Research
Three explanations for biodiversity hotspots: small range size,
geographical overlap and time for species accumulation. An
Australian case study
Lyn G. Cook1, Nate B. Hardy2 and Michael D. Crisp3
1
School of Biological Sciences, The University of Queensland, Brisbane, Qld 4072, Australia; 2Department of Entomology and Plant Pathology, Auburn University, Auburn, AL 36849, USA;
3
Research School of Biology, The Australian National University, Canberra, ACT 0200, Australia
Summary
Author for correspondence:
Lyn G. Cook
Tel: +61 7 3365 2070
Email: [email protected]
Received: 31 July 2014
Accepted: 28 October 2014
New Phytologist (2014)
doi: 10.1111/nph.13199
Key words: biodiversity hotspot,
geographical range, niche limitation, range
overlap, southwest Australia Floristic Region,
time for species accumulation.
To understand the generation and maintenance of biodiversity hotspots, we tested three
major hypotheses: rates of diversification, ecological limits to diversity, and time for species
accumulation.
Using dated molecular phylogenies, measures of species’ range size and geographical clade
overlap, niche modelling, and lineages-through-time plots of Australian Fabaceae, we compared the southwest Australia Floristic Region (SWAFR; a global biodiversity hotspot) with a
latitudinally equivalent non-hotspot, southeast Australia (SEA).
Ranges of species (real and simulated) were smaller in the SWAFR than in SEA. Geographical overlap of clades was significantly greater for Daviesia in the SWAFR than in SEA, but the
inverse for Bossiaea. Lineage diversification rates over the past 10 Myr did not differ between
the SWAFR and SEA in either genus.
Interaction of multiple factors probably explains the differences in measured diversity
between the two regions. Steeper climatic gradients in the SWAFR probably explain the
smaller geographical ranges of both genera there. Greater geographical overlap of clades in
the SWAFR, combined with a longer time in the region, can explain why Daviesia is far more
species-rich there than in SEA. Our results indicate that the time for speciation and ecological
limits hypotheses, in concert, can explain the differences in biodiversity.
Introduction
Biodiversity is unevenly distributed across the globe, with 35 hotspots of diversity formally recognized (e.g. Myers et al., 2000;
Mittermeier et al., 2004; Williams et al., 2011), but why some
areas harbour more species than others is still under debate.
When considering this question, it is important to consider the
evolutionary history of organisms, because past opportunities and
shared traits influence current distributions (Ricklefs, 1987).
Increasingly complex phylogenetic models have been developed
to try to explain biodiversity hotspots and these often include
multiple assumptions whose interactions and influence on the
outcome can be unclear (Warren et al., 2014). Here we used a
simple approach in which we applied different methods, in a
phylogenetic framework, to tease apart three competing hypotheses advanced to explain the existence of biodiversity hotspots. We
focused on the largely mediterranean-climate regions of temperate southern Australia, but the findings are relevant to understanding the global distribution of biodiversity in general.
Mediterranean climates are heavily represented as biodiversity
hotspots of global significance (Cowling et al., 1996; Myers et al.,
2000; Sauquet et al., 2009). These are regions characterized by
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cool wet winters and warm dry summers, and typically occur at
temperate latitudes on the western margins of continents (Fig. 1
in Myers et al., 2000). The mediterranean-climate flora, such as
in the western Cape of South Africa, southwest Western Australia
and around the Mediterranean Basin, is especially rich on nutrient-poor soils, and is recurrently disturbed by frequent fire
regimes or heavy grazing (Cowling et al., 1996; Orians &
Milewski, 2007).
Numerous hypotheses have been proposed to explain the floristic diversity of the mediterranean-climate biodiversity hotspots
and, in general, they fall into three nonexclusive major categories
(Table 1). These categories roughly equate to the three major
ideas about the limits to diversity more generally, not just in
mediterranean-climate regions.
(1) Faster speciation and/or lower extinction rates (rates of
species accumulation hypothesis; e.g. Cardillo et al., 2005).
(2) Higher levels of co-existence (ecological limits to diversity
hypothesis; e.g. MacArthur & Wilson, 1967; Rabosky & Glor,
2010).
(3) More time for species accumulation (time for species accumulation hypothesis; reviewed in Stephens & Wiens, 2003;
Wiens, 2011).
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Table 1 Hypotheses proposed to explain relatively high plant diversity in mediterranean-climate biodiversity hotspots
Hypothesis
Explanation
Reference
against
Reference in support
Diversification (higher speciation and/or lower extinction)
Speciation across morphological
and environmental axes
Selection for specialization across gradients
(e.g. topographical and edaphic heterogeneity)
leads to reduction in gene flow and consequent
speciation
Cowling et al. (1992); Goldblatt (1997);
Linder (2003) and Cowling et al. (2009)
Low dispersal
Low gene flow leads to speciation
Cowling et al. (1992) and Goldblatt (1997)
Relatively stable climate
Speciation rates similar to elsewhere but milder
climate results in lower extinction rates and
hence higher diversification rates
Cowling et al. (1992, 1996); Goldblatt
(1997); Linder et al. (2003); Cowling
et al. (2009); Verboom et al. (2009) and
Valente & Vargas (2013)
Fast speciation rates
Multiple causes of faster speciation rates
Richardson et al. (2001); Linder (2003);
Linder et al. (2003); Sauquet et al. (2009);
Schnitzler et al. (2011) and Valente &
Vargas (2013)
Cardillo & Pratt
(2013)
Short generation time in seeding response to
fire increases speciation rate
Wells (1969)
Verdu et al. (2007)
Selection for pollinator specialization leads to
reduced gene flow and consequent speciation
Linder (2003) and Johnson (2010)
Schnitzler et al.
