Early Arrival and Climatically-Linked Geographic Expansion of New World Monkeys from Tiny African Ancestors

Early arrival and climatically-linked geographic expansion of New World monkeys from tiny African ancestors

Daniele Silvestro1,2,3, Marcelo F. Tejedor1,4,5, Martha L. Serrano-Serrano2,

Oriane Loiseau2, Victor Rossier2, Jonathan Rolland2,6, Alexander Zizka1,3,

Sebastian Hohna¨ 7, Alexandre Antonelli1,3,8, Nicolas Salamin2

1 Department of Biological and Environmental Sciences, University of Gothenburg, 413 19

Gothenburg, Sweden;

2 Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland;

3 Gothenburg Global Biodiversity Center, Gothenburg, Sweden;

4 Instituto Patagonico de Geologia y Paleontologia (CCT CONICET-CENPAT), Boulevard

Almirante Brown 2915, 9120 Puerto Madryn, Chubut, Argentina;

5 Facultad de Ciencias Naturales, Sede Trelew, Universidad Nacional de la Patagonia ‘San Juan

Bosco’, 9100 Trelew, Chubut, Argentina;

6 Department of Zoology, University of British Columbia, Vancouver, Canada;

7 Division of Evolutionary Biology, Ludwig-Maximilians-Universit¨at,M¨unchen,Germany ;

8 Gothenburg Botanical Garden, Carl Skottsbergs gata 22A, 413 19 Gothenburg, Sweden;

1 Supplementary Text

Simulated data sets

We assessed the accuracy and the eﬃciency of our Bayesian framework by analyzing

simulated data sets and comparing the estimated rates, trends, and ancestral states with

the true values. For each simulation, we generated a complete phylogenetic tree (extinct

and extant taxa) under a constant rate of birth-death with 100 extant tips (sim.bd.taxa

function with parameters λ = 0.4, and µ = 0.2, in the R package TreePar, (Stadler 2011)).

The number of fossils simulated on the tree was deﬁned by a Poisson distribution with expected number of occurrences (N) equal to the total branch length times the preservation rate (q). We ﬁxed the number of expected fossils N = 20 by setting q to the

ratio between N and the sum of branch lengths. Then, each fossil was placed randomly on

the tree and extinct tips without fossils were pruned out (note that this procedure is

equivalent to that implemented in the FBD original paper (Heath et al. 2014)). Finally, the

fossilized tree was re-scaled to an arbitrary root height of 1.

Phenotypic data were simulated on every tree with the following parameters: rate of

2 evolution (σ ), phenotypic trend (µ0), number of shifts in rate, number of shifts in trend, magnitude of the shifts. We simulated data sets under six evolutionary scenarios:

2 1. constant σ drawn from a gamma distribution Γ(2, 5), and ﬁxed µ0 = 0

2 2. ﬁxed σ = 0.1, and constant µ0 drawn from a normal distribution (2, 0.5) N

2 3. baseline σr = 0.1, ﬁxed µ0 = 0, and one rate shift in a randomly selected clade so that σ2 = m σ2, where m (8, 16). i × r ∼ U

2 2 4. baseline σ = 0.1, ﬁxed µ0 = 0, and two shifts in σ drawn from an uniform distribution representing a change with magnitude between 8 and 16 fold

1 2 5. ﬁxed σ = 0.1, ﬁxed initial µ0 = 0, and one shift in µ0 drawn from a normal distribution (2r, 0.5), where r was randomly set to -1 or 1 (simulating negative or positive trends, N respectively).

2 6. ﬁxed σ = 0.1, ﬁxed µ0 = 0, and two shifts in µ0 each one drawn from a normal distribution (2r, 0.5), where r was randomly set to -1 or 1 (simulating negative or N positive trends, respectively).

2 For the last two scenarios the location of shifts (in σ or µ0) was randomly selected by choosing clades that contained between 25 to 50 tips. The branches within the chosen

clades were assigned with the new parameter value before performing the phenotypic data

simulation using a mapped tree (make.era.map function in R package phytools (Revell

2012)). We simulated 100 data sets under each of the six scenarios.

Analysis of simulated data

We analyzed each simulated dataset to estimate the rate and trend parameters of

2 the BM model (σ and µ0) and the ancestral states. Each dataset was run for 500,000 MCMC generations, sampling every 500 steps. We summarized the results in diﬀerent

ways. First, for each simulation we graphically inspected the results by plotting the

phylogeny with the width of the branches proportional to the true and estimated rates. We

2 plotted the true versus estimated σ , and µ0 for each branch on the tree (Fig. 2, main text). The true and estimated ancestral states are compared in a phenogram plot (Revell 2012).

Secondly, we numerically quantiﬁed the overall accuracy of the parameter estimates

across all simulations using diﬀerent summary statistics for each set of parameters. For the

BM rate parameters (σ2) we calculated the mean absolute percentage error (MAPE),

deﬁned as: N ˆ2 2 ! 2 1 X σi σi MAP Ej(σ ) = | − | (1) N σ2 i=1 i

2 ˆ2 2 where j is the simulation number, σi is the estimated rate at branch i, σi is the true rate at branch i, and N is the number of branches in the tree. Because the trend parameter can take both negative and positive values, we used the mean absolute error (MAE) to quantify the accuracy of its estimates:

N 1 X MAE (µ ) = µˆi µi (2) j 0 N 0 0 i=1 | − |

î i where µ0 is the estimated trend at branch i and µ0 is the true trend at branch i. We quantified the accuracy of the ancestral state estimates in terms of coefficient of determination (R2) between the true and the estimated values. These summary statistics were computed for each simulation scenario (across 100 replicates) and are provided in

Figs. 2 (main text) and S1.

Finally, we assessed the ability of the BDMCMC algorithm to identify the correct

BM model of evolution in terms of number of shifts in rate and trend parameters. We calculated the mean probability estimated for models with diﬀerent number of rate shifts

(Kσ2 ranging from 0 to 4) and shifts in trends (Kµ0 ranging from 0 to 4). Note that K = 0 indicates a model with constant rate and/or trend parameter across branches. The estimated posterior probability of a given number of rate shifts was obtained from the frequency at which that model was sampled during the MCMC (Stephens 2000). We averaged these probabilities across 100 simulations under each scenario. We additionally calculated the percentage of simulations in which each model was selected as the best

ﬁtting model (i.e., it was sampled most frequently). These summary statistics are provided in the Table S8.

3 Robustness under incomplete sampling

We assessed the eﬀect of incomplete sampling on the accuracy of trait evolution

estimates in two ways. First we ran 100 additional simulations for scenarios (1) and (2)

with a lower number of fossils (N= 5, 1, and 0) adjusting the q parameter. For simulations

with one fossil, we simulated fossils under a Poisson process with N = 20 and kept only the oldest occurrence in the trait analysis. These simulations show the eﬀects of low preservation rates (i.e. poor fossil record).

