Geographic distribution and genetic structure
Natural populations of a species in a heterogeneous landscape may have very different patterns of distribution, which can influence its population genetic characteristics (Fig. 3) as reviewed by Charlesworth & Charlesworth (2010). The classical island model assumes populations of equal finite constant size, with equal migration rates between them (Wright, 1931). These assumptions can be relaxed, with variable migration rates and changing population sizes. Species can also be distributed in large continuous populations where parts of the range are connected by symmetric gene flow, as described in the isolation by distance model by Wright (1943). Populations located at range margins represent a special case, as they are at the edge of environmental gradients where carrying capacity may be limited. In such cases, there is more migration from the core populations to the margin than vice versa, resulting in asymmetric gene flow (Kirkpatrick & Barton, 1997).
Figure 3. Schemes of the population models used to discuss evolutionary responses. The three different schematic models of population structure encountered in tree species illustrated by the different cases of Picea omorika (one limited population), P. pinaster (several fragmented populations) and P. sylvestris (large and continuous population). The color of the circle indicates the environmental condition of the population which is either undefined (in gray) or following a temperature gradient from warm (in red) to cold (in blue). The arrows represent gene flow connecting populations, with thickness indicating gene flow intensity. For the fragmented populations, the brown line symbolizes a physical barrier to gene flow, such as a mountain.
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Many economically important temperate and boreal species have large populations covering vast areas, but other tree species do not fit this distribution model. We examined the population structure of European conifers in the Pinaceae (including pines, spruces and firs), a limited group of species with very good distributional and reasonable population genetics information. A compilation of the distributions of these 27 species (and sometimes subspecies; from Jalas & Suominen, 1973), allowed us to classify them as having northern or central large, southern large or southern small or fragmented distributions (Table 1). Note that the classification is based on the current distributions, although some currently fragmented species may have had much larger distributions in the past (Soto et al., 2010). Species with a predominantly northern distribution, but also occurring in the south (e.g., P. sylvestris) were classified as northern species. Figure 3 shows examples of distributions of three species (P. omorika, P. pinaster, and P. sylvestris). There are 11 species with predominantly northern or central, large, continuous distributions, and four southern species with large, but somewhat fragmented distributions. About half of the European conifers (12) have southern, small, or fragmented distributions. Furthermore, the southern margin of most species seems to consist of fragmented small populations, whereas in the north, the range margin is part of a continuous distribution for several species. This analysis shows that in many tree populations, the threats associated with climate change are accompanied by and likely exacerbated by the effects of fragmentation at southern range margins (see also Lynch, 1996). However, if there is still extensive gene flow among the fragments, the population structure should resemble that of a continuous population.
Table 1. Distribution range and genetic estimates for the 27 European conifers
|Species||Range||Distribution||Mean QSTa||QST rangea|| F ST || H e ||Reference b|
| Abies nebrodensis ||Sicilia||South small|| || || ||0.201||Ducci et al. (1999)|
| Abies pinsapo ||Andalusia||South small|| || || ||0.056||Scaltsoyiannes et al. (1999)|
