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The shape of mammalian phylogeny: Patterns, processes and scales

Philosophical Transactions B

September 2011
366(1577):2462-77

DOI:10.1098/rstb.2011.0025

Source
PubMed

Authors:

Andy Purvis

Natural History Museum, London

Jesús Rodríguez

Centro de Investigación sobre la Evolución Humana

Show all 5 authorsHide

Mammalian phylogeny is far too asymmetric for all contemporaneous lineages to have had equal chances of diversifying. We consider this asymmetry or imbalance from four perspectives. First, we infer a minimal set of 'regime changes'-points at which net diversification rate has changed-identifying 15 significant radiations and 12 clades that may be 'downshifts'. We next show that mammalian phylogeny is similar in shape to a large set of published phylogenies of other vertebrate, arthropod and plant groups, suggesting that many clades may diversify under a largely shared set of 'rules'. Third, we simulate six simple macroevolutionary models, showing that those where speciation slows down as geographical or niche space is filled, produce more realistic phylogenies than do models involving key innovations. Lastly, an analysis of the spatial scaling of imbalance shows that the phylogeny of species within an assemblage, ecoregion or larger area always tends to be more unbalanced than expected from the phylogeny of species at the next more inclusive spatial scale. We conclude with a verbal model of mammalian macroevolution, which emphasizes the importance to diversification of accessing new regions of geographical or niche space.

(a) I w signature for the whole mammalian phylogeny. Points are within-bin I w ; vertical lines indicate + 1 s.e. (Note the large s.e. of the second point from the right, associated with a small sample size.) Horizontal dashed line, ERM expectation . Solid line is weighted regression, with sample sizes as the weights: slope ¼ 0.025, t 8 ¼ 2.573, p ¼ 0.03. (b) b signature for the whole tree. Points are within-bin estimates of b; vertical lines are confidence intervals under a x 2 approximation. (The second point from the right is again unusual but uncertain.) Horizontal dashed line, ERM expectation. Solid line is weighted regression, with the inverse of the confidence interval width as weights: slope ¼ 0.088, t 8 ¼ 2.238, p ¼ 0.06. (c) I w signature for each of four superorders. Points are within-bin mean I w . Triangles, Marsupialia; diamonds, Afrotheria; squares, Laurasiatheria; circles, Euarchontoglires. Dotted line is regression through Marsupialia and Afrotheria (slope ¼ 20.067, t 14 ¼ 21.768, p ¼ 0.1); solid line is regression through Laurasiatheria and Euarchontoglires (slope ¼ 0.032, t 17 ¼ 2.685, p ¼ 0.02); regressions weighted as in (a).

…

Diversification rate shifts localized on the supertree. Major clades are summarized with pictograms (clockwise from top: Rodentia, 'Marsupials', 'Afrotheria', 'Eulipotyphyla', Chiroptera, Carnivora, Cetartiodactyla, Primates, Lagomorpha, Sciuridae). Filled circles indicate subtrees with betas different from that of the whole tree (see text for details). More unbalanced: red (non-overlapping CI) and pink (maximum-likelihood estimate of b for subtree outside whole-tree CI); more balanced: green (non-overlapping CI) and light green (ML estimate of b for subtree outside whole-tree CI). Triangles indicate nodes with significant (p 0.05) upshifts as indicated by D 1 (upwards pointing) and putative downshifts (downwards pointing).

…

I w and b signatures for (a,d) plants, (b,e) arthropods and (c,f ) non-mammalian vertebrates. Top row as in figure 1a. Bottom row as in figure 1b. One point in f lies well above the plotting region (b ¼ 3.54), and is also an outlier in c.

…

I w and b signatures for six simulation models, which are described fully in the text. (a,c) Instantaneous speciation rate depends linearly on the values of an evolving trait (squares, binary trait; circles, gradually evolving trait; diamonds, punctuational trait). (b,d) Models in which close relatives often have very different speciation rates. Squares, rates depend on a fast-evolving binary trait; circles, rates decline exponentially with time since speciation; diamonds, spatial model; dashed line, ERM expectation. Bestfit lines for vertebrate data (solid lines) and non-vertebrate data (dotted lines) are provided for comparison.

…

Maps of (a,c,e) b and (b,d,f ) b dev for (a,b) biorealms, (c,d) ecoregions and (e,f ) assemblages. See text for explanation.

…

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Research

The shape of mammalian phylogeny:

patterns, processes and scales

Andy Purvis1,2,*, Susanne A. Fritz3, Jesu

´s Rodrı

´guez4,

Paul H. Harvey5and Richard Grenyer2,6

Department of Life Sciences, Imperial College London, Silwood Park, Ascot SL5 7PY, UK

NERC Centre for Population Biology, Imperial College London, Silwood Park,

Ascot SL5 7PY, UK

Department of Biology, Centre for Macroecology, Evolution and Climate, University of Copenhagen,

2100 Copenhagen, Denmark

Centro Nacional de Investigacio

´n de la Evolucio

´n Humana (CENIEH), 09002 Burgos, Spain

Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK

School of Geography and the Environment, University of Oxford, South Parks Road,

Oxford OX1 3QY, UK

Mammalian phylogeny is far too asymmetric for all contemporaneous lineages to have had equal

chances of diversifying. We consider this asymmetry or imbalance from four perspectives. First,

we infer a minimal set of ‘regime changes’—points at which net diversiﬁcation rate has changed—

identifying 15 signiﬁcant radiations and 12 clades that may be ‘downshifts’. We next show that

mammalian phylogeny is similar in shape to a large set of published phylogenies of other vertebrate,

arthropod and plant groups, suggesting that many clades may diversify under a largely shared set of

‘rules’. Third, we simulate six simple macroevolutionary models, showing that those where speciation

slows down as geographical or niche space is ﬁlled, produce more realistic phylogenies than do models

involving key innovations. Lastly, an analysis of the spatial scaling of imbalance shows that the

phylogeny of species within an assemblage, ecoregion or larger area always tends to be more unba-

lanced than expected from the phylogeny of species at the next more inclusive spatial scale. We

conclude with a verbal model of mammalian macroevolution, which emphasizes the importance to

diversiﬁcation of accessing new regions of geographical or niche space.

Keywords: macroevolution; phylogenetic imbalance; evolutionary radiations; simulation;

stochastic model; community phylogenetics

1. INTRODUCTION

As our ability to reconstruct phylogenies has devel-

oped, it has become clear that the Tree of Life’s

structure is much less regular than most early depic-

tions: proliferation has been much more rapid in

some branches and at some times than others

(reviewed by [1–3]). In mammals, for instance, a

recent taxonomy listed 2277 species of rodent and

1166 species of bat, but only one aardvark, two

ﬂying lemurs and three elephants [4]—even though

all these clades are tens of millions of years old.

There is a long history of both deterministic and sto-

chastic explanations for such diversity differences,

both in general (e.g. [5–7]) and in mammals (e.g.

[6,8,9]).

Phylogenies of present-day species provide two

valuable—though still incomplete—lines of evidence

that can be analysed to gain more precise insight into

macroevolution [1–3]. First, analysing the temporal

spacing of nodes can reveal changes in diversiﬁcation

rates over time. Such an analysis of mammalian phylo-

geny found evidence for a pulse of early radiation

about 100– 85 Ma, followed by low rates until about

10 Myr after the cretaceous– paleogene (or K– Pg)

boundary, when diversiﬁcation again sped up [10].

However, analyses of nodes ages depend on the ade-

quacy of the methods used to estimate dates—still a

contentious area (e.g. [11,12])—and are unlikely to

estimate extinction history accurately [13–15].

In this paper, we therefore focus on the second line

of evidence—the phylogeny’s asymmetry or imbal-

ance. If two sister clades have diversiﬁed to very

different extents, their species have probably had

different underlying probabilities of diversifying [16].

Measures of imbalance seek to capture the difference

in species-richness between sister clades [1], and are

commonly used to test whether contemporaneous

species could all have had the same chances of diversi-

ﬁcation, a null model known as the equal-rates Markov

(ERM) model. We begin by analysing the overall

imbalance of mammalian phylogeny before using a

*Author for correspondence (a.purvis@imperial.ac.uk).

One contribution of 12 to a Theme Issue ‘Global biodiversity

of mammals’.

Phil. Trans. R. Soc. B (2011) 366, 2462–2477

doi:10.1098/rstb.2011.0025

2462 This journal is q2011 The Royal Society

range of empirical and simulation approaches to try to

understand mammalian macroevolution.

2. THE SHAPE OF MAMMALIAN PHYLOGENY

We used a modiﬁcation [17] of Bininda-Emonds et al.’s

[10] phylogeny because it adopts a more recent taxon-

omy [4]. We excluded four domesticated species,

generated by artiﬁcial rather than natural selection,

from all of our analyses. The resulting tree has 925

bifurcations with enough present-day descendants to

be informative about imbalance. Importantly, it con-

tains the great majority (5016/5416) of the recognized

species: the imbalance of highly incomplete phylogenies

will be an unbiased estimate of the true imbalance

if sampling is random, but strict randomness may

be unlikely [18]. Wilkinson et al.[19] noted that the

method used to construct the supertree is biased with

respect to tree shape, tending to favour more unba-

lanced topologies. We therefore also analysed two

near-complete, well-resolved, species-level ‘superma-

trix’ Bayesian phylogenies of orders—Carnivora [20]

and Cetartiodactyla [21]: comparing the imbalance of

these trees with the relevant portions of the super-

tree provides a preliminary assessment of whether the

supertree’s imbalance could be artifactual.

We characterized imbalance in two ways. The ﬁrst is

a purely phenomenological measure of pattern. We

used Fusco & Cronk’s [22] approach to compute an

imbalance score, I, for each of the 925 informative

nodes, according to

I¼Bm

Mm;

where Bis the number of species in the more diverse

sister clade, mis the smallest value Bcould take and

Mthe largest possible value for B. These Ivalues

range from zero (as symmetric as possible) to one (com-

pletely asymmetric). We applied weights as in Purvis

et al.[23] such that, under ERM, the weighted mean I

(hereafter, I

) has an expectation of 0.5 for any node

size (i.e. total number of extant descendant species).

