Limited evolutionary rescue of locally adapted populations facing climate change.

2013·0.68 MB·English

by  Schiffers, Katja

Limited evolutionary rescue of
locally adapted populations facing
climate change
Katja Schiffers1, Elizabeth C. Bourne2,3,4, Se´bastien Lavergne1,
rstb.royalsocietypublishing.org Wilfried Thuiller1 and Justin M. J. Travis2
1Laboratoired’Ecologie Alpine, Universite´ Joseph Fourier,Grenoble 1, UMR-CNRS5553, BP53,
38041Grenoble Cedex 9,France
2Institute ofBiologicaland EnvironmentalSciences, UniversityofAberdeen,Zoology Building,
TillydroneAvenue, Aberdeen AB242TZ,UK
Research
3TheJames Hutton Institute, Craigiebuckler, AberdeenAB158QH,UK
4Institut fu¨r Biologie—Botanik, FreieUniversita¨t Berlin, Altensteinstrasse6,14195Berlin, Germany
Cite this article: Schiffers K, Bourne EC,
Lavergne S, Thuiller W, Travis JMJ. 2012 Dispersal is a key determinant of a population’s evolutionary potential. It
Limited evolutionary rescue of locally adapted facilitatesthe propagation of beneficial alleles throughout the distributional
range of spatially outspread populations and increases the speed of adap-
populations facing climate change. Phil
tation. However, when habitat is heterogeneous and individuals are locally
Trans R Soc B 368: 20120083.
adapted,dispersal may, atthesametime, reduce fitnessthrough increasing
http://dx.doi.org/10.1098/rstb.2012.0083
maladaptation. Here, we use a spatiallyexplicit, allelic simulation model to
quantify how these equivocal effects of dispersal affect a population’s evo-
One contribution of 15 to a Theme Issue lutionary response to changing climate. Individuals carry a diploid set of
chromosomes, with alleles coding for adaptation to non-climatic environ-
‘Evolutionary rescue in changing
mental conditions and climatic conditions, respectively. Our model results
environments’.
demonstratethattheinterplaybetweengeneflowandhabitatheterogeneity
may decrease effective dispersal and population size to such an extent that
Subject Areas: substantially reduces the likelihood of evolutionary rescue. Importantly,
evolution evenwhenevolutionaryrescuesavesapopulationfromextinction,itsspatial
rangefollowingclimatechangemaybestronglynarrowed,thatis,therescueis
only partial. These findings emphasize that neglecting the impact of non-
Keywords:
climatic, local adaptation might lead to a considerable overestimation of a
allelic model, dispersal, gene flow, habitat population’sevolvabilityunderrapidenvironmentalchange.
heterogeneity,migrationload,rapidadaptation
Author for correspondence:
1. Introduction
Katja Schiffers
e-mail: [email protected] Facing one of the most drastic global changes in the Earth’s history, a funda-
mental objective of current ecological and evolutionary research is to
understand and predict species’ responses to changing environmental con-
ditions [1]. Three key types of response may ameliorate the threat of
extinction:bufferingagainstnegativeeffectsofdeterioratinghabitatbypheno-
typicplasticity[2–5],trackingsuitableclimatethroughrangeshifting[6,7]and
adaptingtochangingconditionsbyrapidevolution[8,9].Someauthorssuggest
thatmostspecieswillmorelikelyshifttheirdistributionalrangesorrespondby
phenotypicplasticityratherthanadaptinsitutonewconditions[6,10].Thisis
mainly because plasticity and range shifting may be substantially faster in
matching phenotypic preferences with environmental conditions than evo-
lutionary processes. Nonetheless, a number of species have been shown to
adapt with remarkable rapidity in response to environmental change [11,12],
and numerous studies have identified heritable population differentiation in
ecologically relevant traits, providing indirect evidence for the potential of
adaptive evolution over ecological time-scales [8,13,14]. It thus seems impera-
Electronic supplementary material is available
tive to consider the role of evolutionary rescue—the phenomenon of once
at http://dx.doi.org/10.1098/rstb.2012.0083 or
declining populations evolving back to positive growth by evolutionary
via http://rstb.royalsocietypublishing.org.
&
2012TheAuthors.PublishedbytheRoyalSocietyunderthetermsoftheCreativeCommonsAttribution
License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original
authorandsourcearecredited.
