Ecology Letters, (2010) 13: 1270–1279
doi: 10.1111/j.1461-0248.2010.01521.x
LETTER
Key innovations within a geographical context
in flowering plants: towards resolving Darwin!s
abominable mystery
Jana C. Vamosi*† and Steven M.
Vamosi†
Department of Biological
Sciences, University of Calgary,
2500 University Drive NW,
Calgary, AB T2N 1N4, Canada
*Correspondence: E-mail:
[email protected]
†
Both the authors contributed
equally to this work.
Abstract
Elucidating factors associated with diversification have been attempted in lineages as
diverse as birds, mammals and angiosperms, yet has met with limited success.
In flowering plants, the ambiguity of associations between traits and diversification has
sparked debate since Darwin!s description of angiosperm diversification as an
"abominable mystery!. Recent work has found that diversification is often diversitydependent, suggesting that species richness depends on geographical area available more
than on traits or the time available to accumulate species. Here, we undertake
phylogenetic generalized least squares analyses that jointly examine the effects of age,
ecoregion area and four ecological traits on diversification in 409 angiosperm families.
Area explained the most variation, dwarfing the effect of traits and age, suggesting that
diversity-dependent diversification is controlled by ecological limits. Within the context
of area, however, traits associated with biotic pollination (zygomorphy) exhibited the
greatest effect, possibly through the evolution of specialization.
Keywords
Angiosperms, biogeography, diversification, ecological limits, extinction, key innovations, latitudinal biodiversity gradient, lineage age, pollination, speciation.
Ecology Letters (2010) 13: 1270–1279
INTRODUCTION
The species richness of lineages shows tremendous variation
amongst clades, originally sparking widespread support for
the idea that traits influence speciation and ⁄ or extinction
rates. In plants, putative evolutionarily successful traits
include self-incompatibility (Igic et al. 2008), floral asymmetry (Sargent 2004) and short generation time (Barraclough &
Savolainen 2001; Smith & Donoghue 2008), whereas traits
associated with evolutionary dead-ends include dioecy
(Heilbuth 2000) and selfing (Igic et al. 2008). The reasons
why certain traits are associated with increased or decreased
diversification are often intuitively appealing: some traits
inherently encourage speciation via increased genetic diversity (self-incompatibility, herbaceous growth form), or an
association with increased opportunities for the evolution of
specialization (floral asymmetry and fleshy fruit). Other
traits may decrease extinction rates by providing an escape
from predators (latex canals), while still others may be
affiliated with less stochastic pollen receipt (hermaphroditism vs. dioecy).
! 2010 Blackwell Publishing Ltd/CNRS
The extreme variance in diversification rates amongst
clades was hard to reconcile with early theories of
gradualism (Darwin, in a letter to J. D. Hooker, 1879),
leading to Darwin to speculate that a particular trait could
spur rapid angiosperm diversification and describe the
evolutionary success of angiosperms as "an abominable
mystery!. The answer to whether any of the above traits are
consistent predictors of diversity of a given clade remains
elusive to this day (Davies et al. 2004a). The low explanatory
power of traits in determining tree imbalance has led some
researchers to posit that the rate of lineage growth depends
more upon geographical rather than biological traits, such as
geographical extent (i.e., total area occupied by a clade) and
climate (Ricklefs 2003; Jansson & Davies 2008). Others have
suggested that neither geographical nor biological traits
determine diversification on their own but rather certain
traits (or combinations thereof) may stimulate diversification
within a particular geographical context (De Queiroz 2002).
Some ambiguity in the effects of traits comes from the
different metrics used to characterize evolutionary success.
For example, recent studies have called into question the
Ecological limits vs. key innovations 1271
Letter
wisdom of using diversification rates (i.e., log-transformed
species richness of a lineage divided by its age) because older
lineages will be unduly penalized. Even when estimators of
diversification rate incorporate extinction (as in Magallon &
Sanderson 2001), the accumulation of species within a
lineage is expected to increase with time, resulting in the null
expectation that species richness will show positive linear
relationships with age. Surprisingly, however, recent evidence suggests that this pattern rarely exists (Rabosky
2009a,b). In the absence of a strong relationship between
lineage age and species richness (Ricklefs 2007), it has been
suggested that log-transformed species richness alone may
be a better measure of evolutionary success (Rabosky
2009a). Recent studies (Rabosky 2009b) have concluded that
the near-zero correlation between age and species richness is
unlikely to be attributed to variation in diversification rates
between lineages. Instead, the lack of a relationship between
age and species richness is best explained by diversitydependent diversification, where diversity is initially rapid
but slows as lineages age, implying that lineages approach a
"carrying capacity! set by ecological limits. Geographical area
might be what place these limits on diversification (Ricklefs
2007; Rabosky 2009a), such that speciation declines as
competition increases from the ever-increasing number of
species within a particular clade (Phillimore and Price 2008).
Extinction rates may also increase due to competition from
other species, yet this process appears to exert little
influence (Rabosky 2009b).
Key innovations may still operate to alter the evolutionary
success of lineages upon this biogeographical backdrop, yet
the mechanism of operation is different. Examining these
traits after the "explosive diversification! phase may instead
provide evidence that certain traits influence the carrying
capacity of a lineage (Rabosky 2009a). Whether traits are
instrumental in altering diversification rates or carrying
capacities should be evident, if one examines the interaction
between the presence of a trait and time. If the differences
in species richness between sister clades increases with age,
it implies that the trait affects net diversification rates (and
not ecological limits) because the difference in species
richness should increase over time.
