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Biogeography: Where do we go from here?

October 2013
Taxon 65(2):912-927

DOI:10.12705/625.15

Authors:

Jun Wen

Smithsonian Institution

Richard H Ree

Field Museum of Natural History

Stefanie M Ickert-Bond

University of Alaska Fairbanks

Ze-Long Nie

Jishou University, Jishou, China

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Biogeography is a multidisciplinary science concerned with how and why organisms are distributed as they are on Earth. It links fields such as systematics, ecology, paleontology, and climatology, and occupies a central position in evolutionary biology, being fundamental to the study of processes such as speciation and adaptive radiation. Here we provide a brief overview of some particularly dynamic areas of inquiry and offer some perspectives on future directions for the field. We hope that some historical debates, such as those over the importance of dispersal, or the validity of molecular dating, are finally being put to rest. Over the last decade, biogeography has become increasingly integrative, and has benefited from advances in statistical methods for inferring geographic range dynamics in a phylogenetic context, molecular estimation of lineage divergence times, and modeling lineage birth and death. These are enabling greater insights into patterns of organismal diversification in time and space. In the next decade, analytical challenges are emerging on several fronts. For example, phylogenies are increasing in size and taxonomic breadth and new sequencing technologies enabling phylogenetic and phylogeographic data sets are increasingly genomic in depth. In addition, geographic occurrence data are accumulating in online repositories, yet tools for data mining and synthetic analysis are lacking for comparative multi-lineage studies. Biogeography is thus entering an era characterized by phylogenomic data sets, increasingly comprehensive sampling of clades, and interdisciplinary synthesis. We anticipate continued progress in our understanding of biodiversity patterns at regional and global scales, but this will likely require greater collaboration with specialists in bioinformatics and computational science. Finally, it is clear that biogeography has an increasingly important role to play in the discovery and conservation of biodiversity. Lessons learned from biogeographic studies of islands are being applied to better understand extinction dynamics as continental ecosystems become more fragmented, and phylogeography and ecological niche modeling offer innovative paths toward the discovery of previously unknown species distributions and priority areas for conservation. The future of biogeography is bright and filled with exciting challenges and opportunities.

International biogeography data portal — toward a vision of a data core with biogeography as its central mission, with a few exemplar data sources.

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INTRODUCTION

Biogeography is the study of the distribution of life forms

over geographic space and time. Two subdisciplines of bioge-

ography have often been recognized: ecological biogeography

and historical biogeography (Lieberman, 2003; Crisci, 2006;

Lomolino & al., 2010a). New integrative trends in biogeography

as well as the utilization of phylogenetics in both subdisci-

plines have weakened this dichotomy, nevertheless, this review

focuses on the latter, which emphasizes studies on the distri-

butional patterns through time at macroevolutionary scales.

Biogeography has a long and interesting history, and vari-

ous starting points can be selected (Briggs & Humphries, 2004).

One can begin with Alexander von Humboldt (1769–1859), who

is often recognized as the father of plant biogeography. The

publication of his 24-volume work (1805–1837) covering his

five years of exploration in South America (1789–1804) ex-

cited the public as well as the scientific community in Europe

and inspired a whole generation of natural history explorers

to travel around the world, including Darwin, Hooker, and

Wallace (see below).

Charles Darwin’s observations and comparisons (Darwin,

1845) on his five-year around-the-world journey (1831–1836)

stimulated his theory about descent with modifications and its

mechanisms (Mayr, 1982; Crisci & Katinas, 2009). In fact he

dedicated two of the fifteen chapters of the Origin to geographic

distributions (chapters 12 and 13, Darwin, 1859). Darwin (1859)

also acknowledged the importance of migration from one part

of the world to another, the inf luence of climatic changes, and

the many means of dispersal in shaping modern distribution

patterns rather than separate creations in the context of plant dis-

junctions between eastern Asia and North America (Wen & al.,

2010). However, a great deal of the credit for the development

of biogeography as a fact-based science should go to Joseph

Dalton Hooker (1817–1911), a colleague and friend of Darwin,

whose various voyages around the globe such as Antarctica in

Biogeography: Where do we go from here?

Jun We n,1 Richard H. Ree,2 Stefanie M. Ickert-Bond,3 Zelong Nie4 & Vicki Funk1

1 Department of Botany, National Museum of Natural History, MRC166, Smithsonian Institution, Washington, D.C. 20013-7012, U.S.A.

2 Department of Botany, The Field Museum, 1400 South Lake Shore Drive, Chicago, Illinois 60605, U.S.A.

3 UA Museum of the North Herbarium and Department of Biology and Wildlife, University of Alaska Fairbank s, Fairbanks, Alaska

99775-6 96 0, U.S . A.

4 Key Laboratory of Biodiversity and Biogeography, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan

650204, P.R. China

Author for correspondence: Jun Wen, wenj@si.edu

Abstract

Biogeography is a multidisciplinary science concerned with how and why organisms are distributed as they are on

Earth. It links fields such as systematics, ecology, paleontology, and climatology, and occupies a central position in evolutionary

biology, being fundamental to the study of processes such as speciation and adaptive radiation. Here we provide a brief overview

of some particularly dynamic areas of inquir y and offer some perspectives on future directions for the f ield. We hope that some

historical debates, such as those over the importance of dispersal, or the validity of molecular dating, are finally being put to

rest. Over the last decade, biogeography has become increasingly integrative, and has benefited from advances in statistical

methods for inferring geographic range dynamics in a phylogenetic context, molecular estimation of lineage divergence times,

and modeling lineage bir th and death. These are enabling greater insights into patter ns of organismal diversification in time and

space. In the next decade, analytical challenges are emerging on several fronts. For example, phylogenies are increasing in size

and taxonomic breadth and new sequencing technologies enabling phylogenetic and phylogeographic datasets are increasingly

genomic in depth. In addition, geographic occurrence data are accumulating in online repositories, yet tools for data mining

and synthetic analysis are lacking for comparative multi-lineage st udies. Biogeography is thus entering an era characterized by

phylogenomic datasets, increasingly comprehensive sampling of clades, and interdisciplinar y synthesis. We anticipate continued

progress in our understanding of biodiversity patterns at regional and global scales, but this will likely require greater collabo-

ration with specialists in bioinformatics and computational science. Finally, it is clear that biogeography has an increasingly

important role to play in the discovery and conservation of biodiversity. Lessons learned from biogeographic studies of islands

are being applied to better understand extinction dynamics as continental ecosystems become more fragmented, and phylogeog-

raphy and ecological niche modeling offer innovative paths toward the discovery of previously unk nown species distributions

and priority areas for conservation. The future of biogeography is bright and filled with exciting challenges and opportunities.

Keywords

biogeography; biogeography data portal; historical biogeography; parametric models; phylogenomics;

phylogeography

Received: 21 July 2013; accepted: 25 Aug. 2013. DOI: http://dx.doi.org/10.12705/625.15

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1839–1843, the Himalayas and India in 1847–1851, Palestine in

1860, Morocco in 1871, and the western U.S.A. in 1877, led to

his development of what was probably the first explicit biogeo-

graphic method, expressed in his discussion of the distributions

of plants in the Southern Hemisphere (see Hooker, 1844–1860).

Hooker’s method was to place species from the areas in question

into groups that summarized their total distribution and then

compare them across localities (Brundin, 1966). After exam-

ining the shared species he came to the conclusion that these

“bands of affinity” demonstrated that there had been a single

center of evolution in the Southern Hemisphere that had been

broken up. Darwin, in contrast, preferred long-distance disper-

sal over a “permanent geography” (Darwin, 1859).

Alfred Russel Wallace (1823–1913) was another great ex-

plorer who used his years of fieldwork, in the Amazon Basin

(1848–1852) and the Malay Archipelago (1854–1862), to de-

velop important concepts about biogeography (Wallace’s line)

and evolution (natural selection and divergence). He was a key

founder of zoogeography, and his six biogeographic regions

of the world based on animal distributions (Wallace, 1876;

modified from Sclater’s treatment of 1858) are still of relevance

today (Brown & Lomolino, 1998; Holt & al., 2013). Over the

next 100 years the concepts of evolution and the role of disper-

sal in geographic patterns were expanded, but there was little

real change in methodology (see Brown & Lomolino, 1998).

Historical biogeography experienced a wave of conceptual

and methodological developments in the 1970s through the 1990s

(see reviews by Morrone & Crisci, 1995; Funk, 2004; Posadas

& al., 2006; Morrone, 2009) based partially on work done in the

1950s and 1960s. These changes were fueled by the introduc-

tion of two new areas of scientific research: (1) the theory of

continental drift and plate tectonics, and (2) the recognition of

the importance of having a phylogeny-based method for evalu-

ating biogeographic patterns. Several analytical biogeographic

approaches became available such as phylogenetic biogeogra-

phy (Hennig, 1950, 1966; Brundin, 1966, 1988), panbiogeogra-

phy (not phylogeny-based; e.g., Croizat, 1962), and vicariance

biogeography (Nelson & Platnick, 1981), which developed into

cladistic biogeography (Humphries & Parenti, 1999). Ronquist

(1994, 1995, 1996) referred to the above approaches as “pattern-

based” methods, because they are based on the idea that taxa

sharing similar phylogenetic and/or distributional relationships

have a shared biogeographic history. Additional examples of the

pattern-based approaches include Brooks Parsimony Analysis

(Brooks, 1985), Bremer’s Ancestral Area Analysis (Bremer,

1992, 1995), and Component Analysis (Nelson & Platnick, 1981;

Page, 1990). However while some biogeographers hold that area

cladograms (a combination of the cladogram of a group of taxa

and their distributions) indicated the past biogeographic history

of the clade others do not use a phylogeny at all.

Each of these methods has its own strengths and weak-

nesses, and they have been previously reviewed (Funk, 2004;

Posadas & al., 2006; Ronquist & Sanmartín, 2011), eliminating

the need to do so here. However there are a few key points that

are relevant to the discussion of the current and future trends

in historical biogeography. One is the disagreement among the

methods on the relative importance of dispersal and extinction.

The cladistic biogeographic approach emphasizes vicariance in

forming biogeographic patterns, but diminishes or ignores the

impact of processes such as dispersal and extinction (Ronquist,

1996). In fact both dispersal and extinction were/are often con-

demned as untestable and unimportant in many biogeographic

papers in the 1980s to today (Nelson & Rosen, 1981; Craw & al.,

1999; Parenti & Ebach, 2009; Heads, 2012). In recent years the

preponderance of well-supported and dated phylogenies that

effectively demonstrate long-distance dispersal makes it clear

that the preference of vicariance over dispersal cannot be used

as a general rule (Lieberman, 2003; Sanmartín, 2003; Renner,

2004a, b; De Queiroz, 2005). There are hundreds of examples

that show that dispersal and vicariance as well as combinations

of both must be considered when developing biogeographic

hypotheses (e.g., Sanmartín & Ronquist, 2004; Wen & Ickert-

Bond, 2009; Wen & al., 2010). The search for general patterns

is made difficult because of the tendency of different groups

of organisms to respond differently to various biogeographic

barriers, and because organisms diversified at different times

in geologic history so they were exposed to different types of

barriers (Wen, 1999; Tiffney & Manchester, 2001; Lieberman,

2003; Sanmartín & Ronquist, 2004).

In the last decade, the vast majority of those that prac-

tice historical biogeography have moved on to adopt what has

been called event-based biogeography because it allows the

data to be used to evaluate various models (Ronquist, 1995,

1996; Sanmartín, 2007; Ronquist & Sanmartín, 2011). Ronquist

(1996, 1997, 2001) introduced dispersal-vicariance analysis

(DIVA) as a quantitative method of parsimoniously recon-

structing ancestral distributions and patterns of vicariance on

a phylogeny using a three-dimensional step matrix. Although

DIVA favors vicariance over dispersal (Ronquist & Sanmartín,

2011), it was, nevertheless, a useful tool to help determine any

underlying patterns in the data. Nylander & al. (2008) proposed

the Bayes-DIVA method, which uses DIVA to reconstruct an-

cestral areas over a sample of Bayesian topologies resulting

from phylogenetic analysis (also see a similar method by Harris

& Xiang, 2009). The Bayes-DIVA method provides a statistical

framework to evaluate the results from DIVA analyses, and

has been used in many empirical studies (e.g., Antonelli & al.,

2009; Jønsson & al., 2011; Nie & al., 2012). It is implemented

in the Statistical Dispersal-Vicariance Analysis (S-DIVA) (Yu

& al., 2010) and Reconstruct Ancestral State in Phylogenies

(RASP) (Yu & al., 2011).

During the last decade, much progress has been made on

developing parametric methods of inferring biogeographic his-

tory that can more fully exploit the information contained in

molecular phylogenies (reviewed by Lamm & Redelings, 2009;

Ree & Sanmartín, 2009; Ronquist & Sanmartín, 2011). These

methods have been more profitably used in combination with

the profusion of time-calibrated trees made available by in-

creasingly sophisticated algorithms of relaxed molecular-clock

analysis and divergence-time estimation (Sanderson, 2002,

2003; Thorne & Kishino, 2002; Renner, 2005; Rutschmann,

2006). These methods for reconstructing range evolution and

divergence times are briefly reviewed in the following sections,

with an eye toward future developments.

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Biogeography and ecology continue to converge in a

number of areas, especially concerning phylogeography and

analyses such as those related to niche modeling, biogeo-

graphic diversifications, and phylogenetic community assem-

bly (Cavender-Bares & al., 2009; Jenkins & Ricklefs, 2011;

Ricklefs & Renner, 2012). Phylogenetic biogeographic analy-

ses that infer geographic range evolution provide a means of

tracing the evolution of geography and ecological tolerances

(Ronquist, 1997; Ree & al., 2005) and the recent and future

development of model-based biogeographic approaches allows

investigators to explore important processes, such as ecologi-

cal interactions and climate changes, in biogeographic infer-

ence (Ree & Smith, 2008; Ree & Sanmartin, 2009; Ronquist

& Sanmartin, 2011). However, biogeographers still need to in-

corporate ecology better especially when exploring the origins

of large-scale biogeographic patterns (Donoghue & Moore,

2003; Ricklefs, 2004; Wiens & Donoghue, 2004; Wiens, 2011).

It is abundantly clear that historical biogeography is an

integrative science that continues to intrigue scientists. Our

goal in this paper is to encourage this fascination by providing

our perspectives on recent advances and by discussing what one

might expect during the next decade. As the methodological de-

tails of pattern-based and event-based biogeographic methods

have been reviewed recently (see Morrone, 2009, and Ronquist

& Sanmartín, 2011, respectively), this paper focuses on current

challenges where we think the field is headed. We hope that it

will encourage discussions on where we are and where we need

to go in our quest to understand the distributions of organisms

through space and time.

PARAMETRIC MODELS OF RANGE

EVOLUTION

A recent trend in biogeographic research has been the de-

velopment and adoption of methods that use parametric models

for the evolution of geographic ranges in a phylogenetic context.

Some of these extend directly from DIVA, while others adopt

models closer to those employed in studies of character evolution.

An example of the former is the dispersal-extinction-

cladogenesis (DEC) method (Ree & al., 2005; Ree & Smith,

2008), which takes the conceptual framework of DIVA and

applies it to an explicit model of stochastic dispersal and local

extinction in which probabilities of ancestor-descendant range

evolution are a function of phylogenetic branch length (time).

This method thus provides a means of calculating the likelihood

of a phylogenetic tree with geographic range data at its tips. It

also allows various kinds of temporal and spatial information,

such as from fossils, sea levels, climate, and continental posi-

tions, to be brought to bear on establishing model constraints.

For example, the positions of continental landmasses through

time were used to construct temporally stratified constraints on

dispersal in reconstructing the biogeographic histories of the

conifer family Cupressaceae (Mao & al., 2012) and monocot

family Araceae (Nauheimer & al., 2012).

DIVA and DEC are based on the premise that a single lin-

eage may be widespread, i.e., occupy more than one out of a set

of discrete areas, and that ranges develop by dispersal and local

extinction events. By contrast, for some study systems, a more

suitable model is that lineages have only single-area ranges,

and transitions between them are analogous to the evolution

of a multistate character.

This is the case for the Bayesian continuous-time Markov

chain (CTMC) method of Lemey & al. (2009), which is de-

signed for discrete-area phylogeographic analysis in which the

number of areas, and hence the number of potential area-to-area

transition rates, may be large. The method uses a statistical

technique known as Bayesian stochastic search variable selec-

tion (BSSVS), in which a Markov-chain Monte Carlo (MCMC)

sampling procedure determines which rates (cells in the transi

tion matrix) are not supported by the data and may be set to

zero. It integrates over uncertainty in the phylogeny and model

parameters to yield posterior probability estimates for ancestral

areas at internal nodes. The assumptions of Bayesian CTMC

render it best applied to shallow time frames (Lemey & al.,

2010; Drummond & al., 2012; Filipowicz & Renner, 2012).

