Download The role of biotic forces in driving macroevolution: beyond the Red

Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
rspb.royalsocietypublishing.org
The role of biotic forces in driving
macroevolution: beyond the Red Queen
Kjetil L. Voje, Øistein H. Holen, Lee Hsiang Liow and Nils Chr. Stenseth
Review
Cite this article: Voje KL, Holen ØH, Liow LH,
Stenseth NC. 2015 The role of biotic forces in
driving macroevolution: beyond the Red
Queen. Proc. R. Soc. B 282: 20150186.
http://dx.doi.org/10.1098/rspb.2015.0186
Received: 3 February 2015
Accepted: 14 April 2015
Subject Areas:
evolution, ecology, palaeontology
Keywords:
microevolution, macroevolution, Court Jester,
theoretical ecology, food-web models,
biotic interactions
Authors for correspondence:
Kjetil L. Voje
e-mail: [email protected]
Nils Chr. Stenseth
e-mail: [email protected]
Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo,
PO Box 1066 Blindern, Oslo 0316, Norway
A multitude of hypotheses claim that abiotic factors are the main drivers of
macroevolutionary change. By contrast, Van Valen’s Red Queen hypothesis
is often put forward as the sole representative of the view that biotic forcing
is the main evolutionary driver. This imbalance of hypotheses does not
reflect our current knowledge: theoretical work demonstrates the plausibility
of biotically driven long-term evolution, whereas empirical work suggests a
central role for biotic forcing in macroevolution. We call for a more pluralistic
view of how biotic forces may drive long-term evolution that is compatible
with both phenotypic stasis in the fossil record and with non-constant
extinction rates. Promising avenues of research include contrasting predictions from relevant theories within ecology and macroevolution, as well as
embracing both abiotic and biotic proxies while modelling long-term evolutionary data. By fitting models describing hypotheses of biotically driven
macroevolution to data, we could dissect their predictions and transcend
beyond pattern description, possibly narrowing the divide between our
current understanding of micro- and macroevolution.
1. Introduction
‘A fundamental question on how the physical world relates to evolution remains
unsolved. Is physical change the necessary pacemaker of speciations and extinctions, or do living entities drive themselves to evolve and disappear even in its
absence?’, Vrba [1, p. 24] asked two decades ago. The same question had previously been raised by Maynard Smith [2], and still lacks a satisfactory answer.
Many palaeobiologists argue that biotic interactions drive evolution only on
shorter time scales, while changes in the physical environment are necessary to
trigger macroevolutionary changes on geological time scales (e.g. [1,3–9]).
Barnosky [6] compiled eight ‘Court Jester’ hypotheses from the literature,
each claiming that abiotic change is the most important factor driving major
evolutionary change. He contrasted this collection of Court Jester hypotheses
with a class of ‘Red Queen’ hypotheses that claim that biotic factors are more
important in driving macroevolution—a class that in practice boils down to a
single hypothesis: Van Valen’s Red Queen [10]. Barnosky’s strong emphasis
on abiotic factors driving macroevolution was echoed by Benton [7, p. 728],
who claimed that ‘Competition, predation, and other biotic factors shape ecosystems locally and over short time spans, but extrinsic factors such as
climate and oceanographic and tectonic events shape larger-scale patterns
regionally and globally, and through thousands and millions of years.’ Likewise,
many others have also stressed the dominance of abiotic factors. For instance,
Eldredge [11, p. 28] stated that ‘the conclusion seems inevitable to me that nothing much in the way of adaptive evolutionary change takes place unless and
until physical disturbance impacts ecosystems and that the amount of evolution
is proportional to the amount of disturbance and extinction that affects ecological and genealogical systems’. Likewise, Lieberman [9, p. 182] argued that
‘abundant evidence has been gathered to suggest that it may be principally
abiotic factors, such as geologic change, climatic change, and a taxon’s presence
in a geographically complex region, which causes clades to undergo rampant
diversification, rather than organismal adaptation’.
& 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
2. Models of long-term evolution driven
by biotic interaction
The strictest interpretation of the claim that abiotic forces drive
macroevolution is that abiotic change is absolutely necessary
to drive evolution on longer time scales and that evolution
would cease in its absence. This is sometimes referred to as
the ‘stationary model’ [4,6,26], and implies that dinosaurs
would be ruling the Earth morphologically unaltered if abiotic
conditions ceased changing around 70 Ma.
A number of theoretical models have investigated evolutionary dynamics in the absence of extrinsic change. Some
early models were directly motivated by Van Valen’s Red
Queen. These models investigated the evolutionary dynamics
of evolutionary lag, or ‘lag load’, a measure of the difference
in fitness between the fittest possible genotype in a population and the current mean fitness of the same population,
keeping all other species constant (e.g. [25,26,41]). Maynard
Smith [25] showed that the zero-sum assumption was consistent with a constant rate of evolution, just as Van Valen
claimed, but also argued that the zero-sum assumption was
untenable. Stenseth & Maynard Smith [26] demonstrated
that Red Queen evolution could be produced without the
zero-sum assumption and that evolutionary stasis was also
2
Proc. R. Soc. B 282: 20150186
seriously. Abiotic changes in the form of volcanism, tectonic
events, asteroid impacts and climate changes have been present
throughout the history of life. Yet any theory of evolution that
aims to describe the macroevolutionary consequences of abiotic change needs a notion of what long-term evolution would
be like in its absence. The proposition that major evolutionary change will continue in the absence of abiotic
perturbations therefore remains fundamental to evolutionary
biology [1,2]. A more pluralistic approach that moves beyond
the formulation of the original Red Queen hypothesis is
needed: just as there are multiple Court Jester hypotheses
that detail how abiotic forcing might initiate and drive macroevolution [6], we find it necessary to encourage development of
alternative hypotheses for how biotic forces might drive macroevolution. Further testing and discussion of the specific
assumptions of Van Valen’s original Red Queen (e.g. constant
extinction rates or the zero-sum assumption) will not settle the
issue. This point has perhaps been most convincingly demonstrated by the many theoretical models that exhibit intrinsically
driven continuous trait evolution or repeated lineage branching and extinction [33–40]. Because these models are built on
assumptions that are very different from those of the original
Red Queen, their validity is unaffected by the criticisms
raised against her. They in fact lend plausibility to the view
that long-term evolution may occur independent of changes
in the physical environment.
