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ORIGINAL ARTICLE
Environmental effects on fine-scale spatial genetic structure
in four Alpine keystone forest tree species
Elena Mosca
1,2
|
Erica A. Di Pierro
1
|
Katharina B. Budde
3
|
David B. Neale
4
|
Santiago C. Gonz
alez-Mart
ınez
3
1
Research and Innovation Centre,
Fondazione Edmund Mach (FEM), S.
Michele all’Adige, Italy
2
Faculty of Science and Technology, Free
University of Bolzano, Bolzano, Italy
3
BIOGECO, INRA, University of Bordeaux,
Cestas, France
4
Department of Plant Sciences, University
of California at Davis, Davis, CA, USA
Correspondence
Santiago C. Gonz
alez-Mart
ınez, BIOGECO,
INRA, University of Bordeaux, Cestas,
France.
Email: santiago.gonzalez-martinez@inra.fr
Funding information
European Commission, Grant/Award
Number: 21000677; Agence Nationale de la
Recherche, Grant/Award Number: ANR-
12-EBID-0003; Autonomous Province of
Trento, Grant/Award Number: 23, 12 June
2008
Abstract
Genetic responses to environmental changes take place at different spatial scales.
While the effect of environment on the distribution of species’genetic diversity at
large geographical scales has been the focus of several recent studies, its potential
effects on genetic structure at local scales are understudied. Environmental effects
on fine-scale spatial genetic structure (FSGS) were investigated in four Alpine
conifer species (five to eight populations per species) from the eastern Italian Alps.
Significant FSGS was found for 11 of 25 populations. Interestingly, we found no sig-
nificant differences in FSGS across species but great variation among populations
within species, highlighting the importance of local environmental factors. Interan-
nual variability in spring temperature had a small but significant effect on FSGS of
Larix decidua, probably related to species-specific life history traits. For Abies alba,
Picea abies and Pinus cembra, linear models identified spring precipitation as a
potentially relevant climate factor associated with differences in FSGS across popu-
lations; however, models had low explanatory power and were strongly influenced
by a P. cembra outlier population from a very dry site. Overall, the direction of the
identified effects is according to expectations, with drier and more variable environ-
ments increasing FSGS. Underlying mechanisms may include climate-related changes
in the variance of reproductive success and/or environmental selection of specific
families. This study provides new insights on potential changes in local genetic
structure of four Alpine conifers in the face of environmental changes, suggesting
that new climates, through altering FSGS, may also have relevant impacts on plant
microevolution.
KEYWORDS
Alpine plants, climate, environmental change, fine-scale spatial genetic structure, single
nucleotide polymorphisms
1
|
INTRODUCTION
Environmental changes, including climate change, are rapidly modify-
ing plant community composition (Franks et al., 2013). For example,
during the last decades, the Alps experienced an increase in mini-
mum temperature of about 2°C, three times the global average
(IPCC 2014), with changes being more acute at higher altitudes
(Auer et al., 2007; Brunetti et al., 2009; Haeberli & Beniston, 1998;
Philipona, 2013). Global warming has also increased the frequency
and intensity of extreme events (e.g., floods; Wilhelm et al., 2013).
As a consequence, range shifts and vegetation community changes
are strongly affecting Alpine ecosystems (Pauli, Gottfried, Reiter,
Received: 17 June 2016
|
Revised: 15 November 2017
|
Accepted: 22 November 2017
DOI: 10.1111/mec.14469
Molecular Ecology. 2018;1–12. wileyonlinelibrary.com/journal/mec ©2017 John Wiley & Sons Ltd
|
1
Klettner, & Grabherr, 2007; Theurillat & Guisan, 2001; Thuiller,
Lavorel, Araujo, Sykes, & Prentice, 2005). Environmental effects on
genetic variation of Alpine keystone plants, such as forest trees, are
still understudied. So far, research has focused on the macrogeo-
graphical scale of population genetic structure while environmental
effects at local scale, that is, within natural populations, have often
been neglected (Manel & Holderegger, 2013; Richardson, Urban,
Bolnick, & Skelly, 2014). Several recent studies point to genetic
variation within natural populations as a potential source of evolu-
tionary change that could mitigate new environmental impacts (see
references in Scotti, Gonz
alez-Mart
ınez, Budde, & Lalag€
ue, 2016;
Valladares et al., 2014).
Abiotic factors, such as climate or soils, shape the genetic
makeup of individuals, populations and species at different spatial
scales (Fischer et al., 2013; Turner, Bourne, Von Wetterberg, Hu, &
Nuzhdin, 2010). In Alpine plants, temperature and precipitation
together with soils (calcareous vs. siliceous) play a central role in
shaping plant genetic variation at macrogeographical spatial scales
(Alvarez et al., 2009; Manel et al., 2012; Mosca, Gonz
alez-Mart
ınez,
& Neale, 2014; Mosca et al., 2012a). Temperature and plant physiol-
ogy are strongly correlated, influencing plant growth and survival.
Several studies suggested that forest tree species may require expo-
sure to chilling (0–10°C) or freezing (<0°C) temperatures to acquire
maximum cold tolerance (Bigras, Ryypp€
o, Lindstr€
om, & Stattin,
2001; Beck, Heim, & Hansen, 2004; Søgaard, Granhus, & Johnsen,
2009; reviewed in Strimbeck & Kjellsen, 2010), and many species
also need a chilling period to prevent budburst during midwinter
warm periods (Harrington, Gould, & St Clair, 2010). Moreover, the
increase in temperature associated with climate change causes shifts
in the timing of bud burst (i.e., earlier flushing; Bissolli, 2006),
extending the length of the growing season, with shifts in the flow-
ering phenology affecting plant fecundity, an important fitness com-
ponent (Vitasse et al., 2011). Recent studies suggest that, in the
absence of sufficient plasticity, environmental changes will affect
the distribution of genetic diversity at the macrogeographical scale
(e.g., Jaramillo-Correa et al., 2015), due to species migration and/or
genetic adaptation in situ (reviewed in Aitken, Yeaman, Holliday,
Wang, & Curtis-McLane, 2008). We hypothesize that phenological
and physiological changes caused by new environments will also
have important consequences for genetic structure at the microgeo-
graphical scale.
Fine-scale spatial genetic structure (FSGS), that is, the nonran-
dom distribution of genotypes within populations, is determined by
the combined effects of dispersal (Hardy & Vekemans, 1999),
genetic drift and natural selection (Epperson, 1990; Rousset, 2004).
High FSGS often just reflects higher relatedness between neighbour-
ing individuals, compared to more distant ones, due to restricted dis-
persal (i.e., isolation by distance, IBD; Wright, 1943), while low FSGS
is associated with a random distribution of genotypes. Typically, life
history traits related to pollen and seed dispersal determine the
strength of FSGS (Hardy et al., 2006), with wind-pollinated and/or
outcrossing species showing lower FSGS than animal-pollinated and/
or selfing species (Dick, Hardy, Jones, & Petit, 2008; Vekemans &
Hardy, 2004). In addition, FSGS is higher in fragmented/peripheral
populations than in continuous/core ones (De-Lucas, Gonz
alez-
Mart
ınez, Vendramin, Hidalgo, & Heuertz, 2009; Gapare & Aitken,
2005; Leonardi et al., 2012; Pandey & Rajora, 2012; Yao, Zhang, Ye,
& Huang, 2011). Importantly, Audigeos, Brousseau, Traissac, Scotti-
Saintagne, and Scotti (2013) showed that divergent selection caused
by heterogeneous microenvironments (i.e., seasonally flooded bot-
tom lands versus seasonally dry soils) can also shape the genetic
structure within populations of forest trees. In such cases, isolation
by environment (IBE) is expected (Andrew, Ostevik, Ebert, & Riese-
berg, 2012; Nosil, Egan, & Funk, 2008). IBD and IBE are not mutu-
ally exclusive and can both contribute to FSGS (Van Heerwaarden
et al., 2010). The strength of FSGS is relevant for plant populations
at evolutionary timescales as strong FSGS can lead to biparental
inbreeding and thereby decrease genetic diversity (reviewed in
Heywood, 1991).