In the future, we see a particular need for methods that em-

phasize general inferences about range dynamics drawn from

multiple lineages. A promising step in this direction is a Bayes-

ian method for island biogeography (Sanmartín & al., 2008),

implemented in the forthcoming MrBayes v.4.0. This method

takes as input molecular sequence alignments and range data

for multiple clades of co-distributed taxa, and uses MCMC to

estimate carrying capacities (equilibrium frequencies of spe-

cies diversity) and rates of dispersal between geographically

isolated areas (islands). These biogeographic parameters are

estimated simultaneously with the dated tree of each group,

allowing for differences in root ages, models of sequence

evolution, and area-specific dispersal rates. It was originally

presented as a means of studying lineage dynamics in island

systems, but Sanmartín & al. (2010) have also demonstrated

its use in a continental setting.

THE USE OF FOSSILS IN BIOGEOGRAPHIC

INFERENCE

Fossils, in recording the occurrence of taxa in geological

time and geographic space, have been important in testing

hypotheses about the biotic connections between areas (Tiffney

& Manchester, 2001; Lieberman, 2003), but their utility in

phylogenetic biogeography has largely been relegated to cali-

brating molecular-clock estimates of lineage divergence times

(Rutschmann & al., 2007; Forest, 2009). In other words, their

use has mostly benefited the temporal, but not spatial, com-

ponent of biogeographic inference on a phylogeny. Statistical

methods for molecular dating continue to be improved, in terms

of methods that implement increasingly complex models of

substitution rate heterogeneity and prior densities for calibra-

tions (Drummond & al., 2006; Drummond & Rambaut, 2007),

and optimized algorithms for large trees (Smith & O’Meara,

2012). Nevertheless, major challenges persist, including the in-

completeness of fossil record, taphonomic biases, and the prob-

lem of accurately deter mining where in a tree a fossil should be

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placed as a calibration point (Magallón, 2004; Ho & Phillips,

2009; Sauquet & al., 2012). Clade age estimates might also

improve if factors such as fossil abundance and stratigraphic

confidence intervals are incorporated into methods and tools

(Forest, 2009).

Recent developments may spur progress along these lines

on incorporating the use of fossils in biogeography. In a no-

table shift away from considering fossils solely as post hoc age

constraints applied to internal nodes on a phylogeny, Pyron

(2011) and Ronquist & al. (2012) proposed that fossils should

be explicitly incorporated into phylogenetic analysis as extinct

(non-contemporaneous) terminal taxa, such that the topology

and node ages of a chronogram are estimated simultaneously

based on “total evidence”: namely, a combination of molecular

and morphological data for the extant taxa, and morphological

data and age priors for the fossil taxa. In a Bayesian MCMC

framework, as implemented in MrBayes v.3.2 (Ronquist & al.,

2012), this allows posterior distributions of node ages to be es-

timated while integrating over uncertainty in the phylogenetic

positions of fossil taxa and other model parameters.

We are still at the infancy of exploring how fossil evidence

may be integrated into phylogenetic inferences of ancestral

areas and range dynamics. Just as a fossil placed on a phy-

logeny can be used to constrain a node’s age, it can also be

thought of as a constraint on its ancestral range; in other words,

it records both the time and place of an ancestral lineage (Ree

& Sanmartín, 2009). The spatial component can be encoded as

a constraint in a Lagrange (likelihood analysis of geographic

range evolution) analysis (Ree & Smith, 2008) if the fossil can

be placed at a particular node or branch on the tree; ranges

inferred for the node of interest are consequently required to

include the area of fossil occurrence. In practice, however,

using fossils as node-based spatial constraints entails many of

the same assumptions and potential pitfalls that are associated

with node-based age calibrations.

Looking toward the future, a promising approach that com-

bines total-evidence fossil dating with parametric inference

of ancestral areas is demonstrated by Wood & al. (2013). In

their analyses of archaeid spiders, fossils are treated as non-

contemporaneous terminal taxa in reconstr ucting the phylog-

eny itself (using BEAST) and in estimating ancestral areas

(using Lagrange and DIVA). They argue that treating the fossils

as terminal taxa within a Bayesian framework removes the

subjectivity involved in assigning priors to node calibrations.

Their results, which indicate that diversification of the northern

and southern archaeid lineages was congruent with breakup

of Pangaea, are more precise and sensible than those obtained

when fossils are used only as node constraints.

ENHANCED RESOLUTION

IN BIOGEOGRAPHY IN THE

PHYLOGENOMIC ERA

Phylogenetic uncertainty has been a challenging issue

for biogeographic inferences (Nylander & al., 2008; Harris

& Xiang, 2009; Lee & al., 2009; Smith, 2009; Harris & al.,

2013). Evolutionary radiations are common in the history of life

and represents hard phylogenetic uncertainty (Linder, 2008;

Alfaro & al., 2009; Funk & al., 2009; Givnish & al., 2011;

Drummond & al., 2012). Much higher phylogenetic resolution

can be achieved for lineages using multigene or phylogenomic

approaches (Moore & al., 2007, 2010; Zimmer & Wen, 2012).

The advent of next-generation sequencing (NGS) has revolu-

tionized genomic and transcriptomic approaches to evolution-

ary biology (Davey & al., 2011). These new sequencing tools

are also valuable for the discovery, validation and assessment

of genetic markers for phylogenetics, hence benefiting studies

of historical biogeography. The rapid advances in plant phy-

logenomics afforded by the speed and decreasing cost of NGS

methodology also may soon become the standard for produc-

tion of large numbers of phylogenetically informative sites for

many previously unresolvable lineages (Straub & al., 2012;

McCormack & al., 2013; also see other papers from a special

issue of the American Journal of Botany, Feb. 2012).

Many labs have been successful in generating near-

complete chloroplast genomes or plastomes on an NGS platform

(Cronn & al., 2008; Parks & al., 2009; Parks, 2011; Straub & al.,

2012). In an analysis of 21 species of strawberry (Fragaria L .)

using plastome data, Njuguna & al. (2013) hypothesized an

eastern Asian origin of the group, while the clade containing

the diploid F. vesca L. subsp. bracteata (A. Heller) Staudt,

the octaploids F. viriginiana Mill., F. chiloensis Duchesne,

and F. ×ananassa (Weston) Duchesne ex Rozier subsp. cunei-

folia (Nutt. ex Howell) Staudt and the decaploid F. iturupensis

Staudt are inferred to have a North/South American origin.

Furthermore, the western North American octaploid F. vesca

subsp. bracteata is inferred as the sister to the remaining North/

South American octaploid/decaploid Fragaria species. The use

of whole plastomes in phylogenomic studies is not without chal-

lenges, for example: large genome sizes in non-model organ-

isms, extensive variation in the proportion of organellar DNA

as compared to the total DNA, polyploidy, and gene number/

redundancy (Cronn & al., 2012). To overcome these challenges

additional approaches for generating targeted enrichment strat-

egies are being developed (summarized in Cronn & al., 2012).

To resolve recent shallow divergences, some labs have

focused on genomic reduction approaches such as reduced-

representation libraries (RRLs; Carstens & al., 2012; Lemmon

& Lemmon, 2012; McCormack & al., 2012; Zellmer & al.,

2012) by sequencing a subset of the genome. McCormack & al.

(2012) showed the utility of the RLL approach to resolve re-

cent divergence in four phylogenetically diverse avian systems.

Using single nucleotide polymorphisms (SNPs) mined from

the hundreds of loci in 20 individuals each from the four avian

systems, they detected a case of ecological speciation in rails

(Rallus), a rapid postglacial radiation in the genus Junco, recent

in situ speciation among hummingbirds (Trochilus) in Jamaica,

and subspecies population structure of white-crowned spar-

rows (Zonotrichia leucophrys) along the Pacific coast. Zellmer

& al. (2012) studied deep divergences of Sarracenia alata

(Alph. Wood) Alph. Wood using the reduced-representation

library approach from 86 individuals across ten populations

from throughout the range of the species. They found that the

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pattern of diversification in Sarracenia L. implies that riverine

barriers are the primary factor promoting diversification in this

group, with divergence across the Mississippi River dating to

60,000 generations before present in the Pleistocene.

Biogeographic analyses can benefit from new genomic

data, which provide resolution at deep phylogenetic levels. For

example, transcriptome sequences recently have been shown

to provide a rich set of characters to produce phylogenies in

eukaryotes and are more eff icient and cost-effective than tra-

ditional PCR-based and EST (expressed sequence tag)-based

methods (Hittinger & al., 2010). Recent studies have demon-

strated the utility of transcriptome data for resolving the re-

lationships of mosquitoes (Hittinger & al., 2010), mollusks

(Kocot & al., 2011; Smith S.A. & al., 2011a), the large group

consisting of turtles, birds and crocodiles (Chiari & al., 2012),

and plants (Wen & al., in press). For example, even though

the phylum has an excellent fossil record, deep relationships

of mollusks have been uncertain when molecular phylogenies

used a few genes. With a transcriptome approach, the major

groups were resolved with highly significant statistical support.

The growing ease of use of NGS and even 3rd-generation

sequencing technologies no doubt accelerates the growth rate

of phylogenetic data matrices (Zimmer & Wen, 2012). Yet an

emerging challenge lies in the interpretation of the strength

and the assessment of the quality of the phylogenetic signal

in constructing phylogenomic trees (Meusemann & al., 2010;

Wen & al., in press). As phylogenetic trees become much big-

ger, it also adds computational complications for biogeographic

inferences. Most existing parametric statistical biogeographic

programs will need to be improved or developed to handle

large and more complex data from phylogenomics. The need of

input from computer scientists into integrated phylogenetic and

biogeographic pipeline development has become more urgent

than ever. With genomic data and difficulties in resolving deep

relationships, supertrees (also see Smith & al., 2009) may be

used for phylogenies. But supertree approaches will not enable

the effective use of statistic biogeographic methods, such as

DEC, because these comparative methods assume accurate

branch lengths calibrated to units of time.

PHYLOGEOGRAPHY

Phylogeography is concerned with range dynamics over

relatively shallow time frames and with relatively fine-grained

distribution data at or below the species level (Avise, 1989,

2000, 2008). Phylogeography bridges ecological and historical

biogeography and has been a highly dynamic subdiscipline;

we thus include a brief discussion on it in this review. Phylo-

geographic analyses can be conducted on single species or on

several unrelated species (comparative phylogeography). The

latter seeks to elucidate the mechanisms responsible for the cur-

rent patterns of phylogenetic relationships and co-distribution

of different unrelated species (Avise, 2000; Riddle & al., 2000;

Soltis & al., 2006).

Phylogeography has developed very rapidly in the last

decade especially concerning statistical phylogeography

(Knowles & Maddison, 2002), which uses statistical ap-

proaches based on coalescent models for parameter estima-

tion and hypothesis testing, even though phylogeography was

earlier criticized as being highly descriptive. The new advances

of statistical phylogeography have been discussed by Hickerson

& al. (2010; also see Lemey & al., 2009, 2010).

So far comparative phylogeographic studies have had

mixed success in identifying common phylogeographic pat-

terns among co-distributed taxa in various regions. Some have

found similar general patterns across multiple co-distributed

taxa (e.g., Hewitt, 2000; Riddle & al., 2000; Smith C.I. & al.,

2011), but many others have found that the histories of dif-

ferent species are much less congruent, especially when the

time or dating component is considered (e.g., Qiu & al., 2011;

Ornelas & al., 2013). As an example, Ornelas & al. (2013) used

comparative phylogeographic analyses to identify patterns

of co-vicariance among 15 species of seed plants, birds and

rodents that are co-distributed in the cloud forest habitats of

northern Mesoamerica. The hierarchical approximate Bayesian

computation (HABC) method as implemented in the program

msBayes (Hickerson & al., 2007) was used to test simultane-

ous versus non-simultaneous divergence of the cloud forest

lineages. Shared phylogeographic breaks were detected that

correspond to the Isthmus of Tehuantepec, Los Tuxtlas, and

the Chiapas Central Depression, with the Isthmus representing

the most frequently shared break among taxa. Yet dating analy-

ses support that the phylogeographic breaks corresponding to

the Isthmus occurred at different times in different taxa. This

complexity is likely attributable to differences among species

in ecological niche requirements and dispersal capabilities.

Phylogeography in plants is generally hampered by the

lack of DNA-sequence regions that provide sufficient varia-

tion in intra-specific lineages to reveal historical patterns.

Recent advances in next-generation sequencing can provide

a fast and cost-effective way to generate multilocus sequences

for phylogeographic analysis. As the sequencing of whole

plastomes via the NGS platform becomes more and more af-

fordable, whole plastomes can be utilized in phylogeographic

analyses in plants. Several other methods such as restriction

site–associated DNA tags (RAD tags; Baird & al., 2008), and

double digest RAD sequencing (ddRAD; Peterson & al., 2012)

have been used recently in various animal groups, and they can

be developed and employed in plant phylogeographic studies

in the near future.

The RAD tags were recently shown to resolve previously

unresolved genetic structure and detect direction of evolution in

the pitcher plant mosquito, Wyeomyia smithii, from a refugium

in the southern Appalachian Mountains following recession

of the Laurentide Ice Sheet at 22,000–19,000 B.P. (Emerson

& al., 2010). RAD sequencing or RADseq uses multiplex-

ing and is a cost-effective way to sequence multiple samples

(about 50 samples) per individual lane using high-throughput

platforms such as an Illumina HiSeq (Baird & al., 2008). It

uses restriction enzymes to shear genomic DNA into smaller

fragments, and ligates adapter sequences (tags) to both end of

the fragments so that primers containing the sequence of the

adapters can be used to sequence the fragment. A drawback of

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the RADseq method is the inability to target a specific frac-

tion of the sampled genome (Peterson & al., 2012). The RAD

tag method provided detailed patterns of phylogeography in

this nonmodel species without any prior genomic data, and

identified incipient speciation and genome-wide variation in

natural populations. It has been recently used to explore plant

evolution (Eaton & Ree, 2013) and phylogenetics (Rubin & al.,

2012; Cariou & al., 2013).

An approach similar to RAD tags, the ddRAD method

(Peterson & al., 2012) is a very cost effective NGS method and

is suited for studies that need a large amount of genomic data

such as single nucleotide polymorphisms (SNPs). Instead of

randomly shearing DNA fragments after the treatment with

restriction enzymes which is done in RADseq, ddRAD se-

quencing uses a double restriction enzyme digestion and a size-

selection for DNA fragments, thereby allowing a more precise

as well as more flexible selection of fragments as compared to

RADseq approaches.

Phylogeographic parameter estimates and model testing

can be integrated with ecological niche models (Hugall & al.,

2002; also see the following section) to elucidate how phylo-

geographic patterns are directly or indirectly linked to abiotic

factors (Kozak & al., 2008). Comparative phylogeography can

be integrated with analyses of community assembly to provide

insights into the processes of range expansion and contrac-

tion of major component taxa in the community (Emerson

& Gillespie, 2008; Cavender-Bares & al., 2009). Like in bio-

geographic inferences at higher taxonomic levels, the statistical

tools for comparative phylogeography across multiple lineages

are still in their infancy (Hickerson & al., 2010).

NICHE MODELS AND BIOGEOGRAPHY

A challenge in biogeographic studies is often to reconstruct

the distribution of a species in geographic space as well as on

a temporal scale and to determine why species are present in

certain areas, but are lacking from others (Wiens & Graham,

2005). Range limits of species or lineages of species are often

determined by ecological factors and, of course, the dispers-

ability of the species (Wiens, 2011, also see Sexton & al., 2009),

but another interesting dimension are the temporal dynamics

of a species. Stochastic processes such as colonization and

extinction dynamics need to be considered when trying to un-

derstand species distributions at both regional and global scales

(Ricklefs, 2004; Chase & Meyers, 2011).

The future synergy between ecology and biogeography

will depend on the integration of scales. Ecological biogeog-

raphers are mainly working at temporal scales that are much

shallower than those typically aimed at in historical biogeo-

graphic studies. Nevertheless, an increasingly large number of

studies are being devoted to niche modeling at both regional

and global scales (Hugall & al., 2002; Graham & al., 2004;

Phillips & al., 2006; Carstens & Richards, 2007; Loarie & al.,

2008; Crisp & al., 2009; Evans & al., 2009; Ackerly & al.,

2010; Loera & al., 2012). Ecological Niche Modeling (ENM) or

Species Distribution Modeling (SDM) combines distribution

data from specimens with data on climate, topography, soil

properties, species interactions, and physiological conditions

to predict the areas where a species could exist, given its eco-

logical requirements. Progress in understanding large-scale

biogeographic patterns will be made by carefully integrating

niche modeling with biogeographic approaches. The inclusion

of trait-based assembly mechanism in studying biogeography

diversifications might also yield important insights (Weiher

& al., 2011).

