1 Mapping diversification metrics in macroecological ...

bioRxiv preprint first posted online Feb. 8, 2018; doi: http://dx.doi.org/10.1101/261867. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. 1

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Mapping diversification metrics in macroecological studies: Prospects and challenges

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Julián A. Velasco

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& Jesús N. Pinto-Ledezma

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Museo de Zoología Alfonso L. Herrera, Facultad de Ciencias, Universidad Nacional Autónoma de México 1

, Mexico City, Mexico.

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Department of Ecology, Evolution, and Behavior, University of Minnesota, 1479 Gortner Ave,

Saint Paul MN 55108, United States.

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Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel René Moreno,

Av. Irala 565, C.C. 2489, Santa Cruz de la Sierra, Bolivia.

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*Corresponding author: [email protected]

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ORCID: https://orcid.org/0000-0002-2183-5758

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Abstract

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The intersection of macroecology and macroevolution is one of the most active research areas

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today. Macroecological studies are increasingly using phylogenetic diversification metrics to

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explore the role of evolutionary processes in shaping present-day patterns of biodiversity.

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Evolutionary explanations of species richness gradients are key for our understanding of how

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diversity accumulated in a region. For instance, the present-day diversity in a region can be a

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result of in situ diversification, extinction, or colonization from other regions, or a combination of

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all of these processes. However, it is unknown whether these metrics capture well these

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diversification and dispersal processes across geography. Some metrics (e.g., mean root distance

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-MRD-; lineage diversification-rate -DR-; evolutionary distinctiveness -ED-) seem to provide very

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similar geographical patterns regardless of how they were calculated (e.g., using branch lengths

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or not). The lack of appropriate estimates of extinction and dispersal rates in phylogenetic trees

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can limit our conclusions about how species richness gradients emerged. With a review of the

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literature and complemented by an empirical comparison, we show that phylogenetic metrics by

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itself are not capturing well the speciation, extinction and dispersal processes across the

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geographical gradients. Furthermore, we show how new biogeographic methods can improve our

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inference of past events and therefore our conclusions about the evolutionary mechanisms

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driving regional species richness. Finally, we recommend that future studies include several

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approaches (e.g., spatial diversification modelling, parametric biogeographic methods) to

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disentangle the relative the role of speciation, extinction and dispersal in the generation and

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maintenance of species richness gradients.

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ords: Phylogeny, Geography, Spatial diversification, Macroevolution, Species

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Keyw

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richness, Regional assemblages


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Introduction

The causes of spatial variation of biodiversity are one of the most fundamental questions

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in ecology, biogeography and macroecology (Brown 1995, 2014, Brown and Lomolino 1998,

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Hawkins et al. 2012, Fine 2015, Jablonski et al. 2017). Current studies are integrating in a single

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framework the ecological and evolutionary mechanisms driving regional species diversity

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(McGaughran 2015, Pärtel et al. 2016, Cabral et al. 2017, Leidinger and Cabral 2017). However,

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only three macroevolutionary processes ultimately can modify the number of species in a region:

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speciation, extinction and dispersal (Wiens 2011, Fine 2015, Jablonski et al. 2017) (Figure 1). These

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processes can be modulated by species’ traits varying within clades (Paper et al. 2016, 2017,

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Jezkova and Wiens 2017, Moen and Wiens 2017), age of region (e.g., time-for-speciation effect;

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Stephens & Wiens, 2003), geographical area (Losos and Schluter 2000), or climatic conditions

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(Condamine et al. 2013, Lewitus and Morlon 2017).

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The integration of different disciplines such as molecular phylogenetics, palaeontology,

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and historical biogeography have allow to infer a series of macroevolutionary processes across

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geography (Diniz-Filho et al. 2013, Fritz et al. 2013). It is well-known that fossil information is key

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to estimate with high confidence rates of speciation and extinction (Sepkoski 1998, Foote 2000,

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Quental and Marshall 2010, Rabosky 2010b). New methods correcting for sampling bias are able

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to generate improvements in the estimates of the speciation, extinction and net diversification

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rates (Silvestro et al. 2014, 2016). However, the causal mechanisms that underlying the

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geographical diversity gradients only can be established with greater confidence for a few

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taxonomic groups with adequate fossil record, such as marine bivalves (Jablonski et al. 2006,

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2017), mammals (Silvestro et al. 2014) or plants (Antonelli et al. 2015).


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As fossil data is not available or incomplete for most extant groups, model-based

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approaches used to estimate speciation and extinction rates in palaeontology were adapted to

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study the macroevolutionary dynamics using phylogenetic information (Nee et al. 1994, Morlon

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et al. 2010, Stadler 2013). Molecular phylogenies are becoming essential to the study of

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diversification dynamics across temporal and spatial scales for extant taxa (Wiens and Donoghue

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2004, Rabosky and Lovette 2008, Stadler 2013, Morlon 2014, Schluter and Pennell 2017).

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Theref

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of a phylogeny using a set of birth-death models (Nee et al. 1994, Nee 2006, Morlon et al. 2010,

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Stadler 2013, Morlon 2014). These birth-death models allow infer either a homogeneous process

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for an entire clade (Nee et al. 1994, Magallón and Sanderson 2001) or a heterogeneous process

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varying in time or in specific subclades of a tree (Paper et al. 2006, Rabosky and Lovette 2008,

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Alfaro et al. 2009). However, these birth-death models only account for temporal variation of the

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macroevolutionary processes and how translate these processes to the geography is still a matter

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of debate.

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, it is possible to reconstruct past diversification process based on the branching events

Macroecological studies use two main approaches to link the estimates of diversification

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with the geographical ranges of species (Hawkins et al. 2007, Algar et al. 2009, Qian et al. 2014,

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Pinto-Ledezma et al. 2017, Velasco et al. 2018). The first one uses a set of phylogenetic metrics as

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a proxy to capture the geographical signature of lineage diversification dynamics (Diniz-Filho et

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al., 2013; Fritz et al., 2013; Table 1; Figure 2). These metrics provide either an estimate of a per-

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species rate of diversification (e.g., mean root distance –MRD-, residual phylogenetic diversity –

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rPD-, mean diversification rate –MDR-), the phylogenetic structure of regional assemblages (e.g.,

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phylogenetic species variability –PSV-) or the average age of co-occurring lineages in a given area

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(e.g., mean age; see Table 1). Each metric is calculated for each species in the phylogeny;

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therefore, we can associate the species’ values to its corresponding geographical range and


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generate a map with average values for cells or regions. Although these phylogenetic metrics only

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account for speciation events, macroecologists have used these maps as a proxy to test some

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evolutionary-based hypothesis in macroecological research (Diniz-Filho et al., 2013; Fritz et al.,

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2013; see Table 2 for a compendium of these hypotheses).

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The second approach used by macroecologists consists in the explicit estimation of

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diversification parameters across geography (Goldberg et al. 2005, 2011, Ramiadantsoa et al.

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2017). For instance, the geographic state speciation and extinction model –GeoSSE (Goldberg et

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al. 2011; Table 1) allows estimating speciation, extinction and dispersal parameters across two

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regions. It is possible to disentangle the relative role of each one of these processes on the

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generation and maintenance of the geographical diversity gradients (Rolland et al. 2014, Pulido-

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Santacruz and Weir 2016, Pinto-Ledezma et al. 2017). In addition, a recently developed Bayesian

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approach (BAMM; Rabosky 2014, Rabosky et al. 2014) allows to infer the balance of speciation

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and extinction in the generation of these biodiversity gradients (Rabosky et al. 2015, Sánchez-

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Ramírez et al. 2015, Morinière et al. 2016, Pinto-Ledezma et al. 2017). The BAMM approach allows

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both the inference of macroevolutionary dynamics for an entire clade (i.e., a macroevolutionary

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regime; Rabosky 2014) and also get estimates of per-species diversification rates (i.e., as a

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phylogenetic metric; Rabosky 2016) that can be mapped in a geographical domain. Although all

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these methods aim to obtain a geographical picture of the diversification processes, it remains

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unexplored if they can effectively capture these dynamics across regions.

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In this paper, we conducted a review on macroecological literature to evaluate how

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evolutionary and biogeographic processes contribute to shape geographical species richness

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gradients. We review only those papers that make explicit use of phylogenetic metrics and/or

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explicit diversification approaches (Table 1). We divided our review in three main sections. In the


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first one, we discuss how studies use phylogenetic metric to test some evolutionary-based

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hypotheses underlying geographical diversity gradients and we explore some limitations of these

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metrics (see also Table 2). Also, we discuss to what extent these metrics are able to capture

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macroevolutionary dynamics in a spatial explicit context. We illustrate these using three case

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studies (Furnariides birds, Hylid frogs, and Anolis lizards; Figure 1) and explore how another

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approaches (e.g., diversification modelling and biogeographical approaches) can complement our

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inferences about diversification process across geography. In the second section, we discuss how

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dispersal and extinction processes are limiting these diversification inferences and we propose

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some research avenues to attempt to solve these problems. Using an explicit biogeographical

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approach, we test the role of dispersal on the geographical species richness patterns of the three

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case studies. Finally, in the third section, we call for the adoption of complementary approaches

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(e.g., extensive simulations, parametric biogeographical methods) in macroecological research

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with the aim to evaluate the relative role of speciation, extinction and dispersal process driving

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geographical biodiversity gradients.

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LITERATURE REVIEW

We conducted a literature search in Web of Science for studies that explicitly have

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addressed questions on how speciation, extinction and dispersal have shaped geographical

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species richness gradients. We selected those papers that used either phylogenetic metrics (e.g.,

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mean root distance -MRD-, phylogenetic diversity -PD-, phylogenetic species variability -PSV-;

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diversification rate –DR-; mean Ages; Table 1) or explicit macroevolutionary approaches (e.g.,

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GeoSSE, BAMM; Goldberg et al., 2011; Rabosky 2014). We compiled a list of 44 papers (Table A1),


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but we are aware that this likely is not an exhaustive search. The majority of papers reviewed are

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testing historical process shaping latitudinal diversity gradients (LDG) in various taxa.

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TESTING EVOLUTIONARY HYPOTHESIS USING PHYLOGENETIC METRICS AND EXPLICIT DIVERSIFICATION APPROACHES Here, we discuss how phylogenetic metrics are used to test evolutionary hypotheses

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related to the generation and maintenance of geographical diversity. Several studies used the

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mean root distance -MRD- metric to evaluate whether regional assemblages are composed of

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“basal” or “derivate” linages. First, this terminology should be avoided because it provides an

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incorrect interpretation of the phylogenetic trees (Baum et al. 2005, Crisp and Cook 2005,

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Omland et al. 2008). Although this metric does not incorporate information from branch lengths

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(Algar et al. 2009, Qian et al. 2015), it does provide the average number of nodes separating each

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species in a given region from the root of the phylogeny (Kerr and Currie 1999). MRD therefore

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provides information about the number of cladogenetic events (splits) that have occurred through

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the history of co-occurring lineages in each region (Pinto-Ledezma et al. 2017, Velasco et al.

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2018). Under this view, MRD should be interpreted as a metric of total diversification (Rabosky

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2009), where high MRD values indicating regional assemblages dominated by extensive

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cladogenesis and low MRD values indicating assemblages with few cladogenetic events. A main

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concern with this metric concern with the fact that it does not provide any information about

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what macroevolutionary dynamics have taken place in a region. For example, it is very hard to

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establish whether MRD allows to distinguish between diversity-dependent (Rabosky 2009,

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Rabosky and Hurlbert 2015) or time-dependent (Wiens 2011, Harmon and Harrison 2015)

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processes dominating regional diversity. Although the distinction between these two dynamics,


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and its relationship with the origin and maintenance of regional diversity, is an intense topic in the

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macroevolutionary literature (Rabosky 2009, 2013, Wiens 2011, Cornell 2013, Harmon and

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Harrison 2015, Rabosky and Hurlbert 2015). However, more empirical and theoretical work is

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necessary to establish what scenario plays a significant role in regional species richness assembly

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(Rabosky 2012, Etienne et al. 2012, Valente et al. 2015, Graham et al. 2018). It might worth to

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establish whether local ecological process scaling up to regional scales or emergent effects (i.e.,

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the existence of a strong equilibrium process) governed the build-up of regional diversity (Cornell

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2013, Harmon and Harrison 2015, Rabosky and Hurlbert 2015, Marshall and Quental 2016).

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The time-for-species effect hypothesis state that the regional build-up of species richness

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is directly proportional to the colonization time of its constituent clades (Stephens and Wiens

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2003b; Table 2). However, many phylogenetic metrics used to test this hypothesis, did not

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incorporate any age information (Fritz and Rahbek 2012, Qian et al. 2015). Qian et al. (2015) did

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some additional predictions for the time effect hypothesis regarding the phylogenetic structure of

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regional assemblages. We suggest that these predictions are not easily deduced from the original

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statement of the time-for-speciation effect hypothesis (Stephens & Wiens 2003). F

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Qian et al. (2015, p. 7) predicted that regions with low species richness (e.g., extra-tropical

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regions) should be composed of more closely related species than regions with high species

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richness (e.g., tropical regions). This assumes that regions with low species richness were

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colonized recently and therefore these lineages had little time for speciation. However, it is also

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plausible consider that high extinction occurred in these poor species richness regions by

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marginal climatic niche conditions preventing adaptive diversification (Wellborn and Langerhans

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2015). By contrast, regions with high species richness might also be assembled by multiple

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dispersals from nearby regions becoming to be a macroevolutionary sink (Goldberg et al. 2005). In

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this latter case, the species richness was not build-up by in situ speciation mainly but by continued

or instance

,


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dispersal through time. To evaluate which of these scenarios is more plausible it is necessary to

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adopt an approach that explicitly infer the number of the dispersal and cladogenetic events across

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areas (Roy and Goldberg 2007, Dupin et al. 2017).

