Can we predict the fate of forest tree
communities under climate change?
Eleni Kotoula
Supervisor: Prof.dr. Jos Verhoeven
Group: Ecology and Biodiversity
Utrecht University 2013
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Abstract
Climate is changing and human acts are accelerating that change raising many concerns for living organism’s
survival. Under climate change tree species will probably either become extinct, or migrate or adapt. A
crucial role in their adaptation and survival will be played by their phenotypic plasticity. Many experiments
have been carried out and many models designed in order to predict the trees species’ future response.
Experiments like common garden experiments and models based on Breeder’s equation used to record and
predict the responses of different species to predicted climate change. Here, we mention the limitation of
these experiments in models and the crucial gaps to our knowledge in order to precisely predict future fate
of forest communities in global scale. Many abiotic factors such as the rapid climatic changes, low
precipitation and fluctuations in temperature usually are not included in many models and experiments.
Moreover, many biotic factors are excluded. Herbivory might have a significant impact on plants’ migration
or/and survival and plant-plant interactions (e.g competition) can alter vegetation composition are usually
not be taken into account. Moreover, many anthropogenic actions such as the introduction of new species
and the fragmentation due to changes in land use influence the maintenance of native species and reduces
the extend of gene flow respectively. All these parameters are changing the strength of selection and the
genetic variation of populations in an unexpected way which in addition to our knowledge gaps on
phenotypic plastic response, fitness traits and generally on ecosystem dynamics set other limiting factors for
accurate prediction.
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Contents
Abstract………………………………………………………………………………….…2
Contents…………………………………………………………………………………..3
1. Introduction………………………………………………………………………………...4
1.1Migration……………………………………………………………..….……….……5
1.2Phenotypic plasticity……………………………..…………………...…………..7
1.3Adaptation………………………………………………………………..….……….9
2. Knowledge gaps and limitation of models and experimental
Designs…………………………………………………………………………………….10
2.1 Genetic correlations-selection…………………………………………..….….10
2.2 Gene flow-fragmentation………………………………………………………...11
2.3 Biotic Interactions…………………………….……………………………..….….11
2.3a Hybridization…………………………………………………………………….11
2.3b Density of population-interspecific interactions……….………….………..12
2.3c Soil composition and limits in climate change predictions……….……...…13
3. Future directions…………………………….……………………..……………….….…14
4. Conclusion………………………………………………………………………………….…15
5. References………………………………………………..………………………………....16
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1. Introduction
Climate change is altering the environment in which all living organisms develop. Measurements showed that almost
the entire globe has experienced surface warming, with the Northern hemisphere having likely the warmest 30-year
period of the last 1.400 years. Moreover, it has been observed that CO2 concentrations have increased 40% since the
pre-industrial period primarily from fossil fuels and secondarily from net emission due to land use change affecting
global Carbon cycle. Climate change, whether driven by natural or human forcing, can lead to changes in the likelihood
of the occurrence or strength of extreme weather and climate events or both (IPCC, 2013). All these changing
phenomena have impacts on forest communities which except from their ecological services; also provide economic,
social and aesthetic services to natural systems and mankind (Hasson et al., 2005). For example, forests contain around
three quarters of the earth’s terrestrial biomass and thus are tightly linked with atmospheric carbon budgets.
Moreover, they sustain the hydrological cycle through evapotranspiration, which cools climate through feedbacks with
clouds and precipitation. Therefore, forest’s survival is really important since forests are influencing many aspects on
ecosystems services from the climate to various life forms.
Modern plant taxa have persisted for a long period facing different environments, including large changes in
temperature, humidity, CO2 levels. Many studies have been done (e.g. Huntley et al., 1988) or are in progress in order
to investigate species distribution and record tree abundances changes in space and time via macrofossils (e.g. Jackson
et al., 2000) and species evolution and genetic changes during time via DNA recovered from fossil pollen (e.g. Yoshihisa
et al., 1996). That kind of studies will probably facilitate the predictions for future plant responses to the changing
environment. However, the earth has entered an era of rapid environmental change without precedent in the past
since many anthropogenic stressors such as pollution were never faced before. Therefore other studies were created in
order to predict future species distribution and survival. Some of these studies are recording species responses by using
simulated environments, common garden and selection experiments and with the contribution of several mathematical
modeling efforts to predict the effects of global warming on trees and forest communities. It is generally accepted that
the responses of many tree populations are likely to be inadequate to cope with the speed and magnitude of climatic
change, leaving groups vulnerable to decline and extinction. Extinction can be avoided through phenotypic plasticity, by
moving to a new location corresponding to environmental conditions they are adapted to, by genetically adapting to
the new conditions, or by combinations of these responses (Aitken et al., 2008).
However, can we accurately predict the future distribution of species? Which are the limitations of the model designs
and experiments? Is our knowledge sufficient to predict the fate of forest tree communities in a changing environment?
Here, we are trying to point out the difficulties in predicting future forest composition. We are exploring the changes of
tree species, migration adaptation or extinction, including the contribution of phenotypic plasticity to climate-induced
responses and we are mentioning the limitation of experimental set-ups and models to accurately predict the fate of
tree species in future.
Glossary
Adaptation lag: the trend of natural populations to show sub-optimal averages of phenotypes for their environments (Mátyás and Yeatman,
1992).
Adaptive divergence: diverge for phenotypic traits that influence survival and reproduction when the population is exposed to different
ecological environments.
Environmental stochasticity: the type of variability in population growth rates. It refers to variation in birth and death form one season to the
next in response to weather, disease, competition, predation or other factors external to population.
Effective population size (Ne): the size of ideal population that would undergo the same amount of random genetic drift as the actual
population (Wright, 1983).
Epialleles: genes which have the same DNA sequence but differ in the extent of DNA modifications causing heritable phenotypic differences.
Introgression: the movement of a gene (gene flow) from one species into the gene pool of another by the repeated backcrossing of an
interspecific hybrid with one of its parent species. Purposeful introgression is a long-term process; it may take many hybrid generations
before the backcrossing occurs.
Pleiotropy: when one gene influences multiple, seemingly unrelated phenotypic traits causing genetic correlations and hence correlated
responses to selection.
Linkage disequilibrium: the non-random association of alleles at two or more loci that descend from single, ancestral chromosomes (Reich,
2001)
Transgressive segregation: the formation of extreme phenotypes, or transgressive phenotypes, observed in segregated hybrid populations
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compared to phenotypes observed in the parental lines. The appearance of these transgressive (extreme) phenotypes can be either positive
or negative in terms of fitness.
1.1 Migration of plant species
Each population of species has a limited range of tolerance to climatic variables which is called its climatic envelop.
Historical shifts in the distribution of species and ecosystems had occurred as a result of climate change during the last
glacial period (Davis, 1986). Tree species occupy shifting geographic ranges, as documented in the pollen and fossil
record (Huntley and Birks, 1983; Willis and Van Andel, 2004). Range expansions and contractions have left their marks
both in the plastid DNA and the nuclear genes of current populations (Heuertz et al. 2006, Petit et al. 2003).