At a shallow level, by projecting niche models onto recon-

structions of the climate during Pleistocene glacial cycles, it is

possible to identify refugia where species might have weath-

ered such events and understand speciation dynamics at dif-

ferent temporal, spatial and ecological scales (Knowles, 2004;

Knowles & al. 2007; Hope & al., 2013). Another aspect of these

modeling approaches is to predict the future distributions of

species and clades under a climate change scenario, which is

of particular interest to conservation management (Ackerly

& al., 2010; Wiens, 2011; Diazgranados, 2012; Hof & al., 2012).

Niche modeling of paleo-distributions (Maguirea & Stigall,

2009; Wiens, 2011) may also provide an additional line of evi-

dence toward the resolution of biogeographic histories. Smith

& Donoghue (2010) recently used climatic niche modeling and

divergence time estimates to explore the evolution of climate

variables in the Caprifolium clade of Lonicera L. with 25 spe-

cies distributed around the Northern Hemisphere. Divergence

time estimation and biogeographic analyses over the posterior

distribution of dated trees suggest that a widespread ancestor

was distributed across the Northern Hemisphere some 7 to

17 million years ago. Climatic models were projected from one

continent (such as North America) into the others (e.g., Asia

and Europe) to examine whether species living in these areas

occupy similar climates. This study demonstrates the utility

of combining niche modeling with historical biogeographic

analyses and documents significant climatic niche evolution

within a group of species distributed throughout the Northern

Hemisphere. These results suggest a possible model of con-

vergent shift to drier Mediterranean climates in western North

America and Europe for the origin of the Madrean–Tethyan

disjunction pattern (also see review by Axelrod, 1975; Wen

& Ickert-Bond, 2009; Kadereit & Baldwin, 2012), supporting

convergent evolution in some lineages of the Madrean–Tethyan

disjunction in the broad Northern Hemisphere intercontinental

biogeographic disjunction (Wen, 1999, 2001; Donoghue & al.,

2001; Donoghue & Smith, 2004).

Maguirea & Stigall (2009) used ecological niche model-

ing to reconstruct the geographic distribution of species of the

subfamily Equinae in the Great Plains region of North America

during two time slices from the middle Miocene through early

Pliocene. Their method combines known species occurrence

points with environmental parameters inferred from sedimen-

tological variables to model each species’ fundamental niche.

The geographic range of each species is then predicted to oc-

cupy the geographic area within the study region wherever the

set of environmental parameters that constrain the fundamental

niche occurs. They report that patchy geographic distributions

were more common in the middle Miocene when speciation

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rates were high. During the late Miocene, when speciation

rates were lower, continuous geographic ranges were common.

Equid species tracked their preferred habitat within the Great

Plains as well as throughout North America. However, as cli-

mate continued to deteriorate in the late Miocene, extinction

rates increased for species of all range sizes.

It is known that the recently developed niche models have

various sources of error and uncertainties such as model mis-

specification, missing variables, small sample size, and data

error such as misidentifications from publicly available online

data (Barry & Elith, 2006; Lozier & al., 2009). It is also im-

portant to bear in mind that paleo-niche models predict the

potential ranges under the assumption of niche conservatism

(Losos, 2008). Model-based biogeographic inferences can be

used in the future to test ancestral range predictions inferred

based on alternative niche assumptions (also see Crisp & al.,

2011). Also comparative niche modeling analyses across mul-

tiple lineages of taxa with a common biogeographic pattern

such as the eastern Asian-North American disjunctions (Wen,

1999; Wen & al., 2010) and the Madrean–Tethyan disjunctions

(Wen & Ickert-Bond, 2009) may provide insights into extinc-

tion processes in the formation of important intercontinental

disjunct patterns formed in the Tertiary.

GEOGRAPHIC ASPECTS OF LINEAGE

DIVERSIFICATIONS

Molecular phylogenies and the relaxed molecular clocks

provide a framework for analyzing species diversif ication pat-

terns and processes (Harvey & al., 1994; Nee, 2006; Moore

& Donoghue, 2009). Statistical methods for inferring rates of

speciation and extinction from time-calibrated phylogenies

have seen dramatic developments over the past decade, and the

trend shows no signs of abating (e.g., Rabosky, 2006; Rabosky

& al., 2007; Alfaro & al., 2009; Wertheim & Sanderson, 2010;

Silvestro & al., 2011). The tempo of lineage diversification has

often been explored using lineage-through-time (LTT) plots,

which are graphical representations of the cumulative number

of reconstructed lineages over time based on a chronogram

(Harvey & al., 1994; Nee, 2006). To test whether the temporal

pattern of diversification in a lineage departs from a constant

rate model, a gamma statistic (Pybus & Harvey, 2000) and/or a

birth-death likelihood (BDL) method (Rabosky, 2006; Rabosky

& al., 2007) are commonly used to test several variable-rate

models against the null hypothesis of constant diversification

rate. The method MEDUSA (Alfaro & al., 2009) has been em-

ployed as an extension to the birth-death likelihood approach

(Rabosky & al., 2007) and allows inference of rate shifts at

one or more topological positions. MEDUSA minimizes the

problem of unresolved incomplete and/or nonrandomly sam-

pled lineages, and is analogous to maximizing the likelihood

while including a penalty for additional parameters (Burnham

& Anderson, 2002). The methodological developments have

stimulated many broad to local-scale diversification analyses,

which have identified numerous radiations throughout the Tree

of Life in the past decade.

Insights into patterns of lineage diversifications using a

dated phylogenetic framework are limited by incomplete sam-

pling, nonrandom sampling, background extinction, and uncer-

tainties associated with divergence time estimates (Rabosky,

2006; Stadler, 2009; Cusimano & Renner, 2010; Wertheim

& Sanderson, 2010; Cusimano & al., 2012). Nevertheless,

nearly complete phylogenies are emerging. Jetz & al. (2012)

present the first complete dated phylogeny of extant bird spe-

cies (9993 species) and explore global patterns and rates of

diversifications. They report the main geographic differences

in diversification rates are east-west hemispheric, rather than

latitudinal (cf. Weir & Schluter, 2007), with average rates dis-

tinctly lower in the Eastern Hemisphere than that in the West-

ern Hemisphere (cf. Xiang & al., 2004 for different results in

plant diversification in eastern Asia and North America). Yet

no significant difference in rates is found between Northern

and Southern Hemisphere species. Also a strong increase in

diversification rate is detected from about 50 million years ago

to the near present, with a number of radiations in the temperate

zone. Furthermore, the past diversification rate shifts are scat-

tered throughout the avian tree and across geographic space.

Leslie & al. (2013) were also able to sample 80% of all

extant conifer species to explore the impact of the large-scale

distribution of the Earth’s landmasses on patterns of biological

diversity in the Northern and Southern Hemispheres. Their

dated phylogeny of conifers, calibrated with well-documented

fossils, suggests a much older distribution of divergence ages in

the Southern Hemisphere than those of the taxa in the Northern

Hemisphere. The distribution and abundance of more recent

divergences in the Northern clades are mostly concentrated to

western North America and southern China in eastern Asia

and are not directly related to the latitudes nor habitat types.

The Southern Hemisphere older clades seem to have survived

in scattered wet environments. The conifer diversif ication pat-

terns are interpreted to have resulted from large-scale differ-

ences in climatic and landscape history.

The analysis of diversification rates in the context of ana-

genetic change, e.g., the evolution of phenotypic or ecological

traits, has been transformed by the development of models

that unite both character evolution and species diversification,

avoiding biases that occur when the two are treated separately.

The first of these was the binary-state speciation and extinction

(BiSSE) model (Maddison & al., 2007), which estimates the

speciation and extinction rates separately for each state of a

binary character as well as the rate of change between states.

BiSSE has been extended in various ways, e.g., to allow for

the analysis of multi-state characters (MuSSE; Fitzjohn, 2012)

and continuous traits (QuaSSE; Fitzjohn, 2010). For biogeo

graphic inference, the BiSSE framework was extended with

principles drawn from the DEC model of geographic range

evolution (Ree & al., 2005; Ree & Smith, 2008) to produce the

geographic-state speciation and extinction (GeoSSE) model

(Goldberg & al., 2011). GeoSSE enables testing of hypotheses

of range-dependent diversification, such as: Are regional dif-

ferences in the standing diversity of a clade due to asymmetry

in diversification or migration? Does range expansion increase

the probability of lineage divergence?

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BiSSE, GeoSSE, and related models offer the prospect of

macro-evolutionary inferences that integrate anagenetic and

cladogenetic processes much more effectively than previous

methods; however, they come with associated caveats related to

data sampling and model complexity (Davis & al., 2013). Most

importantly, they implicitly assume that the phylogenetic posi-

tions of all extant species are known, if not sampled, with the

consequence that gaps in taxon sampling must be accounted for

statistically (Fitzjohn & al., 2009). This is because the models

explicitly incorporate diversification parameters (speciation

and extinction) and thus require unbiased samples of diversity

as inputs. At a more pragmatic level, the mathematical methods

used to jointly infer anagenetic and cladogenetic processes do

not yet scale well with model complexity—that is, for GeoSSE,

the number of potential free parameters increases geometrically

with the number of component areas. This poses significant

computational challenges for even “simple” biogeographic

problems involving a handful of areas.

Empirical application of GeoSSE has been hampered

by such computational considerations, but the simpler

BiSSE method has made inroads into biogeographic studies.

Drummond & al. (2012) evaluate the hypothesis of multiple

radiations in the genus Lupinus L. (Leguminosae), which ex-

hibits some of the highest known rates of net diversification in

plants. They detected three significant shifts to increased rates

of net diversification relative to background levels in the genus.

Bayesian ancestral state reconstructions and BiSSE analyses

indicated that increased rates of speciation are strongly asso-

ciated with the derived evolution of perennial life history and

invasion of the montane ecosystems.

Empirical analyses also showed the importance of bio-

geography in understanding the patterns of species diversity

(Rabosky & al., 2007; Wiens & al., 2009; Almeida & al., 2012).

Analyses in the past decade have often investigated the evo-

lutionary patterns at higher levels (Magallón & Sanderson,

2001; McPeek & Brown, 2007; Smith S.A. & al., 2011b ; Jetz

& Fine, 2012; Jetz & al., 2012). Nevertheless, future work

needs to emphasize analyses of evolutionary radiations and

rate shifts at lower taxonomic scales with complete sampling

and better integration of geographic and ecological parameters

to gain insights into the lineage diversification processes and

mechanisms. Integration of patterns and rates of lineage di-

versifications in the context of space, time and form provides

an important dimension on integrative biogeography and the

mechanisms behind species diversif ication and persistence

(Lexer & al., 2013) and should be emphasized in the future.

BIOGEOGRAPHIC DIVERSIFICATION

ON ISLANDS

Many biogeographic studies involve islands. While neither

the patterns nor the processes of diversification on islands are

unique to islands, it is their special circumstances that make

islands “natural laboratories” for the development and testing

of theories. These laboratories have drawn the attention of a

wide variety of naturalists, many of whom have developed

theories that have furthered our understanding of evolution as

well as biogeography. Perhaps the chief characteristic of im-

portance in islands is their isolation since that influences both

gene flow and colonization. The early, now classic publications

based on magnificent voyages bear careful study since these

naturalists were greatly influenced by the time they spent on

islands. As early as 1778, the writings of Forster introduced the

ideas of species-isolation and species–area relationships. He

was followed by Darwin (1859), Hooker (1867), and Wallace

(1911). More recently MacArthur & Wilson (1967), Carlquist

(1974), and others inspired generations of biogeographers by

introducing conceptual ideas such as adaptive radiation, taxon

cycles, assembly rules, and an equilibrium theory of island

biogeography. Because islands have higher extinction rates than

continental areas, biogeographic studies on islands have played

a big role in conservation biology (Whittaker & al., 2007). Of

course, island biogeography does not just apply to islands but

serves as a model for a variety of ecosystems that are isolated

in some way: sky islands (high-mountain habitats), land-locked

lakes, and disjunct populations of wide ranging species. This

ensures that biogeographic concepts developed on islands can

be tested on a broader scale.

Island biogeography is undergoing a resurgence as is evi-

denced by two recent standalone meetings that focused mostly

on the current research and future possibilities: “The theor y

of island biogeography revisited” (Losos & Ricklefs, 2010)

and in 2011 the conference on “Evolution of life on Pacific

islands and reefs: Past, present and future” (http://botany.si.edu

/events/2011_pacific/). Currently there is an ever-increasing

amount of research on islands combining systematic, ecologi-

cal, and evolutionary studies; one example is the work on com-

munity assembly (Emerson & Gillespie, 2008). Also, there is a

push to develop an Integrative Theory of Island Biogeography

that works across scales (Lomolino & al., 2010b). Finally, the

development of testable hypotheses for long-distance dispersal

may temper some of the negative discussions on dispersal vs.

vicariance (Gillespie & al., 2012) especially with the addition

of Bayesian methods (Sanmartín & al., 2008). These advances

ensure that island studies will continue to provide theories

that can then be tested in continental ecosystems (Sanmartín

& al., 2010).

EMPIRICAL BIOGEOGRAPHIC ANALYSES

AT GLOBAL AND LOCAL SCALES

As molecular phylogenetics moves into the next decade

with better sampling and increased resolution, it will be logical

for biogeographers to explore more empirical biogeographic

analyses at the global scale (e.g., Xiang & al., 2004; Jønsson

& al., 2011; Jetz & al., 2012; Leslie & al., 2013) which will

enable the analyses of large-scale patterns in space and time.

The availability of user-friendly software packages should

inspire more biogeographic analyses at both the global and

local scales. Empirical biogeographers need to emphasize

hypothesis-testing in their biogeographic analyses, as mod-

els are becoming increasingly more complex, sophisticated

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and realistic (Crisp & al., 2011). The models can be used for

hypothesis testing by varying the degree of parameters within

a statistical framework (Nathan & al., 2008).

Increased interest in empirical biogeographic analyses may

also be due to much easier sampling in different continents

due to easier travel to more remote places and more efficient

communications and collaborations among colleagues. Sci-

ence advancement in developing countries that harbor rich

biodiversity is vitally important for healthy development of

biogeography as a field, especially at the local level and in the

subdiscipline of conservation biogeography (Whittaker & al.,

2005). It will be more fruitful if western biogeographers can

train local biologists and students and involve them in the field

as well as during analytical phases of biogeographic studies,

and inspire the interests of local biologists to pursue biogeo-

graphic questions.

In-depth biogeographic and phylogeographic analyses

have been increasingly conducted in various regions in devel-

oping countries, especially with the advancement of in-country

biogeographic expertise, such as in China, Brazil, Argentina,

and Mexico, as witnessed by many excellent publications led by

colleagues there (e.g., Fiaschi & Pirani, 2009; Morrone, 2009;

Xie & al., 2009; Hoorn & al., 2010; Yu & al., 2010; Qiu & al.,

2011; Nie & al., 2012; Ornelas & al., 2013). Yet it is necessary

to enhance capacity building in biogeography in many other re-

gions, especially in mega-diverse regions such as Indonesia, the

island of New Guinea, the Philippines, Vietnam, Madagascar,

India, many parts of Africa, and other parts of the Neotropics

such as Colombia, Peru, and Bolivia. The international bioge-

ography community needs to establish coordinated efforts in

capacity building and training in developing countries.

INTEGRATIVE BIOGEOGRAPHY

DATA PORTAL

Large volumes of distributional, genetic, phylogenetic,

ecological, and fossil data are accumulating in online data-

bases (e.g., GBIF - www.gbif.org/; Museum collection records

such as those from the National Museum of Natural History

- http://collections.mnh.si.edu/search/, TreeBase - treebase.org;

GenBank - www.ncbi.nlm.nih.gov/genbank/; World Climate

Data - www.worldclim.org/; U.S.A. National Phenology Net-

work - www.usanpn.org/; Time Tree Of Life - www.timetree

.org/; Paleobiology - paleodb.org/; Barcode of Life - http://

www.barcodeoflife.org/). As a result, it seems inevitable that

data mining methods will become essential tools of the trade

for biogeographic research. In the phylogenetic front, Peters

& al. (2011) offered a bioinformatics pipeline for retrieving,

processing, filtering, editing and analyzing large amounts of

sequence data from GenBank in a phylogenetic context, with

its open nature and applicability to any taxonomic group of

interest. However, while the potential increase in data for bio-

geographic studies using such online resources is huge, one

must keep in mind that such online data are not the same as

the personally developed much smaller amounts of data that

individual scientists and groups have accumulated (Soberon

& Peterson, 2004; Lozier & al., 2009). For instance, in many,

if not most, online portals no one has checked the identifica-

tions of the specimens: some are even made up of the original

catalogue records and so have many misapplied names. Also

important is that the georeferencing (prior to the common use

of GPS devices) was often done based on gazetteer records of

the nearest landmark. All these data must be vetted carefully

before they can be used.