(2011)
Spatial heterogeneity
Specialization to different soils or topography
reduces competition and favours co-existence
across landscape mosaics
Bond (1983); Cowling et al. (1992);
Hopper & Gioia (2004); Cowling et al.
(2009); Schnitzler et al. (2011);
Laliberte et al. (2013) and Linder
et al. (2014)
Disturbance (temporal
heterogeneity)
Specialization to different times in disturbance
cycles (e.g. fire or grazing) reduces
competition by separating organisms temporally
Bond et al. (1992); Huston (1994);
Verdu et al. (2007) and Schnitzler
et al. (2011)
Low competition
Low productivity as a result of poor soils and
relatively arid conditions facilitates co-existence
without dominance
Cody (1991); Huston (1994);
Sauquet et al.
(2009) and Huston (2012)
Different life history strategies, for example,
seeding/resprouting and nutrient acquisition
traits, enable co-existence
Linder (2003) and Pekin et al. (2012)
Ancient soils with ancient
lineages
Ancient stable soils allow the accumulation of
highly adapted species
Hopper (2009)
Long-term stable climate
and landscape
Long-term lack of major landscape or climate
changes allows accumulation of species.
Similar to reduced extinction increasing
diversification rates
Linder et al. (2003); Cowling et al.
(2009); Hopper (2009); Schnitzler
et al. (2011); Valente & Vargas
(2013) and Linder et al. (2014)
Pre-adaption for xerophylly
Sclerophylly was long favoured on poor soils
in dry climates. This resulted in less extinction
as climates became more arid in the
Miocene–Pliocene
McLoughlin & Hill (1996)
Pollinator specialization
High co-existence
Time for species accumulation
Various combinations of factors falling under several of these
categories have been proposed to explain the plant diversity in
the Cape Floristic Region (CFR) of South Africa. For example,
several authors have argued for species accumulation through
long-term climatic stability, with spatial heterogeneity in climate
and landscape, and low dispersal rates, increasing diversification
(Goldblatt, 1997; Linder, 2003; Cowling et al., 2009; Valente &
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Orians &
Milewski (2007)
Vargas, 2013). Each of the hypotheses (Table 1) provides testable
components but, to date, the relevance of each has mostly been
inferred from correlations, with many not accounting for evolutionary history and regional differences.
The introduction of new methods using relaxed molecular
clock dating has allowed testing of several hypotheses, particularly relating to the timing and rate of diversification using
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plant lineages occurring in the mediterranean-climate regions.
In general, authors of these tree-based studies have argued for
increased diversification rates in mediterranean-climate regions.
For example, for Phylica (Richardson et al., 2001) and Restionaceae (Linder et al., 2003), authors concluded that lineages had
diversified rapidly in the CFR of South Africa. By contrast,
Linder et al. (2003) argued that low extinction, rather than
high speciation, contributed to high diversity of Restionaceae
in the southwest Western Australia Floristic Region (SWAFR).
However, these studies did not compare diversification rates in
the mediterranean-climate hotspots with those in other regions,
and therefore could not examine whether differences in rates
per se might explain any diversity differences.
A link between geographical range size and diversification also
has the potential to explain global patterns of biodiversity
(Ricklefs, 2006). This idea has received some support from a
recent study of Protea, in which Valente et al. (2010) found that
the proteas of the CFR of South Africa (a global biodiversity hotspot: Myers et al., 2000) were more diverse and had smaller geographical range sizes than proteas elsewhere in Africa. Although
range size alone did not explain species richness of proteas in the
CFR, Valente et al.’s finding fits the hypothesis that range size
might play a role in the generation of global biodiversity hotspots. More recently, Valente & Vargas (2013) tested all three
hypotheses listed above by comparing diversity patterns and
dynamics of disjunct taxa shared between the CFR and Mediterranean Basin. They found support for a role for all three in the
CFR, which has a higher species density than in the Mediterranean Basin, whereas there was evidence for recent high species
turnover in the latter, and they suggested that a diversity limit
might have been reached there in several clades. Valente & Vargas (2013) concluded that ecological limits have not been
reached in the CFR because of finer niche subdivision there but
did not explicitly quantify and compare range- or niche-size overlap between the two regions.
Here, we assessed hypotheses in the three categories by comparing two regions at similar latitudes. The temperate zone in
Australia is large and comprises two major regions, the SWAFR,
which is recognized as a global biodiversity hotspot (Myers et al.,
2000), and southeast Australia (SEA). These regions span similar
latitudes and are separated by 1000 km or more of arid shrubland. Many groups of plants have diversified in both Australian
temperate regions, and thus multiple comparisons can be made
among closely related lineages with highly similar life histories
and ecologies.
To date, attempts to understand the generation and maintenance of the SWAFR biodiversity hotspot have focused solely on
the region itself (e.g. Hopper & Gioia, 2004) or have involved
comparative studies with other globally identified hotspots (e.g.
Sauquet et al., 2009). However, such studies have had limited
power to discriminate among competing hypotheses for biodiversity hotspot generation and maintenance because they have not
compared and contrasted the macroevolution of hotspots with
that of latitudinally equivalent and historically associated nonhotspot regions. Comparisons among continents typically
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compare patterns formed by biotas comprising different taxonomic groups and/or different dominance.
We addressed several expectations of differences between the
SWAFR (mediterranean climate; global biodiversity hotspot) and
latitudinally similar SEA (mostly not mediterranean climate)
under the above hypotheses.