The empirical I

for the whole set of nodes was com-

pared with the null expectation using a weighted

t-test. We likewise tested marsupials, all placentals and

four placental superorders separately. On rejecting

ERM, we then constructed imbalance signatures to pro-

vide a richer description of imbalance; we grouped

nodes into 10 bins of equal width on the log(node

size) axis between the clade’s minimum and maximum

node size, computed I

within each bin and regressed I

against mean log(node size) among bins. Although the

nodal Ivalues have a strongly non-normal distribution,

use of within-bin I

gives regressions a much more nor-

mally distributed error term. In these regressions, node

size might be acting as a proxy for node age—whatever

processes are causing imbalance can operate for longer

in older clades. We tested this possibility using weighted

multiple regression of nodal Ion log(node size) and

log(clade age). The R package caper (C. D. L. Orme

2011, unpublished data, http://caper.r-forge.r-project.

org/) was used to compute nodal imbalance scores.

We also used a process-based measure of imbalance,

namely the estimate of

from Aldous’s [24]

-split

model of clade growth. The range of

is from – 2 to

þ1; trees where

,0 are more unbalanced than

average ERM trees, and those with

.0aremore

balanced. Two trees that grew under the same

-split

model should yield very similar

estimates. Aldous

[24] was motivated by mathematical simplicity rather

than biological considerations, and biological meaning

of particular

values remains opaque [25]. Nonethe-

less, a

of approximately – 1 has been found to

provide a good ﬁt to a wide range of (incomplete) pub-

lished phylogenies [25], perhaps implying a common

macroevolutionary process. At the very least,

reﬂects

tree shape pattern differently from I

.Toestimate

,we

modiﬁed the function maxlik.betasplit in the R package

apTreeshape [26] in two ways. First, we removed the

requirement for a fully resolved phylogeny. (We used

simulations, not shown, to check that estimates of

were not systematically biased by randomly collapsing

branches in ERM phylogenies.) Second, to avoid

underﬂow errors, we amended one of the internal func-

tions to work with the log(beta) distribution rather than

the beta distribution. We then optimized

for the

whole tree and for the same subtrees and binned sets

of nodes as identiﬁed above.

Table 1. Estimates of I

and

for portions of mammalian phylogeny. n¼number of informative nodes. Signiﬁcance of

departure from the equal-rates Markov expectation is assessed by weighted t-test for I

and from 1000 parametric bootstrap

replicates for

. 95% CI, conﬁdence interval for

from bootstrapping.

clade nI

+s.e.

95% CI

Mammalia 925 0.631 +0.013*** 20.857*** 20.974 to 20.710

Marsupialia 110 0.650 +0.038*** 20.904*** 21.215 to 20.392

Eutheria 812 0.627 +0.014*** 20.847*** 20.985 to 20.684

Afrotheria 15 0.657 +0.090 20.878 21.671 to 6.113

Xenarthra 7 0.420 +0.160 7.264 22.000 to .500

Laurasiatheria 398 0.603 +0.020*** 20.765*** 20.955 to 20.498

Euarchontoglires 389 0.653 +0.020*** 20.938*** 21.110 to 20.731

Carnivora: supertree 108 0.590+0.040 20.752*** 21.104 to 20.137

Carnivora: cyt b tree 140 0.672 +0.033 21.102*** 21.276 to 20.855

Cetartiodactyla: supertree 69 0.616 +0.032 20.770 21.216 to 0.014

Cetartiodactyla: cyt b tree 148 0.662 +0.032 21.002*** 21.186 to 20.732

*p,0.05; **p,0.01, ***p,0.001.

The shape of mammalian phylogeny A. Purvis et al. 2463

Phil. Trans. R. Soc. B (2011)

Both I

and

indicate that chances of diversiﬁca-

tion have varied among contemporaneous species

(table 1; ﬁrst row). The values of I

and

are very

similar for marsupials and placentals separately, and

for three of the four placental superorders (table 1),

the exception being Xenarthra, which had only seven

informative nodes. The cyt b phylogenies were slightly

more unbalanced than the corresponding portions of

the supertree (table 1), suggesting that the supertree’s

imbalance is not artifactual.

The imbalance signature in ﬁgure 1ashows that I

increases with node size across the whole phylogeny.

The regressions of I

on log(node size) did not differ

signiﬁcantly among placental superorders (omitting

Xenarthra on grounds of sample size; F

2,21

¼1.67, p.

0.2), but placental and marsupial imbalance signatures

do differ signiﬁcantly (F

1,31

¼4.83, p¼0.04). However,

a clearer distinction is that I

increases more with

log(node size) in the two more diverse placental super-

orders (Laurasiatheria and Euarchontoglires) than

in Afrotheria or Marsupialia (F

1,31

¼6.25, p¼0.02;

ﬁgure 1c). ANCOVA showed no differences between

the imbalance signatures of cyt b tree and supertree

for either order (Carnivora: F

2,244

¼1.30, p.0.2;

Cetartiodactyla: F

2,213

¼0.61, p.0.5).

Multiple regression suggests that node age does not

confound the relationship seen in ﬁgure 1a: nodal I

does not depend on log(node age) (t

922

¼21.085,

p.0.2) but increases weakly with log(node size)

(slope ¼0.024, t

923

¼2.418, p¼0.02). Although the

two predictors are correlated, variance inﬂation factors

were less than 2, indicating no strong collinearity. Con-

trary to expectation,

shows a weak tendency ( p¼

0.06) to become less negative—closer to ERM—for

larger nodes (ﬁgure 1b).

The ﬁnding that mammalian phylogeny is too unba-

lanced for ERM is unsurprising: analyses of large

individual trees (e.g. [27,28]) and collections of smal-

ler trees (e.g. [18,29 –31]) have repeatedly shown that

complete or nearly complete empirical phylogenies are

markedly too asymmetric for ERM. The estimate of

is signiﬁcantly less negative than the value of 21

suggested by Blum & Francois [25] for their smaller,

incomplete trees, but very close to the estimate derived

from their largest fully bifurcating trees (20.89).

The dependence of I

on node size is in line with

some previous studies. Purvis & Agapow [31], analysing

a set of 61 phylogenies across a wide range of taxa, found

that nodes with a given number of higher taxa (e.g.

genera) descended from them were more unbalanced

on average than nodes having the same number of

species descended from them. Holman [32] showed

that I

increased roughly linearly with log(node size)

across the same set of phylogenies, and a similar pattern

was also reported for a collection of highly incomplete

trees [33]. Freckleton et al.[34] showed I

to rise with

log(node size) in carnivora but to decrease in New

World primates. An increase in I

with node size indi-

cates the presence of small basal clades; however,

clades are unlikely to persist for long at low numbers

under diversity independent dynamics. The frequency

of small, old clades (especially in Laurasiatheria and

Euarchontoglires) therefore suggests either that their

dynamics are diversity-dependent or that they are now

declining deterministically (having previously grown

deterministically). The surprising ﬁnding that—unlike

—

becomes closer to ERM expectations at deeper

nodes suggests that it is best not to view the value of

as a process parameter; it is not constant. A possible

mechanism is if many clades having a greatly reduced

diversiﬁcation rate die out before they become old,

making the tree self-pruning.

The overall shape of mammalian phylogeny is com-

plex, reﬂecting a mixture of diversiﬁcation rates. The

0.2

0.4

0.6

0.8

1.0

(a)

(b)

(c)

within-bin weighted mean I

−1.0

−0.5

0.0

0.5

1.0

within-bin b

234567

0.2

0.4

0.6

0.8

1.0

(node size)

within-bin weighted mean I

Figure 1. (a)I

signature for the whole mammalian phylogeny.

Points are within-bin I

; vertical lines indicate +1s.e. (Note

the large s.e. of the second point from the right, associated

with a small sample size.) Horizontal dashed line, ERM expec-

tation. Solid line is weighted regression, with sample sizes as the

weights: slope ¼0.025, t

¼2.573, p¼0.03. (b)

signature

for the whole tree. Points are within-bin estimates of

; vertical

lines are conﬁdence intervals under a

approximation. (The

second point from the right is again unusual but uncertain.)

Horizontal dashed line, ERM expectation. Solid line is

weighted regression, with the inverse of the conﬁdence interval

width as weights: slope ¼0.088, t

¼2.238, p¼0.06. (c)I

signature for each of four superorders. Points are within-bin

mean I

. Triangles, Marsupialia; diamonds, Afrotheria;

squares, Laurasiatheria; circles, Euarchontoglires. Dotted line

is regression through Marsupialia and Afrotheria

(slope ¼20.067, t

¼21.768, p¼0.1); solid line is regres-

sion through Laurasiatheria and Euarchontoglires (slope ¼

0.032, t

¼2.685, p¼0.02); regressions weighted as in (a).

2464 A. Purvis et al. The shape of mammalian phylogeny

Phil. Trans. R. Soc. B (2011)

next section is to identify clades that stand out as

unusual compared with the tree as a whole.

3. WINNERS AND LOSERS: PINPOINTING SHIFTS

IN DIVERSIFICATION RATE

Here, we distinguish between two kinds of deviation

from the tree-wide imbalance signature. Individual

nodes with unusual imbalance can suggest which

intrinsic and extrinsic phenomena may have regulated

diversiﬁcation [16,22]. On the other hand, consistent

variation in imbalance—i.e. a whole subtree with

imbalance differing from the parental phylogeny—is

analogous to non-stationarity in models of spatial or

time-series data: it suggests that different processes

have been operating in different parts of the phylogeny.