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Differences in species diversification are als

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considered as a main driver of the

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geographical diversity gradient for many groups (Kennedy et al. 2014, Pinto-Ledezma et al. 2017).

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This hypothesis states (Table 2) that differences in net diversification rates between areas are the

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main driver of differences in regional species richness between areas. Davies and Buckley (2011)

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used the phylogenetic diversity controlled by species richness (i.e., residual PD –rPD-) to

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distinguish areas with different evolutionary processes. These authors predicted that areas where

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rapid speciation and low immigration events from other areas occurred, are dominated by large

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adaptive radiations (e.g., large islands; Losos and Schluter 2000). By contrast, areas with slow

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speciation and colonized by multiple lineages through time should have high values of residual

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PD.

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The “out of the tropics” -OTT- hypothesis (Jablonski et al. 2006; Table 2) states that

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latitudinal diversity gradient is due to that the majority of lineages originated in the tropics and

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then migrated to extratropical regions. Under this hypothesis, tropics harbour higher net

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diversification rates (higher speciation and lower extinction) than extratropical regions and

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dispersal rates are higher from the tropics to extratropical regions than the reverse (Jablonski et

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al. 2006; Table 1). For instance, Rolland et al. (2014) used the GeoSSE model to test this

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hypothesis in the generation of the latitudinal mammal diversity gradient. They found that net

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diversification rates (i.e., the balance of speciation minus extinction) was higher in tropical than in

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temperate regions and dispersal rates were higher from the tropics to temperate regions than the

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reverse. Also, Pinto-Ledezma et al. (2017) used the GeoSSE model to test an analogue hypothesis

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to OTT, as form of Out of the Forest hypothesis (OTF), using Furnariides birds as a clade model.


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Their favoured a model where open areas have higher speciation, extinction and dispersal rates

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than forest habitats. All these results suggest that it is reasonable to use either phylogenetic

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metrics or explicit diversification approaches (e.g., the GeoSSE model) to evaluate a set of

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evolutionary-based hypotheses as a main driver of geographical diversity gradients. However, we

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show here (see below) that these approaches fail to capture the evolutionary and biogeographic

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processes at spatial scales.

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Are phylogenetic metrics capturing well the diversification process across geography?

A deep understanding of evolutionary processes affecting regional species assemblages is

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coming from the integration of molecular phylogenies and fossil record (Quental and Marshall

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2010, Marshall 2017). From this integration of neontological and paleontological perspectives, it is

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clear that both approaches are necessary to test evolutionary-based hypothesis in

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macroecological research. Several hypotheses were proposed to explain geographical diversity

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patterns, particularly the latitudinal diversity gradient –LDG- (see Table 2 for a summary and

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compilation of the main hypotheses reported in the literature). Although the ideal approach is to

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generate robust conclusions from multiple lines of evidence (e.g., fossil record, molecular

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phylogenies, biogeographical inference) it is clear that this information is scarce for many

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taxonomic groups. Many macroecological studies have adopted either phylogenetic metrics or

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explicit diversification approaches (e.g., the GeoSSE model) to evaluate the relative contribution

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of speciation, extinction and dispersal on the resulting geographical diversity gradients (Table S1).

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Phylogenetic metrics can be easily visualized in a geographical context and several

inferences about ecological (e.g., dispersal) and evolutionary (e.g., speciation) process can be


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done. As these metrics provide a per-species level diversification metric for each species in a

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phylogeny, it is possible to associate these values with the corresponding species’ geographical

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range and obtain a mean value for cells or regions in a given geographical domain (Table 1; Figure

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2). By contrast, explicit diversification approaches (e.g., GeoSSE; BAMM; fitting models) provide a

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per-lineage level diversification metric for a given clade or a regional assemblage (Rabosky 2016a).

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However, in some cases, it is possible to generate a per-species level diversification metric with

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these approaches. For instance, Pérez-Escobar et al. (2017) used the function GetTipsRates in

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BAMMtools (Rabosky et al. 2014) to map speciation rates for Neotropical orchids.

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These two approaches (phylogenetic metrics and lineage diversification) potentially can

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provide complementary pictures about how macroevolutionary dynamics have taken place in the

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geography. One the one hand, it is possible to estimate diversification rates for a given clade

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using the number of species, its age and a birth-death models (Magallón and Sanderson 2001,

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Nee 2006, Sánchez-Reyes et al. 2017). These model-fitting approaches allow to whether diversity-

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or time-dependent diversification process has taken place in a regional assemblage (Etienne et al.

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2012, Rabosky 2014, Valente et al. 2015). On the other hand, per-species diversification rate

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metrics allow establishing the potential of each individual species to generate more species (Jetz

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et al. 2012, Rabosky 2014, 2016a). However, these approaches imply at least a different process,

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which left a different signature on the geography. Phylogenetic metrics captures a total

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diversification process (Rabosky 2009), whereas lineage diversification approaches (e.g., BAMM)

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can potentially provide information about an individual diversification process (Rabosky 2013). In

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addition, still is not clear whether phylogenetic metrics can provide an accurate description of the

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diversification dynamics across geography.

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The first step to clarify how well these phylogenetic metrics behave is to establish a

comparison within and between different taxonomic groups. To evaluate how different


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phylogenetic metrics vary across geography and their relationship with species richness, we used

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two empirical data sets from our own empirical work (furnariid birds and anole lizards; (Pinto-

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Ledezma et al. 2017, Velasco et al. 2018) and a data set compiled from several sources (hylid

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frogs; (Wiens et al. 2006, Algar et al. 2009, Pyron 2014a). We mapped across geography five

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phylogenetic metrics (Table 1, Figure 2). We selected these three data sets because previous work

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analysed how evolutionary-based hypotheses affected the present-day species richness gradient

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(Wiens et al. 2006, Algar et al. 2009, Pinto-Ledezma et al. 2017, Velasco et al. 2018).

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Figure 2 shows the geographical pattern of species richness and the five phylogenetic metrics

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for Anolis lizards, hylid frogs and Furnariides birds. For all clades, there is a higher species

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concentration near to the Ecuador. Higher species concentration for hylids and Furnariides can be

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found in the Amazon and the Atlantic forest and for Anolis lizards in Central America and the

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Caribe (Figure 2A-C; see also Algar et al. 2009, Pinto-Ledezma et al. 2017, and Velasco et al. 2018,

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for a detailed description of the geographical species richness pattern for these clades,

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respectively). In terms of the geographical pattern of each phylogenetic metric (Figure 2D-R), in

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most of the cases cells with higher metric values are related to cells that contain high species

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richness and vice versa (Figure 2D-R; Figure A1). However, the degree and the direction of this

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relationship changes according to the phylogenetic metric used. For example, MRD, a metric of

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species derivedness, shows a negative correlation with species richness (Figure 2J-L; Figure A1).

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Importantly, the spatial relationships between species richness and phylogenetic metrics found in

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our analysis could simply be the result of aggregated species-level attributes within cells or

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assemblages (Hawkins et al. 2017). Hence, any conclusion derived from these relationships needs

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to be considered carefully. In addition, there are different levels of correlation between

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phylogenetic metrics (Figure A1). For example, MDR - MA present a high but negative correlation,

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and rPD - PSV and MRD - MDR present a mid-high positive correlation (Figure A1). Although


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there are few studies comparing correlations between metrics (Vellend et al. 2010, Miller et al.

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2017), to our knowledge, none previ

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metrics (Table 1). However, some of these metrics sharing mathematical assumptions, which

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increase the likelihood of correlation between them. For example, for ultrametric trees, metrics as

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MDR could be approximated by considering the mean root distance (i.e. MRD metric) from the

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tips to the root (Freckleton et al. 2008), so further studies exploring the mathematical relation

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between metrics are needed.

ous

study compares the similarity

of these

diversification

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In order to assess if the cells/assemblages on average do not represent a random sampling

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from the species pool, we applied a simple permutation test to explore the non-randomness in

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each of the phylogenetic metrics. We applied a null model where the presence-absence matrix

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(i.e., PAM) was randomly shuffled 1000 times, but maintaining the frequency of species

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occurrence and observed richness in the cells/assemblages (Gotelli 2000). This kind of null model

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is standard in studies at the community/assemblage level that use phylogenetic information

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(Cavender-Bares et al. 2004, 2006). Interestingly, none of the phylogenetic metrics deviates from

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the null expectation for the three clades (Figure 3). Also, very few cells/assemblages present p-

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values below the 0.05 threshold, thus indicating that the cells/assemblages present random

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association among species (Figure 3). These results should be supported by repeating analyses

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with more clades at different spatial extents, but again, we stress that any result obtained with

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the use of phylogenetic metrics need to be interpreted carefully.

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A brief comparison between phylogenetic metrics and explicit diversification and biogeographic approaches ompared the

We c

ogenetic

phyl

metrics enunciated in Table 1, which have been the

ored

geographical

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most used in macroecological research. We expl

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phylogenetic metrics in three empirical examples coincide with the macroevolutionary dynamics

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inferred using explicit m

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GeoSSE model to estimate the three parameters (speciation, extinction, and dispersal) between

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two areas in each taxonomic group (Table 3 and 4). In addition, we used the BAMM approach to

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generate the per-species level diversification metric implemented in the software BAMM 2.5.0

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(Rabosky 2014). In the following section, we discuss each metric and we compare them with the

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explicit diversification approaches.

odelling diversification

whether the

patterns of these

approaches. In particular, we implemented the

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Residual Phylogenetic Diversity (rPD)

In the case of furnariid birds, we show that forest areas tend to exhibit slightly higher

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values of rPD in contrast with open areas (Figure 4; see also Figure 2). According to Davies and

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Buckley’s logic, these areas exhibit slow diversification and frequent dispersal from open areas.

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Pinto-Ledezma et al. (2017) using GeoSSE and BAMM approaches indicated that open areas

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exhibit higher net diversification rates than open areas (Table 3). For hylid frogs, we found that

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tropical areas tend to exhibit higher rPD values than extratropical regions (Figure 5). However, by

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adopting an explicit diversification approach (GeoSSE and BAMM), we found that net

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diversification rates were similar in both regions (Table 3). In the case of Anolis lizards, the rPD

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values were higher in the continent than in the island areas (Figure 6). However, using GeoSSE


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and BAMM, we found that both rates were similar (Table 3). In a recent paper, Poe et al. (in press)

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also found that macroevolutionary rates are similar between insular and mainland clades. All

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these results suggest that rPD likely does not provide an accurate signature of the

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macroevolutionary dynamic at spatial scales. In fact, it seems that rPD tends to overestimate

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differences between regions when a stationary diversification process is occurring across

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geography. A potential solution might be rethinking the way in which we visualize rPD across

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geography in contrast with the original meaning by Davies & Buckley (2011; see also Forest et al.

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2007).

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Mean root distance (MRD)

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As we discussed, MRD captures a total diversification value portraying the number of

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cladogenetic events co-occurring in a given region. In the case of furnariid birds, we found that

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MRD values tend to be higher in open than forest areas (Figure 4). Accordingly, this metric

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suggests that more cladogenetic events were accumulated in open areas (i.e., more total

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diversification; Rabosky 2009). Therefore, this metric, for this bird clade, is consistent with results

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from explicit diversification approaches (Table 3). For hylid frogs, it seems that there are no

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differences in MRD values between extratropics and tropics areas (Figure 5). However, tropical

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areas have some cells with very high values. Again, MRD provide an accurate description of the

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total diversification pattern in this clade across the latitudinal gradient. In Anolis lizards, we found

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that MDR values tend to be lower in islands in comparison with mainland areas (Figure 6). In this

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case, MRD did not provide an accurate description of the evolutionary processes occurring

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between the mainland and insular anole assemblages. However, there is also a high probability

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that the high MRD values in the mainland are a direct reflect of an idiosyncratic evolutionary


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trajectory of each one of the two clades that radiated there (i.e., Draconura and Dactyloa clade;

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see Poe et al. 2017, Velasco et al. 2018). These two clades seems t

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diversificati

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necessary to evaluate these differences.

on

o

exhibit differential

dynamics across geography (Velasco et al. 2018) but further research might be

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Phylogenetic species variability (PSV)

The PSV metric provides information about how related are the species in a given

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regional assemblage. In hylid frogs, we found that tropical assemblages tend to be composed of

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more related species than extratropical assemblages (Figure 5). Hylid assemblages in

346

extratropical areas are composed of multiple lineages that dispersed from tropical areas and then

347

diversified there. We found higher dispersal rates from tropical to temperate regions than vice

348

versa (Table 3 and 4). The same tendency is present in the case of furnariid birds where open

349

areas exhibit higher PSV values than forest areas (Figure 4) and dispersal rates were higher from

350

open to forest areas than the reverse (Table 3). By contrast, we did not find any evidence for

351

differences in PSV values between island and mainland Anolis assemblages (Figure 6). In addition,

352

the dispersal rates were very low between these tw

353

results confirm that the PSV metric can provide some insights about how dispersal process have

354

shaped regional assemblages. We find evidence that low PSV values (i.e., phylogenetically over-

355

dispersed faunas) are influenced by multiple dispersals along its evolutionary history.

356

357

Mean diversification rate (MDR)

o

regions (Table 3; Poe et al. 2017). All these


358

Jetz et al. (2012) proposed MDR metric as a species-level speciation rate metric based in

359

the branch length along the path from the root of a tree to each individual species. In furnariid

360

birds, we noted that MDR was slightly higher in open versus forest areas and the same pattern is

361

present using the BAMM approach (Pinto-Ledezma et al. 2017; Figure 5; Table 3). For hylid frogs,

362

extratropical regions tend to exhibit higher values than tropical regions (Figure 5). MDR seems to

363

capture well the differences in macroevolutionary diversification for these taxa along the

364

latitudinal diversity gradient. A similar pattern is present when the BAMM approach is used (Table

365

3). We consider that both metrics (MDR vs per-species diversification rate from BAMM) leave the

366

same signature in the geography. In Anolis, we found that insular assemblages tend to exhibit

367

higher MDR values than continental assemblages (Figure 6), However, there is no difference in

368

the macroevolutionary dynamic between these two areas for the Anolis lizards clade (Poe et al. in

369

press, Velasco et al. 2018).