Comparisons of molecular and quantitative data suggest that selection, which occurred after postglacial colonization, is
the predominant factor that shapes present quantitative trait variation (Collignon et al. 2002, Kremer et al. 2002).
Predictions for current changes in the environment suggest that geographical distributions of organisms will probably
equally significant. Warming is expected to occur at higher levels and the impacts on biological communities might be
more severe than these in the past (Davis and Zabinski, 1992; Ritchie and Macdonald, 1986; Delcourt and Delcourt,
1987; King and Herstrom, 1997; Clark, 1998; Wilkinson, 1998; Malcolm et al., 2002).
On a global scale, the composition and distribution of biomes is largely determined by climatic parameters (Walther et
al., 2003).The biological responses to warming at the individual level affect the population responses and can lead to
changes in the distributional ranges of species. Simulations have predicted that ranges of species will be shifted
polewards and towards higher elevations in response to future climatic scenarios (Thuiller at al., 2005; Hickler et al.,
2012). Leading edged populations of the species range are the most possible candidates to expand and occupy new
territory. Indeed, there are already indications that climate change has induced biome shifts. Peñuelas and Boada
(2003), based on historical records, vegetation maps and aerial photographs, provide evidences that deciduous beech
(Fagus sylvata) forests from north-eastern Spain are gradually being replaced at its lower margin by evergreen oak
(Quercus ilex) forests and other vegetation types. On the other hand, deciduous beech claims new territories at the
upper elevation margins. An upward shift of 70 m was detected in the altitudinal distribution of deciduous beech, which
was related to a rise of the annual mean temperature of 1.2-1.4°C during the last 55 years (Peñuelas and Boada, 2003;
Peñuelas et al., 2007). Another example for a possible vegetation shift that is about to take place can be found at the
broad land of northern Italy and southern Switzerland. This area is located in the transition zone of deciduous to
evergreen broad leaved vegetation (Klötzli, 1988; Klötzli et al., 1996). A minor climatic shift made the environment
more suitable for warmer temperate species establishment and especially for exotic species which were widely
cultivated in gardens and parks (Schröter 1936; Schmid 1956). That made it clear that a new ecological niche has been
occupied by these introduced thermophilous species (Klötzli et al. 1996; Walther 1997; Carraro et al. 1999; Walther
2000, 2003).
The fascinating question of how plants are distributed on Earth in space and time has a long history which has inspired
many biogeographers and ecologists to demand explanations. Most
Procedure for species distribution modeling
modeling approaches developed for predicting plant or animal species
distributions have their roots in quantifying species–environment
1. Gather relevant data
relationships. Many species distribution models (SDMs) have often been
2. Assess the sufficiency both for the species
used to predict the impacts of climate change on biota by relating current
data and for predictors and their correlation
species distributions to climate and then projecting future distributions
and variables
3. Select the algorithm
under various climate change scenarios (Elith and Leathwick, 2009-see
4. Fitting modeling algorithm to training data
also Sidebar). There are now a variety of statistical techniques used to
5. Model evaluation (fitted response
develop SDMs (Elith et al., 2006), although most studies continue to use a
functions, model’s fit to data e.t.c.)
single modeling approach (Hanspach et al., 2011). These models are based
6. Mapping prediction to geographical space
on current niche preference of species and predict future species
7. Threshold selection if continuous
distribution by applying the environmental parameters associated with
predictions need reduction to a binary map
the present distribution onto maps representing future climate scenarios
8. Rehearsal of the model
(Pearson and Dawson 2003). Environmental predictors can effect directly
(Elith and Leathwick, 2009)
or indirectly effect species and are divided in three categories (modified
from Guisan and Zimmermann, 2000; Huston, 2002): (i) the limiting factors (or regulators) which are controlling species
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ecophysiology (e.g. temperature, water, soil composition); (ii) disturbances, defined as all types of perturbations
affecting environmental systems (natural or human-induced) and (iii) resources, defined as all compounds that can be
assimilated by organisms (e.g. energy and water).
Problems for forest migration/predictions
Few methods to date have the capacity to include factors that determine the difference between the theoretical,
complete and the realized niche of a species. Characteristics such as age to sexual maturity, fecundity and seed
dispersal and the degree of range fragmentation are really crucial here (Pearson and Dawson 2003; Guisan and Thuiller,
2005). These factors can have a major impact on the migratory capacity of a species, thus future distributions of
appropriate habitat and realized species distributions may differ considerably. Specifically, modeling studies suggest
that dispersal distance is the dominant plant property determining velocities of plant movement. Seed terminal velocity
and wind properties (spread and turbulence coefficient) are the factors that mostly influence spread rate (Coutts et al.,
2011). For long-lived species time to maturity became increasingly important and both fecundity and seed-to-adult
survival can have a significant impact in certain circumstances (Coutts et al., 2011; Caplat et al., 2012). However, a
model which combines these factors as well the spatial heterogeneity is still missing (Coutts, 2011).
Moreover, some other environmental aspects may be responsible for blocking migration. Some of these might be poor
soils, competitive pressure from surrounding herbaceous vegetation or interaction with other species such as
pathogens, insects and fungi. Consequently, it remains unclear whether climate change is the main driver of change.
Disentangling all possible drivers of distribution changes is difficult, since detection at one point or across a narrow
range of elevation involves both large scale climatic variables (primary factors) and local scale biotic interactions
(secondary factors) that control species altitudinal distribution. Across the species migration there are different factors
which may positively contribute to migration such as facilitation of seedling establishment (Cairns and Moen, 2004). On
the other hand, there are other factors which might negatively affect migration. Some examples are land-use change
(Gehrig-Fasel et al., 2007) and species interactions such as herbivory pressure (Cairns and Moen, 2004) leading to
altered community composition (e.g. Herrero et al., 2012).
Whereas there are many studies which have detected significant shifts of treeline and suggest a causal relationship
between climate change and the establishment of tree seedlings beyond the forest margin, other studies describe a
relatively stable treeline position in the last half century. For example, boreal forest ecotones have migrated little
during the last several decades in response to current warming (Payette & Filion, 1985; Suarez et al., 1999; Masek,
2001) indicating that migration is species-specific and is also influenced by other parameters than climatic change.