Even though there are many current databases available

that contain biogeographic information, none of them currently

focuses on biogeography. The biogeography community needs

to develop such a data portal (Fig. 1) with biogeography as the

core, one that has the capacity to integrate data from phylogeny,

ecology, paleobiology, specimens, distribution, and genomics,

and synthesize the biogeographic diversification of plants and

animals. The portal needs to be effective to disseminate the

biogeographic data to the scientific community, guide conser-

vation efforts, and educate the public about the exciting results

of biogeography! Perhaps an organization like the International

Biogeography Society (IBS) should be involved in developing

such a portal.

Fig. 1.

International biogeography data portal — toward a vision of a

data core with biogeography as its central mission, with a few exem-

plar data sources.

Data

Sources

International Biogeography

Portal

Matrix Assembly

Choice of Inference Methods

& Algorithms

(DEC, GeoSSE, BEAST, MrBayes …)

Outputs

Elucidate evoluon of past

& present distribuon,

guide conservaon eﬀorts,

educate

Paleobiology.org GBIF.org

WorldClim

Global Climate Change GenBank

National Phenology

Network

TimeTree of Life

Muldimensional

synthesis of

geography,

phylogeny,

ecology,

geology &

genomics

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CONCLUDING REMARKS

Biogeography has become increasingly integrative and is

closely linked to systematics, ecology, paleontology, and con-

servation biology. It has a central role in evolutionary biology

and tackles questions on space, time and form. The investiga-

tion of many processes of evolution such as adaptive radiation

(Gavrilets & Losos, 2009; Losos, 2010; Givnish & al., 2011;

Diazgranados, 2012), rates of evolution (Knope & al., 2012),

and speciation (Avise & al., 1987; Barraclough & al., 1998;

Losos & Glor, 2003; Pyron & Burbrink, 2010) can only be

examined within the context of biogeography. Phylogeography

and ecological niche modeling using recent locality records and

readily available environmental coverage data offer innovative

potential for the discovery of unknown distributional areas and

unknown species (e.g., Raxworthy & al., 2003). The combina-

tion of biogeography and niche modeling is powerful in identi-

fying areas for conservation (Crisp & al., 2009; Ackerly & al.,

2010). In addition, islands have had a higher rate of extinction

than continents, partially because of their isolation. However

the rates of extinction in continental ecosystems are rising,

mostly because of ongoing fragmentation of ecosystems creat-

ing virtual islands or habitat islands. Predictive models from

island biogeography are being used to understand the dynam-

ics in these habitat islands (Whittaker & al., 2005, 2007). The

new subdiscipline of conservation biogeography has emerged,

which applies the concepts and methods of biogeography to ad-

dress conservation problems (Ladle & Whittaker, 2011; Herzog

& al., 2013).

The last decade has witnessed the development of bio-

geographic methods and the integration of biogeography with

diverse sources of data associated with the development of para-

metric methods. In turn, global and local patterns of biogeogra-

phy are once again being rigorously tested. Meanwhile, advances

in the fields of molecular dating and historical biogeography can

be combined to provide insights into patterns of species diver-

sification across both time and space, with the development of

new methods to estimate rates of speciation and extinction from

phylogenetic data. We envision more statistical methods to be

developed in the phylogenomic era of biogeography with very

large datasets and big phylogenies. Biogeography is moving

toward multidimensional syntheses of geography, phylogeny,

ecology, geology, paleontology, physiology and genomics. In

this regard, powerful statistical methods need to be developed

to integrate data of biogeography, geology, diversification, trait

evolution and ecology, and to explore the mechanisms of species

diversification and persistence across space and through time. A

few recent studies (e.g., Hoorn & al., 2010; Almeida & al., 2012;

Jetz & Fine, 2012; Jetz & al., 2012; Leslie & al., 2012; Holt & al.,

2013) have illustrated a definite trend in that for certain clades, it

is now (or soon will be) feasible to generate near-complete dated

phylogenies and global-scale datasets, which allow large-scale

patterns to emerge. With rapid advances in empirical biogeog-

raphy of many lineages, multi-lineage synthesis is needed for

various biogeographic regions as well as at the global scale in

the next decade. Some broader syntheses have been produced

for certain regions (Hughes & al., 2013).

ACKNOWLEDGEMENTS

We thank IAPT and the organizers of the symposium “Where is

Plant Systematics Headed in the Next Ten Years?”, held at the Botany

2012 Meeting, for the invitation to par ticipate in the symposium and

prepare a paper. We are grateful to Mauricio Diazgranados, Dick

Olmstead, Beryl Simpson, Tod Stuessy and Editor-in-Chief Joachim

Kadereit for their constructive comments.

LITERATURE CITED

Ackerly, D.D., Loarie, S.R., Cornwell, W.K., Weiss, S.B., Hamil-

ton, H., Branciforte, R . & Kraft, N.J.B. 2010. The geography

of climate change: Implications for conservation biogeography.

Diversity & Distrib. 16: 476–487.

http://dx.doi.org/10.1111/j.1472-4642.2010.00654.x

Alfaro, M.E., Santini, F., Brock, C.D., Alamillo, H., Dornburg, A.,

Rabosky, D.L., Carnevale, G. & Harmon, L. J. 2009. Nine ex-

ceptional radiations plus high tu rnover explain species diversity in

jawed ver tebrates. Proc. Natl. Acad. Sci. U.S.A. 32: 13410–13414.

ht tp://d x. doi.o rg /10.10 73/ pn as .081108710 6

Almeida, E.A.B., Pie, M.R., Brady, S.G. & Danforth, B.N. 2012 .

Biogeography and diversification of colletid bees (Hymenoptera:

Colletidae): Emerging patter ns from the southern end of the world.

J. Biogeogr. 39: 526–544.

ht t p://d x.d oi .or g /10.1111 /j .1365- 26 99.2011.0 2624. x

Antonelli, A., Nylander, J.A.A., Persson, C. & Sanmartín, I. 2009.

Tracing the impact of the Andean uplift on Neotropical plant evolu-

tion. Proc. Natl. Acad. Sci. U.S.A. 106: 9749–9754.

http://dx.doi.org/10.1073/pnas.0811421106

Avise, J.C. 1989. Gene trees and organismal histories: A phylogenetic

approach to population biology. Evolution 43: 119 2 –120 8.

http://dx.doi.org/10.2307/2409356

Avise, J.C. 2000. Phylogeography: The history and formation of spe-

cies. Cambridge: Harvard University Press.

Avise, J.C. 2008. Phylogeography: Retrospect and prospect. J. Bio-

geog r. 36: 3–15. ht tp ://d x. doi .or g/ 10.1111/j .1365- 269 9.2 008. 020 32. x

Avise, J.C., Arnold, J., Ball, R.M., Bermingham, E., Lamb, T.,

Neigel, J.E., Reeb, C.A. & Saunders, N.C. 1987. Intraspecific

phylogeography: The mitochondrial DNA bridge between popula-

tion genetics and systematics. Annual Rev. Ecol. Syst. 18: 489 –522 .

http://dx.doi.org/10.1146/annurev.es.18.110187.002421

Axelrod, D.I. 1975. Evolution and biogeography of Madrean–Tethyan

sclerophyll vegetation. Ann. Missouri Bot. Gard. 62: 280–334.

http://dx.doi.org/10.2307/2395199

Baird, N.A., Etter, P.D., Atwood, T.S., Currey, M.C., Shiver, A.L.,

Lewis, Z.A., Selker, E.U., Cresko, W.A. & Johnson, E.A. 2008.

Rapid SNP discovery and genetic mapping using sequenced RAD

markers. PLoS ONE 3: e3376. doi /10.1371/journal.pone.0003376

Barraclough, T., Vogler, A. & Harvey, P. 1998. Revealing the factors

that promote speciation. Philos. Trans., Ser. B 353: 241–249.

http://dx.doi.org/10.1098/rstb.1998.0206

Barry, S. & Elith, J. 2006. Error and uncertainty in habitat models.

J. Appl. Ecol. 43: 413–423.

ht t p://d x.d oi .or g /10.1111 /j .1365- 26 6 4.2 0 06.0113 6. x

Bremer, K. 1992. Ancestral areas: A cladistic reinterpretation of the

center of origin concept. Syst. Biol. 41: 436 – 445.

http://dx.doi.org/10.1093/sysbio/41.4.436

Bremer, K. 1995. Ancestral areas: Optimization and probability. Syst.

Biol. 44: 255–259. http://dx.doi.org/10.1093/sysbio/44.2.255

Briggs, J. & Humphries, C.J. 2004. Early classics. Pp. 5–13 in:

Lomolino, M.V., Sax, D.F. & Brown, J.H. (eds.), Foundations of

biogeography: Classic papers with commentaries. Chicago: Uni-

versity of Chicago Press.

922

TAX ON

62 (5) • October 2013: 912–927

Wen & al.

• New trends in biogeography

922 Version of Record (identical to print version).

Brooks, D.R. 1985. Historical ecology: A new approach to st udying the

evolution of ecological associations. Ann. Missouri Bot. Gard. 72:

660– 680. http://dx.doi.org/10.2307/2399219

Brown, J.H. & Lomolino, M.V. 1998. Independent discover of the

equilibrium theory of island biogeography. Ecology 70: 19 54–1957.

htt p://dx.doi.org/10.2307/1938125

Brundin, L. 1966. Transantarctic relationships and their signif icance, as

evidenced by ch ironomid midges with a monography of the subfam-

ilies Podonominae and Aphroteniinae and the austral Heptagyiae.

Kongl. Svenska Vetensk. Acad. Handl., ser. 4, 11(1): 1–472.

Brundin, L.Z. 1988. Phylogenetic biogeography. Pp. 343–369 in:

Myers, A.A. & Giller P.S. (eds.), Analytical biogeography. New

York: Chapman & Hall.

ht tp://d x. doi.o rg /10.10 07/978-94-0 09-1199- 4_14

Burnham, K.P. & Anderson, D.R. 2002. Model selection and multi-

model inference: A practical information theoretic approach, 2nd

ed. New York: Springer.

Cariou, M., Duret, L. & Charlat, S. 2013. Is RAD-seq suitable for

phylogenetic inference? An in silico assessment and optimization.

Ecol. Evol. 3: 846–852. http://dx.doi.org/10.1002/ece3.512

Carlquist, S. 1974. Island biology. New York, London: Columbia Uni-

versity Press.

Carstens, B.C. & Richards, C.L. 2007. Integrating coalescent and eco-

logical niche modeling in comparative phylogeography. Evolution

61: 1439–1454 . h t t p: //dx. doi.o rg/10 .1111/j.155 8-564 6.2 0 07.0 0117. x

Carstens, B., Lemmon, A.L. & Lemmon, E.M. 2012. The promises

and pitfalls of next-generation sequencing data in phylogeography.

Syst. Biol. 61: 713–715. http://dx.d oi.org/10.1093/sys bio/s ys050

Cavender-Bares, J. , Kozak, K., Fine, P. & Kembel, S. 2009. The merg-

ing of community ecology and phylogenetic biology. Ecol. Letters

12: 6 93 –715. http://d x.d oi.or g /10.1111 /j.14 61-02 48. 2009. 01314. x

Chase, J.M. & Meyers, J.A. 2 011. Disent angling the importance of

ecological niches from stochastic processes across scales. Philos.

Trans., Ser. B 366: 2351–2363.

ht tp://d x. doi.o rg /10.10 98/rstb.2 011.0063

Chiari, Y., Cahais, V., Galtier, N. & Delsuc, F. 2012. Phylogenomic

analyses support the position of turtles as the sister group of birds

and crocodiles (Archosauria). B. M. C. Biol. 10: 65.

ht tp://d x. doi.o rg /10.1186 /1741-7007-10 -65

Craw, R.C., Grehan, J.R. & Heads, M.J. 1999. Panbiogeography:

Tracking the history of life. New York: Oxford University Press.

Crisci, J. 2006. L. A. S. Johnson Review No. 4. Bridging historical and

ecological approaches in biogeography. Austral. Syst. Bot. 19: 1–10.

ht tp://d x. doi.o rg /10.10 71/SB0 5006

Crisci, J.V. & Katinas, L. 2009. Darwin, historical biogeography, and

the impor tance of overcoming binary opposites. J. Biogeogr. 36:

1027 –10 32 . ht tp: //dx. doi .o rg/10 .1111/j.13 65 -2699.2 0 09.0 2111. x

Crisp, M.D., Arroyo, M.T.K., Cook, L .G., Gandolfo, M.A., Jordan,

G.J., McGlone, M.S., Weston, P.H., Westoby, M., Wilf, P. &

Linder, H.P. 200 9. Phylogenetic biome conservatism on a global

scale. Nature 458: 754–756. http://dx.doi.org/10.1038/nature07764

Crisp, M.D., Trewick, S.A. & Cook, L.G. 2011. Hypothesis testing in

biogeography. Trends Ecol. Evol. 26: 6 6–72.

ht tp://d x. doi.o rg /10.1016/j.tr ee .2010.11.005

Croizat, L. 1962 . Space, time, form: The biological synthesis. Caracas:

by the author.

Cronn, R., Liston, A., Parks, M., Gernandt, D.S., Shen, R. &

Mockler, T. 2008. Mutiplex sequencing of plant chloroplast ge-

nomes using Solexa sequencing-by-synthesis technology. Nucl.

Acids Res. 36: e122. http://dx.doi.org/10.1093/nar/gkn502

Cronn, R., Knaus, B. J., Liston, A., Maughan, P.J., Parks, M.,

Syring, J.V. & Udall, J. 2012. Targeted en richment strategies for

next-generation plant biology. Amer. J. Bot. 99: 291–311.

http://dx.doi.org/10.3732/ajb.1100356

Cusimano, N. & Renner, S.S. 2010. Slowdowns i n diversif ication rates

from real phylogenies might not be real. Syst. Biol. 59: 458–4 64.

http://d x.doi.or g/10.1093/sysbio/syq032

Cusimano, N., Stadler, T. & Renner, S.S. 2012. A new method for

handling missing species in diversification analysis applicable to

randomly or nonrandomly sampled phylogenies. Syst. Biol. 61:

785–792. http://dx.doi.org/10.1093/sysbio/sys031

Darwin, C.R. 18 45. Journal of researches into the natural history and

geology of the countries visited during the voyage of H.M.S. Beagle

round the world, 2nd ed, corr. and with additions. London: John

Murray. http://dx.doi.org/10.5962/bhl.title.2107

Darwin, C.R. 18 59. On the origin of species by means of natural selec-

tion, or, The preservation of favoured races in the struggle for life.

London: John Murray.

http://dx.doi.org/10.5962/bhl.title.59991

Davey, J.W., Hohenlohe, P.A., Etter, P.D., Boone, J.Q., Catchen J.M.

& Blaxter, M.L. 2011. Genome-wide genetic marker discover y and

genotyping using next-generation sequencing. Nat. Rev. Genet. 12:

499 –510. ht t p://dx.doi.org /10.1038/n rg3012

Davis, M.P., Midford, P.E. & Maddison, W. 2013. Exploring power

and parameter estimation of the BiSSE method for analyzing spe-

cies diversification. B. M. C. Evol. Biol. 13: 38.

ht t p://d x. do i. or g /10.1186/1471-2148 -13 -38

De Queiroz, A. 2005. The resurrection of oceanic dispersal in historical

biogeography. Trends Ecol. Evol. 20: 68–73.

ht tp://d x. doi.o rg /10.1016/j.tr ee .200 4.11.0 06

Diazgranados, M. 2 012. Phylogenetic and biogeographic relationships

of frailejones (Espeletiinae, Compositae): An ongoing radiation in

the tropical Andes. Dissertation, Saint Louis University, St. Louis,

Missouri, U.S.A.

Donoghue, M. J. & Moore, B.R. 2003. Toward an integrative historical

biogeography. Integr. Comp. Biol. 43: 261– 270.

http://dx.doi.org/10.1093/icb/43.2.261

Donoghue, M. J. & Smith, S.A. 2004. Patterns in the assembly of tem-

perate forests around the Northern Hemisphere. Philos. Trans.,

Ser. B 359: 1633–1644. http://dx.doi.org/10.1098/rstb.2004.1538

Donoghue, M. J., Bell, C.D. & Li, J.H. 2001. Phylogenetic patterns in

Northern Hemisphere plant geography. Int. J. Pl. Sci. 162: S41– S52.

ht tp://d x. doi.o rg /10.10 86/323278

Drummond, A.J. & Rambaut, A. 2007. BEAST: Bayesian evolutionary

analysis by sampling trees. B. M. C. Evol. Biol. 7: 214.

ht t p://d x. do i. or g /10.1186/1471-2148 -7-214

Drummond, A.J., Ho, S.Y.W., Phillips, M.J. & Rambaut A. 2006.

