for males and females).

An additional sample of 161 sub-adult and juveniles Cebus and 90 Saimiri skulls of varied age were also measured. Dental eruption sequence for all New World Monkeys was described in detail by [8]. I use the same developmental age (DA) criteria described in [30] and DA6 correspond to adult specimens. The following samples sizes were available for Cebus : DA1 (N = 25), DA2 (N = 22), DA3 (N = 41), DA4 (N = 13), DA5 (N = 60), DA6 (N = 379); and for Saimiri : DA1 (N = 9), DA2 (N = 2), DA3 (N = 4), DA4 (N = 10), DA5 (N = 65), DA6 (N = 226). Young samples (DA1 to DA4) not always present sex or species identification available and often lack any information regarding locality or accompanying skin that would allow proper identification of species and sex. The sampling here was as complete and throughout as possible but this lack of information result in a lack of power to perform growth analyses controlling for sexual and interespecific differentiation. However, most young and sub-adult samples (DA1 to DA5) are concentrated on two species, Cebus apella (99% of all specimens) and Saimiri sciureus (81%). Because the major goal here is to understand the differentiation and evolution of size and shape between the two genera the effect of uncontrolled sexual and specific variation within each genus would be to increase dispersion among points and consequently blur any observed pattern among genera. Results presented here are straightforward in this respect with a clear characterization of evolutionary and ontogenetic allometry (Figures 7 and 8) that seems robust for these other uncontrolled sources of variation (sex and species).

The specimens are deposited at the following institutions: American Museum of Natural History (AMNH), Museu de Zoologia da Universidade de São Paulo (MZUSP), Museu Nacional do Rio de Janeiro (MNRJ), Museu Paranaense Emílio Goeldi (MPEG) and National Museum of Natural History (USNM). A complete list of measured specimens sorted by taxon and museum collection may be obtained from the author upon request. Only adult crania were used in the subsequent analyses, except where specifically noted. Specimens were considered adult when they had fully erupted and functional dentition as well as closed or fused spheno-occipital and/or spheno-ethmoid sutures. Non-adult specimens correspond to a mixed age sample containing all tooth stages from a completely deciduous dentition to a permanent dentition except a functional canine or third molar [8].

Three-dimensional co-ordinates were recorded for 36 landmarks (Figure 9 and Table 9) using a Polhemus 3Draw or a Microscribe 3Dx digitizer. A small scale experiment was performed measuring a sub-sample of 20 specimens twice in each of the two digitizers. No significant differences were found between the digitizers. The general procedure for measuring specimens follows [6]. A set of 70 linear measurements describing cranial morphology was calculated from the co-ordinate values. This was reduced to a set of 39 measurements, after averaging measurements present on both sides of the skull (Tables 9 and 10). Whenever one of the skull sides was damaged, preventing me from taking any particular measurement, the other side is used. All results are presented in millimeters. All statistical analyses were performed using SYSTAT 11 (Richmond, CA).
Table 9

22 Landmarks digitized

Landmark

Description

Position(s)

Order

IS

Intradentale superior, A

Midline

1

PM

Premaxillary suture at the alveolus, A

Right, left

2, 21

NSL

Nasale, A

Midline

3

NA

Nasion, A

Midline

4

BR

Bregma, AP

Midline

5

PT

Pterion, AP

Right, left

6, 22

FM

Fronto-malare, A

Right, left

7, 23

ZS

Zygomaxillare superior, A

Right, left

8,24

ZI

Zygomaxillare inferior, A

Right, left

9, 25

MT

Maxillary tuberosity, A

Right, left

10, 26

PNS

Posterior nasal spine, A

Midline

11

APET

Anterior petrous temporal, A

Right, left

12, 27

BA

Basion, AP

Midline

13

OPI

Opisthion, AP

Midline

14

EAM

Anterior external auditory meatus, A

Right, left

15, 28

PEAM

Posterior external auditory meatus, A

Right, left

16, 29

ZYGO

Inferior zygo-temporal suture, A

Right, left

17, 30

TSP

Temporo-spheno-parietal junction, A

Right, left

18, 31

TS

Temporo-sphenoidal junction at the petrous, AP

Right, left

19, 32

JP

Jugular process, AP

Right, left

20, 33

LD

Lambda, P

Midline

34, 35

AS

Asterion, P

Right, left

36

Landmarks recorded in Cebinae primates skulls using the three-dimensional digitizer. The designation A (anterior) or P (posterior) after the landmark name indicates in which position(s) the landmark was recorded. Landmarks are also identified in Figure 9. The order that each landmark was recorded is also presented (see additional movies material).