Moreover, species-specific responses to climate change are likely to be much more complex than those simply
predicted by the effects of warming temperatures and changing precipitations. Whereas there are numerous reports of
species moving towards higher elevations in response to the rising temperature (Klanderud and Birks, 2003; Walther et
al., 2005; Pauli et al., 2007; Kelly and Goulden, 2008; Lenoir et al., 2008; Parolo and Rossi, 2008; Vittoz et al., 2008,
Lenoir et al., 2009), and evidence for significant upslope migration now seems to occur regardless of the position along
latitudinal (Klanderud and Birks 2003, Konvicka et al. 2003, Wilson et al. 2005, Raxworthy et al. 2008, Chen et al. 2009)
or elevational gradients (Walther et al., 2005; Pauli et al. 2007; Kelly and Goulden, 2008; Lenoir et al., 2008; Vittoz et
al., 2008; Lenoir et al., 2009). In a study in Western Europe, Lenoir et al., (2008) found that two-thirds of studied plant
species (171 in total) shifted their optima upward and one-third shifted their optima downward between the periods
1905-1985 and 1986-2005. That indicates that the majority of the plant species has migrated the last century, behaving
although in a seemingly idiosyncratic way in response to climate change due to the different rates of movement.
Migration of species however, is also affected by other parameters. Plants should adapt to a previously unoccupied
environment and when a new founder population is established, phenotypic plasticity of most tree species will play a
significant role for its persistence.
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1.2 Phenotypic plasticity
Phenotypic plasticity is the capacity of a given genotype to render different phenotypic values for a given trait under
different environmental conditions. Plants express plasticity in a given trait probably through molecular-mediated
process (Schlichting and Smith, 2002) since plasticity is under genetic control (Jump and Peñuelas, 2005). Genetic
diversity and phenotypic plasticity are two significant advantages for tree adaptation in changing environments. First of
all, high levels of genetic variation within natural population improve the potential to withstand and adapt to abiotic
and biotic changes. A portion of that genetic variation is responsible for sensing the environmental changes and
produce plastic responses. Indeed, common garden experiments have shown clinal variation in adaptive traits
according to climate in tree populations (Morgenstern 1996; Howe et al. 2003; Premoli et al., 2007; Vitasse et al. 2009a;
Viveros-Viveros et al. 2009). Secondly, short-term phenotypic plasticity is a really significant mechanism for plants
reaction and confrontation to the changing environment (Sultan, 2004; Pigliucci et al., 2006; Valladares, 2006;
Ghalambor et al. 2007). It provides a protection against rapid climate change and assists rapid adaptation (Lande, 2009;
Jump, 2009) increasing the probabilities for extinction when crucial for fitness traits experienced low phenotypic
plasticity (Rehfeldt et al., 2001).
Phenotypic plasticity can be directly enhanced by natural selection in heterogeneous environments (Thompson 1991;
Schlichting and Pigliucci, 1993), and has been proposed as a catalyst in evolutionary processes for local adaptation
(Pigliucci et al., 2006). Many developmental transformations have been shown to be controlled by environmental
signaling and cross-talk pathways that sense abiotic cues such as light and nitrogen (Krouk et al., 2006), drought (Nilson
et al., 2010) and temperature. It was found, for example, that heat-tolerance responses are related to leaf
morphological traits like leaf size and thickness (Roth-Nebelsick, 2001; Godoy et al., 2011). Generally, leaf morphology
is among the best-studied effects of climate change (Morin et al., 2009). Many plants showed shifts in phenology to
earlier spring timing (Parmesan and Yohe, 2003), however these changes can be both genetic and plastic (Franks and
Weiss, 2008). Therefore, to assess population responses to climate change, it is crucial to quantify both the magnitude
of phenotypic plasticity and the rate of genetic evolution of the fitness traits.
Problems for phenotypic plasticity quantification/prediction
Many studies have shown that plants are plastic for numerous ecologically important traits, ranging from morphology,
physiology and anatomy, to developmental and reproductive timing, breeding system and offspring developmental
patterns (Sultan, 2000). Such traits generally evolve by polygenic response to selection, even though major genes may
be involved at first and accelerate evolution in response to strong selective pressures (Lande and Arnold, 1983;
Gomulkiewicz et al. 2010). However, for most reported cases of phenotypic change in the wild, it is unclear whether
they are caused by a change in the genetic composition of the population in response to natural selection (i.e. genetic
evolution), or to phenotypic plasticity. Longitudinal studies of single populations help determine whether genetic
changes in traits have evolved or instead have occurred through plasticity (determined by the environment). Plasticity
and adaptive evolution are not mutually exclusive (Nicotra et al. 2010). Some traits or populations may respond
through plasticity, others through evolution, and others through a combination of the two. The Illingworth lodgepole
pine (Pinus contorta) provenance trial in British Columbia, Canada provides a useful example of a study in order to
separate plastic from adaptive responses (Rehfeldt et al. 1999, 2001). In 1974, seeds which had been collected from
120 different locations across the species range were planted in 60 filed sites. Data on tree growth were collected for
three decades. Although each provenance was not planted in all different sites, they managed to derive the population
reaction norm (trait vs. environment plots; Pigliucci 2001) to site climatic variables (Rehfeldt et al. 1999, 2001; Wang et
al. 2010). With that experiment, researchers developed a ‘universal response function’ approach to describing
phenotypic variation as a multidimensional function of provenance climate and site climate (Wang et al. 2010). That
function facilitates the calculation of the proportion of the phenotypic variation due to plastic responses versus that
due to genetic adaptation. Similarly, Anderson et al. (2012a) showed that genetic variation explained about 20% of the
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phenotypic variation, with phenotypic plasticity explaining the majority of variation in response to test site
temperature. Therefore, it seems likely that phenotypic plasticity and adaptive evolution both contribute to shifts in
phenology. Using quantitative studies there is an opportunity to predict the opportunities for adaptation to the
continued climate change; however, there are limitations since it is hard to determine whether adaptive evolution will
allow populations to reach new phenotypic optima rapidly enough to keep pace with climate change (Anderson et al.,
2012).
Since the interplay between evolution and demography in changing environments is at least partly mediated by
phenotypic plasticity (Gienapp et al. 2008; Charmantier et al. 2008; Ozgul et al. 2010; Anderson et al., 2012), it is
difficult to accurately predict shifts in vegetation. In addition, little is known about the phenotypic plasticity of many
plants, particularly those with a long lifespan such as trees which may experience large changes in climate conditions
during their life time (Rehfeldt et al., 2001; Valladares et al. 2005) resulting in the inability to predict extinctions.
Moreover, there are several examples of different plastic responses under different environmental challenges. In order
to predict the contribution of phenotypic plasticity in adaptation to the new environment, it is necessary firstly to know
the range of response and dynamics of phenotypic plasticity under that environment since plant development in
different environmental gradients also leads to differential selective pressures. For example, Mimulus guttatus plants
flower early in unfavorable conditions, whereas they delay flowering in favorable conditions to allocate more biomass
to vegetative growth (Galloway, 1995). A selection experiment confirmed that these contrasting reproductive patterns
reflect different fitness priorities in the two types of environment: in poor sites, plants have shorter life spans and
maximizing early flower production is advantageous; in favorable sites, where plants live longer, greater allocation to
vegetative growth followed by later flowering maximizes fitness (Galloway, 1995). That capacity of plasticity in
important traits will undoubtedly play a significant role in the early stages of population persistence (Jump and
Peñuelas, 2005) and therefore will reduce the changes for extinction, but even plasticity that is not currently adaptive
can provide phenotypes important in phenotypic evolution (Lande, 2009; Chevin, 2010). In addition, although plastic
responses of plants to contrasting environments have been frequently reported as adaptive (e.g. Poorter and Lambers,
1986; Valladares and Pearcy, 1998; Donohue et al., 2003; Dudley, 2004), this is not always the case (van Kleunen and
Fischer, 2005), and examples of maladaptive plasticity also exist (Sánchez-Gómez et al., 2006a; Ghalambor et al., 2007).