Relaxed phylogenetics and dating with confidence. PLoS Biol. 4:

e88. http://dx.doi.org/10.1371/journal.pbio.0040088

Drummond, C.S., Eastwood, R.J., Miotto, S.T.S. & Hughes, C.E.

2012. Multiple continental radiations and correlates of diversifica-

tion in Lupinus (Leguminosae): Testing for key innovation with

incomplete taxon sampling. Syst. Biol. 61: 443– 460.

http://dx.doi.org/10.1093/sysbio/syr126

Eaton, D.A.R. & Ree, R.H. 2013. Inferring phylogeny and introg res-

sion using RADseq data: An example from flowering plants

(Pedicularis: Orobanchaceae). Syst. Biol. 62: 689 –706.

http://d x.doi.or g/10.1093/sysbio/syt032

Emerson, B.C. & Gillespie, R.G. 2008. Phylogenetic analysis of com-

munity assembly and structure over space and time. Trends Ecol.

Evol. 23: 619–630. ht tp://dx.doi.org/10.1016/j.tree.2008.07.005

Emerson, K.J., Merz, C.M., Catchen, J.M., Hohenlohe, P.A., Cresko,

W.A., Bradshaw, W.E. & Holzapfel, C.M. 2010. Reso lving p ost-

glacial phylogeography using high throughput sequencing. Proc.

Natl. Acad. Sci. U.S.A. 107: 16196 –1620 0.

http://dx.doi.org/10.1073/pnas.1006538107

Evans, M.E.K., Smith, S.A., Flynn, R. & Donoghue, M.J. 20 09.

Climate, niche evolution, and diversification of the “bird-cage”

evening primroses (Oenothera, sections Anogra and Kleinia).

Amer. Naturalist 173: 225–240.

ht tp://d x. doi.o rg /10.10 86/595757

Fiaschi, P. & Pirani, J.R. 2009. Review of plant biogeographic studies

in Brazil. J. Syst. Evol. 47: 477–496.

ht t p://d x.d oi .or g /10.1111 /j .175 9-68 31. 200 9. 0004 6.x

923

Wen & al.

• New trends in biogeography

TAX ON

62 (5) • October 2013: 912–927

923Version of Record (identical to print version).

Filipowicz, N. & Renner, S.S. 2012. Brunfelsia (Solanaceae): A genus

evenly divided between South America and radiations on Cuba and

other Antillean islands. Molec. Phylogen. Evol. 6 4: 1–11.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2012.02 .026

FitzJohn, R.G. 2010. Quantitative traits and diversification. Syst. Biol.

59: 619– 633. http://dx.doi.org/10.1093/sysbio/syq053

FitzJohn, R.G. 2012. Diversitree: Comparative phylogenetic analyses

of diversification in R. Meth. Ecol. Evol. 3: 1084–1092.

ht t p://d x.d oi .or g /10.1111 /j .20 41-210 X.2 012. 0 02 34. x

FitzJohn, R.G., Maddison, W.P. & Otto, S.P. 2009. Estimating t rait-

dependent speciation and extinction rates from incompletely re-

solved phylogenies. Syst. Biol. 58: 595–611.

http://dx.doi.org/10.1093/sysbio/syp067

Forest, F. 2009. Calibrating the Tree of Life: Fossils, molecules and

evolutionary timescales. Ann. Bot. (Oxford) 104: 789 –79 4.

http://dx.doi.org/10.1093/aob/mcp192

Forster, J.R. 177 8. Observations made during a voyage round the

world, on physical geography, natural history, and ethic philoso-

phy. London: G. Robinson.

Funk, V.A. 2004. Revolutions in historical biogeography. Pp. 647–777

in: Lomolino, M.V., Sax, D.F & Brown, J.H. (eds.), Foundations

of biogeography: Classic papers with commentaries. Chicago:

University of Chicago Press.

Funk, V.A., Anderberg, A.A., Baldwin, B.G., Bayer, R.J., Boni-

facino, J.M., Breitwieser, I., Brouil let, L., Carbajal, R., Chan,

R., Coutinho, A.X.P., Crawford, D.J., Crisci, J.V., Dillon, M.O.,

Freire, S.E., Galbany-Casals, M., Garcia-Jacas, N., Gemein-

holzer, B., Gruenstaeudl, M., Hansen, H.V., Himmelreich, S.,

Kadereit, J.W., Källersjö, M., Karaman- Castro, V., Karis, P.O.,

Katinas, L., Keeley, S.C., Kilian, N., Kimball, R.T., Lowrey,

T.K., Lundberg, J., McKenzie, R.J., Mort , M.E., Nordenstam,

B., Oberprieler, C., Ortiz, S., Pelser, P.B., Randle, C.P.,

Robinson, H., Roque, N., Sancho, G., Semple, J.C., Serrano, M.,

Stuessy, T.F., Susanna, A., Tadesse, M., Unwin, M., Urbatsch,

L., Urtubey, E., Vallès, J., Vogt, R., Wagstaff, S., Ward, J.M. &

Watson, L.E. 2009. Compositae metatrees: The next generation.

Pp. 747–777 in: Funk, V.A., Susanna, A., Stuessy, T. & Bayer, R.

(ed s.), Systematics, evolution, and biogeography of Compositae.

Vienna: IAPT.

Gavrilets, S. & Losos, J.B. 2009. Adaptive radiation: Contrasting

theory with data. Science 323: 732–737.

http://dx.doi.org/10.1126/science.1157966

Gillespie, R.G., Baldwin, B.G., Waters, J.M., Fraser, C.I., Nikula,

R. & Roderick, G.K. 2012. Long-distance dispersal: A f ramework

for hypothesis testing. Trends Ecol. Evol. 27: 47– 56.

ht tp://d x. doi.o rg /10.1016/j.tr ee .2011.08.009

Givnish, T.J., Barfuss, M.H.J., Van Ee, B., Riina, R., Schulte,

K., Horres, R., Gonsiska, P.A., Jabaily, R.S., Crayn, D.M.,

Smith, J.A ., Winter, K., Brown, G.K., Evans, T.M., Holst , B.K.,

Luther, H., Till, W., Zizka, G., Berry, P.E. & Sytsma, K.J.

2011. Phylogeny, adaptive radiation, and historical biogeography

in Bromeliaceae: Insights f rom an eight-locus plastid phylogeny.

Amer. J. Bot. 98: 872–895.

http://dx.doi.org/10.3732/ajb.1000059

Goldberg, E.E., Lancaster, L.T. & Ree, R.H. 2011. Phylogenetic infer-

ence of reciprocal effects bet ween geographic range evolution and

diversification. Syst. Biol. 60: 451– 46 5.

http://dx.doi.org/10.1093/sysbio/syr046

Graham, C.H., Ron, S.R., Santos, J.C., Schneider, C.J. & Moritz, C.

2004. Integrating phylogenetics and environmental niche models

to explore speciation mechanisms in dend robatid frogs. Evolution

58: 1781–1793. http://dx.doi.org/10.1554/03-274

Harris, A.J. & Xiang, Q.-Y. 2009. Estimating ancestral distributions

of lineages with uncer tain sister groups: A statistical approach to

dispersal vicariance analysis and a case using Aesculus L. (Sapi n-

daceae) including fossils. J. Syst. Evol. 47: 349–368.

ht t p://d x.d oi .or g /10.1111 /j .175 9-68 31. 200 9. 0004 4 .x

Harris, A.J., Wen, J. & Xiang, Q.-Y. 2013. Inferring the biogeographic

origins of inter-continental disjunct endemics using a Bayes-DIVA

approach. J. Syst. Evol. 51: 117–133.

ht t p://d x.d oi .or g /10.1111 /j s e.12007

Harvey, P.H., May, R.M. & Nee, S. 1994. Phylogenies without fossils.

Evolution 48: 523–529. http://dx.doi.org/10.2307/2410466

Hausdorf, B. & Hennig, C. 2003. Biotic element analysis in biogeog-

ra phy. Syst. Biol. 52: 717–72 3.

ht tp://d x. doi.o rg /10.10 80/106 3515039023558 4

Heads, M. 2 012. Molecular panbiogeography of the tropics. Berkeley:

University of California Press.

http://dx.doi.org/10.1525/califor nia/9780520271968.001.0001

Hennig, W. 1950. Grundzüge einer Theorie der phylogenetischen

Systematik. Berlin: Deutscher Zent ralverlag.

Hennig, W. 1966 . Phylogenetic systematics, trans. D. Davis &

R. Zangerl. Urbana: University of Illinois Press.

Herzog, S.K., Hamel-Leigue, A.C., Larsen, T.H., Mann, D.J., Soria-

Auza, R.W., Gill, B.D. & Edmonds, W.D. 2013. Elevational dis-

tribution and conservation biogeography of Phanaeine dung beetles

(Coleoptera: Scarabaeinae) in Bolivia. PLoS ONE 8(5): e64963.

http://dx.doi.org/10.1371/journal.pone.0064963

Hewitt, G. 2000. The genetic legacy of the Quaternar y ice ages. Nature

405: 907– 913. http://d x.doi .org/10.1038/350160 00

Hickerson, M.J., Stahl, E.A. & Takebayashi, N. 2007. msBayes:

Pipeline for testing comparative phylogeographic histories using

hierarchical approximate Bayesian computation. B. M. C. Bioinf.

8: 268. http://dx.doi.org/10.1186/1471-2105-8-268

Hickerson, M.J., Carstens, B.C., Cavender-Bares, J., Crandall,

K.A., Graham, C.H., Johnson, J.B., Rissler, L., Victoriano, P.F.

& Yoder, A.D. 2010. Phylogeography’s past, present, and future:

10 years after Avise, 2000. Molec. Phylogen. Evol. 54: 291–301.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2009.0 9.016

Hi tti nger,

C.T., Johnston, M., Tossberg, J.T. & Rokas, A. 2010. Le -

veraging skewed transcript abundance by RNA-Seq to increase the

genomic depth of the tree of life. Proc. Natl. Acad. Sci. U.S.A. 107:

1476–1481. http://dx.doi.org/10.1073/pnas.0910449107

Ho, S.Y.W. & Phillips, M.J. 2009. Accounting for calibration uncer-

tainty in phylogenetic estimation of evolutionary divergence times.

Syst. Biol. 58: 367–380. ht tp://dx.doi.org/10.1093/sysbio/syp035

Hof, A., Jansson, R. & Nilsson, R. 2012. Future climate change will

favour non-specialist mam mals in the (sub)Arctics. PLoS ONE

7(12): e52574. http://dx.doi.org/10.1371/journal.pone.0052574

Holt, B.G., Lessard, J.-P., Borregaard, M.K., Fritz, S.A., Araújo,

M.B., Dimitrov, D., Fabre, P.-H., Graham, C.H., Graves, G.R.,

Jønsson, K.A., Nogués-Bravo, D., Wang, Z., Whittaker, R.J.,

Fjeldså, J. & Rahbek, C. 2013. An update of Wallace’s zoogeo-

graphic regions of the world. Science 339: 74 –78.

htt p://dx.doi.org/10.1126/science.1228282

Hooker, J.D. 184 4 –1860. The botany of the Antarctic voyage of H.M.

discover y ships Erebus and Terror in the years 1839–1843, under

the command of Captain Sir James Clark Ross. London: Reeve

Brothers. htt p://dx.doi.org/10.5962/bhl.title.16029

Hooker, J.D. 1867. Insular f loras. Gard. Chron. 1867: 6–7, 27, 50–51,

75–76.

Hoorn, C., Wessel ingh, F.P., Ter Steege, H., Bermudez, M.A., Mora,

A., Sevink, J., Sanmartín, I., Sanchez-Meseguer, A., Anderson,

C.L., Figueiredo, J.P., Jaramillo, C., Riff, D., Negri, F.R.,

Hooghiemstra, H., Lundberg, J., Stadler, T., Särkinen, T. &

Antonelli, A. 2010. Amazonia through time: Andean uplift, cli-

mate change, landscape evolution, and biodiversity. Science 330:

927–931. http://dx.doi.org/10.1126/science.1194585

Hope, A.G., Takebayashi, N., Galbreath, K.E., Talbot, S.L. & Cook,

J.A. 2013. Temporal, spatial and ecological dynamics of speciation

among amphi-Beringian small mammals. J. Biogegr. 40: 415–429.

ht t p://d x.d oi .or g /10.1111 /j b i.1205 6

Hugall A., Moritz, C., Moussalli, A. & Stanisic, J. 2002. Reconciling

paleodistribution models and comparative phylogeography in the

924

TAX ON

62 (5) • October 2013: 912–927

Wen & al.

• New trends in biogeography

924 Version of Record (identical to print version).

wet tropics rainforest land snail Gnarosophia bellendenkerensis

(Brazier 1875). Proc. Natl. Acad. Sci. U.S.A. 99: 611 2– 6117.

http://dx.doi.org/10.1073/pnas.092538699

Hughes, C., Pennington, R.T. & Antonelli, A. 2013. Neotropical plant

evolution: Assembling the big picture. Bot. J. Linn. Soc. 171: 1–18.

ht t p://d x.d oi .or g /10.1111 / boj.12 0 06

Humboldt, A. von 1805 –1837. Voyage aux régions équinoxiales du

nouveau continent, fait en 1799–1804, par Alexandre de Humboldt

et Amie Bonpland. Paris: Levrault, Schoell et Cie.

Humphries, C.J. & Parenti, L.R. 1999. Cladistic biogeography: Inter-

preting patterns of plant and animal distributions, 2nd ed. Oxford:

Oxford University Press.

Jenkins, D.G. & Ricklefs, R.E. 2011. Biogeography and ecology: Two

lenses in one telescope. Frontiers Biogeogr. 3(1): 3 –5.

Jetz, W. & Fine, P.V.A. 2012. Area and productivity of the world’s

biomes integrated over geological time predicts global patterns of

vertebrate diversity. PLoS Biol. 10: e1001292.

ht tp://d x. doi.o rg /10.1371/j ou r na l.pbio.1001292

Jetz, W., Thomas, G.H., Joy, J.B., Hartmann, K. & Mooers, A.O.

2012. The global diversity of birds in space and time. Nature 491:

444–448. http://dx.doi.org/10.1038/nature11631

Jønsson, K.A., Fabrea, P.-H., Ricklefs, R .E & Fjeldså, J. 2011. M ajor

global radiation of corvoid birds originated in the proto-Papuan

archipelago. Proc. Natl. Acad. Sci. U.S.A. 108: 2328–2333.

ht tp://d x. doi.o rg /10.10 73/ pn as .1018956108

Kadereit, J.W. & Baldwin, B.G. 2012. Western Eurasian –western

North American disjunct plant taxa: The dry-adapted ends of for-

merly widespread north temperate mesic lineages—and examples

of long-distance dispersal. Ta xo n 61: 3– 17.

Knope, M.L., Morden, C .W., Funk, V.A. & Fuk ami, T. 2012. Area and

the rapid radiation of Hawaiian Bidens (Asteraceae). J. Biogeogr.

39: 1206 –1216. http://dx.doi.org/10.1111/j.1365-2699.2012.02687.x

Knowles, L . 2004. The bu rgeoning field of statistical phylogeography.

J. Evol. Biol. 17: 1–10.

ht tp://d x. doi.o rg /10.10 46/j.1420 -9101.2003.0064 4.x

Knowles, L .L. & Maddison, W.P. 2002. Statistical phylogeography.

Molec. Ecol. 11: 2623–2635.

ht tp://d x. doi.o rg /10.10 46/j.1365 -29 4X.20 02.01637.x

Knowles, L .L., Carstens, B.C. & Keat, M.L. 20 07. Coupling genetic

and ecological-niche models to examine how past population dis-

tributions contribute to divergence. Cur r. Biol. 17: 940–946.

http://dx.doi.org/10.1016/j.cub.2007.04.033

Kocot, K.M., Cannon, J.T., Todt, C., Citarella, M.R., Kohn, A.B.,

Meyer, A., Santos, S.R., Schander, C., Leonid L., Moroz, L.L.,

Lieb, B. & Halanych, K.M. 2011. Phylogenomics reveals deep

molluscan relationships. Nature 477: 452 –456.

ht tp://d x. doi.o rg /10.10 38/ na ture10382

Kozak, K.H., Graham, C.H. & Wiens, J.J. 2008. Integrating GIS-

based environmental data into evolutionary biology. Trends Ecol.

Evol. 23: 141–148. ht t p://dx.doi.org /10.1016/j.tree.2008. 02.001

Ladle, R.J. & Whittaker, R.J. (eds.) 2011. Conservation biogeogra-

phy. Oxford: Wiley-Blackwell.

ht tp://d x. doi.o rg /10.10 02 /9 78144439 0001

Lamm, K. S. & Redelings, B.D. 2009. Reconstr ucting ancestral ranges

in historical biogeography: Properties and prospects. J. Syst. Evol.