370

371

372

Mean ages (MA).

The average of ages of co-occurring lineages are used to test evolutionary hypothesis

373

about whether a region maintains older lineages than others (e.g., a museum) or a combination of

374

old and recent lineages (e.g., OTT hypothesis, Table 1). Although this metric does not provide any

375

inference of the ancestral area of the clade, it is possible to implement an explicit biogeographic

376

approach to test this (see below). For example, in hylid frogs, we found that extratropical areas

377

are composed of older lineages than tropical regions (Figure 5). The biogeographic parametric

378

approach infers this same area as ancestral for the entire lineage (Figure A2). In furnariid birds,

379

mean ages metric revealed that older lineages have accumulated more in forest than open areas

380

(Figure 4). In accordance, the ancestral area inferred with a parametric biogeographic method


381

was the forest area (Figure A3). In the case of the anole lizards, insular settings tend to be

382

composed of older lineages than continents. However, the ancestral area for the entire anole

383

clade is the mainland, particularly South America (Poe et al. 2017). The mainland Anolis radiation

384

is composed of two clades, one clade that originated in South America (the Dactyloa clade; Poe et

385

al. 2017) and colonized Caribbean islands, and the other clade (the Norops clade; Poe et al. 2017)

386

that originated in the Caribbean islands and then colonized back the mainland in Middle America

387

and then dispersed to South America. Therefore, the biogeographical history of the Anolis

388

radiation is complex and involves multiple dispersals between islands and mainland areas (Poe et

389

al. 2017; Figure A4). In general, mean ages does not provide enough information about the

390

biogeographic origin and maintenance of a clade. This happens because multiple dispersals and in

391

situ cladogenesis might erase any simplistic pattern elucidated for this metric, as found in the

392

case of the Anolis lizards.

393

394

395

HOW DISPERSAL AND EXTINCTION AFFECT INFERENCES OF GEOGRAPHICAL DIVERSIFICATION GRADIENTS? other

396

Dispersal is an

397

species in a region (Roy and Goldberg 2007, Eiserhardt et al. 2013, Rolland et al. 2014, Chazot et

398

al. 2016). H

399

regi

400

2000, Goldberg et al. 2005, 2011, Jablonski et al. 2006). Roy and Goldberg (2007) showed with

401

simulations that dispersal asymmetry between areas had a strong impact in the regional species

402

richness and the average age of these lineages. Accordingly, phylogenetic metrics can be

403

sensitive to dispersal between areas because it is impossible to distinguish which lineages

owever,

on

key macroevolutionary process that ultimately determines the number of a

few studies evaluated explicitly how the direction of dispersals between

contributes to the generation of regional differences between areas (Chown and Gaston


404

originated by in situ speciation or simply due to dispersal from nearby areas. Goldberg et al. (2011)

405

developed the GeoSSE model to evaluate how range evolution affected diversification rates in a

406

phylogenetic comparative approach. The GeoSSE model only considers three states (A: endemic

407

species to a region; B: endemic species to another region; and AB for widespread species) and

408

makes a series of assumptions that can be problematic. The first assumption of the GeoSSE

409

model is that a time-dependent process dominates the diversification dynamic in each region

410

(Stephens and Wiens 2003, Wiens 2011). This assumption conflicts with a diversity-dependent

411

process assumpt

412

Harrison 2015, Rabosky and Hurlbert 2015). The second problematic assumption has to do with

413

the fact that the GeoSSE model consider dispersal rates as stable through time and lineages. In

414

other words, the dispersal ability and therefore the frequency of transitions between areas are

415

constant across the evolutionary history of a clade. There are many empirical evidence sh

416

that dispersal rates vary across time and space among lineages (McPeek and Holt 1992,

417

Sanmartín et al. 2008, Robledo-Arnuncio et al. 2014).

418

ion

and this debate is far from being resolved (Cornell 2013, Harmon and

owing

Regardless of these major assumptions, the GeoSSE model has been adopted to evaluate

419

relative contributions of speciation, extinction and dispersal to the generation of species richness

420

gradients (e.g., Rolland et al. 2014, Pyron 2014b, Staggemeier et al. 2015, Looney et al. 2016,

421

Morinière et al. 2016, Pulido-Santacruz and Weir 2016, Alves et al. 2017, Hutter et al. 2017, Pinto-

422

Ledezma et al. 2017). In a recent study, Rabosky and Goldberg (2015) found that state-dependent

423

diversification models tend to inflate excessively the false discovery rates (i.e., type I error rates).

424

In particular, Rabosky and Goldberg (2015) found that these models tend to find false associations

425

between trait shifts and shifts in macroevolutionary dynamics. Although the Rabosky and

426

Goldberg’s study was not based on the GeoSSE model, it is clear that transitions between areas

427

(i.e., dispersal events) can be falsely associated with shifts in speciation and extinction rates


428

across the phylogeny. Alves et al. (2017) also found that geographical uncertainties in the

429

assignment of species to a given area affect the parameter estimates (i.e., speciation, extinction

430

and dispersal rates) in the GeoSSE model. Same authors also evaluated how incorrect

431

assignments of bat species to tropical or extra-tropical regions can generate erroneous

432

conclusions about the relative role of speciation, extinction and dispersal on a latitudinal diversity

433

gradient. From these studies, it is clear that dispersal is a major issue that needs to be evaluated

434

explicitly in macroecological studies.

435

Pulido-Santacruz and Weir (2016) also used the GeoSSE model to disentangle the relative

436

effect of speciation, extinction and dispersal on the latitudinal bird diversity gradient. They found

437

that extinction was prevalent across all bird clades and therefore they suggest this as a main

438

driver of the geographical bird diversity gradient. Pyron (2014c), also using the GeoSSE model,

439

found that temperate diversity in reptiles is due to higher extinction in these areas. We consider

440

that extinction inferences from the GeoSSE model should be treated with caution. For the few

441

clades where fossil record is abundant (e.g., marine bivalves; Jablonski et al. 2006), studies point

442

out to conclude that extinction differences between regions should be treated with caution due to

443

the potential sampling bias (Jablonski et al. 2006, 2017). In addition, studies based on extensive

444

simulations found that extinction inferences based only in molecular phylogenies are not reliable

445

(Rabosky 2010a, 2016b, Quental and Marshall 2010), although extinction rates can be estimated

446

relatively well using medium to large phylogenies (Beaulieu & O’Meara 2015).

447

In a recent review, Sanmartín and Meseguer (2016) proposed that it is possible to detect the

448

extinction signature in molecular phylogenies using extensive simulations and lineage-through-

449

time –LTT- plots (see also Antonelli and Sanmartín 2011). These authors also found that many

450

birth-death models leave a similar phylogenetic imprint, which make indistinguishable some


451

scenarios. In addition, extinction events can affect substantially the ancestral range estimates,

452

and therefore dispersal and extinction parameters in several parametric biogeographic methods

453

(e.g., Dispersal-Vicariance –DIVA- and Dispersal-Extinction-Cladogenesis –DEC- models;

454

Ronquist 1997, Ree et al. 2005). Sanmartín and Meseguer (2016) finally proposed that the

455

adoption of a hierarchical Bayesian approach using continuous-time Markov Chain models will

456

allow a better estimation of extinction both in geography and in the phylogeny (Sanmartín et al.

457

2008, Sanmartin et al. 2010).

458

Recently, Rabosky and Goldberg (2017) developed a semi-parametric method (FiSSE) to

459

correct the statistical problems found in BiSSE models by themselves in a previous paper

460

(Rabosky and Goldberg 2015). However, the FiSSE method does not allow the evaluation of the

461

contribution of dispersal on regional species richness. In any case, the best suitable framework to

462

estimate relative contributions of speciation, extinction and dispersal might be the GeoSSE

463

model (or parametric biogeographic models; e.g., Matzke 2014; see below), although it requires

464

the simulation of a series of null scenarios to evaluate the statistical power in each case (see Alves

465

et al. 2017, Pinto-Ledezma et al. 2017 for a few examples). For instance, Pinto-Ledezma et al.

466

(2017) developed a parametric bootstrapping approach simulating traits to evaluate whether

467

empirical inferences are different from the simulated. They simulated 100 datasets of neutral

468

characters along a set of empirical phylogenies and using this new information repeated the same

469

procedure with empirical data (see Appendix S1 in Pinto-Ledezma et al. 2017 for details of the

470

bootstrapping approach). This bootstrapping procedure assumes no direct effect of the

471

geographic character states on the parameter estimations (Feldman et al. 2016, Pinto-Ledezma

472

et al. 2017).


473

Finally, it should be clear that more research would be necessary to establish how

474

extinction affect estimation parameters in state-dependent diversification approaches (e.g., the

475

GeoSSE model). For instance, the inclusion-exclusion of extinct species in simulated phylogenies

476

using birth-death models could substantially affect the geographical inferences of speciation,

477

extinction and dispersal parameters in the GeoSEE model. This kind of approach might provide

478

some lights on how to biased can be the parameter estimates with only molecular phylogenies

479

using the GeoSSE model or any other modeling approach.

480

481

482

Parametric biogeographical approaches in macroecological studies. The use of parametric biogeographic approaches is an optimal solution to estimate

483

dispersals across time and space (Matzke 2014, Dupin et al. 2017). These methods are promising

484

in identifying the relative roles of cladogenetic and anagenetic processes shaping regional species

485

richness. Recently, Dupin et al. (2017) developed a biogeographical stochastic mapping to infer

486

the number of dispersals, and other biogeographical events, in the evolutionary history of

487

Solanaceae plants across the world. This approach allows the inference from multiple process

488

including sympatric speciation, allopatric speciation, founder-event speciation, range expansion

489

(i.e., dispersal without speciation) and local extinction (i.e., range contractions) based on a time-

490

calibrated phylogenetic tree and the occurrence of species in geographical regions (see also

491

Matzke 2014 for more detailed description of the method). These explicit biogeographical

492

approaches are promising in macroecological studies since they allow to test simultaneously a set

493

of evolutionary process during the diversification of a clade in a region (Velasco 2018). In addition,

494

with these new approaches it is possible to differentiate effectively between macroevolutionary

495

sources and sink areas (Goldberg et al. 2005, Castroviejo-Fisher et al. 2014, Poe et al. 2017). For


496

instance, Poe et al. (2017) used a parametric biogeographical approach to estimate the number of

497

events among regions and distinguish those areas where many cladogenetic events occurred (i.e.,

498

in situ speciation) and areas where almost all its diversity was build-up from extensive

499

colonization of other regions.

500

The biogeographical stochastic mapping (BSM) method developed by Dupin et al. (2017)

501

is promising to estimate more accurately the number of dispersal events between regions based

502

on a better estimation of the ancestral area for a clade. We evaluated how dispersal rates

503

between regions can affect inferences drawn only from phylogenetic metrics in our three data

504

sets. We implemented GeoSSE and BSM approaches for each data set (Table 3 and 4). For the

505

case of hylid frogs, we counted the inferred number of dispersal events between tropical and

506

extra-tropical regions in the Americas (Table 3; see also (Wiens et al. 2006, Algar et al. 2009). For

507

furnariid birds, we counted the number of dispersal events between open and forest areas (Table

508

3; see also Pinto-Ledezma et al. 2017). Finally, for anole lizards, we counted the number of

509

dispersal events between insular and mainland areas (Table 3; see also Algar and Losos 2011, Poe

510

et al. 2017, Velasco et al. 2018).

511

Using biogeographical stochastic mapping –BSM-, we inferred the number of dispersal

512

events from one region to another for each one of the three taxonomic groups examined (Table

513

3). The BSM approach allows us to disentangle which dispersals were only range expansions and

514

which dispersals generated a speciation event (i.e., a founder-event speciation; (Barton and

515

Charlesworth 1984, Templeton 2008). For furnariid birds, we found that range expansions were

516

three times higher from forest areas to open areas than the reverse and founder events were

517

twice higher from forest to open areas than the opposite (Table 3). This result suggests that

518

differences in species richness between forest and open areas are due by recurrent dispersal


519

events along the furnariid diversification history (Pinto-Ledezma et al. 2017). Pinto-Ledezma et al.

520

(2017) found a similar result using the GeoSSE approach, but they conducted a parametric

521

simulation approach to evaluate whether there was a direct effect of the geographic location on

522

the parameter estimates. Their results show that the GeoSSE approach, in this case, had limited

523

power to detect a signature of geographic region on speciation, extinction and dispersal rates.

524

With the implementation of the BSM approach here, we corroborate Pinto-Ledezma et al.’s

525

findings with improved statistical power. In the case of hylid frogs and the transitions between

526

tropical and extra-tropical areas, we found that the BSM approach inferred more dispersal events

527

from tropical to extra-tropical regions (Table 3 and 4). However, the number of founder events

528

was relatively low in comparison with range expansions (Table 3). These results suggest that few

529

dispersal events have occurred across the diversification of hylid frogs and corroborate that the

530

species richness in each region largely originated by in situ speciation modulated by climatic

531

factors (Wiens et al. 2006, Algar et al. 2009). Finally, for Anolis lizards, we found that dispersal

532

events between insular and mainland regions were relatively low (Table 3 and 4). We did not find

533

evidence of any expansion range events from mainland to island or vice versa. This also

534

corroborates previous findings that evolutionary radiation of anole in insular and mainland

535

settings is due to extensive in situ diversification (Poe et al. in press, 2017, Algar and Losos 2011).