adaptation—in assessments of the likely impacts of global fromthosethatundergotemporalchanges.Asimpleexample 2
changeon species abundance,distribution andpersistence. canillustratethisstatement.Manyplantpopulationsarelocally
The theoretical foundations of adaptive dynamics have adaptedtovaryingabioticconditions(e.g.edaphicfactors)or rstb
been established over the past decades by a growing biotic context (e.g. presence/absence of herbivores), but the .ro
y
numberofstudiesinthefieldsofpopulationandquantitative mosaic of this local adaptation will mostly be decoupled als
o
genetics. A key theorem states that the rate of adaptation is from currently changing climatic gradients. Under these cir- c
ie
predominantly driven by the amount of available additive cumstances,thecentralquestionis:howdothecontradictory ty
p
u
genetic variance and the strength of environmental selec- effects of dispersal influence the evolutionary response of b
lis
tion [15,16]. In principle, given sufficient genetic variance, populationstoenvironmentalchange? h
in
populations should adapt to virtually any environmental Inthisstudy,weaddresstheabovequestionbyintegrat- g.o
condition [17]. However, under natural conditions, an ing the key processes that have until now typically been rg
often-complex interaction between demographic processes studiedseparately:theroleofdispersalasthemechanismdis-
P
h
and evolutionary dynamics may result in failure of adap- tributing adapted alleles across populations and the il
T
tation and ultimate extinction of the population [18–20]. To feedbacks between dispersal and local adaptation. We do ra
n
s
gain initial insights into such interactive processes, synthetic thiswithinthecontextofanallelicmodel,wherepopulation R
approaches linking genetics with population demography genetics are coupled with population ecology by condition- So
c
are being applied increasingly frequently, addressing ques- ing demographic rates on the match of genetically variable B
3
tions on, e.g. the formation of species range edges [21–23] traits to environmental characteristics. We use our model to 6
8
:
andinvasiondynamics[24,25],includinginvasiondynamics examine how the interplay between dispersal and local 2
0
1
inheterogeneous landscapes [26,27]. adaptationacrossspatiallyheterogeneoushabitatsinfluences 2
0
0
Inthecontextofeco-evolutionaryprocesses,dispersalisa the probability of evolutionary rescue of populations facing 8
3
keydeterminantofpopulationdynamics,owingtoitsimpact changing climatic conditions. We also examine how the
on both spatial demography [28,29] and the speed of local geneticarchitectureofadaptivetraitsmodulatesthisinterplay.
adaptation [29–31]. As a consequence, dispersal is likely
to be crucial for evolutionary rescue. The main responsible
mechanismisthespreadingofnewlyarising,beneficialalleles 2. The model
throughout a population’s distributional range [32–35]. In a
recent study, Bell & Gonzalez [35] empirically tested these We developed an allelic, spatially explicit and individual-
theoretical predictions with an experiment on bakers yeast: based simulation model to investigate the interactive effects
they demonstrated that spatially structured populations had of gene flow and local adaptation on the evolutionary
asignificantlyhigherchanceofsurvivingaperiodofdeterior- response of populations to environmental change. The full
atinggrowthconditionsandadaptingtothenewstate,when source code and an accompanying readme file are available
dispersalallowedforgeneflowacrosssubpopulations. as electronic supplementary material, and a maintained ver-
In contrast to its beneficial effect for rapid adaptation sion of the model is downloadable from http://www.katja-
under temporally changing conditions, dispersal is known schiffers.eu/docs/allele_model.zip.
to have an overall negative influence on population fitness Themodelorganismwehadinmindduringimplementa-
undermostscenariosoflocaladaptation[36,37].Inaspatially tion was a bisexual, annual plant species with xenogamous
heterogeneous environment, mismatches between immi- breeding system. Population dynamics take place within a
grants’ genotypes and the environmental conditions at their continuous region of 32(cid:2)32 grid cells. To avoid arbitrary
destination locations result in a reduction in overall fitness, edge effects, the area is simulated as a torus, i.e. the edges
termedmigrationload.Withanalyticalpredictionsandindi- of both axes are joined. Grid cells are characterized by two
vidual-based simulations, Lopez et al. [37] have illustrated environmentalconditions: (i) localenvironmental conditions
how, under gene flow through both pollen and seed move- such as edaphic parameters or particular biotic settings,
ment, migration load increases with the degree of habitat which follow a fractal distribution and are stable over time;
heterogeneity. In a further theoretical study, Alleaume- and(ii)climaticconditions,e.g.maximalannualtemperature,
Benhariraetal.[38]demonstratedthatinapatchypopulation, whichchangeduringthesimulatedperiod.Forsimplicity,we
distributed across an environmental gradient, intermediate assume climate to be homogeneous across space. Each grid
rates of dispersal optimized fitness. This was the result of a cell can support a number of individuals, the maximal
trade-off between some dispersal having benefits in terms number given by the local carrying capacity, which is con-
of purging deleterious alleles, especially from smaller mar- stant across the region. Individuals are diploid, carrying
ginal populations, and increasing dispersal resulting in two copies of eitherone or several chromosomes coding for
higher migration load owing to gene flow between patches an individual’s level of adaptation to climatic and local
of differinglocal conditions. environmentalconditions.Individualsarelocatedincontinu-
Clearly, adaptation to heterogeneous habitat and tem- ousspaceandareassignedtothegridcellwithinwhichtheir
poral changes in environmental conditions often occur x-and y-coordinatesfall.