(1) Rates of diversification. Diversification rates should be higher
in the SWAFR, and this is testable using time-calibrated phylogenies (e.g. Valente et al., 2011).
(2) Limits on diversification. Spatial heterogeneity leads to finerscale niche partitioning in the SWAFR, and this predicts that species have smaller geographical range sizes within that region. At
the scale of clades (above the species level), there should be more
geographical overlap if taxa are partitioning resources across finescale mosaics (e.g. Rabosky, 2013).
(3) Time for diversification. Long-term occupancy or stability
leads to greater species accumulation, and this predicts that
regions occupied for longer have more species (e.g. Cowling
et al., 1996).
The SWAFR flora is not especially species-rich per grid cell
compared with several other regions in Australia, such as the Sydney sandstones, the wet tropics and the Border Ranges (in onedegree grid cells; Crisp et al., 2001), which also score as highly as
the SWAFR for endemism (species with a range of one to four
one-degree grid cells; Crisp et al., 2001). Diversity analysis of
global regions by Beard et al. (2000) shows that the species diversity–area relationship for the SWAFR is on the same slope as
Australia as a whole. The SWAFR stands out from other regions
in Australia in scoring uniformly highly on weighted endemism
(species richness down-weighted by geographical range sizes;
Crisp et al., 2001), and in having a high proportion of species
and habitat at risk from human-mediated change. It is this latter
measure (in addition to endemism) that elevated the SWAFR to
a global biodiversity hotspot in the assessment of Myers et al.
(2000). Nevertheless, the difference in species per total area and
greater weighted endemism in the SWAFR compared with most
regions in the similar latitudes of SEA provide an opportunity to
tease apart processes leading to regional biodiversity differences
in general.
We compared the geographical scale of species diversity in two
Australian endemic genera of egg-and-bacon peas (Fabaceae),
Daviesia and Bossiaea, which both occur across southern Australia
with strong representation in both the SWAFR and SEA (Supporting Information Table S1). They comprise the only data set,
other than Banksia (Cardillo & Pratt, 2013), for which there are
sufficient numbers of species and dense enough sampling available to enable comparative diversification studies for these regions
in a phylogenetic framework. This allows a direct comparison of
species diversity and range sizes across taxa in similar latitudes
and within the same lineages of plants. We also compared the
rate and pattern of lineage accumulation in each region, and
assessed two facets of the interaction between evolutionary lineages and their environment: geographical range size and overlap.
Although it is commonly assumed that taxa in the SWAFR have
small range sizes (e.g. Hopper & Gioia, 2004), this has seldom
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been quantified in a comparative manner (but see Cowling et al.,
1994), and never in a phylogenetic framework.
Materials and Methods
Phylogenetic relationships and relaxed molecular dating
We used phylogenies of Bossiaea (Fabaceae: Bossiaeeae) and
Daviesia (Fabaceae: Mirbelieae) derived from three loci: one
nuclear (Internal Transcribed Spacers, ITS) and two chloroplast
(NADH dehydrogenase F, ndhF, and the trnL(UAA)-trnF
(GAA) region, trnL-F). Sequence data used by Crisp & Cook
(2009) were supplemented with 30 additional species and 263
newly obtained sequences, using the methods described therein.
Bossiaea and Daviesia are each widely represented in both the
SWAFR and SEA and we sampled most of the species for phylogenetic analysis – 106 of 127 species of Daviesia (83%) and 61 of
70 species of Bossiaea (87%) (Table S1) – together with multiple
species and genera of outgroups (Table S2).
We used BEAST v1.5.4 (Drummond & Rambaut, 2007) to estimate the joint posterior probability of phylogeny and divergence
times, conditioned on models of nucleotide substitution, substitution rate variation among branches, phylogenetic diversification, and prior probabilities imposed on node ages for which
calibration data were available. A birth-death model (Nee, 2006)
was used for the phylogenetic branching process. Each data set
was partitioned by locus; ndhF was further divided into two partitions, one comprising third codon position sites, and another
composed of first and second codon positions. Parameter values
for a GTR+I+G substitution model, and an uncorrelated log normal model of among-lineage rate variation, were estimated independently for each partition. For each data set, two (Daviesia) or
six (Bossiaea) runs of 40 million Markov chain Monte Carlo
(MCMC) generations were performed, sampling every 4000 and
6000 generations, respectively, with the log and tree files then
combined. We used TRACER v1.5 (BEAST package) to determine
when BEAST began to sample from the stationary distribution, and
that all post-burnin parameter estimates had effective sample sizes
> 200.
No primary (i.e. fossil-based) calibration data are available
for either Daviesia or Bossiaea, so we used the calibration priors
in Crisp & Cook (2009). The following secondary calibration
priors were used in the present study, based on the best estimates from multiple analyses by Crisp & Cook (2009) of the
mean and 95% confidence interval (CI) of relevant nodes. For
the stem node of Daviesia, the previous mean estimate of 43.9
million yr ago (Ma) (95% CI 39–49 Ma) was applied with a
lognormal zero offset 39, mean 1.7, SD 0.4. For the crown
node of Daviesia, the previous estimate (mean 29.1 Ma; 95%
CI 22.7–34.0 Ma) was given a lognormal prior with zero offset
22.7, mean 2.0 (= 29.0), and SD 0.3. The Bossiaea data set was
rooted at the divergence between Hypocalyptus and Mirbelieae
plus Bossiaeeae, which had been dated at 54.1 Ma, SD 1.2, by
Lavin et al. (2005) and we calibrated this as a secondary constraint with a lognormal prior: offset of 54, mean 0.01 and SD
1.0.