Despite several recent FSGS studies (e.g., Duminil et al., 2016;
Fajardo, Torres-D
ıaz, & Till-Bouttraud, 2016; Sork, Smouse, Grivet, &
Scofield, 2015; Torroba-Balmori et al., 2017), the effects of environ-
mental factors on spatial genetic structure within populations are yet
to be investigated. Different nonexclusive processes could contribute
to environmental effects on FSGS, being climate effects of particular
relevance in Alpine species, where spring phenology is triggered by
climate factors and not by photoperiod (Zohner, Benito, Svenning, &
Renner, 2016). First, high spring temperatures can result in early
growth resumption after winter, which in turn leads to longer growing
seasons (Cleland, Chuine, Menzel, Mooney, & Schwartz, 2007; H€
anni-
nen & Tanino, 2011; Linkosalo, Hakkinen, & Hanninen, 2006). Early
spring warming can increase flowering synchrony (Wang, Tang, &
Chen, 2016), which could in theory blur FSGS but would also make
trees more prone to late frost events (increasing the variance of
reproductive success and thus FSGS, see next point). Second, because
climate is strongly associated with fecundity, it can affect the variance
of reproductive success, a key mating parameter that strongly influ-
ences FSGS (see, e.g., De-Lucas et al., 2009). Sites with more favour-
able years for growth and reproduction (e.g., cool years with high
spring precipitation; see, e.g., Oberhuber, 2004 for Pinus cembra), with
few or no late frosts, may reduce the variance of reproductive suc-
cess, and in turn FSGS, by allowing most trees to contribute to repro-
duction, while in sites experiencing hotter, drier years, many trees
might be effectively sterile, increasing FSGS. Third, masting events
(i.e., the interindividual synchronization of seed crops in particular
years) are driven by climate factors, in particular spring/summer tem-
peratures and rainfall (Bisi et al., 2016; Kelly et al., 2013). Climate
could then affect the variance of reproductive success, and thus
FSGS, in forest trees with marked masting, such as Larix decidua, Picea
abies or Abies alba. Fourth, local environment can also affect genetic
structure patterns due to uneven postdispersal mortality. For exam-
ple, low spring precipitation could favour high mortality due to
intraspecific competition, selecting particular families with higher
competition ability (e.g., Vizca
ıno-Palomar, Revuelta-Eugercios,
Zavala, Al
ıa, & Gonz
alez-Mart
ınez, 2014) and thus also increasing
FSGS. Hence, environmental factors responsible for phenological and
2
|
MOSCA ET AL.
physiological traits could, at least in theory, influence both IBD and
IBE processes, thus affecting the strength of FSGS.
The knowledge on which environmental factors may contribute
to FSGS would improve our understanding on how changing envi-
ronmental conditions affect genetic variation and fitness within natu-
ral plant populations. Twenty-five natural populations of A. alba,
L. decidua, P. abies and P. cembra were sampled across the eastern
part of the Italian Alps. For each population, we examined FSGS
using single nucleotide polymorphism (SNP) markers and determined
differences across species and across populations within species due
to nonclimatic factors known to be relevant in Alpine environments
(i.e., biogeographical regions, elevation and soils). Then, we estimated
the effects of different climatic variables related to temperature and
precipitation, which are closely associated with plant physiology and
growth, on FSGS while considering other relevant factors in Alpine
ecosystems.
2
|
MATERIALS AND METHODS
2.1
|
Study species and sampling
Alpine forests are characterized by the presence of several conifer
species, with natural populations growing across a wide altitudinal
range (c. 800–2,250 m a.s.l.). The eastern part of the Italian Alps is
covered by pure and mixed stands of Abies alba and Picea abies, sub-
stituted by pure stands of Larix decidua and Pinus cembra and mixed
pine forests of P. cembra and Pinus mugo, at higher elevation. Alpine
conifer species have different preferences for light, temperature and
water availability. Abies alba tolerates a wide range of soils, but it is
sensitive to moisture availability and temperature (Mauri, de Rigo, &
Caudullo, 2016). Picea abies is a shade-tolerant species and has its
optimum on deep, nutritious and humid soils (Skrøppa, 2003). Larix
decidua is a typical pioneer that grows on disturbed soils, but it suffers
from the competition with other species (Matras & P^
aques, 2008).
Pinus cembra is well adapted to the severe upper subalpine climate
(Ulber, Gugerli, & Bozic, 2004). Moreover, Alpine conifer species show
different strategies to cope with environmental stress. For example,
L. decidua is a deciduous species that sheds its needles, reducing win-
ter transpiration to 2.3% of its annual photosynthetic carbon gain
(Havranek & Tranquillini, 1995), whereas P. cembra has strong roots
that penetrate a large volume of soil in their search for water. Bisi
et al. (2016) found marked interannual variation in cone production,
with years resembling masting events in P. abies,A. alba and L. decid-
ua, whereas P. cembra had less interannual variation in cone-crop size.
All species are wind-pollinated, and seeds are exclusively dispersed by
wind in A. alba,L. decidua and P. abies, whereas in P. cembra, seed
dispersal is tightly associated with a bird, the European nutcracker
(Nucifraga caryocatactes), and establishment is strongly affected by
biotic interactions (Neuschulz, Merges, Bollmann, Gugerli, & B€
ohning-
Gaese, 2017). In this study, five to eight natural populations per spe-
cies (total of 25 populations) of A. alba,P. abies,L. decidua and
P. cembra, four Alpine conifer species with distinct life history traits
(Table S1), were sampled in the eastern Italian Alps (Figure 1),
covering their environmental variation in altitude and soils (see
Table S2). For each population, coordinates of each tree (latitude, lon-
gitude and elevation, as provided by GPS; Trimble Technologies, Sun-
nyvale, CA, USA), geographical position of the sampling site (East or
West) relative to the Adige River, which constitutes an important bio-
geographic barrier (Thiel-Egenter et al., 2011), and soil types (calcare-
ous or siliceous), were recorded in the field. Fresh needles were
sampled from 65 adult trees in each population for molecular analyses
(see Mosca et al., 2012a for sampling details).
2.2
|
Climatic variables and ecological indexes
For each sampling site, monthly and annual cumulative precipitation
for the period 1981–2010 was obtained from the European Climate
Assessment & Dataset time series (spatial resolution of 0.25°; Hay-
lock et al., 2008), and temperature data were obtained from daily
reconstructed MODIS LST time series data for the period 2002–2012
(resolution—pixel size—of 250 m; Neteler, 2005, 2010). In conifers,
high temperatures from late winter to early spring cause an earlier
initiation of cambial activity and consequently a longer annual growth
period (Rossi, Deslauriers, & Anfodillo, 2007). Cambial activity is also
regulated by rainfall and photoperiod (Begum, Nakaba, Yamagishi,
Oribe, & Funada, 2013). Thus, we focused the analysis of climate
effects on FSGS on the minimum April temperature (tmin) and the
spring (1 April to 30 June) precipitation (precQ2). These two variables
were uncorrelated (Pearson’srof .10) and thus appropriate to be
combined in linear models (see below). We also included interannual
variability in spring temperature, defined as the mean of the standard
deviation of March, April and May average temperatures between
2000 and 2010 (Zohner et al., 2016). Sites with high interannual vari-
ability in spring temperature have also a higher probability to experi-
ence late frost events. Moreover, to account for the effect of winter
temperature on plant growth resumption, two ecological indexes
were calculated using daily mean temperature: chilling degree days
(CDD) and freezing degree days (FDD). Since in Alpine conifers cam-
bial activity occurs only above a mean daily temperature of 5.8–8.5°C
(Rossi et al., 2007), we calculated CDD as the number of days with
mean temperature between 0°C and 5°C in the period from 1 Jan-
uary to 31 March. Moreover, as Alpine trees only suffer damage from
freezing below 10°C (Neuner, 2014), we computed FDD as the
number of days with mean temperature below 10°C in the period
from 1 November to the end of February (Greuell et al., 2015). CDD
and FDD were calculated for each year and averaged for the period
2000–2010. Both averages for the period 2000–2010 and outlier
years/periods (i.e., years/periods with low correlation with the aver-
age), which in our case corresponded to year 2005 for CDD and
period 2006–2007 for FDD, were used in the models (see below).