| Pinus nigra ssp dalmatica ||South Croatia||South small|| || ||0.091||0.292||Nikolic & Tucic (1983)|
| Picea omorika ||Croatia Serbia||South small|| || ||0.261||0.067||Ballian et al. (2006)|
| Pinus nigra ssp laricio ||Corsica Calabria Sicilia||South small|| || ||0.005||0.182||Scaltsoyiannes et al. (1994)|
| Abies cephalonica ||Balkans||South small||0.140||0.100–0.170||0.048||0.221||Fady & Conkle (1993)|
| Pinus peuce ||Balkans||South small|| || ||0.083||0.124||Zhelev & Tsarska (2009)|
| Pinus brutia ||Aegean Sea||South fragmented||0.040|| ||0.053||0.196||Kara et al. (1997)|
| Pinus heldreichii ||Balkans||South fragmented|| || ||0.054||0.177||Boscherini et al. (1994)|
| Abies borisii-regis ||Balkans||South fragmented|| || || ||0.273||Scaltsoyiannes et al. (1999)|
| Pinus nigra ssp pallasiana ||Greece Serbia Bulgaria||South fragmented||0.028||0.020 - 0.040||0.070||0.114||Tolun et al. (1999)|
| Pinus nigra ssp salzmannii ||East Spain South France||South fragmented|| || || ||0.216||Scaltsoyiannes et al. (2009)|
| Pinus nigra ssp nigra ||North Italy Croatia Greece||South large fragmented|| || || ||0.264||Scaltsoyiannes et al. (2009)|
| Pinus pinaster ||South West Europe||South large fragmented||0.616||0.441–0.791||0.076||0.142||Salvador et al. (2000)|
| Pinus pinea ||South Europe||South large fragmented|| || ||0.279||0.011||Fallour et al. (1997)|
| Pinus halepensis ||South Europe||South large fragmented||0.130|| || ||0.040||Schiller et al. (1986)|
| 16 species with small or fragmented range || ||0.192|| ||0.082c||0.171c|| |
| Pinus cembra ||Alps Romania||North large continuous||0.830|| ||0.040||0.081||Belokon et al. (2005)|
| Pinus uncinata ||Central West Europe||North large continuous|| || ||0.006||0.260||Lewandoski et al. (2000)|
| Larix decidua ||Central Europe||North large continuous|| || ||0.051||0.223||Maier (1992)|
| Pinus sibirica ||East Siberia||North large continuous|| || ||0.027||0.278||Goncharenko et al. (1992)|
| Pinus mugo ||Central East Europe||North large continuous|| || ||0.041||0.214||Slavov and Zhelev (2004)|
| Abies alba ||Central Europe||North large continuous||0.075||0.000–0.150|| ||0.252||Ducci et al. (1999)|
| Abies sibirica ||Siberia||North very large continuous|| || ||0.102||0.083||Semerikova & Semerikov (2006)|
| Larix sibirica ||Siberia||North very large continuous|| || ||0.082||0.159||Semerikov et al. (1999)|
| Pinus abies ssp obovata ||Lapland Siberia||North very large continuous|| || ||0.011||0.213||Krutovskii & Bergmann (1995)|
| Pinus abies ssp abies ||North Central Europe||North very large continuous||0.417||0.106 - 0.727||0.044||0.252||Krutovskii & Bergmann (1995)|
| Pinus sylvestris ||Whole Europe||North very large continuous||0.519||0.080 - 0.860||0.033||0.286||Goncharenko et al. (1994)|
| 11 species with continuous range || ||0.463|| ||0.044||0.209|| |
Consistent with the theoretical predictions, the European conifers with continuous distributions have higher genetic diversity (He) than the fragmented ones (Table 1). The widespread northern species such as P. abies and P. sylvestris have low levels of genetic differentiation (FST) in their main range (Heuertz et al., 2006; Pyhäjärvi et al., 2007). Similar findings have been made in North America for species such as P. menziesii (Eckert et al., 2010), P. sitchensis, P. glauca, and P. mariana (Namroud et al., 2008; Chen et al., 2009; Holliday et al., 2010a,b). In contrast, the level of population differentiation is almost twice for the southern fragmented species compared with the northern widely distributed ones (Table 1). Thus, the genetic data available are broadly consistent with the population structure classification based on species distribution and census size. However, current census size may ignore effects of past demographic history such as population size changes or hybridization, and we do not expect the current distributions to account for all variation in patterns of diversity.