If different carrying capacities distinguish the species
richness amongst clades and if carrying capacities are indeed
set by available area, then key innovations may influence the
carrying capacity of a lineage in two main ways. First, a trait
may alter the ability of a lineage to expand its range, which
then in turn increases the carrying capacity of the lineage.
Of the traits that may influence geographical extent, life
history (Cardillo et al. 2003) and dispersal (Roy et al. 2009)
has emerged as most influential in animal lineages and
associations between growth form, seed ⁄ fruit size and
geographical extent in plants may indicate a similar pattern
(Morin & Chuine 2006). Second, certain traits may appear to
be key innovations because they allow for greater species
packing upon a landscape (e.g., through increased specialization). Thus, ecological limits are present but the carrying
capacity is set higher for some lineages over others for a
given amount of space. While not a key innovation in the
same sense, an important contributor to this pattern might
be if diversification occurs within the tropics, as the tropics
has long been acknowledged for supporting more species
per unit area (Pianka 1966). While the latitudinal biodiversity
gradient has garnered much attention, most evolutionary
investigations have focused on whether diversification rates
are higher in tropical lineages (through increased speciation
or decreased extinction) or whether the tropics have simply
had more time to accumulate more species (Mittelbach et al.
2007). Whether a temperate environment simply sets lower
ecological limits on species richness of a given lineage has
not been previously examined.
Here, we examine interconnected issues related to
characterizing, and understanding the basis of, variation in
diversification amongst lineages when controlling for
phylogenetic relatedness and age. We apply an information-theoretic approach in a phylogenetically informed
multiple regression framework to decipher the contributions
of, and plausible interactions between, four putative key
traits (growth form, fruit type, sexual system and floral symmetry), and available geographical area (summed area of the
ecoregions occupied by a lineage) to species richness of 409
angiosperm families. Finally, we examine the effects of
geographical distribution of a given ecozone area by
examining whether lineages that have a predominantly
tropical distribution exert effects on species richness
independently or in concert with the above traits.
METHODS
Investigation of broad, global patterns necessitates the
gathering of large amounts of information from disparate
sources. We generated geographical extent (i.e., total area
occupied by the entire family) for 396 of the 409
angiosperm families by digitizing family maps (Stevens
2001 onwards). With reference to these maps and family
treatments for the 12 families without complete family
maps, we subsequently calculated the area potentially
available for expansion (hereafter available area; see Fig. 1)
for all 409 families. We assessed the effect of ecological
limits by approximating available area using the
presence ⁄ absence of each family in eight biogeographical
realms (see also Fig. 1), or ecozones, based on Udvardy
(1975): Nearctic (area = 54.1 · 106 km2), Neotropic
(19.0 · 106 km2), Palaearctic (87.7 · 106 km2), Afrotropic
(22.1 · 106 km2), Indo-Malaya (7.5 · 106 km2), Australasia
(7.6 · 106 km2), Oceania (1.0 · 106 km2) and Antarctic
(0.3 · 106 km2). Available area was calculated by summing
! 2010 Blackwell Publishing Ltd/CNRS
1272 J. C. Vamosi and S. M. Vamosi
GE
E
the areas for all ecozones that a family was present in.
For example, the family Achatocarpaceae, with member
species in the Nearctic and Neotropic ecozones, was
assigned a value of 73.1 · 106 km2 for available area.
Geographical extent and available area were cube-root
transformed for consistency with other studies (Jansson &
Davies 2008). Information on predominant growth form
(herbaceous, woody), fruit type (fleshy, dry), breeding
system (cosexual, dioecious), distribution (tropical or temperate) and species richness for these same 409 angiosperm
families was collated from Vamosi & Vamosi (2005) with
information updated as required using (Stevens 2001;
onwards). Family descriptions often offered only rough
descriptive resolution of trait states: e.g., "mostly shrubs and
trees! would be coded as "woody!. Fruit type is apparently
unknown for the poorly studied monotypic Haptanthaceae,
which reduced our sample size to N = 408 families for
subsequent multi-model analyses of species richness. Floral
symmetry (zygomorphic, actinomorphic) was collated from
Sargent (2004) with information updated as required using
(Stevens 2001 onwards). Species richness was log-transformed prior to analyses. Trait data are provided in
Supporting information, Data S1.
! 2010 Blackwell Publishing Ltd/CNRS
Letter
Figure 1 A representative example of the
geographical variables used. The Afrotropic
Ecozone (E) has an area of 22.1 · 106 km2
(light grey), while the family Huaceae (dark
grey) has a geographical extent [GE; redrawn
from Stevens 2001 onwards)] of 2.73 ·
106 km2 within this ecozone. If the area of
these ecozones (E), defined largely according
to plate tectonics, places limits on species
richness (and GE) then families restricted to
smaller ecozones should have fewer species
than those restricted to larger ecozones (or
present in more ecozones).
The phylogenetic relationship amongst families was
obtained using the angiosperm APGIII consensus tree
(R20091110) from Phylomatic (Webb & Donoghue 2005).