Second, we assessed the eﬀects of missing extant taxa. We designed these simulations to reﬂect the level of taxon sampling and the size of the empirical phylogeny of platyrrhines analyzed in this study. We therefore simulated complete phylogenies of 200 extant species and removed randomly 56% of them from the tree prior to the analysis. The mean number of fossils was kept to 20, which is actually fewer than the 34 fossils included in our empirical analysis. The traits were simulated under scenarios (1) and (2). We simulated 100 data sets and analyzed them under the same settings used for the other simulations.)

Performance tests

We compared the performance of our implementation using Gibbs sampling for ancestral states with that of an implementation using a Metropolis-Hasting algorithm to update the ancestral states, i.e. the method used in other software such as Geiger (Slater et al. 2012; Slater and Harmon 2013; Pennell et al. 2014) and phytools (Revell 2012) (note however that faster alternative implementations are available, e.g. (Ho and Ane 2014)). We implemented the latter option in our code by updating the ancestral states individually using sliding window proposals and acceptance probability based on the posterior ratio.

We ran the two implementations on trees with 50, 100, 500, and 1,000 tips for 100,000

4 MCMC iterations, sampling every 50 iterations and using the true parameter values as

starting values in the MCMC to avoid burnin. We simulated the data using a constant

2 Brownian rate (σ = 0.1) and no trend (µ0 = 0) and ran the analyses by constraining the MCMC to use the simplest Brownian model (i.e. no rate shifts and no trends). We then calculated for each simulation the run time and the eﬀective sample size (ESS) of the posterior and use them to estimate the run time necessary to reach an ESS = 1000. These performance tests (summarized in Fig. S3a), show that the Gibbs sampler achieved the target ESS of 1000 in a much shorter time than the alternative Metropolis-Hastings algorithm. Importantly, the time required to reach ESS = 1000 scaled linearly with tree size using the Gibbs sampler and exponentially using the Metropolis-Hastings.

The joint estimation of model parameters (rates and trends) and ancestral states allow us to compute the likelihood as a product of normal densities (Fig. 1, main text) while avoiding the use of a variance-covariance (VCV) matrix (commonly used in comparative methods (Felsenstein 1988; O’Meara 2012)), which can be very expensive to compute especially for large trees. We ran simulations to assess the performance of the two approaches to calculate the likelihood of the data (product of normal densities against standard calculation using the vcv matrix). We simulated trees of 50, 100, 500, 1,000, and

5,000 tips under a pure birth process (only extant taxa) and trait data under a simple

2 constant Brownian model (σ = 0.1, µ0 = 0). On each data set we ran 100,000 MCMC iterations and recorded the run time. The results of these simulations are summarized in

Fig. S3b and show that the two approaches to calculate the model likelihood perform similarly for small trees (around 100 tips). However, the time to calculate the likelihood by inverting the VCV scales exponentially with tree size, while our implementation with the product of normal densities scales linearly with tree size. Thus, for instance, our implementation was 1.7 times faster than the alternative with 500 tips and 16.4 times faster with 5,000 tips.

5 Episodic Fossilized-Birth-Death Process

Our species-diversiﬁcation model is based on the reconstructed process (Nee et al.

1994) and its extension, the fossilized-birth-death process (Stadler 2010; Heath et al. 2014).

Most importantly, we assume that rates —speciation, extinction, and fossilization— are constant within episodes, hence using a Episodic-Fossilized-Birth-Death (EFBD) Process

(see Fig. S10a). An episodic model has previously been applied to the birth-death process without fossilization (Stadler 2011; H¨ohna2015; May et al. 2016) and we will follow the same logic.

Let us consider an example, as shown in Fig. S10, where speciation (λ), extinction

(µ) and fossilization (φ) rates shift at given times s. In the example provided in Fig. S10a,

the rates shift at times = s0 =0, s1 =10, s2 =20, s3 =30 . Furthermore, in our model we S { } assume that the process starts with a single lineage at the time of the origin, tor million years in the past. Then, every living lineage at some time t speciates with rate λ(t), goes extinct with rate µ(t), and leaves a recovered fossil with rate φ(t). Our model is strictly bifurcating, that is, at a speciation event, the lineage is replaced by two daughter lineages

(see Fig. S10b). Also note that in our model fossilization does not require extinction, which means that fossil taxa do not have to be terminal taxa. We do not condition the process on survival because we could also observe a phylogeny without extant taxa.

To derive the probability density of observing a reconstructed fossil tree, we break the branches of the phylogeny into branch segments, where a segment begins at either a speciation, fossilization, or rate-shift event and terminates at either a speciation, fossilization, or rate-shift event. Within a segment there must not be any speciation, fossilization, or rate-shift event of the reconstructed fossil tree, thus, the branch segments connect any two consecutive events along a lineage (see Fig. S10.C). Let us denote the start time of a branch segment by to (i.e., older time) and the end time of a branch

6 segment by ty (i.e., younger time). First, we will deﬁne the diﬀerential equation for the probability of extinction in a

small interval ∆t as

h i E(t + ∆t) = µ(t)∆t + (1 µ(t)∆t) (1 φ(t)∆t) 1 λ(t)∆tE(t) + λ(t)∆tE(t)2 (3) | {z } | − {z } | − {z } | − {z } | {z } i ii iii iv v

which accounts for the three scenarios that could occur in the interval: (i) the process goes

extinct in the interval; (ii) no extinction and (iii) no fossil is sampled, and (iv) no

speciation event occurs but the process goes extinct later; or (v) a speciation event in that

interval and both daughter lineages eventually going extinct and are not sampled (neither

as a fossil nor as an extant tip). Rearranging the equation, eliminating terms of order ∆t2, subtracting E(t) from E(t + ∆t) and dividing by ∆t leads to the diﬀerential equation

dE = µ(t) E(t)(λ(t) + µ(t) + φ(t)) + λE(t)2 . (4) dt −

Assuming that rates are constant within an episode —our deﬁnition of the episodic

fossilized-birth-death process— and that si+1 > t > si we obtain the solution of the diﬀerential equation as

−A (t−s ) 1+Bi−e i i (1−Bi) λi + µi + φi Ai −A (t−s ) 1+Bi+e i i (1−Bi) Ei(t) = − (5) 2λi

where

q 2 Ai = λi µi φi + 4 λi φi (6) − − × ×

1 2Ei−1(si) λi + µi + φi Bi = − (7) Ai

7 with initial condition E(0) = 1 ρ (Stadler et al. 2013; Zhang et al. 2016). − Next, we deﬁne the diﬀerential equation computing the probability of a

branch-segment (applied to both lineages leading to extant and sampled extinct taxa) as

h i D(t + ∆t) = (1 µ(t)∆t) (1 φ(t)∆t) D(t)(1 λ(t)∆t) + 2 D(t) λ(t) ∆t E(t) (8) | − {z } × | − {z } | −{z } | {z } i ii iii iv which again accounts for the various scenarios that could occur in the interval, ∆t, which are that the process (i) does not go extinct and (ii) does not experience a fossilization event ∆t and (iii) does not speciate, or (iv) there is a speciation event in the interval but one of the two lineages goes extinct before the present, which occurs with probability Ei(t). Similar rearrangements as above give the diﬀerential equation as

dD = D(t)(λ(t) + µ(t) + φ(t)) + 2D(t)λ(t)E(t) . (9) dt −

The analytical solution to this diﬀerential equation, using si+1 > t > si, is

4e−Ai(t−si) Di(t) = (10) −A (t−s ) 2 1 + Bi + e i i (1 Bi) −

when using the initial condition D(t = 0) = 1 and using the same function Ai and Bi as before (Stadler et al. 2013; Zhang et al. 2016).