hand in hand [39], confronting populations with multiple Withineachgeneration,thefollowingprocessesaresimu-
sources of potential maladaptation. However, the few exist- lated: (i)reproduction with mutation,recombination,gamete
ing studies investigating population responses to a spatially dispersal and subsequent death of the parental generation,
and temporally changing optimum focus predominantly on (ii)dispersaloftheoffspring,(iii)selectionactingonthesurvi-
a single environmental variable [40–42]. Such an approach val probabilities of the juveniles, and (iv) density-dependent
neglects a situation that is likely to be very common in nat- mortality. Selection takes the form of density-independent,
ural conditions, that is where the spatially heterogeneous hardselectionforanindividual’sadaptationtoboth climatic
conditions driving local adaptation of populations differ andlocalenvironmentalconditions.
(a) Genetic architecture To gain sufficient computational efficiency, we do not 3
explicitly simulate the dispersal of pollen. Instead, we use
Anumberofpreviousstudieshaveshownthattraitsaffecting
species’ adaptation, particularly to climatic conditions, are the following algorithm. As for offspring dispersal, rstb
usually polygenic. For example, 12 quantitative trait loci x/y-coordinates are chosen randomly in the neighbourhood .ro
y
have been identified for climatic adaptation in Arabidopsis ofthefocalindividual.Thematingpartneristhenrandomly als
o
drawn from all individuals inhabiting the grid cell within c
thaliana[43],33forbud-flush,nineforautumncoldhardiness ie
which the random position is located. In case the selected ty
and nine for spring cold hardiness in Pseudotsuga menziesii p
u
grid cell is empty, the procedure is repeated up to 99 times. b
[44](seealsoFalconer&Mackay[45]forageneraloverview). lis
If all trials are unsuccessful, we assume the ovule not to h
Onthebasisofthisinformation,wesimulatedgenomescom- in
posedofn¼15lociforeachofthetwoconsideredtraits.To be fertilized. g.o
Totestforpotentialundesirableeffectsofthissimplifica- rg
representtwocontrastingscenariosoflinkage,weconsidered
tion,wealsodevelopedimplementationofgametedispersal
thegenome tobe composedeitherofone or several pairsof P
h
chromosomes.Inthefirstcase,alllociaresituatedonasingle thatismorepreciseinthesenseoflinkingfertilizationprob- il
T
chromosome and, as we do not allow for crossovers during ability to the exact distance between individuals. For each ran
s
recombination,thelociarefullylinked.Effectively,thiscould individual of the population, the probability of fertilizing a R
also be considered a single locus with multiple alleles and specificovuleiscalculatedbasedontheinter-individualdis- Soc
tances and theshape of thedispersal kernel. Followingthat, B
pleiotropiceffects.Inthesecondcase,weassumetheopposite
3
the probabilityof no fertilization can be determined. Rescal- 6
possibleextremecaseofnolinkage.Thismaycorrespondtoa 8
:
ing all resulting probabilities so that they add up to unity 2
situation where a genome is made of 30 chromosome pairs 0
1
eachcarryingasinglelocus,implyingcompletelyindependent thenallowssamplingofthepollendonorbyadrawofauni- 20
0
inheritanceofalleles.Or,thismightmimicasituationwhereall form randomnumberbetweenzero andunity. Comparisons 83
betweenthetwoapproachesshowedthattherearenoobvious
thelociareonasingle(ormultiple)chromosome,butwithsuf-
differences at the level of evolutionary or demographic
ficient distance between the loci and sufficient frequency of
dynamics. We thus chose the former, computationally much
crossovereventsthattheyareeffectivelyunlinked.Allelesare
lessintensivemethod.
described by continuous values and are additive within and
between loci, i.e. neither epistatic nor pleiotropic effects are
considered. Individuals’ phenotypes are directly determined (iii) Selection
by their genotypes, that is, environmental effects on pheno-
Selection acts on population demography by modulating
types are neglected, and heritability is thus assumed to be
juvenilesurvivalprobability.Eachindividual’ssurvivalprob-
unity[21–23,46].
