climate change drives microevolution in a wild bird · supplementary information for climate change...
TRANSCRIPT
Supplementary information for
Climate change drives microevolution in a wild bird
Patrik Karell, Kari Ahola, Teuvo Karstinen, Jari Valkama & Jon E. Brommer
This document includes:
-Supplementary Figures S1-S4
-Supplementary Tables S1-S6
-Supplementary Methods
Data collection
Genetics
Capture-Mark-Recapture modelling and model selection
The role of phenotypic plasticity for the temporal change in frequency of color morphs
Temporal trends in pre- recruitment selection on tawny owl colour morphs
Population-genetic model
Predicted effects of further climate change on colour polymorphism in tawny owls
-Supplementary References
a b
1980 1985 1990 1995 2000 2005
Year
-0.4
-0.2
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Pro
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its -
Pro
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0 0.1 0.2 0.3 0.4 0.5
Frequency of brown fledglings t-2
0
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f b
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Supplementary Figure S1. Pre-recruitment selection on tawny owl colour morphs
The figure shows the relationship between frequency of brown nestlings and frequency of
brown breeding adults. Data consists of fledglings and recruits that were deduced to be brown
or grey based on observed ratios described in Figure 2 in the main text. a) Bubble plot
showing the difference in the proportion of local recruits that are deduced to be brown (prop
B recruits) minus the proportion of fledglings that were deduced to be brown (prop B
fledglings). Positive values indicate that local recruits are more likely to be brown than
expected on the basis of the proportion of brown fledglings (pre-recruitment selection in
favour of brown). A larger size of the plotted bubble indicates a larger sample size (number
of individuals). b) The frequency of brown adults as a function of the frequency of brown
fledglings produced two years earlier (t-2). See Supplementary Methods for statistics.
1980 1985 1990 1995 2000 2005 2010
Year
0
0.2
0.4
0.6
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Pro
po
rtio
n b
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n im
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1980 1985 1990 1995 2000 2005 2010
Year
0
0.2
0.4
0.6
0.8
1
Pro
port
ion
bro
wn
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its
b
a
Supplementary Figure S2. Immigration and recruitment of tawny owl colour morphs
Bubble plots showing temporal variation in the proportion of brown immigrants (a) and
recruits (b) between 1981 and 2008 in the study population. Bubble size refers to sample size.
A shift in frequency of the brown phenotype should result in a simultaneous shift in the
frequency of brown immigrants and recruits if these are neutral properties of the population
dynamics. The proportion of both immigrated and locally recruited brown individuals
increased moderately over the study period (Immigration, binomial GLM: year, b = 0.030 ±
0.003 s.e.m., z = 9.84, p < 0.0001; Recruitment; binomial GLM: year, b = 0.067 ± 0.015
s.e.m., z = 4.37, p < 0.0001).
0 50 100 150 200
Time
0.67
0.68
0.69
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0.71S
urv
iva
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0 50 100 150 2000.1
0.2
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Pro
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le
0 50 100 150 200
Time
0.3
0.4
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Pro
p B
mo
rph
Average BB + Bg
gg
a b
c
Supplementary Figure S3. A population-genetic model. Output of a population-genetic
model assuming selection against the brown morph with a survival advantage for the
heterozygous brown morph. We assume that there is a one locus – two allele inheritance with
the allele for brown (B) dominant over grey (g). There is survival selection against the brown
morph, which after 50 time steps changes into a pattern where selection against the brown
morph diminishes over time (panel a). We further assume that survival for the heterozygous
brown morph (Bg) is 9% higher than the survival for the brown morph (which is the average
of the survival of genotypes Bg and BB weighted by their frequency in the population). The
brown morph never has a higher survival than the grey morph, but because the heterozygous
genotype has a high survival, an equilibrium frequency of the B allele is reached where it
remains in the population (panel b, first 50 time steps). Diminishing selection against the
brown morph (from time step 50 onwards) is associated with an increase in the frequency of
the B allele (panel b) and the brown morph (panel c).
Supplementary Figure S4. Climate change predictions and colour polymorphism
Conceptual figure of the fitness of brown and grey tawny owls in different environments as
predicted by the theory of evolution of genetic polymorphism. The left part of the graph
represents the temporal pattern observed in the data (drawn from Figure 3c in the main text)
and the right part represents the predicted trend based on empirical studies (listed in
Supplementary Table S6) as the climate gets warmer. The error bars are standard errors of the
estimated survival probability.
Supplementary Table S1. Hardy-Weinberg frequency table. The table shows all possible
mating crosses of a 1 locus 2 allele system and the frequency of the offspring genotypes each
cross produces. Phenotypic morphs of the crosses are denoted as brown (B) or grey (G)a.
Because of the full dominance of the B alleleb, certain phenotypic crosses can refer to several
genotypic crosses. For each genotypic cross, the probabilities of producing the three different
offspring genotypes are presented.
Mating Genotypes of offspring
–––––––––––––––––––––––––––––– ––––––––––––––––––––––––––––
Phenotype Genotype Frequency Freq. BB Freq. Bg Freq. gg
—————————————————————————————————————
B B BB BB P2 1 0 0
B B BB Bg PQ 0
B B Bg BB QP 0
B B Bg Bg Q2
G B gg BB RP 0 1 0
B G BB gg PR 0 1 0
B G Bg gg QR 0
G B gg Bg RQ 0
G G gg gg R2 0 0 1
a Alleles are denoted in italics where the dominant allele is for brown (B), and the recessive
allele is for grey (g).
b The frequency of the B allele in the population is p, and the frequency in the population of
genotype BB is denoted as P (p2), frequency of Bg is denoted Q (2p[1-p]) and of bb as R
([1-p]2).
Supplementary Table S2. Observed and expected number of pairs of mating crosses.
Colour morph crosses do not take into account the sex of the parent only their plumage colour
(G = grey, B = brown). Expected numbers of offspring morphs are denoted between brackets
and are generated under the assumption of Hardy-Weinberg equilibrium and full dominance
of the ‘brown’ allele over the ‘grey’ allele at a single locus.