Finally, we obtain the probability density of a reconstructed fossil phylogeny under

8 the episodic fossilized-brith-death process as

2N+M−1 f(Ψ) = (i) probability of the speciﬁc tree topology (N + M)!× ρN (ii) probability of sampling the extant taxa × Y [φ(t) E(t)] (iii) probability of a fossil tip leaving no sampled descendant × × t∈F Y h i φ(t) (iv) probability of a sampling event for the fossil ancestors × t∈A Y h i λ(t) (v) probability of a speciation event × t∈N Y hD(to)i (vi) probability of each branch-segment D(ty) t∈B (11) where ρ is the sampling probability at the present of an extant tips, N is the number of extant tips and M is the number of fossil tips, are the ages of the fossil tips, are the F A ages of the sampled ancestor fossils, are the ages of the speciation events (i.e., N bifurcating internal nodes) of the reconstructed fossil phylogeny, and are all B branch-segments (see Fig. S10c) with to being the age of the beginning of the branch-segment (older) and ty being the age of the end of the branch-segment (younger).

Autocorrelated prior model on diversiﬁcation rates

We model changes in speciation and extinction using an autocorrelated model.

Therefore, we divide time into equal-length intervals (bins) and assume that the log-transformed rates λ(t) and µ(t) are drawn from a normal distribution with the rates in

9 the next time interval centered at the rates in the current time interval

E[log(λ(t))] = log(λ(t ∆t)) and (12) − E[log(µ(t))] = log(µ(t ∆t)) (13) −

Hence, we apply a normal distribution with mean λ(t ∆t) and standard deviation σλ on − the rate parameter λ(t). Similarly, we apply a normal distribution with mean µ(t ∆t) − and standard deviation σµ on µ(t). The standard deviations σλ and σµ regulate the amount of change in each time interval. For example, if we set the standard deviation

σλ = 0, then our model collapses to the constant rate process. Due to the scarcity of fossil occurrences in platyrrhines (as compared to many other mammalian clades, e.g. Pires et al.

(2015)), we assumed a constant rate of fossilization in our analysis.

Software implementation and availability

The episodic fossilized-birth-death process with autocorrelated diversification rates is implemented in the Bayesian phylogenetics software RevBayes v1.0.7 (Höhnaet al. 2014; Höhnaet al. 2016). However, the implementation is not restricted to the autocorrelated model we used here because RevBayes provides many possible prior distributions that can be used, such as independent lognormal or independent uniform rates. RevBayes is open-source and freely available from https://github.com/revbayes/revbayes. The analyses from this paper are described in detail in several tutorials available at http://revbayes.github.io/tutorials.html.

10 Supplementary Tables

11 Table S1: Summary of the main taxonomic hypotheses that have been proposed for platyrrhines.

Schneider (2000) Groves (2001) Rylands & Mittermeier Rosenberger (2011), including (2009) extinct genera (†) 1. Family Cebidae 1. Family Cebidae 1. Family Callitrichidae 1. Family Cebidae Callithrix, Cebuella, Saguinus, Leontopithecus, Callimico, Callibella, Mico

1.1. Subfamily 1.2. Subfamily 2. Family Cebidae 1.1. Subfamily Cebinae Callitrichinae Callitrichinae Cebus, Saimiri, †Neosaimiri, Callithrix, Cebuella, Mico, Callithrix, Leontopithecus, †Laventiana,†Dolichocebus, Leontopithecus, Callimico Saguinus, Callimico †Killikaike, †Acrecebus

1.2. Subfamily Cebinae 1.3. Subfamily Cebinae 2.1. Subfamily Cebinae 1.2. Subfamily Callitrichinae Cebus, Saimiri Cebus Cebus Cebuella, Callithrix, Saguinus, Leontopithecus, Callimico, †Mohanamico, †Micodon, †Patasola

1.3. Subfamily Aotinae 1.4. Subfamily Saimiriinae 2.2. Subfamily Saimiriinae 1.3. Subfamilies incertae sedis Aotus Saimiri Saimiri Branisella, Szalatavus, Chilecebus

2. Family Pitheciidae 2. Family Aotidae 3. Family Aotidae 2. Family Pitheciidae Aotus Aotus 2.1. Subfamily Pitheciinae 3. Family Pitheciidae 4. Family Pitheciidae 2.1. Subfamily Pitheciinae Pithecia, Chiropotes, Pithecia, Chiropotes, Cacajao, Cacajao †Proteropithecia, †Nuciruptor, †Cebupithecia, †Soriacebus, †Mazzonicebus 2.1. Subfamily Callicebinae 3.1. Subfamily Pitheciinae 4.1. Subfamily Pitheciinae 2.2. Subfamily Homunculinae Callicebus Pithecia, Chiropotes, Pithecia, Chiropotes, Callicebus, †Homunculus, Aotus Cacajao Cacajao (including †A. dindensis), †Xenothrix, †Antillothrix, †Miocallicebus, †Insulacebus, †Tremacebus, †Carlocebus 3. Family Atelidae 3.2. Subfamily Callicebinae 4.2. Subfamily Callicebinae 2.3. Subfamily incertae sedis Callicebus Callicebus †Lagonimico

3.1. Subfamily Alouattinae 4. Family Atelidae 5. Family Atelidae 3. Family Atelidae Alouatta

3.2. Subfamily Atelinae 4.1. Subfamily Alouattinae 5.1. Subfamily Alouattinae 3.1. Subfamily Alouattinae Ateles, Lagothrix, Alouatta Alouatta Alouatta, †Stirtonia, †Paralouatta, Brachyteles †Protopithecus, †Solimoea

4.2. Subfamily Atelinae 5.2. Subfamily Atelinae 3.2. Subfamily Atelinae Ateles, Brachyteles, Ateles, Brachyteles, Ateles, Brachyteles, Lagothrix, Lagothrix, Oreonax Lagothrix †Caipora

