Evol Ecol (2014) 28:205–227
DOI 10.1007/s10682-013-9671-x
ORIGINAL PAPER
Effects of an adaptive zone shift on morphological
and ecological diversification in terapontid fishes
Aaron M. Davis • Peter J. Unmack • Bradley J. Pusey
Richard G. Pearson • David L. Morgan
•
Received: 3 July 2013 / Accepted: 29 August 2013 / Published online: 14 September 2013
Ó Springer Science+Business Media Dordrecht 2013
Abstract A fundamental goal of evolutionary ecology is understanding the processes
responsible for contemporary patterns of morphological diversity and species richness.
Transitions across the marine–freshwater interface are regarded as key triggers for adaptive
radiation of many clades. Using the Australian terapontid fish family as a model system we
employed phylogenetic analyses to compare the rates of ecological (dietary) and morphological evolution between marine and freshwater species of the family. Results suggested significantly higher rates of phenotypic evolution in key dietary and morphological
characters in freshwater species compared to marine counterparts. Moreover, there was
Electronic supplementary material The online version of this article (doi:10.1007/s10682-013-9671-x)
contains supplementary material, which is available to authorized users.
A. M. Davis (&)
Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), and School of Marine
and Tropical Biology, James Cook University, Townsville, QLD 4811, Australia
e-mail: [email protected]
P. J. Unmack
National Evolutionary Synthesis Center, Durham, NC 27705-4667, USA
P. J. Unmack
Institute for Applied Ecology and Collaborative Research Network for Murray-Darling Basin Futures,
University of Canberra, Canberra, ACT 2601, Australia
B. J. Pusey
Centre of Excellence in Natural Resource Management, University of Western Australia, Albany,
WA 6330, Australia
R. G. Pearson
School of Marine and Tropical Biology and TropWater, James Cook University, Townsville,
QLD 4811, Australia
D. L. Morgan
Freshwater Fish Group and Fish Health Unit, Murdoch University, South St., Murdoch, WA 6150,
Australia
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significant correlation between several of these dietary and morphological characters,
suggesting an underlying ecomorphological aspect to these greater rates of phenotypic
evolution in freshwater clades. Australia’s biogeographic history, which has precluded
colonisation by many of the major ostariophysan fish families that make up much global
freshwater fish diversity, appears to have provided the requisite ‘ecological opportunity’ to
facilitate the radiation of invading marine-derived fish clades.
Keywords Dietary radiation Morphological disparity Marine–freshwater
transition Trophic ecology Ecomorphology
Introduction
One of the primary questions in evolutionary biology concerns the drivers of differences in
diversity between clades (Simpson 1953; Schluter 2000; Yoder et al. 2010). According to
ecological theories of adaptive radiation, shifts between adaptive zones and ‘ecological
opportunity’ in the form of ‘empty niches’ may catalyse rapid diversification in invading
lineages, as well as the evolution of a variety of ecomorphologies that maximise resource
utilisation along the niche spectrum (Rosenzweig 1995; Grant 1999; Gillespie 2004; Yoder
et al. 2010). The adaptive zone shift from marine to freshwater environments constitutes
one of the most dramatic evolutionary transitions in the history of life (Lee et al. 2012).
Ecological transitions from marine to freshwater habitats have occurred in several major
animal groups, including annelids, molluscs, crustaceans, lampreys, elasmobranchs and
teleost fishes (McDowall 1997; Lee and Bell 1999; Vega and Wiens 2012). With a dramatic gradient in global biodiversity existing between marine and freshwater environments, freshwater habitats clearly provide a wealth of ecological resources and
opportunities for lineage diversification (Vega and Wiens 2012). While some fish lineages
have apparently undergone multiple marine–freshwater transitions (Lovejoy et al. 2006;
Betancur-R 2010; Yamanoue et al. 2011; Bloom et al. 2013), several otherwise ecologically diverse lineages (e.g., Labridae, Acanthuridae) are entirely restricted to marine
environments (Lee and Bell 1999; Nelson 2006). Phylogenetic studies on marine-derived
fish groups (e.g., ariid catfishes, pufferfishes and anchovies; Betancur-R 2010; Yamanoue
et al. 2011; Bloom and Lovejoy 2012; Santini et al. 2013; Bloom et al. 2013) have
suggested that competition from incumbent lineages may be a major force regulating the
frequency and ultimate scale of freshwater transitions. While the effects of marine to
freshwater invasions on processes of lineage diversification are being increasingly documented, the specific effects of these transitions on the ecological and morphological trajectories of invading lineages remain largely unstudied (although see Betancur-R et al.
2012; Santini et al. 2013).
The evolutionary outcome of marine to freshwater adaptive zone transition is particularly relevant on the Australian continent because of its long isolation and consequent
evolution of its freshwater fish largely independent of other continental faunas (Lundberg
et al. 2000; Unmack 2001, 2013; Allen et al. 2002). Australia is particularly unusual for its
prevalence of acanthopterygian fishes, which typically dominate marine environments, and
an almost complete lack of ostariophysan fishes, which usually dominate freshwater
habitats on other continents. Accordingly, the majority of Australia’s freshwater fish fauna
comprises ‘secondary’ freshwater acanthopterygian species, many of which have strong
affinities with tropical Indo-Pacific marine fishes (Williams and Allen 1987; Allen et al.
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2002). Colonisation of fresh waters with low pre-existing diversity (i.e., few incumbent
competitors) should result in increased rates of lineage and ecomorphological diversification in invading clades. There is growing evidence that Australia’s unique biographic
history has provided ample scope for continental-scale adaptive radiations in invading
lineages: for example, despite repeated independent reinvasions of fresh waters in multiple
geographic areas by ariid catfishes, only in Australia–New Guinea has an ariid invasion
achieved substantial radiation (Betancur-R et al. 2012).
Another promising group in which to test these ideas is the terapontid grunters (Terapontidae), a family of small- to medium-sized perciform fishes found in freshwater,
estuarine and near-shore marine waters of the Indo-west Pacific region (Vari 1978). Terapontidae diversity comprises 54 extant species (Eschmeyer and Fong 2013) in 16 genera,
with a fossil record dating back at least 40 million years (Turner 1982). Most of the
diversity within Terapontidae is found in the fresh waters of the Australia–New Guinea
region, with 34 endemic species, 28 of which only live in fresh waters. Recent comparative
studies suggested that an initial transition from estuarine and marine habitats prompted this
freshwater diversification, with freshwater clades subsequently diversifying *3 times
faster than euryhaline-marine counterparts (Davis et al. 2012a). This lineage diversification
was also apparently coupled with a substantial expansion of dietary resource exploitation,
including evolution of herbivorous, frugivorous, omnivorous, insectivorous and detritivorous habits from carnivorous euryhaline ancestors (Davis et al. 2011a, 2012b). However,
rates of terapontid dietary diversification in relation to habitat affiliation are yet to be
assessed. Similarly, the effects of this major adaptive zone shift on ecological characters
such as niche breadths and trophic partitioning are poorly known.