—————————————————————————————————————
Parents Offspring
--------------------------------------- ------------------------------------------
Cross N obs N exp N Grey N Brown
————————————————————————————————————---
B B 15 16.05 4 (9.14) 55 (49.86)
G B 43 40.90 81 (79.34) 91 (92.66)
G G 25 26.05 83 (87.00) 4 (0.00)
Total 83 168 (175.48) 150 (142.52)
G2 = 0.2197, P = 0.86a G1 = 0.308, P = 0.58
b
a G test statistic (with degrees of freedom in subscript) denoting deviation between observed
(N obs) and expected (N exp) number of crosses.
b G test statistic (with degrees of freedom in subscript) denoting deviation between the total
number of grey and brown morphs observed and expected. See text for alternative approaches
for testing the offspring’s morph frequencies.
Supplementary Table S3. Annual number of captured male and female tawny owls. This
data from the study population consist of 1065 observations of 466 individuals and is used in
the capture-mark-recapture analyses.
______________________________________________
Year N males N females_
1981 4 7
1982 25 25
1983 29 30
1984 15 16
1985 23 24
1986 22 25
1987 4 4
1988 15 15
1989 23 25
1990 12 12
1991 21 21
1992 22 24
1993 14 17
1994 24 24
1995 25 25
1996 11 11
1997 14 14
1998 14 15
1999 26 27
2000 21 21
2001 23 24
2002 27 27
2003 25 26
2004 11 11
2005 15 16
2006 22 23
2007 8 8
2008 26 27_______
Supplementary Table S4. Correlations between tawny owl survival and climate.
Different time windows (5-44 days) and their correlations between snow depth and tawny
owl annual survival, and between temperature and tawny owl annual survival during 1981-
2008. Using a sliding window approach, annual average temperature and snow depth during
all possible windows of a given length were considered as explanatory variables. Survival
estimates ( ) are taken from a full time-dependent CMR model tpt.
________Snow depth________ ________Temperature______
Window length (days) rPearson Window dates rPearson Window dates
5 -0.40358 23-27.12a 0.56525 25-29.12
6 -0.38524 23-28.12 0.56812 24-29.12a
7 -0.37017 22-28.12 0.56642 24-30.12
8 -0.35563 22-29.12 0.55771 23-30.12
9 -0.33797 22-30.12 0.54951 22-30.12
10 -0.32542 22-31.12 0.5412 22-31.12
11 -0.3132 21-31.12 0.52859 21-31.12
12 -0.30779 18-29.12 0.51749 21.12-1.1
14 -0.27298 22.12-4.1 0.51219 23.12-5.1
15 -0.28641 17-31.12 0.49955 21.12-4.1
16 -0.25784 22.12-6.1 0.51562 22.12-6.1
18 -0.26417 18.12-4.1 0.50669 21.12-7.1
20 -0.25184 16.12-4.1 0.48641 14.12-2.1
21 -0.25084 17.12-6.1 0.48594 19.12-8.1
22 -0.2184 20.12-10.1 0.48217 20.12-10.1
24 -0.21508 12.12-4.1 0.44972 12.12-4.1
27 0.19776 18.12-13.1 0.44844 19.12-14.1
28 0.19096 22.3-18.4 0.43786 22.12-18.1
30 -0.1857 1.4-30.4 0.42496 30.5-28.6
32 -0.18454 12.12-12.1 0.4359 10.12-10.1
33 -0.19242 24.3-25.4 0.44815 29.5-30.6
36 0.16598 7.3-10.4 0.41674 23.12-27.1
40 -0.1921 24.3-2.5 0.36855 11.2-22.3
44 -0.16566 25.2-9.4 0.41651 27.5-9.7 aThe time window periods producing the highest correlation are in bold. This period around
New Year is the time when a permanent snow cover is formed and a steep decline in winter
temperature begins in Southern Finland40
.
Supplementary Table S5. Within- and between-individual variation in coloration as a
function of winter climate. Estimates from linear mixed models on the within- and between-
individual effects of snow depth in preceding winter on plumage coloration (score 4-14) in
tawny owls with individual ID as random effect. Shown are (1) a standard model with
combined within- and between-individual effect, and (2) and (3) are models with within- and
between-individual effects separated. Significance testing is reported for the slope of the
fixed effect(s).
Model Estimate ± s.e.m. t d.f. P
1: y = ß0 + ßCa + u + e
Intercept 8.36 ± 0.14
C (Combined effects) -0.03 ± 0.01 -2.80 591 0.005
2: y = ß0 + ßW(obsb-mean
c) + ßB(mean
c) +u + e
Intercept 8.86 ± 0.21
W (Within-individual effect) -0.02 ± 0.01 -1.36 590 0.18
B (Between-individual effect) -0.11 ± 0.02 -4.06 590 0.0001
3: y = ß0 + ßW(obs b) + ßB(mean
c) +u + e
Intercept 8.86 ± 0.21
W (Within-individual effect) -0.02 ± 0.01 -1.36 590 0.18
B (Between-individual effect) -0.09 ± 0.03 -3.21 590 0.001
a Within- and between-individual effects are combined
b ‘obs’ stands for an individual’s observed value in a given year
c ‘mean’ stands for an individual’s mean value from each observation
Supplementary Table S6. Climate change predictions and colour polymorphism.
Different environmental factors which are predicted to change due to climate warming and to
cause a change in fitness of colour morphs in future. Both direct effects of climate change
and indirect effects of climate change on biotic interactions are predicted to alter morph-
specific selection pressure in future. See Supplementary Figure S4 for an illustration of the
predicted change in fitness of colour morphs in a climate change scenario based on the
empirical studies listed in this table.
_________________________________________________________________________
Environmental factor Predicted Reference Predicted Reference
climate effecta fitness effect
b
_________________________________________________________________________
Temperature + 21 G < B 28, 36, 42-43
This study
Snow depth - 21 G < B 28
This study
Vole cycle amplitude - 22 G > B This study
0 23 G = B This study
Humidity + 21 G < B 28, 36
Parasite richness + 2, 41 G < B 17, 30, 44
_________________________________________________________________________ aincrease is denoted by ‘+’, no effect by ‘0’ and decrease by ‘-’
bG = B, no fitness difference predicted between grey (G) and brown (B) morphs; G > B,
selection against brown predicted; G < B, selection against grey predicted.