To locate individual changes in diversiﬁcation rate,

we used the D

statistic [35,36] as implemented in

apTreeshape [26]. D

considers two pieces of evidence

in deciding whether diversiﬁcation rate has increased

along a branch: the imbalance of the ancestral node

and the imbalance of the descendant. Nodal imbal-

ance is expressed as the ratio between the likelihood

of the observed split under a single-rate ERM process,

and the likelihood of the same split if the larger daughter

clade had diversiﬁed with a 100-fold higher rate; larger

likelihood ratios therefore indicate higher imbalance. D

is simply the difference between the likelihood ratios of

the nodes at the start and the end of a branch; signiﬁ-

cance is assessed by simulating a single-rate ERM

process. An upshifted descendant clade will tend to

make the parental clade appear to be upshifted too (a

phenomenon known as ‘trickle down’; [37]). In such

cases, both likelihood ratios will be large but the D

between them relatively small. Consequently (following

the contingency table in [36]), here we ascribe an

increase in diversiﬁcation rate only to branches where

a signiﬁcant D

is observed between an unbalanced

ancestral node and a more balanced descendant. Of

1335 bifurcating nodes in the supertree, 15 provided

such evidence of an increase in diversiﬁcation rate.

These nodes are presented in table 2, and depicted

with an upward-facing black triangle on ﬁgure 2.

Moore et al.[37] argue that D

can only detect

increases in diversiﬁcation rate, because clades under-

going a decrease are unlikely to survive to be observed.

We suggest that D

can also be used to pinpoint some

decreases in diversiﬁcation rate (‘downshifts’), albeit

with caveats. For small clades, the likelihood under a

single-rate ERM process will be similar to that under

a heterogenous rate, as there is little evidence to differ-

entiate between the two: small clades have inherently

low likelihood ratios. As a result, a signiﬁcant D

can

be found along a branch between an unbalanced

ancestral node and a smaller descendant. If imbalance

at the ancestral node cannot be ascribed to an increase

in rate on the branch leading to its larger daughter, and

if a signiﬁcant D

is observed on the branch leading to

its smaller daughter, we contend this to suggest a

decrease in net diversiﬁcation rate in the smaller

Table 2. Rate-shifted clades. MSW taxon ¼name of clade in Wilson & Reeder [4]. n¼number of species in clade, n

sister

number of species in sister clade, n

ancs

¼number of species in sister group to ancestral node.

MSW taxon (3rd edn) age (Myr) nn

sister

ancs

node label

(a) Clades arising from signiﬁcant ( p0.05) upshifts in diversiﬁcation rate (100-fold shift in

sister

) as measured by D

[37]

‘Boreoeutheria’ 96.2 4614 29 69 8

Dipodomyinae þPerognathinae þsome Geomyidae (Rodentia) 35.9 96 2 1456 443

Sciurinae þXerinae þCalloscurinae (Rodentia; Sciuridae) 40.2 275 1 1 585

Sciurinae (Rodentia; Sciuridae) 18.2 80 4 7 681

within Sylvivagus (Lagomorpha; Leporidae) 8.3 10 1 2 809

Simiiformes 52.4 265 7 79 885

Carnivora 64.1 282 8 325 1330

Hipposideridae þRhinolophidae (Chiroptera) 47.5 147 16 5 1702

Artibeus (Chiroptera; Phyllostomidae) 8.6 17 1 26 1845

Molossus and allies (Chiroptera; Molossidae) 22.8 58 4 10 1898

Subgenus Sorex (Soricomorpha; Soricidae) 24 47 3 3 2162

Phalangeriformes þMacropodiformes (Diprotodontia) 45.8 122 4 1 2285

Macropus (Diprotodontia; Macropodidae) 10.1 13 1 2 2357

Dasyuridae (Dasyuromorphia) 25.4 63 1 2 2391

Didelphinae (Didelphimorphia; Didelphidae) 51.6 79 5 216 2464

(b) Clades tentatively identiﬁed as resulting from downshifts in diversiﬁcation rate

Monotremata 63.6 4 5012 n/a 2

Anomaluromorpha (Rodentia) 54.9 9 1447 98 15

Castoridae (Rodentia) 12.1 2 96 1456 442

Sciurotamias (Rodentia; Sciuridae) 5 2 120 6 674

Dermoptera 12.8 2 351 20 1115

Girafﬁdae þAntilocaprinae (Cetartiodactyla) 20.2 3 189 8 1251

Camelidae (Cetartiodactyla) 21.8 3 306 16 1315

Harpionycteris (Chiroptera; Pteropodidae) 3.8 2 85 12 1617

Noctilionidae (Chiroptera) 3.5 2 158 1 1763

Desmodontinae (Chiroptera; Phyllostomidae) 26.8 3 147 8 1772

Solenodeontidae (Soricomorpha) 40.5 2 390 1669 2216

‘Afrotheria’ 89.5 69 4643 300 2236

The shape of mammalian phylogeny A. Purvis et al. 2465

Phil. Trans. R. Soc. B (2011)

daughter clade. Such downshifted clades are depicted

with downward triangles in ﬁgure 2 and table 2 gives

details. We caution that our evidence is only sugges-

tive, and note that our approach does not permit the

identiﬁcation of single species as downshifts.

localizes rate shifts to an individual branch, but its

performance is yet to be evaluated in the context of gra-

dual changes across the phylogeny. These would

probably best be detected using the temporal spacing

of nodes, but the supertree’s incomplete resolution

and dating complicates such an analysis. Instead, we

used local variation in Aldous’

to paint a wider picture

of variation in imbalance across the supertree, and to

look for subtrees having consistent but unusual imbal-

ance signatures. Our motivation is that any whole-tree

imbalance statistic, while useful, is a measure of the

central tendency of imbalance for a set of nodes and

can therefore average away interesting biological

patterns. We therefore calculated the maximum-likeli-

hood estimate of

for every subtree in the phylogeny.

Several subtrees yield

values whose conﬁdence

intervals do not overlap with those of the whole-tree

estimate. Primates are signiﬁcantly more balanced

and sciurids signiﬁcantly more unbalanced, as shown

by the coloured circles in ﬁgure 2 (red subtrees are

unbalanced, dark green are balanced). Figure 2 also

shows that variation in imbalance is not randomly

located around the phylogeny: many other clades have

imbalance signatures that differ consistently, although

not signiﬁcantly, from the whole-tree imbalance (pink

and mint green subtrees have a

outside the 95% CI

for the whole-tree estimate). Whole-tree imbalance

measures do seem to obscure interesting variation,

although in mammals the effect size may be slight.

What mechanisms might underlie this variation

in imbalance across mammalian phylogeny? Compar-

ing the topologies of Primates and Sciuridae—the

clades with the most extreme balance and imbalance,

respectively—suggests that the answer could lie in

the interaction of historical biogeography and biotic

responses to global change. The differing geographical

nature of diversiﬁcation in response to global change

has, we suspect, played a large part in generating the

observed differences in imbalance.

Crown-group Primates are old for their habitat. We

infer Simiiformes to be a signiﬁcant radiation, at about

52.4 Ma, a timing that closely matches the ﬁrst ap-

pearance of crown-group Primates (Euprimates) in

the fossil records of Europe, Asia and North America

at the onset of the Eocene [38,39]. However, by the

Mid-Eocene, Primates as a whole were beginning to

decline even as crown-group Simiiformes were diversi-

fying. Simiiformes radiated in a world that was cooling

and drying throughout the Eocene [40], as the area of

habitat similar to modern tropical forests was declining

[41] and possibly ﬁlling up with competitors such as

arboreal rodents [42]. New World primates, in par-

ticular, show an extremely balanced topology.

Repeated variation in the availability of tropical forest

has been suggested to govern diversiﬁcation regimes

Figure 2. Diversiﬁcation rate shifts localized on the supertree. Major clades are summarized with pictograms (clockwise from top:

Rodentia, ‘Marsupials’, ‘Afrotheria’, ‘Eulipotyphyla’, Chiroptera, Carnivora, Cetartiodactyla, Primates, Lagomorpha, Sciuridae).

Filled circles indicate subtrees with betas different from that of the whole tree (see text for details). More unbalanced: red

(non-overlapping CI) and pink (maximum-likelihood estimate of

for subtree outside whole-tree CI); more balanced: green

(non-overlapping CI) and light green (ML estimate of

for subtree outside whole-tree CI). Triangles indicate nodes with

signiﬁcant (p0.05) upshifts as indicated by D

(upwards pointing) and putative downshifts (downwards pointing).

2466 A. Purvis et al. The shape of mammalian phylogeny

Phil. Trans. R. Soc. B (2011)

in South American vertebrates [43] and speciﬁcally

the topological balance of Primates (the ‘simultaneous

across-lineage vicariance’ of Heard & Cox [44]). We

suggest that the balanced phylogeny of Primates is a

consequence of such vicariant speciation within a

limited geographical region, throughout much of the

history of crown-group Primates (and speculate

that Xenarthra—also an unusually balanced clade

(table 1)—may have had a similar history). Towards

the end of the Eocene, sciurids experienced the ﬂip

side of the same coin, as xeric habitats, savannah and

temperate forest all increased rapidly in area leading,

we speculate, to a diffusion of lineages through newly

available space and a resulting unbalanced topology.

Many of the diversiﬁcation rate upshifts in table 2 can

be argued to correspond to sudden access to novel geo-

graphical or habitat space, although palaeontological

data are patchy, limiting the scope for meta-analysis.

Kisel et al.[45] present evidence that, in mammals,

colonization of new geographical areas usually elevates

diversiﬁcation.

Downshifts in diversiﬁcation rate are even harder

than upshifts to infer or date reliably, so interpretation

of the clades in table 2 is necessarily more speculative.

Limited geographical space, geographical isolation or

disproportionate extinction can all be expected to

lead to a depauperate extant clade [46,47]. In some

cases, extinction does appear to be a contributory

factor, either in deep time—Afrotherians, and perhaps

monotremes—or more recently—camelids, girafﬁds

(e.g. [48]) and solenodons [49]. Most of the down-

shifted clades are moderately widespread and some

(e.g. Castoridae and Camelidae) are very wide-ran-

ging. From an ecological perspective, many seem to

be highly unusual mammals: vampire and ﬁshing

bats, high-altitude xeric artiodactyls, lodge-building

riparian rodents, toothless monotremes and non-

chiropteran gliders (including one highly balanced

clade in the otherwise universally unbalanced Sciuri-

dae). Unusualness is a statistical concept, of course,

and species-rich groups are bound to appear typical,

but specialization to a narrow adaptive zone may be

the best predictor of species-poverty. We speculate

that such specializations may reduce the likelihood of

radiation—because the niches are not broad enough

to support multiple species in sympatry—but at the

same time confer a strong incumbency advantage

[50] that reduces the per-species background extinc-

tion rate if niches are stable over time [51]. Such a

mechanism would lead to species in relictual groups

tending to be highly specialized, which is certainly

true for some of the species-poor basal clades (e.g.