12 Table S2: Summary of the molecular data used in this study (Part 1).

Partition for Position in alignment Position in Percentage substitution model Springer et al. (2012) platyrrhine alignment of species sampled Autosomal-4 16874–17433 1–560 63.22 Autosomal-3 17434–17849 561–976 64.37 X-linked 17850–18349 977–1476 72.41 X-linked 18350–18928 1477–2055 64.37 Autosomal-2 18929–19600 2056–2727 54.02 Autosomal-1 19601–20119 2728–3246 47.13 Autosomal-2 20120–21067 3247–4194 65.52 Autosomal-2 21068–22051 4195–5178 62.07 X-linked 22052–22823 5179–5950 64.37 Autosomal-1 22824–23384 5951–6511 42.53 Autosomal-3 23385–24636 6512–7763 56.32 Autosomal-3 24637–25427 7764–8554 36.78 Autosomal-4 25428–25808 8555–8935 65.52 Autosomal-2 25809–26804 8936–9931 66.67 Autosomal-3 26805–27232 9932–10359 56.32 Autosomal-2 27233–27859 10360–10986 45.98 Autosomal-2 27860–28464 10987–11591 68.97 Autosomal-1 28465–29001 11592–12128 65.52 Autosomal-2 29002–29968 12129–13095 64.37 Autosomal-3 29969–30716 13096–13843 60.92 X-linked 30717–31446 13844–14573 65.52 Autosomal-1 31447–32165 14574–15292 56.32 Autosomal-4 32166–32635 15293–15762 66.67 Autosomal-2 32636–33198 15763–16325 68.97 Autosomal-3 33199–33844 16326–16971 43.68 Autosomal-1 33845–34457 16972–17584 56.32 Autosomal-3 34458–35249 17585–18376 37.93 Autosomal-3 35250–36010 18377–19137 45.98 Autosomal-3 36011–36704 19138–19831 48.28

13 Table S3: Summary of the molecular data used in this study (Part 2).

Partition for Position in alignment Position in Percentage substitution model Springer et al. (2012) platyrrhine alignment of species sampled Autosomal-1 36705–37359 19832–20486 67.82 Autosomal-1 37360–37916 20487–21043 59.77 Autosomal-1 37917–38456 21044–21583 68.97 Autosomal-1 38457–39061 21584–22188 68.97 Autosomal-2 39062–39711 22189–22838 68.97 Autosomal-2 39712–40049 22839–23176 63.22 Autosomal-4 40050–40362 23177–23489 62.07 X-linked 40363–40966 23490–24093 67.82 Autosomal-3 40967–41683 24094–24810 70.11 Autosomal-2 41684–42754 24811–25881 64.37 Autosomal-2 42755–43444 25882–26571 67.82 Autosomal-2 43445–44130 26572–27257 70.11 Autosomal-1 44131–44726 27258–27853 68.97 Autosomal-1 44727–45371 27854–28498 67.82 X-linked 45372–45700 28499–28827 59.77 Y-linked 45701–46638 28828–29765 33.33 Y-linked 46639–47105 29766–30232 34.48 Autosomal-1 47106–47261 30233–30388 55.17 Autosomal-4 47262–48139 30389–31266 55.17 Autosomal-3 48140–48614 31267–31741 66.67 Autosomal-1 48615–49219 31742–32346 60.92 Y-linked 49220–49590 32347–32717 44.83 X-linked 49591–50401 32718–33528 67.82 Y-linked 50402–51254 33529–34381 39.08 X-linked 51255–51801 34382–34928 70.11 mitochondrial 51802–52938 34929–36065 82.76

14 Table S4: Results of models testing across alternative substitution models for each partition (Part 1). The best ﬁtting model is the one with the lowest (Akaike information criterion) AIC score.

Autosomal 1 Autosomal 2 Model -lnL AIC -lnL AIC GTR+G 18171.96 36707.92 29410.55 59185.09 HKY+G 18198.40 36752.81 29424.62 59205.24 GTR 18317.04 36996.08 29800.83 59963.66 HKY 18342.97 37039.94 29816.94 59987.89 SYM+G 18342.36 37042.73 29456.76 59271.53 K80+G 18351.23 37052.47 29465.72 59281.43 SYM 18487.80 37331.61 29849.04 60054.07 K80 18498.19 37344.37 29861.63 60071.25 F81+G 18496.14 37346.27 30228.49 60810.97 JC+G 18633.89 37615.77 30267.30 60882.59 F81 18638.67 37629.34 30614.91 61581.81 JC 18778.05 37902.10 30654.48 61654.96

Autosomal 3 Autosomal 4 Model -lnL AIC -lnL AIC GTR+G 26041.88 52447.75 12913.01 26190.03 HKY+G 26059.57 52475.14 12932.43 26220.86 GTR 26273.78 52909.57 13099.64 26561.29 HKY 26291.80 52937.61 13121.34 26596.69 SYM+G 26243.73 52845.47 12929.99 26217.98 K80+G 26248.68 52847.37 12945.06 26240.13 SYM 26482.76 53321.52 13121.38 26598.76 K80 26488.67 53325.34 13137.83 26623.67 F81+G 26636.81 53627.63 13258.48 26870.95 JC+G 26799.33 53946.66 13270.45 26888.90 F81 26868.48 54088.95 13445.12 27242.24 JC 27035.58 54417.15 13459.61 27265.21

15 Table S5: Results of models testing across alternative substitution models for each partition (Part 2). The best ﬁtting model is the one with the lowest (Akaike information criterion) AIC score.

mitochondrial X-linked Model -lnL AIC -lnL AIC GTR+G 15719.08 31802.15 15118.04 30600.07 HKY+G 15727.93 31811.85 15125.16 30606.32 GTR 17562.30 35486.60 15288.38 30938.76 HKY 17697.30 35748.59 15297.45 30948.90 SYM+G 15948.26 32254.53 15145.88 30649.76 K80+G 16136.77 32623.53 15149.23 30648.47 SYM 17663.05 35682.10 15321.27 30998.54 K80 17999.63 36347.25 15326.52 31001.05 F81+G 17361.88 35077.76 15577.04 31508.08 JC+G 17911.10 36170.21 15600.50 31548.99 F81 19182.29 38716.58 15750.74 31853.48 JC 19620.14 39586.29 15776.46 31898.92

Y-linked Model -lnL AIC GTR+G 10751.87 21867.75 HKY+G 10763.49 21882.98 GTR 10901.07 22164.13 HKY 10915.20 22184.39 SYM+G 10798.49 21954.99 K80+G 10806.76 21963.53 SYM 10956.62 22269.23 K80 10966.23 22280.46 F81+G 10929.55 22213.10

16 Table S6: List of fossil taxa used in the phylogenetic analysis of platyrrhines. The taxonomic assignments were used to enforce topological constraints in the FBD analysis. The placement of taxa with no taxonomic constraints (e.g. Branisella boliviana and following) were sampled based on their age and on the parameters of the FBD model, without any a priori assumptions regarding their assignment to the stem or crown of the tree.