2.3
|
SNP genotyping
For each species, a SNP genotyping assay was designed based on SNPs
obtained from Sanger resequencing with PCR primer pairs from
P. taeda (see details in Mosca et al., 2012b; Scalfi et al., 2014; Di Pierro
MOSCA ET AL.
|
3
et al., 2016). SNP genotyping was carried out at the Genome Center of
the University of California, Davis, using the Golden Gate platform
(Illumina, San Diego, CA, USA). After standard filtering and visual
inspection with GenomeStudio software (Illumina, San Diego, CA, USA),
a total of 231, 233, 455 and 214 high-quality SNPs were retained for
FSGS analyses in A. alba,L. decidua,P. cembra and P. abies, respectively.
2.4
|
Genetic diversity and differentiation
Expected heterozygosity, H
E
, was computed for each population fol-
lowing Nei (1978) and averaged for each species. Global F-statistics
(F
IS
and F
ST
) and pairwise genetic differentiation (F
ST
) among popula-
tions within species were calculated following Weir and Cockerham
(1984). Significant levels for F
IS
and pairwise F
ST
were obtained by
10,000 permutations. All genetic diversity and differentiation statis-
tics were computed using SPAGEDI version 1.4 (Hardy & Vekemans,
2002) and ARLEQUIN version 3.5 (Excoffier & Lischer, 2010).
2.5
|
Fine-scale spatial genetic structure (FSGS)
Fine-scale spatial genetic structure (FSGS) was analysed by linear
regression of pairwise kinship coefficients on the logarithm of inter-
tree distances using SPAGEDI. The average kinship coefficients were
calculated for six distance classes with similar tree-pair numbers
(Table S3), following Nason’s method (reported in Loiselle, Sork,
Nason, & Graham, 1995). The significance of the regression slope,
b-log, was assessed by 10,000 permutations. For each population,
we computed the Sp statistic, defined as the negative ratio between
the regression slope, b-log, and (1-F
1
), where F
1
is the mean kinship
coefficient in the first distance class (Vekemans & Hardy, 2004). Pre-
vious FSGS studies in tree species have revealed Sp values varying
from 0.03934, indicating strong FSGS in Vouacapoua Americana, over
Sp =0.00031, indicating weak FSGS in Virola michelii, to nonsignifi-
cant, undetectable FSGS (reviewed by Vekemans & Hardy, 2004). To
test for FSGS differences among species and among populations
within species, a standard ANOVA was performed in Renvironment
(Rversion 3.3.1, R Core Team 2016). In addition, Student’sttests
and Pearson’sr, also performed in R environment, were used to
investigate correlation of FSGS with soil types and biogeographic
regions, and elevation, respectively (i.e., main nonclimatic factors that
may also affect FSGS in the Alpine ecosystems).
2.6
|
Effects of climate on FSGS
Linear regression models were used to investigate a potential
association of temperature/precipitation variables (April minimum
FIGURE 1 Sampling sites for four Alpine conifer forest species: Pinus cembra,Larix decidua,Abies alba and Picea abies. Population codes
and site description are given in Table S2. The blue line indicates the Adige River, a natural biogeographic barrier
4
|
MOSCA ET AL.
temperature, tmin; interannual variability in spring temperature,
SD_springTemp; and cumulative spring precipitation, precQ2) and eco-
logical indexes (CDD and FDD, see above) with FSGS, as evaluated
by Sp, while considering other relevant factors (i.e., species, soil
types and biogeographic regions; see above). Species, soil types and
biogeographic regions were introduced in the models as covariates,
while climatic variables and ecological indexes as linear predictors of
Sp. The step function, a standard stepwise selection method based
on the minimum Akaike’s information criterion (AIC), was used to
select which variables/factors would remain in the final model that
best fitted the data. Previous to analyses, continuous variables
were normalized using the scale function. Final models were fitted
using the lm function. All analyses were performed in Rcomputing
environment.
3
|
RESULTS
3.1
|
Genetic diversity and differentiation
Genetic diversity (H
E
) was similar across tree species, except for sig-
nificantly lower values in Larix decidua (mean H
E
=0.170 in L. decid-
ua vs. mean H
E
=0.237–0.264 in the other tree species; Student’st
test =14.7086, p<.00001). Genetic diversity was also similar for
conspecific populations sampled at sites with contrasting elevation,
soil type and geographical position with respect to the Adige River,
an important biogeographic barrier (Table 1). Inbreeding coefficients
(F
IS
) were close to zero in all species, ranging from 0.132 in L. de-
cidua to 0.027 in Picea abies (Table 1). Genetic differentiation among
populations was lower in Abies alba and P. abies (F
ST
values of
0.0092 and 0.0068, respectively) than in P. cembra and L. decidua
(F
ST
of 0.0191 and 0.0255, respectively). Most pairwise F
ST
values
were highly significant, with the highest values often involving pairs
of populations from different biogeographic regions (Table 1).
3.2
|
Fine-scale spatial genetic structure (FSGS)
Significant fine-scale spatial genetic structure (FSGS) was found for
11 of 25 populations tested (44%), involving one (P. cembra) to four
(A. alba) populations per species (Table 2 and Figure 2). Average Sp
ranged from 0.0018 in P. cembra to 0.0035 in L. decidua (Table 2
and Figure 3), but differences among species were not significant (as
shown by ANOVA;F-value =0.377, p=.771). Interestingly, however,
Sp varied greatly among populations within species, for example,
from 0.0005 (pfor b-log of .3819, i.e., no significant FSGS detected)
to 0.0113 (pfor b-log of .0003, supporting a relatively strong FSGS)
in L. decidua (Table 2), highlighting the importance of local site envi-
ronmental factors. No overall significant correlation of Sp with soil
types, biogeographic regions or elevation was found (Fig. S1).
3.3
|
Effects of climate on FSGS
The best linear model associated FSGS, as evaluated by Sp, with
interannual variability in spring temperature (SD_springTemp), mean
chilling degree days (CDD) and CDD of the extreme year 2005
(CDD
2005
), while retaining species and soil types as significant fixed
factors (adjusted R
2
=.413, p=.018; Table 3, Model A). This model
showed a significant positive effect of SD_springTemp (p=.008) and
mean CDD (p=.083) on FSGS, while CDD
2005
had a negative effect
(p=.011). Among the fixed factors, a significant species effect for
L. decidua (p=.006) and P. cembra (p=.033) as well as for soil type
(p=.042) was found. Removing SD_springTemp variable rendered
the model nonsignificant (Table 3, Model B). Further exploratory
analyses for L. decidua, the species with the strongest fixed effect,
revealed that SD_springTemp was significantly correlated with FSGS
in this species (Pearson’s correlation of 0.807; p=.015; Figure 4),
while for other climate variables and ecological indexes, correlations
were not significant. For the other species, the best linear model
removing L. decidua (Table 3, Model C) included only a significant
negative effect of spring cumulative precipitation (precQ2) on FSGS
(adjusted R
2
=0.2163, p=.034), with no significant fixed factors.
However, this relationship was not significant when P16, a P. cembra
outlier population with very low precQ2 and very high Sp, was
removed from the analyses (Table 3, Model D).
4
|
DISCUSSION
By studying within-population genetic structure in four keystone
Alpine tree species with distinct life history traits, we aimed at
improving our understanding of the complex interactions between
geography, environment and genetics at local scales. Importantly,
most of the genetic variation found in forest trees resides within pop-
ulations (Scotti et al., 2016) and this variation may prove to be of
great value for evolutionary responses to climate change. Although
identified correlations of FSGS with climate factors were weak and
sensitive to the choice of populations and variables, our study sug-
gests that, depending on the species, changes in interannual variability
in spring temperatures and spring rainfall regimes may have conse-
quences for population microevolution in Alpine forests.