Costs of plasticity (genetic, production, maintenance, information acquisition cost and developmental instability) and
limits of plasticity (epiphenotype problem, information reliability, lag-time and developmental range limit) are not well
understood and usually not included in the designed models, which hampers future predictions (see review of DeWitt
et al., 1998). Moreover, plastic organisms fail to exhibit “perfect” or “infinite” plasticity because of inability to
consistently produce the optimum in a changing environment (Futuyma and Moreno, 1988) and as mentioned before it
is hard to know whether adaptive evolution will allow populations to reach new phenotypic optima rapidly enough to
keep pace with climate change (Anderson et al., 2012).
Most of the studies are restricted to specific species-environment interactions and each species under different
environmental conditions may respond differently in terms of phenotypic plasticity and adaptation. It is really hard to
predict for each population whether genetic adaptation or phenotypic plasticity will contribute more in a changing
environment. Under conditions of rapid climate changes, plants experience novel conditions and phenotypic plasticity
seems to be more a successful way to respond since it can occur within a generation in comparison to long-term
evolutionary changes. On the other hand, there are indications (Alpert and Simms, 2002) supporting the ecological
genetic theory which is proposing that predictability favors plasticity. For example theoretical models suggest that
seasonal environmental fluctuation will probably select temporal plasticity for a trait whereas unpredictable
environmental variation probably will select continuous expression of that trait (e.g storage of carbohydrates in
underground storage; Iwasa and Kubo, 1997). Since climate change can lead to changes in the likelihood of the
occurrence or strength of extreme weather and climate events or both (IPCC, 2013), that instability may increase or
decrease plasticity.
Finally, except for the plastic responses of several traits, genome-wide changes can also be triggered, such as the
formation of epialleles (Finnegan et al., 2002, Glossary). Environmental stress can trigger DNA methylation changes in
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plants (Chinnusamy and Zhu, 2009) which may generate nonspecific (random) differences between individuals. These
differences may have adaptive significance between individuals (Rapp and Wendel, 2005), since they increase the range
of variation that natural selection can act upon. Under several forms of biotic and abiotic stress, chromatin structure
alters and that response is reversible and plastic (Pavet et al., 2006; van Zanten et al., 2011; Lang-Mladek et al., 2010;
Pecinka et al., 2010; Tittel-Elmer et al., 2010). Re-organization of chromatin results in reactivation of genes that are
normally silent (Pecinka et al., 2010; Tittel-Elmer et al., 2010), exposure of transposons (Pecinka et al., 2010) and other
sequences of heterochromatin (Jeddeloh et al.,1999; Amedeo et al., 2000; Soppe et al., 2002; Probst et al., 2003;
Lippman et al.,2004). Studies of a number of transposable elements suggest that they can be highly sensitive to various
stresses, both biotic and abiotic, including salt (Naito et al., 2009), wounding (Mhiri et al., 1997), cold, (Naito et al.,
2009; Ivashuta et al., 2002) and heat (Ito et al., 2011), as well as infection by bacteria (Grandbastien et al., 2011) and
viruses (Buchmann, 2009). Not surprisingly, in some cases, insertion of transposable elements upstream of host genes
has conferred stress responsiveness on those genes as well. It is clear that transposable elements are capable of
causing many kinds of genetic variation, and it is also clear that they have had important effects on the course of plant
evolution. The implicit assumption is that the trajectory of plant evolution would be very different in their absence and
that a substantial portion of the genetic variation that has been necessary for plant adaptation has been due to
transposable elements activity (see review Lisch et al., 2013).
1.3 Adaptation
Adaptation to changes in the environment is an important and nearly universal aspect of biotic responses to climate
change. Paleontologists have argued that rapid environmental changes overwhelm evolutionary processes (Bennet
1997; Webb 1997; Jackson 2000) based on Darwin’s perspective that differences among organisms need vast periods of
geologic time in order to collectively result in major differences in biological forms. However, recent studies have
highlighted that evolutionary change can be rapid in a number of taxa (Hendry et al., 2008), including in species that
have invaded new areas (Whitney et al., 2008). Moreover, studies (McGraw and Fetcher 1992; Hairston et al. 1999;
Kerfoot et al., 1999) with resurrected populations provide evidence for evolutionary change in adaptive traits, even in
organisms with relatively long generation times, disproving the idea that rapid environmental change overwhelms
evolutionary processes. The evolutionary responses of populations can then be pictured as a race where populations
are tracking the moving optima both in time and space (Pease et al. 1989; Polechova et al. 2009).
Evolutionary responses play a major role in maintenance of species diversity within and among species. Adaptation is
one evolutionary force by which genetic divergence may occur between the generation within a population, between
different populations and between different degrees of fitness as a response to environment and leading to higher
fitness (Stearns et al., 1992). Moreover, phenotypic divergence for growth and physiological traits occurring over very
short distances within a habitat mosaic (Brousseau et al., 2013) indicating that large genetic reservoirs are maintained
by trait filtering due to the habitant (Russo et al., 2005; Kraft and Ackerly, 2010) and by local adaptive processes
Brousseau et al., 2013). Kremer et al., (2013) found that phenotypic traits showed a very large in situ variation with
variation in altitude and latitude by looking genetically driven population differentiation, phenotypic and genetic
patterns of three genera of Quercus and Eucalyptus. Climate also contributes to that differentiation; it increases spatial
heterogeneity which promotes genetic differentiation among populations and development of local adaptation. Forest
trees are plant species with wide geographical range and occurring in environments that vary significantly in important
parameters like temperature and moisture, and since they are sessile organisms with long generation times, they
inherently require local adaptation.
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Future population predictions
Local adaptation has been demonstrated in many studies since the pioneering work of Turesson (1940) and Clausen et
al., (1940). Kawecki and Ebert (2004) attempting to give a definition of local adaptation, used transplant experiments
and proposed that when the local population has always a higher fitness at its home than in a transplanted site, then
the population is locally adapted. Adaptive genetic differentiation in response to spatial environmental heterogeneity
can also be assessed by common garden experiments (see review of Howe et al., 2003; Savolainen et al., 2007) and
more recently by studying associations between the home environment and molecular marker polymorphisms (see
review of Sork et al., 2013). Moreover, common garden experiments were applied in order to assess the relative
importance of plastic and genetic effects on adaptation (e.g. Etterson et al., 2001) and their combination with
transplant experiments gives the opportunity to demonstrate genetic differences over time and space of traits related
with reproduction (e.g. Franks et al., 2007). However, in order to observe changes in spatial patterns, experiments
should be repeated across years when the stored material such as seeds is available (Franks et al., 2007) and when the
direction of selection is clear. All these types of experiments help to predict the fate of a population and the
evolutionary potential of some population to selection pressures associated with climate change.