Taxon Taxonomy (Constraints for fossil placement) Genus Subfamily Family Cartelles coimbraﬁlhoi - Alouattinae Atelidae Paralouatta varonai - Alouattinae Atelidae Solimoea acrensis - Alouattinae Atelidae Stirtonia tatacoensis - Alouattinae Atelidae Stirtonia victoriae - Alouattinae Atelidae Caipora bambuiorum - Atelinae Atelidae Acrecebus fraileyi Stem Cebus Cebinae Cebidae Dolichocebus gaimanensis - Cebinae Cebidae Killikaike blakei - Cebinae Cebidae Laventiana annectens Stem Saimiri Cebinae Cebidae Neosaimiri ﬁeldsi Stem Saimiri Cebinae Cebidae Panamacebus transitus - Cebinae Cebidae Patasola magdalenae - Cebinae Cebidae Micodon kyotensis - Callitrichinae Cebidae Aotus dindensis - Aotinae Pitheciidae Tremacebus harringtoni - Aotinae Pitheciidae Carlocebus carmenensis - Homunculinae Pitheciidae Homunculus patagonicus - Homunculinae Pitheciidae Miocallicebus villaviejai - Homunculinae Pitheciidae Mazzonicebus almendrae - Pitheciinae Pitheciidae Soriacebus ameghinorum - Pitheciinae Pitheciidae Proteropithecia neuquenensis - Pitheciinae Pitheciidae Cebupithecia sarmientoi - Pitheciinae Pitheciidae Nuciruptor rubricae - Pitheciinae Pitheciidae Antillothrix bernensis - - Pitheciidae Insulacebus toussentiana - - Pitheciidae Xenothrix mcgregori - - Pitheciidae Branisella boliviana --- Canaanimico --- Chilecebus carrascoensis --- Lagonimico conclucatus --- Mohanamico hershkovitzi --- Szalatavus attricuspis --- Perupithecus ucayaliensis ---

17 Table S7: Age, estimated body mass and geographic coordinates of the fossil records used in phylogenetic and trait evolution analyses.

Taxon Age (Ma) Body mass (g) Latitude Longitude Cartelles coimbraﬁlhoi 0.02 23,500 -10.2 -40.9 Paralouatta varonai 1.255 (2.5–0.01) 8,444 22.4 -83.7 Solimoea acrensis 7.5 (9–6) 8,000 -10.9 -69.9 Stirtonia tatacoensis 12.5 (13.2–11.8) 5,513 -3.2 -75.2 Stirtonia victoriae 12.5 (13.2–11.8) 10,000 -3.2 -75.2 Caipora bambuiorum 0.02 24,000 -10.2 -40.9 Acrecebus fraileyi 7.5 (9–6) 12,000 -10.9 -69.9 Dolichocebus gaimanensis 20 2,700 -43.4 -65.5 Killikaike blakei 16.5 2,000 -51.6 -69.4 Laventiana annectens 12.5 (13.2–11.8) 605 -3.2 -75.2 Neosaimiri ﬁeldsi 12.5 (13.2–11.8) 768 -3.2 -75.2 Panamacebus transitus 20.93 (+/- 0.17) 2,700 9.0 -79.7 Patasola magdalenae 12.5 (13.2–11.8) 480 -3.2 -75.2 Micodon kyotensis 12.5 (13.2–11.8) 400 -3.2 -75.2 Aotus dindensis 12.5 (13.2–11.8) 1,054 -3.2 -75.2 Tremacebus harringtoni 20 1,800 -42.5 -68.3 Carlocebus carmenensis 17 (17.5–16.5) 3,500 -47.0 -70.7 Homunculus patagonicus 16.5 2,700 -51.2 -69.1 Miocallicebus villaviejai 12.5 (13.2–11.8) 1,500 -3.2 -75.2 Mazzonicebus almendrae 20 1,602 -45.7 -68.7 Soriacebus ameghinorum 17 (17.5–16.5) 1,483 -47.0 -70.7 Proteropithecia neuquenensis 15.7 1,600 -40.0 -70.2 Cebupithecia sarmientoi 12.5 (13.2–11.8) 1,602 -3.2 -75.2 Nuciruptor rubricae 12.5 (13.2–11.8) 2,000 -3.2 -75.2 Antillothrix bernensis 1.32 (+/- 0.1) 1,500 18.4 -68.6 Insulacebus toussentiana 0.01 4,805 18.3 -74.1 Xenothrix mcgregori 0.002 5,720 17.7 -77.2 Branisella boliviana 26.42 (27.02–25.82) 1,000 -17.1 -67.6 Canaanimico amazonensis 26.56 (+/- 0.07) 2,000 -7.5 -75.0 Chilecebus carrascoensis 20.09 (+/- 0.27) 1,000 -34.9 -70.4 Lagonimico conclucatus 12.5 (13.2–11.8) 595 -3.2 -75.2 Mohanamico hershkovitzi 12.5 (13.2–11.8) 1,000 -3.2 -75.2 Szalatavus attricuspis 26.42 (27.02–25.82) 550 -17.1 -67.6 Perupithecus ucayaliensis 37.5 (40–35) 400 -9.5 -72.8

18 Table S8: Model testing across simulations. We summarized the mean probability (Pr) estimated for models with diﬀerent number of rate shifts (Kσ2 ranging from 0 to 4) and shifts in trends (Kµ0 ranging from 0 to 4) across 100 simulations under each scenario (see Supplementary Methods). We additionally calculated the percentage of simulations in which each model was selected as the best model (%bm). Values in bold represent the settings used to simulated the data. n. shifts Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Pr %bm Pr %bm Pr %bm Pr %bm Pr %bm Pr %bm

Kσ2 = 0 0.72 97 0.81 96 0.01 1 0 0 0.80 97 0.76 92

Kσ2 = 1 0.23 3 0.17 3 0.73 89 0.05 7 0.17 2 0.19 7

Kσ2 = 2 0.04 0 0.02 1 0.22 9 0.68 91 0.03 1 0.04 1

Kσ2 = 3 0 0 0 0 0.03 1 0.22 2 0.01 0 0 0

Kσ2 = 4 0 0 0 0 0 0 0.04 0 0 0 0 0

Kµ0 = 0 0.59 98 0.61 86 0.57 92 0.57 94 0.04 4 0.01 1

Kµ0 = 1 0.31 2 0.3 14 0.32 7 0.32 6 0.55 87 0.12 14

Kµ0 = 2 0.08 0 0.08 0 0.09 1 0.09 0 0.3 9 0.53 79

Kµ0 = 3 0.01 0 0.01 0 0.02 0 0.02 0 0.09 0 0.26 5

Kµ0 = 4 0 0 0 0 0 0 0 0 0.02 0 0.07 1

19 Table S9: Parameters estimated by the fossilized birth-death analysis of extinct and extant platyrrhines. Posterior mean and 95% credible intervals were estimated from the MCMC samples from two independent runs, after removing burn-in.