4.1
|
Fine-scale spatial genetic structure (FSGS)
across species
The strength of FSGS did not vary significantly among the four stud-
ied Alpine conifer species although it was slightly lower in Pinus cem-
bra and the strongest was detected in Larix decidua. Usually,
differences in FSGS among species have been related to differences
in life history traits that influence pollen and seed dispersal (see, e.g.,
Dick et al., 2008; Vekemans & Hardy, 2004). In our study, differ-
ences in seed dispersal (by wind in Abies alba,L. decidua and Picea
abies vs. by birds in P. cembra) did not affect FSGS. Both wind- and
bird-mediated seed dispersal can render long dispersal distances. In
the specific case of P. cembra, which produces cones with large
seeds that are an attractive food source for the European nutcracker
(Tomback, 2005), seed dispersal up to 15 km from the mother tree
has been observed (Mattes, 1982). Seed dispersal by wind can also
MOSCA ET AL.
|
5
TABLE 1 Genetic diversity (H
E
), pairwise genetic differentiation (F
ST
) and population inbreeding (F
IS
), for each of 25 populations in four Alpine forest tree species. Numbers in italics are
significant at 0.001 level, after 10,000 permutations. The biogeographic region (Geo) is indicated as E (East side) or W (West side) with respect to the Adige river; soil types are calcareous (C)
and siliceous (S)
Species ID NGeo Elevation [m] Soil type F
IS
H
E
Pairwise F
ST
Mean SD
Abies alba P1 P2 P3 P4 P5 P6
P1 65 W 1,168 C –0.031 0.241 0.162 –––––
P2 65 W 1,187 C –0.049 0.259 0.156 0.0099 ––––
P3 65 W 1,581 C –0.052 0.250 0.154 0.0099 0.0038 –––
P4 65 E 1,288 S –0.005 0.248 0.152 0.0101 0.0079 0.0084 ––
P5 65 W 940 S –0.066 0.243 0.158 0.0118 0.0084 0.0042 0.0121 –
P6 63 E 1,340 C –0.026 0.252 0.151 0.0133 0.0126 0.0081 0.0085 0.0094
Larix decidua P7 P8 P9 P10 P11 P12 P13 P14
P7 64 E 1,707 C –0.117 0.172 0.165 –––––––
P8 65 W 1,883 S –0.051 0.173 0.156 0.0181 ––––––
P9 63 E 1,906 S –0.132 0.182 0.159 0.0360 0.0288 –––––
P10 65 W 1,881 C –0.062 0.152 0.159 0.0319 0.0082 0.0377 ––––
P11 65 W 1,480 S –0.123 0.176 0.161 0.0451 0.0251 0.0236 0.0302 –––
P12 65 W 2,144 S –0.125 0.181 0.159 0.0481 0.0274 0.0292 0.0371 0.0014 ––
P13 64 W 1,631 S –0.112 0.158 0.159 0.0315 0.0074 0.0411 0.0113 0.0281 0.0332 –
P14 65 W 2,217 S –0.066 0.164 0.153 0.0290 0.0055 0.0347 0.0083 0.0209 0.0240 0.0046
Pinus cembra P15 P17 P18 P19 P16
P15 63 W 2,053 S –0.007 0.247 0.153 ––––
P17 65 E 2,032 C –0.018 0.242 0.157 0.0155 –––
P18 65 W 2,149 S –0.035 0.238 0.159 0.0150 0.0057 ––
P19 65 E 1,885 S 0.027 0.214 0.167 0.0104 0.0214 0.0193 –
P16 61 E 2,227 C –0.001 0.246 0.155 0.0273 0.0252 0.0183 0.0345
Picea abies P20 P21 P22 P23 P24 P25
P20 64 E 1,242 S –0.007 0.266 0.157 –––––
P21 62 E 1,701 S –0.005 0.261 0.161 0.0024 ––––
P22 65 W 1,683 S –0.006 0.262 0.158 0.0102 0.0107 –––
P23 65 W 1,757 C –0.005 0.267 0.156 0.008 0.0083 0.0046 ––
P24 64 E 1,879 S 0.022 0.266 0.158 0.0043 0.0051 0.0089 0.0071 –
P25 64 E 1,805 C 0.0004 0.264 0.158 0.0057 0.0043 0.0112 0.006 0.0049
6
|
MOSCA ET AL.
reach long distances, for example, up to about 50% of seeds went
over 3 km in Fraxinus excelsior (Bacles, Lowe, & Ennos, 2006). Other
factors such as soil types, elevation or biogeographic regions did also
not affect patterns of FSGS. These results contrast with a study by
Nardin et al. (2015) in L. decidua, where the intensity of FSGS was
found to vary with elevation. However, the authors interpreted their
findings rather as the effect of human activities and recent recolo-
nization than as an effect of changing environmental conditions
along the altitudinal gradient.
4.2
|
Environmental effects on FSGS
Interestingly, we found more variation in FSGS among populations
of the same species than among species, highlighting the importance
of local site environmental factors. Overall, our results suggested
that, depending on the species considered, high interannual variabil-
ity in spring temperature (L. decidua) or lower spring precipitation
(A. alba,P. cembra and P. abies) may result in increased FSGS.
Although the latter correlation was weak and sensitive to the choice
of populations, the result was in agreement with our expectations. It
also pointed to a potential effect of climatic extremes since the sig-
nificant correlation was driven by an outlier (P. cembra) population
living in exceptionally dry conditions. Our study is exceptional in that
it includes a large number of sites (total of 25 populations) for which
FSGS could be assessed, but still this sample size is insufficient to
reveal weak effects associated with extreme environmental condi-
tions. Thus, the associations between FSGS and climate discussed
below, although pointing to relevant environmental factors to be
considered in further studies, must also be taken with caution.
High variability in interannual spring temperatures is associated
with a higher risk of late frost events that, in turn, could increase the
variance of reproductive success and thus FSGS in L. decidua. Interest-
ingly, L. decidua is the only deciduous species in our study and the one
that flushes and flowers the earliest (Zohner & Renner, 2014;
Table S1). In this species, cones are produced every 3–6 years (Poncet
et al., 2009 and personal observations), and specific responses to cli-
mate in masting patterns may have also enhanced FSGS. In contrast to
L. decidua, spring cumulative precipitation, especially in very dry sites,
seemed to play a more relevant role than temperature in shaping
FSGS in the other species. Lower precipitation in spring can both
reduce the number of individuals that contribute to reproduction and
increase postdispersal mortality, resulting in unequal representation of
genotypes in the next generation and, as suggested in this study,
greater FSGS. Mathiasen and Premoli (2013) obtained similar results
for Nothofagus pumilio, where high elevation populations growing
under harsher environmental conditions showed stronger FSGS than
low elevation populations. However, N. pumilio is a partly clonal spe-
cies and other factors such as disturbance regimes played also a role
in their study, impeding direct comparisons.
Other environment-related population features could also underlie
the observed FSGS patterns. From those, the potentially most impor-
tant is population density. Lower density populations are expected to
have higher FSGS (Vekemans & Hardy, 2004). Thus, environment
could interact indirectly with FSGS by modifying population density,
for example, if low-density populations typically grow in environments
with lower spring precipitation and/or less adequate soils. However,
correlations between the climate factors selected in the linear models
in this study and a proxy for population density in regular stands (basal
area in m
2
/ha) for the four Alpine tree species studied were not signifi-
cant (Table S2), allowing us to discard this possibility.