47: 369–382. http://dx.doi.org/10.1111/j.1759-6831.2009.00042.x

Lee, M.S.Y., Oliver, P.M. & Hutchinson, M.N. 2009. Phylogenetic

uncertainty and molecular clock calibrations: A case study of leg-

less lizards (Pygopodidae, Gek kota). Molec. Phylogen. Evol. 50:

661– 666. ht tp://d x. doi.o rg /10.1016/j.y mp ev.2008.11.024

Lemey, P., Rambaut, A., Drummond, A.J. & Suchard, M.A. 2009.

Bayesian phylogeography finds its roots. PLoS Computat. Biol.

5(9): e1000520. http://dx.doi.org/10.1371/journal.pcbi.1000520

Lemey, P., Rambaut, A., Welch, J.J. & Suchard, M.A. 2 010. Phylo-

geography takes a relaxed random walk in continuous space and

time. Molec. Biol. Evol. 27: 1877–85.

ht tp://d x. doi.o rg /10.10 93/ molbev/msq067

Lemmon, A.R. & Lemmon, E.M. 2012. High-throughput identifica-

tion of informative nuclear loci for shallow-scale phylogenetics

and phylogeography. Syst. Biol. 61: 745 –761.

http://dx.doi.org/10.1093/sysbio/sys051

Leslie, A.B., Beaulieu, J.M., Rai H. S., Crane, P.R., Donoghue, M. J.

& Mathews, S. 2013. Hemisphere-scale differences in conifer

evolutionary dynamics. Proc. Natl. Acad. Sci. U.S.A. 109: 16217 –

16221. http://dx.doi.org/10.1073/pnas.1213621109

Lexer, C., Mangili, S., Bossolini, E., Forest, F., Stölting, K.N.,

Pearman, P.B., Zimmermann, N.E. & Salamin, N. 2013. ‘Next

generation’ biogeography: Towards understanding the drivers of

species diversification and persistence. J. Biogeogr. 40 : 1013–10 22.

ht t p://d x.d oi .or g /10.1111 /j b i.1207 6

Lieberman, B.S. 2003. Paleobiogeography: The relevance of fossils to

biogeography. Annual Rev. Ecol. Syst. 34: 51– 69.

http://dx.doi.org/10.1146/annurev.ecolsys.34.121101.153549

Linder, H.P. 2008. Plant species radiations: Where, when, why? Philos.

Trans., Ser. B 363: 3 097–310 5.

http://dx.doi.org/10.1098/rstb.2008.0075

Loarie, S.R., Carter, B.E., Hayhoe, K., McMahon, S., Moe, R.,

Knight, C.A. & Ackerly, D.D. 2008. Climate change and the

futu re of California’s endemic flora. PloS ONE 3: e2502.

http://dx.doi.org/10.1371/journal.pone.0002502

Loera, I., Soza, V. & Ickert-Bond, S.M. 2012. Diversification in North

American arid lands: Niche conser vatism, divergence and expansion

of habitat explain speciation in the genus Ephedra. Molec. Phylogen.

Evol. 65: 437– 450. htt p://dx .doi.org/10.1016/j.ym pev.2012.0 6.025

Lomolino, M.V., Riddle, B.R., Whittaker, R. J. & Brown, J.H. 2010a.

Biogeography, 4th ed. Sunderland, Massachusetts: Sinauer.

Lomolino, M.V., Brown, J.H. & Sax, D.F. 2010b. Island biogeog-

raphy theor y: Reticulations and reintegration of a biogeography

of the species. Pp. 13–51 in: Losos, J.B. & Ricklefs, R.E. (eds.),

The theor y of island biogeography revisited. Princeton: Princeton

University Press.

Losos, J.B. 2008. Phylogenetic niche conservatism, phylogenetic signal

and the relationship between phylogenetic related ness and ecologi-

cal similarity among species. Ecol. Letters 11: 9 95 –1003.

ht t p://d x.d oi .or g /10.1111 /j .14 61-0 2 48 . 200 8.01 229. x

Losos, J.B. 2010. Adaptive radiation, ecological opportunity, and evo-

lutionar y determinism. Amer. Naturalist 175: 623– 639.

ht tp://d x. doi.o rg /10.10 86/652 433

Losos, J.B. & Glor, R. 2003. Phylogenetic comparative methods and

the geography of speciation. Trends Ecol. Evol. 18: 220–227.

ht tp://d x. doi.o rg /10.1016/S0169-5347(03)0 0037-5

Losos, J.B. & Ricklefs, R.E. 2010. The theory of island biogeography

revisited. Princeton: Princeton University Press.

Lozier, J.D., Aniello, P. & Hickerson, M. J. 2009. Predicting the dis-

tribution of Sasquatch in western North America: Anything goes

with ecological niche modeling. J. Biogeogr. 36: 16 23 –1627.

ht t p://d x.d oi .or g /10.1111 /j .1365- 26 99.20 0 9.02152 .x

MacArthur, R.H. & Wilson, E.O. 1967. Island biogeography. Princ-

eton: Princeton University Press.

Maddison, W., Midford, P. & Otto, S. 2007. Estimating a binary char-

acter’s effect on speciation and extinction. Syst. Biol. 56: 701–710.

ht tp://d x. doi.o rg /10.10 80/106 35150701607033

Magallón, S. 2004. Dating lineages: Molecular and paleontological

approaches to the temporal framework of clades. Int. J. Pl. Sci. 165

(Suppl. 4): S7–S21. http://dx.doi.org/10.1086/383336

Magallón, S. & Sanderson, M. J. 2001. Absolute diversification rates

in angiosperm clades. Evolution 55: 1762–178 0.

ht tp://d x. doi.o rg /10.1111/j.0014-38 20.20 01.t b0 0826.x

Maguirea, K.C. & Stigall, A.L. 2009. Using ecological niche model-

ing for quantitative biogeographic analysis: A case study of Mio-

cene and Pliocene Equinae in the Great Plains. Paleobiolog y 35:

587–611. htt p://dx.doi.org/10.1666/0094-8373-35.4.587

Mao, K., Milne, R.I., Zhang, L., Peng, Y., Liu, Thomas, P., Mill,

R.R. & Renner, S.S. 2012. Distribution of living Cupressaceae

925

Wen & al.

• New trends in biogeography

TAX ON

62 (5) • October 2013: 912–927

925Version of Record (identical to print version).

reflects the breakup of Pangea. Proc. Natl. Acad. Sci. U.S.A. 109:

7793–7798. http://dx.doi.org/10.1073/pnas.1114319109

Mayr, E. 1982. The growth of biological thought: Diversity, evolution,

and inheritance. Cambridge, Massachusetts: The Belknap Press

of Harvard University Press.

McCormack, J.E., Maley, J.M., Hird, S.M., Derryberry, E.P.,

Graves, G.R. & Brumf ield, R.T. 2012. Next-generation sequenc-

ing reveals phylogeographic structu re and a species tree for recent

bird divergences. Molec. Phylogen. Evol. 62: 397– 406.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2011.10.012

McCormack, J.E., Hird, S.M., Zellmer, A.J., Carstens, B.C. &

Brumfield, R.T. 2013. Applications of next-generation sequenc-

ing to phylogeography and phylogenetics. Molec. Phylogen. Evol.

66: 526 –538. h ttp://d x.doi .org/10.1016/ j.ympev.2 011.12.007

McPeek, M.A. & Brown, J.M. 2007. Clade age and not diversification

rate explains species richness among animal taxa. Amer. Naturalist

169: E97–E10 6. http://d x.doi.org /10.10 86/512135

Meusemann, K., Reumont, B.M. von, Simon, S., Roeding, F., Kück,

P., Strauss, S., Ebersberger, I., Walzl, M., Pass, G., Breuers, S.,

Achter, V., Haeseler, A. von, Burmester, T., Hadrys, H., Wägele,

J.W. & Misof, B. 2010. A phylogenomic approach to resolve the

arthropod tree of life. Molec. Biol. Evol. 27: 2451–2464.

ht tp://d x. doi.o rg /10.10 93/ molbev/msq130

Moore, B.A. & Donoghue, M. J. 2009. A Bayesian approach for evalu-

ating the impact of historical events on rates of diversif ication.

Proc. Natl. Acad. Sci. U.S.A. 106: 4307– 4312.

http://dx.doi.org/10.1073/pnas.0807230106

Moore, M.J., Bell, C.D., Soltis, P.S. & Soltis, D.E. 2007. Using plastid

genome-scale data to resolve enigmatic relationships among basal

angiosperms. Proc. Natl. Acad. Sci. U.S.A. 104: 19363 –19368.

http://dx.doi.org/10.1073/pnas.0708072104

Moore, M.J., Soltis, P.S., Bell, C.D., Burleight, J.G. & Soltis, D.E..

2010. Phylogenetic analysis of 83 plastid genes further resolves the

early diversification of eudicots. Proc. Natl. Acad. Sci. U.S.A. 10 7:

4623–4628. http://dx.doi.org/10.1073/pnas.0907801107

Morrone, J.J. 20 09. Evolutionary biogeography: An integrative ap-

proach with case studies. New York: Columbia University Press.

Morrone, J.J. & Crisci, J.V. 1995. Historical biogeography: Introduc-

tion to methods. Annual Rev. Ecol. Syst. 26: 373– 401.

ht tp://d x. doi.o rg /10.1146/ann urev.es.26.110195.002105

Nathan, N., Schurr, F.M., Spiegel, O., Steinitz, O., Trakhtenbrot, A.

& Tsoar, A. 2008. Mechanisms of long-distance seed dispersal.

Trends Ecol. Evol. 2 3: 63 8– 647.

http://dx.doi.org/10.1016/j.tree.2008.08.003

Nauheimer, L., Metzler, D. & Renner, S.S. 2012. Global history of the

ancient monocot family Araceae inferred with models accou nting

for past continental positions and previous ranges based on fossils.

New Phytol. 195: 938–950.

ht t p://d x.d oi .or g /10.1111 /j .14 69- 81 37. 2012 .04 220.x

Nee, S. 2006. Birth-death models in macroevolution. Annual Rev. Ecol.

Evol. Syst. 37: 1–17.

http://dx.doi.org/10.1146/annurev.ecolsys.37.091305.110035

Nelson, G. & Platnick, N. 19 81. Systematics and biogeographpy: Cla-

distics and vicariance. New York: Columbia University Press.

Nelson, G. & Rosen, D. 1981. Vicariance biogeography: A critique.

New York: Columbia University Press.

Nie, Z.L., Sun, H., Manchester, S.R., Meng, Y., Luke, Q. & Wen, J.

2012. Evolution of the intercontinental disjunctions in six con-

tinents in the Ampelopsis clade of the grape family (Vitaceae).

B. M. C. Evol. Biol. 12: 1–17.

htt p://dx.doi.org/10.1186/1471-2148-12-17

Njuguna, W., Liston, A., Cronn, R., Ashman, T.-L. & Bassil, N. 2 013.

Insights into phylogeny, sex function and age of Fragaria based on

whole chloroplast genomes sequencing. Molec. Phylogen. Evol. 66:

17–29. ht tp://d x.doi.o rg /10.1016/j.y mp ev.2012 .08.0 26

Nylander, J.A.A, Olsson, U., Alström, P. & Sanmartín, I. 2008. Ac-

counting for phylogenetic uncertainty in biogeography: A Bayesian

approach to dispersal-vicariance analysis of the thrushes (Aves:

Tur dus). Syst. Biol. 57: 257–268.

ht tp://d x. doi.o rg /10.10 80/106 35150802044003

Ornelas, J.F., Sosa, V., Soltis, D.E., Daza, J.M., González, C., Soltis,

P.S., Gutiérrez-Rodríguez, C., Espinosa de los Monteros, A.,

Castoe, T.A., Bell, C. & Ruiz-Sanchez, E. 2 013. Comparative

phylogeographic analyses illustrate the complex evolutionary his-

tory of threatened cloud forests of northern Mesoamerica. PLoS

ONE 8(2): e56283.

http://dx.doi.org/10.1371/journal.pone.0056283

Page, R.D.M. 1990. Component analysis: A valiant failure? Cladistics

6: 119 –136 . ht tp: //dx. doi.o rg/10 .1111/j.10 96- 0031.19 9 0. tb00 532. x

Parenti, L.R. & Ebach, M.C. 20 09. Comparative biogeography: Dis-

covering and classifying biogeographical pattern of a dynamic

earth. Berkeley: Univeristy of California Press.

Parks, M. 2 011. Plastome phylogenetics in the genus Pinus using mas-

sively parallel sequencing technology. Dissertation, Oregon State

University, Corvallis, Oregon, U.S.A.

Parks, M., Cronn, R. & Liston, A. 2009. Increasing phylogenetics

resolution at low taxonomic levels using massively parallel se-

quencing of chloroplast genomes. B. M. C. Biol. 7: 84.

ht tp://dx .d oi.org /10.1186/1741-7 00 7-7- 84

Peters, R. S., Meyer, B., Krogmann, L., Borner, J., Meusemann,

K., Schütte, K., Niehuis, O. & Misof, B. 2011. The taming of an

impossible child: A standardized all-in approach to the phylogeny

of Hymenoptera using public database sequences. B. M. C. Biol.

9: 55. http://dx.doi.org /10.1186/1741-7007-9-55

Peterson, B.K., Weber, J.N., Kay, E.H., Fisher, H.S. & Hoekstra,

H.E. 2012. Double digest RADseq: An inexpensive method for

de novo SNP discover y and genotyping in model and non-model

species. PLoS ONE 7: e37135.

htt p://dx.doi.org /10.1371/jou rnal.pone.0037135

Phillips, S. J., Anderson, R.P. & Schapire, R.E. 2006. Maximum en-

tropy modeli ng of species geographic d istributions. Ecol. Modelling

190: 231–259. htt p://dx.doi.org/10.1016/j.ecolmodel.2005.03.026

Platnick, N.I. 1991. On areas of endemism. Austral. Syst. Bot. 4: 11–12.

Posadas, P., Crisci, J.V. & Katinas, L. 2006. Histor ical biogeography:

A review of its basic concepts and critical issues. J. Arid Environm.

66: 389 – 403. h ttp: //dx.doi .or g/10.1016/ j.jar id en v.2006.01.00 4

Pybus, O.G. & Harvey, P.H. 2000. Testing macro-evolutionary models

using incomplete molecular phylogenies. Proc. Roy. Soc. London,

Ser. B, Biol. Sci. 267: 2267–2272.

http://dx.doi.org/10.1098/rspb.2000.1278

Pyron, R .A. 2011. Divergence time estimation using fossils as terminal

taxa and the origins of Lissamphibia. Syst. Biol. 6 0: 466–481.

http://dx.doi.org/10.1093/sysbio/syr047

Pyron, R.A. & Burbrink, F.T. 2010. Hard and soft allopat ry: Physi-

cally and ecologically mediated modes of geographic speciation.

J. Biogeogr. 37: 2005–2015.

http://dx.doi.org/10.1111/j.1365-2699.2010.02336.x

Qiu, Y.-X., Fu, C.-X. & Comes, H.P. 2011. Plant molecular phylogeog-

raphy in China and adjacent regions: Tracing the genetic imprints

of Quater nary climate and environmental change in the world’s

most diverse temperate f lora. Molec. Phylogen. Evol. 59: 225–244.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2011.01.012

Rabosky, D.L. 2006. Likelihood methods for detecting temporal shif ts

in diversification rates. Evolution 60: 1152 –1164 .

Rabosky D.L., Donnellan S.C., Talaba A.L. & Lovette I.J. 200 7.

Exceptional among-lineage rate variation in diversification rates

during the radiation of Australia’s most diverse vertebrate clade.

Proc. Roy. Soc. London, Ser. B, Biol. Sci. 274: 2915–2923.

http://dx.doi.org/10.1098/rspb.2007.0924

Raxworthy, C.J., Martinez-Meyer, E., Horning, N., Nussbaum,

R.A., Schneider, G.E., Ortega-Huerta, A. & Peterson, A.T.

2003. Predicting distributions of known and un known reptiles

species in Madagascar. Nature 42 6: 837– 841.

http://dx.doi.org/10.1038/nature02205

926

TAX ON

62 (5) • October 2013: 912–927

Wen & al.

• New trends in biogeography

926 Version of Record (identical to print version).

Ree, R.H & Sanmartín, I. 2009. Prospects and challenges for para-

metric models in historical biogeographical inference. J. Biogeogr.