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Species missing from the molecular analyses were placed
with their presumed closest relatives on the basis of morphology. For lineages-through-time (LTT) estimates, the aim was
to minimize artifacts caused by incomplete sampling (Pybus &
Harvey, 2000). The missing taxa were connected midway
along the terminal branch of their closest relative, or the
branch was segmented in thirds if there were two missing taxa
(Figs S1, S2).
Definition of SEA and the SWAFR
SEA and the SWAFR were defined using regions delimited under
the Interim Biogeographic Regionalisation for Australia (IBRA7
regions; http://www.environment.gov.au/topics/land/nationalreserve-system/science-maps-and-data/australias-bioregions-ibra),
which are listed in Table S3. SEA and the SWAFR contain those
IBRA regions for which average annual rainfall is > 300 mm,
with most not falling in summer. Several IBRA regions in
Queensland with these conditions were excluded from the SEA
category because high evaporation makes them too arid to be
comparable with the rest (Australian Bureau of Meteorology;
http://www.bom.gov.au/, accessed June 2012). Each species’ distribution was assigned as SEA, SWAFR, Eremaean (average
annual rainfall < 300 mm), Australian Monsoon Tropics (> 85%
of rainfall from November to April) or polymorphic.
Ancestral area reconstruction
Ancestral area was estimated for Bossiaea and Daviesia separately
using both maximum parsimony and maximum likelihood
(MK1) optimality criteria in MESQUITE ver. 2.75 (Maddison &
Maddison, 2011). For each genus, the whole phylogeny, including sister groups, was included in reconstructions. In case
phylogenetic uncertainty might have affected the results, parsimony reconstructions were also assessed on 100 trees sampled
evenly (to minimize autocorrelation) across the respective BEAST
posteriors.
Geographical range size
Distributional data for species of Daviesia and Bossiaea were
sourced from the Australian Virtual Herbarium online database
(http://avh.chah.org.au, accessed June–August 2010). Each genus
is heavily represented – the Australian Virtual Herbarium held
10 559 records of Bossiaea and 15 949 records of Daviesia. Obviously incomplete entries (i.e. those with inadequate or incorrect
latitude and longitude data) and those with incorrect taxonomic
names were pruned from the data sets. The remaining data were
then imported into BIODIVERSE v. 0.14 (Laffan et al., 2010) and
converted into a 0.5° grid format, which was an appropriate scale
for the environmental measures used (see next section). Areas of
occupancy (AOOs; sensu Gaston, 1991 – number of grid cells
with a record of each species) were then calculated using a Python
script written by NBH (occuPy: available at http://apes.skullisland.info/node/6).
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We calculated phylogenetic autocorrelation of geographical
range sizes (AOO scores) using Moran’s I in the ‘ape’ R library
(Paradis et al., 2004). Significant autocorrelation could confound
tests of geographical range size and diversification because, if
range size were linked to other traits conserved within lineages,
geographical range might not be disentangled from other intrinsic factors influencing diversification (Goldberg et al., 2011).
Comparisons between areas of occupancy of species within
SEA versus those in the SWAFR were calculated using speciesarea data for Bossiaea and Daviesia independently. Twelve species
whose distributions extended beyond SEA and the SWAFR were
excluded from the analysis. Tests for differences in the area of
occupancy of species in the two regions were performed using the
nonparametric Mann–Whitney U test and, after log-transformation, using simple t-tests implemented in GRAPHPAD PRISM 5.0d
(GraphPad Software Inc., San Diego, CA, USA).
Might climate explain differences in geographical range
size?
To assess whether any differences in species’ range sizes were
likely to be the result of differences in environmental factors
between the two regions, we modelled climatic envelopes for constituent species. Using MAXENT (Phillips et al., 2006), available in
the spatial tools of the Atlas of Living Australia (http://spatial.ala.
org.au/), we used point data for species and two basic climatic
variables (Bio1, mean annual temperature (MAT), and Bio12,
mean annual rainfall (MAR)) to estimate matching of climate
between regions for five species from each genus from each of the
SWAFR and SEA regions, totalling 20 species representing each
of the major clades within each genus (Table S4). Although this
is, perhaps, a simplistic view of variables likely to be contributing
to species distributions, we wanted to determine whether there
were differences even under the simplest model. The resulting
species distribution model (SDM) for each species was applied to
the whole of Australia, and the area of high probability (≥ 0.6)
represented in the SWAFR and SEA was calculated using pixel
counts in ADOBE PHOTOSHOP. To check that results were not
attributable to some peculiarity of the chosen species distributions, we repeated the analyses using multiple random rectangles
of points on the map (c. 0.7° longitude by 1° latitude).
Lineages-through-time
Lineages-through-time plots were calculated for all SEA and
SWAFR lineages of Bossiaea and Daviesia separately by summing
the number of lineages inferred to exist (from the chronograms)
at each time-point. Lineages that do not occur in each region
were excluded. To assess whether the patterns of LTT differed
between the SWAFR and SEA, the numbers of lineages of each
at each time-point were plotted against each other and assessed
for deviation using linear regression implemented in GRAPHPAD
PRISM.
To test for diversification shifts in lineages, we used BAMM
v1.0.0 (http://bamm-project.org), which explores multiple models (‘configurations’) of diversification-rate heterogeneity using
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reversible-jump MCMC simulations (Rabosky, 2014). Unlike
previous packages, BAMM proposes complex configurations
including multiple rate-shifts, using the Bayesian framework to
address model uncertainty and to compare configurations using
marginal likelihoods. Post-analysis and visualization were carried
out using the R package BAMMtools. We pruned each genus tree to
obtain separate phylogenies of the species occurring in each
region, and the four resulting trees were analysed separately.