Monotremata, Xenarthra).

If biogeographic history has been important in

shaping mammalian diversity [52], then it should be con-

sidered alongside ecological or life-history differences

when trying to understand clade-richness patterns [53].

It may also explain why ecological or life-history differ-

ences alone have so far explained little of the variance

in species-richness amongmammalian clades inphyloge-

netic comparative analyses. A test of whether dispersal

ability is associated with richness in mammals, as it is

in birds [54], would be of great interest.

4. IS MAMMALIAN PHYLOGENY UNUSUAL?

How common is the shape of mammalian phylogeny?

Previous studies suggest that tree shape may be con-

sistent (and consistently too unbalanced for ERM)

among groups as different as plants, insects and ver-

tebrates [29,30,55]. However, these studies had low

power: they all reduced each phylogeny’s shape to a

single number, and the largest compilation had 120

trees. More recent suggestions of a general shape for

phylogenies [25,56] have been based on incomplete

trees. To provide a more stringent test, we compare

the mammalian imbalance signature to that of a com-

pilation from the literature of 243 non-overlapping and

non-mammalian near-complete phylogenies. The

compilation includes all the non-mammalian trees

analysed previously by Purvis & Agapow [31] and

Holman [32], supplemented by some more recent

phylogenies [57,58]. All trees selected met the criteria

used in Purvis & Agapow [31], excepting that Bayesian

0.2

0.4

0.6

0.8

1.0 (a)(b)(c)

(d)(e)(f)

within-bin Iw

24681012

−2.0

−1.0

0.5

1.0

log (node size)

within-bin b

24681012

log (node size)

24681012

log (node size)

Figure 3. I

and

signatures for (a,d) plants, (b,e) arthropods and (c,f) non-mammalian vertebrates. Top row as in ﬁgure 1a.

Bottom row as in ﬁgure 1b. One point in flies well above the plotting region (

¼3.54), and is also an outlier in c.

The shape of mammalian phylogeny A. Purvis et al. 2467

Phil. Trans. R. Soc. B (2011)

trees (summarized as the maximum clade credibility

tree from the posterior distribution) were also used.

Outgroups were removed prior to analysis.

The phylogenies provided 2496 informative nodes,

representing many groups from three major clades—

arthropods (73 trees, 582 informative nodes); plants

(66 trees, 812 nodes); and non-mammalian vertebrates

(102 trees, 1102 nodes). Figure 3 shows how I

and

vary with log(node size) in these three groups. The I

regression lines for plants and arthropods are not

statistically distinct (ANCOVA: F

2,16

¼0.511, p.0.6;

pooled intercept ¼0.622 +0.0167 s.e., pooled slope ¼

0.0159 +0.0034 s.e.); neither are the mammalian and

non-mammalian vertebrate lines (ANCOVA: F

2,16

1.355, p.0.2; pooled intercept ¼0.532 +0.0217

s.e.; pooled slope ¼0.0306 +0.0077 s.e.). Vertebrates

(mammals þnon-mammals) and non-vertebrates

(plants þarthropods) do not reject a pooled slope

(ANCOVA: F

1,36

¼3.402, p¼0.07; slope ¼0.0194 +

0.0035) but their intercepts differ signiﬁcantly (non-ver-

tebrates 0.607 +0.0179 s.e.; vertebrates 0.561 +

0.0145 s.e.; F

1,37

¼10.09, p¼0.003). This overall

model explains 61.5 per cent of the variance in I

among bins with three parameters, providing a succinct

phenomenological description of imbalance.

The relationship between

and log(node size) is

harder to model because of

’s asymmetric error dis-

tribution and apparent nonlinearity in plants (ﬁgure

3d) and arthropods (ﬁgure 3e). A least-squares regres-

sion, weighted by the inverse of the conﬁdence interval

width, has a signiﬁcantly positive slope within plants

(slope ¼0.036 +0.014 s.e.; t

¼2.62, p¼0.03) and

arthropods (slope ¼0.057 +0.021 s.e., t

¼2.73,

p¼0.03; these regressions are not distinct: F

2,16

0.713, p.0.5) but not in non-mammalian vertebrates

¼0.106, p.0.9). Like I

is consistent with a

model having a pooled slope (0.094 +0.0424 s.e.)

but an intercept that is further from ERM expectation

for the non-vertebrates (21.466 +0.304 s.e.) than for

the pooled vertebrates (21.040 +0.241 s.e.).

The supertree’s shape is not particularly unusual

compared with the other vertebrate phylogenies in our

dataset. However, vertebrate imbalance patterns differ

from those seen in plants and arthropods. This differ-

ence was not detected in the previous comparisons of

phylogeny shape [25,29,55,56], but emerges from

using more informative summaries of shape and from

analysing near-complete phylogenies. The difference

parallels one in the temporal spacing of nodes reported

by McPeek [59], who found that molecular phylogenies

of chordates showed more evidence of slowdown than

did those of arthropods or an angiosperm group (we

are unable to repeat McPeek’s test as our non-mamma-

lian phylogenies mostly lack timescales). The parallel

may simply be coincidence, but both a slowdown and

relatively balanced nodes near the tips of the tree are

to be expected if competition with closely related species

inhibits speciation; perhaps this situation is more

common in vertebrates than in arthropods or plants.

Whether competition is or is not part of why these

groups show different imbalance signatures, it cannot

explain why phylogenies are unbalanced relative to

ERM expectations. Why is imbalance so widespread?

Many models of clade growth have been proposed that,

when simulated, generate trees that are more unbalanced

than ERM (reviewed by Mooers et al.[3]). In the next

partofthepaper,wesimulateseveralofthesemodels

and analyse the phylogenies they produce. If only some

models produce trees whose imbalance patterns resemble

those in ﬁgure 3, we may be closer to understanding the

sorts of processes that have structured diversity.

5. WHAT SIMPLE PROCESSES CAN PRODUCE

REALISTIC TREE SHAPES?

We simulated the growth of 100 phylogenies to 1000

‘species’ each under six stochastic macroevolutionary

models, detailed in table 3 (this is obviously not an

exhaustive set: e.g. [3]), and constructed imbalance

signatures using the pooled data from each model.

Table 3. Details of the six macroevolutionary models that were simulated. X

¼ancestral value of trait X;X

¼value of trait

Xin species i;

¼instantaneous speciation rate in species i;

¼instantaneous speciation rate for species in the 0 state

or 1 state, respectively; r

¼per-lineage rate of transition of Xfrom state ato state b;t

¼time since species iwas last

involved in a speciation event. Models 1–5 were simulated using MeSA (www.agapow.net/software/mesa), model 6 with

PhyloGen (tree.bio.ed.ac.uk/software/phylogen/).

model details

1. punctuationally evolving key

trait [60,61]

¼100; X

changes (in both daughters) only at speciation events; changes are drawn

from a normal distribution,

¼0,

¼50. If X

becomes negative it is set to

¼0.001 þX

/100 000

2. gradually evolving key trait

[60,61]

¼100; Xchanges continuously by Brownian motion with

¼0,

¼5 per time

unit; X

and hence

were assessed every 0.1 time units and at every speciation event.

Negative X

were truncated to 0.

¼0.0001 þX

/10 000

3. binary key trait X

¼0,

¼1,

¼10. r

¼r

¼0.05. States and rates were assessed every time unit

and at every speciation event

4. fast-evolving binary key trait X

¼0,

¼1,

¼10. r

¼r

¼10. States and rates were assessed every time unit

and at every speciation event

5. patency [61]

¼max(5–15t

, 0.6); ages and rates were assessed every 0.001 time units and at every

speciation event

6. spatial model Initial

¼1. Ancestral species placed on an inﬁnite square grid (i.e. each cell is

adjacent to four others). Species occupy only one cell; they are selected at random to

speciate but can do so only if they are adjacent to at least one empty cell

2468 A. Purvis et al. The shape of mammalian phylogeny

Phil. Trans. R. Soc. B (2011)

The ﬁrst three models have speciation probabilities

depending on a slowly evolving key trait, a class of

models ﬁrst simulated by Heard [60]; in the fourth,

the binary trait that determines speciation rate evolves

so rapidly that it changes along most of the phylogeny’s

branches. The ﬁfth model (ﬁrst simulated by [61], see

also [62]) has speciation rates depending not on

species’ traits but on their ages, declining smoothly

from a high value straight after speciation to a back-

ground rate; we term this model patency to contrast

it with Losos & Adler’s [63] latency model in which

newly speciated lineages are unable to speciate again

for a while. Although it is implausible that species

age per se determines speciation rate, age could be

proxy for a macroecological trait that does itself

change as species age, like global population or

geographical range size [64,65]. The last process we

simulate is a spatial model in which species occupy

squares in a grid and can speciate only if they are

next to empty squares. In this model, species are

selected at random to speciate and, if they are

able to do so, the new species is placed in a rando-

mly chosen adjoining empty cell. The axes can be

viewed as latitude and longitude or dimensions of

niche space.

Figure 4 shows the I

and

signatures from each of

the six models. The models in which speciation rates

depend on slowly evolving traits produce qualitatively

different signatures (ﬁgure 4a,c) from those seen in

our empirical phylogenies (ﬁgure 3). These models

generated pronounced imbalance only deep within

the phylogeny: near the tips, nodes are consistent

with the ERM (because there is little or no interspecies

variation in the key trait on which clade selection can

act: [60,66]). The remaining three processes produce

more realistic signatures, with signiﬁcant imbalance

even among small nodes (ﬁgure 4b,d).

The poor ﬁt of models in which speciation rates

evolve only slowly suggests that (i) the relationship

between diversiﬁcation rate and phenotype is more

complex than that simulated, (ii) different traits

affect diversiﬁcation in different clades, or (iii) most

variation in diversiﬁcation rate near the tips of the phy-

logeny does not depend on slowly evolving traits. A

previous simulation study [60] concluded that

models with slowly evolving traits could produce

imbalance comparable to that seen in empirical phylo-

genies, but that study measured the shape of each

simulated or empirical tree using an index that is

most sensitive to imbalance at the root.