4.3
|
Expected trends under climate change
In the Alpine region, climate change is expected to increase tempera-
ture (about +2.7°C in spring at the end of the 21st century) but not to
change overall annual precipitation, according to current models
(Gobiet et al., 2014). However, a different distribution of the
TABLE 2 Fine-scale spatial genetic structure (FSGS) in four Alpine
conifer species; N: sample size; b-log: slope of the regression of
kinship with the logarithm of the distance; F
1
: mean kinship of the
first distance class (0–10 m, except for Larix decidua that is 0–20 m);
p-value was calculated using 10,000 permutations, and significant
values at 0.05 level are given in italics
Species ID Nb-log p-value F
1
Sp
Abies alba P1 65 0.0064 .0009 0.0123 0.0064
P2 65 0.0029 .0488 0.0088 0.0029
P3 65 0.0001 .4556 0.0019 0.0001
P4 65 0.0025 .0404 0.0105 0.0026
P5 65 0.0032 .0315 0.0114 0.0033
P6 63 0.0010 .6673 0.0112 0.0010
Overall 0.0094 0.0024
Larix decidua P7 64 0.0107 .0003 0.0523 0.0113
P8 65 0.0005 .3819 0.0097 0.0005
P9 63 0.0043 .0415 0.0374 0.0044
P10 65 0.0045 .0220 0.0182 0.0046
P11 65 -0.0017 .2045 0.0232 0.0018
P12 65 0.0022 .2610 0.0313 0.0022
P13 64 0.0019 .1468 0.0177 0.0019
P14 65 0.0014 .2156 0.0123 0.0015
Overall 0.0253 0.0035
Pinus cembra P15 63 0.0006 .3790 0.0139 0.0006
P16 61 0.0085 .0002 0.0385 0.0088
P17 65 0.00002 .4846 0.0104 0.0000
P18 65 0.0018 .8634 0.0041 0.0018
P19 65 0.0014 .2888 0.0165 0.0014
Overall 0.0167 0.0018
Picea abies P20 64 0.0010 .2675 0.0057 0.0010
P21 62 0.0034 .0119 0.0082 0.0034
P22 65 0.0042 .0073 0.0119 0.0042
P23 65 0.0034 .0192 0.0091 0.0034
P24 64 0.0011 .1935 0.0045 0.0011
P25 64 0.0016 .1792 0.0067 0.0016
Overall 0.0077 0.0025
MOSCA ET AL.
|
7
precipitation along the year, with less precipitation in summer (-20.4%)
and more precipitation in winter (+10.4%), is also predicted (Gobiet
et al., 2014). Reduced water availability during spring and summer
could be exacerbated by the expected reduction in snow amount and
duration (i.e., an upward snowline shift of over 300–600 m by the end
of the 21st century; Gobiet et al., 2014). Our results suggest that
these changes (i.e., reduced water availability during reproduction and
seedling establishment) may result in increased FSGS in Alpine tree
species, affecting tree population microevolution, as higher FSGS may,
in the long-term, increase population inbreeding (Frankham, Ballou, &
Briscoe, 2002). Climate is also expected to become more unpre-
dictable in the Alps, with sudden decreases in temperature after cam-
bial reactivation becoming more frequent (late spring frosts). This may
enhance FSGS in temperature-sensitive Alpine species, as our study
suggests for L. decidua. Bisi et al. (2016) showed that spring and sum-
mer temperatures and precipitations of one and two years prior to
seed maturation influence cone-crop size in Alpine conifer species.
Based on their model and taking into account climate change scenar-
ios, they do not expect marked changes in masting patterns in Alpine
forests. However, interannual spring temperature variability was not
taken into account in their study.
Climate and other environmental changes will have profound
impacts on Alpine ecosystems. We suggest that new environments
FIGURE 2 Average kinship coefficient (F
ij
) plotted against mean geographical distance between individuals in each distance class, for each
of 25 populations sampled in four Alpine conifer forest species. Significant autocorrelograms are indicated with an asterisk
FIGURE 3 Box plots showing Sp statistics for each population
across species. Greater variability in FSGS among populations within
species than among species is observed
8
|
MOSCA ET AL.
will not only modify how genetic diversity is distributed among pop-
ulations at large geographical scales but will also affect, in complex
ways, how fine-scale spatial genetic structure builds within popula-
tions, being relevant for plant microevolution.
ACKNOWLEDGEMENTS
The authors wish to thank Luca Delucchi from the GIS and Remote
Sensing Unit at Fondazione Edmund Mach for providing the environ-
mental data and Yuri Gori for providing Forest Inventory data. Thanks
are extended to Berthold Heinze (BFW, Austria), Marjana Westergren
(SFI, Slovenia) and two anonymous referees for valuable suggestions
that helped to improve the manuscript. This study was realized within
the ACE-SAP project, which was partially funded by the Autonomous
Province of Trento (Italy), with the regulation No. 23, 12 June 2008, of
the University and Scientific Research Service. This project was also
supported by Cost Action FP1202 ’Strengthening conservation: a key
issue for adaptation of marginal/peripheral populations of forest trees
to climate change in Europe (MaP-FGR)’, EU H2020 GenTree project
(21000677) and BiodivERsA ERAnet (TipTree project, ANR-12-EBID-
0003), which included the French National Research Agency (ANR) as
national funder (part of the 2012 BiodivERsA call for research propos-
als).
DATA ACCESSIBILITY
SNP genotypes and sampling locations for A. alba,P. cembra and
L. decidua, Dryad Digital Repository: https://doi.org/10.5061/dryad.
tm33d. SNP genotypes and sampling locations for P. abies, Dryad
Digital Repository: https://doi.org/10.5061/dryad.n818s. Environ-
mental data used in linear models associating climate with FSGS,
Dryad Digital Repository: https://doi.org/10.5061/dryad.6d831.
AUTHOR CONTRIBUTIONS
E.M. and E.A.D.P. collected the needle samples. E.M. and S.C.G.M.
realized statistical analyses with the contributions from E.A.D.P. and
K.B.B. E.M., E.A.D.P. and K.B.B. were responsible for figure prepara-
tion. E.M. and S.C.G.M. wrote the manuscript with contributions
from D.B.N., E.A.D.P. and K.B.B. All authors read, edited and
approved the final manuscript.
ORCID
SANTIAGO C. GONZ
ALEZ-MART
INEZ HTTP://ORCID.ORG/0000-
0002-4534-3766
REFERENCES
Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T., & Curtis-McLane, S.
(2008). Adaptation, migration or extirpation: Climate change
TABLE 3 Best linear model correlating climatic factors and FSGS, as identified by AIC, after stepwise selection. A: complete model based on
all variables; B: model with the same variables as A except for SD_springTemp; C: model without Larix decidua; D: model without L. decidua and
with outlier population P16 removed. SD_springTemp: interannual variability in spring temperature; precQ2: spring cumulative precipitation;
CDD: mean chilling degree days; CDD
2005
: chilling degree days in 2005
Model
Variable estimates Fixed factors
Adj. R
2
pSD_springTemp precQ2 CDD CDD
2005
Species Soils
Larix decidua Picea abies Pinus cembra Siliceous
A 0.7084 –1.3325 –0.7468 3.0173 1.6046 2.5575 –0.8550 .4134 .0182
B––1.0482 –0.4932 2.5459 2.1263 1.9972 –1.2503 .1588 .1655
C––0.5150 ––– – – – .2163 .0344
D––0.2494 ––– – – – .0622 .3517
FIGURE 4 (a) Regression of interannual variability in spring
temperature (SD_springTemp, defined as mean standard deviation of
March, April and May temperatures from 2000–2010) and FSGS, as
evaluated by Sp,inLarix decidua. (b) A scatterplot showing no
significant relationship in the other species; the discontinuous line
indicates Sp =0
MOSCA ET AL.
|
9
outcomes for tree populations. Evolutionary Applications,1,95–111.
https://doi.org/10.1111/j.1752-4571.2007.00013.x
Alvarez, N., Thiel-Egenter, C., Tribsch, A., Holderegger, R., Manel, S.,
Sch€
onswetter, P., ... K€
upfer, P. (2009). History or ecology? Substrate
type as a major driver of spatial genetic structure in Alpine plants.
Ecology Letters,12, 632–640. https://doi.org/10.1111/j.1461-0248.
2009.01312.x
Andrew, R. L., Ostevik, K. L., Ebert, D. P., & Rieseberg, L. H. (2012).
Adaptation with gene flow across the landscape in a dune sunflower.
Molecular Ecology,21, 2078–2091. https://doi.org/10.1111/j.1365-
294X.2012.05454.x
Audigeos, D., Brousseau, L., Traissac, S., Scotti-Saintagne, C., & Scotti, I.
(2013). Molecular divergence in tropical tree populations occupying
environmental mosaics. Journal of Evolutionary Biology,26, 529–544.
https://doi.org/10.1111/jeb.12069
Auer, I., B€
ohm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., ...