Tawny owl data collection and colour scoring. Tawny owls were studied by authors KA
and TK in a study area of about 250 km2 in southern Finland (60º 15N´, 24º 15E´). The study
area is dominated by mixed forests, agricultural areas and small freshwater courses and was
established in 1977-78. From 1980 onwards approximately 125 nest boxes in suitable habitats
were available for tawny owls to breed in. Considerable effort was put into finding the nests
of all tawny owls in the study population by searching for natural nest sites and new boxes set
up by private individuals in the approximate area where hooting individuals had been
recorded earlier in spring and where a breeding thus was expected. The same monitoring
scheme was used by KA and TK during all years and the effort remained similar in all years.
Practically all breeding male and female owls in the area were caught, ringed, measured and
their plumage coloration scored by KA and TK using a semi-continuous scale. The colour
scoring method is described by Savolainen45
and focuses on the degree of brown
phaeomelanin pigmentation in four different parts of the plumage: facial disc 1-3 points,
breast 1-2 points, back 1-4 points, and general appearance 1-5 points. This gives a score from
4 (grey) to 14 (red). A low score indicates less pigmentation and grey dominated plumage,
whereas a higher score indicates higher degree of reddish-brown phaeomelanin pigmentation.
All colour scoring was done by KA, TK or (mainly) by both. Because each individual was
colour scored in every year it was caught by the same observers, repeatability and changes in
colour as an individual ages can be studied. A previous study of the same tawny owl
population has found that this colour scoring method is highly repeatable for both sexes
(females: repeatability r = 0.90; males: r = 0.92), that plumage colour score is not affected by
age and sex, and that the frequency distribution of colour morphs is bimodal for both sexes18
(see also Figure 1 in the main article). Based on this distribution we categorised individuals
into two morphs, either grey (colour less than 10) or brown (colour 10 or more). More
information on the monitoring scheme and the colour scoring procedure is given in Brommer
et al18
and Karell et al24
.
As a reference point to the colour morph data from the study population, author PK colour
scored 126 tawny owl skin specimens that have been collected 1915-1980 by the public and
stored by the Natural History Museum, University of Helsinki. Colour scoring was done in
the same way as in the study population. At the time of colour scoring the Museum
specimens, author PK had been trained for four years by KA and TK in using this method.
Skin specimens in the collection are mainly victims of the traffic or of other accidents. Data
on bird skin specimens are informative on temporal trends in colour morph frequencies36
.
National level individual tawny owl data was obtained from the Finnish Ringing Centre and
has been collected by amateur owl ringers all over Finland. We extracted the data on all
records of adult ringed and recaptured tawny owls in Finland to which colour morph (grey or
brown) had been assigned during 1961-2008 (30 % of all data, 3194 / 10601 records).
Climate and prey abundance data. Weather data were obtained from the Finnish
Meteorological Institute. We used daily measures of temperature (T) and snow depth (cm)
collected at Helsinki-Vantaa Airport, which is located c. 50 km from the center of the study
area. Extraction of estimates of temperature and snow depth relevant for tawny owl survival
is described in the section on capture-mark-recapture modelling.
Voles are the main prey of tawny owls and their abundance varies drastically between
years24
. Snap trapping of small mammals was carried out each October during the years
1981-2008 by KA and TK within the study area in order to estimate the abundance of prey.
Snap trapping was conducted in two localities on each trapping event: one in the eastern part
of the study area and one in the western part of the study area. Each trapping locality consists
of open (field/clearcut) habitat and forest habitat. Traps were set as a transect with a total of
48 traps / habitat (96 traps per replicate). All traps were triggered for two consecutive nights
(192 traps for two nights yields 384 trap nights in total per trapping). Mainly field voles
(Microtus agrestis) and bank voles (Myodes glareolus) were caught in the traps, but also to
some extent wood mice (Apodemus flavicollis) and shrews (Sorex araneus). We include all
species in the analysis of prey abundance (number of individuals caught per 100 trap nights).
We use this autumn prey abundance to estimate the over-winter food availability. Similar
autumnal prey abundance indices have been found to explain over-winter survival variation
in the closely related Ural owl46
.
Animal model. Estimation of heritability of plumage colour scores was based on a Restricted
Maximum Likelihood (REML) linear mixed model on the individual’s average colour score,
where the additive genetic effects were estimated using pedigree-derived estimates of
relatedness across all individuals (‘animal model’)19
.
We considered the linear mixed model
c = Xß+ Zu + e, (S1)
where c is a vector with the average colour score of all the individuals (possibly categorised
by an additional state variable), ß denotes a vector of fixed-effects, u a vector of random
effects which each are related to the appropriate individual through design matrices X and Z
respectively. The vector e contains the effect of residual variance not attributed to model
terms. Equation (S1) can be solved for the genetic (co)variance matrix G for the vector u by
using information on the coefficient of co-ancestry ij between individuals i and j, which is
directly obtained from the pedigree. The variance-covariance matrix of additive genetic
effects is equal to G = A A2, where A has elements Aij = 2 ij. The additive genetic effects and
residual effects are assumed to be normally distributed with mean of zero. As fixed effects we
considered sex and the year the individual started breeding. However, none of these effects
were significant which provides further evidence that tawny owl plumage colouration is a
real polymorphism14
.
In our analyses, we used the reduced pedigree where 167 individuals whose phenotype was
measured and who had at least one relative with a measured phenotype in the pedigree were
included. Extra-pair paternity is low in this species20
, and we are thus confident in inferring
relatedness on the basis of the social pedigree. Sex, age (1, 2, 3 years) and year of first
breeding were tested as fixed effects, but were not significant, and were not included in the
final models. Because the additive genetic effects are estimated only on the basis of these
individuals, including other (non-related) individuals does not change the results, especially
since none of the considered fixed effects were significant. This basic animal model based on
the colouration scoring method (see Figure 1 in main text) revealed that plumage colouration
was 79.8 ± 13.8 (s.e.m.) % heritable.