Next, we examine the patterns of quantitative genetic variation in tree species in general and in these European conifers in particular to evaluate the effects of selection for local adaptation. We reviewed the literature of provenance trials and found a total of 112 studies on 19 relevant traits related mostly to phenology, growth, cold or drought tolerance or other ecophysiological traits, among which were 36 studies on European conifers (Table S1). Among 59 tree species studied, most were native to Europe and North America (23 and 29 species, respectively) while conifers were more studied than angiosperms (36 and 23 species, respectively). Only three traits had been measured in a sufficiently large number of experiments (Table 2) to make general comparisons and draw general patterns. We focused on the patterns of genetic variation for height increment and for the timing of bud flush, at the beginning of the growing season in spring, and the timing of bud set, an indication of cessation of growth in fall. Among all studies, these three traits had comparable levels of genetic differentiation between populations (mean value equal to 0.249, 0.324, and 0.392 for bud flush, height increment, and bud set, respectively; Table 2).
Table 2. Genetic differentiation (QST) estimates for the 19 quantitative traits studied in provenance trials
|Trait||Category||QST estimatesa||Qualitative estimation b|
|Mean QST||QST range||Nbc||Trend||Nbc|
|Dark respiration||Ecophysiology|| || ||0||Moderate||2|
|Leaf mass per area||Ecophysiology||0.022||0.000 – 0.044||2||Variable||6|
|Net assimilation||Ecophysiology||0.045||0.015 – 0.075||2||Variable||8|
|Nitrogen leaf content||Ecophysiology||0.042||0.000 – 0.083||2||Variable||6|
|Photosynthetic capacity||Ecophysiology||0.101||0.000 – 0.201||2||Variable||1|
|Stomatal conductance||Ecophysiology||0.061||0.000 – 0.150||4||Variable||4|
|Stomatal density||Ecophysiology||0.028||0.000 – 0.056||2||Low||5|
|Water use efficiency (A/gs)||Ecophysiology||0.075|| ||1||Variable||7|
|Water use efficiency (δ13C)||Ecophysiology|| || ||0||Variable||6|
|Fall frost hardiness||Frost hardiness||0.581||0.030 – 0.890||9||High||10|
|Spring frost hardiness||Frost hardiness||0.126||0.000 – 0.352||4||Variable||3|
|Winter frost hardiness||Frost hardiness||0.170||0.000 – 0.291||3|| ||0|
|Growth rate per day||Growth||0.284||0.050 – 0.710||8||Moderate||3|
|Height increment||Growth||0.324||0.040 – 0.880||36||High||33|
|Root allocation||Growth||0.340||0.251 – 0.430||2||Moderate||4|
|Bud flush||Phenology||0.249||0.000 – 0.700||24||Moderate||37|
|Bud set||Phenology||0.392||0.040 – 0.904||16||High||16|
|Germination||Phenology||0.521||0.200 – 0.940||6||High||3|
|Senescence||Phenology||0.108||0.080 – 0.180||5||Low||3|
Quantitative variation in fragmented populations
In Europe, small and fragmented conifer populations occur mainly in the southern Mediterranean area. Provided population sizes are sufficiently large, species with greater differences among populations in local phenotypic optimum and higher levels of genetic variance would be expected to have higher equilibrium differentiation. Gene flow in contrast, would reduce differentiation (Hendry et al., 2001). In general, if there is strong differential selection between populations, we would expect that the proportion of total genetic variance found between populations, QST, should be higher than FST calculated from neutral markers with appropriate mutation rates (Leinonen et al., 2008; Edelaar et al., 2011).
