Discussion
Predicting range expansions with ecology and evolution occurring on the
same timescale is a challenging task. Building on previous ecological
range expansion studies (2, 3), we included short-term evolution from
standing genetic variation in a simple model parameterised for our
laboratory system and confronted predicted evolutionary outcomes with
results from experimental range expansions. Both model and experiment
show rapid divergence between range core and front treatments, with
selection for higher dispersal at the front. The repeated fixation of
particular COI genotypes in the experimental lines corresponded to
strains identified as most likely winners in the model. This match
between predicted and observed outcomes suggest a certain predictability
of range expansions, even when evolutionary change occurs. Over longer
time scales, experimental range core and front populations continued to
diverge, indicating de novo evolution and resulting in the emergence of
dispersal syndromes.
Dispersal and growth rate are main targets of selectionIn the context of reaction-diffusion models, dispersal (diffusion) and
population growth at low densities are the two key traits for
understanding and predicting range expansion dynamics (14, 15).
Consistent with this view and previous studies (13, 33), dispersal and
population growth were here identified as main targets of selection.
Higher dispersal was immediately selected from standing genetic
variation at the range front and weakly selected against in the range
core in the model as well as in the experiment, where range front
populations showed increased dispersal already after the first few
cycles. Such strong and fast selection on dispersal in the vanguard
front populations has been found in similar experiments (5, 25–29), but
also in natural populations (4, 22). Dispersal evolution might therefore
accelerate the speed of range expansion already over very short time
scales (21).
Contrary to more standard views of range expansion with r- and
K-selection (17, 21), growth rate was under positive short-term
selection in both range core and front treatments. This can be explained
by the fact that populations in all treatments experienced regular
bottlenecks, thus imposing general selection for increased growth rate,
a trait for which there was ample variation among founder strains (SI
Appendix, Fig. S4). Importantly, however, our model shows that dispersal
and growth rate can be simultaneously selected in the range front
treatment (Fig. 3). Whether one or the other trait has more weight
depends on the stochasticity introduced by the dispersal bottlenecks,
implemented in the model via the quasi-extinction threshold. With small
bottlenecks, even weak dispersers make it into the new patch and can
subsequently regrow to high density. Indeed, additional model scenarios
show that, when we decrease the quasi-extinction threshold, selection
for growth rate overrides selection for dispersal and the strain with
the highest growth rate becomes fixed in all treatments (SI Appendix,
Figs. S8.1-S8.3). However, the model scenario that fits the observed
data indicates a large enough extinction threshold in our experiment,
putting equal selective weight on dispersal and growth rate (Fig. 2) and
allowing selection to pick the best possible disperser strain that still
has a high growth rate.
Predictability of outcomesGenetic analysis indicates that the experimental lines became fixed for
single COI genotypes. Despite limited resolution (several strains have
the same COI), there is a good correspondence with model predictions:
Range core and control treatments were fixed for the b05 COI genotype,
and the two b05 strains in the founder population were the most likely
winners in the model, due to their particularly high growth rate (Table
S1). In the range front treatment, there is more uncertainty (12 strains
carry the b07 genotype fixed in this treatment), but among the possible
candidates only the predicted most likely winning strain (goe_14) has
both high dispersal and high growth rate (Fig. 3). Additional sequencing
would be required to determine whether these lines are fixed for the
same or different (combinations of) strains.
Although our model seems to correctly identify the most likely winner
strains, it nonetheless predicts the frequent fixation of strains with
alternative COI genotypes (Fig. 3). Indeed, according to the model, our
exclusive finding of b05 strains in all 9 range core and control lines
is highly unlikely (0.289 < 0.0001).
Similarly, even in the front treatment, the expected probability of
exclusive fixation of b07 strain(s) is well below 5%
(0.486 = 0.01). In this sense, our experiment was more
deterministic than the model. Possibly, when we determined dispersal and
growth of the individual strains, a large measurement error was added to
biologically relevant variation (Fig. S4). This additional noise then
cascades through the model, from the strain posterior distributions
(making them wider) to the phenotypic composition of the founder
population (making strains more similar) to model outcomes (making them
more variable). Alternatively, our model may be missing additional
factors, such as direct strain-strain interactions or density-dependent
dispersal, which potentially amplify among-strain variation in
performance. In addition, our experiment allowed relatively long periods
(6 days) of population build-up in-between dispersal events, thus
resembling a pushed-wave expansion scenario (36). Under such conditions,
advancing range fronts are fuelled by dispersal from large populations.