36: 1211–1220.

ht t p://d x.d oi .or g /10.1111 /j .1365- 26 99.20 0 8. 020 68. x

Ree, R.H. & Smith, S.A. 2008. Maximum likelihood inference of

geographic range evolution by dispersal, local Extinction, and

cladogenesis. Syst. Biol. 57: 4 –14.

ht tp://d x. doi.o rg /10.10 80/106 35150701883881

Ree, R.H., Moore, B.R., Webb, C.O. & Donoghue, M.J. 20 05. A

likelihood framework for inferring the evolution of geog raphic

range on phylogenetic trees. Evolution 59: 22 99–2 311.

http://dx.doi.org/10.1111/j.0014-3820.2005.tb00940.x

Renner, S.S. 2004a. Plant dispersal across the tropical Atlantic by wind

and sea currents. Int. J. Pl. Sci. 165: S23 –S33.

http://dx.doi.org/10.1086/383334

Renner, S.S. 2004b. Multiple Miocene Melastomataceae dispersal be-

tween Madagascar, Af rica and India. Philos. Trans., Ser. B 359:

1485–1494. http://dx.doi.org /10.1098/rstb.2004.1530

Renner, S.S. 2005. Relaxed molecular clocks for dating histor ical plant

dispersal events. Trends Pl. Sci. 10: 550 –558.

ht tp://d x. doi.o rg /10.1016/j.tpla nt s.20 05.09.010

Ricklefs, R.E. 2004. A comprehensive framework for global patterns

in biodiversity. Ecol. Letters 7: 1–15.

http://dx.doi.org/10.1046/j.1461-0248.2003.00554.x

Ricklefs, R.E. & Renner, S.S. 2012. Global correlations in t ropical

tree species richness and abundance reject neutrality. Science 335:

464 –467. htt p://dx.doi.org/10.1126/science.1215182

Riddle, B.R., Hafner, D.J., Alexander, L.F. & Jaeger, J.R. 2000.

Cryptic vicariance in the historical assembly of a Baja California

Peninsular Deser t biota. Proc. Natl. Acad. Sci. U.S.A. 97: 14 438–

14443. http://dx.doi.org/10.1073/pnas.250413397

Ronquist, F. 1994. Ancestral areas and parsimony. Syst. Biol. 43: 2 67–

274. http://dx.doi.org/10.1093/sysbio/43.2.267

Ronquist, F. 1995. Reconstructing the history of host-parasite associa

tions using generalized parsimony. Cladistics 11: 73–89.

http://dx.doi.org/10.1111/j.1096-0031.1995.tb00005.x

Ronquist, F. 1996. DIVA, version 1.1. Computer program and manual

available from Uppsala University. http://www.ebc.uu.se/systzoo

/re search/d iva/diva.ht ml

Ronquist, F. 1997. Dispersal-vicar iance analysis: A new approach to the

quantification of historical biogeography. Syst. Biol. 46: 195–203.

http://dx.doi.org/10.1093/sysbio/46.1.195

Ronquist, F. 2001. Dispersal Vicariance Analysis (DIVA), version 1.2.

http://www.ebc.uu.se/syszoo/research/diva/diva.html

Ronquist, F. & Sanmartín, I. 2011. Phylogenetic methods in biogeog-

ra phy. Annual Rev. Ecol. Evol. Syst. 42: 441–46 4.

ht tp://d x. doi.o rg /10.1146/ann urev-e co lsys-102209-144710

Ronquist, F., Teslenko, M., Van der Mark, P., Ayres, D.L., Darling,

A., Höhna, S., Larget, B., Liu, L., Suchard, M.A. & Huelsen-

beck, J.P. 2012. MrBayes 3.2: Efficient Bayesian phylogenetic

inference and model choice across a large model space. Syst. Biol.

61: 539–542. http://dx.doi.org/10.1093/sysbio/sys029

Rubin, B.E .R., Ree, R.H. & Moreau, C.S. 2012. Inferr ing phylogenies

from RAD sequence data. PLoS One 7: e33394.

http://dx.doi.org /10.1371/jour na l.pone.0033394

Rutschmann, F. 2006. Molecular dating of phylogenetic trees: A brief

review of cur rent methods that estimate divergence times. Diver-

sity & Distrib. 12: 35–48.

ht t p://d x.d oi .or g /10.1111 /j .136 6 -9 516 .20 0 6.0 0210.x

Rutschmann, F., Eriksson, T., Abu Salim, K. & Conti, E. 2007.

Assessing calibration uncert ainty in molecular dating: The as-

signment of fossils to alternative calibration points. Syst. Biol. 56:

591– 60 8. ht tp://d x. doi.o rg /10.10 80 /106 35150701491156

Sanderson, M.J. 2002. Estimating absolute rates of molecular evolution

and divergence times: A penalized likelihood approach. Molec.

Biol. Evol. 19: 101–109.

http://dx.doi.org/10.1093/oxfordjournals.molbev.a003974

Sanderson, M.J. 2003. r8s: Infer ring absolute rates of molecular evo-

lution and divergence dates in the absence of a molecular clock.

Bioinformatics 19: 301–302.

http://dx.doi.org/10.1093/bioinformat ics/19.2.301

Sanmart ín, I. 2003. Dispersal vs. vicariance in the Mediterranean: His-

torical biogeography of the Palearctic Pachydeminae (Coleoptera,

Scarabaeoidea). J. Biogeogr. 30 : 18 83 –1897.

http://dx.doi.org/10.1046/j.0305-0270.2003.00982.x

Sanmartín, I. 2007. Event-based bigeography: Integrating pat terns,

processes, and time. Pp. 135–156 in: Ebach, M.C. & Tangney, R.S.

(ed s.), Biogeography in a changing world. Systematics Association

Volume Series. London: Taylor & Francis.

Sanmartín, I. & Ronquist, F. 2004. Souther n Hemisphere biogeogra-

phy inferred by event-based models: Plant versus animal patterns.

Syst. Biol. 53: 216 –243.

ht tp://d x. doi.o rg /10.10 80/106 35150 49042 3430

Sanmartín, I., Van der Mark, P. & Ronquist, F. 2008. Inferring dis-

persal: A Bayesian approach to phylogeny-based island biogeog-

raphy, with special reference to the Canary Islands. J. Biogeogr.

35: 428–449. http://dx.doi.org/10.1111/j.1365-2699.2008.01885.x

Sanmartín, I., Anderson, C.L., Alarcon, M., Ronquist, F. &

Aldasoro, J.J. 2010. Bayesian island biogeography in a continental

setting: The Rand Flora case. Biol. Letters 6: 703–707.

ht tp://d x. doi.o rg /10.10 98/rsbl .2 010.0 09 5

Sauquet, H., Ho, S.Y.W., Gandolfo, M.A., Jordan, G.J., Wilf,

P., Cantrill, D.J., Bayly, M.J., Bromham, L., Brown, G.K.,

Carpenter, R.J., Lee, D.M., Murphy, D.J., Sniderman, J.M.K.

& Udovicic, F. 2012. Testing the impact of calibration on molecular

divergence times using a fossil-rich group: The case of Nothofagus

(Fagales). Syst. Biol. 61: 289 –313.

http://dx.doi.org/10.1093/sysbio/syr116

Sclater, P.L. 1858. On the general geographical distribution of the mem-

bers of the class Aves. J. Linn. Soc. Zool. 2: 13 0 –145.

ht t p://d x.d oi .or g /10.1111 /j .10 9 6 -364 2.185 8.t b02 549. x

Sexton, J.P., McIntyre, P.J., Angert, A.L. & Rice, K.J. 2009. The

evolution and ecology of geographic range limits. Annual Rev.

Ecol. Evol. Syst. 40: 415–436.

http://dx.doi.org/10.1146/annurev.ecolsys.110308.120317

Silvestro, D., Schnitzler, J. & Zizka, G. 2011. A Bayesian framework

to estimate diversif ication rates and their variation through time

and space. B. M. C. Evol. Biol. 11: 311.

ht t p://d x.d oi .or g /10.118 6/1471-2148-11-311

Smith, C.I., Tank, S., Godsoe, W., Levenick, J., Strand, E., Esque, T.

& Pellmyr, O. 2011. Comparative phylogeography of a coevolved

community: Concerted population expansions in Joshua trees and

four Yucca moths. PLoS ONE 6(10): e25628.

htt p://dx.doi.org /10.1371/jou rnal.pone.0025628

Smith, S.A. 2009. Taking into account phylogenetic and divergence-

time uncertainty in a parametric biogeographic analysis of the

Northern Hemisphere plant clade Caprifolieae. J. Biogeogr. 36:

2324–2337. http://dx.doi.org/10.1111/j.1365-2699.2009.02160.x

Smith, S.A. & Donoghue, M.J. 2010. Combining historical biogeog-

raphy with niche modeling in the Caprifolium clade of Lonicera

(Caprifoliaceae, Dipsacales). Syst. Biol. 59: 322– 341.

ht tp://d x. doi.o rg /10.10 93/sysbi o/s yq011

Smith, S.A. & O’Meara, B.C. 2012. treePL: Divergence time estima-

tion using penalized likelihood for large phylogenies. Bioinformat-

ics 28: 2689–2690. http://dx.doi.org/10.1093/bioinfor matics/bts492

Smith, S.A., Beaulieu, J.M. & Donoghue, M.J. 2009. Mega-phylogeny

approach for comparative biology: An alternative to super- tree and

supermatrix approaches. B. M. C. Evol. Biol. 9: 37.

ht t p://d x. do i. or g /10.1186/1471-2148 -9-37

Smith, S.A., Wilson, N.G., Goetz, F.E., Feehery, C., Andrade,

S.C.S., Rouse, G.W., Giribet, G. & Dunn, C.W. 2011a. Resolv-

ing the evolutionary relationships of molluscs with phylogenomic

tools.

Nature

480: 364 –367.

ht tp://d x. doi.o rg /10.10 38/ na ture10526

927

Wen & al.

• New trends in biogeography

TAX ON

62 (5) • October 2013: 912–927

927Version of Record (identical to print version).

Smith, S.A., Beaulieu, J.M., Stamatak is, A. & Donoghue, M.J.

2011b. Understanding angiosperm diversification using small and

large phylogenetic trees. Amer. J. Bot. 9 8: 404 –417.

http://dx.doi.org/10.3732/ajb.1000481

Soberon, J. & Peterson, A.T. 2004. Biodiversity infor matics: Manag-

ing and applying primary biodiversity data. Philos. Trans., Ser. B

359: 689–698. http://dx.doi.org/10.1098/rstb.2003.1439

Soltis, D.E., Morris, A.B., McLachlan, J.S., Manos, P.S. & Soltis,

P. S. 2006. Comparative phylogeography of unglaciated easter n

North America. Molec. Ecol. 15: 4261–4293.

ht t p://d x.d oi .or g /10.1111 /j .1365- 29 4X. 200 6 .0 3061.x

Stadler, T. 2009. On incomplete sampling under birth-death models and

connections to the sampling-based coalescent. J. Theor. Biol. 261:

58–66. ht tp://d x.doi.o rg /10.1016/j.jtbi. 20 09.07.018

Straub, S.C.K., Parks, M., Weitemier, K., Fishbein, M., Cronn,

R.C. & Liston, A. 2012. Navigating the tip of the genomic iceberg:

Next-generation sequencing for plant systematics. Amer. J. Bot. 99:

349–364. http://dx.doi.org/10.3732/ajb.1100335

Thorne, J.L. & Kishino, H. 2002. Divergence time and evolutionary

rate estimation with multilocus data. Syst. Biol. 51: 689–702.

ht tp://d x. doi.o rg /10.10 80/106 3515029010 245 6

Tiffney, B.H. & Manchester, S.R. 2001. The use of geological and

paleontological evidence in evaluating plant phylogeographic

hypotheses in the Northern Hemisphere tertiary. Int. J. Pl. Sci.

162(suppl): S3–S17. http://dx.doi.org/10.1086/323880

Wallace, A.R. 1876. The geographical distribution of animals, with a

study of the relations of living and extinct faunas as elucidating the

past changes of the earth’s surface. London: Macmillan.

http://dx.doi.org/10.5962/bhl.title.46581

Wallace, A.R. 1911. Island life, 3rd ed. London: Macmillan.

Weiher, E., Freund, D., Bunton, T., Stefanski, A., Lee, T. &

Bentivenga, S. 2011. Advances, challenges, and a developing syn-

thesis of ecological community assembly theory. Philos. Trans.,

Ser. B 366: 2403–2413.

ht tp://d x. doi.o rg /10.10 98/rstb.2 011.0056

Weir, J.T. & Schluter, D. 2007. The latitudinal gradient in recent spe-

ciation and extinction rates of birds and mammals. Science 315:

1574–1576. htt p://d x.doi.org/10.1126 /science.1135590

Wen J. 1999. Evolution of eastern Asian and eastern North American

disjunct distributions in f lowering plants. Annual Rev. Ecol. Evol.

Syst. 30: 421–455.

http://dx.doi.org/10.1146/annurev.ecolsys.30.1.421

Wen, J. 2001. Evolution of easter n Asian and eastern North American

biogeographic pattern: A few additional issues. Int. J. Pl. Sci. 162:

S117–S122. http://dx.doi.org/10.1086/322940

Wen, J. & Ickert-Bond, S.M. 2009. Evolution of the Madrean-

Tethyan disjunctions and the North and South American am-

phitropical disjunctions in plants.

J. Syst. Evol. 47: 331–348.

ht t p://d x.d oi .or g /10.1111 /j .175 9-68 31. 200 9. 0005 4. x

Wen, J., Ickert-Bond, S.M., Nie, Z.-L. & Li, R. 2010. Timing and

modes of evolution of eastern Asian–North American biogeo-

graphic disjunctions in seed plants. Pp. 252–269 in: Long, M.,

Gu, H. & Zhou, Z. (eds.), Darwin’s heritage today: Proceedings of

the Darwin 200 Beijing International Conference. Beijing: Higher

Education Press.

Wen, J., Xiong, Z., Nie, Z.-L., Mao, L., Zhu, Y., Kan, X.-Z., Ickert-

Bond, S.M., Gerrath, J., Zimmer, E.A. &. Fang, X.-D. In press.

Transcriptome sequences resolve deep relationships of the grape

fa m il y. PLoS ONE.

Wertheim, J.O. & Sanderson, M.J. 2010. Estimating diversification

rates: How useful are divergence times? Evolution 65: 309–320.

ht t p://d x.d oi .or g /10.1111 /j .1558 -5 6 46. 2010.01159. x

Whittaker, R.J., Araújo, M.B., Jepson, P., Ladle, R.J., Watson,

J.E.M. & Willis, K.J. 2005. Conservation biogeography: Assess-

ment and prospect. Diversity & Distrib. 11: 3–2 3.

ht t p://d x.d oi .or g /10.1111 /j .136 6 -9 516 .20 05.00143.x

Whittaker, R.J., Fernandez-Palacios & Wen J.M. 2 0 07. Island bio-

geography: Ecology, evolution, and conservation, 2nd ed. New

York: Oxford University Press.

Wiens, J. J. 2011. The niche, biogeography and species interactions.

Philos. Trans., Ser. B 366: 2336–2350.

ht tp://d x. doi.o rg /10.10 98/rstb.2 011.0059

Wiens, J.J. & Donoghue, M. J. 2004. Historical biogeography, ecology,

and species richness. Trends Ecol. Evol. 19: 639– 64 4.

ht tp://d x. doi.o rg /10.1016/j.tr ee .200 4.09.011

Wiens, J.J. & Graham, C.H. 2005. Niche conser vatism: Integrating

evolution, ecology and conservation biology. Annual Rev. Ecol.

Evol. Syst. 36: 519–539.

http://dx.doi.org/10.1146/annurev.ecolsys.36.102803.095431

Wiens, J.J., Sukumaran, J., Pyron, R.A. & Brown, R.M. 2 009. Evo-

lutionar y and biogeographic origins of high tropical diversity in

old world frogs (Ranidae). Evolution 63: 1217–1231.

ht t p://d x.d oi .or g /10.1111 /j .1558 -5 6 46. 200 9. 00610. x

Wood, H.M., Matzke, N.J., Gillespie, R.G. & Griswold, C.E. 2013.

Treating fossils as terminal taxa in divergence time estimation

reveals ancient vicariance patterns in the palpimanoid spiders.

Syst. Biol. 62: 264–284. http://dx.doi.org/10.1093/sysbio/sys092

Xiang, Q.-Y., Zhang, W.H., Ricklefs, R.E., Qian, H., Chen, Z.D.,

Wen, J. & Li, J.H. 2004. Regional differences rates of plant spe-

ciation and molecular evolution: A comparison between eastern

Asia and eastern North America. Evolution 58: 2175– 2184.

htt p://dx.doi.org/10.1554/03-712

Xie, L., Wagner, W.L., Ree, R.H., Berry, P.E. & Wen, J. 2009. Mo-

lecular phylogeny, divergence time estimates, and historical bio-

geography of Circaea (Onagraceae) in the Norther n Hemisphere.

Molec. Phylogen. Evol. 53: 9 95 –10 09.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2009.0 9.0 09

Yu, Y., Harris, A. & He, X. 2010. S-DIVA (Statistical Dispersal-

Vicariance Analysis): A tool for inferring biogeographic histories.