In this paper, we briefly summarize theoretical work that
shows that long-term evolution driven by biotic interactions
is plausible, and point to empirical evidence from the fossil
record that indicates that biotic forcing is important in macroevolution. We also argue that evolution driven by persistent
biotic forcing may be consistent with observed phenotypic
stasis in the fossil record and with varying extinction probabilities. We end by discussing some hypotheses for how
biotic forces shape macroevolutionary patterns, challenges
that lie ahead in studying biotic factors in macroevolution
and paths that may be challenging but fruitful to explore.
rspb.royalsocietypublishing.org
Although the evidence that abiotic change drive macroevolutionary change is beyond dispute (e.g. [12–14]), many
palaeobiologists disagree with the categorical idea that abiotic
perturbations are the only drivers and allow for a significant
role for biotic factors in driving macroevolutionary change
[15–18]. For example, competition-driven clade–clade replacements have been hypothesized for diverse taxonomic groups,
including cheilostome bryozoans replacing cyclostome bryozoans [19], angiosperms replacing gymnosperms [20] and
bivalves replacing brachiopods [16]. Moreover, macroevolutionary turnover driven by interspecific interactions can be
observed as diversity-dependent diversification (see review
by Rabosky [21]).
For more than four decades, Van Valen’s Red Queen
hypothesis [10] has been central in the discussion on the role
of biotic factors in evolution (see review by Liow et al. [22]). In
essence, Van Valen proposed that biotic interactions suffice to
drive species to evolve indefinitely, independent of abiotic
changes. He argued that evolutionary advantages gained by
one species cause evolutionary disadvantages to other species
in the community, an idea dating back to Darwin [23] and
Fisher [24]. Furthermore, the negative effects adaptation in
one species have on other interacting species result in a continuous evolutionary race in which species catch up or go extinct,
resulting in a constant, age-independent risk of extinction.
Thus, the Red Queen hypothesis invokes a microevolutionary
process to explain a macroevolutionary pattern.
Upon publication, the Red Queen hypothesis received
immediate criticism from both neontologists and palaeobiologists. One criticized aspect of the hypothesis (e.g. [25,26]) was
the underlying assumption that an increase in fitness in one
species should be exactly balanced by a loss of fitness in
other species (the ‘zero-sum’ assumption), which Van Valen
had based on the argument that the amount of energy in a
community stays approximately constant. The inference that
extinction probability should be constant was also criticized
(e.g. [27,28]); the observation of this pattern was what had
motivated Van Valen to formulate his hypothesis in the first
place. It was also claimed that the Red Queen hypothesis predicts gradual evolution and would be incompatible with
widespread observations of phenotypic stasis in the fossil
record [26,29,30]. Despite widespread criticism, the Red
Queen continued to attract attention, being the only major
theory that gave biotic factors the central role in driving macroevolution. As Hoffman [31, p. 2] succinctly observed, the Red
Queen hypothesis ‘has been repeatedly dethroned and restored
to power but the issue has not yet been settled’. The situation is
much the same today, with the Red Queen still having an influential hold on our imaginations. The review by Liow et al. [22]
presented a view of the Red Queen that was close to the
one originally presented by Van Valen [10], while Vermeij &
Roopnarine [32, p. 561] devoted an entire paper to dethroning
the Red Queen, in the process repeating many of the above criticisms and concluding that ‘the evolutionary turf over which
the Red Queen reigns is so small that the Red Queen is best
eased into retirement’. Regardless of the view one takes, it is
clear that the Red Queen hypothesis has been incredibly productive, in the sense that it has stimulated much research and
served as a justification for questioning the view that abiotic
perturbations are necessary for macroevolutionary change.
As we see it, the central proposition of the Red Queen
hypothesis—that evolution will continue without changes in
the physical environment—needs and deserves to be taken
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
3. Studies of long-term evolution driven
by biotic factors
A more moderate interpretation of the claim that abiotic
forces drive macroevolution is that abiotic forces (the ‘Court
Jester’) have played such a dominant role throughout the history of life that the effects of biotic forces on macroevolution
are hard, or next to impossible, to detect [3,6,7,9]. A variant
of this claim is that biotic factors are restricted to affecting
evolutionary change on small temporal and spatial scales,
whereas abiotic factors are responsible for change on larger
scales [6,7].
The hypothesis that abiotic factors have been the dominant
macroevolutionary force throughout the history of life may
seem most plausible if we restrict our attention to taxonomic
turnover. This is especially true for mass extinctions (e.g.
[12]). For extinction in general, however, a role for biotic factors
cannot be ruled out merely by showing that abiotic factors
can explain some of the variation in turnover (e.g. [50]), or
vice versa (e.g. [51]). Only a handful of studies of taxonomic
turnover simultaneously consider data from both abiotic
and biotic proxies when investigating which factor is the stronger driver of macroevolutionary change: Aberhan et al. [52]
reported that the changes in sea level, climate and seawater
chemistry could not explain Mid-Mesozoic relative abundance
of different marine guilds, while increasing levels of predation
and biotic ‘bulldozing’ explained the decline in the abundance
of vulnerable epifaunal taxa. Likewise, Ezard et al. [53] used
morphological traits as proxies for species ecology, and
found that while both biotic and abiotic factors contributed
to species diversity changes, extinction was more strongly
affected by climate while speciation was more strongly
3
Proc. R. Soc. B 282: 20150186
coevolutionary dynamics in the food-web models. Dieckmann
et al. [37] presented a different and more tractable model of
community coevolution based on competition alone. In this
model, a handful of species within a single trophic level coevolve,
and exhibit perpetual turnover of species through branching
and extinction. The patterns of extinction seem almost regular
(underdispersed) in the community coevolution model by
Dieckmann et al. [37] when compared with the more complex
patterns seen in the food-web models.
In summary, theoretical models based on many different
sets of assumptions have shown that biotic interactions have
the potential to drive long-term evolution. The food-web
models in particular have become increasingly sophisticated
and relevant for understanding the evolution of ecosystems,
and several exhibit diversity-dependent turnover dynamics.
Few (if any) of these theoretical developments are discussed
in the palaeobiological literature. It is still the Red Queen
models from the 1970s and 1980s that tend to be discussed
(e.g. [6,7,32,45,46]), probably because these were motivated
directly by Van Valen’s [10] thoughts on the Red Queen,
and are more easy to relate to macroevolution than later
work. However, the food-web models differ in their detailed
dynamics and suggest that biotically driven lineage extinctions do not produce a single ‘signature’ pattern. It is time
to exchange the constantly running Red Queen and its constant extinction probability with a more pluralistic view of
biotically driven evolution. The theories of ecosystems and
food webs, and the palaeobiological literature, already
present us with a diversity of hypotheses (table 1).
rspb.royalsocietypublishing.org
a possible outcome (the stationary model). In retrospect, it is
clear that the definition of lag load used in these papers only
makes sense because there was no intraspecific frequency
dependence of fitness, a limitation these models shared
with other coevolutionary models of that time (discussed in
[37,41,42]). Other early models featured trait evolution, and
showed (not surprisingly) that predators and victims would
evolve more and more extreme arms levels if the improvements were always both beneficial and cost-free and the
trait space was unbounded (e.g. [41]).