Instances of uncertainty in the tree topology (collapsing of
uncertain nodes from source trees) were left as soft
polytomies, making the phylogeny as insensitive to tree
uncertainty as can be reasonably accomplished with a
dataset of this size. We dated as many nodes in the tree as
possible using data from TimeTree (Hedges et al. 2006),
with the criterion that the topology used TimeTree matched
that of APGIII. This procedure resulted in the calibration of
95 nodes of the tree. While broad consensus is emerging on
the relationships between angiosperm orders, there is less
confidence in the relationships between families within
orders. As the accuracy of dates generated with smoothing
algorithms are sensitive to topology (Drummond et al. 2006;
Smith et al. 2009), we did not attempt to date nodes within
orders and instead used the branch length adjuster function
in Phylocom (Webb et al. 2008) to estimate these undated
branch lengths within the phylogeny, inputting our dated
nodes as calibration points. A visualization of the dated
phylogeny used for all analyses is provided in Supporting
information, Data S2. Although programs such as BEAST
Ecological limits vs. key innovations 1273
Letter
could potentially incorporate uncertainty in the phylogeny,
the level of uncertainty at each node would still be
dependent on the genes and species included in the tree,
which clearly have limits imposed by the present level of
data and computing power. In total, the procedures used
here attempt to incorporate our broadest knowledge of
angiosperm systematics to produce the most comprehensive
phylogenetic hypothesis.
Statistical analyses
We assessed associations between various traits and
diversification, while taking account of their evolutionary
history (Freckleton et al. 2002). An advantage of the
statistical analysis approach adopted below, is that, unlike
independent contrasts that only compare sister species at
defined nodes (Davies et al. 2004b), our approach includes a
more comprehensive coverage of the angiosperm phylogeny. The approach we take is to use the lambda (k)
transformation suggested by Pagel (1999). This allowed us
to determine whether the available phylogeny improves the
statistical description of the data compared to treating all
data points as independent. While outstanding potential to
dissect speciation and extinction individually on phylogenies
exists with programs such as Diversitree (Fitzjohn et al.
2009), the accuracy of the estimates diminishes with
trees that are < 50% complete at the species-level and have
< 400 tips.
The phylogenetic signal for growth form, fruit type,
breeding system, floral symmetry, species richness, geographical extent and available area was initially estimated for
each single trait via maximum likelihood (sensu Freckleton
et al. 2002), as implemented in the "ape! package (Paradis
et al. 2004) in R version 2.10.1 (R Development Core Team
2008). The phylogenetic covariance structure was multiplied
by a phylogenetic signal value (k), ranging from 0 (no
phylogenetic autocorrelation) to 1 (maximum phylogenetic
autocorrelation), and the log-likelihood of each run was
recorded; from the resulting likelihood surface a maximumlikelihood value of k was obtained (Pagel 1999). The
parameter k measures the degree to which the variation ⁄ covariation of traits across a tree agrees with a
Brownian process (Freckleton et al. 2002). In the context
of analysing species richness, for example, a value of k = 0
implies that species richness is distributed amongst families
at random with respect to phylogeny. A value of k = 1
indicates that species richness exhibits a phylogenetic
signal; that is, closely related groups have more similar
species richness values than would be expected by chance
(Appendix S1).
Before proceeding with our primary investigations of
factors associated with variation in diversification in
angiosperm families, we assessed whether the appropriate
dependent variable was species richness (i.e., log N ) or
diversification rate (i.e., ln[N] ⁄ t ) (see also Ricklefs 2007;
Rabosky 2009a). Species richness was not significantly
predicted by family age (non-phylogenetic analysis:
r = )0.07, P = 0.14; Fig. 2). Other analyses indicate
(Rabosky 2009b) that this coefficient is out of the range
that can be obtained through variation between families in
diversification rates (0–0.8), unless there is a uniform
distribution of diversification rates (range of coefficients
)0.4 to 0.5). We did not have a uniform distribution of
diversification rates (v23,408 = 624; P < 0.001), and therefore concluded that subsequent analyses of diversification
were best conducted on log-transformed species richness as
the response.
For the main analyses of species richness in angiosperm
families, we evaluated the fit of a series of candidate models
(Appendix S2) using an information-theoretic approach
(Burnham & Anderson 2002). Each variable in isolation has
been hypothesized to affect diversification rates, with the
state typically being associated with higher rates being:
being present in more ⁄ larger ecozones (Rabosky 2009b),
tropical distribution (hereafter "tropicality!; Jansson &
Davies 2008), herbaceous growth form (Tiffney & Mazer
1995), fleshy fruits (Eriksson & Bremer 1992), hermaphroditism (Heilbuth 2000; Vamosi & Vamosi 2004) and
zygomorphic flowers (Sargent 2004). Adhering to the rule
of thumb that the maximum number of structural
parameters in a regression should be N ⁄ 10 (Burnham &
Anderson 2002), our global model contained all main
effects and all two-way interactions between the main
effects (K = 22, including intercept). Subsequent reduced
models (K = 3–13) evaluated the relative importance of
Figure 2 Relationship
between estimated family age and
ln-transformed species richness (SR). There is a marked lack of
the expected positive relationship (logSR = 1.83)0.0025 [family
age], P = 0.13, R2 < 0.01), indicating that differences in diversification of families may be more due to differences in their
ecological limits (Rabosky 2009b).