Parameter Mean 95% credible interval Net diversiﬁcation 0.089 0.041 – 0.136 Turnover rate 0.745 0.571 – 0.908 Speciation rate 0.362 0.241 – 0.490 Extinction rate 0.273 0.137 – 0.415 Preservation rate 0.255 0.066 – 0.495

20 Table S10: Evolution of body mass and latitude in platyrrhines: estimated number of shifts in rate and trend parameters averaged over 100 phylogenies of extinct and extant taxa. We summarized the mean probability (Pr) estimated for models with different number of rate shifts (Kσ2 ranging from 0 to 7) and shifts in trends (Kµ0 ranging from 0 to 7). We also computed the proportion of analyses in which is model configuration was selected as best fitting model (bfm). For both traits constant rate BM models received very little support and the number of rate shifts ranged between 1 and 4 depending on the tree (Figs. S6, S7). This heterogeneity of BM models estimated across different trees is likely to capture the uncertainties associated with the placement of fossil lineages and branching times. We found little evidence of shifts in trend parameters (S6, S7).

Body mass Mid latitude n. shifts Pr bfm n. shifts Pr bfm

Kσ2 = 0 0.04 0.05 Kσ2 = 0 0.02 0.01

Kσ2 = 1 0.23 0.25 Kσ2 = 1 0.14 0.13

Kσ2 = 2 0.3 0.3 Kσ2 = 2 0.3 0.37

Kσ2 = 3 0.26 0.25 Kσ2 = 3 0.33 0.38

Kσ2 = 4 0.12 0.11 Kσ2 = 4 0.15 0.09

Kσ2 = 5 0.04 0.02 Kσ2 = 5 0.04 0.01

Kσ2 = 6 0.01 0.02 Kσ2 = 6 0.01 0.01

Kσ2 = 7 0.0 0.0 Kσ2 = 7 0.0 0.0

Kµ0 = 0 0.79 0.87 Kµ0 = 0 0.83 0.98

Kµ0 = 1 0.18 0.12 Kµ0 = 1 0.16 0.02

Kµ0 = 2 0.03 0.01 Kµ0 = 2 0.02 0.0

Kµ0 = 3 0.0 0.0 Kµ0 = 3 0.0 0.0

Kµ0 = 4 0.0 0.0 Kµ0 = 4 0.0 0.0

Kµ0 = 5 0.0 0.0 Kµ0 = 5 0.0 0.0

Kµ0 = 6 0.0 0.0 Kµ0 = 6 0.0 0.0

Kµ0 = 7 0.0 0.0 Kµ0 = 7 0.0 0.0

21 Table S11: Posterior estimates of the ancestral body mass for some of the main nodes in the platyrrhine phylogeny. Ancestral states are summarized from 100 analyses as median and 95% credible intervals. For comparison, we show the estimated ancestral states obtained from the analysis of both extinct and extant taxa as well as for analyses based on phylogenies of living taxa only. Note that in trees pruned of all extinct lineages the most recent common ancestor (MRCA) of all extant species coincides with the root node.

Ancestral body mass (Kg) using fossils using only extant data MRCA of median 95% CI median 95% CI Homunculinae 1.58 0.74 – 3.06 1.23 0.73 – 1.86 Pitheciinae 1.68 1.06 – 2.61 2.13 1.22 – 3.35 Alouattinae 6.54 3.28 – 10.21 6.13 4.57 – 7.86 Cebinae 2.09 0.93 – 3.17 1.36 0.78 – 2.12 Aotinae 0.91 0.55 – 1.42 0.89 0.65 – 1.16 Callitrichinae 0.63 0.28 – 1.12 0.65 0.41 – 0.97 Atelinae 6.91 1.40 – 12.75 6.30 3.81 – 9.30 Cebidae 1.67 0.83 – 2.57 1.28 0.78 – 1.91 Atelidae 3.94 0.85 – 7.82 4.06 2.21 – 6.37 Pitheciidae 1.59 0.58 – 2.69 1.70 0.93 – 2.68 All extant species 1.18 0.50 – 2.26 1.71 0.95 – 2.71 Clade origin 0.40 0.26 – 0.89 1.71 0.95 – 2.71

22 Table S12: Posterior estimates of the mid latitude for some of the main nodes in the platyrrhine phylogeny. Ancestral states are summarized from 100 analyses as median and 95% credible intervals. For comparison, we show the estimated ancestral states obtained from the analysis of both extinct and extant taxa as well as for analyses based on phylogenies of living taxa only. Note that in trees pruned of all extinct lineages the most recent common ancestor (MRCA) of all extant species coincides with the root node.

Ancestral latitude using fossils using only extant data MRCA of median 95% CI median 95% CI Homunculinae -20.11 -49.36 – 1.41 -7.13 -20.11 – 6.41 Pitheciinae -29.42 -49.90 – -2.52 -3.33 -18.84 – 11.24 Alouattinae -7.56 -31.69 – 17.66 -11.24 -25.21 – 7.86 Cebinae -25.73 -51.99 – 9.94 -5.73 -20.36 – 8.61 Aotinae -7.21 -22.00 – 6.92 -3.44 -11.91 – 5.23 Callitrichinae -17.78 -36.07 – -1.20 -7.39 -19.65 – 4.28 Atelinae -15.42 -37.13 – 5.62 -7.04 -24.65 – 11.07 Cebidae -29.40 -46.99 – -8.87 -6.08 -18.66 – 6.82 Atelidae -17.60 -38.49 – 2.34 -7.53 -24.08 – 9.91 Pitheciidae -34.27 -53.63 – -12.38 -5.29 -20.63 – 10.43 All extant species -22.48 -45.69 – -5.03 -5.84 -21.16 – 9.31 Clade origin -9.49 -34.39 – -0.23 -5.84 -21.16 – 9.31

23 Table S13: Estimated body mass (g) of Eocene anthropoids and parapithecoids from North Africa.

Taxon Epoch Locality Body Family mass (g) Abuqatrania basiodontos Late Eoc. Egypt 341 Parapithecidae Qatrania wingi Late Eoc. – Early Olig. Egypt 242 Parapithecidae Qatrania ﬂeaglei Late Eoc. – Early Olig. Egypt 510 Parapithecidae Biretia piveteaui Late Eoc. Algeria 383 Incertae sedis Biretia fayumensis Late Eoc. Egypt 273 Incertae sedis Biretia megalopsis Late Eoc. Egypt 400 Incertae sedis Arsinoea kallimos Late Eoc. Egypt 552 Incertae sedis Proteopithecus sylviae Late Eoc. Egypt 800 Proteopithecidae Serapia eocaena Late Eoc. Egypt 1029 Proteopithecidae Talahpithecus parvus Late Eoc. Libya 376 Incertae sedis

24 Table S14: Episodic fossilized birth-death process: model parameter names and prior distributions. See text for complete description of model parameters.

Parameter X f(X)

Variation in speciation rates σλ Exponential(1.0)

Variation in extinction rates σµ Exponential(1.0)

Variation in fossilization rates σφ Exponential(1.0) Log-transformed speciation rate at present log(λ(0)) Uniform(-10,10) Log-transformed extinction rate at present log(µ(0)) Uniform(-10,10) Log-transformed fossilization rate at present log(φ(0)) Uniform(-10,10)

Log-transformed speciation rate log(λ(i)) Normal(log(λ(t ∆t)),σλ) − Log-transformed extinction rate log(µ(i)) Normal(log(µ(t ∆t)),σµ) − Log-transformed extinction rate log(φ(i)) Normal(log(φ(t ∆t)),σφ) − Phylogeny (‘observed’) Ψ Episodic-fosslized-birth-death(λ, µ, φ, t0)

25 Table S15: MCMC moves used for autocorrelated episodic fossilized-birth-death analyses. Samples were drawn from the MCMC each iteration, where each iteration consisted of 163 diﬀerent moves in a random move schedule with 383 moves per iteration.