536

These results point out that the BSM approach (Dupin et al. 2017) is a promising approach

537

when we are interested in testing the role of anagenetic and cladogenetic events on the resulting

538

geographical species richness gradients. Although parametric biogeographic approaches are still

539

in their infancy (Sanmartín 2012, Matzke 2014, Dupin et al. 2017), these methods allow us to

540

evaluate macroevolutionary dynamics (i.e., speciation and extinction) in an explicit geographical

541

context. These methods are statistical powerful and make use of a series of explicit geographic

542

range evolution models (Velasco 2018).


543

544

545

546

TOWARD AN INTEGRATION OF BIOGEOGRAPHICAL AND SPECIES DIVERSIFICATION APPROACHES IN MACROECOLOGICAL STUDIES. Although different parametric biogeographic methods have been developing at least for

547

the last 20 years (Ronquist 1997, Ree et al. 2005, Landis et al. 2013, Matzke 2014, Dupin et al.

548

2017), the ad

549

geographical diversity gradient has been rare.

550

the effect of dispersal events in the generation of regional species richness assemblages. It sh

551

clear that the current paradigm in biogeography makes a call for an evaluation of the relative

552

frequency of cladogenetic and anagenetic process during the biogeographical history of lineages.

553

The adoption of parametric approaches in future macroecological studies will contribute to an

554

improvement of the estimation of speciation, extinction and dispersal processes as drivers of the

555

geographical diversity gradients. In addition, we also think that it is necessary to establish which

556

macroevolutionary dynamics govern regional assemblages. Phylogenetic approaches based on

557

fitting diversification models help to test whether regional species richness is due to diversity

558

dependence (i.e., ecological limits), time dependence, or environmental factors (Rabosky and

559

Lovette 2008, Etienne et al. 2012, Etienne and Haegeman 2012, Condamine et al. 2013). We also

560

stress that the adoption of many approaches providing multiple lines of evidence will help to

561

disentangle the evolutionary and ecological causes of biodiversity gradients. Some recent studies

562

have pointed toward this strategy and have begun to provide evidence from many lines to

563

understand how evolutionary processes underlying species richness gradients works (Hutter et al.

564

2017, Pinto-Ledezma et al. 2017).

565

option of

these methods to test evolutionary-based hypotheses underlying

For instance

, few studies examined here tested

ould


566

567

Conclusions and recommendations The resulting geographical pattern of several phylogenetic metrics did not provide any

568

robust evidence of a spatially explicit diversification dynamic. As we have shown, these resulting

569

geographical patterns did not differ from that generated by a simple null model. It is hard to

570

untangle causal mechanisms (i.e., speciation, extinction, and dispersal) from only the

571

geographical signature that these metrics attempt to capture. We recommend that phylogenetic

572

metrics should be used only to visualize geographical patterns of total diversification (e.g., MRD,

573

residual PD; MDR), phylogenetic structure (e.g., PSV), or mean ages of co-distributed species

574

(e.g., MA) (Table 1). We suggest that conclusions about the role of evolutionary processes in the

575

generation and maintenance of species richness gradients based only in these phylogenetic

576

metrics should be avoided and additional approaches always should be used.

577

Some explicit diversification approaches (e.g., model fitting approaches; (Etienne et al.

578

2012, Rabosky 2014, Valente et al. 2015) are useful to establish the macroevolutionary dynamics

579

operating at regional scales. Although some approaches (e.g, the GeoSSE model) allow us to

580

evaluate the relative role of the ultimate process that modify the regional species diversity, its

581

statistical power (e.g., high Type I errors) has been challenged by simulation and empirical

582

studies. Furthermore, the extinction and dispersal estimates inferred by the GeoSSE model tend

583

to be unbiased. Parametric biogeographic approaches are becoming a standard tool to evaluate

584

how evolutionary processes can explain the geographical distribution of extant taxa. These

585

approaches are promising and should be extensively used because allow us to estimate the

586

relative frequency of cladogenetic and anagenetic process shaping the regional species richness.

587

588

It is necessary that macroecological studies use a combination of explicit diversification

approaches and parameter biogeographic methods with the aim to clarify how evolutionary


589

process have shaped regional species richness assemblages. As Jablonski et al. (2017) have

590

outlined, one of the main obstacles to generate an appropriate understanding of the causal

591

mechanisms underlying geographical diversity gradients has been that many studies have tested

592

a single hypothesis, either evolutionary or ecological, as an explanatory factor. We suggest that

593

ecological and evolutionary hypotheses should be tested simultaneously to explain the relative

594

contribution of each process to the regional diversity. As shown by our empirical comparison of

595

phylogenetic metrics, explicit diversification models, and historical biogeographic methods have

596

showed, it is necessary to obtain evidence of different approaches to guarantee sound

597

conclusions about the evolutionary causes of these biodiversity gradients.

598

599

Acknowledgments

600

We thank Josep Padullés for reviewing the English. JAV is supported by a Postdoctoral fellowship

601

from DGAPA program at Facultad de Ciencias of the UNAM. JAV thanks to Oscar Flores Villela for

602

providing a working space at Museo of Zoologia “Alfonso L. Herrera” and insightful discussions

603

about historical biogeography and evolutionary biology. JNPL is supported by a Postdoctoral

604

research fellowship from the Grand Challenge in Biology Postdoctoral Program, College of

605

Biological Sciences, University of Minnesota.

606

607

608

609

610

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861

Table 1: Phylogenetic metrics and explicit diversification approaches used in macroecological

862

studies to address evolutionary questions related with the geographical diversity gradients.

Metric

MRD

Author

Description

Kerr and Currie 1999

MRD is calculated by counting the number of nodes separating each terminal species in a regional assemblage or cell from the tips to root metricTester of the phylogenetic tree. This metric does not need that trees be ultrametric or have branch lengths.

PD (residual) Faith 1992

PSV

Helmus et al. 2007, Algar et al. 2009

Software / R package

PD is calcultated by summing all the branch lengths of species co-occurring in a regional assemblage or cell. Residual PD is obtained from an ordinary least square regression between PD and species richness.

picante, metricTester, pez

PSV is calculated from a matrix where their diagonal elements provide the evolutionary divergence (based on the branch lengths) of each terminal species from the root to the tips of the tree, and the off-diagonal elements provide the degree of shared evolutionary history among species. Values close to zero indicates that all species in a regional assemblage or cell are very close related whereas values close to one indicate that species are not related.

picante, metricTester, pez

DR is calculated as the inverse of a measure of evolutionary isolation (Redding & Mooers 2006) which sum all the edge lengths from a species to the root of the tree. The inverse of this FiSSE evolutionary isolation metric therefore capture the level of splitting rate of each species (i.e., its path to a top).

mean DR

Jetz et al. 2012

Mean age

The mean age of co-occurring species in a regional assemblage or cell simply is calculated Latham and Ricklefs tallying the age of each most recent common None 1993 ancestor (MRCA) for each species and the averaged.


GeoSSE

BAMM

The geographic state speciation and extinction -GeoSSE- model is a trait-dependent diversification method linking geographic Goldberg et al. 2011 occurrence with diversification rates. These method allow to infer both speciation and extinction rates as movement (dispersal) rates among two regions.

Rabosky 2014

Diversitree R package

BAMM is a method that attempt to identify whether a phylogeny exhibit a single or various macroevolutionary regimes (i.e., different BAMM software and diversification dynamics). As speciation, BAMMtools R extinction and net diversification rates are package considered to be heterogeneous across the phylogeny it is possible to estimate a rate for each branch or species in the tree.

Table 2. Description of some evolutionary hypothesis tested in macroecological studies as causal mechanisms of regional species richness.

Hypothesis

References

Phylogenetic Wiens and niche Graham conservatism 2005 (PNC)

Description

Predictions

Metrics and/or methods used to test

PNC predicts that regions where a clade originated Phylogenetic will accumulate more niche species simply due to conservatism is more occupation time and the tendency of diversification rates tend related species to to be similar between MRD, Mean inherit niche regions. The tropical age, GeoSSE, requirements niche conservatism BAMM from its the most hypothesis (TNC; Wiens recent common and Donoghue 2004) is ancestors (Wiens based on PNC to explain & Graham 2005). differences in species richness in tropical and temperate regions.

Limitations 1) MRD metric fails to capture spatially dynamics of the balance of speciation and extinction and it is very hard to establish whether species richness in a region is only generated by higher speciation rates. Furthermore, MRD does not capture dispersal dynamic across regions and species richness in a given region can be generated from only dispersals from nearby regions (e.g., macroevolutionary sinks; CITA). 2) Mean age provide partial is able to test the role of PNC on geographical species richness because only it is possible to establish which regions have, in average, old clades and this not reflects whether many speciation events occurred there. 3) GeoSSE is potentially the only one approach that allow to disentangle these three process but it is only limited to two regions (e.g., tropical vs. temperate). In addition, GeoSSE has been criticized due its low statistical power (see Rabosky and Goldberg 2015).

bioRxiv preprint first posted online Feb. 8, 2018; doi: http://dx.doi.org/10.1101/261867. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

863

1

Species were generated in the tropical regions and dispersed to Out of the Jablonski et extratropical tropics (OTT) al. 2006 regions but maintain its presence in its ancestral areas

Time for speciation effect (TEE)

Stephens and Wiens 2003

Tropical regions accumulated more species because their clades had more time to speciate than temperate regions.

RD predicts that regions with striking differences in species richness are due residual PD, GeoSSE, to differences in BAMM macroevolutionary dynamics between regions.

1) Residual PD can be used to discriminate regions with rapid and slow diversification based on the expected phylogenetic diversity given species richness (Buckley et al. 2010). However, this metric ignores the contribution to dispersal to PD in a given region or cell. 2) GeoSSE can estimate speciation, extinction and dispersal rates between regions but again is limited to two regions. 3) BAMM potentially could be used to estimate speciation rates for regional clades but this method is unable to estimate dispersal rates between regions.

High rates of speciation are predicted in tropical regions in contrast with temperate regions. MRD, Mean Asymmetric dispersal age, GeoSSE have occurred along the biogeographical history of a taxa from tropical to temperate areas.

These metrics are the same used to test the PNC/TNC hypothesis as we discuss above.

Regions recently colonized had lower species richness than Mean age regions where clades colonized very early in the history of a clade.

1) Mean age does not provide an accurate description of which lineages colonized first a region. To test this hypothesis, it might be necessary to perform an ancestral range reconstruction of all co-occurring clades and estimate its diversification rates (i.e., total diversification for each independent colonized clade; Rabosky 2009; 2012).


Regional Buckley et diversification al. 2010 (RD)

Differences in the balance of speciation and extinction across geography can explain differences in species richness between regions.


864 865 866 867

Table 3. Parameter estimates from the GeoSSE model for three taxonomic groups (Furnariides birds, hylid frogs, and Anolis lizards) across two regions. Areas for each taxonomic group as follows: Furnariides birds: A: Forest; B: Open areas; Hylid frogs: A: Extra tropics; B: Tropics; Anolis lizards: A: Islands; B: Mainland. Group

Furnariides birds

Hylid frogs

Anolis lizards

868

Rates

A

B

AB

Speciation

0.139 ± 0.020

0.223 ± 0.065

0.041 ± 0.020

Extinction

0.040 ± 0.025

0.107 ± 0.075

-

Dispersal

0.021 ± 0.004

0.311 ± 0.114

-

Net diversification

0.099 ± 0.005

0.116 ± 0.01

-

Speciation

0.044 ± 0.003

0.044 ± 0.003

0.041 ± 0.025

Extinction

0.002 ± 0.002

0.002 ± 0.002

-

Dispersal

0.001 ± 0.001

0.035 ± 0.010

-

Net diversification

0.042 ± 0.003

0.042 ± 0.003

-

Speciation

0.058 ± 0.003

0.058 ± 0.003

1.245 ± 1.303

Extinction

0.001 ± 0.001

0.001 ± 0.001

-

Dispersal

0.002 ± 0.001

0.0003 ± 0.000

-

Net diversification

0.057 ± 0.002

0.057 ± 0.002

-


1 869 870 871 872

Table 4. Frequency of dispersal events inferred using biogeographical stochastic mapping (BSM) for three taxonomic groups (Furnariides birds, hylid frogs, and Anolis lizards) across two regions. Areas for each taxonomic group as follows: Furnariides birds: A: Forest; B: Open areas; Hylid frogs: A: Extra tropics; B: Tropics; Anolis lizards: A: Islands; B: Mainland.

873 Event

Range expansions

Group

Regions

A

B

Furnariides birds

A

0

92.62 ± 4.39

B

31.4 ± 4.29

0

A

0

0.64 ± 0.78

B

12.92 ± 0.88

0

A

0

0

B

0

0

A

0

24.46 ± 2.54

B

10.88 ± 2.22

0

A

0

0.66 ± 0.66

B

4.3 ± 1.42

0

A

0

2.02 ± 0.14

B

0.12 ± 0.33

0

Hylid frogs

Anolis lizards

Furnariides birds

Founder events

Hylid frogs

Anolis lizards

874 875


2 876

FIGURE LEGENDS

877 878 879 880

Figure 1. Diagram illustrating how differences in speciation, extinction, and dispersal rates between regions can generate a geographical species richness gradient. The phylogenetic trees below illustrate how the differences in speciation and extinction rates between two regional assemblages can shape a gradient of species richness (degraded blue colour).

881 882 883 884 885 886 887

Figure 2. Geographical patterns of some phylogenetic metrics used in macroecological studies to explore evolutionary process underlying geographical diversity gradients (see also Table 1 for a detailed explanation). Left column Anolis lizards; Middle column: Hylid frogs; Right column: Furnariides birds. (A-C) observed richness patterns; (D-F) rPD: residual phylogenetic diversity (i.e., after controlling for species richness); (G-I) PSV: phylogenetic species variability; (J-L) MRD: mean root distance; (M-O) MDR: mean diversification rate; (P-R) Mean ages: average ages of species.