In the limited set of provenance trials on European conifers, estimates of quantitative genetic differentiation among populations for species with small or fragmented range were low over all traits (mean QST = 0.192, five species; Table 1). This average is about twice as high as the neutral FST (0.082; nine species; Table 1). Even though sampling across an environmental gradient is clearly not concordant with the assumptions of the island model, data of this kind are frequently analyzed by comparing QST and FST estimates for distinct samples from large and continuous populations. The average QST estimate for large populations in northern areas is 0.463 while average FST is 0.044. Thus, in this small set of studies, the ratio of QST to FST is much lower for species with small or fragmented range than that found in more widespread species. In small populations or fragments, selection for local adaptation is less efficient because of the effects of genetic drift on individual loci, and further, on the associations of alleles at different loci (Le Corre & Kremer, 2012). A review by Leimu & Fischer (2008) found that in plants only about 50% of all population pairs in reciprocal transplantations studies showed evidence of local adaptation, i.e., each population at its native site had higher fitness than other populations introduced to that site. Local adaptation was much less likely in small than large populations. However, QST values could also differ because the studies on species with limited distributions have sampled a smaller range of environmental variation than studies in species with large ranges, or because the scale of fragmentation does not match the scale of environmental variation. Reciprocal transplant experiments are needed to assess the level of local adaptation directly. In the large provenance trial data set over all 19 traits and 59 tree species, 264 of 294 analyses (around 90%) showed significant differentiation across populations (Table S1), in most cases likely due to climatic selection.
There is also some evidence in the literature for local climatic adaptation in southern European fragmented populations, such as for water use efficiency in P. halepensis (Voltas et al., 2008). Furthermore, some allelic variants at candidate loci for drought tolerance have also been found to be associated with environmental variables (Grivet et al., 2011). In some of these species, selection may have been strong enough for local adaptation to evolve. Clearly, more studies on the patterns of local adaptation are needed in the species with fragmented southern distributions. Forests at Mediterranean southern limits are threatened by faster changes in precipitation than in the northern range limit. If indeed their adaptive capacity is lower, this could make southern fragmented populations even more vulnerable.
It is also possible that these populations have evolved high adaptive phenotypic plasticity in response to environmental variability instead of genetic differentiation, either for some specific traits or across the genome (Nicotra et al., 2010). This could be likely if there is also a strong temporal component of environmental variation (Hedrick, 2006). In a changing climate, the responses due to phenotypic plasticity may maintain fitness despite climatic changes. More growth chamber or reciprocal transplant experiments will be needed to assess the response functions for these species.
Quantitative variation in continuous populations along environmental gradients
Species present in Central and Northern Europe generally have continuous distributions covering large areas encompassing much heterogeneity in abiotic and biotic environmental factors with large effective population sizes connected by extensive gene flow. If there is differential selection along environmental gradients, we expect to see patterns of clinal variation in traits (Barton, 1999). These patterns can be described by the slope of a regression along an environmental gradient. The proxies for environmental gradients most frequently used are latitude and altitude. For height increment, populations from warmer environments generally grew faster in the provenance trials (see Table S1), but quantitative estimates of the slopes were rarely available. Populations from cold environments cease growth earlier, as an adaptation to the approaching winter (see e.g., Savolainen et al., 2004). To compare slopes of clinal variation, we focused on the two phenological traits, the timing of bud flush and the timing of bud set, and compared altitudinal and latitudinal clines. To summarize data across species and environments, we considered that one degree of latitude corresponds approximately to a temperature change of 0.6 °C, and correspondingly, 100 m of altitude (Jump et al., 2009). We show examples of an altitudinal cline in bud flush in Q. petraea (Fig. 4a) and a latitudinal cline in bud set in P. sylvestris (Fig. 4b).
Figure 4. Clines of phenological traits along environmental gradients. (a) Timing of bud flush along an altitudinal gradient in Quercus petraea, based on data from Alberto et al. (2011). The timing of bud flush is expressed as the number of days from 1st January to reach the fourth developmental stage of leaf unfolding. Means of populations (large diamonds) are plotted against the altitude of origin. Bars represent standard deviations of the populations. Means of maternal tree progenies (small diamonds) in populations located at 131 m and 1235 m of elevation illustrate high additive genetic variance within populations, slightly decreasing with increasing altitude. Dark colored points represent populations and maternal trees from Luz valley while light colored points represent populations from Ossau valley. (b) Timing of bud set along a latitudinal gradient in P. sylvestris, based on data from Mikola (1982). The timing of bud set is measured as the number of days from the day of sowing. Means of populations (large diamonds) are plotted against latitude of origin. Bars represent standard deviations of the populations.