Table 10

39 Linear distances and cranial regions

Functional/Developmental group

Region

Trait

Oral

Face

ISPM

Nasal

Face

ISNSL

Oral, nasal

Face

ISPNS

Oral

Face

PMZS

Oral

Face

PMZI

Oral

Face

PMMT

Nasal

Face

NSLNA

Nasal

Face

NSLZS

Oral, nasal

Face

NSLZI

Cranial vault

Neurocranium

NABR

Orbit

Neurocranium

NAFM

Nasal

Face

NAPNS

Cranial vault

Neurocranium

BRPT

Cranial vault

Neurocranium

BRAPET

Orbit

Neurocranium

PTFM

Cranial vault

Neurocranium

PTAPET

Cranial vault

Neurocranium

PTBA

Cranial vault

Neurocranium

PTEAM

Zygomatic

Face

PTZYGO

Cranial vault, zygomatic

Neurocranium, Face

PTTSP

Orbit

Neurocranium

FMZS

Zygomatic

Face

FMMT

Oral

Face

ZSZI

Oral

Face

ZIMT

Zygomatic

Face

ZIZYGO

Zygomatic

Face

ZITSP

Oral

Face

MTPNS

Cranial base

Neurocranium

PNSAPET

Cranial base

Neurocranium

APETBA

Cranial base

Neurocranium

APETTS

Cranial base

Neurocranium

BAEAM

Zygomatic

Face

EAMZYGO

Zygomatic

Face

ZYGOTSP

Cranial vault

Neurocranium

LDAS

Cranial vault

Neurocranium

BRLD

Cranial vault

Neurocranium

OPILD

Cranial vault

Neurocranium

PTAS

Cranial base

Neurocranium

JPAS

Cranial base

Neurocranium

BAOPI

Thirty-nine linear skull measurements (distances between landmarks) and membership in the six functional/developmental groups and two major cranial regions. Table 1 defines each landmark and Figure 9 shown their locations in a generalized Platyrrhine skull.

http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-7-20/MediaObjects/12862_2006_Article_314_Fig9_HTML.jpg
Figure 9

New World Monkey skull with landmarks. Craniofacial landmarks recorded from Cebinae skulls using three-dimensional digitizer. See Tables 9 and 10 for landmarks and measurements details.

A total of 564 adult and 251 juveniles skulls with all 39 measurements (without missing values) were used in the analyses below. Juveniles were only used in the allometry analyses and were not included in the differentiation analyses. In this study I tested for differences between the taxa, the sexes and interaction between the sexes and taxa using multivariate analysis of variance (MANOVA). Given that squirrel and capuchin monkey species present sexual dimorphism with males usually larger than females, sexes were analyzed separately.

Analyses

Interespecific Differentiation – Differences among Cebinae skulls were examined using the general linear model (GLM) module in SYSTAT 11 to perform a MANOVA and canonical variate analyses. Moreover, because the sampling includes several species within each genus and is not balanced in terms of the numbers of specimens per species, the MANOVA was performed for each sex with species nested within genus. In this way the between species variation within genus is accounted for so that the between genera differentiation is not inflated. Therefore the general linear model includes genus and species nested within genus as the two independent factors. For estimating the degree of differentiation among Cebus and Saimiri, Mahalanobis D2 distances between group averages in the canonical function were calculated.

Allometry and scaling correction – The first principal component extracted from the ln-transformed data pooled within-group variance/covariance matrix of each genus and sex was computed. Because sexual variation in allometric patterns were small, detailed comparisons of allometric coefficients are presented only for the two genera. The thirty-nine standardized PC1 coefficient values of each group were divided by (1/√39) to assess divergence from isometry [16]. In order to compare allometric coefficients among Cebinae, it is important to determine the associated error of those values. A bootstrap procedure was used to set 95% confidence limits (L1 and L2) to the allometric coefficients (AC's) (see page 34 in [22]). A hundred bootstrap samples of N = 300 were taken and used to set up 95% confidence limits to AC's. Allometric coefficients with L2 below 1.0 were considered to be negatively allometric and conversely those AC's with L1 above 1.0 were considered positively allometric. For the juveniles a hundred bootstrap samples of N = 101 for Cebus and N = 26 for Saimiri were used to set 95% confidence limits to AC's.