888 889 890 891 892 893

Figure 3. P-values distribution for each phylogenetic metric obtained through the null model (see main text for details). The vertical red lines represent the empirical 0.05 cut-off. Note that for all cases very few cells are below the 0.05 cut-off. (A-C) rPD: residual phylogenetic diversity (i.e., after controlling for species richness); (D-F) PSV: phylogenetic species variability; (G-I) MRD: mean root distance; (J-L) MDR: mean diversification rate; (M-O) Mean ages: average ages of species.

894 895 896 897 898

Figure 4. Variation of phylogenetic metric values for Furnariides birds in forest and open areas. rPD: residual phylogenetic diversity (i.e., after controlling for species richness); PSV: phylogenetic species variability; MRD: mean root distance; MDR: mean diversification rate; Mean ages: average ages of species.

899 900 901 902 903

Figure 5. Variation of phylogenetic metric values for Hylid frogs in tropics and extra-tropics regions. rPD: residual phylogenetic diversity (i.e., after controlling for species richness); PSV: phylogenetic species variability; MRD: mean root distance; MDR: mean diversification rate; Mean ages: average ages of species.

904 905 906 907 908 909

Figure 6. Variation of phylogenetic metric values for Anolis lizards in continental and insular areas. rPD: residual phylogenetic diversity (i.e., after controlling for species richness); PSV: phylogenetic species variability; MRD: mean root distance; MDR: mean diversification rate; Mean ages: average ages of species.

DIVERSITY GRADIENTS

Higher speciation Lower extinction Dispersal (out)

Speciation = 0.5; Extinction = 0.05

Lower speciation Higher extinction Dispersal (in)

Speciation = 0.3; Extinction = 0.15

Anolis

(A)

Hylids

(B)

20 10 5

(E)

(F)

(H)

1.0 0.8 0.6 0.4 0.2 0.0

(J)

100 80 60 40 20

150 100 50 0 −50 −100 −150

150 100 50 0 −50 −100 −150 −200

0.1 0.0 −0.1 −0.2 −0.3 −0.4 −0.5

(G)

(C)

40 30 20 10

15

(D)

Furnariides

(I)

1.0 0.8 0.6 0.4 0.2 0.0

(K)

1.0 0.8 0.6 0.4 0.2 0.0

(L)

20 20

25

15


15

10

10

5

20 15 10

0

(M)

(N)

(Q)

0.08 0.06 0.04 0.02

0.7 0.6 0.5 0.4 0.3 0.2 0.1

0.20 0.15 0.10 0.05 0.00

50 40 30 20 10

(P)

(O)

(R)

35 30 25 20 15 10 5 0

14 12 10 8 6 4 2

250

(B)

0.2

0.4

0.6

0.8

1.0

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

PD p−vals

300

250

(E)

(F)

0.8

1.0

0.2

0.4

0.6

0.8

1.0

0.0

PSV p−vals

0.8

1.0

0.2

0.4

0.6

0.8

0.0

0.8

1.0

0.2

0.4

0.6

0.8

1.0

0.6

MA p−vals

0.0

Frequency

400

(N)

0.2

0.8

1.0

0.4

0.6

0.8

1.0

(O)

0

0 0.4

1.0

MDR p−vals

200

Frequency

300 200 100 0

0.2

0.8

(L)

MDR p−vals

600

MDR p−vals

0.0

0.6

0 0.0

(M)

0.4

300 Frequency

300 0

100

Frequency

300 100

0.6

0.2

MRD p−vals

(K)

0

0.4

300

1.0

MRD p−vals

(J)

0.2

1.0

0 0.0

MRD p−vals

0.0

0.8

200

Frequency

400 0

200

Frequency

400 200

0.6

0.6

(I)

(H)

0

0.4

0.4

PSV p−vals

(G)

0.2

0.2

100

PSV p−vals

0.0

200 0

0.0

200

0.6

100

0.4

100 200 300 400

0.2

100

Frequency

200 0

100

Frequency

150

0.6


0 50

Frequency

0.4

PD p−vals

(D)

0.0

Frequency

400 0

0.2

PD p−vals

Frequency

200

Frequency 0.0

300

0.0

Frequency

(C)

0 50

150

Frequency

200 100 0

Frequency

(A)

Furnariides 600

Hylids

300

Anolis

0.0

0.2

0.4

0.6

MA p−vals

0.8

1.0

0.0

0.2

0.4

0.6

MA p−vals

0.8

1.0

rPD

100

150

Richness ● ● ● ● ●

80

● ● ● ●

●

60

● ● ● ● ● ● ● ●

50

●

0

−150

20

40

−50

● ● ●

Forest

Open

● ● ● ● ● ● ● ● ● ● ● ●

Forest

0.4

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

0.2

● ● ●

●

0.0

●

18

● ● ● ●

16

0.6

● ●

14

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ●

12

●

● ● ● ● ● ● ● ● ●

10

● ● ● ● ● ●

20

MRD

● ● ●

●

Forest

Open

8

0.8

1.0

PSV

●

Forest

Open MA

●

15

20

●

● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ●

5

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

10

0.5

0.7

MDR

0.3

Open

0.1

●

Forest

Open

● ● ● ● ●

0

●

● ● ● ● ● ● ● ● ● ● ● ● ●

Forest

Open

rPD

−100

● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

−200

● ●

0

10

20

0

30

100

40

Richness

Extratropics

Tropics

● ● ●

Extratropics MRD

● ● ● ● ● ● ● ● ●

20

1.0

PSV

Tropics

●

0.6

● ● ● ●

15

0.8

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ●

0.4

10

● ● ● ● ● ● ● ● ● ●

●

0.2

5

●

● ●

●

Extratropics

0

0.0

● ●

Tropics

MDR

●

●

Extratropics

Tropics

MA

●

0.10

● ● ●

● ● ● ● ●

● ●

● ● ● ● ● ● ● ●

● ● ● ● ●

5

● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

0

● ● ● ● ● ● ● ● ●

10 15 20 25 30

0.20

●

●

● ● ● ●

0.00

●

Extratropics

Tropics

Extratropics

Tropics

Richness

rPD

10

−0.1

15

0.1

20

● ●

●

● ● ●

−0.5

5

−0.3

● ●

Continent

Islands

●

Continent MRD ● ●

0.4

●

● ●

0.0

● ●

10

0.2

●

● ● ● ● ● ● ● ●

●

●

●

●

● ● ● ●

Continent

Islands

Continent

Islands MA

0.08

●

● ●

● ● ●

● ● ● ● ● ●

● ● ● ● ● ● ●

Continent

● ●

● ● ●

●

20

● ● ●

● ●

0.06

40

●

●

Islands

0.04

●

30

●

●

0.02

50

60

MDR

10

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

15

● ● ● ● ● ● ● ● ● ● ●

● ●

20

●

●

0.6

●

●

●

0.8

1.0

PSV

Islands

● ● ● ● ● ●

● ●

Continent

Islands

● ●


Anolis 1

SR 0.8

0.5

0.6

rPD

0.4

0.99

0.57

PD 0.2

0.26

0.65

0.32

PSV

0.06

0.04

0.02

0.03

0

−0.2

MRD −0.4

−0.14

0.27

−0.09

0.56

0.02

MDR

0.35

0.04

0.29

−0.21

0.26

−0.76

−0.6

−0.8

MA −1

Hylids

Furnariides 1

SR

1

SR 0.8

0.21

0.6

rPD

0.8

0.14

0.6

rPD

0.4

0.98

0.27

0.4

0.99

PD

0.22

PD

0.2

0.87

0.47

0.88

PSV

0

0.2

0.82

0.5

0.86

PSV

0

−0.2

0

0.27

0.01

0.03

−0.2

−0.58

MRD

−0.53

−0.63

−0.76

MRD

−0.4

−0.17

−0.22

−0.17

−0.21

0.54

MDR

0.69

0.44

0.71

0.69

−0.13

−0.66

−0.6

−0.4

−0.79

−0.47

−0.82

−0.85

0.77

MDR

0.76

0.56

0.8

0.88

−0.81

−0.87

−0.6

−0.8

MA −1

−0.8

MA −1

Hylid frogs

Anolis lizards

0.3

Furnariid birds ●

●

●

●

●

●

●

●

● ● ●

● ● ● ● ●

● ● ● ● ●

● ● ●

●

● ●

● ●

● ●

● ● ● ●

● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ●

● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●

●

●

●

● ● ●

●

● ● ● ● ●

10

● ● ● ● ●

●

0.4

● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ●

0.2

● ● ● ● ● ●

●

● ● ● ● ●

●

● ● ● ● ● ● ● ●

● ● ● ● ● ●

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ●

● ● ●

● ● ● ●

● ● ●

● ● ● ●

● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

0.1

Rates

Rates

● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

5

●

● ● ● ● ● ● ● ● ● ● ● ● ●

Rates

● ● ● ●

● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

●

0.2

●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ●

● ● ● ● ●

0.0

● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Dispersal A

● ● ● ● ● ● ● ●

● ●

0

●

Dispersal B

Extinction

Parameters

Speciation

Speciation AB

●

●

Dispersal A

Dispersal B

● ●

Extinction

Parameters

● ●

● ● ● ● ● ●

0.0 Speciation

Speciation AB

Dispersal A

Dispersal B

Extinction A

Extinction B

Parameters

Speciation A Speciation B

● ● ●

0.1 ●

0.0 −0.1 −0.2 ●

●

●

Forest

Forest−Open

Open

0.02470

● ● ●

●

0.02468 ●

0.02466

●

● ●

● ●

● ●

● ● ● ●

0.02464

●

0.02462

●

Extratropical

Regions

Tropical

7.975

● ● ●

●

7.973

●

● ● ●

● ●

7.971

● ●

●

●

●

●

● ●

● ● ● ● ●

●

●

7.969 ●

Tropical−Extratropical

Islands

Anole lizards 9.566

● ●

● ● ●

0.12

0.08

0.03058

0.03056 ● ● ●

●

● ●

●

0.03054

● ● ● ●

● ● ●

●

●

● ●

●

Speciation rates

● ● ● ●

●

Speciation rates

● ●

●

Mainland

Regions

Hylid frogs

0.16

●

Regions

Furnariid birds Speciation rates

Anole lizards Net diversification rates

Hylid frogs ●

Net diversification rates

Net diversification rates

Furnariid birds

● ●

9.564 9.562 ●

●

●

9.560

● ●

●

0.04

●

Forest

Forest−Open

Open

Extratropical

Regions

Tropical


Islands

Regions

Furnariid birds

Regions

Hylid frogs

0.16

●

●

Anole lizards 1.3825

0.00516

● ●

Mainland

●

● ● ● ● ●

● ● ● ● ● ●

0.12

0.08

●

0.00514

●

●

● ●

●

●

●

0.00512

●

●

● ●

●

●

● ● ● ●

0.00510

●

Forest−Open

Regions

Open

1.3775

●

●

●

1.3750

● ● ● ●

● ● ●

● ● ● ● ●

●

Forest

1.3800

1.3725

●

0.04

Extinction rates

Extinction rates

Extinction rates

●

Extratropical

Tropical

Regions


Islands

Mainland

Regions

Stochastic map ancstates: global optim, 2 areas max. d=5; e=0; j=2.9172; LnL=−102.92