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The results of the summary in Table 3 show that the two phenological traits differ in their patterns. For bud flush, both altitudinal and latitudinal clines showed similar shallow slopes, but the direction of adaptation varied greatly among species (Table 3a). For example, high altitude populations from the same transect flushed late in Q. petraea (Fig. 4a), whereas in F. sylvatica they flushed early (Vitasse et al., 2009). This could reflect different compromises in the adaptive tradeoff between maximizing the growing season length and exposing new leaves to late frosts. Bud flush is triggered by the accumulation of cold (or chilling) sums followed by heat (or forcing) sums above a threshold temperature sum. These genetically determined critical temperature sums and thresholds may vary among species, and to a lesser extent among populations of the same species (Hänninen & Tanino, 2011). Bud flush in late successional species is also more influenced by photoperiod than in early successional species (Körner & Basler, 2010; Basler & Körner, 2012). Bud set showed steeper slopes for both gradients and in all species more northern or higher altitude populations had earlier bud set (Table 3b). These data suggest that differential selection on bud set is systematically stronger than on bud flush. Bud flush may display higher phenotypic plasticity as temperatures increase. In contrast, bud set is largely governed by photoperiods, and modulated by temperatures and drought, which results in a more predictable environmental signal from year to year and location to location (Böhlenius et al., 2006). In a warming climate, spring phenology can likely respond and advance without much genetic change, as has already been seen in many species (Gienapp et al., 2008), provided that the chilling requirement has been met. However, if chilling temperature requirements have not been met, in some cases bud flush may even be delayed (Hänninen & Tanino, 2011), as already seen recently in Tibet (Yu et al., 2010). In the fall, a change in bud set date is more likely to require a genetic change in photoperiodic responses. Some studies suggest that the heritability of bud flush is higher than for bud set (Howe et al., 2003), but estimates of the additive genetic component are rarely available in the literature. The latitudinal slopes were also much steeper than the altitudinal ones (Table 3b). Sundblad & Andersson (1995) have suggested that along the altitudinal gradients there may be more gene flow so populations do not become as differentiated. The simple calibration factors we used also may not capture all aspects of the environment.
Table 3. Slopes of the linear regressions of (a) bud flush and (b) bud set along altitudinal, and latitudinal gradients
|Altitudinal|| Abies amabilis ||5||High early||−1.18||Worrall (1983)|
| A. lasiocarpa ||2||High early||−0.83||Worrall (1983)|
| Fagus sylvatica ||9||High early||−0.43||Vitasse et al. (2009)|
| F. sylvatica ||158||High early||−0.17||Von Wuehlisch et al. (1995)|
| Pseudotsuga menziesii ||7||High early||−4.38||Acevedo-Rodriguez et al. (2006)|
| P. menziesii ||18||No cline||0.00||Rehfeldt (1978)|
| Picea abies ||23||No cline||−0.22||Chmura (2006)|
| P. abies ||8||No cline||−0.03||Skroppa & Magnussen (1993)|
| A. alba ||6||No cline||−0.20||Vitasse et al. (2009)|
| Acer pseudoplatanus ||7||No cline||−0.20||Vitasse et al. (2009)|
| Fraxinus excelsior ||9||Low early||1.90||Vitasse et al. (2009)|
| Larix occidentalis ||82||Low early||0.23||Rehfeldt (1982)|