The patency model can produce trees similar to

the empirical data for non-vertebrates (dotted line in

ﬁgure 4b,d). A slightly more complex spatial model

might produce quantitatively as well as qualitati-

vely realistic imbalance signatures: unrealistic features

of our simulation include that the initial ancestor

could diversify equally in every direction (leading to

balanced nodes at the root of the tree), and that

space was divided into contiguous equal-sized units.

Patency’s relatively good ﬁt adds to growing evidence

that many clades may be at or near equilibrium diver-

sity. The North American Cenozoic mammalian fossil

record shows negative diversity-dependence [67] at the

species level, as does Phanerozoic marine macroinver-

tebrate genus-level diversity [68]. Complete molecular

phylogenies with timescales typically indicate that

0.4

0.5

0.6

0.7

0.8

0.9 (a)(b)

(c)(d)

within−bin Iw

234567

−2.0

−1.5

−1.0

−0.5

0.0

0.5

(node size)

within−bin b

234567

(node size)

Figure 4. I

and

signatures for six simulation models, which are described fully in the text. (a,c) Instantaneous speciation rate

depends linearly on the values of an evolving trait (squares, binary trait; circles, gradually evolving trait; diamonds, punctuational

trait). (b,d) Modelsin which close relatives often have very different speciation rates. Squares, rates depend on a fast-evolving binary

trait; circles, rates decline exponentially with time since speciation; diamonds, spatial model; dashed line, ERM expectation. Best-

ﬁt lines for vertebrate data (solid lines) and non-vertebrate data (dotted lines) are provided for comparison.

The shape of mammalian phylogeny A. Purvis et al. 2469

Phil. Trans. R. Soc. B (2011)

speciation rates have declined as clades have grown

[58,59]. More generally, numbers of species in non-

nested clades usually do not depend strongly on their

ages (e.g. [15,27,46,69]). Mammalian lineages that

disperse successfully to a different biogeographic

realm tend to be more diverse than their sister clades

in their ancestral regions [45]. Dispersal ability is the

strongest known predictor of clade richness among

bird families [54], but good broad-scale data on dis-

persal ability are not, so far as we are aware, available

for mammals (or indeed for many non-avian groups).

Taken together, our results may explain why studies

ﬁnding signiﬁcant correlates of diversity (reviewed by

Coyne & Orr [70]) have nearly all compared old,

rather than young, lineages. They may also help to

explain the dearth of signiﬁcant correlates, and why

the explanatory power has typically been low. We

suggest that study of lineages’ opportunity for diversi-

ﬁcation, as well as their intrinsic attributes, is likely to

prove fruitful. The next section therefore considers the

spatial context of mammalian faunas alongside the

imbalance patterns they show.

6. THE GEOGRAPHICAL SCALE

OF PHYLOGENETIC IMBALANCE

Phylogenies arise through ecology and evolution over

long time periods and, crucially, in a geographical con-

text. Despite the importance of geography, only one

study has so far analysed phylogeny shape geographically.

Heard & Cox [44], in a study of primates in African and

South American nature reserves, showed how imbalance

can be partitioned into a hierarchy of spatial scales. The

shape of the phylogenyof a local assemblage can be com-

pared with that of the regional source pool, and the latter

compared with global imbalance. Such a decomposition

can shed light on the spatial scale at which imbalance is

generated, and perhaps on the processes that produce

it. The approach has strong conceptual links with com-

munity phylogenetics [71,72]. However, we are trying

not only to see how the local community is assembled

from a source pool, but also whether local processes

scale up to shape species diversity at larger scales.

We considered four spatial scales: global, ‘biorealm’ (a

convenient shorthand for unique combinations of World

Wide Fund for Nature (WWF) biome and biogeographic

realm: deﬁnitions from Olson et al.[73]), WWF ecore-

gion [73] and local assemblage (from local species

checklists). We computed

for the phylogeny of mam-

mals within each unit (e.g. each biorealm) at each level;

we chose

over I

because it varies less with node size

(ﬁgure 1). We also compared the imbalance of each

unit with a biogeographic null expectation [44]—the

value expected if the unit’s species were drawn at

random from the species within the next spatial scale

up. For example, an ecoregion’s

would be compared

with the distribution of

values obtained from 1000 ran-

domizations, each of which samples the ecoregion’s

number of species from the biorealm that contains it.

As a summary statistic, we subtract from each observed

the mean of the corresponding null distribution; we

term this difference

dev

. If ecoregions’ species are

random subsets of those in the corresponding biorealms,

the

dev

will be centred around 0.

Cooper et al.[72], in a community phylogenetic

analysis, found that mammalian assemblages tended

to contain fewer closely related species than predicted

under the biogeographic null model, consistent with

competitive exclusion among close relatives. Given

that mammalian phylogeny overall is unbalanced, an

overdispersed sample—in which species-rich groups

will tend to be under-represented—will tend to be

less unbalanced. We therefore predict that the

dev

for assemblages within ecoregions will tend to be posi-

tive (i.e. assemblages more balanced than ecoregions),

though we note that Cooper et al.[72] restricted their

analyses to species within the same family. At large

scales, in situ diversiﬁcation presumably becomes an

important process [74], leading to diversifying clades

being over-represented relative to the biogeographic

null. However, the same pattern could be caused by

habitat ﬁltering—a top-down rather than bottom-up

process, whereby some clades are under-represented

because ecological conditions do not suit them. For

both processes, we expect

dev

at the large scale to

be negative, as spatial units should be more unba-

lanced than the biogeographic null. Competitive

exclusion might also be more likely in species-rich,

energy-poor or ecologically uniform systems.

Species lists for biorealms and ecoregions were gen-

erated by overlaying maps of the units (downloaded

from www.worldwildlife.org/science/ecoregions/item

1267.html, accessed August 2006) with distribution

maps from the Global Mammal Assessment [75]; his-

torical, uncertain or introduced species ranges were

excluded, and only extant non-marine species present

in the phylogeny were considered. The ﬁnal dataset

contained 4846 mammalian species within 62 bior-

ealms and 796 ecoregions. Assemblage data came

from a literature compilation of 242 species checklists

[76,77] containing a total of 1911 species and repre-

senting 140 ecoregions; the lists were for non-volant

mammals only, so Chiroptera (bats) were pruned

from the trees prior to randomizations of assemblages

within ecoregions. Because estimates of

from small

datasets seemed highly variable, we excluded all

samples with fewer than 20 species, leaving us with

59 biorealms, 728 ecoregions and 233 assemblages.

Figure 5 presents maps of

and

dev

at each spatial

scale. As expected, we ﬁnd that strong imbalance at

the largest spatial scale (biorealms) largely reﬂects

the global imbalance of mammalian phylogeny. All

59 biorealms yielded negative estimates of

,37of

them signiﬁcantly negative (ﬁgure 5a), whereas the

mean

dev

for biorealms (ﬁgure 5b) was not signiﬁ-

cantly different from 0 (mean ¼0.02; Wilcoxon

signed-rank test, V¼943, p.0.05). Eight biorealms

had phylogenies signiﬁcantly more unbalanced than

the biogeographic null while four biorealm phylogenies

were signiﬁcantly more balanced.

Imbalance was again ubiquitous at the intermediate

scale of ecoregions within biorealms: the estimate of

was positive in only 19 of the 728 ecoregions (none

signiﬁcantly), and was signiﬁcantly negative in 112

(ﬁgure 5c). Furthermore, the mean

dev

showed sig-

niﬁcant imbalance when compared with the biorealm

source pools (mean ¼20.07; Wilcoxon signed-rank

test, V¼101 487, p,0.001). The biogeographic

2470 A. Purvis et al. The shape of mammalian phylogeny

Phil. Trans. R. Soc. B (2011)

null model was rejected in 82 of the 720 ecoregions

where it could be tested (the ecoregion and biorealm

had the same set of species in the other eight eco-

regions); of these, 56 ecoregion phylogenies were

more balanced than expected and 26 more unbalanced

(ﬁgure 5d).

Moving to the level of assemblages within ecoregions,

was negative in 209 of 233 assemblages but signiﬁ-

cantly so in only two (ﬁgure 5e); this lack of

signiﬁcance is unsurprising given that the assemblage

phylogenies are seldom large. However, contrary to

our expectations,

dev

is signiﬁcantly negative on average

(mean ¼–0.12; Wilcoxon signed-rank test, V¼8503,

p,0.001), indicating that assemblage phylogenies

tend to be unbalanced compared with their ecoregion

source pools. This ﬁnding suggests that habitat ﬁltering

(or, less likely at this small scale, in situ speciation) dom-

inates over competitive exclusion, though community

phylogenetic approaches (e.g. [71,74]) would provide

a more direct test. Twelve assemblages rejected the bio-

geographic null model, out of 215 that could be tested;

seven were more balanced than expected and ﬁve more

unbalanced (ﬁgure 5f).

To investigate environmental inﬂuences on imbalance

signatures, we used multiple regression to predict

dev

from different variables that reﬂected current envi-

ronmental conditions or system characteristics. The

environmental variables for our spatial units were calcu-

lated using an ArcInfo macro; only assemblages with

a digitized polygon were included in this analysis

(215 assemblages; [76]). Variables and their sources

were: mean annual actual evapotranspiration (AET, in

millimetres; [78]; downloaded from http://www.grid.

unep.ch); mean annual temperature (in 8C; [79],

downloaded from http://www.worldclim.org/); mean

elevation and range in elevation (in metres; [80], down-

loaded from http://edc.usgs.gov); and ecosystem count

as a coarse measure of habitat heterogeneity ([81],

downloaded from http://edcsns17.cr.usgs.gov/glcc). Pre-

cipitation was included at ﬁrst but had to be excluded

because of variance inﬂation (it was highly collinear

with AET). All models also included the area (log-trans-

formed) and number of species within the focal unit.