Are clade range overlaps the same in the SWAFR and SEA?
We calculated extent of occurrence (EOO; sensu Gaston (1991)
– area of minimum polygon containing all points) for SWAFRand SEA-restricted clades in Bossiaea and Daviesia. For these
comparisons, we tried to remove clade age as a factor. If there
were a relationship between clade age and geographical range
(e.g. McPeek & Brown, 2007; Ricklefs, 2007; Wiens et al.,
2011), then differences might be found on the basis of clade age
alone. Therefore, we defined clades of the same age within each
genus using the following cut-off node ages: Daviesia (14 Ma; 10
clades) and Bossiaea (8 Ma; 11 clades). For each combination of
genus and region (SWAFR and SEA), minimum-area polygons
were constructed around the geographical ranges of clades
defined by the cut-off ages defined above. The parts of species’
ranges for a clade that were outside the regions of interest were
excluded. We calculated an index of overlap for the SWAFR and
SEA as the total overlap of clades (summed across all pair-wise
comparisons) divided by the area of the minimum polygon of all
clades combined, for example, (XY + YZ + XZ)/(XYZ) as shown
in Fig. 1. Higher values indicate greater overlap. This index differs from those used by Barraclough & Vogler (2000) (degree of
sympatry) and Kozak & Wiens’ (2010) index of clade overlap, in
that it considers the total area occupied and can incorporate more
than two overlapping clades.
Results
Chronograms for each genus (Figs S1, S2) were not substantively
different from those derived previously with fewer taxa. Geographical range sizes were not autocorrelated along lineages in
either Bossiaea (P = 0.68) or Daviesia (P = 0.60), allowing tips
(species) to be used as independent observations in comparisons
of range size between SEA and the SWAFR.
In both genera, the modal size of AOOs was small (≤ 10 halfdegree grid cells) and few species had large AOOs (Fig. S3).
There was no significant difference in the average range size of
species of Daviesia compared with the average range size of species of Bossiaea, across all distributions (P = 0.93), in the SWAFR
(P = 0.90) or in SEA (P = 0.76). Species’ range sizes (AOOs) were
significantly smaller (about one-third the size), on average, in the
SWAFR than in SEA in both Bossiaea (0.01 < P < 0.05) and
Daviesia (0.00001 < P < 0.0001) (Fig. 2). All species distribution
models showed good matching to observed distributions (Area
Under the Curve, AUC ≥ 0.94; Table S4) and predicted a greater
area of matched climate in SEA than in the SWAFR irrespective
of whether the prediction was based on species limited to the
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X
Y
SWAFR
Z
SEA
150
(b)
(c)
Fig. 1 Index of clade overlap: the total geographical overlap of clades
(summed across all pair-wise comparisons) divided by the area of the
minimum polygon of all clades combined. Each red, blue or yellow square
represents the minimum polygon around the distribution of a clade (extent
of occurrence). The black outline represents the area calculated as the total
area occupied by the clades of interest. Clade overlap is shown by overlay
of colours. In (a), there is no overlap of areas occupied by clades X, Y and
Z. In (b), the red clade (Y) overlaps both the blue (X) and yellow (Z) clades,
but the latter two do not overlap. There are two pair-wise overlaps (red–
blue and red–yellow). In (c), all clades overlap so there are three pair-wise
overlaps (red–blue, red–yellow and blue–yellow). The situation in (c)
would yield a higher overlap index than (b), and the index for (a) would be
0. The lower bound on the index is 0 and there is no upper bound (limited
only by total number of clades).
No. 0.5 degree grid cells
(a)
100
t-test P = 0.0475
MWU P = 0.0253
t-test P < 0.0001
MWU P = 0.0001
50
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SE
A
R
vie
sia
–
AF
Da
vie
Da
Bo
ss
sia
ia
–
ea
–
SW
AF
SW
–
ea
ia
ss
Bo
SWAFR or limited to SEA (Table S4), or on artificial rectangular
distributions (Table S5).
The SWAFR is estimated to be the ancestral region for extant
lineages of Daviesia (Maximum Parsimony, MP, Fig. S2;
Maximum Likelihood, ML proportional likelihood = 0.989),
and SEA is estimated to be the ancestral region for Bossiaea (MP,
Fig. S1; ML proportional likelihood = 0.999). All reconstructions
were the same across the respective 100-tree posterior samples,
except that two reconstructions were ambiguous (either SWAFR
or SEA) for Bossiaea. Daviesia exhibited more clade overlap in the
SWAFR than in SEA, but Bossiaea had more clade overlap in
SEA than in the SWAFR (Table 2).
Lineage-through-time plots for both Bossiaea and Daviesia
showed an increase in diversification rate beginning c. 15–
10 Ma (marked by vertical broken lines in Fig. 3a,b). From c.
10 Ma, LTTs were very similar for SEA and the SWAFR lineages in both Bossiaea and Daviesia (Fig. 3c,d), with linear
regressions showing strong correlations in each (P < 0.0001).