! 2010 Blackwell Publishing Ltd/CNRS
1274 J. C. Vamosi and S. M. Vamosi
Letter
"geography! (i.e., ecozone area, tropicality) vs. "intrinsic
traits! (Appendix S2). By following the suggestion of testing
4–20 candidate models (Burnham & Anderson 2002),
we do not expect that we captured the "true! model in our
set. Therefore, in addition to choosing the best approximating model(s) with reference to Akaike!s information
criterion (AICc) values, we calculated Akaike weights for all
candidate models, which provide a measure of relative
support for each model. Using the presence ⁄ absence of
terms from a model and the model!s Akaike weight, we
subsequently calculated model-averaged estimates of effect
sizes and associated P-values of all variables and interaction
terms present in models with non-zero Akaike weights to
assess their relative contributions to amongst-family variance in SR. All models were run scaling the covariance
between predictors by the global maximum-likelihood
estimate of k estimated for the relationship between
variables in each particular model.
Clearly, not all clades will occupy all of the available area
(see Fig. 1) and the extent to which the available area is
occupied might influence species richness. We thus tested
the influence of available area, tropicality and the four traits
on geographical extent (N = 396 families for which reliable
geographical extent values were available; Supporting
information, Data S1) following the same framework as
described above, with the exception of specifying geographical extent as the response variable. Because it can be argued
that geographical extent is merely a proxy for species
richness, we were especially interested in whether the
ranking of the relative importance values for the four
intrinsic traits differed between the two sets of analyses.
Finally, we assessed the relative importance of traits in the
context of age, in a third set of analyses. To avoid the
inherent circularity of using age as an independent variable
in a phylogenetically informed framework, we applied nonphylogenetic analyses. For all variables, with the exception
of available area, we specified models: (1) SR ! trait (e.g.,
growth form), (2) SR ! trait + age and (3) SR ! trait +
age + trait · age. We used goodness-of-fit tests to determine whether retention of the interaction between age and
the focal trait (e.g., herbaceousness) and ⁄ or the effect of age
was justified. Although each of the five sets had too few
variables or models to justify the information-theoretic
approach, ranking by AIC scores produced the same results.
RESULTS
We found limited evidence for phylogenetic signal overall,
with maximum-likelihood estimates of k being significantly
greater than zero for only two of our five predictor variables
(growth form and floral symmetry) and neither of our
response variables (Appendix S1). We nonetheless conducted phylogenetically informed analyses for two main
reasons. First, the analysis framework we used did not suffer
from the reduced power that may accompany independent
contrast methods with discrete traits (i.e., if k = 0, the
model becomes equivalent to a non-phylogenetic model).
Second, the observed significant phylogenetic signal in some
of the traits resulted in a significant maximum-likelihood
value of k of the overall relationships between the variables
for several models of species richness, including all of
the best-supported ones (k = 0.27–0.32, P = 0.002–0.03;
Table 1a).
Two candidate models of species richness, SR6 (available
area + tropicality) and SR10 (available area + tropicality + fleshy fruits + zygomorphy), received nearly equivalent empirical support (Table 1a). Despite our analysis
omitting some putative key innovations (e.g., self-incompatibility), the best models explained 50–51% of the
amongst-family variation in species richness. With reference
to model-averaged estimates, we obtain the following
ranking of the contribution of the main effects: available
area (estimate: 0.0072; P < 0.0001) > zygomorphy (0.287;
P = 0.018) > tropicality (0.203; P = 0.074) ! cosexuality
(0.201; P = 0.066) > fleshy fruits (0.071; P = 0.41) > herbaceousness (0.001; P = 0.89). Examination of Fig. 3
(panel a) suggests that available area places limits on
species richness, with families having zygomorphic flowers
containing more species on average than those with
comparable available area values but actinomorphic flowers. We found little evidence for significant two-way
interactions, with only that between available area and
Table 1 Phylogenetically informed models of (a) species richness (SR; N = 408 families) and (b) geographical extent (GE; N = 396 families),
ranked by relative support; number of parameters (K ), model lambda statistic (k), small sample Akaike!s information criterion (AICc), Akaike
weight (wi), R2 values; total area of occupied ecoregions (E), tropics (T), herbaceousness (H), fleshy fruits (F), cosexuality (C) and zygomorphy
(Z). Only models with substantial empirical support (i.e., AICc difference £ 2) reported here (see Appendix S2 for all models)
Response variable
Model
Terms
K
k
AICc
wi
R2
Species richness
SR6
SR10
GE3
E+T
E+T+F+Z
E + T + H + F + C + Z + E:T
3
5
8
0.32
0.29
0.00
960.35
960.64
4485.9
0.34
0.30
0.81
0.50
0.51
0.72
Geographical extent
! 2010 Blackwell Publishing Ltd/CNRS
Ecological limits vs. key innovations 1275
Letter
Figure 3 Species richness (a–c) and geographical extent (d–f ) as a function of ecozone area with the three traits that received empirical
support displayed. Zygomorphic flowers (a), tropicality (b), and cosexuality (c) are associated with higher species richness for a given
ecoregion area, while tropicality (d), herbaceous growth form (e) and cosexuality (f ) are associated with higher geographical extents. In all
cases, solid circles indicate the trait state associated with higher values.
tropicality being present in candidate models with non-zero
Akaike weights (SR5, SR3; Appendix S2). However, we
note little empirical support for this term, based on model
averaging ()0.0005; P = 0.55).