The next generation of Red Queen models focused solely
on trait evolution instead of lag loads and incorporated
intraspecific frequency-dependence of fitness [33 –35,40].
These models assumed that evolution is driven by mutations
of small effect, and showed that relatively simple ecological
interactions might lead to cyclic evolutionary trait dynamics
[33,34,40] or repeated cycles of branching and extinction in
lineages [35]. The zero-sum assumption played no role in
these models, and they did not require an unbounded trait
space. Their major limitation from a Red Queen perspective
was their focus on pairwise interacting species only.
The last decade has seen a series of important theoretical
papers that explore evolving food webs and their nonequilibrium dynamics [36–39,43]. These papers make no
reference to the Red Queen. The models are based on trait evolution and show how complex food webs that contain many
species at several trophic levels can evolve and be dynamically
maintained through evolutionary branching and extinction,
driven by biotic processes such as predation and competition.
They are consistent with the observation that diversification
is diversity-dependent. For instance, they show that in the
absence of extrinsic forcing, biotic interactions can create coevolutionary trait dynamics in which the number of species
fluctuates widely in a periodic or chaotic fashion [36]. Biotically driven mass extinctions or extinction cascades are also
demonstrated in these models [36,39,43]. Takahashi et al.’s
[39] model features food webs that alternate between two community states: they repeatedly build up and evolve towards a
quasi-stable state that is very vulnerable to perturbation,
where demographic stochasticity triggers an extinction cascade, starting the cycle again. In Allhoff et al.’s [43] model,
periods of increasing specialization in the food web are interrupted by extinction cascades; the trigger for the cascades is
the arrival of a new mutant form that escapes specialist predation, and therefore can increase to great numbers and displace
other species. Together, the food-web models demonstrate
that non-equilibrium evolutionary dynamics in food webs
can be very rich, and that evolution can last for a very long
time in the absence of abiotic forcing, taking the community
through many cycles of expansion and contraction. The
models are simulation-based and not analytically tractable,
and no direct claim is made in any of them that evolution
never ceases (indeed this is not their focus). They are nevertheless highly relevant for understanding the processes
shaping macroevolution. Other models produce food webs
with a very stable structure [44], and there are ongoing efforts
to identify the factors (e.g. functional responses and trophic
interactions) that stabilize and destabilize evolving food
webs (see discussions in [39,43]).
It is interesting to note here that while Van Valen [10] phrased
his Red Queen scenario in terms of a struggle for resources,
which emphasizes the role of competition, predator–prey interactions seem to be an important driver of the continuous
f
across taxa
ecosystem stability due to
evolved interdependencies
natural enemies drive
morphological diversification;
the Escalation hypothesis [15,47]:
followed by radiations, and
clade replacements
‘key innovations’:b macroevolution
dominated by key innovations
community
fragile ecosystems
negligible
varying
build-up
depending on
stage of
evolutionary build-up and
occasional collapse of inherently
varying,
constant
no
no
yes
no
no
yes
no
no
The ‘expansion and crash’ hypothesis is loosely based on ideas from ecosystem theory [48] and food-web models (e.g. [39]).
The ‘key innovations’ hypothesis is loosely based on ideas on phylogenetic diversification (e.g. [16,21,49]).
b
a
abiotic
perturbations
absence of
change will
continue in
major evolutionary
deterioration in others
‘expansion and crash’:a slow
species exactly balanced by
Van Valen’s Red Queen [10]:
evolutionary advance in one
hypothesis
related
species tend
to go extinct
together
yes
no clear
prediction
prediction
no clear
no
periods of
true
phenotypic
stasis
prediction
no clear
occasional bursts
build-up
community
depending on
stage of
varying,
constant
rate of
biotically
driven
speciation
prediction
no clear
no
no
no
evolution
ceases in
absence of
abiotic forcing
Proc. R. Soc. B 282: 20150186
biotically
driven
extinction
cascades
new evolutionary
opportunities
predominant cause of
extinction, which opens up
abiotic forcing is the
new niches that can be
accessed by key innovations
changes in the abiotic
environment may create
gradual evolutionary buildup of a new community
setting the stage for
forcing, may cause fragile
ecosystems to collapse, thus
abiotic forcing, just like biotic
abiotic forcing
none; species are constantly
evolving regardless of
qualitative changes of
dynamics due to abiotic
forcing
rspb.royalsocietypublishing.org
rate of
biotically
driven
extinction
Table 1. Hypotheses of biotically driven macroevolution and their predictions. A number of hypotheses are compatible with the idea that macroevolution is driven by biotic forces, although most discussions tend to focus on Van Valen’s
Red Queen. Other plausible hypotheses of biotically driven macroevolution can be formulated based on ecosystem and food-web theories, or the literature on phylogenetic diversification, and these yield predictions that differ strikingly
from that of the original Red Queen.
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
4
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
It has been argued that the Red Queen is incompatible with
observations of stasis in the fossil record (e.g. [26,29,30,32]).
Indeed, the original Red Queen metaphor suggests that all
species have to continuously evolve so as not to lag behind
other species and that they have constant (age-independent)
probabilities of extinction. Together, this suggests a uniformity of process that is clearly incompatible with zero evolution
(e.g. [4,26]). It would be grossly misleading, however, to generalize from this and conclude that biotically driven evolution
is incompatible with observations of stasis on geological
time scales.
First, it is necessary to acknowledge that phenotypic stasis
is itself a matter of interpretation. Stasis can be operationally
defined as a state of minimal net rate of evolutionary change
over some period of time, usually modelled as temporal variation around a constant mean phenotype (e.g. [62,63]). If
stasis truly represents negligible evolution, the small-scale temporal fluctuations in phenotype observed during periods
defined as stasis may simply reflect a combination of preservation artefacts, varying sampling probabilities, and other
types of noise and measurement error. Under this
5
Proc. R. Soc. B 282: 20150186
4. Biotically driven evolution and the paradox
of stasis
interpretation, stasis may be caused by factors like stabilizing
selection and evolutionary constraints (reviewed in [64]).
Understood in this way, phenotypic stasis is difficult to reconcile with the high levels of additive genetic variance [65,66] and
strength of selection (e.g. [67]) typically observed in contemporary populations. This is known as the ‘paradox of stasis’
[68], and it applies with equal force to biotically and abiotically
driven evolution.
The alternative interpretation of observed stasis is that
the temporal small-scale fluctuations observed during periods
of stasis represent adaptive evolutionary change towards a
constrained but non-static fitness optimum on the adaptive
landscape. The rate of evolution around such an optimum
may be fast or slow, depending on the evolvability of the population and the tempo of the movement of that fitness optimum.