While previous research on Terapontidae supports the expectation that ecological
habitat transitions promoted lineage diversification (Davis et al. 2012a), the resultant
effects on ecomorphological diversification are yet to be assessed. If the relatively rapid
rate of speciation in Australian freshwater terapontids has occurred without substantial
divergence in ecologically important phenotypic characters (i.e., adaptive disparity), it
could indicate that ecological opportunity and natural selection were not critical in the
diversification of these fishes. Freshwater habitats typically provide greater opportunities
for allopatric speciation without effects on trophic diversity, than marine environments
(May 1994; Burridge et al. 2006). However, an increased rate of diversification in trophic
characters in freshwater terapontids would be consistent with the idea that, following a
major adaptive zone shift, ecological opportunity and natural selection have played a
fundamental role in the rapid rate of diversification of these fishes. In this study we test
whether: (1) the transition to freshwater habitats promoted increased rates of diversification
in terapontid dietary habits; (2) the ecological adaptive zone promoted any increased rates
of disparification in the morphology of terapontids; (3) whether morphological disparity
correlates with the trophic diversity of the family; and (4) whether this major adaptive zone
shift has had any effects on niche breadths or trophic partitioning between macrohabitats.
Materials and methods
Taxon sampling, molecular markers and phylogeny reconstruction
A phylogenetic analysis of 28 terapontid species was performed using combined nuclear
and mitochondrial DNA (mtDNA) sequences from Davis et al. (2012a). The ingroup
consisted of 28 species and included all nine Australian marine-euryhaline species, all
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genera and 18 of the 24 species of Australian freshwater terapontids, and one species
present only in New Guinea. Two representative sequences of one species (Hannia
greenwayi Vari 1978) were included due to their different placement in the topology. The
28 species encompass all of the major trophic habits displayed by Australia’s freshwater
terapontids: invertivores, generalist carnivores, omnivores, herbivores and detritivores–
algivores (Davis et al. 2011a, 2012a).
Sequence data consisted of an 1,141 base pair (bp) fragment of the mtDNA gene
cytochrome b (cytb) and 3,896 and 905 bp fragments of the nuclear recombination activation genes RAG1 and RAG2 (hereafter referred to as RAG) respectively for a total of
5,942 bp for each individual. We used the dataset of Davis et al. (2012a) (Dryad Digital
Repository doi:10.5061/dryad.4r7b7hg1), trimmed out taxa for which we lacked morphological data, then realigned the dataset. Cytb was aligned by eye while RAG sequences
were aligned using the online version of MAFFT 6.822 (Katoh and Toh 2010) using the
accurate G-INS-i algorithm with the scoring matrix for nucleotide sequences set to 1PAM/
K = 2, a gap opening penalty of 1.53 and an offset value of 0.1. Combined partitioned
phylogenetic analyses were performed with maximum likelihood (ML) using GARLI 2.0
(Zwickl 2006). We identified the best-fitting model of molecular evolution using the
Akaike Information Criterion (AIC) in Modeltest 3.7 (Posada and Crandall 1998) using
PAUP* 4.0b10 (Swofford 2003). For cytb Modeltest identified TrN ? I ? G as the best
model and for RAG GTR ? I ? G was the best model. We ran GARLI with 10 search
replicates using the default settings with two partitions representing cytb and RAG with
their respective models. For bootstrapping we ran 1,000 replicates with the previous settings except that the options genthreshfortopoterm was reduced to 10,000 and treerejectionthreshold was reduced to 20 as suggested in the GARLI manual to speed up
bootstrapping. The concatenated sequence data file and tree files were deposited in Dryad,
http://dx.doi.org/10.5061/dryad.h30t5. Trees were rooted with representatives from several
related families based on Yagishita et al. (2009) (see Davis et al. 2012a).
To address uncertainty in tree topology, branch lengths and the evolutionary history of
habitat affiliation (following Collar et al. 2009; Price et al. 2011), we generated a collection
of trees using the Bayesian method BEAST 1.7.1 (Drummond and Rambaut 2007) and
generated input files using BEAUti 1.7.1. The analysis used an uncorrelated lognormal
relaxed molecular clock with rate variation following a tree prior using the speciation
birth–death process, and the same models of sequence evolution for the nuclear and
mtDNA partitions as for our ML analysis. BEAST analyses were run for 50 million
generations, with parameters logged every 100,000 generations. Multiple runs were conducted to check for stationarity and that independent runs were converging on a similar
result. The treefile was summarized using TreeAnnotator 1.7.1. The first 10 % of trees
were removed as burn-in, providing 450 trees for reconstructing ancestral habitat
affiliations.
Specimen collection
Fish for dietary and morphometric analysis were collected from freshwater and marine
habitats across Australia (Davis et al. 2011a) and Papua New Guinea, as well as being
sourced from museum collections (Davis et al. 2011b). Fishes were primarily preserved in
buffered formalin or ethanol. For larger specimens, incisions in the body wall or fixative
injected by hypodermic syringe into the body cavity were used to aid fixation of internal
organs. Due to the pronounced dietary and morphological changes evident in the life
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histories of many terapontids (Davis et al. 2011a, 2012b), morphological and dietary
description focused on the largest available size classes of each species.
Dietary quantification
Stomachs were excised from the body cavity and, if [20 % full, were transferred to a
watch glass for estimation of the proportion of each food item by the indirect volumetric
method of Hyslop (1980). Food items were grouped into seven dietary categories:
unidentified component, aquatic insects and zooplankton, macrocrustacea and molluscs;
fishes; aquatic vascular plants and filamentous algae; terrestrial plant material; and detritussediment. Stomach content data was aggregated into species-level mean proportions for
each species. The ‘unidentified’ category was excluded from all analyses and the resultant
dataset for each species was normalized to 100 % and arcsine transformed to improve
normality (Sokal and Rohlf 1995). Sample sizes for dietary ranged from two to 70 specimens per species (median = 19). Raw dietary data, specimen numbers and size ranges are
provided in Table S1).