Despite the significant correlation between species
richness and geographical extent in our data set (nonphylogenetic analysis: r = 0.82, t1,395 = 28.41, P < 0.0001),
we observed marked differences in the relative importance
of the six predictor variables in the corresponding set of
candidate models with the latter response variable. Perhaps
unsurprisingly, models with available area explained a
greater amount of amongst-family variation in geographical
extent (R2 = 0.72 for best model) than in species richness.
A single model, GE3 (all main effects + available
area · tropicality), received substantial empirical support
(Appendix S3). With reference to model-averaged estimates,
we obtain the following ranking of the contribution of the
main effects: available area (estimate: 1.200; P <
0.0001) > tropicality (109.022; P = 0.0009) > herbaceousness (16.286; P = 0.020) > cosexuality (16.302; P =
0.051) > zygomorphy (12. 782; P = 0.11) > fleshy fruits
(6.797; P = 0.24). Model averaging revealed a considerably
greater contribution of the available area · tropicality
interaction ()0.284; P < 0.0001) than with species richness,
which explains the moderate support for model GE5
(Appendix S3). As with species richness, the remaining twoway interactions (all in model GE11) did not receive much
empirical support (all P > 0.26).
Finally, we found little support for the importance of
family age (Table 2), and we note that the sign of its
association with species richness was negative in all
candidate models (see also Fig. 2). The retention of age
and the trait · age interaction was warranted only when
the focal trait was fleshy fruits (Table 2). However, this
trait did not explain much of the amongst-family variation
in either species richness or geographical extent (see
above).
DISCUSSION
Our analyses reveal that available area for expansion is the
most critical determinant of increased diversification in
flowering plants, followed by zygomorphy, as revealed by
model-averaged estimates. Our best models consistently
incorporated these features, explaining up to 51% of the
variation in species richness. Age explained little of the
variation in species richness, indicating diversity-dependent
diversification consistent with previous studies (Rabosky
! 2010 Blackwell Publishing Ltd/CNRS
1276 J. C. Vamosi and S. M. Vamosi
Letter
Table 2 Tests of the relative importance of family age (A) and
trait · family age interactions (e.g., H:A) as covariates in models of
species richness in 409 families (N = 408 for analyses with fruit
type)
Trait
Model
Age
Interaction
GOF
H
H + A + H:A
H+A
H
F + A + F:A
F+A
F
C + A + C:A
C+A
C
Z + A + Z:A
Z+A
Z
T + A + T:A
T+A
T
0.83
0.17
–
0.009
0.13
–
0.21
0.12
–
0.14
0.19
–
0.34
0.13
–
0.19
–
–
0.019
–
–
0.50
–
–
0.46
–
–
0.86
–
–
–
0.19
0.17
–
0.019
–
–
0.50
0.12
–
0.46
0.19
–
0.86
0.13
F
C
Z
T
Goodness-of-fit (GOF) F-tests were used to determine whether
retention of the interaction term and ⁄ or the main effect of age
were supported; significant (i.e., < 0.05) P-values provide support
for retaining the model with more terms (simplest model supported indicated in bold for trait). Trait abbreviations are given in
Table 1.
2009a). There was no indication that particular trait
combinations, rather than isolated traits, lead to higher
diversification rates (Ricklefs & Renner 1994; Vamosi &
Vamosi 2004, 2005).
Diversity-dependent diversification and upper limits
on species richness
Recent simulations indicate that if no limits are placed on the
number of species within a clade, then species richness
should simply increase with clade age, even when diversification rates vary between lineages. The decided lack of a
positive correlation between clade age and species richness is
consistent with previous studies in angiosperms (Rabosky
2009b) and indicates instead that clades experience diversitydependence in cladogenesis. Our results support the idea
that ecological limits (e.g., set by the size of ecozones) play a
major role (Rabosky 2009b) in setting the asymptote, or
"carrying capacity! for a lineage. While other interpretations
cannot be completely ruled out (e.g., taxonomists may tend
to split older diverse clades into smaller, younger families), a
separate analysis using a different taxonomical system
resulted in identical results (results not shown). We find
that geographical area of the ecozones upon which a clade is
diversifying exerts the greatest limitation on angiosperm
species richness as it does in birds (Ricklefs 2006). In short,
! 2010 Blackwell Publishing Ltd/CNRS
larger ecozones support families with higher species richness.
To what degree this is because larger ecozones will support
families with more larger-ranged species (and decrease the
per-species extinction rate) or because larger ecozone area
provides a larger landscape for increased allopatric speciation
remains an interesting question. While one can envision the
causal arrow pointed in the opposite direction (high
diversification leading to a larger area covered), such a
relationship would not result in the observed lack of
relationship between species richness and age (lineages
should continue to diversify after filling all the available area).
Phylogenetic signal
Overall, phylogenetic signal at the family level of analysis
was relatively modest for most variables considered.
Examining the whole models, there was a phylogenetic
signal in the models with species richness but not in models
of geographical extent. Our result of low phylogenetic signal
in diversification is not consistent with previous work
(Savolainen et al. 2002; Davies et al. 2004a), which may be a
result of changes in the angiosperm phylogeny topology or
our use of species richness instead of diversification rate.