Recent theoretical and empirical work indeed indicates that
bounded but fluctuating changes are the norm (e.g. [69–71]),
which indicates that phenotypic change during stasis could
be more than error and noise. Selection is inevitable as long
as there is a mismatch between the average phenotype in a
population and an optimum in the adaptive landscape, regardless of whether it is caused by abiotic and biotic forces. Just as
climate changes have led to adaptive and genetically based
responses in phenotypes on microevolutionary time scales in
a wide range of organisms (e.g. [72–74]; reviewed by Merilä
[75]), so have biotic factors, including competition for food
resources, interactions between predator and prey, frequency
and density dependence, variability of mate choice driven by
sexual selection, and sexual conflict (e.g. [49,76–78]). Even
migration alone can displace populations off the adaptive hilltop as gene flow counteracts local adaptation (see review by
Lenormand [79]).
Second, the broader proposition that biotic factors drive
macroevolution does not in any sense rely on the original
Red Queen in all its detail. In particular, there is absolutely
no reason to assume that biotically driven evolution should
be a uniform process. As Vrba [4, p. 441] stated, ‘One might
well ask whether the Red Queen could lead to punctuated
equilibria. Perhaps the metaphorical lady is not running constantly without getting anywhere, but instead is an
occasional sprinter to new places where she rests at length
before the next sprint (which would of course not be Lewis
Carroll’s Red Queen).’ In other words, perhaps biotic factors
trigger minor change most of the time but major change
some of the time. Selection pressures may be constant but varying, and species may frequently be subject to various genetic or
developmental constraints that must be overcome before large
responses can be made. This is likely to hold equally true for
evolution driven by biotic and abiotic forces.
The recent models of food-web evolution (e.g. [36–39])
show clearly that extinctions can be diversity dependent and
may occur in cascades. The fact that the models feature phylogenetic gradualism rather than punctuated equilibrium is
likely to be a consequence of the models being purely phenotypic and allowing only mutations of small effect: evolution
slows down only when the fitness landscape flattens (e.g.
near a fitness peak or a branching point) or when the population size becomes very small. The incorporation of a more
realistic genetic architecture might allow for periods of stasis
that are punctuated by fast transitions when the organisms
finally break free from developmental and genetic constraints
[80]. Superior phenotypes that are not mutationally accessible
from current genotypes may become accessible at some later
rspb.royalsocietypublishing.org
influenced by diversity dependence in the last 65 Myr.
Mayhew et al. [54] reported that both temperature and turnover
rates in previous time intervals have effects on marine invertebrate richness in 10 Myr time intervals. Meachen & Samuels
[55] reported that trait evolution in Pleiostocene coyotes was
probably attributable to biotic interactions rather than climatic
changes. While we applaud these studies for considering biotic
and abiotic proxies simultaneously, additional studies investigating other taxonomic groups and temporal scales are needed
before any firm conclusions can be drawn on the relative contributions of biotic and abiotic drivers to taxonomic turnover in
the fossil record. There are also many technical challenges in
estimating taxonomic turnover and diversity on geological
time scales, not least because of incomplete sampling in the
fossil record, the lack of application of statistical frameworks
for addressing causality, and the auto-correlation of time-series
data. It is necessary to address these challenges with appropriate
statistical tools to undertake a more robust evaluation of biotic
and abiotic factors on evolutionary processes.
Macroevolution in the broader sense also includes the
evolution of traits and novel structures (e.g. [56,57]). The
origin of eyes, the evolution of wings and the transition
from fins to limbs are classic macroevolutionary transitions
that have largely been discussed without evoking abiotic factors (the ‘Court Jester’) as probable primary driving forces.
Such evolutionary novelties allow new functions, may open
new adaptive zones [58], and even trigger adaptive radiations
[21,49]. Similarly, the long-term temporal trends in increasing
complexity of adaptations as well as ecospace seen in the
fossil record are usually linked to biotic factors [15,47,59].
For instance, the escalation of more complex traits due to predation pressure has been thought to have taken place during
both the Cambrian Radiation [60] and the Mesozoic Marine
Revolution (e.g. [47]). Moreover, intraspecific competition is
key to understanding evolutionary trends in sexual dimorphism, like Rensch’s rule [61]. Biotic forces are important in our
understanding of trait evolution on long time scales.
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
5. Macroevolution and ecosystem theory: the
need for a synthesis
6. Challenges ahead
We have emphasized the valid reasons for expecting biotic
factors to be important in determining macroevolutionary processes and hold that they should be included more often in our
analyses of macroevolutionary data than is currently the case.
This is easier said than done. Biotic interactions are notoriously
difficult to study in deep time and come with many caveats.
However, there is no way around facing this challenge if we
want to disentangle the effects of biotic and abiotic drivers of
macroevolutionary change. Palaeoenvironmental proxies that
purportedly reflect past temperature, acidity and other climatic
or environmental variables are often used in studies of macroevolution. An analogous solution for the study of biotic
factors requires identification of proxies that meaningfully represent biotic variables. If specific morphological traits indicate
functional differences among species, such traits may be
used as proxies for the ecology of different taxa, as did Ezard
et al. [53]. However, such proxies must always be chosen and
estimated with care.
6
Proc. R. Soc. B 282: 20150186
To develop a theory of biotic forcing in macroevolution, it
might be useful to contrast observations and predictions
from related and complementary fields. Historically, comparing data and theory from palaeontology and neontology has
indeed helped shape our current understanding of tempo
and mode in evolution (e.g. [82–84]). Moving beyond the
Red Queen, one may argue that the most clearly formulated
and well-developed grand theory on how biotic forces drive
macroevolution and biological complexity is the escalation
hypothesis [15,47]. It claims that natural enemies (i.e. strong
competitors and consumers) are the main driving forces
behind the evolution of morphological diversification and
hence taxa. Moreover, it posits that coevolution tends to
create an increasing number of interdependencies (including
mutualisms) among species that render ecosystems more
robust and stable. Although the escalation hypothesis is largely
a theory of biotic evolution, it shares elements with the Court
Jester hypotheses: Vermeij [85, p. 222] argued that ‘Although
the directions of evolution are determined largely by organisms, the timing of evolutionary events is dictated by extrinsic
causes related to climate, sea level, tectonic movements, and
mass extinctions.’
Just like Van Valen’s Red Queen, the escalation hypothesis
is also founded upon a number of uncertain assumptions—a
fact that is rarely discussed. For instance, the escalation hypothesis suggests that communities in the absence of abiotic
perturbation will evolve towards robustness and greater stability via evolved interdependencies between species [47].