Morphological variables
Body size, in itself, is an important morphological character from both an evolutionary and
ecological perspective, and provides a simple descriptor to integrate a large amount of
biological and ecological information regarding an organism (Peters 1986). If habitat shifts
had produced ecological opportunity for invading lineages, we predicted that those lineages would retain a signature of elevated rates of body-size evolution, reflecting a burst of
ecological diversification (Harmon et al. 2010). To compare rates of body-size evolution
across freshwater and marine terapontids we created a maximum body-length dataset,
using Fishbase (Froese and Pauly 2012), published literature (e.g., Allen et al. 2002), and
our own records.
We quantified morphological diversity within the Terapontidae using 11 linear, two
meristic and two categorical variables (Fig. 1). Morphological variables emphasized
characters that have predictable consequences for a fish’s capacity for prey capture and
processing, and that are directly relevant to the range of feeding habits investigated in this
study (Winemiller 1991; Hugueny and Pouilly 1999; Davis et al. 2012a). Linear measurements were made to the nearest 0.1 mm for measures \150 mm and to the nearest
1 mm for measures [150 mm for: standard length (SL); body depth (BD); maxilla length
(ML); head length (HL); snout length (SNL); eye diameter (ED); mouth width (MW); eye
height (EH); head depth (HD); caudal peduncle depth (CPD) and intestinal length (IL).
A single morphological shape (ratio) variable relating to relative eye position in the
dorso-ventral plane was also created by standardizing eye height against head depth in all
individuals of a species. In addition to the linear, mensural variables, two meristic variables
in the form of anal fin soft ray counts and gill raker counts on the first gill arch were
quantified (Fig. 1). Two coded variables were scored with integer values: mouth orientation (MO), coded according to the inclination of a plane perpendicular to the longitudinal
axis of the body and tangential to upper and lower jaws when the fish mouth is open
defined as 1 = supra-terminal, 2 = terminal and 3 = sub-terminal; and tooth shape (TS),
coded as 1 = conical, 2 = slightly flattened, 3 = moderately flattened, and 4 = highly
flattened dentition (following Vari (1978). Sample sizes for morphological quantification
within species ranged from two to 22 specimens (median = 14). Raw morphological data,
specimen numbers and size ranges are provided in Table S2. To make continuous
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13
2
9
5
6
3
7
11
4
10
8
12
1
Fig. 1 A representative terapontid species, Hephaestus fuliginosus, with 13 mensural and meristic
morphometric variables indicated. 1 = standard length (SL), 2 = body depth (BD), 3 = maxilla length
(ML), 4 = head length (HL), 5 = snout length (SNL), 6 = eye diameter (ED), 7 = mouth width (MW),
8 = eye height (EH), 9 = head depth (HD), 10 = intestinal length (IL), 11 = caudal peduncle depth
(CPD), 12 = anal fin ray count (AFR), and 13 = gill raker count (GRC)
characters dimensionally similar, variables were transformed to their natural logarithms,
except for the eye position ratios, which were included as raw values.
Data analyses
To correct species values for phylogenetic history and potential biases associated with
allometry and body size variation, regressions of each morphological variable against SL
were performed using phylogenetic size correction with the ‘‘phyl.resid’’ function from the
‘‘phytools’’ R package (Revell 2012). In order to eliminate multicollinearity and reduce the
dimensionality of data matrices, a phylogenetically corrected principal component analysis
(PPCA; Revell and Harrison 2008) was conducted using the maximum clade credibility
tree and transformed morphological and dietary data. Both PPCAs were calculated on the
evolutionary correlation matrix. Species scores on principal component (PC) axes were
used as character values in subsequent model-fitting analysis.
Evolutionary model fitting and rate comparisons
Maximum-likelihood estimates of rates of evolution for dietary diversification and morphological disparification were calculated and compared using a modified O’Meara et al.
(2006) censored-rates test (Burbrink et al. 2011). This analysis compares an evolutionary
model in which rates vary among clades according to habitat affiliation against a singlerate model across the whole phylogeny.
The collection of 450 BEAST-generated trees were used in character stochastic mapping (SIMMAP; see Nielsen 2002; Huelsenbeck et al. 2003; Bollback 2006) to create an
evolutionary character history of marine-euryhaline or freshwater habitat affiliation mapped onto each topology. Phenotypic evolutionary rates (r2) of species’ scores for each PC
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axis were obtained for the euryhaline-marine and freshwater clades using maximumlikelihood estimates and the selection of trees generated above. The null hypothesis that
rates do not differ between the one- and two-rate BM models was assessed using the
modified Akaike Information Criterion (AICc) which is AIC with a correction for small
sample size—appropriate when the number of observations, in this case species, is
\40 times the number of estimated parameters (Burnham and Anderson 2002). To select
the best fitting model for each PC, given uncertainty in phylogeny and ancestral habitats,
we evaluated the average model fit over the 450 reconstructions and compared mean AICc
across models. Averaging AICc in this way is valid because the data for species are the
same for all iterations of model fitting. The 450 reconstructions are not considered as
independent data sets but as alternative estimates of phylogeny and ancestral habitat states
sampled in proportion to their posterior probabilities given the same data. Averaging AICc
over this sample allowed us to quantify model fit in a way that integrates uncertainty in
these estimates. An DAICc value of C2.0 was taken as an indication of support for one
model over the other (Burnham and Anderson 2002).
In using the censored-rates test, we fit multiple-rate Brownian motion (BM) models (see
Hutcheon and Garland 2004; O’Meara et al. 2006), which adequately describe adaptive
evolution under a variety of conditions (Hansen and Martins 1996). While other models
could better fit the data than BM in adaptive radiations (Freckleton and Harvey 2006), the
BM model is directly applicable to the macroevolutionary prediction of adaptive radiation:
morphological variance under a Brownian process should be generated at a greater rate
within an adaptive radiation relative to outgroups.
If there are substantial differences in the average rates of evolution due simply to habitat
affiliation, the censored-rates test should explicitly account for that difference. However, if
substantial rate variation exists in sub-clades within either of the a priori euryhaline-marine
or freshwater clades, these analyses will not detect this type of within-clade ‘trickle-down’
rate divergence effect (i.e., Moore et al. 2004). For example, the ‘trickle-down’ effect
could mislead the censored-rates analysis due to the dominant influence of a rapidly
diversifying sub-clade evolving within the broader freshwater clade. Therefore, we also
used the ‘‘evol.rate.mcmc’’ function in the R package ‘‘phytools’’ (Revell et al. 2011) to
detect any rate shifts in morphological diversification. This analysis uses a Bayesian
Markov Chain Monte Carlo approach to identify the branch with the strongest support for
an acceleration or deceleration in phenotypic (morphological) evolution in the phylogenetic tree for a univariate trait, without assuming any a priori defined group (Revell 2012).