Because in situ diversiﬁcation is expected to produce

concentrations of newly formed species, we additionally

included as a predictor the median terminal branch

length for the species in each spatialunit on the complete

mammalian phylogeny as a proxy for the recency of the

species; however, because terminal polytomies in the

supertree typically reﬂect lack of knowledge, we adjusted

the branch lengths following Mooers et al.[3].

Following Lichstein et al.[82] and Dormann et al.

[83], we tested for the presence of spatial autocorrela-

tion in the regression residuals from our initial,

ordinary least-squares (OLS) model with Moran’s

Icorrelograms, and accounted for the spatial autocor-

relation with spatial autoregression (SAR, simultaneous

autoregressive model). We used an SAR

error

model,

which models the autoregressive process in the error

term and has been recommended as a reliable spatial

method [84]. Moran’s Icorrelograms and spatial

models were generated with the R packages spdep [85]

and ncf [86]. We tested standardized Ivalues for signiﬁ-

cance with a one-tailed randomization test for positive

autocorrelation (999 permutations; [82]). In the SAR

models, we deﬁned neighbours as data points closer to

each other than a model-speciﬁc maximum distance,

which was chosen by optimizing the model AIC value

(Akaike’s information criterion) following Cooper &

Purvis [87]. We used a row-standardized coding

–1.61

(a)(b)

–2.18

–1.25 –0.75 –0.25 0.25 0.75 4.52 –0.60 –0.20 0.20 0.60 1.00 4.26

bb dev

(c)(d)

(e)(f)

Figure 5. Maps of (a,c,e)

and (b,d,f)

dev

for (a,b) biorealms, (c,d) ecoregions and (e,f) assemblages. See text for explanation.

The shape of mammalian phylogeny A. Purvis et al. 2471

Phil. Trans. R. Soc. B (2011)

scheme for the spatial weights matrix [84], calculated r

values using Nagelkerke’s formula [88] and assessed the

contribution of each variable to OLS and SAR model

ﬁts with likelihood-ratio tests for nested models [82].

Although

dev

was signiﬁcantly negative on average

at each spatial scale (meaning that the phylogeny of the

species present in the spatial unit tended to be more

unbalanced than the biogeographic null), the signiﬁ-

cant environmental predictors of

dev

depended on

scale (table 4), perhaps reﬂecting the differing relative

importance of top-down and bottom-up controls on

imbalance (though we caution that no model has

high explanatory power, and that the precision of

environmental variables is often scale-dependent).

At the largest scale, biorealm phylogenies were more

unbalanced relative to the global phylogeny when the

biorealm contained more different ecosystems and, in

OLS models, was at high altitude. The altitude term

distinguishes the montane biome from the remainder.

Habitat ﬁltering (a top-down process) and in situ diver-

siﬁcation (bottom-up) are both likely to be important

at this broad scale. Indeed, the latter may follow

from the former—if few clades are able to persist at

high altitudes, those that can are likely to radiate into

a wider range of niches than they might occupy in

less harsh environments.

Moving to ecoregions within biorealms, the map of

dev

(ﬁgure 5d) suggests that the most negative values

are in deserts, the Northern high latitudes, and the

Himalayas; the Amazonian basin fauna, by contrast,

has a relatively balanced phylogeny (positive

dev

The regression models ﬁnd stronger imbalance to be

associated with small ecoregions containing few

species and, in OLS models, having low AET and

more different ecosystems. Habitat ﬁltering is likely

to be more important at this scale than at the broader

scale, and in situ diversiﬁcation less so.

In situ diversiﬁcation is unlikely at the scale of

assemblages within ecoregions, whose patterns pre-

sumably reﬂect a mixture of habitat ﬁltering and

competitive exclusion. The generally negative

dev

values suggest that the former dominates, especially

in warm, topographically heterogeneous areas, but

the signs of the environmental predictors of

dev

are

consistent with a role for competitive exclusion in

assemblages that are species-rich, in cold, uniform

places.

Although interpretation of our results remains ten-

tative, the spatial decomposition of imbalance in

mammalian phylogeny reveals many patterns that

cannot be attributed to the biogeographic null. This

shows the possible usefulness of Heard & Cox’s [44]

groundbreaking approach for understanding how

bottom-up and top-down processes might interact to

control clade diversity and hence imbalance. Their rela-

tive lack of non-null results may reﬂect their choice of

the only large mammalian clade—Primates—that does

not have an unbalanced phylogeny at a global level.

7. A DESCRIPTIVE MODEL OF MAMMALIAN

MACROEVOLUTION

What do our analyses suggest about the processes that

have shaped mammalian phylogeny? The I

and

signatures of the supertree highlight the persistence

of small basal clades, and also show that closely related

species often have very different chances of diversiﬁca-

tion. Our simulations indicate that the latter is more

easily achieved if speciation rates decrease with time

since speciation or, more plausibly, with the occu-

pancy of adjacent areas or niches, than if they are

determined by slowly evolving traits. The occupancy

model is also compatible with our analysis of the

spatial scale of imbalance. It suggests that diversiﬁca-

tion will show negative diversity-dependence, a

pattern seen in many molecular phylogenies [58,59]

and the mammalian fossil record [67]. Equilibrium

diversity seems likely to depend on both the geographi-

cal area available to the clade and the amount of

energy available to support populations of its species

in the face of competition from other lineages.

Four main ways have been proposed by which a

clade or subclade’s diversity might increase determi-

nistically beyond this initial equilibrium (e.g. [6,9,53,

59,89–92]):

—Spreading in space. A species that colonizes new

geographical space, by dispersal to a disjunct

region or through an expansion of suitable habitat,

may diversify taxonomically without markedly

expanding its ecological niche [53,91,93]. The

equilibrium diversity of such a clade should

depend on the size of the new area and the

number of competitors present.

—Spreading in niche space. An adaptive breakthrough

to new niche space (a new adaptive zone; [9,93])

may permit a lineage to diversify more rapidly

than its sister clade [53,89]; again, equilibrium

diversity presumably depends on the size and occu-

pancy of the adaptive zone [89,94].

—Improvement. An adaptation that makes a species

markedly better at occupying its niche (through,

for example, increased metabolic efﬁciency or

disease resistance) may enable the species to out-

compete other species both within and at the

margin of its niche [9,89,93]. How far the resulting

clade is expected to spread through niche space is

governed by how much of a competitive edge the

improvement gives. Clades with such key inno-

vations are always expected to be more diverse

than their sister clades without them (though they

may competitively wipe out their sister clades; [89]).

—Narrower species. The ﬁrst three mechanisms raise

diversity by increasing the resource base; an

alternative is to slice the resource base more

thinly [89,95]. Sexual selection by female choice,

for example, makes rapid speciation more likely,

and is repeatedly associated with high diversity in

sister-clade comparisons within birds [69,96]. Eco-

logical specialization [97], behavioural and societal

complexity, philopatry, changes to karyotypic

architecture, or a ‘cellular’ population structure

might also be relevant to mammals.

If each of these four classes of event occurs at random

with respect to phylogeny (a reasonable null starting

point) then, by the law of averages, they are more

likely to happen within species-rich clades than in

2472 A. Purvis et al. The shape of mammalian phylogeny

Phil. Trans. R. Soc. B (2011)

Table 4. Non-spatial multiple regression (OLS) and spatial autoregression (SAR) model results for

dev

at each of three spatial levels. Apart from characterizing each model, we also show

each variable’s slope, signiﬁcance and likelihood-ratio test result (LR).

biorealms ecoregions assemblages

OLS SAR OLS SAR OLS SAR

AIC 235.3 234.1 320.6 82.5 335.6 314.5

d.f. 50 48 694 692 171 169

28.7 29.6 17.6 41.4 15.1 25.4

neighbourhood distance n.a. 1970.79 n.a. 626.9 n.a. 700.6

autocorrelation parameter n.a. 0.173 n.a. 0.614 n.a. 0.4

slope LR slope LR slope LR slope LR slope LR slope LR

AET ,0.001 ,0.1 ,0.001 ,0.1 ,0.001** 222.9 ,0.001 2.7 ,0.001 5.1 ,0.001 0.3

temperature 20.001 1.9 .20.001 1.5 20.004*4.1 ,0.001 ,0.1 20.019*6.8 20.016 3.2

mean elevation .20.001*5.2 .20.001 2.8 ,0.001 ,0.1 ,0.001 0.2 ,0.001 2.1 ,0.001 1.6

elevation range ,0.001 0.6 ,0.001 0.1 ,0.001 ,0.1 .20.001 0.2 .20.001*4.7 .20.001 3.0

ecosystem count 20.008** 10.2 20.007** 5.3 20.006*** 13.3 20.002 1.5 20.005 0.2 ,0.001 ,0.1

area 20.016 0.8 20.013 0.5 0.020*4.6 0.028** 10.2 20.010 0.1 20.003 ,0.1

species number ,0.001 0.7 ,0.001 0.3 0.002*** 47.0 0.001** 7.4 0.015*** 12.7 0.014*** 12.6

median terminal branch length 20.197 1.0 20.173 0.7 0.091 1.1 0.117 1.4 20.411 3.3 20.442 3.3

Signiﬁcance levels: *** p,0.001, ** p,0.01, *p,0.05.

The shape of mammalian phylogeny A. Purvis et al. 2473

Phil. Trans. R. Soc. B (2011)

species-poor ones; any initial pattern of imbalance in

the phylogeny is therefore accentuated. Adaptive

improvements will, likewise by the law of averages,

tend to arise in species adapted to wide rather than

narrow adaptive zones and living in large rather than

small regions. Individuals within these species will

tend to outcompete those in related species in the

same region, causing competitive extinctions as

Darwin [98] envisaged; and there may be successive

waves of improvement. For an innovation to spread

further, however, the clade bearing it will have to

either colonize new areas or break through to new

niches, both of which may have long waiting times

(i.e. low probabilities per unit time). ‘Unimproved’

lineages can therefore persist as relicts in isolated

regions from which competitors are absent (e.g. Sole-

nodon), or in isolated niches (e.g. myrmecophagy in

Pholidota and Tubulidentata, or haematophagy in

Desmodontinae) where their incumbency advantage

permits them to outcompete more ‘advanced’ but

less well-adapted species. If these relictual lineages

have diversity-dependent dynamics, they may persist

indeﬁnitely even at low diversity; this provides a plaus-

ible mechanism for the long persistence of small basal

clades suggested by the I

signatures.