Apart from studies applying an experimental approach, many models also have been created. Because the phenotype
emerges from the complex interplay of genes and environment and due to the fact that traits such as behaviour,
morphology, physiology, and the susceptibility to disease, are quantitative, biologists are compelled to take a statistical
approach to study the evolution of quantitative traits. The most basic form of this approach is the breeder's equation
(Lush 1937). According to that equation, the response to selection depends on the strength of selection, on the amount
of genetic variation, and its ratio to total phenotypic variation (heritability; see Falconer and Mackay, 1996). That model
can be used to predict the response of a population to climate change across a generation and to test whether the
response is adequate, assuming that one trait has overriding importance in population survival (Sinervo et al., 2010).
Two other models were developed which are studying the genetic prediction as far as extinction risk is concerned
(Lynch and Lande, 1993; Bürger and Lynch, 1995). The first one indicates that population can survive by having a steady
rate of adaptation and as long as the change can be covered by the changing genetic variation, individual fecundity,
effective population size, environmental stochasticity (Glossary) and strength of selection. The second one indicates
that the extinction risk is higher when the population sizes are small due to the combined effects of genetic drift and
demographic stochasticity, where drift in itself does not have any affect on usually large populations of forest species.
When the population is large (Ne> 1000; Ne: effective population size-Glossary) drifts do not seem to affect genetic
variance of the population (Johnson and Barton, 2005). Gomulkiewicz and Holt (1995) have developed similar models
for population persistence while they are facing a shift resulting in the same conclusion as Johnson and Barton (2005).
Finally, Chevin et al., (2010) designed a model which also includes phenotypic plasticity. However, it is really difficult to
predict the fate of a population fate in a changing environment due to gaps in our knowledge and the limitation of
designed experiments and models.
2. Knowledge gaps- limitations of models and experimental designs
2.1 Genetic correlations-selection
Most of the previous models were dealing with one trait that is under selection pressure. Local adaptation (Kawecki and
Ebert, 2004) of a population is determined by several traits in order to create a new phenotypic optimum under the
current environment. Local adaptation of populations to climate revealed high among-population levels of genetic
variation for quantitative traits related to adaptation, geographic structuring of that variation along climatic gradients
and genotype-by-environment interaction (Kawecki and Ebert, 2004). In addition to high genetic variability of forest
tree populations in many quantitative traits (Cornelius, 1994; Morgenstern, 1996; Howe et al., 2003), it is really hard to
predict responses in breeding populations and far more difficult in the wild where the environment is more unstable
10
and there are additional abiotic interactions. Moreover, fitness traits might be correlated through pleiotropy, meaning
that a range of possible phenotypic combinations can occur which slow down the rate of evolution. That is happening
for example under genetic correlations that based on linkage disequilibrium (Etterson and Shaw, 2001-Glossary).
Hellmann and Pineda-Krch (2007) simulated the adaptational lag (Glossary) to a changing environment of two linked
traits. They confirmed that genetic correlation increases the lag through directional selection to one trait and disruptive
selection to the other changing species optima (Bürger and Krall, 2004) and facilitating the response (Duputie et al.,
2012). That points out that in natural organisms not only directional selection is taking place but also stabilizing
selection. Although, these underlying genetic correlations are so far unknown in trees due to their large generation
time.
2.2 Gene flow-fragmentation
The gene flow rate and the future fragmentation of the environment constitute two different parameters that are really
difficult to predict. Whether a population of trees is equipped with the new genotypic compositions required for future
local adaptation is difficult to tell. Forest trees have generally been found to harbour a high level of genetic diversity at
both neutral and quantitative trait loci (e.g. Hamrick, 2004). They also often have good dispersal ability and high rates
of gene flow which promote the establishment of better adapted genotypes in local populations (Austerlitz et al., 2000;
Davis and Shaw, 2001; Savolainen et al., 2007; Aitken et al., 2008). Trees are capable of long-distance gene flow, which
can promote adaptive evolution in novel environments by increasing genetic variation for fitness. Gene flow in plants is
accomplished through pollen and propagule dispersal between populations; migration of species can take place when
habitat patches are well connected. However, humankind activities lead to heavily fragmented landscapes and as a
consequence to the fragmentation of the range of species and increased genetic isolation of their population (Young et
al. 1996; Aldrich et al. 1998; Knutsen et al. 2000; Williams et al. 2003). Fragmentation not only leads to gene flow
reduction, but also to a reduced size of the population resulting in loss of genetic diversity and an increase in
differentiation because of drift and inbreeding (Young et al. 1996; Chen, 2000b). However, long lived-species like trees
probably do not follow these theoretical expectations indicating that these assumptions may be invalid or that the
number of generations that had experienced fragmentation were too few for these responses to be detected (Hamrick
2004; Lowe et al. 2005). A recent study of Wang et al,. (2011) confirmed the findings for a relative weak negative effect
of fragmentation on genetic diversity among long-lived, wind pollinated trees, it highlighted the sensitivity of really
disturbed communities to fine-scale spatial genetic structure (SGS) and pointed SGS as an early indicator for
fragmentation in plant populations. However, the changing climate and the future migration of species to higher
altitudes and latitudes may cause asynchrony in reproductive phenology which will probably limit gene flow among
distant populations inhabiting different climates despite long-distance dispersal of pollen.
Common garden experiments in forest trees suggest that genotypes can perform poorly when transferred to climates
far from their location of origin (Rehfeldt et al., 1999, 2002). Maladaptation of long-distant migrants could thus reduce
the mean fitness and if the gene flow is extensive then species will have difficulty to reach the fitness optimum (e.g
Nilsson et al., 1995). Moreover, high gene flow may also create adaptive divergence (Glossary) along the different
environmental gradients (Garcia-Ramos and Kirkpatrick et al., 1997). Considerably, except for the fact that models and
experiments exclude gene flow from the prediction of forest future fate, there is also limited knowledge about the
effect and contribution of gene flow in tree population adaptation.