This ensures that the largest shift will be found solely on the basis of data and phylogeny.
The PC scores were analysed as continuous traits on the maximum clade credibility tree.
We ran the MCMC algorithm for 100,000 generations for each morphological trait,
sampling every 10 generations and excluding the first 20,000 generations as burn-in.
Correlation between diet and morphology
Canonical correspondence analysis (CCA) (ter Braak 1986) has long history of use in fish
ecomorphological studies where data sets are naturally divisible into morphological and
environmental data for each species or group (e.g., Winemiller 1991, Hugueny and Pouilly
1999; Davis et al. 2012a). CCA selects the linear combinations (canonical variables) from
two datasets (in this case diet and morphology), with the constraint that the two canonical
variables are maximally correlated. The second and any subsequent CCA axes also select
linear combinations of morphological variables that maximize dispersion of diet scores, but
with the stipulation of being uncorrelated to previous CCA axes. We tested the relationship
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between terapontid morphology and diet utilising a recent variation of this approach,
Phylogenetic Canonical Correspondence Analysis (PCCA; Revell and Harrison 2008).
This analysis controls for phylogenetic non-independence of species data using the phylogenetic generalized least squares (PGLS) approach of Grafen (1989). For ease of
interpretability we conducted the PCCA by comparing the original arcsine-transformed
dietary data (six categories) against morphological PCA axes.
Dietary indices—niche breadth
The effects of a major macrohabitat shift or ecological release in the trophic habits of
terapontids could be manifest in a range of ways such as expansion or contractions of niche
breadth, or shifts in diet (see Schluter 2000). We used a range of dietary indices to compare
several aspects of trophic utilisation among the terapontids.
Levin’s standardised measure of dietary breadth, BA (Hurlbert 1978) was used to
compare the levels of dietary specialization between marine and freshwater terapontids as
follows:
X p2i and BA ¼ ðBL 1Þ=ðn 1Þ
BL ¼ 1=
where: BL = Levin’s
P measure of niche breadth; pi = proportional contribution of resource
i to the total diet ( pi = 1.0); BA = Levin’s standardised niche breadth; n = number of
possible resource (diet) categories. It is useful to standardize BL to a scale of 0 (minimum
niche breadth and maximum specialisation) to 1 (maximum niche breadth and minimum
specialisation) to allow comparisons among species (Krebs 1999). Standardised niche
breadth was calculated for each species, and the significance of any difference in the
average levels of dietary specialization (niche breadth) evident between marine and
freshwater groups was tested using the phylogenetic ANOVA of Garland et al. (1993)
using the maximum clade credibility topology. A post hoc comparison of mean niche
breadth between groups was conducted by phylogenetic simulation (1,000 simulations) in
‘phytools’ (Revell 2012).
Dietary indices—dietary overlap
The level of dietary overlap evident between marine and freshwater terapontids was
compared using the Bray–Curtis similarity matrix (Legendre and Legendre 1998). To
contrast the average levels of resource overlap occurring between macrohabitats, average
Bray–Curtis similarity values for diet were calculated for each species compared to all
other species found within the same habitat, and means between macrohabitats were
compared using the phylogenetic ANOVA (Garland et al. 1993). To compare the average
diet of terapontids across habitats (essentially the degree of dietary overlap between
habitats) we calculated average Bray–Curtis similarly values for each marine terapontid
compared to all freshwater species and compared this to average Bray–Curtis similarity
values for each freshwater species and other species from the same macrohabitat.
All phylogenetic analyses were performed using R version 2.15.0 (R Core Team 2012).
All multivariate analyses (PPCA, PCCA) and censored-rate tests were conducted in the R
package ‘phytools’ (Revell 2012), with call functions from the packages ‘ape’ (Paradis
et al. 2004), ‘geiger’ (Harmon et al. 2008), ‘ade4’ (Dray and Dufour 2007), ‘caper’ (Orme
et al. 2012) and ‘CCA’ (Gonzalez et al. 2008).
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Results
Phylogenetic analysis
Maximum likelihood recovered one tree with a likelihood score of -34,413.698284
(Fig. 2). Overall, most nodes within the freshwater radiation in the tree were well resolved
with strong support (Hillis and Bull 1993) evidenced by bootstrap values mostly [80
(Supplementary Material (Fig. S1). Marine-euryhaline species relationships mostly had no
bootstrap support.
Principal components analysis
The first three axes of the dietary PCA explained 86 % of the variation in the dataset
(PC1 = 44 %; PC2 = 24 %, and PC3 = 18 %) and were used in subsequent analyses.
Dietary items with highest loadings on PC1 were terrestrial and aquatic plants (positively
loaded) and fishes and macrocrustacea (negatively loaded) (Table 1), which separated the
generalist carnivores from other dietary modes along PC1 in ordination space (Fig. 3).
Aquatic invertebrates and detritus (positively loaded) and aquatic plants (negatively
Lu
Sp
Sb
Hf
Tj
Ps
Fig. 2 ML phylogeny of terapontids with a single possible map of habitat affiliation (marine-euryhaline
habitat in red and freshwater in black) generated through stochastic character mapping (SIMMAP; Bollback
2006). Images indicating some of the range of morphological variation in head shape and intestinal length
between marine and freshwater forms are identified by initials of genus and species nearby in the tree
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Table 1 Loadings and percentage variance from the phylogenetic PCA of terapontid
morphology and diet
Evol Ecol (2014) 28:205–227
PC1
PC2
PC3
17.73
PC4
Dietary habits
% Variance
44.49
24.19
Terrestrial plants
0.82
0.09
0.08
Aquatic plants
0.20
-0.95
-0.08
Aquatic invertebrates
0.43
0.44
0.71
Detritus
0.31
0.56
-0.73
Fishes
-0.89
0.17
0.06
Macrocrustacea
-0.95
0.09
0.07
Morphology
% Variance
Intestinal length
31.78
18.24
13.34
8.95
0.80
-0.09
-0.07
-0.37
-0.03
Body depth
0.61
-0.48
-0.28
Caudal peduncle depth
0.46
-0.33
-0.60
0.27
Mouth orientation
0.46
-0.41
0.68
0.00
Tooth shape
0.35
0.14
0.50
0.20
Anal fin ray count
0.24
0.26
0.12
-0.23
Gill raker count
Mouth width
0.21
-0.55
0.52
-0.33
-0.35
-0.08
-0.33
-0.81
Eye diameter
-0.37
-0.78
-0.29
0.15
Snout length
-0.64
-0.58
0.26
-0.04
Eye position
-0.73
0.56
0.08
-0.13
Head length
-0.77
-0.42
0.00
-0.08
Maxilla length
-0.81
-0.12
0.15
0.21
loaded) were the dietary items with highest loadings for PC2, while detritus (negative
loading) and aquatic invertebrates (positively loaded) had the highest loadings on PC3
(Fig. 3).