abilityWiscalculatedastheproductofitsconditionrelated
to climate W and its condition related to the local environ-
C
ment W . Both of these values, W , are functions of the
(b) Simulated processes E C,E
difference between the individual’s phenotype z , and
C,E
(i) Reproduction the optimal phenotype under the current climatic or local
environmental conditions Q . They follow a normal
Allindividualscanpotentiallybearoffspring.Thenumberof C,E
distribution with maximum unityandvariancev2 :
ovulesproducedbyeachindividualisdrawnfromaPoisson C,E
distribution with average R¼100. Whether, and by which " ðz (cid:3)Q Þ#
matingpartner,singleovulesarefertilizedismodelledstochas- WC;EðzÞ¼exp (cid:3) C;E2v2 C;E ;
C;E
tically with the probabilities of fertilization derived from the
individuals’ distances and the shape of the pollen dispersal wherev2istraditionallyreferredtoasselectionstrength,but
kernel(see§2b(ii)).Gametesarecomposedbyduplicatingpar- canalsobeinterpretedasameasureofthespecies’tolerance
entalchromosomes,oneofeachhomologouspairbeingchosen tosuboptimalconditions,i.e.itsnichebreadth[40].Here,for
randomly.Allelesmutatewithaprobabilityofm¼1027,which the default parameter settings, this value was fixed at 0.1,
representstheaverageratefoundfortheannualplantspecies resulting in W ,0.01 and thus in a negative population
C
Arabidopsis thaliana [47–49]. The mutational effect, i.e. the growth rate (given the average number of offspring per
amountbywhichtheallelicvalueischanged,isdrawnfrom individualis100)whentemperaturehaschangedbyapproxi-
a zero-mean normal distribution with variance a2¼0.2, mately18C,assumingthephenotypeisfixed.Inanumberof
approximatelyfittingempiricalobservations[47]. additionalruns,selectionstrengthwasreducedbyincreasing
v2to 0.2.
(ii) Dispersal
(iv) Density-dependent mortality
There are two phases of dispersal in each generation cycle:
We assume a simple ceiling form of density dependence
pollendispersalandoffspringdispersal.Botharecharacterized
(similar to [39,51]): whenever the number of individuals
bylognormal,isotropicdispersalkernelswithanaveragedis-
within a grid cell exceeds its carrying capacity, K, resident
tancedforbothgametesandoffspringandashapeparameter
individuals are subjected to a density-dependent mortality
of0.5.Thelognormaldistributionhasbeenfoundtoadequately
with probabilityof survival¼12K/N.
representbothlocalandlong-distancedispersal[50].
Offspring dispersal is simulated explicitly: dispersal dis-
(c) Simulations
tance and direction are chosen randomly with probabilities
followingtheshapeofthedispersalkernel.Theoffspringisposi- Simulationswererun totesttheinteractiveeffectsof disper-
tioned at the resulting x/y-coordinates, respecting torroidal sal, habitat heterogeneity and linkage on population
boundaryconditions. dynamics and the likelihood of survival under climate
Table 1. Parameter values for simulationruns. with no initial genetic variation showed that resulting 4
population parameters were not influenced by the chosen
parameter description values startingconditions(seetheelectronicsupplementarymaterial, rstb
figureS5).Forthemainanalysis,climatechangewassimulated .ro
y
V rate of climate shift twounits per 100 bykeepingthetemperatureconstantoverthefirst200gener- als
o
years ations and then gradually increasing it by 2.08C over the cie
following 100 time-steps. After this period of change, ty
H Hurstexponent 0.2 pu
the new climatic conditions were assumed to be stable b
h habitat 0,1,2,3, 4,5,6 lis
H untiltheendof500simulationyears. h
in
heterogeneity g
.o
K carrying capacityper 5 rg
grid cell 3. Results Ph
il
R mean numberof 100 In test runs without environmental change, population size, Tra
n
offspring average individual fitness and additive genetic variance sR
D mean dispersal 0.05,0.1, 0.2, 0.4,0.8, were stable over time, unless mean dispersal distances were So
c
too small to ensure a sufficient number of fertilized ovules B
distance 1.6, 3.2, 6.4 3
to keep growth rates higher than unity. When introducing a 6
8
d shape factor 0.5 :
shape shift in climate, population size started to decline at the 2
0
1
dispersal kernel point where the average individual phenotype lagged so 2
0
L linkage between fully linked, free far behind the optimum Q that W,1/R. In simulations 083
wherethemutationratemwassettozero,populationsinev-
loci recombination
itablydied,becausestandinggeneticvariationalonedidnot
M mutation rate per 1027
provideenoughscope forfulladaptationtonewconditions.
locus Withthedefaultvalueform¼1027,anaveragefamilysizeof
a2 variance of 0.2 100 and a carrying capacity around 5000 individuals,
mutations occurred on average once per generation and
mutational effect
locus. In combination with the given variance of the muta-
v2 selection strength 0.1,0.2 tional effect (a2)¼0.2 and a selection strength (v2)¼0.1,
allelic dynamics resulted in a slow disruption of the initial
normal distribution of allelic values (see the electronic sup-
change (see table 1 for model parameters). The average dis- plementary material, figures S1–S4) during periods of
persal distance d was set to 0.05 grid cell lengths for the stable climate. During phases of temperature rise, mainly
first set of simulations and then repeatedly doubled up to a thefixationofrare,largemutationscontributedtotheadap-
distance of 6.4. Habitat heterogeneity h was controlled by tation process to the new conditions (results not shown),
H
modifying the range of possible local environmental con- leading to punctuated phases of rapid evolution as, for
ditions from 0 units, i.e. no heterogeneity, to a maximum of example, describedinHoltet al. [39].
six units in steps of 1. For testing the effect of linkage, the Population responses to rapid climate change fitted into
two contrasting scenarios of complete versus no linkage three general classes, depending upon the values of some
were compared. The remaining model parameters were key model parameters. We first describe the three main cat-
keptconstantacrosssimulations.Forallpossible112combin- egories of response (figure 1), before providing some detail
ations of d, h and linkage, we ran 100 replicates, recording on howthe keyparametersinfluenced theoutcome.