This prevents drift from becoming too important and can therefore
explain the highly repeatable outcomes observed here for independent
replicates of the same treatment.
Regardless, one main conclusion from this model-experiment confrontation
is that evolution from standing genetic variation can be fairly
predictable, at least in the short term. As already shown for ecological
models (3), realistic predictions can indeed be made about range
expansion dynamics, at least in highly controlled laboratory settings.
Here we infer trait change from knowledge of standing genetic variation
in only a few parameters, suggesting that such models can be readily
extended to include evolution.
Long term evolution of dispersal syndromes and emergence of
trade-offsExperimental evolution studies show that adaptation to novel conditions
may reduce performance in other environments (34). The emergence of such
trade-offs depends on underlying biochemical and life-history
constraints (35), but also on historical contingency, determining the
composition and genetic architecture of the ancestral population, and
thus the available trait space for selection. Generally, natural
populations present high levels of standing genetic variation, on which
selection can act and produce rapid adaptation (36–40). Our case
mimicked this situation and generated short-term response to selection,
but no clear signs of trade-offs. In the long run, however, range front
and core populations continued to diverge in multiple traits (Fig. 4D),
and the increase in dispersal in the front treatment was associated with
a decrease in growth rate (Fig. 4A). Such coupled responses in dispersal
and life-history traits are referred to as dispersal syndrome (8, 9).
Typically, they involve the emergence of a competition-colonisation
trade-off, where dispersal evolution coincides with selection for
opportunistic growth strategies (r-selection). Theoretical and empirical
studies have demonstrated the importance of dispersal syndromes in
generating eco-evolutionary feedbacks and accelerating the pace of range
expansions and biological invasions (4, 17, 20, 21). Dispersal - growth
trade-offs were previously reported for this (41) and another ciliate
species (25). In these systems, growth rate is a good indicator of
competitive ability, and the trade-off with dispersal likely reflects a
true life-history constraint, mediated through energy costs of foraging
activity (25). The evolved differences between core and front lines are
stable, even after switching core and front treatments for multiple
cycles (SI Appendix, Fig. S6.1). Moreover, mixes of core and front lines
readily respond to dispersal selection (SI Appendix, Fig. S6.2), making
new evolutionary experiments possible, where phenotypic measurements
change can easily be combined with the tracking of COI genotype
frequencies.
Advantages and limitations of an asexual reproduction
scenarioIn this study, we consider asexual reproduction in both model and
experiment. Hence advantageous allele combinations are not broken apart
or reshuffled by sex and recombination (42, 43), such that strains with
favourable trait combinations rapidly increase in frequency in our range
and core treatments. Similar results were reported for experimental
range expansions of the plant Arabidopsis thaliana , where the
fastest-dispersing clonal genotype became predominant in multiple
replicate lines, all starting from the same initial mix of clones (5).
Thus, asexual reproduction narrows down the variability in the range
expansion outcomes and, as we show here, makes predictions possible with
relatively simple models. Clearly, recombination will make predictions
more difficult, and replicated range expansion experiments with sexually
reproducing organisms already showed higher variability and uncertainty
in final outcomes (26, 28, 29). For example, recombination may slow down
range expansions in the short term, but speed up longer-term responses
by creating novel trait associations not previously available. In our
system, sex may have immediate and strong fitness consequences due to
the nuclear dimorphism typical of all ciliates. Aside from creating
novel genetic variants (in the germline micronucleus), sexual
reproduction also involves the recreation of a new somatic macronucleus
and thereby the loss of any (somatic) adaptation acquired during asexual
life (44).
ConclusionsPredicting evolution is arduous because of the intrinsic tension between
determinism and contingency (45), and it demands an adequate theoretical
representation of the eco-evolutionary processes in the biological
system in question and reliable information on the genetic variation in
the relevant traits (31), as we describe in this work. At least in
simple settings as ours, accurate predictions of the evolutionary
outcomes of range expansions require surprisingly few parameters, and
independent biological realisations can be highly repeatable. Future
studies will need to consider, for example, more realistic landscape
scenarios and interactions with other species occurring during range
expansion. This would imply a more systems-biology approach, with
simulations calibrated on the empirical knowledge of the specific
ecological scenario and biological players (46, 47). More generally,
increasing our capacities to make reliable quantitative predictions of
invasive eco-evolutionary processes is critical to a variety of issues,
from conservation and biocontrol strategies to antibiotic development
and disease management.