In the phylogenetic PCA applied to morphology, the first four axes explained *72 % of
the variation (PC1 = 31.8 %; PC2 = 18.2 %, PC3 = 13.3 %, and PC4 = 8.95 %) and
were used in subsequent analyses. The highest morphological loadings from PC1 were
intestinal length and body depth (positively loaded), and head length and maxilla length
(negatively loaded) (Table 1). The second PC axis was dominated by eye position, fin ray
count (positive loadings), and eye diameter, snout length and gill raker count (negative
loadings). Morphological PC axis 3 was dominated by caudal peduncle depth (negatively
loaded) and mouth orientation, gill raker count and tooth shape (positively loaded), and PC
axis 4 loaded most strongly toward mouth width, intestinal length (negatively loaded) and
caudal peduncle (positively loaded). This morphological divergence is depicted by projecting the phylogeny into morphospace (phylomorphospace, sensu Sidlauskas 2008)
(Fig. 4).
Evolutionary rate comparisons
Results of stochastic habitat mapping indicated marine-euryhaline as the most common
root state within 450 character histories, in agreement with previous terapontid habitat
reconstructions using alternative maximum likelihood and maximum parsimony ancestral
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Fig. 3 Phylomorphospace plots of principal component axes obtained from the terapontid dietary habits for
PC1 versus PC2. Each point represents a species phenotypic value (coloured) or a hypothesized ancestral
phenotype (black). Lines connect related species through hypothetical ancestors (Sidlauskas 2008). Species
are identified by epithets formed from initial letters in genus and species names. a Different colours for
species represent habitat association; blue—marine; red—freshwater). b Different colours represent species
in different trophic categories based on stomach content analysis (Davis et al. 2011a); red—generalised
carnivore; brown—invertivore; yellow—omnivore; green—herbivore; and brown—detritivore–algivore
character state reconstructions, and more comprehensive species’ sampling (Davis et al.
2012b). The results of evolutionary rate comparisons for diet and morphology are summarized as means and SE across the 450 character histories for the one- and two-rate
models separately across freshwater and marine taxa (Table 2). There was AICc support
(DAICc C2.0) for dietary PC axes PC1 and PC3 evolving faster in freshwater terapontids
fishes according to the model means. The strong support for a two-rate model for PC1
likely reflects the broad-scale shifts away from carnivorous and macrocrustacean-dominated diets of most marine species, to a diverse diet of invertebrates, terrestrial plants,
aquatic plants and detritus. The significant support for a two-rate model for PC3 highlights,
in large part, the emergence of detritus and aquatic insect larvae as important in the diets of
several freshwater clades, dietary components that are essentially absent in marine
terapontids.
There was strong support for a two-rate model with much faster disparification rates in
freshwater terapontids for Total Length (TL, *10.8 times faster, DAICc = 6.16). This
result underlines the greater disparity in body size seen in freshwater than marine species.
There was also support for morphological PC axes 1, 3 and 4 evolving faster in freshwater
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Evol Ecol (2014) 28:205–227
Fig. 4 Phylomorphospace plot of principal component axes obtained from the terapontid morphometric
data for PC1 versus PC2. Each point represents a species phenotypic value (coloured) or a hypothesized
ancestral phenotype (black). Lines connect related species through hypothetical ancestors (Sidlauskas 2008).
Species are identified by epithets formed from initial letters in genus and species names. a Different colours
for species represent habitat association; blue—marine; red—freshwater). b Different colours represent
species in different trophic categories based on stomach content analysis (Davis et al. 2011a); red—
generalised carnivore; brown—invertivore; yellow—omnivore; green—herbivore; and brown—detritivore–
algivore
terapontid fishes according to the AICc criterion (Table 1). This reflects much greater rates
of disparification in body-shape variables such as maxilla length, intestinal length, head
length body depth (PC1; *7.79 faster), caudal peduncle depth, tooth shape and mouth
orientation (PC3; *69 faster), and mouth width (PC4; *7.89 faster) in freshwater
species. A single evolutionary rate across the family was supported for PC2. Results
overall support the prediction that greater rates of ecological (dietary) and morphological
evolution have occurred in freshwater terapontids compared to marine-euryhaline species.