H
population size over time and the population average and Complete evolutionary rescue occurred when there was a
variance of individuals’ survival probabilities, W , as a sufficient number of beneficial mutations and when they
C,E
measurefortheirconditions. were able to spread unhampered across the landscape. This
Landscapes were initialized with avalue of 258C for the class of response was typically characterized by an initial
climaticconditionsandaHurstexponentof0.2forthefractal phaseduringwhich,astheclimatebegantochange,individ-
distribution of local environmental conditions, their ampli- uals’survivalprobabilitiesdeclined.Subsequently,asoneor
tude being controlled by h . The values assigned to h more beneficial mutations occurred and spread across the
H H
resulted in average differences between neighbouring cells landscape, the average individual’s fitness increased,
of 0.03, 0.09, 0.15, 0.38, 0.9 and 1.46 units, respectively. The thetotalpopulationsizefullyrecoveredandultimatelyindi-
spatial population was initialized by colonizing each grid viduals’ phenotypes were a good match to the new climate
cell with three individuals. Individuals were, on average, conditions (figure 1a).
optimally adapted to both climatic and local environmental Partialevolutionaryrescueoccurredunderconditionswhere
conditions, but exhibited normally distributed additive gen- beneficialallelesarosebutwereunabletospreadowingtoinef-
etic variation with a within-cell variance of 0.01. This value fective gene flowacross space.In this classof response, only
corresponds approximately to the mutation–selection equi- fragments of what was previously fully occupied habitat
librium reached after 1000 generations in previous test runs were populated following climate change. This effective
under stable conditions (see the electronic supplementary reductioninthesuitablehabitatnicheforthepopulationsome-
material, figures S1–S4). It has to be noted that allelic timesresultedinsubstantiallyreducedtotalpopulationsizes
values typically did not follow normal distributions at the following climate change (figure 1b). Importantly, this effect
end of these runs, particularly when habitat heterogeneity waspersistent,lastinguntiltheendofsimulations,whichran
waslow.However,comparisonswithadditionalsimulations for200generationsafterclimatechangeceased.
(a) 1.5 5
1.0 rstb
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n 0.5 als
of adaptatio 0 ocietypublish
(b) evel 1.5 ing.o
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0 R
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si 3
(c) ulation 1.5 68:201
pop 1.0 2008
3
0.5
0
0 100 200 300 400 500
generations
Figure 1. Three example runs depicting (a) full rescue, (b) partial rescue, and (c) population extinction. Solid lines represent population size, short dashed lines
representthelevelofadaptationtoclimaticconditions, W,andlong dashedlines representthelevelofadaption,W,tolocal environmental conditions. Onthe
C E
right-hand side the density of individuals is shown after 500 time-steps with darker values indicating higher densities.
Extinction, due to the failure of evolutionary rescue, observed for intermediate values between 0.4 and 2 grid
occurred when the frequency of beneficial mutations was cell units. Within that range, the peak of rescue probability
too low. Under these conditions, individuals’ phenotypes depended on the level of habitat heterogeneity and shif-
rapidly became very poorly matched to the prevalent ted towards shorter dispersal distances with increasing
climatic conditions, resulting in lower offspring viability heterogeneity(figure5a–c).
and ultimatelyanon-viablepopulation (figure1c). With increasing spatial heterogeneity, there was also an
increased likelihood that, when rescue occurred, it was only
(a) Effects of dispersal and habitat heterogeneity partial.Thus,whilethepopulationhadatleastsomeprobability
of surviving climate change through evolutionary rescue, the
In accordance with our expectations based on previous
landscapewasnotfullyoccupiedafterclimatechangeandthe
studies[36,37],inaspatiallyheterogeneousenvironment,dis-
total population size was substantially reduced (figure 5).
persalgenerallyhadanegativeeffectonindividuals’levelsof
Under a heterogeneity of h ¼5, the average relative popu-
H
adaptationto environmental conditions W (figure2).