Inferring Mendelian genetics based on nestlings and their parents. Because the estimated
heritability was high, and because the distribution of colour scores is so clearly bimodal (see
Figure 1 in the main article), allowing the separation of two colour morphs, we wanted to test
whether a ‘major gene’ could be responsible for creating this pattern. The colour scoring
method is only applicable in individuals that have obtained their full adult plumage. Tawny
owls (and owls in general) fledge from their nest well before they attain all adult
characteristics. The colour morph was scored (by author PK) of 318 nestlings in 83 nests
during 2006-2009 when the oldest nestling was 25 days old (i.e. close to fledging). On the
basis of the coloration of the interior part of the tertiary wing feather, these offspring was
categorised as either grey (no brown pigmentation) or brown (brown pigmentation present).
Their parents were previously caught and colour scored and categorised into brown or grey as
explained above. Note that colour scoring of the parents was done by a different researcher at
another time point than the colour scoring of offspring, and the offspring are thus
characterised independently and without reference to the colour score of the adults. Because
we only started to colour score offspring recently, we only have data on seven individuals
that recruited back into the breeding population (three males and four females) that were
categorised into colour morphs as offspring. Four of these recruits were given the colour
score 6 (grey), one was given the score 8 (grey), and two were given the score 13 (brown).
For six out of seven individuals the colour morph categorisation as offspring and adult
corresponded, and only the individual with adult colour score 8 (which is close to the
threshold value for being considered a brown morph) was wrongly scored as a brown morph
offspring. Given the data at hand we assume that the characterisation of offspring colouration
corresponds well with colour scoring as adult (see also Gasparini et al17
).
Tawny owls mate random with respect to colour18
, which is a critical requirement for
applying the Hardy-Weinberg equilibrium. We first show random mating also with respect to
the inferred Mendelian genetics. Under Hardy-Weinberg equilibrium, mates are a random
‘draw’ from the genotypes present in the population. The expected frequencies of the
different genotypes mating are presented in Supplementary Table S1. The frequency of the
dominant ‘brown’ allele (p) can be easily derived from the observed frequency of the double
recessive ‘grey’ allele, which is the proportion of observed grey phenotypes (=(1-p)2) Of the
166 (2 x 83) adults involved in the crosses, 55.36% are grey (Supplementary Table S2), thus
p = 0.2515. The frequency of the different crosses follows the expected distribution closely
(Supplementary Table S2), indicating that mating is random with respect to our Mendelian
genetics model.
We next calculated the expected frequency of offspring genotypes per mating between
phenotypes. This was done by multiplying the expected genotypic frequencies of each cross
with the expected frequency of offspring genotypes (both given in Supplementary Table S1)
and applying the thus obtained ratio to the total offspring produced by that particular
phenotypic cross. For example, the three genotypic crosses that underlie the phenotypic cross
‘B G’ produce a ratio of brown:grey offspring of (PR+RQ) : RQ. One cannot compare
observed and expected value if the expected value equals zero (which is the expected
proportion of brown offspring from a ‘G G’ cross, Supplementary Table S2). We therefore
calculated the G-test statistic between the observed and expected brown offspring across all
crosses, which showed a good model fit (Supplementary Table S2, Figure 2 in the main text),
and which is the test statistic reported in the text. We further explored whether these findings
were robust to different approaches for generating expected values. Firstly, following the
above-outlined approach, but ignoring the ‘G G’ cross (which contains a ‘0’ expected
value) produces a good fit for the other two crosses (G2 = 4.2432, P = 0.12). Second, we
assumed that the reproductive output of each cross is similar (justified since Brommer et al18
showed no difference in reproductive output across colour morphs, see also supplementary
results below). Thus, we may derive the expected numbers of offspring morphs directly on
the basis of the full Hardy-Weinberg frequency table (Supplementary Table S1) and the
assumption that the ‘B’ allele occurs with frequency 0.2515 (see above). Expected numbers
of brown and grey offspring are then generated using this frequency distribution multiplied
with the total number of offspring (as opposed to the cross-specific number offspring). This is
a method that is thus highly independent of the actual data (since it requires only information
on the proportion of grey adult individuals and the total number of offspring produced), yet
produces a satisfactory fit to the observed frequencies of brown and grey nestlings (G1 =
1.314, P = 0.25).
Repeating the above, but assuming that the allele for grey morph is dominant over the allele
for brown leads to the following expected number of offspring (following the order and
terminology of Supplementary Table S2, the number expected (observed) offspring that are
grey (G) or brown (B), cross BxB: 0 (4) G / 59 (55) B; BxG: 95.7 (81) G / 76.3 (91) B; GxG:
75.1 (83) G / 11.9 (4) B; G-test over the last two crosses with non-zero expected numbers: G2
= 12.98, P = 0.0015). Clearly, there are more brown offspring observed than expected when
assuming full dominance of the grey allele over brown (see Figure 2 in the main text).
We further implemented a purely additive genetic model. This approach used information on
the colour score (4 to 14) of the adults. Offspring colour score were predicted on the basis of
the mid-parent colour score (standardised to a mean of zero) and assuming a heritability of
80% (based on the results from the animal model). That is, offspring colour = 0.8 * midparent
value + N(0, SDerror), where N(0, SDerror) is a normally distributed random value with mean of
zero and a standard deviation SDerror = SQRT[Var(mid-parent value) – Var(0.8*mid-parent
value)]. Offspring colour score was then classified into two morphs using the same criteria as
for adults (colour score 10 is brown). This procedure was repeated 10,000 times, and the
average predicted number of grey and brown offspring for each cross was used as the
expected value. This purely additive model fitted the data poorly (number expected
(observed) offspring that are grey (G) or brown (B), cross BxB: 0 (4) G / 59 (55) B; BxG:
132.1 (81) G / 39.9 (91) B; GxG: 87 (83) G / 0 (4) B; G-test over the one cross with non-zero
expected numbers: G1 = 70.9, P < 0.0001). In this particular case, the colour scores of the
adults were such that the additive model predicted that all offspring from monomorphic
crosses were the same morph as the parents; thus, these crosses are not informative for
testing. However, focusing on the heteromorphic (BxG) cross, it is clear that there are again
more offspring of the brown morph observed than expected (see Figure 2 in the main text).