In Daviesia, there was a mismatch between the LTT of
SWAFR lineages and that of SEA before c. 10 Ma. In each
genus, LTTs for SEA and the SWAFR show a decreased diversification rate towards the present. The inferences from the
LTTs were supported by the BAMM analyses. In all four genusregion phylogenies, the maximum credibility rate-shift configuration included a strong upwards shift during the period 15–
10 Ma (Figs S4, S5), as marked by the broken lines in Fig. 3.
The tree for Daviesia in the SWAFR also included an upwards
shift c. 30 Ma, when the genus first began diversifying (Figs
SE
R
A
0
Fig. 2 Map showing delineation of the southwest Australian Floristic
Region (SWAFR) and southeast Australia (SEA). Box and whisker plots are
shown of species’ areas of occupancy for Bossiaea and Daviesia, with
those from each region analysed separately. The error bars extend to the
2.5 and 97.5 percentiles, the box represents the 25–75 percentiles and the
central bar represents the median. Species range sizes of both Bossiaea
and Daviesia are significantly smaller in the SWAFR than in SEA. MWU,
Mann–Whitney U test.
Table 2 Index of clade overlap in Bossiaea and Daviesia in the southwest
Australia Floristic Region (SWAFR) and southeast Australia (SEA)
Genus
Region
No. of clades
Index
Bossiaea (8 Ma clades)
SWAFR
SEA
SWAFR
SEA
4
7
6
4
0.6
3.1
4.2
1.6
Daviesia (10 Ma clades)
The index is the total of pair-wise overlap divided by the total area covered
by the clades, as illustrated in Fig. 1. There is greater overlap of clades of
Daviesia in the SWAFR than in SEA, and more overlap of clades of
Bossiaea in SEA than the SWAFR.
S4d, S5d). In all four trees, a downwards rate trend from c.
10 Ma to the present was reconstructed, with the steepest
decline occurring in Daviesia in the SWAFR (Fig. S4).
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(c)
Bossiaea
10
1
20
10
60
Bossiaea
40
20
10
0
20
30
No. of lineages SEA
Time (Ma)
(d)
(b)
No. of lineages SWAFR
100
Daviesia
10
1
30
Discussion
In explaining diversity differences between the SWAFR and SEA,
our results support both the ‘limits on diversification’ and ‘time
for species accumulation’ hypotheses but we reject the ‘different
rates of diversification’ hypothesis.
Rates of diversification
The increase in diversification rate in both Bossiaea and Daviesia
between 15 and 10 Ma coincides with increasing aridification
across Australia, and globally, from the late Miocene (Byrne
et al., 2008). Whether the upturn in rate at that time was the
result of an increase in speciation stimulated by climate change or
recovery after a major extinction event caused by the rapid cooling cannot be determined without an extensive fossil record
(Crisp & Cook, 2009; Fiz-Palacios & Valcarcel, 2013), but our
results (Fig. 3) indicate that lineages in the SWAFR and SEA
responded similarly. This timing of shift in diversification rate in
both genera coincides also with the uplift of the Nullarbor Plain
(Crisp & Cook, 2007), which currently separates the floras of the
SWAFR and SEA through being an aridity and edaphic barrier.
The coincidence in the timing of the Nullarbor Plain uplift and
Miocene cooling and drying initiated a pulse of speciation in the
Australian southern temperate flora as a whole (Crisp & Cook,
2007), and here also we find that it coincides with a shift in diversification rate of taxa on either side of the Nullarbor.
Such an increase in plant diversification from the late Miocene
has long been argued to be one of the major contributors to the
diversity of mediterranean-climate hotspots, particularly for the
CFR of South Africa (e.g. Axelrod & Raven, 1978; Goldblatt &
Manning, 2000; Cowling et al., 2009; Valente et al., 2011). In
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No. of lineages SWAFR
100
30
No. of lineages (log scale)
Fig. 3 Lineages-through-time (LTT) plots of
all extant lineages in the southwest
Australian Floristic Region (SWAFR; red
triangles) and southeast Australia (SEA; blue
circles) for (a) Bossiaea and (b) Daviesia.
Dashed lines indicate the mid-Miocene
timing, in millions of years ago (Ma), of
inferred increases in diversification rate.
Correlations between lineages present in the
SWAFR and SEA from the mid-Miocene are
shown on the right for each genus
(respectively c, d). The rate of diversification
does not differ between lineages in the
SWAFR and SEA for either Bossiaea or
Daviesia over the past 10 Myr, but Daviesia
underwent an earlier radiation in the SWAFR
than in SEA.
No. of lineages (log scale)
(a)
20
Time (Ma)
10
0
100
80
Daviesia
60
40
20
10
20
30
No. of lineages SEA
support of this idea has been the finding that many lineages of
plants within these regions show signatures of increased diversification around the Miocene–Pliocene boundary (e.g. Hopper
et al., 2009; Verboom et al., 2009; Fiz-Palacios & Valcarcel,
2013). It has also been argued that diversification in the CFR
began well before the mid-Miocene, for instance in Restionaceae
(Linder et al., 2003) and at least a dozen other taxa (Valente &
Vargas, 2013).
In their study using Proteaceae, Sauquet et al. (2009) compared diversification rates of lineages in three mediterraneanclimate hotspots (SWAFR, CFR and Chile) with non-hotspot
regions and determined that rates in the mediterranean-climate
regions were higher. In the present study, however, we found that
there was no difference between the SWAFR and SEA in diversification rate of lineages of Bossiaea and Daviesia. The different
conclusions reached by these two studies could be attributable to
the use of different analysis methods. In the Proteaceae study,
diversification rates were compared against general background
rates, that is, rates derived across the entire tree. Therefore, the
results might have been confounded by differences in diversities
and rates in more arid and more mesic biomes, or at different latitudes, which are well-established correlates of regional differences in biodiversity (e.g. latitudinal gradient in biodiversity;
Cardillo et al., 2005). Also, the Proteaceae study did not have
extensive sampling of species and was primarily sampled at the
genus level. The effect of this approach might have been to detect
rate differences at deeper time-scales and might even reflect
extinction (slowing rates) in more mesic biomes from the end of
the Eocene. Indeed, in comparing diversification rates at the species level in one genus of Proteaceae (Banksia), Cardillo & Pratt
(2013) found no significant difference between the mediterranean-climate SWAFR and other regions of Australia.