X Move Weight

σλ Scale(λ = 1.0) 5

σµ Scale(λ = 1.0) 5

σφ Scale(λ = 1.0) 5 log(λ(0)) Slide(δ = 1.0) 2 log(µ(0)) Slide(δ = 1.0) 2 log(φ(0)) Slide(δ = 1.0) 2 log(λ(i)) Slide(δ = 1.0) 50 x 2 log(µ(i)) Slide(δ = 1.0) 50 x 2 log(φ(i)) Slide(δ = 1.0) 50 x 2 log(λ) VectorSlide(δ = 1.0) 10 log(µ) VectorSlide(δ = 1.0) 10 log(φ) VectorSlide(δ = 1.0) 10

log(λ) σλ Shrink-Expand(λ = 1.0) 10 ∪ log(µ) σµ Shrink-Expand(λ = 1.0) 10 ∪ log(φ) σφ Shrink-Expand(λ = 1.0) 10 ∪ log(tor) Slide(δ = 1.0) 2 Total 163 383

26 Supplementary Figures

a) Rate parameters 0.5 0.4 0.3 MAPE sig2 0.2 0.1 0.0 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6

b) Trend parameters 0.5 0.4 u0 0.3 MAE m 0.2 0.1 0.0 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6

c) Ancestral states 1.00 0.95 al states 0.90 R2 Ancest r 0.85 0.80

Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Figure S1: Accuracy of parameter estimation summarized across 100 simulations under six simulation settings (see Supplementary Methods). Mean absolute percentage errors (MAPE) are reported for the rate parameters (a; σ2), Mean absolute errors (MAE) are used for the 2 trend parameters (b; µ0) and the coeﬃcient of determination R is used for ancestral states (c).

27 a) True trends b) Estimated trends c) Traitgram 0.5 0. 0 phenotype − 0. 5

2.5 2.5

trend trend − 1.0 0 parameter 0 parameter − 2.5 − 2.5 0.00 0.25 0.50 0.75 1 time

d) True rates e) Estimated rates f) Traitgram 1.5 1. 0 0. 5 0. 0 phenotype 0. 5 − 1.0 − 1.5 − 1.5 1.5 rate rate parameter parameter 0 0 0.00 0.25 0.50 0.75 1 time Figure S2: Example of simulated and estimated parameter estimates. Plots in a-c show a simulation from scenario 5, in which one clade evolves under negative trend. Plots in d-f show a simulation from scenario 4, where two rate shifts occur in the phylogeny. The phenogram (c and f) show the true trait evolutionary history (in blue) and the estimated one (in red).

28 a) Gibbs sampler vs Metropolis-Hastings b) Product of Normal densities vs VCV likelihood

● ● ● 500 40000 Time 400 ations (sec) 30000 ●

300 0 1000 2000 3000 4000 5000 20000 200

● or 100K ite r ● ● Time to ESS = 1000 (sec) 10000

100 ● ● Time f ● ● ● ● ● ● ● ● 0 ● 0

50 100 500 1000 50 100 500 1000 Number of tips Number of tips Figure S3: a) Performance of two implementations using Metropolis-Hastings (red) or a Gibbs sampler (blue) to estimate ancestral states

29 Callithrix humeralifera 0.62 0.54 Callithrix mauesi Callithrix emiliae 0.9 0.99 Callithrix argentata Callithrix pygmaea Callithrix jacchus Callithrix penicillata 0.92 Callithrix kuhlii Callithrix geoffroyi Callithrix aurita Callimico goeldii 0.98 Leontopithecus chrysopygus Leontopithecus rosalia Leontopithecus chrysomelas Saguinus bicolor Saguinus martinsi Saguinus midas Saguinus geoffroyi Saguinus oedipus Saguinus labiatus Saguinus mystax Saguinus imperator Saguinus graellsi 0.68 Saguinus nigricollis Saguinus melanoleucus 0.73 Saguinus tripartitus Saguinus fuscicollis Aotus azarae Aotus infulatus Aotus nigriceps Aotus nancymaae Aotus griseimembra 0.66 Aotus lemurinus Aotus trivirgatus 0.98 Aotus vociferans Cebus apella Cebus libidinosus Cebus robustus Cebus xanthosternos Cebus albifrons 0.98 Cebus olivaceus Cebus capucinus Saimiri oerstedii Saimiri sciureus Saimiri ustus Saimiri boliviensis Alouatta macconnelli Alouatta seniculus Alouatta nigerrima Alouatta sara Alouatta caraya 0.96 Alouatta belzebul 0.73 Alouatta guariba Alouatta coibensis Alouatta palliata Alouatta pigra Ateles belzebuth Ateles geoffroyi Ateles hybridus Ateles fusciceps Ateles chamek Ateles paniscus Brachyteles arachnoides Brachyteles hypoxanthus Lagothrix cana Lagothrix lagotricha Callicebus hoffmannsi 0.58 Callicebus moloch 0.79 Callicebus brunneus Callicebus cupreus Callicebus caligatus Callicebus donacophilus Callicebus coimbrai Callicebus personatus Callicebus nigrifrons Callicebus lugens Callicebus torquatus Cacajao ayresi Cacajao hosomi Cacajao melanocephalus Cacajao calvus Chiropotes chiropotes Chiropotes israelita Chiropotes utahicki Pithecia irrorata Pithecia monachus Pithecia pithecia