We conclude from the above exercises based on comparing fledgings’ morphs with their
parents’ morphs that we find evidence for genetic dominance of brown over grey, whereas
dominance of grey and purely additive genetic effects are not supported by this data. While
this modelling is not exhaustive, it does show that the tawny owl phenotypic morph has a
genetic architecture that relies on few genes with large effects.
We used individual capture history data on 466 tawny owls (1065 observations) from 1981-
2008 for the capture-mark-recapture (CMR) modelling, because vole abundance data
gathering was initiated in 1981 and this vole data was tested as a covariate in the CMR
modelling. The data is summarized in Supplementary Table S3. Survival of adult grey and
brown tawny owls was estimated using CMR methodology on live encounters data
(Cormack-Jolly-Seber model, CJS) using the program MARK38
. With the CJS model one can
separate survival probability ( ) from recapture probability (p) using a maximum likelihood
approach. In explaining the CMR models, we adhere to a short-hand notation where the
interaction (e.g. between colour and temperature (col*temp), also includes the main effects of
these variables; hence, col*temp = col + temp + col*temp).
In order to extract relevant climate variables for tawny owl survival we first estimated time-
specific (t) survival and recapture based on a basic model (t)p(t). We then used a sliding-
window approach to find the time period during which survival correlated best with climate
(average temperature and snow depth). Mean temperature and snow depth during all possible
time windows between 5 and 44 days were considered and the Pearson’s r correlation
coefficients compared. Annual survival of tawny owls was highly correlated with temperature
and snow depth (Supplementary Table S4). A relatively short time window of five and six
days prior to the turn of the year correlated best with the tawny owl survival estimates.
Consistency among time windows was high for both snow depth and temperature as all the
highest correlations across window sizes were always in the same period, around the turn of
the year (Supplementary Table S4). In particular, 75% (18/24) of the tested time windows
correlated with snow depth and 83% (20/24) correlated with temperature and these were all
centred around the same time period (Supplementary Table S4). We therefore conclude that
early to mid winter severity in terms of both snow depth and temperature is important for
tawny owl adult survival in the study population. It is during these best fitting selected time
periods when a permanent snow cover is being formed and when cold spells are likely to
occur40
and it is also during this time period when snow depth and temperature is expected to
change largely in the near future according to climatological models of the IPCC applied to
Finland47
. Hence, variation in climatic estimates during the selected periods reflects a period
sensitive to climate change and to overall length and severity of the winter. Measures of
temperature and snow depth from the time window which correlated best with annual tawny
owl survival were selected as covariates for further modelling (Supplementary Table S4).
We first tested the goodness of fit of the full colour morph (col) and time dependent (t) model
(col * t)p(col * t) using program U-CARE48
in order to assess whether the data meets the model
assumptions. The results of the global goodness of fit test indicated that the data met the
assumptions of homogeneity (2
141 = 143.55, P = 0.42). We then entered the data into
program MARK37
with sexes pooled and colour morph (col) as grouping variable. We
estimated over-dispersion in the full time dependent model by running 500 bootstrap
simulations of this model and calculated the value by dividing the observed model deviance
with the mean deviance of the simulated models. A value of 1.19 obtained from the
bootstrap simulation was entered into all models to adjust for over-dispersion. We ran all
models nested under the full model (N = 25) and models were ranked by their QAICc value.
The best model, i.e. the model with the lowest QAICc value, was a model with colour morph
and time dependent survival and time dependent recapture ( (col + t)p(t)) which was selected
for further analyses. In order to check that there are no survival differences between sexes we
run a similar model with sex differences accounted for ( (col +sex+ t)p(t)). This model received a
poorer fit with higher QAICc value than the above mentioned model without sex differences
( QAICc > 4, see also below for a climate constrained model). Also earlier analyses of a part
of the same data set (years 1981-1995) found that models coding for sex or sex * vole cycle
phase did not receive high QAICc support24
, and another previous CMR analysis of data from
the same population found that colour dependent survival was the best model for both
sexes18
. Since we found no main effect of the variable ‘sex’ then allowing interactions with
variable ‘sex’ should not improve model fit. Hence, we proceeded with modelling by
focusing on the variable with the highest main effect (i.e. colour morph), which reduced the
number of candidate models. This way we describe model space without the need to model
each of the candidate models.
We tested for the effects of real covariates by replacing the dummy variable ‘time
dependence (t)’ in model (col + t)p(t) with interactions of colour morph and prey abundance
(col*vole), temperature (col*temp) and snow depth (col*snow) in both survival ( )
estimation as well as recapture (p). The full constrained model was
(col*vole+col*temp+col*snow)p(col*vole+col*temp+col*snow) (S2)
In order to reduce the number of potential candidate models nested under the full constrained
model we followed the model selection criteria described in Karell et al24
: First we tested all
nested models for survival while keeping recapture rate constant at p(col*vole+col*temp+col*snow).
The models with highest QAICc (deltaQAICc < 2, N = 4) were selected and all potential
recapture models under the full constrained model were tested while keeping constant. In
total 150 constrained models were considered. We applied model averaging on the candidate
models to get a more conservative estimate of and p.
We compared the best model ( (temp + col*snow)p(t), described in detail in Table 1 in the main
text) with a similar model which included sex differences ( (temp + sex*col*snow)p(t)). The model
accounting for sex differences received a poorer fit ( QAICc = 7.9), which indicates that the
climate variables have a similar effect on the survival of both sexes. Recapture probability
was not explained by prey abundance, temperature or snow depth as none of the models
including these variables received any support.