The overall similarity of the allometric patterns is quantified with vector correlations, which measure similarity of vector orientation in a p-dimensional space (p being the number of traits). Vector correlations are equal to the cosine of the angle between vectors. The expected range of vector correlations commonly occurring among 39-element vectors by chance alone is -0.4 < r < 0.4 [1] with an average of 0.127 and a standard deviation of 0.095. Additionally, because there is a sampling error associated with each estimated allometric vector we use a self-correlation procedure to calculate allometric vector repeatability [6, 23]. Allometric vector repeatability was estimated by correlating the observed PC1 and each of the 100 PC1 obtained from a bootstrap sample of replicates. These correlations provide a distribution of self-correlation [4]. The mean of this distribution is then used to measure allometry vector repeatability. To help judging how high allometric vector correlations are among genera and sex we adjust the observed vector correlations for estimation error by dividing the observed correlation by the square root of the product of the two vector repeatabilities (see [6, 23]). I also use the strategy described by ([41], chapter 13, page 337) and compare each allometric vector to 100 random permutation of its elements. The rational underlying this approach is that if two vectors are "size" or "allometric" vectors with all elements positive, the range of vectors correlations is actually much smaller that from zero to one. Therefore every vector is permuted a 100 times and correlated with this random sample in order to test, using the corresponding average and confidence interval, whether or not correlation among any two vectors are indeed more similar that expected by chance alone.

I also used another strategy to analyze the relationship between size, shape and development based on [31] restriction of the term heterochrony and his focus on growth trajectories. Under this restriction heterochrony is a uniform change in the rate or timing of some ontogenetic process, with no change in the nature of the biological interactions going on within that process [31]. Uniform changes in the growth trajectory (trait × time) can be detected by comparing them (see Fig. 4 in [31]). One caveat in the analyzes performed here is that neither the Saimiri or Cebus data have time (age) available, given that the specimens were wild caught. Therefore, I plotted all 39 traits against skull size (all data ln-transformed in order to linearize the relationship), assuming that size is good proxy to time (see results for an indirect test of this assumption). To help visualize whether or not trajectories were linear and similar every plot included a LOWESS smooth function with tension equal to 0.3 (SYSTAT 11, Richmond, CA). The smoothing is produced by running along the X values and finding predicted values from a weighted average of nearby Y values. The surface is allowed to flex locally to better fit the data.

Given variation in squirrel and capuchin monkey size and, consequently, in allometric shape variation associated with those size differences, a normalization technique to scale data and remove allometric effects was applied [20, 24]. This method, which I will refer from now on as "Multivariate Allometric Size-Scaling (MASS)", is derived from theoretical equations of allometric growth removing all the information related to size, not only scaling all individuals to the same size, but also adjusting their shape to account for allometry [20]. Here I follow Marroig and [24] modifying the [20] method by using the first principal component (PC1) score of the natural log data as the overall size measure and regressing all 39 traits onto PC1. The [20] correction is

Y*i = Yi [X0/Xi]b

Where Yi and Xi are the values of a specific trait and overall size (PC1 score) in individual 'i', respectively, Y*i is the theoretical value for the trait at the average size, X0 is the average antiloge of the PC1 scores, and 'b' is the PC1 coefficient for each of the 39 traits. Notice that 'b' is equal to the regression coefficient of the trait Y upon the PC1 scores. After this correction, the original data of all Cebinae are scaled to the same size, also adjusting their shapes for allometric scaling. These scale-corrected data were used to explore whether differences among Saimiri and Cebus were size dependent. This was done comparing the results of the MANOVA using the original (unscaled) and scale-corrected (MASS) data.