2.3 Biotic interaction
2.3a Hybridization
Hybridization is an important process in plant evolution and ecology, affecting speciation (Arnold, 1992; Riesberg and
Willis, 2007), coevolution and community composition (Boecklen and Spellenberg, 1990; Whitham et al., 1994), and
local adaptation (Dodd and Afzal-Rafii, 2004). However, there are arguments that extensive hybridization in the wild is
rare (Coyne and Orr, 2004), many species pairs are capable of interspecies hybridization including Picea, Pinus, Populus
and Quercus. Periodic or ongoing introgression may provide novel alleles for adaptation to new environments, and may
11
have been significant for adaptation to new conditions in the past (Morjan and Rieseberg, 2004). It has even been
suggested that hybridization, followed by backcrossing, may have played a role in the migration of some oak species
following the last glacial period (Petit et al., 2003). Backcrossing or further introgression (Glossary) and selection can
lead to transgressive segregation (Glossary) which appeared to be frequent and an expected consequence of the
genetic architecture of differentiated populations or
species (Reiseberg et al., 1999). That result reinforces the
Species may exhibit four patterns with regard to hybridization:
idea that hybridization provides the raw material for rapid
(1) complete reproductive isolation which is usually rare and
adaptation (Stebbins, 1959; Lewontin and Birch, 1966;
leads to speciation (Arnold, 1992; Riesberg and Willis, 2007); (2)
Arnold, 1997; Grant and Grant, 1998).
formation of a hybrid zone. Hybridization occurs more
frequently in these hybrid zones than in their geographical or
ecological margins (Stebbins, 1950) due to the unfavorable
conditions existing at the edge of each species' range (Williams
et al, 2001 and references therein); Classic hybrid zone theory
(Endler, 1977; Barton and Hewitt, 1985, 1989) predicts that the
extent and the shape of hybrid zones depend upon a balance of
migration and selection, with the latter involving a combination
of ‘exogenous’ and ‘endogenous’ forms of selection (Barton,
2001); (3) introgression (Glossary) over a more widespread area.
Introgression may be facilitated when species co-occur in an
area with limited heterogeneity of the environment (Rushton,
1993; Valbuena-Carabana et al., 2007); or (4) the formation of a
hybrid swarm, where most individuals exhibit intermediate
morphologies and/or mixed genetic characteristics.
Numerous studies have examined hybridization rates;
however its quantification has been shown to be hard. The
exact tools used are important for reliable quantification.
For example, high allelic diversity of the microsatellite
markers in genetic studies can underestimate the rate of
hybridization (Hendrick et al., 1999). Moreover, the life
stage of the plant can provide different hybridization levels.
For instance, seeds give high rates of hybridization (Bacilieri
et al., 1996; Streiff et al., 1999; Salvini et al., 2009) whereas
adults provide lower rates (Craft et al., 2002; Muir and
Schlotterer, 2005; Curtu et al., 2007; Burgarella et al.,
2009). Finally, environmental perturbations, including
climate change and land use, can influence the opportunity
for hybridization and the consequences of gene
interspecific gene flow. Dodd and Afzal-Rafii (2004) found that for Quercus wislizeni, the level of introgression from two
other species was correlated with the temperature and moisture of the site.
2.3b Density of the population-interspecific interactions
Another limitation of the previous models is that they do not take into account the density of the population. In high
densities competition among individuals increases, which in turn influences tree light interception, photosynthetic
capacity, dry matter allocation growth and survival (Pacala et al. 1996). Bjorklund et al., (2009) designed a model in
which selection is dependent on density. They performed individual-based simulations with different shapes of the
fitness curve, different heritabilities, different levels of density compensation, and different autocorrelation of
environmental noise imposed on an environmental trend to study the ability of a population to adapt to changing
conditions. They found that most important factors determining that ability were width of selection function and the
level of heritability whereas levels of density-dependence had only a small impact. The level of density-dependence
matters as well, but only when the selection is strong and mostly affect population size. Selective pressure varies with
the population for the most of the tree species, as for example under decreased competition for resources.
Density of the population also increases interspecific interactions and as different populations of different species will
probably respond differently in climate, the probabilities for forest composition alterations are enhanced. Many
experiments have been done in order to examine the changes in competitions rate in changing environments. Most of
them have been done in the seedling stage since the strongest viability selection of traits in forest trees is observed at
the seedling stage (Müller-Starck, 1985; Jump et al., 2006). For instance, Reekie and Bazzaz (1989) have experimentally
demonstrated changes in competitive ability of seedlings of five different tropical tree species in response to elevated
CO2 levels leading in changes in species competition. Similarly, Muhamed et al., (2013) found that strong canopy and
climate conditions affect interactions between understory shrubs and oak seedlings from negative to positive
interaction under stressful sites. That pointed out that interaction between species might also change due to the
climate change and verified that plant-plant interactions are crucial drivers for early establishment of tree seedlings
12
(Lortie et al., 2004 and Brooker et al., 2009). Moreover, climate change, including extreme climatic events and human
activities (transportation, land degradation, agricultural systems) can enhance the invasion process, from initial
introduction through establishment and spread (Foley et al., 2005; Walther et al., 2009; Diez et al., 2012). Indeed, the
last 30 years invasive species have increased by 76% in Europe (Butchart et al., 2010) increasing competition between
native and introduced species. Consideration of all the factors that affecting seedling establishment in one study is
really hard to be done. Moreover, many of interaction are not well-understood and many of them probably not
identified yet.
Climate change may also result in the introduction of new pests, as for instance the mountain pine beetle (Robertson et
al., 2009) or new pathogens (Netherer & Schopf, 2010) but also can directly (Uvarov, 1931; Ayres, 1993) or indirectly
affect herbivores and pathogens on forest communities. Changes in temperature, precipitation, soil moisture, and
relative humidity influence the sporulation and colonization success of some forest pathogens (Brasier, 1996, Lonsdale
and Gibbs, 1996, Chakraborty, 1997, Houston, 1998) and insects. For example, in temperate and boreal forests,
increases in summer temperatures will generally accelerate the development rate of insects (and other poikilotherms)
and will commonly increase their reproductive potential (Sharpe and DeMichele, 1977, Asante et al., 1991 and Porter et
al., 1991). In addition to the direct effects of climate change on herbivores and pathogens, other effects may result
from climate-induced changes in tree physiology and tree defences (Landsberg and Smith, 1992, Ayres, 1993 and Coley,
1998). Climate change could further impact the epidemiology of herbivores and pathogens through effects on other
organisms within the community. This is particularly clear for the numerous disease syndromes that involve both
insects and pathogens (Hatcher, 1995, Paine et al., 1997). For example, the distribution of Dutch elm disease could be
influenced by climatic effects on the beetle that transports it (Hansen and Somme, 1994). Forest soil communities are
also likely to sustain indirect effects from herbivores and pathogens. For instance, herbivory and other environmental
effects on trees can influence the extent and type of mycorrhizal infection in tree roots (Gehring and Whitham, 1994,
Gehring and Whitham, 1995, Klironomos and Allen, 1995, Power and Ashmore, 1996, Gehring et al., 1997 and Gehring
et al., 1998). Finally, climate change can also have impacts on these threats. For example, herbivores may develop
faster at higher temperature and survival may even be enhanced, but these insects may consequently have lower adult
weight and fecundity (Cardenas and Gallardo, 2012).