E lation size (of the surviving populations) at the end of the
Inscenariosoffulllinkage,thelevelofadaptationincreased
simulation time was, across a broad range of dispersal dis-
again for very high values of dispersal and heterogeneity
tances, reduced to an average of around 50 per cent of pre-
(figure 2a), owing to an increased mortality of strongly mala-
climate-changedensities(figure5c).Interestingly,theparameter
dapted individuals and consequently higher averages for the
valuesthatmaximizedtheprobabilityofrescuedidnotnecess-
survivingfractionofthepopulation(resultsnotshown).
arilyresultinamorecompleterescue.Forexample,whenh ¼
H
On the other hand, model results also confirmed the
5, there was the greatest probability of population survival
beneficial effect of dispersal on a population’s adaptation to
whendispersal¼0.4.Forthisscaleofdispersal,however,sur-
temporally changing conditions. This was demonstrated by
viving populations were reduced on average to roughly one-
increasing values of W with increasing dispersal distances
C sixth of their initial abundance. By contrast, when dispersal
(figure 3). However, this pattern appeared to be more
occurredacrossagreaterrange(e.g.dispersal¼2.5),thepopu-
sensitive to stochastic effects than results regarding the
lationssurvivedonly10percentofthetime,butthenrecovered
adaptationto local environmental conditions.
toanaverage50percentofinitialabundance.
The likelihood of evolutionary rescue was strongly
reduced oreven hindered fora range of dispersal distances,
(b) Effect of linkage
for which rapid adaptation would have been possible with-
out local adaptation (figure 4). Because both high dispersal Theassumptionsregardingtheformoflinkagehadastrong
distances,aswellasverylowdistances,decreasedtheprob- effect on the overall probability of evolutionary rescue.
ability of evolutionary rescue, highest survival rates were Independent inheritance allowed for much fasteradaptation
(a) (b) 6
6
1.00
rstb
5 .ro
0.95 ya
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ha 2 P
h
0.75 il
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ra
1 n
s
0.70 R
S
o
c
0 B
1 2 3 4 5 6 1 2 3 4 5 6 36
8
dispersal distance dispersal distance :2
0
1
Figure2.Average valuesfor thelevelofadaptationtolocal environmental conditions,WE, during thephase oftemperaturerisefor (a)full linkage and (b)free 200
recombinationofloci.Depictedaretheaveragevaluesover100replicatesforallcombinationsofhabitatheterogeneityh andaveragedispersaldistancesdingrid 8
H 3
cell length.
(a) (b)
6
0.60
5
0.55
y
eit 4
n 0.50
e
g
o
er 3
et 0.45
h
at
bit 2 0.40
a
h
0.35
1
0.30
0
1 2 3 4 5 6 1 2 3 4 5 6
dispersal distance dispersal distance
Figure3.Averagevaluesforthelevelofadaptationtoclimaticconditions,W,duringthephaseoftemperaturerisefor(a)fulllinkageand(b)freerecombination
C
of loci. Depicted are theaverage values over 100 replicates forall combinations of habitatheterogeneity h and average dispersal distances din grid cell length.
H
to both spatially (figure 2) and temporally changing con- extinction. Understanding the factors determining the
ditions (figure 3) so that the negative effect of local likelihoodthatpopulationsadaptsufficientlyrapidlytochan-
adaptation was strongly ameliorated (figure 4). However, ging environmental conditions is at the heart of research on
the overall pattern of intermediate dispersal distances evolutionaryrescue.
resulting in highest evolutionary potential was consistently Allelic simulation models, as used in this study, provide
observedforboth scenarios. an ideal tool for integrating the available knowledge on
eco-evolutionary dynamics from different organizational
levels and to reflect the complex nature of adaptive and
4. Discussion demographic processes. However, to date, most modelling
studies have been highly abstracted, for example, assuming
Global environmental change is confronting natural popu- unrealistically high mutation rates and panmictic popula-
lations simultaneously with rapid climate change and tions. Here, we have taken a first step towards quantitative
increasing habitat loss and deterioration. The combination predictions of population response to environmental change
of habitat fragmentation and limited dispersal will prevent by establishing an individual-based model that is both
many populations from tracking suitable climate in space. spatiallyandgeneticallyexplicit,andthat,asfaraspossible,
For these species, in situ adaptation to changing climate is has been parametrized realistically for both genetic and
likelytoprovidetheonlynaturalmeansofavoidingultimate demographic functions.
(a) (b) 7
6
1.0
rstb
5 .ro
y
a
0.8 ls
o
eity 4 ciety
n p
e u
g 0.6 b
ero 3 lish
et in
h g
bitat 2 0.4 .org
a
h P
h
il
1 0.2 Tra
n
s
R
S
0 0 oc
1 2 3 4 5 6 1 2 3 4 5 6 B
3
6
dispersal distance dispersal distance 8
:
2
Figure 4. Probabilityof full rescue depending on habitat heterogeneity hH and average dispersal distances d in grid cell length for (a) full linkage and (b) free 012
recombination of loci. Calculated from 100 simulation runs for each parameter combination. 00
8
3
(a)
0.8
0.4
e
z
n si 0
o
ati
(b) ul
p
o
p
e 0.8
v
ati
el
d r 0.4
n
a
e
u
c 0
s
e
(c) of r
y
bilit
a 0.8
b
o
pr
0.4
0
0 1 2 3 4 5 6
dispersal distance
Figure 5. Probability of evolutionary rescue (partial and full rescue, solid lines) and relative population sizes (population size at generation 500/K, extinctions
excluded, dashed lines) in dependence on dispersal distance d for habitat heterogeneities of (a) h ¼0, (b) h ¼3, and (c) h ¼5. Results are based on 100
H H H
simulation runs for full linkage of loci.