To some degree, the lack of phylogenetic signal in species
richness mirrors the lack of phylogenetic signal in ecozone
area, which may have a large stochastic component based on
whether propagules experience intercontinental dispersal
(see below). We also find that most of the traits considered
had moderate (fleshy fruit) or effectively no (breeding
system, tropicality) phylogenetic signal, which indicates that
these traits transition between families frequently upon the
angiosperm phylogeny. Floral symmetry and growth form,
on the other hand, exhibited significant and marked
phylogenetic signals [e.g., zygomorphic (actinomorphic)
families were often sister to other zygomorphic (actinomorphic) families]. The finding of frequent transitions in
many putative key innovations may be tied to the lack of
phylogenetic signal in species richness at the family level.
These traits that provide recognizable common traits for
higher taxa of angiosperms are possibly the same traits that
enable them to rapidly diversify within a novel "adaptive
zone! (Rabosky 2009b). That variance in traits between
higher taxonomic levels is often greater than between
related species and ⁄ or genera has been previously recognized (Smith 2004), as well as the connection that these
same key traits that often mark synapomorphies (or
common traits) of families are also important in causing
divergent diversification (see Burlando 1993; Schaefer 2009).
We would further add that these key innovations could
effectively "reset! the biogeographic arena for a renewed
bout of exponential diversification when they evolve,
allowing for interplay between traits and geography in
diversification (Ricklefs 2007).
Ecological limits vs. key innovations 1277
Letter
Key Innovations and ecological limits
With ecozone area included in the models, we consistently
found evidence that zygomorphy was the primary trait
contributing additively to increased species richness. The
effects of tropicality and cosexuality also received marginal
empirical support. Considering that there was little evidence
for interactions between traits and age in our dataset, we
expect that few lineages were in the exponential phase of
growth and would argue that zygomorphy conveys a higher
"carrying capacity! per unit area, potentially evolving into
ever-narrower specialist "niches! (Rabosky 2009a). This
finding further suggests that the effect of key innovations
on diversification rates (speciation or extinction) is minimal
relative to the effect on carrying capacities within a
geographical landscape. The rather moderate effect of
tropicality may be surprising in light of recent findings
(Jansson & Davies 2008) but, despite numerous differences
in data and methodology, the relative ranking of area vs.
latitudinal distribution remains consistent with these previous studies. Taken together, the picture emerges that the
latitudinal gradient in angiosperm diversification relies to
some degree on the more specialized relationships between
plants and their pollinators in the tropics (Olesen & Jordano
2002) as well as tropical lineages occupying larger areas (see
below).
Studies in other systems have found associations between
fast life history (Cardillo et al. 2003), and dispersal (Roy et al.
2009) and range size, leading to predictions that growth
form (tree vs. herb) and ⁄ or fruit type (fleshy vs. dry) may
increase the geographical extent covered within a given
ecozone area, which in turn may elevate the available area
(and thus the carrying capacity) of herbaceous and ⁄ or
fleshy-fruited clades. Our analysis revealed that herbaceousness was indeed a strong additional predictor of geographical extent. In a post hoc analysis, herbaceousness was also
strongly correlated with clades with higher ecozone area
(F2,408 = 35.80; P < 0.0001), lending further support to the
idea that herbs are more widely dispersed, leading to
increased species richness. Fleshy fruits, on the other hand,
exhibited few strong additive effects on species richness or
geographical extent, even when previously reported growth
form · fruit interactions (Tiffney & Mazer 1995) were
included in our models, indicating that any association
between species richness and fruit may be due to complex,
multidimensional effects with other traits.
Other predictors of a large geographical extent within an
ecozone include a tropical distribution and, marginally,
cosexuality. Interestingly, a strong negative interaction
between tropicality and ecozone area was present in the
best models. This interaction indicates that, while tropical
clades were larger in range than temperate ones for more
restricted lineages (and thus could elevate the carrying
capacity of a lineage), the tropical lineages occupied a
smaller amount of available area than temperate clades as
ecozone area expands. Opposite to the effect of herbaceousness, there was a non-significant tendency for tropical
families to occupy smaller ecozone areas in general
(F2,408 = 1.94; P = 0.16). In total, these effects (1) may
show some support for the tropical conservatism hypothesis
(Wiens & Donoghue 2004) in that tropical clades remain
constrained close to the tropical band in more widely
distributed clades and (2) diminish the overall effect of
tropicality on species richness.
Higher geographical extent may produce higher species
richness through buffering from extinction (Payne &
Finnegan 2007), yet we expect this effect plays only a
minor role in the mechanism of most key innovations.
We would expect such an effect to manifest itself with a trait
showing significant effects in both geographical extent and
species richness for a given ecoregion area and only
cosexuality had equivalent dual effects (and these were
relatively weak). Thus, we venture that traits can act as key
innovations through two main mechanisms: by increasing
speciation through increased specialization within a given
area (e.g., zygomorphy), being associated with increases in
the amount of area occupied (e.g., herbaceousness), or some
combination of the two (e.g., tropicality).
CONCLUSIONS
Understanding broad-scale determinants of angiosperm
diversification in terms of the origin of key traits has had
a long history (see Crepet & Niklas 2009 for review), yet
recent analyses have questioned whether key traits account
for a meaningful amount of variation (cf. Davies et al.
2004a). We find that several key traits are associated with
species richness and geographical extent but that their
effects are best seen when accounting for ecoregion area.
These constraints on the "carrying capacity! of a lineage are
emerging as critically important in disparate lineages and
placing the most severe bounds on the species richness of a
lineage (Ricklefs 2007; Rabosky 2009b). Geography, thus,
determines the species richness of a clade far more than age
as lineages rapidly expand and diversify upon a landscape.