Results of the MCMC evolutionary rate test (Revell et al. 2011) with no a priori habitat
affiliations yielded some different results from the censored-rates test (Table 3). The
maximum credibility tree with the branch lengths scaled proportionally to their associated
evolutionary rates identified from the MCMC analysis for each morphological variable is
presented in Supplementary Material (Fig. S2). In contrast to the censored-rates test results,
MCMC testing for maximum body size identified only a marginally faster rate shift at the
most recent common ancestor of all freshwater species (Fig. 5), and the estimated posterior
123
0.41
SD
PC4 mean
SD
PC3 mean
SD
PC2 mean
SD
0.98
-10.73
1.12
-16.63
1.23
-21.01
1.60
-29.28
SD
PC1 mean
7.54
1.10
-9.33
1.20
-13.87
3.85
-23.20
Total length
mean
Morphology
SD
PC3 mean
SD
PC2 mean
SD
PC1 mean
Diet
One rate model
Log likelihood
(single rate)
0.11
1.16
0.18
1.74
0.26
2.36
0.61
4.19
0.02
0.33
0.11
1.05
0.15
1.44
0.97
2.85
Brownian
rate
1.96
27.16
2.24
38.97
2.46
47.73
3.20
64.27
0.83
-9.36
2.19
24.37
2.40
33.45
7.69
52.12
1 Rate
AICc
0.94
-6.16
0.94
-12.87
1.04
-20.11
1.33
-24.78
0.74
12.76
1.09
-0.96
1.49
-11.15
2.77
-17.14
Two rate model
Log likelihood
(multiple rate)
0.17
1.54
0.26
2.29
0.36
2.82
0.85
5.56
0.04
0.46
0.18
1.51
0.27
1.89
1.33
3.91
Freshwater
Brownian rate
Table 2 Model fitting and model parameters from the one- and two-rate Brownian motion models
0.07
0.20
0.04
0.37
0.12
1.28
0.10
0.72
0.01
0.04
0.07
0.05
0.06
0.42
0.04
0.29
Marine
Brownian rate
1.89
22.32
1.88
35.75
2.09
50.22
2.66
59.55
1.49
-15.52
2.18
11.92
2.99
32.30
5.53
44.27
2 Rate
AICc
-4.84
-3.22
2.50
-4.72
-6.16
-12.45
-1.15
-7.85
DAICc
7.85
6.13
2.21
7.68
10.77
29.40
4.54
13.65
FW versus
marine
Evol Ecol (2014) 28:205–227
217
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Evol Ecol (2014) 28:205–227
Table 3 Estimates of evolutionary diversification rate shifts for terapontid morphology derived from
‘‘evol.rate.mcmc’’ analysis of Revell et al. (2011)
Morphological
variable
Evolutionary rate
prior to rate shift (r21)
Evolutionary rate
after rate shift (r22)
Ratio of estimated
rates (r22/r21)
Total length
0.35
0.38A
1.09
PC1
3.26
7.99B
2.45
PC2
2.28
2.35A
1.03
PC3
0.87
3.51C
4.03
PC4
0.85
1.51C
1.78
A
Rate shift occurs at ancestor of entire freshwater clade
B
Rate shift occurs at ancestor of Scortum, Hephaestus, Syncomistes freshwater sub-clade
C
Rate shift occurs at ancestor of major Bidyanus, Pingalla, Scortum, Hephaestus, Syncomistes freshwater
clade
densities for the location for rate shifts for these variables were diffusely distributed near
the tips of the tree (Fig. S2). This suggests that for this particular variable, rate shifts are
relatively recent, and/or there is significant evolutionary lability in these variables (i.e.,
considerable variability even between closely related species). The MCMC analysis
identified a rate shift for PC1 (positively correlated with intestinal length, negatively
correlated with maxilla length) occurring at the node sub-tending the freshwater clade
containing the genera Syncomistes, Scortum and Hephaestus. The inferred evolutionary
rate of PC1 after the rate shift (r22) was *2.5 times faster compared to the rest of the tree
(r21). A similar result emerged for PC3 (positively correlated with mouth orientation and
tooth shape, negatively with caudal peduncle depth and mouth width), whereby PC3
evolved at a rate in this clade *4 times faster than the rest of the tree. Results for PC4
(positively correlated with caudal peduncle depth and negatively with mouth width), also
identified a rate shift for the same clade whereby PC4 evolved at a rate in this clade
*1.8 times faster than the rest of the tree.
Results for morphological PC2 (for which previous censored rate testing suggested no
evolutionary rate difference between habitats) identified a rate shift at the node sub-tending
all freshwater species. However, this rate shift was only marginally faster for this variable
compared to the rest of the tree, and like maximum body size, the estimated posterior
densities for the location for rate shifts for this variable were diffusely distributed near the
tips of the tree (Fig. S2). When interpreting the results of this Bayesian MCMC approach it
should be noted that smaller trees can influence the scale of identified rates shifts. The ratio
of estimated rates (i.e., r22/r21) tends to be downwardly biased for small trees (\30 tips),
such that r1 and r2 are more similar. This tendency is a consequence of integrating across
uncertainty in the location of the rate shift and, similarly, the posterior density for the
location of a given rate shift also tends to be much more diffuse on smaller trees (Revell
et al. 2011).
PCCA
Tests of dimensionality for the canonical correlation analysis indicated a significant
relationship between morphology and diet, with three of the five canonical dimensions
significant at P \ 0.01. The canonical correlations between the sets of variables for
dimensions 1–3 respectively explained 33, 32 and 25 % of the total variation captured in
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Evol Ecol (2014) 28:205–227
219
TL/PC2
PC3/PC4
PC1
Fig. 5 Phylogeny of terapontids with a single possible map of habitat affiliation (marine-euryhaline habitat
in red and freshwater in black) generated through stochastic character mapping (SIMMAP; Bollback 2006).
Arrows indicate approximate position of evolutionary rate shift derived from ‘‘evol.rate.mcmc’’ analysis of
Revell et al. (2011). Scale of rate shifts (r22/r21) for each variable are outlined in Table 3
the analysis (Table 4). Eigenvalues dropped off sharply after these first three axes so only
the variation in canonical axes 1–3 were interpreted. The first canonical axis was most
strongly influenced by a negative correlation between morphological PC1 (weighted
toward longer intestines, and smaller maxilla and head lengths) and consumption of fish
and terrestrial plant material. This reflects an association between larger gapes, longer head
lengths and shorter intestines in terapontid species feedings on larger prey items such as
fish and riparian fruit. The second canonical axis was positively correlated with body size
(standard length) and negatively with PC4 (strongly loaded toward smaller mouth widths)
strongly loading toward consumption of detritus. This highlights an ecomorphological
association between large body size, smaller gape and detrital feeding. The third canonical
dimension was strongly influenced by a negative correlation between morphological PC3
(most strongly loaded toward sub-terminal mouth orientation and flattened dentition), PC4
(most strongly loaded toward smaller mouth widths) with consumption of terrestrial plant
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Evol Ecol (2014) 28:205–227
Table 4 Summary of phylogenetic canonical correlation analysis (PCCA) between terapontid diet and
morphology
Axis 1
Axis 2
Axis 3
Axis 4
Axis 5
Canonical correlations
0.91
0.90
0.80
0.37
0.33
Eigenvalues
0.83
0.81
0.64
0.14
0.11
Proportion of total variance explained
0.33
0.32
0.25
0.05
0.04
v2
104.25
64.85
28.35
5.85
2.57
P
\0.001
\0.001
\0.01
0.44
0.27
Variables
CA1
CA2
CA3
CA4
CA5
Canonical variable (morphology)
PC1
0.43
-0.24
-0.02
0.04
0.06
PC2
-0.23
-0.16
-0.15
0.28
0.52
PC3
-0.03
-0.14
0.42
0.57
-0.31
PC4
-0.15
-0.26
0.69
-0.45
0.38
0.21
1.07
0.39
0.02
1.07
SL
Canonical variable (diet)
Invertebrates and zooplankton
-2.26
-1.85
0.16
1.49
3.20
Macrocrustaceans
-1.38
-0.61
-0.48
-0.14
3.23
Fish
-2.82
-0.01
-0.09
6.40
2.65
Terrestrial plant material
-2.79
-0.62
-3.08
2.33
7.35
Aquatic vascular plants
-1.40
-1.84
-0.01
2.66
5.32
Detritus
-2.67
-2.84
-0.92
2.36
4.57
material. This likely reflects the need for significant mouth gape and terminal mouth
orientation to consume large, terrestrially derived items such as fruit.