The initial results of our model presented within this Considering the effects of dispersal on local adaptation
paper demonstrate two potent key phenomena that we and environmental change separately, the results of our
consider important, particularly under ongoing habitat model concur with existing studies on each topic. Under
deterioration and fragmentation: first, the potentially com- habitat heterogeneity and local adaptation, dispersal typic-
plex effects of dispersal for a population’s evolutionary ally has negative consequences for the average fitness
response to both spatially heterogeneous habitats and [36,37]. Increased migration load—in our model output
shifting climate. And second, the possibility for partial reflectedbyreducedlevelsofadaptationtothelocalenviron-
evolutionaryrescue,wherebyrapidadaptationsavesapopu- ment—leadtohighermortalityratesandanincreasedriskof
lation from extinction, but both population size and its location extinction, hence a lower chance of rescue. On the
geographicalrangemaybe substantiallyreduced. other hand, as argued and shown recently by Bell &
Gonzalez [35], greater dispersal can be strongly beneficial, stabilizing selection for local environments would account 8
owing to its function in spreading favourable alleles across for most genetic load (i.e. for most fitness reduction).
the populations’ distributional ranges. This was mirrored Second,onecouldalsoexpectthatadaptiveresponsetochan- rstb
byourresults,whenfocusingononlytheadaptationtotem- gingclimatewouldbereducedwhenrecombinationbetween .ro
y
porally changing climate and thus neglecting the distorting climate-relatedlocicanoccurateverygeneration,thusbreak- als
o
effects of migrationload. ing apart adaptive allele combinations and preventing the c
ie
The interplay of these double-edged consequences of populationfrombeingfullyrescued.Whetherandhowlink- ty
p
u
gene flow leadsto the key resultsthat we emphasize in this age may facilitate or impede adaptation to changing b
lis
paper. When dispersal is high and habitat heterogeneous, environmental conditions could be further investigatedwith h
in
the number of viable offspring in each generation can be ourmodel,but is beyondthescope of this paper. g.o
drastically reduced due to the arrival of many maladapted Clearly, a number of genetic, demographic and envir- rg
juveniles.Atthepopulationlevel,thisisoflittleconsequence onmental settings that were neglected in this study can
P
h
whentheclimateisstable,aslongasthenumberofsurviving modulate the effects of spatio-temporal variability on il
T
juvenilescanmaintainthepopulationinasteadystate.How- micro-evolutionary dynamics. Some of these are shortly ra
n
s
ever, when the population needs to adapt to new climatic discussedin thefollowing. R
conditions, the absolute number of beneficial mutations In terms of the genetic basis of adaptation, it has been So
c
becomes crucial. This number depends not only on the shown that the relative amount of genetic versus environ- B
3
mutation rate, but also on the number of potential recruits mental variability in individual phenotypes affects the 6
8
:
that may carry these mutations and pass them on to sub- speed of adaptation and the likelihood of evolutionary 2
0
1
sequent generations. High rates of juvenile dispersal into rescue[39,53].Whiletheprobabilityofpopulationextinction 2
0
0
habitat to which they are ill-adapted reduces the effective isincreasedunderlowerheritabilityofthosetraitscontrolling 8
3
rate at which beneficial mutations on climate-related loci adaptationtotemporallychangingconditions,fortraitscon-
can be fixed in the population (see Barton & Bengtsson trollingadaptationtospatialheterogeneity,lowheritabilities
[52]). Ultimately, this interaction between dispersal, habitat andhighplasticitymayinsteadfacilitatepopulationsurvival:
heterogeneity and temporal environmental change leads to plasticity can buffer the negative effects of local malad-
the observed reduction in the probability of evolutionary aptation, reduce mortality and thus allow for increased
rescue.Thissuggestthatevenunderhighdispersalscenarios, effective dispersal and the spread of beneficial alleles.
populations previously adapted to spatially structured local Weakerselectionwillhaveapositiveinfluence onthesurvi-
environmentsmayhavealowerchancetoadapttochanging val probabilityof populations as well, because the effects of
regionalclimate. maladaptation are reduced. This effect is more pronounced
Thesecondkeyresult—partialevolutionaryrescue—isin whenthehabitatisheterogeneous(seetheelectronicsupple-
itsmechanismcloselylinkedtotheprocessdescribedabove. mentarymaterial, figureS6), becausethe level of adaptation
High habitat heterogeneity, subsequent migration load and to both climate and local conditions determine population
decreased survival probability hamper the spatial spread of development in this case. Furthermore, a number of studies
beneficial alleles, which may become locally abundant. The have demonstrated that characteristics of allelic effects such
positive fitness effect of the beneficial mutation on climate- as epistatis or pleiotropy [54] and the nature of the selection
related loci becomes overridden by the negative effects (i.e.hardversussoftselection)[55]mightchangeevolutionary
duetogeneticswampingbynewlyarrivedindividualscarry- dynamicssubstantially.
