Modeling lineage and phenotypic diversification in the New World monkey (Platyrrhini, Primates) radiation
Graphical abstract
Introduction
The study of lineage and phenotypic diversification in living clades with relatively recent divergences has supported models of adaptive radiation (Simpson, 1953, Schluter, 2000) that predict selective influences arising from ecological opportunity and circumstances (Gavrilets and Losos, 2009, Losos and Mahler, 2010). This process frequently results in great phenotypic variation and species richness relative to a short time frame of phylogenetic divergence (Schluter, 2000, Gavrilets and Losos, 2009, Losos and Mahler, 2010). It is also suspected that similar processes of adaptive radiation have driven the initial diversification of clades that originated in the distant past, for example, at the ordinal and subordinal levels in mammalian clades (Gavrilets and Losos, 2009, Losos and Mahler, 2010). In such cases, ecological opportunity may decrease during the radiation as niches are filled, leading to stasis.
Several evolutionary radiations have been carefully studied with recently developed mathematical models applied to molecular phylogenies and comparative data from extant species (Nee, 2006, Gavrilets and Losos, 2009, Losos and Mahler, 2010). However, it is becoming clear that our capacity to successfully model and understand the diversification processes of ancient clades is limited when only neontological data are considered (Quental and Marshall, 2010, Slater et al., 2012). New World monkeys (Parvorder Platyrrhini), one of the three major groups of living and fossil primates, are a good example of a major mammalian evolutionary radiation that occupies a large temporal scale (i.e., 20–40 million years ago or megannums [Ma] in Central and South America), exhibits a remarkable phenotypic variation (e.g., a body mass spanning two orders of magnitude, from 0.1 to more than 10 kg) and presents a relatively small but informative fossil record (Fleagle, 1999, Fleagle and Tejedor, 2002, r, 2008). Morphological and phylogenetic studies have hypothesized that the diversification of this monophyletic group was mainly linked to the action of deterministic-selective factors related to ecological variables (Rosenberger, 1992, Marroig and Cheverud, 2001, Rosenberger et al., 2009). However, there is no general agreement about the main ecological dimension—e.g., diet, locomotion or a multidimensional niche—behind the platyrrhine diversification (Rosenberger, 1992; Allen and Kay, 2012; Youlatos and Meldrum, 2011; Perez et al., 2011). It has also been suggested that the marked phenotypic diversification of platyrrhines occurred relatively quickly during the initial branching process of the main extant clades in connection with ecological niche opportunity (i.e., an early-burst platyrrhine radiation), followed by a slowdown in evolutionary rates which resulted in the widespread retention of the formative patterns that are characteristic of these lineages (i.e., evolutionary stasis; Rosenberger, 1992, Rosenberger et al., 2009, Perez et al., 2011).
Although some recent studies of evolutionary radiations have included information from the fossil record along with phylogenies and comparative data of extant species in a mathematical modeling framework (Slater et al., 2010, Slater et al., 2012, Etienne et al., 2011), such approach to the study of the dynamic processes of lineage origin and extinction, and phenotypic diversification, has not been applied to explore the platyrrhine evolutionary radiation. Here, we present comparative evidence using data on extant and fossil species to explore alternative evolutionary models in an effort to better understand the process of platyrrhine lineage and phenotypic diversification. Specifically, we compare the likelihood of null models of lineages and phenotypic diversification versus various models of adaptive evolution. Moreover, we explore the main ecological dimension behind the platyrrhine diversification. If the platyrrhine diversification conforms to the adaptive radiation theory, we expect that differentiation of the major extant platyrrhine lineages was concentrated relatively early in the history of the clade and that phenotypic variation—measured as body size—was partitioned among subclades early in their phylogenetic history as well, as a major driver or consequence of ecological niche partitioning and niche-filling. As a starting point, we first estimate a chronophylogenetic tree for most extant platyrrhine species using molecular data and Bayesian methods (Drummond et al., 2006). Then, using this tree and comparative statistical methods we explore the pattern of lineage diversification through time (Nee et al., 1992, Harmon et al., 2003, Ricklefs, 2007, Stadler, 2011a), investigate the pattern of body size diversification through time and the fit of a series of evolutionary models (Harmon et al., 2003, Butler and King, 2004). Finally, given that the inference of the tempo and mode of diversification of a clade using only extant species can be biased (Quental and Marshall, 2010, Slater et al., 2012), we compare the results based on extant species with the estimated body masses and number of lineages inferred from the platyrrhine fossil record. Summarizing, our work contributes to the discussion of platyrrhine evolution and diversification in three different ways: (1) we present one of the most complete molecular phylogenies of extant platyrrhine species to date, sampling published data on 108 taxa and estimating a chronophylogenetic tree for 78 putative “good” species; (2) we mathematically model the pattern of lineage and phenotypic diversification of the platyrrhine clade; and (3) we combine data about extant and fossil species in a novel way not employed before in studies of the platyrrhines to better understand the process of their diversification.
Section snippets
Molecular divergence among species and phylogenetic inference
Phylogenetic trees are used in almost every branch of evolutionary biology. In particular, in comparative analyses they are needed to avoid misinterpreting historical contingencies as causal relationships and to understand the patterns of diversification (Nee, 2006, Losos, 2011, Yang and Rannala, 2012). In our study, the phylogenetic tree itself is not of direct interest but it is a necessary first step for the following statistical comparative analyses. Previous phylogenetic studies have
Results
Our chronophylogenetic tree of platyrrhines is in general agreement with other recent relationship estimations (Fig. 1; Opazo et al., 2006, Wildman et al., 2009, Perelman et al., 2011, Perez et al., 2012), which support the division of platyrrhines into three monophyletic families (Atelidae, Cebidae and Pitheciidae) and suggest a sister-group phylogenetic relationship between Atelidae and Cebidae (Opazo et al., 2006, Wildman et al., 2009). We found Aotus to be phylogenetically related to
Discussion
The process of diversification of the platyrrhines has been investigated phylogenetically and morphologically (e.g., Rosenberger, 1992, Fleagle, 1999, Marroig and Cheverud, 2001, Hodgson et al., 2009, Wildman et al., 2009, Perez et al., 2011, Perez et al., 2012, Perez et al., 2013). Many of these studies have interpreted the diversification of extant New World monkeys as an adaptive radiation in which the major lineages diversified early and into various alternative ecological niches that
Conclusions
The temporal pattern of lineage accumulation and the mode of phenotypic evolution described here based on the extant platyrrhine species might seem contradictory since, as described above, one of the adaptive radiation scenarios predicts an early burst of species origination accompanied by a marked phenotypic diversification. Although both processes may be unlinked, when we also consider the fossil record information this contradiction diminishes as a pattern of an early burst of species
Acknowledgments
We are sincerely grateful to S.F. dos Reis, E. Delson and two anonymous reviewers for their comments on the manuscript. We thank Graham Slater for providing the scripts used to run OUaverage function in R software, and Luz Arias for helping us with the figures. This research is supported by Grants from the FONCyT (PICT-2011-0307).
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