Niche breadth and dietary overlap
There was no significant difference in overall levels of niche breadth (phylogenetic
ANOVA, F = 4.78, T = 2.18, P = 0.31) or Bray–Curtis dietary overlap (phylogenetic
ANOVA, F = 3.41, T = 1.846, P = 0.37) between marine and freshwater macrohabitats.
The average dietary overlap between marine and freshwater terapontids, compared to
average dietary overlap within the freshwater terapontids was significantly different
(phylogenetic ANOVA, F = 15.29, T = 3.9, P = 0.049). These results suggest that while
there has been no significant change in average terapontid niche width following the
freshwater invasion, there has been a significant shift in the average diet of freshwater
terapontids compared to their marine counterparts, an outcome that largely parallels the
PPCA (Fig. 3).
Discussion
Our comparative analyses give quantitative support to the prediction that shifts between
adaptive zones promote speciation, morphological variation and resource partitioning in
invading clades (Simpson 1953; Schluter 2000). Terapontid species within freshwater
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Evol Ecol (2014) 28:205–227
221
clades exhibited significantly greater rates of evolutionary diversification than their marine
counterparts in several of the major axes of both ecological and morphological differentiation. Further, the application of morphological evolutionary rates testing, with no a
priori designation of habitat association, yielded additional insights into the different
phenotypic diversification between habitats. Much of the difference in morphological
disparification evident in freshwater compared to marine species appears to be driven by
relatively rapid morphological evolution within particular sub-clades of the terapontid
freshwater diversification, rather than increased rates of disparification across freshwater
terapontids in their entirety. In particular, the clade containing the herbivorous Scortum
species, detritivorous–algivorous Pingalla and Syncomistes species and large-bodied,
omnivorous Hephaestus species exhibited higher rates of morphological diversification in
characters relating to intestinal length, mouth gape, head length, dentition and mouth
orientation compared to the rest of the terapontid tree.
There were also significant correlations between several of these dietary and morphological traits that diversified rapidly following the freshwater invasion. This suggests a
phenotype-environment fit between the diverse phenotypes of freshwater terapontids and
their divergent dietary ecologies, fulfilling the ‘adaptive’ criteria (Schluter 2000) for an
adaptive radiation. Much of this dietary divergence following the freshwater invasion
appears to be primarily due to shifts in the average diet of species, rather than ecological
processes such as trophic partitioning. The coarse level of these analyses, however, ignores
the particular dietary habits of individual species. Freshwater terapontids exhibit both some
of the most specialised diets in the family (H. transmontanus, S. ogilbyi), and also some of
the broadest, most generalised diets (H. jenkinsi; Table S2). The evolutionary significance
of these divergent trophic strategies is yet to be assessed.
The trophic opportunities available to fishes probably differ greatly among marine,
estuarine and freshwater environments, with submerged aquatic macrophytes, aquatic
insects, allochthonous materials (terrestrial leaves, fruits, seeds, insects and vertebrates)
and detritus being less important sources of food in estuarine-marine environments than in
fresh waters. Consumption of these items accounts for majority of the trophic diversification in freshwater species. Much of the elevated morphological evolution evident in
freshwater terapontids, in characters such as intestinal length, mouth gape, dentition and
mouth orientation, is also significantly correlated with consumption of items such as
detritus and aquatic and terrestrial plant material. This suggests that a phenotype-environment correlation (Schluter 2000) in the form of ecomorphological adaptation to a novel
range of trophic opportunities has played a major role in the morphological radiation of
freshwater-invading terapontids.
Factors other than (or in addition to) resource availability may also have played a role in
the freshwater radiation. Adaptive radiations are known for rapid morphological and
species diversification in response to ‘ecological opportunity’, typically stemming from (1)
colonisation of new areas, (2) extinction of competitors or (3) key innovations, leading to
ecological release and the evolution a variety of ecomorphologies to maximise resource
utilisation along the niche spectrum (Schluter 2000; Losos 2010; Mahler et al. 2010; Yoder
et al. 2010). As the main driver of adaptive radiation, ecological opportunity is a direct
function of resource abundance and an inverse function of competition (Simpson 1953;
Schluter 2000; Losos 2010; Yoder et al. 2010).
Historical ecological opportunity is even more difficult to measure than current ecological opportunity (Losos 2010; Yoder et al. 2010). For this reason, most studies of the
historical effect of ecological opportunity on diversification have been conducted on
oceanic islands, where available niche space is inferred on the basis of the absence of
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Evol Ecol (2014) 28:205–227
competitors (Grant 1999; Gillespie 2004). Similarly, many of the pre-requisites for an
adaptive radiation facilitated by ecological opportunity may have existed on the Australian
continent due to its biogeographic history. The timing of Australia’s isolation from
Gondwana precluded the presence of groups such as cichlids, characiforms, cypriniformes
and most siluriformes. Together, these lineages have evolved the dominant proportion of
dietary diversity in southern hemisphere (excluding Australian) freshwater fishes, particularly herbivorous and detritivorous feeding modes (Schaefer and Lauder 1986; Barlow
2000), one of the significant axes of trophic diversification in freshwater terapontids. With
the exception of terapontids, the dietary diversity of the contemporary Australian fish fauna
is relatively restricted, dominated by carnivores (particularly invertivores) and omnivores
(Davis et al. 2012a). As a result of this incumbent community composition, there was
likely an historical abundance of vacant trophic niche space in Australia for invading
lineages such as terapontids to exploit.