Within the field of ecology, in contrast, the stability of complex
ecosystems has long been a matter of controversy and is regarded as a puzzle to be explained. This dates back to May [48],
who developed a number of mathematical models to show
that complexity in itself does not cause ecosystems to be
stable; complexity may in fact be destabilizing. This prediction
seems to contrast with the claimed and often observed positive
relationship between complexity and stability (e.g. [86–89]).
However, May reconciled his results with empirical observations by suggesting that stable abiotic environments may
permit the build-up of ecosystem complexity. The resulting
complex ecosystems may be stable within only very narrow
parameter ranges, making them inherently fragile and prone
to disintegration and simplification in response to disturbance
[48]. Incidentally, this fragility seems analogous to the complex,
vulnerable community state that a food-web model may reach
before it goes through an extinction cascade (cf. [39]). Ever since
May [48], the hunt has been on in ecology to identify different
mechanisms that may help stabilize complex ecosystems, and
thus explain parts of the puzzle (e.g. [90–92]). Many of these
findings are at odds with assumptions underlying the escalation hypothesis, which proposes that stability arises mainly
from the evolution of an increasing number of interdependencies (including mutualisms) that form among species [15,47].
For instance, complex food webs have been predicted to be
more stable if they are highly compartmentalized, as the compartments limit the number of interdependencies between
species, which helps contain extinction cascades [90,91]. Also,
the presence of mutualisms has been predicted to destabilize
rather than stabilize ecosystems [48,92].
Independent of what causes complex ecosystems to form,
factors that destabilize such systems will be important for setting the pace for species turnover and ecosystem evolution.
Abiotic disturbances are commonly argued to be the major
causes of extinction by palaeontologists [1,3,4,6,11,32]. This
view is shared by Vermeij’s escalation hypothesis, which
claims that strong competitors rarely drive their enemies to complete extinction, as poor competitors usually are able to sustain
viable populations in physiologically marginal environments.
Identifying the crucial factors leading to extinction in nature
is a difficult exercise when studying modern populations
(e.g. [93]), and more so for the fossil record. From the perspective
of ecosystem evolution, however, the processes that cause populations to decline to the point where the stability of ecosystems is
at risk are more important than the processes that cause the last
individual of the last population of a species to disappear. The
death of a species may occur long after that species has ceased
playing an important ecological role. Biotic factors are known
to have potentially devastating effects on local populations
(e.g. [2,94,95]), and this is also reflected in many of the recent
food-web models, where local extinctions happen solely due
to biotic interactions [36,39,43].
A satisfactory theory of biotically driven macroevolution
requires a synthesis of ideas from palaeobiology, ecology and
evolution. Inconsistencies must be confronted head-on, and
relevant processes not conveniently dismissed as operating on
different time scales [3,6,7]. We should use our current understanding of ecosystem stability and community assembly (e.g.
[90,92]) to further develop theories on the role of biotic factors
in driving macroevolution, including factors causing ecosystem
collapse. It is possible to study the relationship between complexity and stability on macroevolutionary time scales [89],
and investigations of the strength of ecosystem-stabilizing
mechanisms using fossil data will be invaluable in resolving
the roles of biotic interactions in community evolution.
rspb.royalsocietypublishing.org
point via nearby genotypes that are attainable by several selectively neutral steps (i.e. they give rise to the same phenotype as
the current one). Punctuations could also represent rare jumps
across fitness valleys (i.e. between the basins of attraction of
different evolutionary attractors [81]).
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
A more mechanistic understanding of macroevolutionary
change may help us reduce the micro- and macroevolutionary
divide. However, this demands sufficiently complex, yet testable and clear hypotheses regarding the roles of biotic and
abiotic factors in driving long-term evolutionary change, not
least because biotic and abiotic forces are likely to act in an
enmeshed manner.
We think it is necessary to confront conflicting theories
in traditionally distant fields such as theoretical ecology
and palaeobiology in order to progress in understanding
the relative importance of the major drivers of macroevolution. Robust observations from both empirical and
theoretical work need to be embraced, even when they
seem to be incompatible, because the resolution of conflicts
through the merging of theory and data from distant fields
may bring forth a more cohesive theory of macroevolutionary
change. Such merging has already successfully contributed to
our understanding of how traits evolve on micro- and macroevolutionary time scales [69 –71], and represents a way
forward for a better understanding of the role of the biota
in its own long-term evolution.
Acknowledgements. This manuscript benefited from discussions with colleagues in the macroevolution group at the Centre for Ecological and
Evolutionary Synthesis, and from thoughtful and constructive comments by Torbjørn Ergon, Barbara Fischer, Thomas F. Hansen, Paul
G. Harnik, Michael Matschiner, Jostein Starrfelt and Thomas
O. Svennungsen. Wolfgang Kiessling and one anonymous reviewer
provided helpful and insightful comments on the manuscript.
Funding statement. The work was supported by grant numbers 227860
and 235073 from the Research Council of Norway.
Authors’ contributions. K.L.V., Ø.H.H., L.H.L. and N.C.S. designed the
study. K.L.V., Ø.H.H. and L.H.L. wrote the draft manuscript, with
K.L.V. having the lead on the writing. All authors contributed to discussions and subsequent revisions, and approved the final version of
the manuscript.
References
1.
2.
3.
4.
Vrba ES. 1995 On the connections between
paleoclimate and evolution. In Paleoclimate and
evolution with emphasis on human origins (eds
ES Vrba, GH Denton, TC Partridge, LH Burckle),
pp. 24 –45. New Haven, CT: Yale University Press.
Maynard Smith J. 1989 The causes of extinction.
Phil. Trans. R. Soc. Lond. B 325, 241 –252. (doi:10.
1098/rstb.1989.0086)
Gould SJ. 1985 The paradox of the first tier: an
agenda for paleobiology. Paleobiology 11, 2–12.
(doi:10.2307/2400419)
Vrba ES. 1993 Turnover-pulses, the Red Queen, and
related topics. Am. J. Sci. 293, 418 –452. (doi:10.
2475/ajs.293.A.418)
5.
6.
7.
8.
Miller AI. 1998 Biotic transitions in global marine
diversity. Science 281, 1157– 1160. (doi:10.1126/
science.281.5380.1157)
Barnosky AD. 2001 Distinguishing the effects of the
Red queen and Court Jester on Miocene mammal
evolution in the northern Rocky Mountains. J. Vert.
Paleontol. 21, 172–185. (doi:10.1671/0272-4634
(2001)021[0172:DTEOTR]2.0.CO;2)
Benton MJ. 2009 The Red Queen and the Court
Jester: species diversity and the role of biotic and
abiotic factors through time. Science 323, 728–732.