Jones, P. (2007). HISTALP—historical instrumental climatologi-
cal surface time series of the Greater Alpine Region. International
Journal of Climatology,27,17–46. https://doi.org/10.1002/(ISSN)
1097-0088
Bacles, C. F. E., Lowe, A. J., & Ennos, R. A. (2006). Effective seed disper-
sal across a fragmented landscape. Science,311, 628. https://doi.org/
10.1126/science.1121543
Beck, E. H., Heim, R., & Hansen, J. (2004). Plant resistance to cold stress:
Mechanisms and environmental signals triggering frost hardening and
dehardening. Journal of Biosciences,29, 449–459. https://doi.org/10.
1007/BF02712118
Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y., & Funada, R. (2013). Reg-
ulation of cambial activity in relation to environmental conditions:
Understanding the role of temperature in wood formation of trees.
Physiologia Plantarum,147,46–54. https://doi.org/10.1111/j.1399-
3054.2012.01663.x
Bigras, F. J., Ryypp€
o, A., Lindstr€
om, A., & Stattin, E. (2001). Cold acclima-
tion and deacclimation of shoots and roots of conifer seedlings. In F.
J. Bigras, & S. J. Colombo (Eds.), Conifer cold hardiness (pp. 57–88).
Norwell, MA, USA: Kluwer Academic Publishers. https://doi.org/10.
1007/978-94-015-9650-3
Bisi, F., von Hardenberg, J., Bertolino, S., Wauters, L. A., Imperio, S., Prea-
toni, D. G., ... Martinoli, A. (2016). Current and future conifer seed
production in the Alps: Testing weather factors as cues behind mast-
ing. European Journal of Forest Research,135, 743–754. https://doi.
org/10.1007/s10342-016-0969-4
Bissolli, P. (2006). European phenological response to climate change
matches the warming pattern. Global Change Biology,12, 1969–1976.
Brunetti, M., Lentini, G., Maugeri, M., Nanni, T., Auer, I., B€
ohm, R., &
Schoener, W. (2009). Climate variability and change in the Greater
Alpine Region over the last two centuries based on multi-variable
analysis. International Journal of Climatology,29, 2197–2225.
https://doi.org/10.1002/joc.1857
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A., & Schwartz, M. D.
(2007). Shifting plant phenology in response to global change. Trends
in Ecology & Evolution,22, 357–365. https://doi.org/10.1016/j.tree.
2007.04.003
De-Lucas, A. I., Gonz
alez-Mart
ınez, S. C., Vendramin, G. G., Hidalgo, E., &
Heuertz, M. (2009). Spatial genetic structure in continuous and
fragmented populations of Pinus pinaster Aiton. Molecular Ecology,18,
4564–4576. https://doi.org/10.1111/j.1365-294X.2009.04372.x
Di Pierro, E. A., Mosca, E., Rocchini, D., Binelli, G., Neale, D. B., & La
Porta, N. (2016). Climate-related adaptive genetic variation and popu-
lation structure in natural stands of Norway spruce in the South-
Eastern Alps. Tree Genetics & Genomes,12,1–15.
Dick, C. W., Hardy, O. J., Jones, F. A., & Petit, R. J. (2008). Spatial scales
of pollen and seed-mediated gene flow in tropical rain forest trees.
Tropical Plant Biology,1,20–33. https://doi.org/10.1007/s12042-
007-9006-6
Duminil, J., Da
€
ınou, K., Kaviriri, D. K., Gillet, P., Loo, J., Doucet, J.-L., &
Hardy, O. J. (2016). Relationships between population density, fine-
scale genetic structure, mating system and pollen dispersal in a tim-
ber tree from African rainforests. Heredity,116, 295–303. https://doi.
org/10.1038/hdy.2015.101
Epperson, B. (1990). Spatial autocorrelation of genotypes under direc-
tional selection. Genetics,124, 757–771.
Excoffier, L., & Lischer, H. E. L. (2010). Arlequin suite ver 3.5: A new ser-
ies of programs to perform population genetics analyses under Linux
and Windows. Molecular Ecological Resources,10, 564–567. https://d
oi.org/10.1111/j.1755-0998.2010.02847.x
Fajardo, A., Torres-D
ıaz, C., & Till-Bouttraud, I. (2016). Disturbance and
density-dependent processes (competition and facilitation) influence
the fine-scale genetic structure of a tree species` population. Annals
of Botany,117,67–77. https://doi.org/10.1093/aob/mcv148
Fischer, M. C., Rellstab, C., Tedder, A., Zoller, S., Gugerli, F., Shimizu, K.
K., ... Widmer, A. (2013). Population genomic footprints of selection
and associations with climate in natural populations of Arabidopsis
halleri from the Alps. Molecular Ecology,22, 5594–5607. https://doi.
org/10.1111/mec.12521
Frankham, R., Ballou, J. D., & Briscoe, D. A. (2002). Introduction to conser-
vation genetics, Cambridge, UK: Cambridge University Press. https://d
oi.org/10.1017/CBO9780511808999
Franks, P. J., Adams, M. A., Amthor, J. S., Barbour, M. M., Berry, J. A.,
Ellsworth, D. S., ... Norby, R. J. (2013). Sensitivity of plants to chang-
ing atmospheric CO2 concentration: From the geological past to the
next century. New Phytologist,197, 1077–1094. https://doi.org/10.
1111/nph.12104
Gapare, W. J., & Aitken, S. N. (2005). Strong spatial genetic structure in
peripheral but not core populations of Sitka spruce [Picea sitchensis
(bong.) carr.]. Molecular Ecology,14, 2659–2667. https://doi.org/10.
1111/j.1365-294X.2005.02633.x
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., & Stoffel,
M. (2014). 21st century climate change in the European Alps—A
review. Science of the Total Environment,493, 1138–1151. https://d
oi.org/10.1016/j.scitotenv.2013.07.050
Greuell, W., Andersson, J. C. M., Donnelly, C., Feyen, L., Gerten, D.,
Ludwig, F., ... Schaphoff, S. (2015). Evaluation of five hydrological
models across Europe. Hydrology and Earth System Sciences,12,
10289–10330. https://doi.org/10.5194/hessd-12-10289-2015
Haeberli, W., & Beniston, M. (1998). Climate change and its impacts on
glaciers and permafrost in the Alps. Research for Mountain Area
Development: Europe. Ambio,27, 258–265.
H€
anninen, H., & Tanino, K. (2011). Tree seasonality in a warming climate.
Trends in Plant Science,16, 412–416. https://doi.org/10.1016/j.tpla
nts.2011.05.001
Hardy, O. J., Maggia, L., Bandou, E., Breyne, P., Caron, H., Chevallier, M.
H., ... Troispoux, V. (2006). Fine-scale genetic structure and gene
dispersal inferences in 10 neotropical tree species. Molecular Ecology,
15, 559–571.
Hardy, O. J., & Vekemans, X. (1999). Isolation by distance in a continuous
population: Reconciliation between spatial autocorrelation analysis
and population genetics models. Heredity,83, 145–154. https://doi.
org/10.1046/j.1365-2540.1999.00558.x
Hardy, O. J., & Vekemans, X. (2002). SPAGEDi: A versatile computer pro-
gram to analyse spatial genetic structure at the individual or popula-
tion levels. Molecular Ecology Notes,2, 618–620. https://doi.org/10.
1046/j.1471-8286.2002.00305.x
Harrington, C. A., Gould, P. J., & St Clair, J. B. (2010). Modeling the
effects of winter environment on dormancy release of Douglas-fir.
Forest Ecology and Management,259, 798–808. https://doi.org/10.
1016/j.foreco.2009.06.018
Havranek, W. M., & Tranquillini, M. (1995). Physiological processes dur-
ing winter dormancy and their ecological significance. In W. K. Smith,
& T. D. Hinckley (Eds.), Ecophysiology of coniferous forests (pp. 95–
10
|
MOSCA ET AL.