One of the primary axes of terapontid diversity, in which freshwater species had much
higher rates of evolution than marine species, related largely to body size, although evolutionary rate testing with no a priori consideration of habitat affiliation suggests that
diversification in this character evolved relatively recently in terapontids. Marine species
have similar maximum body sizes, mostly between 250 and 350 mm TL (Froese and Pauly
2000). Freshwater terapontids, in contrast, contain some of the largest species in the
family, reaching over 500 mm in length and several kilograms in weight, through to some
of the smallest, at *100 mm or less (Allen et al. 2002). This evolutionary contrast may be
even more pronounced as our samples did not include several freshwater species representing body-size extremes in the family [such as Bidyanus bidyanus (Mitchell 1838),
growing up to 600 mm TL and 8 kg] and Leiopotherapon macrolepis Vari 1978 and
Pingalla midgleyi Allen and Merrick 1984 (*100 mm TL adult size maximum) (Allen
et al. 2002). Several of the major axes of terapontid morphological diversification,
including body size, did not reveal any significant early radiation in the terapontid
freshwater invasion. This is an outcome shared with other comparative studies of evolutionary rate in body size and shape, where rapid bursts in body size are relatively rare, with
constraints appearing instead to shape evolution continually through time (Harmon et al.
2010).
Much of the significant morphological diversification evident in terapontids related to
higher rates of evolution in variables such as intestinal length, gape, dentition and mouth
orientation. These axes of increased morphological disparification also had a significant
correlation with diet, explaining *90 % of the dietary diversification evident in terapontids. Evolutionary rate testing with no a priori consideration of habitat affiliation
suggests that these characters did evolve at a high rate early in the freshwater invasion, but
were especially pronounced in one clade containing omnivorous, herbivorous and detritivorous species. The results concur with the increasingly complex patterns of intestinal
convolution previously documented as evolving in this same clade, which includes
Hephaestus, Scortum, Pingalla and Syncomistes (Vari 1978; Davis et al. 2013), and also
correlating with shifts away from carnivorous diets.
The evolution of plant-detritus-dominated diets is a notable characteristic of terapontids,
with several genera such as Pingalla, Syncomistes, and Scortum evolving specialised diets
dominated by aquatic macrophytes, filamentous algae and detritus (Davis et al. 2010,
2011a). The dominant ecomorphological patterns evident in terapontids (sub-terminal
mouth orientation, long intestine and flattened dentition) positively correlate with algivory
and detritivory, and parallel those in other fish assemblages (Winemiller 1991; Hugueny
and Pouilly 1999; Pouilly et al. 2003). While this ecomorphological information does not
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Evol Ecol (2014) 28:205–227
223
provide an explicit functional link between observed patterns of prey use and morphology
(Norton et al. 1995), the re-occurring ecomorphological relationships among distantly
related fish groups provides compelling evidence for convergence shaped by evolutionary
forces, reducing the likelihood that the ecomorphological pattern is a random event.
Comparisons of model fits and model-averaged rate estimates provided evidence that
ecological and morphological diversification varies as a function of macrohabitat association in terapontid clades. However, like all model-fitting analyses these results are only as
accurate as the a priori models tested (Burnham and Anderson 2002). Therefore, the scope
of our conclusions is limited to the relative fit of these models. Although the superior fit of
the multiple-rate models over the single-rate models supports the hypothesis of habitatassociated variation in ecomorphological diversification, we cannot rule out the possibility
of an unknown evolutionary model better fitting the ecomorphological data. Similarly, our
analysis does not rule out roles for other factors that may have influenced morphological
evolution in terapontid lineages. For example, diversification might also vary with differences in intrinsic factors, such as genetic constraints or functional innovations (Wainwright et al. 2012). Future research investigating the effects of functional constraints on
feeding modes, habitat-related ecomorphology or biotic interactions within habitats on the
pattern we documented would be useful.
Adaptive radiation has been defined as the process of diversification from a single
ancestral form into a variety of ecological or geographic niches to produce new morphologically and ecologically differentiated taxa (Gavrilets and Losos 2009). While popular and evolutionary literature has largely emphasized species richness in adaptive
radiations, more recent syntheses suggest that an evolutionary radiation needs to incorporate two distinct aspects of diversity—species richness and phenotypic diversity (frequently termed ‘adaptive disparity’ to avoid confusion with ‘species diversity’) (Losos and
Mahler 2010). The term ‘adaptive radiation’ should therefore refer to clades exhibiting an
exceptional extent of adaptive disparity (Losos and Mahler 2010). While the Terapontidae
is only moderately species-rich by global standards, it does rank as one of Australia’s most
speciose families (Allen et al. 2002; Unmack 2013). The family also displays much higher
ecological (trophic) diversity than that displayed by the rest of the Australian freshwater
fish fauna (see Kennard et al. 2001 for comparison), as well as significant phenotypic
radiation correlating with ecology. Therefore, terapontid freshwater clades should be
recognised as an adaptive radiation based on the Losos and Mahler (2010) definition.
Our results provide strong evidence that the adaptive zone shift from marine to freshwater habitats has profoundly influenced the evolutionary trajectory of trophic and morphological diversity within terapontids. We have demonstrated that the transition from
marine to fresh water triggered accelerated evolution of dietary and morphological characters. The underlying process of phenotypic evolution across these morphological characters in the freshwater-invading terapontid clade is, however, complex. Increased rates of
morphological evolution are not ubiquitous across freshwater species, and may be largely
restricted to specific clades, particularly those shifting to exploit novel resources (i.e., plant
and detrital diets in the case of terapontids). The timing of these rate shifts can also vary
markedly, with some characters evolving quickly after a macrohabitat shift, while others
rate shifts are relatively recent.
Acknowledgments For their efforts in helping to collect and/or provide specimens, we thank Mark
Adams, Gerald Allen, Jon Armbruster, Michael Baltzly, Cindy Bessey, Joshua Brown, Christopher Burridge, Stephen Caldwell, Adam Fletcher, David Galeotti, Chris Hallett, Michael Hammer, Jeff Johnson,
Mark Kennard, Adam Kerezsy, Alfred Ko’ou, Andrew McDougall, Masaki Miya, Sue Morrison, Tim Page,
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Evol Ecol (2014) 28:205–227
Colton Perna, Ikising Petasi, Michael Pusey, Ross Smith and the Hydrobiology team, Dean Thorburn and
the staff from ERISS and Northern Territory Fisheries. Additional samples for genetic work were provided
by the Australian, Northern Territory, Queensland, South Australian, Western Australian, University of
Kansas and the Smithsonian museums, and we thank their staff and donors. Field collection was partly
funded by the Australian Government’s Natural Heritage Trust National Competitive Component and Land
and Water Australia. PJU was supported by the W.M. Keck Foundation, R.M. Parsons Foundation, Natural
History Museum of Los Angeles County and the National Evolutionary Synthesis Center (NESCent), NSF
#EF-0905606. Two anonymous reviewers are thanked for input that greatly improved the final manuscript.
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