B Hyla_walkeri B Smilisca_puma B B Smilisca_phaeota AB AB AB AB Smilisca_cyanosticta B AB A A Smilisca_fodiens B B B B Smilisca_sila B B B AB B B Smilisca_sordida B B AB AB Smilisca_baudinii B B B B Anotheca_spinosa B B B B Triprion_petasatus B B B B Isthmohyla_pseudopuma B B B B Isthmohyla_zeteki B B B B Isthmohyla_rivularis B B B B B B B B Isthmohyla_tica B B Tlalocohyla_loquax B B A A Tlalocohyla_godmani B B B AB Tlalocohyla_picta B AB AB AB Tlalocohyla_smithii A A Charadrahyla_taeniopus B AB AB AB Charadrahyla_nephila B B B B Megastomatohyla_mixe BB B B Ptychohyla_leonhardschultzei B AB AB AB Ptychohyla_zophodes B B B Ptychohyla_euthysanota B B B B B Ptychohyla_hypomykter B B B B B B Ptychohyla_dendrophasma B B Ptychohyla_spinipollex B B B B Duellmanohyla_rufioculis B B B B Duellmanohyla_soralia B B B B B B Bromeliohyla_bromeliacia B B Ecnomiohyla_minera B B B B Ecnomiohyla_miliaria B B B AB Ecnomiohyla_miotympanum AB AB BAB Plectrohyla_pentheter B B B B Plectrohyla_calthula AB AB AB Plectrohyla_bistincta AB AB AB B B Plectrohyla_ameibothalame AB AB A A Plectrohyla_arborescandens A A B B Plectrohyla_cyclada A A A Plectrohyla_siopela AB AB B B Plectrohyla_matudai B B B Plectrohyla_guatemalensis BB B B Plectrohyla_chrysopleura B BB B B Plectrohyla_glandulosa AB AB A A Exerodonta_smaragdina AB AB ABAB Exerodonta_xera AB AB B B Exerodonta_chimalapa AB AB B B Exerodonta_melanomma B B B AB B B Exerodonta_sumichrasti B AB BB Exerodonta_perkinsi A A AA Exerodonta_abdivita A A Pseudacris_triseriata A A A A Pseudacris_feriarum AA AA Pseudacris_maculata A A A A AA Pseudacris_clarkii A A Pseudacris_fouquettei A A AA Pseudacris_kalmi A A A A AA Pseudacris_nigrita A A Pseudacris_brimleyi A A A A A A Pseudacris_brachyphona A A Pseudacris_ocularis A A A A A A Pseudacris_crucifer A A Pseudacris_streckeri A A A A A A Pseudacris_ornata A A Pseudacris_cadaverina A A A A A A Pseudacris_regilla A A Acris_crepitans A A A A Acris_gryllus B B Osteocephalus_deridens B B B Osteocephalus_fuscifacies BB B B B B Osteocephalus_planiceps B BB B B Osteocephalus_leprieurii BB B B Osteocephalus_taurinus B B B B B B Osteocephalus_oophagus B B Osteocephalus_yasuni B B B B Osteocephalus_buckleyi BB B B Osteocephalus_verruciger B B B B B B B Osteocephalus_cabrerai B B B Osteocephalus_mutabor B B Osteocephalus_alboguttatus B B BB Tepuihyla_rodriguezi B B BB BB Tepuihyla_talbergae B B B Tepuihyla_edelcae B BB B B Tepuihyla_aecii B B Phyllodytes_luteolus B B Trachycephalus_hadroceps B B B B Trachycephalus_resinifictrix B B B B B B Trachycephalus_nigromaculatu B B B B B Trachycephalus_imitatrix B B B Trachycephalus_coriaceus B B B B B B Trachycephalus_mesophaeus B B Trachycephalus_jordani B B B B B B Argenteohyla_siemersi B B B B Nyctimantis_rugiceps B B B B Aparasphenodon_brunoi B B B B Corythomantis_greeningi B B Itapotihyla_langsdorffii B B Dendropsophus_minusculus B B B Dendropsophus_juliani BB B B Dendropsophus_sanborni B B BB B B Dendropsophus_rubicundulus B B B B Dendropsophus_branneri B B Dendropsophus_tritaeniatus B B Dendropsophus_walfordi BB B B B B Dendropsophus_nanus B B BB B Dendropsophus_reichlei B BB B BB B B Dendropsophus_riveroi B B B Dendropsophus_gaucheri B B B B Dendropsophus_berthalutzae B B Dendropsophus_bipunctatus AB AB Dendropsophus_sartori B B B AB B B Dendropsophus_robertmertens B B AB Dendropsophus_microcephalu B B B B B B B Dendropsophus_rhodopeplus B B B B Dendropsophus_leali B B Dendropsophus_minutus B B Dendropsophus_leucophyllatus B B B B Dendropsophus_triangulum B B B B Dendropsophus_sarayacuensis B B B B B B B B Dendropsophus_bifurcus B B B B Dendropsophus_ebraccatus B B Dendropsophus_elegans B B B B Dendropsophus_miyatai B B BB B B Dendropsophus_aperomeus B B BBB B Dendropsophus_anceps B B Dendropsophus_schubarti B B Dendropsophus_pelidna B B B B Dendropsophus_labialis B B B B Dendropsophus_carnifex B B B B Dendropsophus_giesleri B B B B B B Dendropsophus_parviceps B B B B Dendropsophus_brevifrons B B B B Dendropsophus_koechlini B B B B Dendropsophus_marmoratus B B B B Dendropsophus_melanargyreu B B B B B B Dendropsophus_seniculus B B Dendropsophus_allenorum B B B B Xenohyla_truncata B B Pseudis_tocantins B B B B B B Pseudis_fusca B B B Pseudis_paradoxa B B B B B Pseudis_bolbodactyla B B B B Pseudis_cardosoi B B B B B B Pseudis_minuta B B Scarthyla_goinorum B B Scinax_ruber B B B B Scinax_x−signatus B B B B Scinax_nasicus B B B B Scinax_chiquitanus B B B B B B Scinax_fuscovarius B B Scinax_cruentommus B B B B B B Scinax_boesemani Scinax_staufferi B AB B B B B B B Scinax_parkeri B B B B Scinax_squalirostris B B Scinax_crospedospilus B B Scinax_proboscideus B B B Scinax_garbei B B B B B B B Scinax_jolyi B B B B Scinax_rostratus B B B B Scinax_boulengeri B B B B B B B B Scinax_sugillatus B B B B Scinax_nebulosus B B Scinax_acuminatus BB B B Scinax_uruguayus B B Scinax_berthae B B BB B B Scinax_catharinae B B Scinax_peixotoi B B B B Scinax_faivovichi B B B B B B Scinax_perpusillus B B Sphaenorhynchus_dorisae B B B B Sphaenorhynchus_lacteus B B B B Sphaenorhynchus_orophilus B B Hypsiboas_cordobae B B B Hypsiboas_pulchellus B B B B B Hypsiboas_prasinus BB B B Hypsiboas_caingua BB B B Hypsiboas_marginatus B B B B Hypsiboas_bischoffi BB B Hypsiboas_guentheri B B B B B Hypsiboas_balzani B B B B Hypsiboas_marianitae B B B B Hypsiboas_andinus B B B B B B Hypsiboas_riojanus BB Hypsiboas_latistriatus B B BB Hypsiboas_polytaenius B B bioRxiv preprint first posted online Feb. 8, 2018; doi: http://dx.doi.org/10.1101/261867 . The (which was not B B copyrightBholder for thisBpreprint Hypsiboas_leptolineatus B B peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. B B Hypsiboas_semiguttatus B B B B B B Hypsiboas_joaquini B B Hypsiboas_melanopleura B B Hypsiboas_ericae B B B Hypsiboas_lundii B B B B B B Hypsiboas_pardalis B B B B Hypsiboas_faber B B B B Hypsiboas_rosenbergi B B B B Hypsiboas_crepitans B B B B B B B B Hypsiboas_punctatus B B Hypsiboas_albomarginatus B B Hypsiboas_rufitelus B B B B Hypsiboas_pellucens B B B B Hypsiboas_albopunctatus B B B B Hypsiboas_multifasciatus B B B B Hypsiboas_lanciformis B B B B Hypsiboas_calcaratus BB B B B B Hypsiboas_fasciatus B B B B Hypsiboas_dentei B B B B Hypsiboas_raniceps B B Hypsiboas_picturatus B B Hypsiboas_lemai B B BB B B Hypsiboas_ornatissimus B B B B Hypsiboas_microderma B B B B Hypsiboas_nympha B B B B Hypsiboas_roraima B B B B B B Hypsiboas_semilineatus B B B B B B B Hypsiboas_geographicus B B B Hypsiboas_boans B B Hypsiboas_sibleszi B B Hypsiboas_cinerascens B B Aplastodiscus_leucopygius B B B B B B Aplastodiscus_cavicola BB B Aplastodiscus_callipygius B BB B B Aplastodiscus_albosignatus B B B B Aplastodiscus_cochranae B B B B Aplastodiscus_perviridis B B B B Aplastodiscus_weygoldti B B B B B B Aplastodiscus_arildae B B B B Aplastodiscus_eugenioi B B B B Aplastodiscus_albofrenatus B B Bokermannohyla_hylax B B B B Bokermannohyla_circumdata B B B B Bokermannohyla_astartea B B B B Bokermannohyla_martinsi B B Hyloscirtus_pacha B B B B Hyloscirtus_staufferorum B B B B B Hyloscirtus_psarolaimus B B B B B Hyloscirtus_ptychodactylus B B B B Hyloscirtus_larinopygion B B B B B Hyloscirtus_pantostictus B B B B Hyloscirtus_lindae B B B Hyloscirtus_tapichalaca B B B B Hyloscirtus_charazani B B B B B B Hyloscirtus_armatus B B Hyloscirtus_simmonsi B B B B B B Hyloscirtus_alytolylax B B B B Hyloscirtus_colymba B B B B B Hyloscirtus_phyllognathus B B B B B Hyloscirtus_lascinius B B B B Hyloscirtus_palmeri B B Myersiohyla_inparquesi B B Myersiohyla_kanaima B

B B B

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Stochastic map ancstates: global optim, 2 areas max. d=5; e=0; j=2.9704; LnL=−899.05