124). San Diego, CA: Academic Press. https://doi.org/10.1016/B978-
0-08-092593-6.50010-4
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D.,
& New, M. (2008). A European daily high-resolution gridded data set
of surface temperature and precipitation for 1950-2006. Journal of
Geophysical Research,113, D20119. https://doi.org/10.1029/
2008JD010201
Heywood, J. (1991). Spatial analysis of genetic variation in plant popula-
tions. Annual Review of Ecology and Systematics,22, 335–355.
https://doi.org/10.1146/annurev.es.22.110191.002003
IPCC (2014) Summary for policymakers. In O. Edenhofer, R. Pichs-
Madruga & Y. Sokona, et al. (Eds.), Climate Change 2014: Mitigation
of climate change. Contribution of Working Group III to the Fifth assess-
ment report of the Intergovernmental Panel on Climate change. Cam-
bridge, UK and New York, NY, USA: Cambridge University Press.
Jaramillo-Correa, J. P., Rodr
ıguez-Quil
on, I., Grivet, D., Lepoittevin, C.,
Sebastiani, F., Heuertz, M., ... Gonz
alez-Mart
ınez, S. C. (2015).
Molecular proxies of climate maladaptation in a long-lived tree.
Genetics,199, 793–807. https://doi.org/10.1534/genetics.114.
173252
Kelly, D., Geldenhuis, A., James, A., Penelope Holland, E., Plank, M. J.,
Brockie, R. E., & Mark, A. F. (2013). Of mast and mean: Differential-
temperature cue makes mast seeding insensitive to climate change.
Ecology Letters,16,90–98. https://doi.org/10.1111/ele.12020
Leonardi, S., Piovani, P., Scalfi, M., Piotti, A., Giannini, R., & Menozzi, P.
(2012). Effect of habitat fragmentation on the genetic diversity and
structure of peripheral populations of beech in central Italy. Journal
of Heredity,103, 408–417. https://doi.org/10.1093/jhered/ess004
Linkosalo, T., Hakkinen, R., & Hanninen, H. (2006). Models of the spring
phenology of boreal and temperate trees: Is there something missing?
Tree Physiology,26, 1165–1172. https://doi.org/10.1093/treephys/
26.9.1165
Loiselle, B. A., Sork, V. L., Nason, J., & Graham, C. (1995). Spatial genetic-
structure of a tropical understory shrub, Psychotria officinalis (Rubi-
aceae). American Journal of Botany,82, 1420–1425. https://doi.org/
10.2307/2445869
Manel, S., Gugerli, F., Thuiller, W., Alvarez, N., Legendre, P., Holderegger,
R., ... Taberlet, P. (2012). Broad-scale adaptive genetic variation in
alpine plants is driven by temperature and precipitation. Molecular
Ecology,21, 3729–3738. https://doi.org/10.1111/j.1365-294X.2012.
05656.x
Manel, S., & Holderegger, R. (2013). Ten years of landscape genetics.
Trends in Ecology & Evolution,28, 614–621. https://doi.org/10.1016/j.
tree.2013.05.012
Mathiasen, P., & Premoli, A. C. (2013). Fine-scale genetic structure of
Nothofagus pumilio (lenga) at contrasting elevations of the altitudinal
gradient. Genetica,1,95–105. https://doi.org/10.1007/s10709-013-
9709-6
Matras, J., & P^
aques, L. (2008). EUFORGEN technical guidelines for genetic
conservation and use for European Larch (Larix decidua) (p. 6). Rome,
Italy: Bioversity International.
Mattes, H. (1982) Die Lebensgemeinschft von Tannenhaher und Arve. Bir-
mensdorf, Switzerland: Swiss Federal Institute of Forestry Research,
241, 74 pp.
Mauri, A., de Rigo, D., & Caudullo, G. (2016). Abies alba in Europe: Distri-
bution, habitat, usage and threats. In J. San-Miguel-Ayanz, D. de Rigo,
G. Caudullo, D. T. Houston & A. Mauri (Eds), European atlas of forest
tree species (pp. e01493b). Luxembourg: Publ. Off. EU Luxembourg.
Mosca, E., Eckert, A. J., Di Pierro, E. A., Rocchini, D., La Porta, N., Belletti,
P., & Neale, D. B. (2012a). The geographical and environmental deter-
minants of genetic diversity for four alpine conifers of the European
Alps. Molecular Ecology,21, 5530–5545. https://doi.org/10.1111/
mec.12043
Mosca, E., Eckert, A. J., Liechty, J. D., Wegrzyn, J. L., La Porta, N., Ven-
dramin, G. G., & Neale, D. B. (2012b). Contrasting patterns of
nucleotide diversity for four conifers of Alpine European forests. Evo-
lutionary Applications,5, 762–775. https://doi.org/10.1111/j.1752-
4571.2012.00256.x
Mosca, E., Gonz
alez-Mart
ınez, S. C., & Neale, D. B. (2014). Environmental
versus geographical determinants of genetic structure in two sub-
alpine conifers. New Phytology,201, 180–192. https://doi.org/10.
1111/nph.12476
Nardin, M., Musch, B., Rousselle, Y., Gu
erin, V., Sanchez, L., Rossi, J.-P.,
... Rozenberg, P. (2015). Genetic differentiation of European larch
along an altitudinal gradient in the French Alps. Annals of Forest
Science,72, 517–527. https://doi.org/10.1007/s13595-015-0483-8
Nei, M. (1978). Estimation of average heterozygosity and genetic dis-
tance for small number of individuals. Genetics,89, 583–590.
Neteler, M. (2005). Time series processing of MODIS satellite data for
landscape epidemiological applications. International Journal of Geoin-
formatics,1, 133–138.
Neteler, M. (2010). Estimating daily Land Surface Temperatures in moun-
tainous environments by reconstructed MODIS LST data. Remote
Sensing,2, 333–351. https://doi.org/10.3390/rs1020333
Neuner, G. (2014). Frost resistance in alpine woody plants. Frontiers in
Plant Science,5, 654.
Neuschulz, E. L., Merges, D., Bollmann, K., Gugerli, F., & B€
ohning-Gaese,
K. (2017). Biotic interactions and seed deposition rather than abiotic
factors determine recruitment at elevational range limits of an alpine
tree. Journal of Ecology, https://doi.org/10.1111/1365-2745.12818
Nosil, P., Egan, S. P., & Funk, D. J. (2008). Heterogeneous genomic differ-
entiation between walking-stick ecotypes: “isolation by adaptation”
and multiple roles for divergent selection. Evolution,62, 316–336.
https://doi.org/10.1111/j.1558-5646.2007.00299.x
Oberhuber, W. (2004). Influence of climate on radial growth of Pinus
cembra within the alpine timberline ecotone. Tree Physiology,24,
291–301. https://doi.org/10.1093/treephys/24.3.291
Pandey, M., & Rajora, O. P. (2012). Higher fine-scale genetic structure in
peripheral than in core populations of a long-lived and mixed-mating
conifer-eastern white cedar (Thuja occidentalis L.). BMC Evolutionary
Biology,12,48–61. https://doi.org/10.1186/1471-2148-12-48
Pauli, H., Gottfried, M., Reiter, K., Klettner, C., & Grabherr, G. (2007). Sig-
nals of range expansions and contractions of vascular plants in the
high Alps: Observations (1994–2004) at the GLORIA master site
Schrankogel, Tyrol, Austria. Global Change Biology,13, 147–156.
https://doi.org/10.1111/j.1365-2486.2006.01282.x
Philipona, R. (2013). Greenhouse warming and solar brightening in and
around the Alps. International Journal of Climatology,33, 1530–1537.
https://doi.org/10.1002/joc.3531
Poncet, B. N., Garat, P., Manel, S., Bru, N., Sachet, J. M., Roques, A., &
Despres, L. (2009). The effect of climate on masting in the European
larch and on its specific seed predators. Oecologia,159, 527–537.
https://doi.org/10.1007/s00442-008-1233-5
R Core Team (2016). R: A language and environment for statistical comput-
ing. Vienna, Austria: R Foundation for Statistical Computing. URL
https://www.R-project.org/.
Richardson, J. L., Urban, M. C., Bolnick, D. I., & Skelly, D. K. (2014).