We obtained estimates of survival and recapture probabilities through model averaging which
are shown in the main results (Table 1, Figure 3 in the main text). Model averaging calculates
an average value over all models in the candidate model set with common elements in the
parameter structure, weighted by normalized QAIC model weights [exp(- QAIC ⁄ 2) ⁄
(exp(- QAIC ⁄ 2))]. Although averaging is performed over all candidate models, the
weighting by QAIC model weights ensures that the information in the best fitting models
contributes most. In our case, 99.4 % of support stems from the four best models (Table 1 in
the main text) and the averaging can therefore be considered to mainly describe the common
features included in these models (i.e. colour morph – snow depth interaction). For more
information on the modelling approach, see Burnham & Anderson39
.
In order to estimate the effect size of the climate covariates included in the best predictor
model for survival ( (temp + col*snow)p(t)) (Supplementary Table S5) we followed the guidelines
described in Grosbois et al49
. In CMR models the effect size of a covariate is calculated as the
difference in deviance between the constant model ( (col)) and the covariate model ( (temp +
col*snow)) divided by the difference in deviance between the constant model ( (col)) and the
time dependent model ( (col+t)). This statistic implied that the covariates retained in the best
predictor model ( (temp + col*snow)P(t)) accounted for 38.5% of the temporal variation in survival
of tawny owl colour morphs. We conclude that snow depth and temperature are very
influential in predicting survival differences of tawny owl colour morphs in our study
population.
We further tested whether the correlations between annual survival and the best time window
of snow depth and temperature only arose as an artefact due to annual variability in snow
depth and temperature in this short time window by running the best five models (Table 1 in
the main text) with a longer time window of 20 days for both snow depth and temperature. A
longer time window may differ from and be more relevant than a shorter time window if it
picks up different or biologically more meaningful signals of winter anomalies across years.
We chose a 20 day time window because it is reasonably well correlated with annual survival
(Supplementary Table S4), although the correlations are a lot weaker. The results show that
the results are qualitatively the same as in Table 1 in the main text, only the difference
between models gets smaller (QAICc < 2 between all five models). The two best models,
(temp + col*snow)P(t) and (col*temp + col*snow)P(t) are retained as the models with the best fit and the
covariates “temp + col*snow” from the best model still account for 25.5% of the temporal
variation in survival when the values from a 20 day time window is selected as covariate.
Model averaging of these five models with covariates from the 20 day time window show
qualitatively the same results as Figure 3a-c in the main text.
We used a within-subject centering statistical procedure50
to evaluate the role of phenotypic
plasticity in explaining the observed change in frequency of the color morphs as snow depth
decreases. The method is a basic linear mixed model approach in which the within-subject
effect (i.e. phenotypically plastic response) is disentangled from the between-subjects effect
(individually fixed response). We used individual color score data (range 4-14) from 1054
observations of 462 tawny owls (1982-2008) and snow depth in the previous winter (snow t-
1) as an explanatory variable to test if individuals become browner over time (phenotypic
plasticity) or if coloration is fixed within an individual. Snow depth values were the same as
those used in the CMR models (Supplementary Table S4). We then subtracted the
individual’s mean value of snow t-1 from each observed snow t-1 value (snow t-1 –
mean(snow t-1)), which gives a within-individual centred value. The between-individual
variation component was calculated as the mean snow t-1 –value for each individual
(different observations of the individual were given the same mean value. Individual ID was
used as a random effect and the models were run as linear mixed effects models with normal
errors. We then compared a model with combined within- and between-individual effect, a
model with within- and between-individual effects separately, and a model with within-
individual effect and a within- vs between-individual effect difference (see van de Pol &
Wright50
for details). We found that the within-individual effect (i.e. individuals become
browner as snow depth decreases) is non-significant, whereas the between-individual effect
(i.e. individual coloration is fixed) was highly significant (Supplementary Table S5).
We found no evidence that reproductive success would have improved over time for brown
individuals compared to grey ones. Linear mixed models showed no trend over time where
either morph would have improved their fledgling production when analysed separately for
females (LMM year: F1,285 = 1.24, P =0.26, colour: F1,285 = 0.06, P = 0.80, year * colour:
F1,285 = 1.26, P = 0.26) and males (LMM year F1,274 = 1.45, P = 0.23, colour: F1,274 =
1.81, P = 0.18, year * colour: F1,274 = 1.481, P = 0.22).
The capture recapture survival analyses (Table 1 in the main text) consider adults that are part
of the breeding population. In order to assess whether selection on the brown morph occurs
prior to the onset of breeding (i.e. between fledging and recruitment), we retrospectively
classified offspring into colour morphs. Since colour scoring was not done on offspring
before 2006 we used the observed proportions of grey and brown nestlings from 2006-2009
(N = 318 fledglings, Supplementary Table S2) to calculate the production of grey and brown
fledglings of all breeding pairs in the population during the whole study period. Note that by
doing so we do not apply the Mendelian genetics model we developed above, but instead
retrospectively apply the frequency distribution of offspring morphs of the observed data
itself. The proportion of fledglings that this way were deduced to be brown increased over
time (N = 28 years, rSpearman = 0.73, P < 0.001). We found no evidence of selection against
brown individuals between fledging and recruitment as the proportion of deduced brown
recruits did not deviate from the proportion of brown fledglings (rSpearman = 0.15, N = 23, P =
0.49, Supplementary Figure S1a). It takes about two years for a tawny owl fledgling to recruit
in the breeding population24
. We indeed found that the proportion of brown adults in the
population in a given year correlated well with the proportion of brown offspring produced
two years earlier (rSpearman = 0.66, N = 26, P < 0.001, Supplementary Figure S1b). This high
and strongly linear relationship demonstrates further a general absence or at least low
importance of selection on colour morph prior to the onset of breeding. An increase in the
proportion of reproducing brown individuals directly increases the production of brown
individuals, which in turn explains the proportion of brown adults in the population two years
later. Furthermore, both the proportion of brown immigrants and local recruits followed the
same temporal trend as the trend in the study population (Supplementary Figure S2), which
suggests that morph-specific immigration or recruitment is not responsible for the temporal
change in morph frequency in the population.