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Our result of no difference in diversification rates in Daviesia
and Bossiaea between the SWAFR and SEA is congruent with
that found for Banksia and is consistent with global cooling and
drying from the late Miocene affecting lineages across temperate
regions as a whole and not just those with a mediterranean climate (i.e. SWAFR). This means that other explanations are
required for the differences in diversity between regions at the
same latitude.
Geographical range size and environmental gradients
It has commonly been recognized that geographical range sizes in
the SWAFR are small (Cowling et al., 1994; Hopper & Gioia,
2004) and that this contributes to the high endemism scores (raw
and weighted) for the region (Crisp et al., 2001). However, small
range sizes in mediterranean-climate regions have usually been
attributed to rare taxa that are dispersal-limited (e.g. Goldblatt,
1997; Hopper & Gioia, 2004; Hopper, 2009), that are constrained by fine-scale soil mosaics (e.g. Beard et al., 2000; Lambers et al., 2011; Laliberte et al., 2013), or that have undergone
greater speciation across steep gradients (e.g. Hopper & Gioia,
2004; Cowling et al., 2009).
We found that there was a quantifiable difference in average
range size between the SWAFR and SEA that was probably
attributable to an environmental gradient. Our simplified environmental niche model (ENM), using both recorded species distributions and random rectangles, found that the geographical
area corresponding to equivalent climate envelopes in SEA was,
on average, three times larger than that in the SWAFR. This
means that, for any species’ niche defined by the two axes we used
(MAT and MAR), there was about three times more available
geographical area in SEA than in the SWAFR. This alone has the
potential to explain major differences in the species–area relationship between the two regions.
Gradients, such as altitudinal, temperature and water availability, have the potential to affect geographical range sizes if species
have physiological limits. Experimental studies have consistently
found limits to species’ physiological tolerances (Spicer &
Gaston, 1999; Somero, 2005; Stuart et al., 2007) and it is
thought that such bounds are maintained by gene flow and selection (Hoffmann, 2010). That species have physiological constraints on distributions is an underlying assumption of most
approaches to Geographic information system (GIS)-based niche
modelling (e.g. ENM and SDM; Gaston, 2009). But there are
often other contributing factors that also limit species’ distributions, including evolutionary history and opportunity, and physiology might not be a major determinant in some cases (Duncan
et al., 2009; Gaston, 2009).
Nevertheless, we found a consistent difference in geographical
range sizes between taxa in the SWAFR and SEA across two genera, and these differences were of the same magnitude as those
predicted using random rectangles. The larger areas of matched
climate in SEA are consistent with long-term climate maps for
Australia (Australian Bureau of Meteorology; www.bom.gov.au/
climate/averages/maps.shtml accessed June 2012) that show climatic gradients, particularly rainfall, being steeper in the SWAFR
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than in SEA. These results are consistent with studies of flora
along environmental gradients in parts of the SWAFR (Sander &
Wardell-Johnson, 2011), in which species turnover was correlated with annual rainfall. Differences in steepness of rainfall gradients have the potential to affect range sizes at similar latitudes,
but this has been little considered in the current literature. Small
range sizes as a result of steep environmental gradients, combined
with limits to species physiologies, are likely to contribute to
biodiversity hotspots in general, not just in mediterraneanclimate regions (e.g. The Andes (Hughes & Eastwood, 2006);
Madagascar (Pearson & Raxworthy, 2009)).
Time for species accumulation
Long-term occupancy and climate stability are argued to be one
of the major causes of the latitudinal gradient in biodiversity
(reviewed in Stephens & Wiens, 2003; Wiens, 2011), and an
explanation for high species diversity in mediterranean-climate
regions (Table 1). This argument posits that habitat types that
have been present and stable for longer periods of time have had
more time for the gradual accumulation of species with relatively
little extinction.
Daviesia is reconstructed as having a SWAFR origin (Fig. S2),
and it has undergone an earlier radiation in this region (compare
slopes in LTTs before 15 Ma in Fig. 3b). The longer period of
occupancy in the SWAFR for Daviesia might, in part, explain its
much greater species richness there than in SEA irrespective of
similar diversification rates after the mid-Miocene rate increase
(Fig. 3d). Age of co-occurrence is related to the degree of geographical overlap of clades (Barraclough & Vogler, 2000), and
we found that Bossiaea had greater clade overlap in SEA, the
region reconstructed as its ancestral area (Fig. S1), and Daviesia
had greater clade overlap in the SWAFR, the region reconstructed as its ancestral area. Overall, Daviesia had greater geographical clade overlap than Bossiaea, which has a younger crown
age. Thus, our findings provide additional evidence for an effect
of time for species accumulation in contributing to diversity,
which is emerging as a significant explanation of regional and
global biodiversities (Wiens, 2011).
Limits on diversification
An underlying assumption of the time for species accumulation
model is that species diversity is unbounded (Stephens &
Wiens, 2003; Valente & Vargas, 2013). However, in both geographical regions, and in both Bossiaea and Daviesia, we found
indications of limits to ecological diversity. The shape of the
curves was very similar in both genera and for lineages from
both regions, as seen in the regression plots (Fig. 3). LTTs
(Fig. 3) and phylorate plots (Fig. S4) show a decreasing diversification rate towards the present, rather than constant diversification, despite inclusion of all described species. This could
represent a decrease in diversification as potential niches are
filled, as appears to have occurred in Himalayan songbirds
across an altitudinal gradient (Price et al., 2014). Alternatively,
a decreased rate might be the result of increased extinction as
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a consequence of severe aridification during the Plio-Pleistocene, during which the sclerophyll biome decreased in area as
the arid zone expanded (Byrne et al., 2008). Few lineages of
Bossiaea and Daviesia have made the shift from the sclerophyll
biome into the arid zone (Figs S1, S2) and none of those that
did appears to have undergone a radiation.