3 0 2 5 2 0 5 0 5 0 Time (Ma) Figure S4: Phylogenetic relationships among extant platyrrhine species. Nodal support (posterior probabilities) is shown only when smaller than 1. 30 Alouatta_belzebul Alouatta_guariba Alouatta_caraya Alouatta_macconnelli Alouatta_seniculus Alouatta_nigerrima Alouatta_sara Alouatta_coibensis Alouatta_palliata Alouatta_pigra Cartelles_coimbrafilhoi Paralouatta_varonai Stirtonia_tatacoensis Stirtonia_victoriae Ateles_belzebuth Ateles_geoffroyi Ateles_hybridus Ateles_fusciceps Ateles_chamek Ateles_paniscus Brachyteles_arachnoides Brachyteles_hypoxanthus Lagothrix_cana Lagothrix_lagotricha Caipora_bambuiorum Solimoea_acrensis Chilecebus_carrascoensis Mohanamico_hershkovitzi Aotus_azarae Aotus_infulatus Aotus_nigriceps Aotus_nancymaae Aotus_griseimembra Aotus_lemurinus Aotus_trivirgatus Aotus_vociferans Aotus_dindensis Tremacebus_harringtoni Callimico_goeldii Callithrix_argentata Callithrix_emiliae Callithrix_mauesi Callithrix_humeralifera Callithrix_pygmaea Callithrix_aurita Callithrix_geoffroyi Callithrix_jacchus Callithrix_penicillata Callithrix_kuhlii Leontopithecus_chrysomelas Leontopithecus_chrysopygus Leontopithecus_rosalia Micodon_kyotensis Saguinus_bicolor Saguinus_martinsi Saguinus_midas Saguinus_geoffroyi Saguinus_oedipus Saguinus_imperator Saguinus_labiatus Saguinus_mystax Saguinus_fuscicollis Saguinus_graellsi Saguinus_nigricollis Saguinus_melanoleucus Saguinus_tripartitus Cebus_albifrons Cebus_olivaceus Cebus_capucinus Cebus_apella Cebus_libidinosus Cebus_robustus Cebus_xanthosternos Acrecebus_fraileyi Killikaike_blakei Saimiri_boliviensis Saimiri_oerstedii Saimiri_sciureus Saimiri_ustus Laventiana_annectens Neosaimiri_fieldsi Patasola_magdalenae Dolichocebus_gaimanensis Panamacebus_transitus Branisella_boliviana Cacajao_ayresi Cacajao_hosomi Cacajao_melanocephalus Cacajao_calvus Chiropotes_chiropotes Chiropotes_israelita Chiropotes_utahicki Proteropithecia_neuquenensis Cebupithecia_sarmientoi Pithecia_irrorata Pithecia_monachus Pithecia_pithecia Soriacebus_ameghinorum Nuciruptor_rubricae Mazzonicebus_almendrae Insulacebus_toussentiana Xenothrix_mcgregori Antillothrix_bernensis Callicebus_brunneus Callicebus_moloch Callicebus_cupreus Callicebus_hoffmannsi Callicebus_caligatus Callicebus_donacophilus Callicebus_coimbrai Callicebus_personatus Callicebus_nigrifrons Miocallicebus_villaviejai Callicebus_lugens Callicebus_torquatus Carlocebus_carmenensis Homunculus_patagonicus Canaanimico_amazonensis Lagonimico_conclucatus Szalatavus_attricuspis Perupithecus_ucayaliensis

4 0 3 0 2 0 1 0 0 Figure S5: One of the 100 posterior trees of extinct and extant platyrrhines used to perform the trait evolution analyses.

31 a) Body mass 0 −1 −2 −3 Rate (log) −4 −5 −6 −7

Alouattinae Aotinae Atelidae Atelinae Homunculinae Callitrichinae Cebidae Cebinae Pitheciidae Pitheciinae

b) 0.02 0.01 rend 0.00 T −0.01 −0.02

Alouattinae Aotinae Atelidae Atelinae Homunculinae Callitrichinae Cebidae Cebinae Pitheciidae Pitheciinae

Figure S6: Rates and trend parameters estimated for body mass across families and subfamilies of platyrrhines, averaged over 100 trees of extant and extinct taxa.

32 a) Latitude 1 0 −1 Rate (log) −2 −3 −4

Alouattinae Aotinae Atelidae Atelinae Homunculinae Callitrichinae Cebidae Cebinae Pitheciidae Pitheciinae

b) 0.10 0.08 0.06 rend T 0.04 0.02 0.00 −0.02

Alouattinae Aotinae Atelidae Atelinae Homunculinae Callitrichinae Cebidae Cebinae Pitheciidae Pitheciinae

Figure S7: Rates and trend parameters estimated for mid latitude across families and subfamilies of platyrrhines, averaged over 100 trees of extant and extinct taxa.

33 4.5 ● Fossils Phylogeny only ● Recent Phylogeny with fossils

● 4.0 ● ● ●

● ● ●

● 3.5 ● ● ●

● ● ● ● ● ● ● ● ● ●

● ● ● ●

Bodymass [log g] 3.0

●

● ● ●

● ●

2.5

2.0 Eocene Oligocene Miocene Plio. Q.

−40 −30 −20 −10 0 Time [M]

Figure S8: Range of ancestral body mass through time across lineages of platyrrhines. The plot shows a comparison between the estimates obtained when analyzing only extant species and those obtained from the analysis of both extinct and extant taxa. The shaded areas show the minimum and maximum boundaries of the range of estimated body mass averaged over 100 analyses within 1 Myr time bins.

34 ● Fossils Phylogeny only ● 20 Recent Phylogeny with fossils ● ● ●

●

● ● ●

Latitude ●

−20

●

−40 ●

● ●

Eocene Oligocene ● Miocene Plio. Q.

−40 −30 −20 −10 0 Time [M]

Figure S9: Range of ancestral latitudes through time across lineages of platyrrhines. The plot shows a comparison between the estimates obtained when analyzing only extant species and those obtained from the analysis of both extinct and extant taxa. The shaded areas show the minimum and maximum boundaries of the range of estimated latitudes averaged over 100 analyses within 1 Myr time bins.

35 a) rate-shift times 3 2

rate 1 0 0 time30 before20 present10

b) fossilization extinction speciation

c) branch segments } } } } } 0 time30 before20 present10 Figure S10: A cartoon realization of the episodic-fossilized-birth-death process. Plot A depicts the speciation (dotted), extinction (long dashed) and fossilization (short dashes) rates through time. Here we assume that rates are constant within each episode, that is, the rates shift instantly and only at the beginning of an episode. In this example, every episode is 10 million years long. Plot B depicts a realization of the fossilized-birth-death process. We have four extant taxa and three fossilization events, and one of the fossilization events has no sampled extant descendant. Plot C depicts the reconstructed phylogeny. The phylogeny corresponds exactly to the one shown in plot B when all extinct lineages with no fossil are pruned away. We also show how branches are broken into branch segments, that is, every segment consist of a branch with no event (speciation, fossilization, or rate shift) occurring on it. 36 atr oteetmtsotie hnicuigetntlnae nteaayi Fg 5, (Fig. analysis the different in significantly lineages a extinct show including results when These ar- obtained text). estimates. Shaded main estimates posterior rates. the the extinction/speciation of to equals speciation CI pattern (D) equals 95% (C) rate the rate turnover show diversification the Net eas extant speci- and of of Myr. rates number phylogeny per extinction estimated lineage the the – per on indicate events B) based extinction (A, or analysis, rates ation birth-death extinction episodic and Speciation the of platyrrhines. Results S11: Figure C A Net diversification rate Speciation rate −0.2 0.0 0.2 0.4 0.0 0.1 0.2 0.3 0.4 0.5 25 25 20 20 million million 15 15 y y ears ago ears ago 10 10 5 5 0 0 37 D B Turnover rate Extinction rate 0.0 0.5 1.0 1.5 2.0 0.0 0.1 0.2 0.3 0.4 0.5 25 25 20 20 million million 15 15 y y ears ago ears ago 10 10 5 5 0 0 *

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