In conclusion, these analyses support the contention that the main temporal change in
selection is through survival selection (as opposed to fecundity selection) on adults.
Fitness advantage of a heterozygote is one classical population genetic model that allows for
the maintenance of genetic polymorphism despite selection. In our case, we measure
selection on the brown morph, but this morph may consist of two genotypes. We constructed
a population-genetic model that allows disentangling the selection on the morph from
selection on the genotype to study consequences for the evolutionary dynamics. As above
(‘Genetics’ section), we assume a one-locus two allele Mendelian model with the phenotypic
morph determined fully by the allele for brown (B) being 100% dominant over grey (g).
Hence, the brown morph consists of genotypes BB (homozygous brown morph) and Bg
(heterozygous brown morph), whereas all grey individuals are gg.
There are thus 9 different pair combinations (crosses), the number (n) of each of these can be
denoted in the vector
, (S3)
The frequencies of the brown morph and the frequency p of the B allele in the population can
be calculated directly from the vector n. When pooling males and females,
,
where n(t) denotes the total number of pairs at time t.
We assume that the survival is specified for each morph, and denote morph as either grey (G)
or brown (B). Survival of the grey morph from time step t to t+1 is denoted as sG(t) and
survival of the grey morph as sB(t). Hence,
, (S4)
where sBB and sBg are the survival for the homozygous and heterozygous brown morphs.
Equality (S4) is valid for a variety of values sBB and sBg, and is further dependent on the
frequency p of the B allele in the population at time t. This is because the survival of the
brown morph is the weighted average of the survival of the brown genotypes (average
weighted by their frequency in the population, equation (S4)). Hence, in order to produce a
tractable model, one needs to a priori assume a certain relationship between two of the
survival parameters in equation (S4). We further need to construct a dynamics model that
takes into account the frequencies of the different genotypes, because this partly defines their
survival until equilibrium is reached. In addition, survival is of course bounded between 0
and 1 and thus not all mathematical solutions to equation (S4) are biologically possible.
We here assume (1) that sB is given (e.g. because we can estimate the survival of the
phenotypic morph), (2) that sG > sB, and (3) that sBg > sB > sBB. Such a situation would occur
in case pleiotropic effects associated with the B and g alleles affect the survival of the
genotypes. Having one allele g gives the heterozygote an advantage over the homozygous
brown individual. We here implement this by assuming that
sBg(t)= c sB(t), (S5)
and using equation (S4) to solve for sBB(t). We assume this functional relationship because of
its simplicity, and not to imply that this is the underlying biological mechanism. Also other
(e.g. non-linear) functions are likely to produce qualitatively the same pattern. The crux is
that the heterozygous genotype that produces the brown morph may have a survival
advantage over the homozygous brown genotype, but yet – on average – the survival of the
brown morph is below that of the grey morph.
We assume that there is no selection operating prior to being a breeding adult (see section
above). Thus, the frequency of the genotype of the offspring follows directly from applying
the rules of Mendelian inheritance (Supplementary Table S1) to the vector n. The frequencies
of offspring genotypes produced can be denoted as rBB(t), rBg(t) and rgg(t) (which sum to 1).
For simplicity, we assume that an offspring recruits either to the breeding population the next
year, or dies (only breeding individuals have overlapping generations). Once an individual is
a breeding individual, it stays so until death. The dynamics are based on pairs (equation (S3)),
although we here assume the same dynamics for each sex and an equal sex ratio at birth. A
certain amount of bookkeeping is required. Basically, the number of a given cross in t+1 is
determined by three different transitions. (1), Both members of a pair can survive, (2) one
member can die, in which case a new partner will be drawn at random from the offspring
produced, and (3) offspring may form an entirely new pair. For simplicity, we here assume
that a scalar cOff determines the number of new pairs, but including density dependence by
assuming a fixed number of territories does not change the results. Dynamics can be specified
as below, where to simplify notation the time-dependence of all the s and r values is dropped.
nBBxBB(t+1) = (sBB sBB + 2 sBB (1-sBB) rBB) nBBxBB(t) + sBB (1-sBg) rBB nBBxBg(t) + sBB (1-sgg) rBB
nBBxgg(t) + (1-sBg) sBB rBB nBgxBB(t) + (1-sgg) sBB rBB nggxBB(t) + cOff rBB2
nBBxBg(t+1) = (sBB sBg + sBB (1-sBg) rBg + (1-sBB) sBg rBB) nBBxBg(t) + sBB (1-sBB) rBg nBBxBB(t) +
sBB (1-sgg) rBg nBBxgg(t) + (1-sBg) sBg rBB nBgxBg(t) + (1-sgg) sBg rBB nggxBg(t) + cOff rBB rBg
nBBxgg(t+1) = (sBB sgg + sBB (1-sgg) rgg + (1-sBB) sgg rBB) nBBxgg(t) + sBB (1-sBB) rgg nBBxBB(t) + sBB
(1-sBg) rgg nBBxBg(t) + (1-sBg) sgg rBB nBgxgg(t) + (1-sgg) sgg rBB nggxgg(t) + cOff rBB rgg
nBgxBB(t+1) = (sBg sBB + sBg (1-sBB) rBB + (1-sBg) sBB rBg) nBgxBB(t) + (1-sBB) sBB rBg nBBxBB(t) +
sBg (1-sBg) rBB nBgxBg(t) + sBg (1-sgg) rBB nBgxgg(t) + (1-sgg) sBB rBg nggxBB(t) + cOff rBg rBB
nBgxBg(t+1) = (sBg sBg + 2 sBg (1-sBg) rBg) nBgxBg(t) + (1-sBB) sBg rBg nBBxBg(t) + sBg (1-sBB) rBg
nBgxBB(t) + sBg (1-sgg) rBg nBgxgg(t) + (1-sgg) sBg rBg nggxBg(t) + cOff rBg2
nBgxgg(t+1) = (sBg sgg + sBg (1-sgg) rgg + (1-sBg) sgg rBg) nBgxgg(t) + (1-sBB) sgg rBg nBBxgg(t) + sBg
(1-sBB) rgg nBgxBB(t) + sBg (1-sBg) rgg nBgxBg(t) + (1-sgg) sgg rBg nggxgg(t) + cOff rBg rgg
nggxBB(t+1) = (sgg sBB + sgg (1-sBB) rBB + (1-sgg) sBB rgg) nggxBB(t) + (1-sBB) sBB rgg nBBxBB(t) + (1-
sBg) sBB rgg nBgxBB(t) + sgg (1-sBg) rBB nggxBg(t) + sgg (1-sgg) rBB nggxgg(t) + cOff rgg rBB
nggxBg(t+1) = (sgg sBg + sgg (1-sBg) rBg + (1-sgg) sBg rgg) nggxBg(t) + (1-sBB) sBg rgg nBBxBg(t) + (1-
sBg) sBg rgg nBgxBg(t) + sgg (1-sBB) rBg nggxBB(t) + sgg (1-sgg) rBg nggxgg(t) + cOff rgg rBg
nggxgg(t+1) = (sgg sgg + 2 sgg (1-sgg) rgg) nggxgg(t) + (1-sBB) sgg rgg nBBxgg(t) + (1-sBg) sgg rgg
nBgxgg(t) + sgg (1-sBB) rgg nggxBB(t) + sgg (1-sBg) rgg nggxBg(t) + cOff rgg2
Clearly, the above transitions are most easily implemented in a matrix M, such that n(t+1) =
M n(t).