(doi:10.1126/science.1157719)
Myers CE, Saupe EE. 2013 A macroevolutionary
expansion of the modern synthesis and the
importance of extrinsic abiotic factors. Palaeontology
56, 1179 –1198. (doi:10.1111/pala.12053)
9. Lieberman BS. 2012 Adaptive radiations in the
context of macroevolutionary theory: a
paleontological perspective. Evol. Biol. 39,
181–191. (doi:10.1007/s11692-012-9165-8)
10. Van Valen L. 1973 A new evolutionary law. Evol.
Theory 1, 1–30.
11. Eldredge N. 2003 The sloshing bucket: how the
physical realm controls evolution. In Evolutionary
dynamics. Exploring the interplay of selection,
accident, neutrality, and function (eds J Crutchfield,
P Schuster), pp. 3– 30. New York, NY: Oxford
University Press.
7
Proc. R. Soc. B 282: 20150186
— Proving the underlying assumptions of Van Valen’s Red
Queen hypothesis wrong does not rule out biotic factors
as important macroevolutionary drivers, nor does it
show that evolution will cease in the absence of abiotic
change.
— The temporal/spatial correlation of abiotic variables with
turnover rates cannot be used as evidence that biotic
factors are negligible when the latter are not directly
examined in the same analyses.
— If ecosystems commonly evolve towards fragile states, the
effect of an abiotic change may just be to trigger a collapse
that was already imminent.
— Observation of no net evolution over millions of years
(stasis) is not in itself an argument for or against macroevolution being driven by biotic factors.
rspb.royalsocietypublishing.org
Developing testable hypotheses that make up cohesive
theories of biotically driven macroevolution is challenging.
To promote discussion, we list in table 1 different hypotheses
that assert a clear role for biotic forces in shaping macroevolution. While the Red Queen and escalation hypotheses can be
directly ascribed to Van Valen [10] and Vermeij [15,47], respectively, the ‘expansion and crash’ and ‘key innovations’
hypotheses are inspired by a larger body of literature. Statistical
modelling of specific empirical systems will be a major tool in
investigating their predictions. Their investigation should also
be informed by theoretical modelling. Suppose, for example,
that biotic forces indeed tend to build up complex, fragile
food webs that sooner or later will collapse by themselves.
Minor abiotic perturbations might hijack such biotically
driven processes by triggering premature collapses, thus synchronizing biotically driven extinction cascades with abiotic
change. On a wider geographical scale, one might observe
‘pulses’ of turnover where climatic perturbations cause fragile
ecosystems to collapse simultaneously [4]. In such a scenario, it
might be relatively straightforward to show that the observed
extinction cascades correlate with abiotic change, but a very
strong link between theory and data would be needed to
demonstrate that biotic factors drove the communities to the
brink of collapse in the first place.
Detection of discrepancies and overlap in how key concepts
are understood may pave the way for scientific progress and
help us ask relevant questions [96]. For example, ecosystem
theory [90] and Vermeij’s [15,47] escalation hypothesis make
different predictions regarding the effects of mutualistic interactions in stabilizing ecosystems. These predictions can be
tested, given that reliable estimates of the prevalence of mutualisms can be obtained for ecosystems that either survived
or collapsed.
As we continue to study the effects of biotic (and abiotic)
forces on macroevolutionary change, we must be mindful of
the following:
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
45. Gould SJ. 2002 The structure of evolutionary theory.
Cambridge, MA: Harvard University Press.
46. Roopnarine P. 2011 Red queen for a day: models of
symmetry and selection in paleoecology. Evol. Ecol.
26, 1 –10. (doi:10.1007/s10682-011-9494-6)
47. Vermeij GJ. 2013 On escalation. Annu. Rev. Earth
Planet. Sci. 41, 1– 19. (doi:10.1146/annurev-earth050212-124123)
48. May RM. 1973 Stability and complexity in model
ecosystems. Monogr. Popul. Biol. 6, 1–235.
49. Schluter D. 2000 The ecology of adaptive radiation.
New York, NY: Oxford University Press.
50. Hadid Y et al. 2012 Is evolution of blind mole rats
determined by climate oscillations? PLoS ONE 7,
e30043. (doi:10.1371/journal.pone.0030043)
51. Alroy J. 2003 Cenozoic bolide impacts and biotic
change in North American mammals. Astrobiology
3, 119 –132. (doi:10.1089/153110703321632462)
52. Aberhan M, Kiessling W, Fürsich FT. 2006 Testing
the role of biological interactions in the evolution
of mid-Mesozoic marine benthic ecosystems.
Paleobiology 32, 259– 277. (doi:10.1666/05028.1)
53. Ezard THG, Aze T, Pearson PN, Purvis A. 2011
Interplay between changing climate and species’
ecology drives macroevolutionary dynamics. Science
332, 349–351. (doi:10.1126/science.1203060)
54. Mayhew PJ, Bell MA, Benton TG, McGowan AJ. 2012
Biodiversity tracks temperature over time. Proc. Natl
Acad. Sci. USA 109, 15 141– 15 145. (doi:10.1073/
pnas.1200844109)
55. Meachen JA, Samuels JX. 2012 Evolution in coyotes
(Canis latrans) in response to the megafaunal
extinctions. Proc. Natl Acad. Sci. USA 109,
4191– 4196. (doi:10.1073/pnas.1113788109)
56. Levinton JS. 2001 Genetics, paleontology, and
macroevolution, 2nd edn. Cambridge, UK:
Cambridge University Press.
57. Reznick DN, Ricklefs RE. 2009 Darwin’s bridge
between microevolution and macroevolution. Nature
457, 837–842. (doi:10.1038/nature07894)
58. Mayr E. 1963 Animal species and evolution. London,
UK: Oxford University Press.
59. Bush AM, Bambach RK. 2011 Paleoecologic
megatrends in marine metazoa. Annu. Rev. Earth
Planet. Sci. 39, 241– 269. (doi:10.1146/annurevearth-040809-152556)
60. Wood R, Zhuravlev AY. 2012 Escalation and
ecological selectively of mineralogy in the Cambrian
Radiation of skeletons. Earth Sci. Rev. 115,
249–261. (doi:10.1016/j.earscirev.2012.10.002)
61. Rensch B. 1959 Evolution above the species level.
New York, NY: Columbia University Press.
62. Sheets HD, Mitchell CE. 2001 Why the null matters:
statistical tests, random walks and evolution.
Genetica 8, 105– 125. (doi:10.1007/978-94-0100585-2_8)
63. Hunt G. 2006 Fitting and comparing models of
phyletic evolution: random walks and beyond.