Major environmental perturbations would be

expected to move the system away from this quasi-

equilibrial state, changing the breadths of the adaptive

zones. Some clades would then decline or go extinct

whereas others, whose adaptive zones had become

broader, would be able to diversify.

Under this model, phylogenies are unbalanced

because regions and niches vary in the diversity they

can support, because new radiations will probably orig-

inate in already-diverse clades, and because relictual

lineages are hard to extirpate. Different clades radiate

in different places, however, so the global phylogeny

will tend to be less unbalanced than expected from the

phylogeny of species found within a biorealm or ecore-

gion. Apparently downshifted clades arise as survivors

of otherwise supplanted groups or as occupants of

narrow adaptive zones: island endemics may be the

ultimate evolutionary dead-ends [99]. Much of the vari-

ation in richness among clades emerges as being

independent of trait variation, reﬂecting historical and

geographical contingencies.

The model also makes predictions about the tempo-

ral pattern of nodes in phylogeny. A complex overall

pattern is expected: although the overall dynamics

are equilibrial, some subclades in the early phase of

their radiation will be diversifying exponentially,

others approaching an equilibrium diversity, and

others declining deterministically. Furthermore, radi-

ations based on niche broadening might have a

different temporal pattern from those based on ﬁner

subdivision of the niche [59]. One prediction, how-

ever, is that lineages occupying biomes that have

recently expanded in area and energy are expected

to show rapid recent radiation; recent ﬁndings of

high speciation rates in North temperate regions for

birds [100], mammals [28] and an angiosperm clade

(carnations; [101]) are consistent with this prediction.

Our verbal model is an attempt to develop a simple

set of ecologically and evolutionarily plausible

processes that could together have generated the

shape of the mammalian phylogeny. Process-based

simulation models are increasingly used in macroevolu-

tion (e.g. [59,102]), permitting tentative identiﬁcation

of parameters to which diversity patterns might be

most sensitive. Although phylogenies without fossils

are inevitably missing much important information

about clade dynamics, large complete phylogenies are

nonetheless a rich source of patterns against which

model outputs can be compared, especially when com-

bined with environmental or trait data. We anticipate

that multi-scale models, incorporating local processes

such as adaptation and competition as well as global

processes such as global change and tectonic move-

ment, will have much to offer in the quest for global

biodiversity models, and for our understanding of why

so much of the Tree of Life is so asymmetric.

We are grateful to Ingi Agnarsson for sending the cytochrome

b phylogenies of carnivores and cetartiodactyls; to Ally

Phillimore for providing his bird phylogenies; to Andrew

Rambaut for programming the spatial simulation model; to

Paul Agapow for programming the other simulation models;

to Rodolphe Bernard, Natalie Cooper, David Orme and

Gavin Thomas for advice and assistance; to Rob Beck,

Lynsey McInnes and two referees for their extremely helpful

comments; and to Kate Jones for her patience. This work

was supported by the NERC, the Leverhulme Trust, the

Danish National Research Foundation, MICINN project

CGL2009-12703-C03-01, the JCyL GR 249-200 and an

Imperial College Research Excellence Award.

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Molecules and Fossils Tell Distinct Yet Complementary Stories of Mammal Diversification

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Jan 2021

[revised from pre-print posted on Current Biology's SSRN server: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3761886] Reconstructing the tempo at which biodiversity arose is a fundamental goal of evolutionary biologists, yet the relative merits of evolutionary-rate estimates are debated based on whether they are derived from the fossil record or time-calibrated phylogenies (timetrees) of living species. Extinct lineages unsampled in timetrees are known to ‘pull’ speciation rates downward, but the temporal scale at which this bias matters is unclear. To investigate this problem, we compare mammalian diversification-rate signatures in a credible set of molecular timetrees (N=5,911 species, ca. 70% from DNA) to those in fossil genus durations (N=5,320). We use fossil extinction rates to correct or ‘push’ the timetree-based (pulled) speciation-rate estimates, finding a major pulse of speciation ca. 66-56 million years ago (Ma) between the Cretaceous-Paleogene (K-Pg) boundary and the Paleocene-Eocene Thermal Maximum (PETM). However, three-quarters of the K-Pg-to-PETM originating taxa did not leave modern descendants, indicating that this rate signature is realistically not detectable from extant lineages alone. For groups without substantial fossil records, thankfully all is not lost. Pushed and pulled speciation rates converge starting ca. 10 Ma, and are equal at the present-day when recent evolutionary processes can be estimated without bias using species-specific ‘tip’ rates of speciation. Clade-wide moments of tip rates also enable enriched inference, as the skewness of tip rates is shown to approximate a clade’s extent of past diversification-rate shifts. Molecular timetrees need fossil-correction to address deep-time questions, but they are sufficient for shallower time questions where extinctions are fewer.

Network science inspires novel tree shape statistics

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PLOS ONE

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree’s shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality .

Molecules and fossils tell distinct yet complementary stories of mammal diversification

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Full-text available

Jul 2021
CURR BIOL

Reconstructing the tempo at which biodiversity arose is a fundamental goal of evolutionary biologists, yet the relative merits of evolutionary-rate estimates are debated based on whether they are derived from the fossil record or time-calibrated phylogenies (timetrees) of living species. Extinct lineages unsampled in timetrees are known to “pull” speciation rates downward, but the temporal scale at which this bias matters is unclear. To investigate this problem, we compare mammalian diversification-rate signatures in a credible set of molecular timetrees (n = 5,911 species, ∼70% from DNA) to those in fossil genus durations (n = 5,320). We use fossil extinction rates to correct or “push” the timetree-based (pulled) speciation-rate estimates, finding a surge of speciation during the Paleocene (∼66–56 million years ago, Ma) between the Cretaceous-Paleogene (K-Pg) boundary and the Paleocene-Eocene Thermal Maximum (PETM). However, about two-thirds of the K-Pg-to-PETM originating taxa did not leave modern descendants, indicating that this rate signature is likely undetectable from extant lineages alone. For groups without substantial fossil records, thankfully all is not lost. Pushed and pulled speciation rates converge starting ∼10 Ma and are equal at the present day when recent evolutionary processes can be estimated without bias using species-specific “tip” rates of speciation. Clade-wide moments of tip rates also enable enriched inference, as the skewness of tip rates is shown to approximate a clade’s extent of past diversification-rate shifts. Molecular timetrees need fossil-correction to address deep-time questions, but they are sufficient for shallower time questions where extinctions are fewer.

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ZOOL SCR

Gerbillinae is the second largest subfamily (after Murinae) in the Muridae family comprising ~16 genera and more than a hundred species. Phylogenetic relationships among the main taxa of gerbils are still not fully understood. In particular, a major issue is the phylogenetic position of the monotypic genus Ammodillus (endemic to the Horn of Africa), which according to morphological data may be one of the earliest offshoots from the gerbilline stem; however, its precise affiliations have been unknown due to the lack of genetic data. Here, by means of a multilocus dataset including one mitochondrial and five nuclear markers, we for the first time elucidated phylogenetic placement of Ammodillus and provided the most complete tribal phylogeny of Gerbillinae to date. Phylogenetic reconstructions robustly supported Ammodillus as a sister taxon to all other living gerbillines and suggested that the separation of the ammodile lineage dates back to the Middle/Late Miocene boundary (~11.7 Mya). The results are consiste nt with subdivision of the subfamily into four tribes: monotypic Ammodillini, Gerbillini (including Taterillus), Desmodilliscini (including Pachyuromys) and Gerbillurini. In the light of the new data, we discuss possible scenarios of Gerbillinae origin, highlight Ammodillus as a relatively ancient—albeit morphologically advanced—lineage that has never gained diversity and propose the term ‘ancient singleton’ for a taxon with a persistently low diversification rate.

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Large diversifications of species are known to occur unevenly across space and evolutionary lineages, but the relative importance of their driving mechanisms, such as climate, ecological opportunity and key evolutionary innovations (KEI), remains poorly understood. Here, we explore the remarkable diversification of rhacophorid frogs, which represent six percent of global amphibian diversity, utilize four distinct reproductive modes, and span a climatically variable area across mainland Asia, associated continental islands, and Africa. Using a complete species-level phylogeny, we find near-constant diversification rates but a highly uneven distribution of species richness. Montane regions on islands and some mainland regions have higher phylogenetic diversity and unique assemblages of taxa; we identify these as cool-wet refugia. Starting from a centre of origin, rhacophorids reached these distant refugia by adapting to new climatic conditions (‘niche evolution’-dominant), especially following the origin of KEIs such as terrestrial reproduction (in the Late Eocene) or by dispersal during periods of favourable climate (‘niche conservatism’-dominant). By examining climate, geographical and phylogenetic data, the diversification and evolution of rhacophorid frogs is explored

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Article

Feb 2022
SYST BIOL

Phylogenetic trees are a central tool in many areas of life science and medicine. They demonstrate evolutionary patterns among species, genes, and patterns of ancestry among sets of individuals. The tree shapes and branch lengths of phylogenetic trees encode evolutionary and epidemiological information. To extract information from tree shapes and branch lengths, representation and comparison methods for phylogenetic trees are needed. Representing and comparing tree shapes and branch lengths of phylogenetic trees are challenging, for a tree shape is unlabelled and can be displayed in numerous different forms, and branch lengths of a tree shape are specific to edges whose positions vary with respect to the displayed forms of the tree shape. In this paper, we introduce representation and comparison methods for rooted unlabelled phylogenetic trees based on a tree lattice that serves as a coordinate system for rooted binary trees with branch lengths and a graph polynomial that fully characterizes tree shapes. We show that the introduced tree representations and metrics provide distance-based likelihood-free methods for tree clustering, parameter estimation and model selection, and apply the methods to analyze phylogenies reconstructed from virus sequences.