Microgeographic adaptation and the spatial scale of evolution. Trends
in Ecology and Evolution,29, 165–176. https://doi.org/10.1016/j.tree.
2014.01.002
Rossi, S., Deslauriers, A., & Anfodillo, T. (2007). Evidence of threshold
temperatures for xylogenesis in conifers at high altitudes. Oecologia,
152,1–12. https://doi.org/10.1007/s00442-006-0625-7
Rousset, F. (2004). Genetic structure and selection in subdivided popula-
tions. Princeton, NJ: Princeton University Press.
Scalfi, M., Mosca, E., Di Pierro, E. A., Troggio, M., Vendramin, G. G., Sper-
isen, C., ... Neale, D. B. (2014). Micro- and macro-geographic scale
effect on the molecular imprint of selection and adaptation in Nor-
way spruce. PLoS ONE,9, e115499. https://doi.org/10.1371/journal.
pone.0115499
MOSCA ET AL.
|
11
Scotti, I., Gonz
alez-Mart
ınez, S. C., Budde, K. B., & Lalag€
ue, H. (2016).
Fifty years of genetic studies: What to make of the large amounts of
variation found within populations? Annals of Forest Science,73,69–
75. https://doi.org/10.1007/s13595-015-0471-z
Skrøppa, T. (2003). EUFORGEN Technical guidelines for genetic conserva-
tion and use for Norway spruce (Picea abies) (p. 6). Rome, Italy: Inter-
national Plant Genetic Resources Institute.
Søgaard, G., Granhus, A., & Johnsen, O. (2009). Effect of frost nights and
day and night temperature during dormancy induction on frost hardi-
ness, tolerance to cold storage and bud burst in seedlings of Norway
spruce. Trees,23, 1295–1307. https://doi.org/10.1007/s00468-009-
0371-7
Sork, V. L., Smouse, P. E., Grivet, D., & Scofield, D. G. (2015). Impact of
asymmetric male and female gamete dispersal on allelic diversity and
spatial genetic structure in valley oak (Quercus lobata N
ee). Evolution-
ary Ecology,29, 927–945. https://doi.org/10.1007/s10682-015-
9769-4
Strimbeck, G. R., & Kjellsen, T. D. (2010). First frost: Effects of single and
repeated freezing events on acclimation in Picea abies and other bor-
eal and temperate conifers. Forest Ecology and Management,259,
1530–1535. https://doi.org/10.1016/j.foreco.2010.01.029
Theurillat, J.-P., & Guisan, A. (2001). Potential impact of climate change
on vegetation in the European Alps: A review. Climatic Change,50,
77–109. https://doi.org/10.1023/A:1010632015572
Thiel-Egenter, C., Alvarez, N., Holderegger, R., Tribsch, A., Englisch, T.,
Wohlgemuth, T., ... Linder, H. P. (2011). Break zones in the distribu-
tions of alleles and species in alpine plants. Journal of Biogeography,
38, 772–782. https://doi.org/10.1111/j.1365-2699.2010.02441.x
Thuiller, W., Lavorel, S., Araujo, M. B., Sykes, M. T., & Prentice, I. C.
(2005). Climate change threats to plant diversity in Europe. Proceed-
ing of National Academy of Sciences, USA,102, 8245–8250. https://d
oi.org/10.1073/pnas.0409902102
Tomback, D. F. (2005). The impact of seed dispersal by Clark’s nutcracker
on whitebark Pine: Multi-scale perspective on a high mountain mutu-
alism. In G. Broll, & B. Keplin (Eds.), Mountain ecosystems: Studies in
treeline ecology (pp. 181–202). Berlin, Germany: Springer. https://doi.
org/10.1007/b138976
Torroba-Balmori, P., Budde, K. B., Heer, K., Gonz
alez-Mart
ınez, S. C.,
Olsson, S., Scotti-Saintagne, C., ... Heuertz, M. (2017). Altitudinal
gradients, biogeographic history and microhabitat adaptation affect
fine-scale spatial genetic structure in African and Neotropical popula-
tions of an ancient tropical tree species. PLoS ONE,12, e0182515.
https://doi.org/10.1371/journal.pone.0182515
Turner, Tl, Bourne, E. C., Von Wetterberg, E. J., Hu, T. T., & Nuzhdin, S.
V. (2010). Population resequencing reveals local adaptation of Ara-
bidopsis lyrata to serpentine soils. Nature Genetics,42, 260–263.
https://doi.org/10.1038/ng.515
Ulber, M., Gugerli, F., & Bozic, G. (2004). EUFORGEN technical guidelines
for genetic conservation and use for Swiss stone pine (Pinus cembra) (p.
6). Rome, Italy: International Plant Genetic Resources Institute.
Valladares, F., Matesanz, S., Guilhaumon, F., Ara
ujo, M. B., Balaguer, L.,
Benito-Garz
on, M., ... Nicotra, A. B. (2014). The effects of pheno-
typic plasticity and local adaptation on forecasts of species range
shifts under climate change. Ecology Letters,17, 1351–1364.
https://doi.org/10.1111/ele.12348
Van Heerwaarden, J., Ross-Ibarra, J., Doebley, J., Glaubitz, J. C., S
anchez
Gonz
alez, J. D. J., Gaut, B. S., & Eguiarte, L. E. (2010). Fine scale
genetic structure in the wild ancestor of maize (Zea mays ssp.
parviglumis). Molecular Ecology,19, 1162–1173. https://doi.org/10.
1111/j.1365-294X.2010.04559.x
Vekemans, X., & Hardy, O. J. (2004). New insights from fine-scale spatial
genetic structure analyses in plant populations. Molecular Ecology,13,
921–935. https://doi.org/10.1046/j.1365-294X.2004.02076.x
Vitasse, Y., Francßois, C., Delpierre, N., Dufr^
ene, E., Kremer, A., Chuinee,
I., & Delzon, S. (2011). Assessing the effects of climate change on
the phenology of European temperate trees. Agricultural and Forest
Meteorology,151, 969–980. https://doi.org/10.1016/j.agrformet.
2011.03.003
Vizca
ıno-Palomar, N., Revuelta-Eugercios, B., Zavala, M. A., Al
ıa, R., &
Gonz
alez-Mart
ınez, S. C. (2014). The role of population origin and
microenvironment in seedling emergence and early survival in
Mediterranean maritime pine (Pinus pinaster Aiton). PLoS ONE,9,
e109132. https://doi.org/10.1371/journal.pone.0109132
Wang, C., Tang, Y., & Chen, J. (2016). Plant phenological synchrony
increases under rapid within-spring warming. Scientific Reports,6,
25460. https://doi.org/10.1038/srep25460
Weir, B. S., & Cockerham, C. C. (1984). Estimating F-statistics for the
analysis of population structure. Evolution,38, 1358–1370.
Wilhelm, B., Arnaud, F., Sabatier, P., Magand, O., Chapron, E., Courp, T.,
... Bard, E. (2013). Palaeoflood activity and climate change over the
last 1400 years recorded by lake sediments in the north-west Euro-
pean Alps. Journal of Quaternary Science,28, 189–199. https://doi.
org/10.1002/jqs.2609
Wright, S. (1943). Isolation by distance. Genetics,28, 114–138.
Yao, X., Zhang, J., Ye, Q., & Huang, H. (2011). Fine-scale spatial genetic
structure and gene flow in a small, fragmented population of Sino-
jackia rehderiana (Styracaceae), an endangered tree species endemic
to China. Plant Biology,13, 401–410. https://doi.org/10.1111/j.1438-
8677.2010.00361.x
Zohner, C. M., Benito, B. M., Svenning, J.-C., & Renner, S. S. (2016). Day
length unlikely to constrain climate-driven shifts in leaf-out times of
northern woody plants. Nature Climate Change,6, 1120–1123.
https://doi.org/10.1038/nclimate3138
Zohner, C. M., & Renner, S. S. (2014). Common garden comparison of
the leaf-out phenology of woody species from different native cli-
mates, combined with herbarium records, forecasts long-term change.
Ecology Letters,17, 1016–1025. https://doi.org/10.1111/ele.12308
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How to cite this article: Mosca E, Di Pierro EA, Budde KB,
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