Myrmothera_campanisona A Myrmothera_simplex A Hylopezus_nattereri A Hylopezus_berlepschi A Hylopezus_fulviventris A A Hylopezus_auricularis A Hylopezus_ochroleucus B B A Hylopezus_dives A A A A Hylopezus_perspicillatus A A A Hylopezus_macularius A A Grallaricula_nana A AB A A Grallaricula_lineifrons A A Grallaricula_peruviana A A A AA A Grallaricula_cucullata A A AA A Grallaricula_ochraceifrons A AA Grallaricula_ferrugineipectus A AB AA Grallaricula_flavirostris A A A A A A Grallaricula_loricata A A Grallaria_kaestneri A A A A Grallaria_varia A A A A Grallaria_guatimalensis A A AA A Grallaria_squamigera A AA Grallaria_carrikeri A A A A Grallaria_alleni A A Grallaria_excelsa A A A A Grallaria_blakei A A A A Grallaria_przewalskii A AA A A A Grallaria_bangsi A A AA Grallaria_rufula A AB A A Grallaria_erythroleuca A A A AA Grallaria_rufocinerea A Grallaria_chthonia A A Grallaria_nuchalis A A Grallaria_haplonota A A A A AA A A A A A Grallaria_milleri Grallaria_griseonucha A AB A Grallaria_ridgelyi A A A AAAA Grallaria_watkinsi A AB Grallaria_ruficapilla A A Grallaria_capitalis A A AAA A Grallaria_hypoleuca A A Grallaria_gigantea A A A A Grallaria_quitensis A A A A A A Grallaria_albigula A AB A A Grallaria_andicolus B B A A Grallaria_flavotincta A A A A Grallaria_erythrotis A A A A Grallaria_eludens A A A A Grallaria_dignissima A A Acropternis_orthonyx A A A A Teledromas_fuscus B B A A Rhinocrypta_lanceolata A AB A A Psilorhamphus_guttatus A A A A Liosceles_thoracicus A A A A Pteroptochos_megapodius B B Scelorchilus_albicollis B B A AB A AB Scelorchilus_rubecula AB AB AB AB A A Pteroptochos_tarnii AB AB ABAB Pteroptochos_castaneus Myornis_senilis A A Scytalopus_schulenbergi A AB AA A A Scytalopus_altirostris Scytalopus_acutirostris A AB AA Scytalopus_latebricola AB AB AB A AA Scytalopus_spillmanni A A Scytalopus_parvirostris A AB Scytalopus_magellanicus A AB AA Scytalopus_unicolor B B A A A AA A Scytalopus_atratus A A A A Scytalopus_stilesi A A A A AA A Scytalopus_argentifrons A A A A A Scytalopus_macropus A A AA Scytalopus_robbinsi A A Scytalopus_simonsi A A A A Scytalopus_griseicollis A A A A A A AA Scytalopus_urubambae B B Scytalopus_vicinior A A Scytalopus_latrans A AAA A Scytalopus_sanctaemartae A A A A Scytalopus_micropterus A A A AA A Scytalopus_femoralis AB Scytalopus_bolivianus A A Scytalopus_iraiensis A A A AA A A Scytalopus_rodriguezi A A A AA Scytalopus_chocoensis A A A A Scytalopus_panamensis A A A AA A Scytalopus_meridanus A A A Scytalopus_novacapitalis B B A A A A Scytalopus_speluncae AA Scytalopus_fuscus AB AB AB AB Scytalopus_pachecoi A A AA A AB Scytalopus_canus B AB Scytalopus_affinis B B A AA A Scytalopus_caracae A A Scytalopus_superciliaris AB A A AA Scytalopus_parkeri A A A Scytalopus_zimmeri B B A A Eugralla_paradoxa A A Eleoscytalopus_indigoticus A A A A Eleoscytalopus_psychopompu A A A A Merulaxis_ater A A A A Merulaxis_stresemanni A A A A Formicarius_rufipectus Formicarius_moniliger A AB A A A A Formicarius_nigricapillus A A A A Formicarius_analis A A A A Formicarius_colma A A A A A A Formicarius_rufifrons A A Chamaeza_ruficauda A A Chamaeza_meruloides A A A A A AA A A A Chamaeza_nobilis Chamaeza_campanisona A AA A A Chamaeza_mollissima A A A Chamaeza_turdina A A Sclerurus_albigularis A A A A A A Sclerurus_mexicanus Sclerurus_scansor A A AB A A A Sclerurus_guatemalensis A A A A Sclerurus_rufigularis A A A A Sclerurus_caudacutus A A A A Geositta_peruviana B B B B Geositta_cunicularia B B B B Geositta_tenuirostris B B Geositta_rufipennis B B B B B B Geositta_punensis B B BB B Geositta_poeciloptera AB B B Geositta_crassirostris B B B B B Geositta_maritima B Geositta_saxicolina B B B B B B Geositta_isabellina B B BB Geositta_antarctica B B Xenops_tenuirostris A A A A Xenops_minutus A A A A Xenops_rutilans A A Ochetorhynchus_melanura B B B Ochetorhynchus_andaecola B B B BB Ochetorhynchus_phoenicurus B B B B B B Ochetorhynchus_ruficaudus B B A A Pygarrhichas_albogularis B AB Xenops_milleri A A Aphrastura_masafuerae AB AB A AB Aphrastura_spinicauda B AB Phacellodomus_maculipectus AB AB A AB Phacellodomus_inornatus B B Phacellodomus_striaticollis B B A AAA Phacellodomus_ferrugineigula A A Phacellodomus_ruber A AB A A A A Phacellodomus_dorsalis B B B AB Phacellodomus_erythrophthalm A A A A Phacellodomus_sibilatrix A AB Phacellodomus_striaticeps B B AB AB A AB AB Phacellodomus_rufifrons A Anumbius_annumbi B AB B B Coryphistera_alaudina B AB BB Hellmayrea_gularis A A Acrobatornis_fonsecai A A A A Xenerpestes_singularis A A Xenerpestes_minlosi A A Metopothrix_aurantiaca A A A AA A Siptornis_striaticollis A A Thripophaga_cherriei A A AA AA A A A A Thripophaga_macroura AA Thripophaga_berlepschi A A A A A A A A Thripophaga_fusciceps Roraimia_adusta A A Cranioleuca_gutturata A A A A A Limnoctites_rectirostris A B B BB BB Cranioleuca_sulphurifera B B B B Cranioleuca_pallida B AB A A Cranioleuca_vulpecula AAA A Cranioleuca_semicinerea B B A A Cranioleuca_marcapatae A AB A A AA Cranioleuca_albiceps AA Cranioleuca_vulpina A AB A A Cranioleuca_albicapilla A AB Cranioleuca_pyrrhophia A AB A AAA Cranioleuca_muelleri A A A A Cranioleuca_obsoleta A A Cranioleuca_henricae B B A A A A A AB Cranioleuca_subcristata A Cranioleuca_antisiensis AB AB A A AB AA Cranioleuca_curtata A AB B B Cranioleuca_baroni A AA A Cranioleuca_demissa A A Cranioleuca_hellmayri A A AA BB A A Cranioleuca_erythrops A Cranioleuca_dissita A AB Asthenes_luizae B B B B B Asthenes_steinbachi B B B B Asthenes_patagonica B B BB Siptornopsis_hypochondriaca B B Synallaxis_propinqua A A AA Synallaxis_rutilans A A AA Synallaxis_unirufa A A A A Synallaxis_kollari B B A A Synallaxis_cinnamomea A A A A A A Synallaxis_erythrothorax A A Synallaxis_infuscata A AB AA Synallaxis_candei B AB A Synallaxis_scutata B A AB AA B B A Synallaxis_subpudica A A A AA Synallaxis_cherriei A A Synallaxis_courseni A B B A A Synallaxis_ruficapilla A A A A Synallaxis_gujanensis A A A Synallaxis_fuscorufa A A Certhiaxis_mustelinus A A A AB AB AB Certhiaxis_cinnamomeus A A Schoeniophylax_phryganophilu B AB A A A AB Synallaxis_albilora A A AA A A Synallaxis_hypospodia A AB A A Synallaxis_brachyura A AA A Synallaxis_stictothorax A AB Synallaxis_frontalis A AB A A A Synallaxis_maranonica A A AA A Synallaxis_albescens AB Synallaxis_azarae AB BB AA AA B A Synallaxis_whitneyi BB AA Synallaxis_spixi A AB A A Synallaxis_tithys AA A Gyalophylax_hellmayri B B AAA A Synallaxis_cabanisi A A AA Synallaxis_moesta A A A A Synallaxis_albigularis Synallaxis_castanea A A Synallaxis_zimmeri A B B A Synallaxis_macconnelli A A A A Synallaxis_cinerascens A A Pseudoseisura_gutturalis B B Pseudoseisura_cristata B AB B B B B Pseudoseisura_unirufa B AB B B BB Pseudoseisura_lophotes B B Asthenes_humicola B AB B B Asthenes_hudsoni B B B B B B Asthenes_cactorum B AA B Spartonoica_maluroides B B BB B AB Asthenes_maculicauda B B Asthenes_virgata B AB Asthenes_baeri B B B B Asthenes_dorbignyi B B B B Asthenes_berlepschi B B Asthenes_pyrrholeuca B B B B Asthenes_huancavelicae B B Schizoeaca_fuliginosa A A AA Schizoeaca_griseomurina A AB Schizoeaca_helleri AA B BB B A AB Schizoeaca_coryi A A A AA Schizoeaca_palpebralis A A A Schizoeaca_perijana A A A A AA AA Schizoeaca_harterti A A A A Schizoeaca_vilcabambae BB Oreophylax_moreirae A A AA Asthenes_pudibunda B B B B Asthenes_ottonis B B Asthenes_arequipae B B Asthenes_urubambensis B AB Asthenes_flammulata B AB B BB B Asthenes_humilis B B B B B B Asthenes_modesta B B AB AB B B Asthenes_wyatti B B B B B B Asthenes_sclateri B B B B AB Asthenes_anthoides Asthenes_heterura B B Leptasthenura_andicola B AB Leptasthenura_platensis B B B BB B Leptasthenura_pileata B B Leptasthenura_striata B B BB BBB Leptasthenura_fuliginiceps B Leptasthenura_aegithaloides B B B B BB BB Leptasthenura_setaria A A Leptasthenura_striolata A A B B A A Leptasthenura_xenothorax B B B B BB B Leptasthenura_yanacensis B Sylviorthorhynchus_desmursii B AB Pseudocolaptes_lawrencii A A A A Pseudocolaptes_johnsoni A AB A A Pseudocolaptes_boissonneaut A AB A AA A Premnornis_guttuligera AA Tarphonomus_certhioides AB AB A AB BAB Tarphonomus_harterti A A Cinclodes_pabsti Cinclodes_nigrofumosus B AB AA BB BB Cinclodes_taczanowskii B AB Cinclodes_patagonicus B BBABAB Cinclodes_palliatus B B B B B B B Cinclodes_atacamensis B B Cinclodes_olrogi BB BB A A Cinclodes_albidiventris A BBA Cinclodes_oustaleti A Cinclodes_comechingonus BAB AA B BB B BB Cinclodes_antarcticus AB B B B B BB Cinclodes_fuscus Cinclodes_aricomae B B BB Cinclodes_albiventris B B BB Cinclodes_excelsior B AB Upucerthia_albigula B B B B B B Upucerthia_dumetaria B B B BB Upucerthia_validirostris B BB Upucerthia_jelskii B BB B Upucerthia_saturatior B AB Upucerthia_serrana B B Lochmias_nematura B AB B B Limnornis_curvirostris B B B B B B Phleocryptes_melanops B B AB AB Furnarius_cristatus B AB Furnarius_rufus A AB B Furnarius_cinnamomeus B A B Furnarius_torridus A B BA A Furnarius_minor A A A A Furnarius_figulus AB AB A AB Furnarius_longirostris B AB BB Furnarius_leucopus B AB Berlepschia_rikeri A A Automolus_rubiginosus A A A A A AB Hylocryptus_rectirostris A A A A A AB Hylocryptus_erythrocephalus Clibanornis_dendrocolaptoides A A Automolus_leucophthalmus A AB A A A A Automolus_infuscatus A Hyloctistes_virgatus A AB A A A AA A A A A Hyloctistes_subulatus Automolus_ochrolaemus A A AA Cichlocolaptes_leucophrus A A Thripadectes_scrutator A AB AA Thripadectes_holostictus A AA AA A Thripadectes_rufobrunneus A A A A Thripadectes_flammulatus A A AAA Thripadectes_melanorhynchus A A A Thripadectes_virgaticeps A A AA A A A A Thripadectes_ignobilis A A A Automolus_melanopezus A A A A A A Automolus_rufipileatus Ancistrops_strigilatus A A Philydor_rufum A AB Philydor_erythropterum A A AAA A Philydor_erythrocercum A A AA Philydor_lichtensteini AB AA A A Philydor_pyrrhodes A A A Philydor_fuscipenne A A A A A A Philydor_atricapillus A A AAA Philydor_ruficaudatum A A AA Philydor_novaesi A A Heliobletus_contaminatus A A Anabacerthia_amaurotis A A A A A A Anabacerthia_variegaticeps AA AA Anabacerthia_striaticollis A A A A Syndactyla_roraimae A A A A A A Syndactyla_guttulata AA AA Syndactyla_ruficollis A AB A A Syndactyla_rufosuperciliata A AB A A AA A Syndactyla_subalaris AA AA A Syndactyla_dimidiata B AB A A Simoxenops_striatus A A A A Simoxenops_ucayalae Megaxenops_parnaguae B B AA Anabazenops_fuscus A A A A Anabazenops_dorsalis A A Margarornis_stellatus A A A A A A Margarornis_rubiginosus A A Margarornis_bellulus A A A A A A A A Margarornis_squamiger Premnoplex_brunnescens A A A A Premnoplex_tatei A A A A Glyphorynchus_spirurus Dendrocincla_turdina A AB A A Dendrocincla_tyrannina A A A AA Dendrocincla_merula A Dendrocincla_fuliginosa A A AA A A Dendrocincla_anabatina A A A A A A Dendrocincla_homochroa A A Deconychura_longicauda A A A A A A Deconychura_stictolaema A A A A Sittasomus_griseicapillus A A Hylexetastes_perrotii A A A Hylexetastes_brigidai A A A AA Hylexetastes_uniformis A A AA Hylexetastes_stresemanni A A A A Xiphocolaptes_promeropirhync A A A A Xiphocolaptes_falcirostris B B A A A Xiphocolaptes_major A A A A A A AB Xiphocolaptes_albicollis A A A A Dendrocolaptes_picumnus A A A AB Dendrocolaptes_platyrostris A Dendrocolaptes_sanctithomae A A A A A AB A A Dendrocolaptes_certhia AA A A Dendrocolaptes_hoffmannsi A A Dendrexetastes_rufigula A A A A Nasica_longirostris A A Xiphorhynchus_spixii A A A A Xiphorhynchus_elegans A A A A Xiphorhynchus_fuscus A A AB A A Xiphorhynchus_ocellatus A A A A Xiphorhynchus_pardalotus A A Xiphorhynchus_erythropygius A A A A A A Xiphorhynchus_triangularis A A Xiphorhynchus_susurrans AA A A A A Xiphorhynchus_guttatus A A A A Xiphorhynchus_flavigaster A A A A A A A A Xiphorhynchus_lachrymosus Xiphorhynchus_obsoletus A A A AA Dendroplex_picus A A A Dendroplex_kienerii A A Lepidocolaptes_affinis A A A A A A Lepidocolaptes_lacrymiger AA Lepidocolaptes_leucogaster A A A A Lepidocolaptes_albolineatus A A A A AA A AB Lepidocolaptes_angustirostris AA A Lepidocolaptes_souleyetii A A A A Lepidocolaptes_squamatus AB Lepidocolaptes_falcinellus AA A A A Campylorhamphus_pucherani A A A Drymornis_bridgesii A AB AA Campylorhamphus_falcularius A A Campylorhamphus_trochilirostr A AB A A A A Campylorhamphus_procurvoid A A A A Campylorhamphus_pusillus A A Melanopareia_elegans AB AB Melanopareia_maranonica A A AB AB A A Melanopareia_maximiliani A AB A A Melanopareia_torquata A AB AB AB Pittasoma_rufopileatum A A A A Pittasoma_michleri A A Conopophaga_castaneiceps A A A A Conopophaga_ardesiaca A A A A AA Conopophaga_melanogaster A A A A Conopophaga_aurita AA A A Conopophaga_roberti A AB A A Conopophaga_peruviana A A A A A Conopophaga_cearae A A A Conopophaga_melanops A A A A Conopophaga_lineata A AB Terenura_callinota A A A A A A Terenura_sharpei A A Terenura_sicki A AB A A Terenura_humeralis A A A A Terenura_maculata A A A A Terenura_spodioptila A A Myrmotherula_gularis A A A A A A Myrmotherula_urosticta AA Myrmorchilus_strigilatus A AB Myrmotherula_sunensis A A Epinecrophylla_fulviventris A A A Stymphalornis_acutirostris A A AA A Epinecrophylla_fjeldsaai A A A A Epinecrophylla_ornata A A A AA Epinecrophylla_haematonota A A A A A Epinecrophylla_spodionota A A A A AA AA A A Myrmotherula_gutturalis A A Myrmotherula_snowi A A A A Epinecrophylla_leucophthalma A AA Epinecrophylla_erythrura A A A AA A Neoctantes_niger A A A A Myrmotherula_guttata Myrmeciza_pelzelni A A Clytoctantes_atrogularis A A A AA A A Clytoctantes_alixii A A A A Myrmeciza_atrothorax A A A A Myrmeciza_ruficauda A A Microrhopias_quixensis A A A A Myrmeciza_disjuncta A A Sclateria_naevia A A Myrmeciza_hyperythra A A A A Schistocichla_rufifacies A A A A AA Schistocichla_leucostigma A A Schistocichla_saturata A A AAA A A A Schistocichla_brunneiceps A A Schistocichla_schistacea A A AAA A Schistocichla_humaythae A A Schistocichla_caurensis A A Myrmeciza_longipes A A Myrmoborus_melanurus A A A A Myrmoborus_myotherinus A A A A A AA Myrmoborus_lugubris A A A A A Myrmoborus_leucophrys A A Percnostola_rufifrons A A AA Percnostola_arenarum A A A A Percnostola_lophotes A A AA A A A Gymnocichla_nudiceps A Myrmeciza_melanoceps A A AA A A A A Myrmeciza_goeldii A A Myrmeciza_immaculata A A A A A A Myrmeciza_fortis A A AA AA Pyriglena_leucoptera A A A A Pyriglena_atra A A AA Pyriglena_leuconota A A Rhopornis_ardesiacus A AB bioRxiv preprint first posted online Feb. 8, 2018; doi: http://dx.doi.org/10.1101/261867 . The copyright holder for thisApreprint (which was not Myrmeciza_exsul A A Myrmeciza_griseiceps A A AB peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. A A Myrmeciza_laemosticta A A A A Myrmeciza_berlepschi A A AA Myrmeciza_nigricauda A A AA Hypocnemoides_melanopogon A A A A A A Hypocnemoides_maculicauda A Hylophylax_naevius A A A A A A A Hylophylax_naevioides A A A A AA Hylophylax_punctulatus A A Myrmeciza_ferruginea A A A A Myrmeciza_loricata A A A A Myrmeciza_squamosa A A Willisornis_poecilinotus AB AB Skutchia_borbae A A A A Phlegopsis_nigromaculata A A A A Phlegopsis_erythroptera A A Gymnopithys_lunulatus A A A A A A A AB Gymnopithys_salvini A A AA AA Gymnopithys_rufigula A A A A Gymnopithys_leucaspis A A A Rhegmatorhina_berlepschi A Rhegmatorhina_melanosticta A A A A AA A A Rhegmatorhina_gymnops A AAA A Rhegmatorhina_hoffmannsi A A A AA Rhegmatorhina_cristata A A Pithys_albifrons A A A A Pithys_castaneus A A A A Phaenostictus_mcleannani A A Myrmeciza_hemimelaena A A A A A Myrmeciza_castanea A A A A A Drymophila_ochropyga A A A A Drymophila_genei A A AA A A Drymophila_squamata Drymophila_devillei A AA A AA AA A Drymophila_malura A Drymophila_rubricollis A A A AA A Drymophila_ferruginea A A A A Drymophila_caudata A A A A A Hypocnemis_hypoxantha A A A Hypocnemis_striata A A A AA Hypocnemis_flavescens A AA Hypocnemis_subflava A A A A A A A A Hypocnemis_ochrogyna A A A A Hypocnemis_peruviana A A Hypocnemis_cantator A A Cercomacra_manu A A A A A Cercomacra_brasiliana A Cercomacra_melanaria A A A AB Cercomacra_carbonaria A AB A A AA A Cercomacra_cinerascens A A AA Cercomacra_nigricans AA A Cercomacra_ferdinandi A AB AA Cercomacra_laeta A A AA Cercomacra_parkeri A A AA A Cercomacra_nigrescens A A A AA Cercomacra_serva A A AA Cercomacra_tyrannina A A Dysithamnus_stictothorax A A A A Dysithamnus_puncticeps A A A A Herpsilochmus_gentryi AA Herpsilochmus_rufimarginatus A A AA Herpsilochmus_dugandi A A Herpsilochmus_stictocephalus A A AA A A Herpsilochmus_dorsimaculatus A A AA Herpsilochmus_pileatus A A A A A AA Herpsilochmus_motacilloides A A A Herpsilochmus_parkeri A A A A A A AA Herpsilochmus_atricapillus A AB Herpsilochmus_pectoralis A AB Herpsilochmus_sticturus A A AA Herpsilochmus_longirostris A AB A A Herpsilochmus_roraimae AA A AA A A Herpsilochmus_sellowi AB AA A A Herpsilochmus_axillaris Dysithamnus_leucostictus A A A A Dysithamnus_occidentalis A A A AA A Dysithamnus_mentalis A A AA A Dysithamnus_plumbeus A A A Dysithamnus_xanthopterus A A A A Dysithamnus_striaticeps A A Sakesphorus_cristatus A AB Sakesphorus_luctuosus A A A A Thamnophilus_divisorius A A A A Sakesphorus_canadensis A A Xenornis_setifrons A A A A AA Thamnophilus_doliatus A A A A AB AB Thamnophilus_ruficapillus A AB Thamnophilus_torquatus A AB A Thamnophilus_multistriatus A A AA A A A A Thamnophilus_tenuepunctatus A A A A A A Thamnophilus_palliatus Thamnophilus_zarumae A AB Thamnophilus_bridgesi A A A A A A Thamnophilus_atrinucha A A Sakesphorus_bernardi A AB AA Thamnophilus_praecox A A A A A A Thamnophilus_nigriceps A A Thamnophilus_caerulescens A AB Thamnophilus_aethiops A A A AA A Thamnophilus_aroyae A A AA Thamnophilus_unicolor A A AAAA A A A Thamnophilus_ambiguus A A AA A A Thamnophilus_pelzelni A AB A A Thamnophilus_sticturus A A A A A A Thamnophilus_punctatus A A A A Thamnophilus_stictocephalus A A A A A Thamnophilus_cryptoleucus A A A A A Thamnophilus_nigrocinereus Sakesphorus_melanonotus A AB Sakesphorus_melanothorax A A A A A A Thamnophilus_amazonicus A A A A Thamnophilus_insignis A A Thamnophilus_schistaceus A A AA A A Thamnophilus_murinus AA Biatas_nigropectus A A Cymbilaimus_sanctaemariae A A AA Cymbilaimus_lineatus A A A A Taraba_major A A Batara_cinerea A A A AA A Hypoedaleus_guttatus A A Frederickena_unduligera A AA A A A Frederickena_viridis A A A A A A Mackenziaena_severa A A A A Mackenziaena_leachii A A Dichrozona_cincta A A Thamnomanes_ardesiacus A AA A A A A A Thamnomanes_saturninus A A Thamnomanes_caesius A A A A A A Thamnomanes_schistogynus A A Myrmotherula_hauxwelli A A A A A Megastictus_margaritatus A A A Myrmotherula_luctuosa A AB Myrmotherula_longicauda A A A A Myrmotherula_klagesi A A Myrmotherula_ignota A A A A A A Myrmotherula_brachyura A A A A Myrmotherula_ambigua A A A A A Myrmotherula_sclateri A A A Myrmochanes_hemileucus A A A A Myrmotherula_cherriei A A A A A A A A Myrmotherula_surinamensis A A Myrmotherula_pacifica A A A A Myrmotherula_multostriata A A Myrmotherula_unicolor A A Myrmotherula_grisea A A A A A A Myrmotherula_behni A A Myrmotherula_schisticolor A A Myrmotherula_longipennis A A AA AA Myrmotherula_minor A A AA A Myrmotherula_fluminensis A A AA Myrmotherula_axillaris A A A Myrmotherula_iheringi A A Formicivora_intermedia A A AA Formicivora_iheringi A AB A A A A Formicivora_grisea A A A A A AA Formicivora_rufa AB A Formicivora_erythronotos A A A A AAA Formicivora_littoralis A A A A Formicivora_serrana A A Formicivora_melanogaster A AB Myrmotherula_assimilis A A A A Myrmotherula_menetriesii A A Myrmornis_torquata A A A A Pygiptila_stellaris A A A A A A Thamnistes_anabatinus A