Certain traits (herbaceousness and tropicality) encourage
diversification by expanding the size of the landscape upon
which diversification occurs. Once the landscape is "full! of
members of a particular family with a characterizing
adaptation, speciation rates decline (or extinction rates
increase) leaving both medium-aged and old-aged lineages
with equivalent species richness. The variation in species
richness, once the main effect of ecoregion area is removed,
is influenced by traits involved in biotic pollination (von
Hagen & Kadereit 2003; Ree 2005), indicating that this trait
parses a given amount of ecological space more finely.
! 2010 Blackwell Publishing Ltd/CNRS
1278 J. C. Vamosi and S. M. Vamosi
Amongst extant families, the next challenge in understanding differential diversification is now one of characterizing
ecological limits and determining whether biotic interactions
influence the "carrying capacity! of angiosperm clades within
a given area. Future examination of key innovations within a
geographical context will provide more insight into the main
determinants of imbalance on the angiosperm phylogeny.
ACKNOWLEDGEMENTS
Thanks to R. Freckleton and S. Queenborough for advice
on model selection and using R to implement phylogenetic
GLS models. A. Mooers and five anonymous referees
provided invaluable comments on an earlier version of the
manuscript. We are grateful to NSERC (Canada) for
continued financial support of our respective research
programs.
REFERENCES
Barraclough, T.G. & Savolainen, V. (2001). Evolutionary rates and
species diversity in flowering plants. Evolution, 55, 677–683.
Burlando, B. (1993). The fractal geometry of evolution. J. Theor.
Biol., 163, 161–172.
Burnham, K.P. & Anderson, D.L. (2002) Model selection and
multimodel inference: a practical-theoretic approach. In. SpringerVerlag, New York.
Cardillo, M., Huxtable, J.S. & Bromham, L. (2003). Geographic
range size, life history, and rates of diversification in Australian
mammals. J. Evol. Biol., 16, 282–288.
Crepet, W.L. & Niklas, K.J. (2009). Darwin!s second "abominable
mystery!: why are there so many angiosperm species? Am. J. Bot.,
96, 366–381.
Davies, T.J., Barraclough, T.G., Chase, M.W., Soltis, P.S., Soltis,
D.E. & Savolainen, V. (2004a). Darwin!s abominable mystery:
insights from a supertree of the angiosperms. Proc. Natl. Acad.
Sci. USA, 101, 1904–1909.
Davies, T.J., Barraclough, T.G. & Savolainen, V. (2004b). Environmental causes for plant biodiversity gradients. Phil. Trans. R.
Soc. Lond. B, 359, 1645–1656.
De Queiroz, A. (2002). Contingent predictability in evolution: key
traits and diversification. Syst. Biol., 51, 917–929.
Drummond, A.J., Ho, S.Y.W., Phillips, M.J. & Rambaut, A. (2006).
Relaxed phylogenetics and dating with confidence. PLoS Biol.,
4, e88.
Eriksson, O. & Bremer, B. (1992). Pollination systems, dispersal
modes, life forms, and diversification rates in angiosperm families. Evolution, 46, 258–266.
Fitzjohn, R.G., Maddison, W.P. & Otto, S.P. (2009). Estimating
trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Syst. Biol., 58, 595–611.
Freckleton, R.P., Harvey, P.H. & Pagel, M. (2002). Phylogenetic
analysis and comparative data: a test and review of evidence.
Am. Nat., 160, 712–726.
von Hagen, K.B. & Kadereit, J.W. (2003). The diversification of
Halenia (Gentianaceae): ecological opportunity versus key
innovation. Evolution, 57, 2507–2518.
! 2010 Blackwell Publishing Ltd/CNRS
Letter
Hedges, S.B., Dudley, J. & Kumar, S. (2006). TimeTree: a public
knowledge-base of divergence times among organisms. Bioinformatics, 22, 2971–2972.
Heilbuth, J.C. (2000). Lower species richness in dioecious clades.
Am. Nat., 156, 221–241.
Igic, B., Lande, R. & Kohn, J.R. (2008). Loss of self-incompatibility and its evolutionary consequences. Int. J. Plant Sci., 169,
93–104.
Jansson, R. & Davies, T.J. (2008). Global variation in diversification rates of flowering plants: energy vs. climate change. Ecol.
Lett., 11, 173–183.
Magallon, S. & Sanderson, M.J. (2001). Absolute diversification
rates in angiosperm clades. Evolution, 55, 1762–1780.
Mittelbach, G.G., Schemske, D.W., Cornell, H.V., Allen, A.P.,
Brown, J.M., Bush, M.B. et al. (2007). Evolution and the latitudinal diversity gradient: speciation, extinction and biogeography.
Ecol. Lett., 10, 315–331.
Morin, X. & Chuine, I. (2006). Niche breadth, competitive strength
and range size of tree species: a trade-off based framework to
understand species distribution. Ecol. Lett., 9, 185–195.
Olesen, J.M. & Jordano, P. (2002). Geographic patterns in plantpollinator mutualistic networks. Ecology, 83, 2416–2424.
Pagel, M. (1999). The maximum likelihood approach to reconstruction ancestral character states of discrete characters on
phylogenies. Syst. Biol., 48, 612–622.