Molec. Phylogen. Evol. 56: 848–850.

ht tp://d x. doi.o rg /10.1016/j.y mp ev.2010.04.011

Yu, Y., Harris, A. & He, X. 2011. RASP (Reconstruct Ancestral State

in Phylogenies), version 2.0. http://mnh.scu.edu.cn /soft/blog/RASP

Zellmer, A., Hanes, M.M., Hird, S.M. & Carstens, B.C. 2012. Deep

phylogeographic struct ure and environmental differentiation in

the carnivorous plant Sarracenia alata. Syst. Biol. 61: 763–777.

http://dx.doi.org/10.1093/sysbio/sys048

Zimmer, E.A. & Wen, J. 2012. Using nuclear gene data for plant phy-

logenetics: Progress and prospects. Molec. Phylogen. Evol. 65:

774 –785. htt p://dx .doi.org/10.1016/j.ympev.2012.0 7.015

Phylogenomic analyses of the East Asian endemic Abelia (Caprifoliaceae) shed insights into the temporal and spatial diversification history with widespread hybridization

Article

Nov 2021

Background and Aims Abelia (Caprifoliaceae) is a small genus with five species, including one artificial hybrid and several natural hybrids. The genus has a discontinuous distribution in Mainland China, Taiwan Island and the Ryukyu Islands, providing a model system to explore the mechanisms of species dispersal in the East Asian flora. However, the current phylogenetic relationships within Abelia remain uncertain. Methods We reconstructed the phylogenetic relationships within Abelia using nuclear loci generated by target enrichment and plastomes from genome skimming. Divergence time estimation, ancestral area reconstruction and ecological niche modelling (ENM) were used to examine the diversification history of Abelia. Key Results We found extensive cytonuclear discordance across the genus. By integrating lines of evidence from molecular phylogenies, divergence times and morphology, we propose to merge Abelia macrotera var. zabelioides into A. uniflora. Network analyses suggested that there have been multiple widespread hybridization events among Abelia species. These hybridization events may have contributed to the speciation mechanism and resulted in the high observed morphological diversity. The diversification of Abelia began in the early Eocene, followed by A. chinensis var. ionandra colonizing Taiwan Island during the Middle Miocene. The ENM results suggested an expansion of climatically suitable areas during the Last Glacial Maximum and range contraction during the Last Interglacial. Disjunction between the Himalayan–Hengduan Mountain region and Taiwan Island is probably the consequence of topographical isolation and postglacial contraction. Conclusions We used genomic data to reconstruct the phylogeny of Abelia and found a clear pattern of reticulate evolution in the group. In addition, our results suggest that shrinkage of postglacial range and the heterogeneity of the terrain have led to the disjunction between Mainland China and Taiwan Island. This study provides important new insights into the speciation process and taxonomy of Abelia.

Correction to: Marine Meiofauna Diversity and Biogeography—Paradigms and Challenges

Chapter

Jun 2023

A synthesis of the biogeographical distribution of Rotifera in Africa

Article

Dec 2022

The rotifer fauna of Africa has been studied for >100 years, but there has been no recent synthesis. We compiled data from 265 publications that reported information on African rotifers. Our dataset yielded information on the distribution of 765 taxa from ~1850 separate sites; these included both natural and artificial habitats such as lakes, ponds, puddles, oases, artificial systems, rivers and wetlands. A heat map of predicted rotifer biodiversity indicated that the greatest diversity should be present in the sub-Saharan region including a large hotspot in Mali and several smaller ones scattered in that region. Lakes Kariba, Tanganyika and Malawi showed high-predicted diversity, but surprisingly, Lake Victoria had lower diversity than expected. Two regions showed unusually high-predicted diversity: northwestern Algeria extending into Morocco and Egypt. Equatorial Africa is rich in habitats well suited for rotifers, yet their predicted biodiversity seems low. Latitude and elevation were negatively correlated with richness, while permanent water source and littoral zone were positively correlated according to generalized linear modeling results. Partial RDA analyses showed significant correlations among several environmental features and species occurrences. It is clear that more survey work remains to be done to achieve a better understanding of African rotifers.

Towards better-informed dispersal probabilities in historical biogeography: Arachnids as a model lineage

Article

Full-text available

Dec 2022

Historical biogeography is an integrative scientific field critical for testing evolutionary hypotheses pertinent to organismal distributions, but despite recent theoretical and analytical advances, biogeographic reconstructions continue to struggle with accuracy and rigor. Most modern studies include the three elements needed for historical biogeographic inference, namely a time-calibrated phylogeny, contemporary taxonomic distributions, and estimations of organismal dispersal probabilities. The latter, we argue, are particularly vague, and historical biogeography would greatly benefit from dispersal probability estimations that are better informed and biologically meaningful. To achieve that goal, next-generation biogeography should ideally consider: a) dispersal-related traits; b) ecology; c) geological histories; and d) geographical factors. We briefly recap the three case studies on spiders that have pioneered this approach. Due to their old age and mega-diversity—considering both phylogenetic and life style diversity—arachnids are an ideal animal lineage for modern biogeographic research. There is no reason, however, that the concept should not be applied to all life. Further modifications of the proposed concept and, particularly, methodological implementation are needed to render this biogeographic framework widely useful.

The biogeography of grasses (Poaceae)

Article

Full-text available

May 2022

A global phylogenetic regionalisation of vascular plants reveals a deep split between Gondwanan and Laurasian biotas

Article

Full-text available

Feb 2022
NEW PHYTOL

Existing global regionalisation schemes for plants consider the compositional affinities among biotas, but these have not explicitly considered phylogenetic information. Here, we present for the first time, a phytogeographical delineation of the global vascular flora based on species-level evolutionary relationships. We analysed 8,738,520 geographical occurrence records for vascular plants together with a time‐calibrated phylogeny including 67,420 species. We constructed a global phylogenetic regionalisation by estimating species composition and phylogenetic beta diversity among 200×200 km grid cells across the world. We identified de novo 16 phytogeographical units that are deeply split into two clusters: Laurasian and Gondwanan. Our regionalisation broadly matches previous schemes, but also highlights the separation of the Gondwanan region into an Holotropical cluster and an Australian-Neozealandic-Patagonian cluster. In contrast, no clear split among Laurasian and Gondwanan biotas was retrieved when omitting phylogenetic information. The integration of phylogenetic and geographical information provides new insights into the delineation of phytogeographical areas and their historical relationships, enabling the identification of three large, clearly differentiated biotas, here referred to as kingdoms: Holarctic, Holotropical, and Austral. Our results provide further evidence for delineating transition zones and show a clear latitudinal pattern of increasing evolutionary distinctiveness towards the poles.

Evolutionary origin of species diversity on the Qinghai‐Tibet Plateau

Article

Aug 2021

The Qinghai-Tibet Plateau (QTP) sensu lato (sl) houses an exceptional species diversity in Asia. To develop a comprehensive understanding of species diversity in this fascinating region, we reviewed recent progresses from biogeographic, paleogeographic, paleontological and genomic researches of both plants and animals in the QTPsl. Numerous studies have been conducted to examine whether the QTPsl uplift triggered the production of rich species diversity there, whether a Quaternary ‘unified ice sheet’ eliminated plants and animals on the central plateau and how high-altitude species developed the extreme environment adaptations. Major disputes arose about the first issue, mainly from different understanding of the QTP circumscriptions and related uplift, inaccurate dating of molecular phylogenetic trees, and non-causal correlations between uplift and species diversification. The QTPsl uplift is spatially and temporally heterogeneous, and abundant fossils reported recently similarly support such an asynchronous upheaval model across the entire region. Available phylogeographic studies of alpine plants and animals suggested their glacial refugia in the central QTPsl, rejecting a unified ice sheet during the Last Glacial Maximum. Genomic evidence from a limited number of alpine species has identified numerous candidate genes for high-altitude adaptation. In the future, more studies should be focused on speciation and adaptation mechanisms of the alpine species in the QTPsl based on the cutting-edge methods. This article is protected by copyright. All rights reserved.

Marine Meiofauna Diversity and Biogeography—Paradigms and Challenges

Chapter

Mar 2023

Scientists studying the biodiversity and biogeography of meiofauna encounter many uncertainties regarding the causes and consequences of natural and anthropogenic-driven changes in biodiversity patterns they observe worldwide. Recently developed novel analytical and computational technologies are facilitating more systematic and integrated approaches to the study of meiofauna biodiversity. In this chapter, we reflect on the state of the art in biodiversity and biogeography research with a focus on the most abundant and diverse meiofauna taxa including nematodes and copepods. Other occasionally abundant meiofauna taxa such as carbonate-shelled crustacean ostracods and protist foraminiferans, which are present in the fossil record, allow meiobenthologists to understand the links between shifts in biodiversity and major historical events in the marine environment. Sample-size dependency and the lack of standardization across benthic surveys currently hamper the integration of disparate meiofauna studies into wider research of seafloor biodiversity and biogeography. We discuss habitat-specific meiofauna biodiversity patterns that are observed at different scales and identify the main drivers of such patterns. Important factors include physical characteristics of the seafloor, biogeochemical processes, ecosystem productivity, geographical location, but also the interactions of meiofauna with other ecosystem components including their prey, their predators, competitors, and habitat facilitators. We discuss the importance of meiobenthic biodiversity for ecosystem functioning and touch on the biogeography of dominant meiofauna taxa by looking at what we know about the importance of endemism versus cosmopolitanism, the growing insights in population genetics and cryptic speciation, the phylogenic processes underpinning them, and critical gaps in our knowledge. We conclude by identifying some dynamic areas of research and inquiry for future generations of meiobenthologists studying the biodiversity and biogeography of meiofauna.

Collections-based science in the 21st Century: A tribute to the botanical giant Vicki Funk

Article

Mar 2023

Collections‐based systematics in the new age of discovery: Celebrating the legacy and life of Professor Wen‐Tsai Wang

Article

Jan 2023

Molecular Panbiogeography of the Tropics

Book

Jan 2012

Michael Heads

Molecular studies have revealed highly ordered geographic patterns in plant and animal groups, and this book discusses examples from the tropics. During evolution, phases of community immobilism that lead to allopatric differentiation alternate with phases of mobilism that lead to range expansion and overlap of taxa. Both phases are caused by geological and climatic factors rather than group-specific factors such as ecology or means of dispersal. In primates, the endemism of high-level taxa in tropical America (New World monkeys) and Madagascar (lemurs) is attributed to Gondwana breakup, while the extensive overlap of clades in South America, Africa, and Asia may have been caused by range expansion during Cretaceous marine transgressions. In the central Pacific, former high islands of the Cretaceous (now seamounts and atolls) may have been important source areas for the biotas of the current islands. The integration of earth history, biogeography, and ecology proposed here leads to an alternative view of evolution, in which groups are much older than suggested by the fossil record and fossil-calibrated molecular clocks. The book concludes with a critique of adaptation by selection, based on biogeography and recent work in genetics.

Phylogeography: The History and Formation of Species

Book

Jan 2000

JOHN C. AVISE

PHYLOGENIES WITHOUT FOSSILS

Article

Jun 1994
EVOLUTION

Phylogenies that are reconstructed without fossil material often contain approximate dates for lineage splitting. For example, particular nodes on molecular phylogenies may be dated by known geographic events that caused lineages to split, thereby calibrating a molecular clock that is used to date other nodes. On the one hand, such phylogenies contain no information about lineages that have become extinct. On the other hand, they do provide a potentially useful testing ground for ideas about evolutionary processes. Here we first ask what such reconstructed phylogenies should be expected to look like under a birth-death process in which the birth and death parameters of lineages remain constant through time. We show that it is possible to estimate both the birth and death rates of lineages from the reconstructed phylogenies, even though they contain no explicit information about extinct lineages. We also show how such phylogenies can reveal mass extinctions and how their characteristic footprint can be distinguished from similar ones produced by density-dependent cladogenesis.

GENE TREES AND ORGANISMAL HISTORIES: A PHYLOGENETIC APPROACH TO POPULATION BIOLOGY

Article

Sep 1989
EVOLUTION

John C Avise

A "gene tree" is the phylogeny of alleles or haplotypes for any specified stretch of DNA. Gene trees are components of population trees or species trees; their analysis entails a shift in perspective from many of the familiar models and concepts of population genetics, which typically deal with frequencies of phylogenetically unordered alleles. Molecular surveys of haplotype diversity in mitochondrial DNA (mtDNA) have provided the first extensive empirical data suitable for estimation of gene trees on a microevolutionary (intraspecific) scale. The relationship between phylogeny and geographic distribution constitutes the phylogeographic pattern for any species. Observed phylogeographic trees can be interpreted in terms of historical demography by comparison to predictions derived from models of gene lineage sorting, such as inbreeding theory and branching-process theory. Results of such analyses for more than 20 vertebrate species strongly suggest that the demographies of populations have been remarkably dynamic and unsettled over space and recent evolutionary time. This conclusion is consistent with ecological observations documenting dramatic population-size fluctuations and range shifts in many contemporary species. By adding an historical perspective to population biology, the gene-lineage approach can help forge links between the disciplines of phylogenetic systematics (and macroevolutionary study) and population genetics (microevolution). Preliminary extensions of the "gene tree" methodology to haplotypes of nuclear genes (such as Adh in Drosophila melanogaster) demonstrate that the phylogenetic perspective can also help to illuminate molecular-genetic processes (such as recombination or gene conversion), as well as contribute to knowledge of the origin, age, and molecular basis of particular adaptations.

The botany of the Antarctic voyage of H.M. discovery ships Erebus and Terror in the years 1839-1843 under the command of Captain Sir James Clark Ross

Article

J.D. Hooker

Testing macro-evolutionary models using incomplete molecular phylogenies

Article

Nov 2000

Phylogenies reconstructed from gene sequences can be used to investigate the tempo and mode of species diversification. Here we develop and use new statistical methods to infer past patterns of speciation and extinction from molecular phylogenies. Specifically, we test the null hypothesis that per-lineage speciation and extinction rates have remained constant through time. Rejection of this hypothesis may provide evidence for evolutionary events such as adaptive radiations or key adaptations. In contrast to previous approaches, our methods are robust to incomplete taxon sampling and are conservative with respect to extinction. Using simulation we investigate, first, the adverse effects of failing to take incomplete sampling into account and, second, the power and reliability of our tests. When applied to published phylogenies our tests suggest that, in some cases, speciation rates have decreased through time.

A likelihood framework for inferring the evolution of geographic range on phylogenetic trees

Article

Nov 2005

At a time when historical biogeography appears to be again expanding its scope after a period of focusing primarily on discerning area relationships using cladograms, new inference methods are needed to bring more kinds of data to bear on questions about the geographic history of lineages. Here we describe a likelihood framework for inferring the evolution of geographic range on phylogenies that models lineage dispersal and local extinction in a set of discrete areas as stochastic events in continuous time. Unlike existing methods for estimating ancestral areas, such as dispersal-vicariance analysis, this approach incorporates information on the timing of both lineage divergences and the availability of connections between areas (dispersal routes). Monte Carlo methods are used to estimate branch-specific transition probabilities for geographic ranges, enabling the likelihood of the data (observed species distributions) to be evaluated for a given phylogeny and parameterized paleogeographic model. We demonstrate how the method can be used to address two biogeographic questions: What were the ancestral geographic ranges on a phylogenetic tree? How were those ancestral ranges affected by speciation and inherited by the daughter lineages at cladogenesis events? For illustration we use hypothetical examples and an analysis of a Northern Hemisphere plant clade (Cercis), comparing and contrasting inferences to those obtained from dispersal-vicariance analysis. Although the particular model we implement is somewhat simplistic, the framework itself is flexible and could readily be modified to incorporate additional sources of information and also be extended to address other aspects of historical biogeography.

Insular Floras

Article

May 1984

J.D. Hooker

The theory of Island biogeography revisited

Book

Oct 2009

Robert H. MacArthur and Edward O. Wilson'sThe Theory of Island Biogeography, first published by Princeton in 1967, is one of the most influential books on ecology and evolution to appear in the past half century. By developing a general mathematical theory to explain a crucial ecological problem--the regulation of species diversity in island populations--the book transformed the science of biogeography and ecology as a whole. InThe Theory of Island Biogeography Revisited, some of today's most prominent biologists assess the continuing impact of MacArthur and Wilson's book four decades after its publication. Following an opening chapter in which Wilson reflects on island biogeography in the 1960s, fifteen chapters evaluate and demonstrate how the field has extended and confirmed--as well as challenged and modified--MacArthur and Wilson's original ideas. Providing a broad picture of the fundamental ways in which the science of island biogeography has been shaped by MacArthur and Wilson's landmark work,The Theory of Island Biogeography Revisitedalso points the way toward exciting future research.

Early classics

Article