Drivers of an epic radiation: the role of climate and islands in species diversification 1 and reproductive-mode evolution of Old-World tree frogs 2 3

Preprint

Full-text available

Sep 2021

24 Although large diversifications of species occur unevenly across space and evolutionary 25 lineages, the relative importance of their driving mechanisms, such as climate, ecological 26 opportunity and key innovations, remains poorly understood. Here, we explore the 27 remarkable diversification of rhacophorid frogs, which represent six percent of global 28 amphibian diversity, utilize four distinct reproductive modes, and span a climatically variable 29 area across mainland Asia, associated continental islands, and Africa. Using a complete 30 species-level phylogeny, we find near-constant diversification rates but a highly uneven 31 distribution of species richness. Montane regions on islands and some mainland regions have 32 higher phylogenetic diversity and unique assemblages of taxa; we identify these as cool-wet 33 refugia. Starting from a centre of origin, rhacophorids reached these distant refugia by 34 adapting to new climatic conditions ('niche evolution'-dominant), especially following the 35 origin of key innovations such as terrestrial reproduction (in the Late Eocene) or by dispersal 36

Mammals, Biodiversity of

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Jan 2024

Diversification and biogeography of Rhacophoridae -a model testing approach OPEN ACCESS EDITED BY

Article

Full-text available

Sep 2023

A central focus of evolutionary biology is to understand species diversity by studying how they arrived at their current geographic distributions. The biogeography of the Old World tree frogs in the family Rhacophoridae has been extensively studied suggesting an early Paleogene origin in Asia (out of Asia hypothesis) with alternative hypotheses in play. However, these alternative hypotheses especially considering adjacency of biogeographical regions and plate tectonics have not been tested empirically. Here using a comprehensive time calibrated phylogeny and constrained dispersal multipliers we studied the biogeographical history and diversification of Rhacophoridae, distributed in five biogeographical regions. Five hypotheses suggesting different centers of origin, and additional hypotheses considering adjacency and plate tectonics were tested to delineate the biogeographical history of Rhacophoridae. In addition, various diversification models that accounted for factors such as lineage isolation time, diversity-dependence, paleotemperatures, speciation and extinction rates were also used to test patterns of diversification. Results confirmed an East/ Southeast Asian center of origin (out of Asia), with dispersal likely mediated by plate tectonics and adjacency of biogeographical regions, which could be linked to periodic sea level fluctuations and climate changes. The best-fitting diversification models explained diversification through lineage isolation time and paleotemperature regimes, while diversity-dependent models had low support. Speciation was linearly dependent on time and paleotemperatures, while extinction rates were exponentially dependent on time and linearly dependent on paleotemperature. Our findings demonstrate that variable extinction rates contribute towards maintaining a constant diversification rate for rhacophorids. We discuss that episodic major extinction events on the Indian Plate may have played a major role in shaping the early evolution of Rhacophoridae thus favoring an Out of Asia hypothesis in the empirical models. However, current biogeographic models may not be sufficient to explain the origin of Rhacophoridae, as multiple factors are likely at play. Ellepola G and Meegaskumbura M (2023) Diversification and biogeography of Rhacophoridae-a model testing approach.

On the Largest Common Subtree of Random Leaf-Labeled Binary Trees

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David Aldous

The delayed rise of present-day mammals

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Full-text available

Mar 2007

Did the end-Cretaceous mass extinction event, by eliminating non-avian dinosaurs and most of the existing fauna, trigger the evolutionary radiation of present-day mammals? Here we construct, date and analyse a species-level phylogeny of nearly all extant Mammalia to bring a new perspective to this question. Our analyses of how extant lineages accumulated through time show that net per-lineage diversification rates barely changed across the Cretaceous/Tertiary boundary. Instead, these rates spiked significantly with the origins of the currently recognized placental superorders and orders approximately 93 million years ago, before falling and remaining low until accelerating again throughout the Eocene and Oligocene epochs. Our results show that the phylogenetic 'fuses' leading to the explosion of extant placental orders are not only very much longer than suspected previously, but also challenge the hypothesis that the end-Cretaceous mass extinction event had a major, direct influence on the diversification of today's mammals. Molecular data and the fossil record can give conflicting views of the evolutionary past. For instance, empirical palaeontological evidence by itself tends to favour the 'explosive model' of diversification for extant placental mammals 1 , in which the orders with living representatives both originated and rapidly diversified soon after the Cretaceous/Tertiary (K/T) mass extinction event that eliminated non-avian dinosaurs and many other, mostly marine 2 , taxa 65.5 million years (Myr) ago 1,3,4. By contrast, molecular data consistently push most origins of the same orders back into the Late Cretaceous period 5-9 , leading to alternative scenarios in which placental line-ages persist at low diversity for some period of time after their initial origins ('phylogenetic fuses'; see ref. 10) before undergoing evolutionary explosions 1,11. Principal among these scenarios is the 'long-fuse model' 1 , which postulates an extended lag between the Cretaceous origins of the orders and the first split among their living representatives (crown groups) immediately after the K/T boundary 8. Some older molecular studies advocate a 'short-fuse model' of diversification 1 , where even the basal crown-group divergences within some of the larger placental orders occur well within the Cretaceous period 5-7. A partial molecular phylogeny emphasizing divergences among placental orders suggested that over 20 lineages with extant descendants (henceforth, 'extant lineages') survived the K/T boundary 8. However, the total number of extant lineages that pre-date the extinction event and whether or not they radiated immediately after it remain unknown. The fossil record alone does not provide direct answers to these questions. It does reveal a strong pulse of diversification in stem eutherians immediately after the K/T boundary 4,12 , but few of the known Palaeocene taxa can be placed securely within the crown groups of extant orders comprising Placentalia 4. The latter only rise to prominence in fossils known from the Early Eocene epoch onwards (,50 Myr ago) after a major faunal reorganization 4,13,14. The geographical patchiness of the record complicates interpretations of this near-absence of Palaeocene crown-group fossils 14-16 : were these clades radiating throughout the Palaeocene epoch in parts of the world where the fossil record is less well known; had they not yet originated; or did they have very long fuses, remaining at low diversity until the major turnover at the start of the Eocene epoch? The pattern of diversification rates through time, to which little attention has been paid so far, might hold the key to answering these questions. If the Cretaceous fauna inhibited mammalian diversification , as is commonly assumed 1 , and all mammalian lineages were able to radiate after their extinction, then there should be a significant increase in the net per-lineage rate of extant mammalian diversification , r (the difference between the per-lineage speciation and extinction rates), immediately after the K/T mass extinction. This hypothesis, along with the explosive, long-and short-fuse models, can be tested using densely sampled phylogenies of extant species, which contain information about the history of their diversification rates 17-20. Using modern supertree algorithms 21,22 , we construct the first virtually complete species-level phylogeny of extant mammals from over 2,500 partial estimates, and estimate divergence times (with confidence intervals) throughout it using a 66-gene alignment in conjunction with 30 cladistically robust fossil calibration points. Our analyses of the supertree indicate that the principal splits underlying the diversification of the extant lineages occurred (1) from 100-85 Myr ago with the origins of the extant orders, and (2) in or after the Early Eocene (agreeing with the upturn in their diversity known from the fossil record 4,13,14), but not immediately after the K/T boundary, where diversification rates are unchanged. Our findings-that more extant placental lineages survived the K/T boundary than previously recognized and that fewer arose immediately after it than previously suspected-extend the phylogenetic fuses of many extant orders and indicate that the end-Cretaceous mass extinction event had, at best, a minor role in driving the diversification of the present-day mam-malian lineages. A supertree with divergence times for extant mammals The supertree contains 4,510 of the 4,554 extant species recorded in ref. 23, making it 99.0% complete at the species level (Fig. 1; see also

Using interspecies phylogenies to test macroevolutionary hypotheses

Chapter

May 1996

Andy Purvis

As progress in cell developmental biology carries on at a breakneck speed, new techniques constantly arise to plug the gaps left by traditional strategies. Cellular Interactions in Development provides detailed discussion and protocols of some of these new techniques, which allow the manipulation of developing organisms such as Drosophila or plants, when and where cells interact with each other to influence their development. The book looks at the really exciting innovations of the identification and functional test of molecules which control these cellular behaviours. The book also describes a number of new ways of hunting for these important proteins involved in cellular communication. A fully comprehensive manual which will prove indispensable to researchers in the fields of cell, developmental, and molecular biology.

Some Models of Phylogenetic Tree Shape

Article

Jun 2007

Phylogenetic trees represent the evolutionary histories of lineages and so bear the impression of the evolutionary forces that gave rise to those lineages. Advances in molecular and computational techniques continually increase the number and size of our phylogenetic estimates. In the 1990s, both we [41] and Purvis [52] surveyed the two main aspects of phylogenetic tree pattern: variation in realized diversification rate among contemporaneous lineages, and changes in realized diversification rates through time. The techniques highlighted in these reviews have been used very successfully (see, e.g. [4, 10, 11, 62, 63]).

Mammal Species of the World

Book

Jan 2005

Don Wilson

SPATIAL AUTOCORRELATION AND AUTOREGRESSIVE MODELS IN ECOLOGY

Article

Aug 2002

Phylogenetic futures after the latest mass extinction

Chapter

Jan 2001

Sean Nee

Phylogeny is a potentially powerful tool for conserving biodiversity. This book explores how it can be used to tackle questions of great practical importance and urgency for conservation. Using case studies from many different taxa and regions of the world, the volume evaluates how useful phylogeny is in understanding the processes that have generated today's diversity and the processes that now threaten it. The novelty of many of the applications, the increasing ease with which phylogenies can be generated, the urgency with which conservation decisions have to be made and the need to make decisions that are as good as possible together make this volume a timely and important synthesis which will be of great value to researchers, practitioners and policy-makers alike.

The Major Features of Evolution

Book

Dec 1953

George Gaylord Simpson

Evolutionary pattern and process in the sister-group

Article

Jan 1984

E.S. Vrba

1. Biodiversity Dynamics: Niche Preemption and Saturation in Diversity Equilibria: Turnover of Populations, Taxa, and Communities

Chapter

Jan 2001

Michael L. Mckinney

Comparative methods for adaptive radiations

Chapter

Aug 2003

The shape of mammalian phylogeny: Patterns, processes and scales

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