Consequently, although there are well-reasoned and comprehensive predictions of host distributions under varying
climate regimes, the complex and co-evolved host-pathogen interactions must also be considered. In addition,
unpredictable invasions and the suggestion that the invasive species sometimes help each other in order to dominate a
community by creating “invasional melt-down” (Simberloff and Von Holle, 1999) add a considerably degree of
uncertainty to forecasting efforts. Some studies have attempted to take into account more parameters in order to draw
a conclusion for future vegetation. A nice example is the work of Herrero et al., (2011). They analysed the impact of
herbivory at the treeline of two Pine species in order to test whether herbivory damage reinforces or restrains the
climatic responses of these species. They found that the forthcoming dominance of one of the two species at the upper
altitudinal margin due to the ungulate herbivory reinforcement.
2.3c Soil composition and limits in climate change predictions
Soil composition also plays a major role in seedling establishment, with factors ranging from nutrients till microbes.
Plants significantly influence microbe communities which are associated to them, leading to feedbacks that influence
seedling establishment and successional dynamics (Kulmatiski et al., 2008; van der Putten et al., 2013). Root-associated
microbes in their turn are also influenced by many abiotic factors such as nutrient concentration, temperature and
moisture (Heinemeyer et al., 2004; Murray et al., 2010; Lundberg et al., 2012). Gehring et al., (2013) showed that
intraspecific variation and its linked traits significantly influence the abundance of interspecific competitors, the
magnitude of competitive release and the consequences of competition for the surrounding microbial community.
These traits are usually linked with several environmental responses such as drought tolerance and susceptibility to
herbivory indicating that similarly unrelated traits can be also linked. That changes to one trait can have unexpected
effects on others and that climate change may not be the main driver of species composition change.
13
From soil warming experiments, it has been clear that increased temperatures are linked to increases in soil
decomposition (Czimczik and Trumbore, 2007; Contosta et al., 2011) and hence the release of soil carbon to the
atmosphere. That has as a result the acceleration of global warming due to terrestrial carbon feedback (Woodwell,
1995). On the other hand it was found that soil warming increases net nitrogen mineralization (Merrian et al., 1996)
and enhances carbon storage in trees (Bergh et al., 1999; Rayment and Jarvis, 2000) decelerating global warming. There
are also other experiments suggesting that carbon feedback could either substantially accelerate (Cox et al., 2000) or
slow (Friedlinstein, 2003) climate change over 21st century. Results from nitrogen addition experiments have also been
mixed, showing increases, decreases, or no effect at all on soil organic matter (Waldrop and Firestone, 2004; Knorr et
al., 2005; Cusack et al., 2009; Lavoie et al., 2011). Pinder et al (2012) mentioned that nitrogen and temperature will
interact under future climate scenarios causing unknown effects on decomposition processes and the carbon balance.
Moreover, there could be feedback to climate from alterations to forest composition and resulting changes in
ecosystem attributes such as water flux and carbon pools. It is really uncertain how these feedbacks are going to affect
the Earth system since gaps in understanding terrestrial ecosystem process are still unfilled (Cox et al., 2000;
Friedlindstein, 2003).
3. Future directions
Evolutionary responses to a changing environment during the Quaternary have hardly been considered in the vast
literature. Knowing the past tree responses to climate change will undoubtedly contribute to more realistic predictions
of the future responses. Fossil pollen has already contributed to the identification of past vegetation and past climates.
More modern studies are dealing with morphological characteristics of pollen walls to identify different families. Some
techniques are even trying to identify pollen at species level (e.g. Nakazawa et al., 2013). The demographic history of
species has to be reconstructed in order to predict the future migrations and probably enhance our knowledge on the
contribution of gene flow and seed movement (e.g. Ohtani et al., 2013). Landscape genomics is a promising field of
study in order to investigate the geographic patterns of genome-wide genetic variation (Holderegger et al. 2006; Manel
et al. 2010a; Sork and Waits 2010). It can give the opportunity to simultaneously examine the effects of demographic
history, migration and selection based on the combined information on phenotype, genotype and the local
environment of large numbers of spatially collected samples (Sork et al., 2013). Moreover, more data have to be
obtained about current gene flow and hybridization between species. Ability of pollen and seed dispersal under future
climate will probably not be the same (e.g strength of the winds). The potential of future migration, as well as the
creation of new hybrid zones have to been taken into account.
Our knowledge about phenotypic plasticity and its crucial role in plant adaptation is limited. Quantifying phenotypic
population response functions to an environmental factor such as temperature or moisture, requires multiple common
garden test environments, including some that exceed population tolerances (Aitken et al. 2008). Common garden
experiments, both long and short-termed, should be designed with a wide range of population representatives and with
the major representatives of surrounding vegetation types. Changes in climatic conditions should be applied not
directly but gradually, at least as far as temperature, moisture and CO 2 is concerned. Then, based on the new and welldesigned species distribution models, taking into account many direct and indirect factors, appropriate sites have to be
selected. These experiments will provide evidence for future shifts in vegetation composition. However, the
environmental fluctuations are in most of the cases unpredictable, at least for the long-term, making predictions
concerning the evolutionary future of populations rather problematic. However, combinations of common garden
experiments with experiments with mutants and the genome analysis of species will probably provide much useful
information. Such studies will probably contribute to the understanding of different environmental cues on species
plastic responses and their contribution to plant survival and might reveal genes and traits which are responsible for
species tolerance and adaptation to environmental changes. Finally, quantification of the role and mechanism of
epigenetics in different tree types should be pursued, using the extensive tool-box being generated for model
organisms. Moreover, new models should be created regarding the sexual maturity, fecundity and seed dispersal of
trees in response to climatic and biotic conditions.
14
There is also a critical need for a hierarchical approach to the study of climate change impacts on plant-soil interactions
and carbon cycling that incorporates mechanisms and responses that occur across different temporal and spatial scales.
It is crucial to our understanding how microbial respiration respond to global warming and how temperature driven
nitrogen cycle would affect carbon storage in order to predict future climate change by knowing whether carbon
feedbacks will be positive or negative in their responses to climate change.
4. Conclusion
There are a myriad of factors to take into account in order to predict future species composition but there is a
possibility to predict the extinction risk of trees. In most of the cases, studies that have been done so far over- or
underestimate the extinction risk of many communities due to the fact that they do not consider many biotic factors
and disturbances which might prevent or facilitate survival. However, rating plants according their extinction risk and
identification of strategies in order to avoid their loss is probably the best way forward. Provenance trials provide some
knowledge about local adaptation of species which have been used in reforestation (Morgenstern, 1996). Further
kknowledge of population differentiation and potentially important phenotype–environment correlations in
quantitative traits, such as timing of bud burst, leaf morphology, and many others, can be used to design landscape
genomic studies and to guide sampling strategies for forest management and restoration.