ing alleles that are not adapted to local environmental Focusingondemographiceffectsonrapidadaptation,the
conditions. This is obviously most likely when habitat is characteristics and effects of dispersal and gene flow may
strongly heterogeneous. Thus, when the resulting absolute need more detailed inspection. For example, gene flow by
fitness of these individuals is lower than unity, beneficial pollen will affect adaptation processes differently compared
alleles cannot spread throughout the distributional range of with gene flow by dispersal of seeds or individuals [37].
the population, thus preventing a species fully recovering First, the expected level of migration load is only half as
its original geographical range following a shift in regional high for pollen as for seed dispersal, because just half the
climate. In case the surviving subpopulations are too small number of maladapted alleles are placed into a new local
to supply a sufficient amount of new mutations for adap- environment, leading to decreased mortality. Second, the
tation to the conditions in the unpopulated space, we tend direct effect of shifting individuals between locations does
to observe a quasi-stable fragmented distribution of the notapply,partlydecouplingevolutionaryfromdemographic
survivingpopulations. dynamics. Apart from that, it has to be considered that dis-
Our model also demonstrates that different ecological persal capabilities evolve rapidly themselves [56–58]. This
traits—even though not genetically correlated—may interact adds another layer of complexity to forecasting population
with the evolutionary dynamics, because they have dynamics in space and time, but should generally increase
additional effects on individuals’ fitnesses and ultimately populations’ survival probabilities. Furthermore, the tree
onpopulations’demographicrates.Itseemsthatlinkagedis- types of population response—plasticity, adaptation and
equilibrium between adaptive loci indeed has a prominent migration—are not mutually exclusive. Whenever popu-
effect on the chance of evolutionary rescue. We found evo- lations are not limited in their distribution and tracking of
lutionary rescue to be more likely under total genetic suitable habitat is possible, the balance between positive
independence than under full linkage between adaptive and negative effectsof dispersal hasto bereconsidered.
loci. These results are not straightforward given our model Finally, in the context of environmental conditions, it
structure. First, we could have expected that under low should be noted that particularly when habitat is hetero-
linkage between adaptive loci, the evolutionary response to geneous, the condition changing temporally may show
shifting regional climate could be reduced, because variability across space. In this case, contrary to its effect
demonstrated in this study, spatial heterogeneity may even natural populations cannot be captured. Thus, we believe 9
accelerate adaptation to temporal change by increasing the that the type of allelic simulation model we applied in our
geneticvarianceon which evolution can operate [42,59]. study will be needed, if we are to ultimately make robust rstb
quantitative predictions on the likelihood of evolutionary .ro
y
rescue in particular populations or species. Here, we could als
5. Conclusion o
show that the evolutionary potential of populations facing c
ie
deteriorating conditions might be overestimated when ty
In pastyears, someremarkable studies have been published pu
neglecting the effects of local adaptation to heterogeneous b
identifying the genetic basis for variation in traits that are lis
habitat characteristics. This finding will be important, h
important for adaptation under climate change [60–64]. If in
we are to understand under which conditions species will becauseincreasinghabitatdeteriorationwill leadtoreduced g.o
total habitat availability, increased habitat fragmentation rg
be able to build upon this variation to respond to environ-
and stronger spatial habitat heterogeneity, all of which
mental change, an important next step is now to scale up Ph
are likely to impede the ability of species to track their il
the knowledge of the genetics underpinning adaptation to T
preferredclimate. ra
the level of population demography. In a recent study, ns
R
Chevin et al. [5] present a relatively simple evolutionary
WethankOscarGaggiotti,Ire`neTill-BottraudandCarstenUrbachfor So
modeltoassess—foragivencombinationofphenotypicvar- discussion during model development and implementation. Two c
B
iance, heritability, selection strength, growth rate and anonymousreviewersprovidedhelpfulcommentsonanearlierver- 3
6
plasticity—the critical rate of environmental change beyond sionofthemanuscript.W.T.andS.L.acknowledgesupportfromthe 8:
which a population must decline and go extinct. This type EuropeanResearchCouncilundertheEuropeanCommunity’sSeventh 201
FrameworkProgrammeFP7/2007-2013grantagreementno.281422, 2
of analytical model allows for a rigid mathematical analysis andfrombytheFrench‘AgenceNationaledelaRecherche’withthe 008
and can givevaluable insights into the sensitivityand inter- project EVORANGE (ANR-09-PEXT-011). This research was sup- 3
dependence of parameters. On the other hand, many of the ported by a Marie Curie Intra European Fellowship to K.S. within
typically complex dynamics of evolutionary processes in theEuropeanCommunity’sSeventhFrameworkProgramme.
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