Paradis, E., Claude, J. & Strimmer, K. (2004). APE: analyses of
phylogenetics and evolution in R language. Bioinformatics, 20,
289–290.
Payne, J.L. & Finnegan, S. (2007). The effect of geographic range
on extinction risk during background and mass extinction. Proc.
Natl. Acad. Sci. USA, 104, 10506–10511.
Phillimore, A.B. & Price, T.D. (2008). Density-dependent cladogenesis in birds. PLoS Biology, 6(3), e71.
Pianka, E.R. (1966). Latitudinal gradients in species diversity: a
review of concepts. Am. Nat., 100, 33–46.
R Development Core Team (2008). R: A Language and Environment
for Statistical Computing. R Foundation for Statistical Computing,
Vienna.
Rabosky, D.L. (2009a). Ecological limits and diversification rate:
alternative paradigms to explain the variation in species richness
among clades and regions. Ecol. Lett., 12, 735–743.
Rabosky, D.L. (2009b). Ecological limits on clade diversification in
higher taxa. Am. Nat., 173, 662–674.
Ree, R.H. (2005). Detecting the historical signature of key innovations using stochastic models of character evolution and
cladogenesis. Evolution, 59, 257–265.
Ricklefs, R.E. (2003). Global diversification rates of passerine
birds. Proc. R. Soc. Lond. B, 270, 2285–2291.
Ricklefs, R.E. (2006). Global variation in the diversification rate of
passerine birds. Ecology, 87, 2468–2478.
Ricklefs, R.E. (2007). History and diversity: explorations at
the intersection of ecology and evolution. Am. Nat., 170,
S56–S70.
Ricklefs, R.E. & Renner, S.S. (1994). Species richness within
families of flowering plants. Evolution, 48, 1619–1636.
Roy, K., Hunt, G., Jablonski, D., Krug, A.Z. & Valentine, J.W.
(2009). A macroevolutionary perspective on species range limits.
Proc. R. Soc. Lond. B, 276, 1485–1493.
Sargent, R.D. (2004). Floral symmetry affects speciation rates in
angiosperms. Proc. R. Soc. Lond. B, 271, 603–608.
Letter
Savolainen, V., Heard, S.B., Powell, M.P., Davies, T.J. &
Mooers, A.O. (2002). Is cladogenesis heritable? Syst. Biol., 51,
835–843.
Schaefer, C.W. (2009). The reality of the higher taxonomic categories. J. Zool. Syst. Evol. Res., 14, 1–10.
Smith, F.A. (2004). Similarity of mammalian body size across the
taxonomic hierarchy and across space and time. Am. Nat., 163,
672–691.
Smith, S.A. & Donoghue, M.J. (2008). Rates of molecular evolution are linked to life history in flowering plants. Science, 322,
86–89.
Smith, S.A., Beaulieu, J.M. & Donoghue, M.J. (2009). An uncorrelated relaxed-clock analysis suggests an earlier origin for
flowering plants. Proc. Natl. Acad. Sci. USA, 107, 5897–5902.
Stevens, P.F. (2001 onwards) Angiosperm Phylogeny Website. Version 9,
June 2008. Available at: http://www.mobot.org/MOBOT/
research/APweb/
Tiffney, B.H. & Mazer, S.J. (1995). Angiosperm growth habit,
dispersal and diversification reconsidered. Evol. Ecol., 9, 93–
117.
Udvardy, M.D.F. (1975) A classification of the biogeographical
provinces of the world. In: IUCN Occasional Paper no. 18.
Morgues, Switzerland: IUCN.
Vamosi, J.C. & Vamosi, S.M. (2004). The role of diversification in
causing the correlates of dioecy. Evolution, 58, 723–731.
Vamosi, J.C. & Vamosi, S.M. (2005). Present day risk of extinction
may exacerbate the lower species richness of dioecious clades.
Divers. Distrib., 11, 25–32.
Webb, C.O. & Donoghue, M.J. (2005). Phylomatic: tree assembly
for applied phylogenetics. Mol. Ecol. Notes, 5, 181. Available at:
http://www.phylodiversity.net/phylomatic/.
Webb, C.O., Ackerly, D. & Kembel, S. (2008). Software for the
analysis of community phylogenetic structure and character evolution,
Version 4.0.1. Available at: http://www.phylodiversity.net/
phylocom.
Ecological limits vs. key innovations 1279
Wiens, J.J. & Donoghue, M.J. (2004). Historical biogeography,
ecology, and species richness. Trends Ecol. Evol., 19, 639–644.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the
online version of this article:
Appendix S1 Phylogenetic signal for five predictor variables
and two response variables (species richness and geographical extent) for 409 angiosperm families.
Appendix S2 Phylogenetically informed a priori models of
species richness (SR).
Appendix S3 Phylogenetically informed a priori models of
geographical extent (GE).
Data S1 Predominant trait states of angiosperm families.
Data S2 Hypothesized angiosperm family phylogeny based
on Stevens (2001 onwards).
As a service to our authors and readers, this journal provides
supporting information supplied by the authors. Such
materials are peer-reviewed and may be re-organized for
online delivery, but are not copy-edited or typeset. Technical
support issues arising from supporting information (other
than missing files) should be addressed to the authors.
Editor, Arne Mooers
Manuscript received 2 June 2009
First decision made 16 June 2010
Manuscript accepted 13 July 2010
! 2010 Blackwell Publishing Ltd/CNRS