Paleobiology 32, 578– 601. (doi:10.1666/05070.1)
64. Hunt G, Rabosky DL. 2014 Phenotypic evolution in
fossil species: pattern and process. Annu. Rev. Earth
Planet. Sci. 42, 421– 441. (doi:10.1146/annurevearth-040809-152524)
8
Proc. R. Soc. B 282: 20150186
29. Hoffman A, Kitchell JA. 1984 Evolution in a pelagic
planktic system: a paleobiologic test of models of
multispecies evolution. Paleobiology 10, 9 –33.
30. Stenseth NC. 1985 Darwinian evolution in
ecosystems: the Red Queen view. In Evolution:
essays in honour of John Maynard Smith (eds
PJ Greenwood et al.), pp. 55 –72. Cambridge, UK:
Cambridge University Press.
31. Hoffman A. 1991 Testing the Red Queen hypothesis.
J. Evol. Biol. 4, 1– 7. (doi:10.1046/j.1420-9101.
1991.4010001.x)
32. Vermeij GJ, Roopnarine PD. 2013 Reining in the Red
Queen: the dynamics of adaptation and extinction
reexamined. Paleobiology 39, 560–575. (doi:10.
1666/13009)
33. Marrow P, Law R, Cannings C. 1992 The coevolution
of predator– prey interactions: ESSs and Red Queen
dynamics. Proc. R. Soc. Lond. B 250, 133–141.
(doi:10.1098/rspb.1992.0141)
34. Law R, Marrow P, Dieckmann U. 1997 On evolution
under asymmetric competition. Evol. Ecol. 11,
485 –501. (doi:10.1023/A:1018441108982)
35. Kisdi É, Jacobs FJA, Geritz SAH. 2002 Red Queen
evolution by cycles of evolutionary branching and
extinction. Selection 2, 161–176. (doi:10.1556/
Select.2.2001.1-2.12)
36. Ito HC, Ikegami T. 2006 Food-web formation with
recursive evolutionary branching. J. Theor. Biol. 238,
1 –10. (doi:10.1016/j.jtbi.2005.05.003)
37. Dieckmann U, Brännström Å, HilleRisLambers R, Ito
HC. 2007 The adaptive dynamics of community
structure. In Mathematics for ecology and
environmental sciences (eds Y Takeuchi, K Sato,
Y Iwasa), pp. 145–177. Berlin, Germany: Springer.
38. Ito HC, Shimada M, Ikegami T. 2009 Coevolutionary
dynamics of adaptive radiation for food-web
development. Popul. Ecol. 51, 65– 81. (doi:10.1007/
s10144-008-0113-5)
39. Takahashi D, Brännström Å, Mazzucco R, Yamauchi
A, Dieckmann U. 2013 Abrupt community
transitions and cyclic evolutionary dynamics in
complex food webs. J. Theor. Biol. 337, 181– 189.
(doi:10.1016/j.jtbi.2013.08.003)
40. Dieckmann U, Marrow P, Law R. 1995 Evolutionary
cycling in predator–prey interactions: population
dynamics and the red queen. J. Theor. Biol. 176,
91 –102. (doi:10.1006/jtbi.1995.0179)
41. Rosenzweig ML, Brown JS, Vincent TL. 1987 Red
Queens and ESS: the coevolution of evolutionary rates.
Evol. Ecol. 1, 59–94. (doi:10.1007/BF02067269)
42. Abrams PA. 2001 Modelling the adaptive dynamics
of traits involved in inter- and intraspecific
interactions: an assessment of three methods. Ecol.
Lett. 4, 166– 175. (doi:10.1046/j.1461-0248.2001.
00199.x)
43. Allhoff KT, Ritterskamp D, Rall BC, Drossel B, Guill C.
2014 Evolutionary food web model based on body
masses gives realistic networks with permanent
species turnover. See http://arxiv.org/abs/1409.3373.
44. Allhoff KT, Drossel B. 2013 When do evolutionary
food web models generate complex networks?
J. Theor. Biol. 334, 122–129. (doi:10.1016/j.jtbi.
2013.06.008)
rspb.royalsocietypublishing.org
12. Alvarez LW, Alvarez W, Asaro F, Michel HV. 1980
Extraterrestrial cause for the Cretaceous–Tertiary
extinction. Science 208, 1095 –1108. (doi:10.1126/
science.208.4448.1095)
13. van Dam JA et al. 2006 Long-period astronomical
forcing of mammal turnover. Nature 443, 687–691.
(doi:10.1038/nature05163)
14. Faith JT, Behrensmeyer AK. 2013 Climate change
and faunal turnover: testing the mechanics of the
turnover-pulse hypothesis with South African fossil
data. Paleobiology 39, 609–627. (doi:10.1666/
12043)
15. Vermeij GJ. 1987 Evolution and escalation: an
ecological history of life. Princeton, NJ: Princeton
University Press.
16. Sepkoski Jr JJ. 1996 Competition in macroevolution:
the double wedge revisited. In Evolutionary
paleobiology (eds D Jablonski, DH Erwin, JH Lipps),
pp. 211 –255. Chicago, IL: University of Chicago
Press.
17. Jablonski D. 2008 Biotic interactions and
macroevolution: extensions and mismatches across
scales and levels. Evolution 62, 715 –739. (doi:10.
1111/j.1558-5646.2008.00317.x)
18. Alroy J. 2010 Geographical, environmental and
intrinsic biotic controls on Phanerozoic marine
diversification. Palaeontology 53, 1211 –1235.
(doi:10.1111/j.1475-4983.2010.01011.x)
19. Sepkoski Jr JJ, McKinney FK, Lidgard S. 2000
Competitive displacement among post-paleozoic
cyclostome and cheilostome bryozoans. Paleobiology
26, 7–18. (doi:10.1666/0094-8373(2000)
026,0007:CDAPPC.2.0.CO;2)
20. Lupia R, Lidgard S, Crane PR. 1999 Comparing
palynological abundance and diversity:
implications for biotic replacement during the
cretaceous angiosperm radiation. Paleobiology
25, 305–340.
21. Rabosky DL. 2013 Diversity-dependence, ecological
speciation, and the role of competition in
macroevolution. Annu. Rev. Ecol. Evol. Syst. 44,
481–502. (doi:10.1146/annurev-ecolsys-110512135800)
22. Liow LH, Van Valen L, Stenseth NC. 2011 Red
Queen: from populations to taxa and communities.
Trends Ecol. Evol. 26, 349 –358. (doi:10.1016/j.tree.
2011.03.016)
23. Darwin CR. 1859 On the origin of species. London,
UK: John Murray.
24. Fisher RA. 1930 The genetical theory of natural
selection, 1st edn. Oxford, UK: Clarendon Press.
25. Maynard Smith J. 1976 A comment on the Red
Queen. Am. Nat. 110, 325–330. (doi:10.1086/
283070)
26. Stenseth NC, Maynard Smith J. 1984 Coevolution in
ecosystems: Red Queen evolution or stasis. Evolution
38, 870–880. (doi:10.2307/2408397)
27. Sepkoski Jr JJ. 1975 Stratigraphic biases in the
analysis of taxonomic survivorship. Paleobiology 1,
343–355.