A

40

30

A

A AA AAA AA A

20 Millions of years ago

A A A

10

A A A

A A A

0

Stochastic map ancstates: global optim, 2 areas max. d=0.7012; e=0; j=2.713; LnL=−20.91

B Pmarmoratus B Bplumifrons B Ugallardoi B Pscapulatus A A extremus A A A A roquet A A A A aeneus A A A A richardi A A A A trinitatis A A A A griseus A A A A blanquillanus A A A A bonairensis A A AB AB A A luciae B B ruizii B B B B lamari B B B B dissimilis B B caquetae B B solitarius B B B B B B santamartae B B nasofrontalis B B B B pseudotigrinus B B B B B B laevis B B B B tigrinus B B B B B B paravertebralis B B B B umbrivagus B B B B menta B B jacare B B B B B B anatoloros B B vaupesianus B B B B punctatus B B B B philopunctatus B B B B B B phyllorhinus B B transversalis B B fitchi B B B B podocarpus B B soinii AB AB B B B B cuscoensis B B B B boettgeri B B B BB B B B huilae B B chloris B B B B gorgonae B B B B B B festae B B B B fasciatus B B B B anchicayae B B B B peraccae B B ventrimaculatus B B B B gemmosus B B BB B B otongae B B B B poei B B B B B B aequatorialis B B B B B B anoriensis B B B B megalopithecus B B B B eulaemus B B antioquiae B B agassizi B B danieli B B B B propinquus B B insignis B B B B ibanezi B B B B B B B B chocorum B B B B limon B B B B B B fraseri B B B B parilis B B B B B B mirus B B B B kunayalae B B apollinaris B B B B casildae B B B B maculigula B B B B B B squamulatus B B princeps B B B B B B latifrons B B B B frenatus B B ginaelisae B B B B microtus B B proboscis B B euskalerriari B B B B nicefori B B B heterodermus B B B B B inderenae B B B B B B B vanzolinii B B B B B B B tetarii B B orcesi B B calimae B B B B carlostoddi B BB B williamsmittermeierorum B B B B neblininus B B B B bellipeniculus A A bimaculatus A A A A gingivinus A A A A leachii A A A A oculatus A A ferreus A A A A lividus A A A A A A nubilus A marmoratus A A A A A desiradei A A A A A A AB A A chrysops AA A A kahouannensis A A A A terraealtae A A A A sabanus A A forresti A A A A pogus A A A A schwartzi A A A A wattsi A A stratulus A A A A evermanni A A A A A A acutus A A scriptus A A cooki A A A AA A A A monensis AA A A cristatellus A A A A desechensis A A A A A A ernestwilliamsi A A pulchellus A A A A krugi A A A A poncensis A A A A A A gundlachi A A vinosus A A A A aurifer A A A A favillarum A A A A dominicensis A A distichus A A A A A A ravitergum A A A A ignigularis A A A A A A properus A A brevirostris A A marron A A A A A A caudalis A A A A A A altavelensis A A websteri A A lineatopus A A A A reconditus A A grahami A A A A A A conspersus A A A A garmani A A A A A A opalinus A A valencienni B B marsupialis B B B B aquaticus B B B B woodi B B B B biporcatus B B B B B B parvauritus B B notopholis B B B B binotatus B B B B gracilipes B B milleri B B B B cymbops B B B B B B parvicirculatus B B B B barkeri B B B B duellmani B B B B pygmaeus B B alvarezdeltoroi B B B B B B uniformis B B B B compressicauda B B cobanensis B B B B campbelli B B B B B B hobartsmithi B B B B rubiginosus B B B B matudai B B B B B B cuprinus B B schiedii B B B B naufragus B B B B pijolense B B tropidonotus B B B B capito B B kemptoni B B B B fortunensis B B pseudokemptoni B B B B B B gruuo AA B B tolimensis B B B B B B bocourti B B B B fuscoauratus B B B B B B maculiventris B B B B medemi B B tenorioensis B B B B monteverde B B B B altae B B B B B B B B mariarum B B B B antonii B B humilis B B B B quaggulus A A B B B B wampuensis B B B B macrophallus B B B cupreus B B B B B villai B B B B polylepis B B carpenteri BB B B B B rodriguezii B B granuliceps B B B B tropidogaster B B B B B B B B gaigei AB AB B B fungosus B B B B trachyderma B B B B B B townsendi B B B B lynchi B B B B B B rivalis B B B B macrolepis B B lionotus B B B B B B oxylophus B B B B B B poecilopus B B concolor B B B B pinchoti B B B B zeus B B B B vicarius B B B B B B B B apletophallus B B limifrons B B B B biscutiger B B B B cryptolimifrons B B dollfusianus B B B B yoroensis B B B B ocelloscapularis B B serranoi B B B B B B lemurinus B B B B B B bicaorum B B B B B B roatanensis B B vittigerus B B B B lyra B B magnaphallus B B B B pachypus B B B B tropidolepis B B B B benedikti B B B B pseudopachypus B B sulcifrons B B B B ortonii B B wermuthi B B muralla B B B B heteropholidotus BBBB B B B B B B B B rubribarbaris B B B B morazani B B B B B B kreutzi B B B B cusuco AAA A B B B B laeviventris B B sericeus B B B B wellbornae B B B B unilobatus B B sminthus B B B B crassulus B B B B amplisquamosus B B cristifer B B B B charlesmyeri B B B B pentaprion BB B B B B beckeri B B B B utilensis B B macrinii B B peucephilus B B BB B B B B B omiltemanus B B B gadovii B B liogaster B B B B B B taylori B B B B dunni B B B B microlepidotus B B B B B B forbesi B B B B nebulosus B B quercorum B B B B nebuloides B B B B megapholidotus B B B B subocularis B B B B boulengerianus B B lineatus B B annectens B B B B onca B B B B B B auratus B B bombiceps B B B B scypheus B B B B B B planiceps B B B B B B brasiliensis B meridionalis B B B B B B B tandai B B B B chrysolepis B B johnmeyeri B B B B purpurgularis B B B B loveridgei B B B B salvini B B B B B B datzorum B B petersii A A ahli A A A A birama A A A A rubribarbus A A A A imias A A A A allogus A A confusus AA A A A A A A luteosignifer A A A homolechis A A A A A jubar A A A A guafe A A bioRxiv preprint first posted online Feb. 8, 2018; doi: http://dx.doi.org/10.1101/261867 . The copyright holder for thisApreprint (which was not mestrei A A A ophiolepis A A peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission. sagrei A A A A A A A A nelsoni A A A A quadriocellifer A A A A bremeri A A marcanoi A A longitibialis A A A A A A strahmi A A whitemani A A A A haetianus A A A A A A cybotes A A A A armouri A A A A A A shrevei A A breslini A A alfaroi A A A A macilentus A A anfiloquiae A A A A clivicola A A A A A A fugitivus A A juangundlanchi A AA A A A rejectus AA A A A A cupeyalensis A A A A A A cyanopleurus A A vanidicus A A A A spectrum A A vescus A A A A alutaceus A A A A inexpectatus A A loysianus AA A A terueli A A A A A A pumilus A A A A argillaceus A A ruibali A A A A A A A A litoralis A A A A centralis A A porcatus A A A A B B carolinensis A A fairchildi A A A A A A smaragdinus A A allisoni A A AA A A brunneus AA A A A longiceps A AA A A maynardi A A isolepis A A A A A A A A toldo A A A A altitudinalis A A A A oporinus A A garridoi A A A A guazuma A A A A paternus A A A A angusticeps A A A A oligaspis A A A A A A alayoni A A sheplani A A A A placidus A A argenteolus A A A A lucius A A barbouri A A semilineatus A A A A A A alumina A A A A olssoni A A AA A A etheridgei A A A A insolitus A A A A A A fowleri A A cuvieri A A ricordii A A A A baleatus A A A A A A A barahonae A A A roosevelti A A A A eugenegrahami A A A A christophei A A A A barbatus A A A A porcus A AA A chamaeleonides A AA A guamuhaha AA A A agueroi A A coelestinus A A A A chlorocyanus A A A A aliniger A A A A singularis A A A A darlingtoni A A bahorucoensis A A A A A A hendersoni A A A A dolichocephalus A A A A koopmani A A A A rupinae A A A A A A monticola A A A A rimarum A A pigmaequestris A A equestris A A A A A A noblei A A AA A A A A smallwoodi A A baracoae A A A A A A luteogularis A A bartschi A A A A vermiculatus A A occultus B

B

B B

0.15

B B

B

B B B

0.1

0.05 Millions of years ago

0