Geometric morphometrics

I also used a different approach to help visualize and test for differences in size and shape among Cebus and Saimiri. This geometric morphometrics approach was implemented using Morphologika, software developed by Paul O'Higgins and Nicholas Jones (University of York, see [27, 7]). Detailed descriptions of Morphologika and the geometric Morphometrics theory can be found elsewhere [17, 7, 27, 41]. The program uses generalized least squares superimposition to register landmark data. Registration is the basic procedure of translation, scaling, and rotation to remove all information unrelated to shape [41]. The resulting shape coordinates were subject to principal component analysis (PC's 3D from now on) in the tangent space (the Procrustes tangent projection) to Kendall's shape space [17, 7]. What is important here is that this approach allows the separation of absolute size (scale differences quantified by the centroid size), shape differences due to allometry, and shape differences non-associated with size. A sample of 279 skulls was used in this analyses corresponding to all sub-adults and juveniles skulls and adults of the two most abundant species of each genus. Principal component scores were saved and used to test for differences as well as to interpret biologically each PC. One interesting feature in Morphologika is that the software allows the visualisation of the shape variability represented by the PCs which is achieved by reconstruction of the skulls (landmarks) in real time at any point along each PC axis.

The clear cut results in terms of separation and similarity between ontogenetic and evolutionary allometries (see below) arising from this geometric morphometric analysis, presents a new opportunity to develop a new approach to the study of allometry, growth and development. Landmarks configurations were obtained for each genus and age class along the PC1-3D and PC2-3D. The absolute differences between each of those average configurations represent the amount of changes occurring in each landmark along any period of the ontogeny. This allows a quantification of the magnitude of changes in each landmark throughout the ontogeny. Also, each of these differences between age classes defines a vector of changes in landmark position. Therefore is possible to quantify and compare those changes in shape using again vector correlation. These were calculated within each genus for consecutive age classes (age1-age2 × age2-age3, age2-age3 × age3-age4, and so on) as well as for similar age classes between genera (Saimiri age1-age2 × Cebus age1-age2, and so on). For those landmarks collected on both sides of the skull, the average of absolute magnitude of change was used in defining each vector. Therefore, each vector has 22 elements.

Life-history

I also obtained life-history data from the literature [15, 11, 9, 19, 28, 37] for all New World Monkeys. Particularly, data on gestation length, body weight and skull size (my own observations from museum specimens, both skulls and labels), age at first reproduction, age at weaning, and birth weight, all transformed to natural log scale to make their relationships linear. Fetal growth rate was estimated by dividing the natural log of birth weight by the natural log of gestation length. Post-natal growth rate was estimated by regressing the natural log of adult weight by the natural log of the age of first reproduction (in days) and using the regression slope as an estimate of the rate (Table 8 show the life-history data). Association among these variables was tested using the independent contrasts (IC) method to account for the non-independence of phylogenetically structured data [12]. I use the module PDAP [12] within the MESQUITE package [21] to obtain the correlation among variables. The phylogenetic tree used is the same as in [25] based on [35]. Ideally, given that species within genus could vary in their life-histories, it would be necessary to correct for such differences properly accounting for phylogenetic relationships among species. Unfortunately robust and complete (with all species) phylogenetic hypotheses at the species within-genus level are not available for either Cebus or Saimiri. Also, not all species had life-history data available. These two pieces of information would be necessary to estimate ancestor values for the life-history traits. Therefore, in order to at least consider the range of variation in life-history among species within these two genera and check whether or not results from these analyses are consistent I use the minimum and maximum values for each life-history parameter to test the robustness of these regressions.

Declarations

Acknowledgements

To those people and institutions who provide generous help and access to the skeletal material: R. Voss and R. MacPhee (American Museum of Natural History); B. Patterson, B. Stanley and L. Heaney (Field Museum of Natural History); L. Salles, J. Oliveira, F. Barbosa and S. Franco (Museu Nacional do Rio de Janeiro); S. Costa and J. de Queiroz (Museu Paraense Emílio Goeldi); M. de Vivo (Museu de Zoologia da Universidade de São Paulo); and R. Thorington and R. Chapman (National Museum of Natural History (USNM, Washington). Many thanks also to Sue Boinski, Jim Cheverud and Sébastien Couette for reading, correcting, and criticizing an earlier version of this manuscript. Of course, as usual, they are not responsible for any error or misinterpretation of any sort on this paper which should all be billed on me. Many thanks also to Felipe Bandoni who help me in digitizing some of the young's specimens. This research was supported by grants from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), an American Museum of Natural History "Collections Study Grant" and a Field Musem of Natural History "Visiting schoolarship".

Authors’ Affiliations

(1)
Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo

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© Marroig. 2007

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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