28. Raup DM. 1975 Taxonomic survivorship curves and
Van Valen’s Law. Paleobiology 1, 82– 96. (doi:10.
2307/2400136)
Downloaded from http://rspb.royalsocietypublishing.org/ on April 29, 2017
76.
77.
78.
80.
81.
82.
83.
84.
85.
86.
87. Elton CS. 1958 The ecology of invasions by animals
and plants. Chicago, IL: University of Chicago Press.
88. Tilman D, Downing JA. 1994 Biodiversity and
stability in grasslands. Nature 367, 363 –365.
(doi:10.1038/367363a0)
89. Kiessling W. 2005 Long-term relationships
between ecological stability and biodiversity in
Phanerozoic reefs. Nature 433, 410 –413. (doi:10.
1038/nature03152)
90. May RM. 2009 Food-web assembly and collapse:
mathematical models and implications for
conservation. Phil. Trans. R. Soc. B 364,
1643– 1646. (doi:10.1098/rstb.2008.0280)
91. Stouffer DB, Bascompte J. 2011
Compartmentalization increases food-web
persistence. Proc. Natl Acad. Sci. USA 108,
3648– 3652. (doi:10.1073/pnas.1014353108)
92. Allesina S, Tang S. 2012 Stability criteria for
complex ecosystems. Nature 483, 205–208.
(doi:10.1038/nature10832)
93. Potts SG, Biesmeijer JC, Kremen C, Neumann P,
Schweiger O, Kunin WE. 2010 Global pollinator
declines: trends, impacts and drivers. Trends
Ecol. Evol. 25, 345–353. (doi:10.1016/j.tree.2010.
01.007)
94. Skerratt LF, Berger L, Speare R, Cashins S, McDonald
KR, Phillott AD, Hines HB, Kenyon N. 2007 Spread
of chytridiomycosis has caused the rapid global
decline and extinction of frogs. EcoHealth 4,
125–134. (doi:10.1007/s10393-007-0093-5)
95. Bengtsson J. 1989 Interspecific competition
increases local extinction rate in a metapopulation
system. Nature 340, 713–715. (doi:10.1038/
340713a0)
96. Pimm SL. 1984 The complexity and stability of
ecosystems. Nature 307, 321 –326. (doi:10.1038/
307321a0)
9
Proc. R. Soc. B 282: 20150186
79.
Bioessays 34, 811–818. (doi:10.1002/bies.
201200054)
Bonduriansky R, Chenoweth SF. 2009 Intralocus
sexual conflict. Trends Ecol. Evol. 24, 280–288.
(doi:10.1016/j.tree.2008.12.005)
Hori M. 1993 Frequency-dependent natural selection in
the handedness of scale-eating cichlid fish. Science 260,
216–219. (doi:10.1126/science.260.5105.216)
Svensson EI, Friberg M. 2007 Selective predation on
wing morphology in sympatric damselflies. Am. Nat.
170, 101 –112. (doi:10.1086/518181)
Lenormand T. 2002 Gene flow and the limits to
natural selection. Trends Ecol. Evol. 17, 183–189.
(doi:10.1016/S0169-5347(02)02497-7)
Wagner A. 2011 The origins of evolutionary
innovations: a theory of transformative change in
living systems. Oxford, UK: Oxford University Press.
Champagnat N, Ferrière R, Méléard S. 2006 Unifying
evolutionary dynamics: from individual stochastic
processes to macroscopic models. Theor. Popul. Biol.
69, 297– 321. (doi:10.1016/j.tpb.2005.10.004)
Simpson GG. 1944 Tempo and mode in evolution.
New York, NY: Columbia University Press.
Gingerich PD. 2001 Rates of evolution on the time
scale of the evolutionary process. Genetica 112 –
113, 127 –144. (doi:10.1023/A:1013311015886)
Hunt G. 2012 Measuring rates of phenotypic
evolution and the inseparability of tempo and
mode. Paleobiology 38, 351 –373. (doi:10.1666/
11047.1)
Vermeij GJ. 1994 The evolutionary interaction
among species: selection, escalation, and
coevolution. Annu. Rev. Ecol. Evol. Syst. 25,
219 –236. (doi:10.1146/annurev.ecolsys.25.1.219)
MacArthur R. 1955 Fluctuations of animal
populations and a measure of community stability.
Ecology 36, 533 –536. (doi:10.2307/1929601)
rspb.royalsocietypublishing.org
65. Houle D. 1992 Comparing evolvability and
variability of quantitative traits. Genetics 130,
195–204.
66. Hansen TF, Pélabon C, Houle D. 2011 Heritability is
not evolvability. Evol. Biol. 38, 258–277. (doi:10.
1007/s11692-011-9127-6)
67. Hereford J, Hansen TF, Houle D. 2004 Comparing
strengths of directional selection: how strong is strong?
Evolution 58, 2133–2143. (doi:10.1554/04-147)
68. Wake W. 1983 On the problem of stasis in
organismal evolution. J. Theor. Biol. 101, 211–224.
(doi:10.1016/0022-5193(83)90335-1)
69. Estes S, Arnold SJ. 2007 Resolving the paradox of
stasis: models with stabilizing selection explain
evolutionary divergence on all timescales. Am. Nat.
169, 227–244. (doi:10.1086/510633)
70. Uyeda JC, Hansen TF, Arnold SJ, Pienaar J. 2011 The
million-year wait for macroevolutionary bursts. Proc.
Natl Acad. Sci. USA 108, 15 908–15 913. (doi:10.
1073/pnas.1014503108)
71. Arnold SJ. 2014 Phenotypic evolution: the ongoing
synthesis. Am. Nat. 183, 729 –746. (doi:10.1086/
675304)
72. Balanyá J, Oller JM, Huey RB, Gilchrist GW, Serra L.
2006 Global genetic change tracks global climate
warming in Drosophila subobscura. Science 313,
1773–1775. (doi:10.1126/science.1131002)
73. Umina PA, Weeks AR, Kearney MR, McKechnie SW,
Hoffmann AA. 2005 A rapid shift in a classic clinal
pattern in Drosophila reflecting climate change. Science
308, 691–693. (doi:10.1126/science.1109523)
74. Bradshaw WE, Holzapfel CM. 2001 Genetic shift
in photoperiodic response correlated with global
warming. Proc. Natl Acad. Sci. USA 98, 14 509–
14 511. (doi:10.1073/pnas.241391498)
75. Merilä J. 2012 Evolution in response to climate
change: in pursuit of the missing evidence.