| Quercus petraea ||10||Low early||1.15||Alberto et al. (2011)|
| Q. rubra ||4||Low early||1.93||Mc Gee (1973)|
|Total|| || ||−0.17|| |
|Latitudinal|| P. abies ||9||North early||−2.08||Sogaard et al. (2008)|
| P. glauca ||63||No cline||0.43||Li et al. (1997a)|
| P. sitchensis ||17||No cline||−0.08||Mimura & Aitken (2007)|
| P. strobus ||66||No cline||−0.83||Li et al. (1997b)|
| Populus balsamifera ||4||No cline||0.10||Farmer (1993)|
| Fagus sylvatica ||158||South early||0.20||Von Wuehlisch et al. (1995)|
| Q. petraea ||16||South early||4.17||Deans & Harvey (1996)|
| Tsuga heterophylla ||8||South early||2.17||Hannerz et al. (1999)|
|Total|| || ||0.51|| |
|Altitudinal|| A. lasiocarpa ||5||High early||−3.33||Green (2005)|
| L. occidentalis ||82||High early||−1.28||Rehfeldt (1982)|
| P. abies ||23||High early||−9.07||Chmura (2006)|
| P. abies ||8||High early||−2.63||Skroppa & Magnussen (1993)|
| P. glauca ||5||High early||−1.00||Green (2005)|
| P. contorta ||5||High early||−1.67||Green (2005)|
| P. contorta ||173||High early||−0.22||Rehfeldt (1988)|
| Pseudotsuga menziesii ||7||No cline||0.37||Acevedo-Rodriguez et al. (2006)|
|Total|| || ||−2.35|| |
|Latitudinal|| Betula pendula ||7||North early||−4.63||Viherä-Aarnio et al. (2005)|
| P. glauca ||63||North early||−3.83||Li et al. (1997a)|
| P. sitchensis ||17||North early||−4.90||Mimura & Aitken (2007)|
| P. strobus ||66||North early||−3.33||Li et al. (1997b)|
| P. sylvestris ||4||North early||−5.00||Hurme et al. (1997)|
| P. sylvestris ||4||North early||−2.35||Notivol et al. (2007)|
| P. sylvestris ||2||North early||−6.83||Savolainen et al. (2004)|
| P. balsamifera ||4||North early||−5.00||Farmer (1993)|
| P. tremula ||12||North early||−8.33||Luquez et al. (2008)|
|Total|| || ||−4.91|| |
In the large set of provenance trial studies, clinal variation along environmental gradients was very common. In the 112 studies, 309 analyses of clinal variation in different quantitative traits, 243 (78%) showed evidence of clinal variation with latitude, altitude, and sometimes longitude (Table S1).
Adaptation at range margins
An important hypothesis for species range limits is that gene flow constraints adaptation (Haldane, 1932; Mayr, 1963). Many models suggest that gene flow could limit adaptation, and even more so with asymmetrical gene flow toward small peripheral populations (see Lenormand, 2002 for review). In a model of species range involving local adaptation, a strong coupling between fitness and population size favors a feedback effect (a ‘migration meltdown’) that acts to stabilize a range margin, as exemplified in the now well-known Kirkpatrick & Barton (1997) model. However, there is limited evidence to evaluate this model, and some issues that complicate the predictions. Some models assume that genetic variance is fixed (Pease et al., 1989; Kirkpatrick & Barton, 1997), while gene flow may also increase genetic variance and the response to selection (Barton, 2001; Polechova et al., 2009). Evidence in P. contorta suggests that gene flow between populations inhabiting heterogeneous environments can increase levels of standing genetic variation (Yeaman & Jarvis, 2006), but it remains unclear whether this effect would be important in other species. Genetic drift can also reduce genetic variance and thus adaptation in peripheral populations (Alleaume-Benharira et al., 2006; Polechova et al., 2009; Bridle et al., 2010), but gene flow may replenish genetic variation. Gene flow may even introduce better adapted genes than local ones, especially in a changing climate (Alleaume-Benharira et al., 2006).