Implementation of the model. As outlined above, differences in survival between
heterozygous and homozygous brown individuals can be implemented in several ways. As an
example, we consider the situation where sG = 0.70 and sB = 0.68 and cOff = 3. In case c in
equation (S4) equals 1 (the same survival for the heterozygous and homozygous brown
morph), the brown allele is extinct in c. 200 time steps. In case the homozygous brown morph
BB is assumed to have a survival advantage (c < 1), the brown allele B goes extinct. This is
because the most abundant brown morph (BB) typically mates with gg thereby producing
100% Bg offspring, which have a low survival. In general, genetic polymorphism can be
maintained only in case c >1.
In the example provided in the main text, we assume that c in equation (S5) equals 1.09 (the
heterozygote has 9% higher survival than the average for the morph). The initial vector of
pairs n(0) consists of 500 pairs of the heterozygous browns (Bg x Bg). With these values, the
above described model converges rapidly to an equilibrium where the frequency of the brown
morph in the population is 0.3078 (frequency p of the B allele is 0.1575). The brown morph is
thus maintained in the population, despite selection (on the level of the phenotype) acting
against it. This is because the heterozygous brown morph (Bg) has a high survival (0.7412),
but the homozygous brown morph (gg) has an extremely low survival (0.0254). We then
disturb this equilibrium situation by introducing increasing survival over 100 time step to sB =
0.6999. Thus, selection against the brown morph essentially disappears over time, which
captures the qualitative pattern observed in the tawny owl population (see main text). Again,
there is net selection against the brown morph throughout this period. However, because the
survival of the heterozygote (Bg) brown morph is high, the overall frequency of the brown
morph increases to 0.4187 (p = 0.2182) in 100 time steps and later attains its new equilibrium
frequency of 0.4581 of the brown morph (c. 50% increase) in the population with p = 0.2408.
We note that a survival advantage of the heterozygous brown morph over the homozygous
one provides an explanation for two puzzling aspects of the tawny owl system. Firstly, it
provides a possible explanation for how the brown morph can be maintained in the
population despite having a lower survival than the grey morph (Supplementary Figure S3).
Secondly, it provides a mechanism by which the frequency of the brown morph may increase
in a population as a result of diminishing selection against the brown morph, while never
assuming a selective advantage of the brown morph over the grey one (Supplementary Figure
S3).
Further remarks concerning the population-genetic model. The pattern shown in the
example holds for a variety of scenarios, as long as c is such that (1) the survival of the
heterozygous brown morph is above that for the grey morph (and lower than 1). Thus,
overdominance of color alleles in terms of survival is required. (2), The survival of the
homozygous brown morph must lie in the interval [0, 1], which is only possible within a
certain range of c.
How does this model relate to the situation in nature? The model is of course a simplification,
and ignores stochastic variability (including drift) and the fact that offspring have overlapping
generations. It also assumes full genetic control over the color morphs (ignoring minor genes
or the small impact of the environment). The dynamic nature of the model hampers applying
it to real data. The model is dynamic because the frequencies of the genotypes feed back into
determining their survival (given that morph survival is fixed, equation (S5)). Reaching
equilibrium state requires some time, and the outcome of imposing diminishing selection
depends strongly on the initial genotype frequencies. For these reasons, our model should be
considered to provide a conceptual link between diminishing selection on the brown morph
and an increase in frequency of the brown morph in a one locus – two allele system.
In Figure 3c in the main text we show how a milder climate reduces the selection against the
brown morph. We further show in Figure 5 that the change in frequency of the brown morph
(Figure 4b) is explained by the change in survival selection. Hence, climate change-driven
natural selection on a melanin-based colouration trait has lead to evolutionary change in the
population. However, as the climate continues to get warmer in future also other
environmental factors (than winter climate) can potentially have impact on the fitness of the
morphs. In Supplementary Figure S4 we show in a conceptual figure based on the theory of
maintenance of genetic polymorphism26
how natural selection driven by environmental
change is, due to the consequences of a warming climate, expected to alter the genetic
composition of a colour polymorphic population in future. Further evolutionary change is
expected in addition to the direct effects of a warmer winter climate, because also humidity,
prey population dynamics, and disease and parasite richness, are expected to change due to
further global change (Supplementary Table S6). All environmental factors presented in
Supplementary Table S6 have been found to have colour morph-specific fitness effects and,
therefore, based on those studies it is predicted that the (melanistic) brown morph of the
tawny owl would further increase in frequency in Northern environments as a consequence of
climate change.
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