It is difficult to distinguish between these two scenarios (niche
filling or extinction) given that there is virtually no fossil record
for the group. Although mathematical models for estimating
diversification rates and shifts using phylogenies are becoming
more complex, they need to be grounded with biological realities
– it is not possible to disentangle speciation and extinction from
phylogenies alone, nor to determine ancestral traits or species’
niches, without making assumptions that are mostly untested
(e.g. Warren et al., 2014). The future challenge is to better incorporate biological, ecological and fossil data into such estimates.
Soil mosaics and niche packing
The soil mosaics of the SWAFR also have the potential to
explain, at least in part, the smaller areas of occupancy in both
genera and greater overlap of Daviesia clades in the region. Soil
attributes are correlated with species turnover in both SEA (Di
Virgilio et al., 2012) and the SWAFR (Sander & WardellJohnson, 2011), but there is finer scale turnover of soils in the
SWAFR (Beard et al., 2000; Pate & Verboom, 2009). This could
potentially result in finer niche partitioning within the narrow
climatic zones of the SWAFR among soil mosaics. Indeed, soil
type is suggested to be a factor driving plant speciation (Fine
et al., 2005) and phylogeny-based studies for several genera in the
Cape Biodiversity Hotspot of South Africa (Schnitzler et al.,
2011) have inferred niche shifts associated with different soil
types to be important in diversification. Quantitative data for soil
variables considered to directly affect plant diversity are not yet
available at a sufficiently fine-scale resolution to allow assessment
of their importance for species distributions in the SWAFR,
although a research framework to achieve this goal is presented
by Laliberte et al. (2013).
Conclusions
Our results are consistent with the emerging consensus that there
is not a homogeneous explanation for high biodiversity that
encompasses all biodiversity hotspots. The commonalities among
mediterranean-climate regions – the mediterranean climate, with
relatively reliable but marked seasonal differences – probably contribute to high diversity, but each region also differs from others
in relation to the history of its biota and landscape, among other
factors. The CFR of South Africa differs from the SWAFR in
being a region of high physiographical heterogeneity. In the Australian context, the SEA region has greater topographical relief
and, conversely, the SWAFR has older, more weathered soils (fig.
7 in Wilford, 2012). The types of soils differ significantly among
mediterranean-climate regions, with those of the SWAFR and
CFR being mostly old and nutrient-poor, whereas those of Chile
are relatively rich (Cowling et al., 1996; Linder, 2003; Orians &
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Research 9
Milewski, 2007; Lambers et al., 2011), and this might have
played a role in the success or otherwise of particular lineages in
each region (e.g. Proteaceae are diverse in the CFR and SWAFR,
but have low diversity in Chile; Sauquet et al., 2009). Diversification rates also appear to differ for similar lineages in the different
mediterranean-climate regions (Linder et al., 2003; Valente &
Vargas, 2013). Thus, there are clearly historical and regional contingencies that have resulted in differences among regions.
For the Australian egg-and-bacon peas, we have found that
time of occupancy in a region (time for species accumulation)
has been an important factor contributing to current diversity,
but that rates of diversification over the past 10 Myr have not differed between the SWAFR and SEA. Geographical overlap of
clades is correlated with time of occupancy, but the contribution
of resource partitioning across soil mosaics to co-occurrence (and
hence to niche packing and the upper limits of diversity) has not
yet been rigorously tested. The steep climatic gradient in the
SWAFR compared with that in SEA contributes to species turnover and high endemism measures, and this is probably common
to all mediterranean-climate regions and several other global
biodiversity hotspots.
Acknowledgements
This research was supported by Discovery Projects grants to
L.G.C. and M.D.C. from the Australian Research Council, and
funding from The Australian National University and The
University of Queensland. We are grateful for the free access to
distributional data and online GIS tools provided by the Australian Government through the Australian Virtual Herbarium and
the Atlas of Living Australia. Lindsay Popple did some of the laboratory work and initial data analysis. The manuscript was
improved by the helpful comments of the Editor and three anonymous reviewers.
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Supporting Information
Additional supporting information may be found in the online
version of this article.
Fig. S1 Chronogram of Bossiaea.
Fig. S2 Chronogram of Daviesia.
Fig. S3 Distribution of species and frequency histograms of species range size within Bossiaea and Daviesia (all species).
Fig. S4 Speciation rates (phylorates) estimated using BAMM and
plotted through time for all extant lineages of Bossiaea and
Daviesia.
Fig. S5 Maximum credibility (95%) rate shift configuration sets
from the BAMM posterior for all extant lineages of Bossiaea and
Daviesia.
Table S1 Species diversity within Bossiaea and Daviesia
Table S2 Collection, distribution and sequence details for samples used in phylogenetic analyses
Table S3 IBRA regions included in each Floristic region
Table S4 Comparison of matched climatic areas. Species environmental niche models (using MAT and MAR) from one region
were projected onto the other region and sizes compared
Table S5 Comparison of matched climatic areas using rectangles.
Environmental niche models (using only MAT and MAR) estimated for a rectangle in one region were projected onto the other
region and sizes compared
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