Some environments, in particular some polar or arid range margins, are intrinsically less favorable than others, and would sustain only very low population sizes even after a very long history of adaptation. Mainland-island models of local adaptation implicitly address this issue with population sizes, but spatially continuous models are still more informative. In particular, Nagylaki (1975) showed that extrinsic asymmetries in habitat quality strongly modified or could even compensate for asymmetries in selection across habitats. In other words, alleles showing a local advantage can be maintained despite having considerable antagonistic effects in other habitats, provided that the local habitat is of better quality (Nagylaki, 1978). Incorporating differences in carrying capacity in quantitative models could critically affect the potential for population adaptation (Bridle et al., 2010).
The leading and the trailing edge of migrating tree distributions face quite different challenges due to the warming climate (Hampe & Petit, 2005). At the southern range edge (in northern hemisphere), the distributions are likely already limited by high temperatures or drought conditions, and associated biotic and abiotic stresses, whereas at the northern margin, many populations have been limited by the cold temperatures (Rehfeldt et al., 2002). For the southern margin, at least at low altitudes, the environment is clearly deteriorating. The risk of extinctions will come from the interplay of multiple factors. In particular, the reduction of water availability and a longer growing season with excessively warm temperatures (IPCC, 2007) could lead to massive diebacks of trees due to drought stress or carbon starvation (Sabate et al., 2002; Bréda et al., 2006) higher mortality due to reduced defense of trees against insects (Rouault et al., 2006), and more frequent forest fires (Mouillot & Field, 2005). Increased mortality due to heat and drought stress has already been observed in many locations globally (Allen et al., 2010). The impact of environmental change will be higher in small populations due to high demographic or environmental stochasticity (Hampe & Jump, 2011).
At the southern margin, there are no populations further south contributing genes conferring necessary adaptation, but gene flow from similar environments could still increase the variance within populations (Barton, 2001). Experimental evidence of gene flow from like populations increasing fitness at warm range-edges exists for some plant species (e.g., Mimulus species, Sexton et al., 2011), and long distance dispersal can be important in fragmented landscapes (Klein et al., 2006; Fayard et al., 2009; Kremer et al., 2012).
Until now, the severe climatic conditions at boreal northern range margins have constrained growth, pollen production, seed maturation and dispersal (Sarvas, 1962; Savolainen, 1996), as well as survival (Persson, 1998), and have limited expansion to the north (Chuine & Beaubien, 2001; Morin et al., 2007). In the northernmost areas, temperatures are expected to increase by about 4 °C (Kattsov & Källen, 2005). Ecophysiologists have used the immediate plastic responses of trees to increased temperature to predict changes in species composition (Kellomäki & Kolström, 1992; Kellomäki et al., 2001). However, these predictions have not explicitly taken into account the possibilities of genetic response (Davis & Shaw, 2001; O'Neill et al., 2008). The warming in the north will improve survival, increase growth (Rehfeldt et al., 2002; Reich & Oleksyn, 2008), increase sexual reproduction (Andalo et al., 2005), and increase pollen production (Savolainen et al., 2011). Based on modeling studies, pollen and seed are predicted to be dispersed further than before (Kuparinen et al., 2009, 2010). Production of mature filled seed will likely increase many fold (Kellomäki et al., 1997) and the warmer air and soil may result in improved germination and establishment. Northern range margin populations are already colonizing more northern and higher altitude areas (Kullman, 2002; Juntunen et al., 2006; Chen et al., 2011). The increased survival rates of existing, established trees may, however, reduce establishment opportunities for better adapted genotypes generated by gene flow and local selection (Kuparinen et al., 2010).
At altitudinal range limits, adaptation could be facilitated by the short geographical distance between populations, associated with low climate change velocity (Loarie et al., 2009). Gene flow from populations at low altitudes could help the populations at higher altitudes to adapt, as has already been observed, e.g., in oak phenological shifts in situ (Alberto et al., 2010). Both colonization of new areas at higher altitudes, if available, and local selection aided by gene flow may contribute to adaptation, as many altitudinal gradients show clinal genetic differentiation (see above).