15
References
Aitken SN, et al. (2008). Adaptation, migration or extirpation: climate change outcomes for tree populations. Evolutionary
Applications, 1:95–111.
Aldrich PR, Hamrick JL, Chavarriaga P and Kochert G (1998). Microsatellite analysis of demographic genetic structure in fragmented
populations of the tropical tree Symphonia globulifera. Mol. Ecol, 7: 933–944.
Amedeo P, Habu Y, Afsar K, Mittelsten Scheid O and Paszkowski J (2000). Disruption of the plant gene MOM releases transcriptional
silencing of methylated genes. Nature, 405: 203–206.
Anderson JT, Inouye DW, McKinney AM, Colautti RI and Mitchell-Olds T (2012). Phenotypic plasticity and adaptive evolution
contribute to advancing flowering phenology in response to climate change. Proc Biol Sci 279: 3843–3852.
Arnold ML (1992). Natural hybridization as an evolutionary process. Ann Review of Ecol, Evol and Syst. 23:237- 261.
Asante SK, Danthanarayana W and Heatwole H (1991). Biomics and population growth statistics of apterous virginoparae of woody
apple aphid Eriosoma lanigerum at constant temperatures. Entomol Exp Appl 60: 261-270.
Austerlitz F, Mariette S, Machon N, Gouyon PH and Godelle B. (2000). Effects of colonization processes on genetic diversity:
differences between annual plants and tree species. Genetics 154:1309–1321.
Ayres MP (1993). Plant defense, herbivory and climate change. In : Kareive PM, Kingsolver JG, Huey RB, editors. Biotic interaction and
global change. Sunderland MA: Sinauer Associates, 75-94.
Bacilieri R, A Ducousso, RJ Petit and A Kremer (1996). Mating system and asymmetric hybridization in a mixed stand of European
oaks. Evolution 50: 900 – 908.
Bennett KD (1997). Evolution and ecology: the pace of life. Cambridge University Press, Cambridge, UK.
Bergh J, Linder S, Lundmark T et al. (1999) The effect of water and nutrient availability on the productivity of Norway spruce in
northern and southern Sweden. Forest Ecology and Management, 119: 51–62.
Boecklen WJ and R Spellenberg (1990). Structure of herbivore communities in two oak (Quercus spp) hybrid zones. Oecologia 85: 92100.
Brasier CM (1996). Phytophthora cinnamomi and oak decline in southern Europe: environmental constrains including climate
change. Ann Sci For 347- 358.
Brooker RW et al (2009). Don’t diss integration: a comment on Ricklefs’s disintegrating communities. Am. Nat. 174:919–927.
Brousseau PM, Hébert C, Cloutier C0 and Côté SD (2013). Short-term effects of reduced white-tailed deer density on insect
communities in a strongly overbrowsed boreal forest ecosystem. Biodiversity and Conservation 22: 77–92.
Buchmann RC, Asad S, Wolf JN, Mohannath G and Bisaro DM (2009). Geminivirus AL2 and L2 proteins suppress transcriptional gene
silencing and cause genome-wide reductions in cytosine methylation. J. Virol. 83, 5005–5013.
Burgarella C, Lorenzo Z, Jabbour-Zahab R, Lumaret R, Guichoux E, Petit RJ, Soto A and Gil L (2009). Detection of hybrids in nature:
application to oaks (Quercus suber and Q. ilex). Heredity 102: 442–452.
Bürger R and R Lande (1994). On the distribution of the mean and variance of a quantitative trait under mutation-selection-drift
balance. Genetics, 138: 901–912.
Butchart SHM, Collar NJ, Stattersfield AJ and Bennun LA (2010). Conservation of the world’s birds: the view from 2010. Handbook of
the birds of the world, Vol. 15. Weavers to new world warblers (ed. by J. del Hoyo, A. Elliott and D.A. Christie), pp. 13–68. Lynx
Edicions, Barcelona, Spain.
Cairns DM and Moen J (2004). Herbivory influences tree lines. J. Ecol., 92: 1019-1024.
16
Caplat P et al. (2012). Seed terminal velocity, wind turbulence, and demography drive the spread of an invasive tree in an analytical
model. Ecology, 93: 368–377.
Cardenas AM and Gallardo P (2012). The effect of temperature on the preimaginal development of the Jewel beetle, Coraebus
florentinus (Coleoptera: Buprestidae). Eur. J. Entomol, 109: 21–28.
Carraro G, Klötzi F, Walther GR, Gianoni P and R Mossi (1999). Observed changes in vegetation in relation to climate warming. Final
Report NPR 31, vdf Hochschulverlag, Zürich.
Chakraborty S (1997). Recent advances in studies of anthracnose of stylosanthes. V. Advances in research on stylosanthes
anthracnose epidemiology in Australia. Trop Grasslands, 31: 445-453.
Charmantier A and Garant D (2005). Environmental quality and evolutionary potential: lessons from wild populations. Proc. R. Soc. B.
272: 1415–1425.
Chen IC et al. (2009). Elevation increases in moth assemblages over 42 years on a tropical mountain. Proc. Nat. Acad. Sci. USA 106:
1479-1483.
Chevin LM, Lande R and Mace GM (2010). Adaptation, plasticity, and extinction in a changing environment: towards a predictive
theory. PLoS. Biol, 8: 100-135.
Chinnusamy V and Zhu JK (2009). Epigenetic regulation of stress responses in plants. Curr. Opin. Plant Biol. 12: 133–139.
Clark JS (1998). Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord. American Naturalist, 152:
204-224.
Clausen J, Keck DD and WM Hiesey (1940). Experimental studies on the nature of species. I. Effects of varied environments on
western North American plants. Carnegie Institute of Washington. 520.
Coley PD (1998). Possible effects of climate change on plant/herbivore interaction in moist tropical forest. Clim Change, 39: 455-472.
Collignon A-M, Van de Sype H and Favre J-M (2002). Geographical variation in random amplified polymorphic DNA and quantitative
traits in Norway spruce. Can. J. For. Res, 32:266–82.
Contosta AR, Frey SD and Cooper AB (2011). Seasonal dynamics of soil respiration and N mineralization in chronically warmed and
fertilized soils. Ecosphere, 2 36.
Cornelius J (1994). Heritabilities and additive genetic coefficients of variation in forest trees. Canadian Journal of Forest. Research.
24:372–379.
Coutts SR et al (2011). What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we
use this knowledge to aid management? Biol. Invasions, 13: 1649–1651.
Cox PM, Betts RA, Jones CD, Spall SA and Totterdell IJ (2000). Acceleration of global warming due to carbon-cycle feedbacks in a
coupled climate model. Nature, 408: 184-187.
Craft KJ, MV Ashley and WD Koenig (2002). Limited hybridization between Quercus lobata and Quercus douglasii (Fabaceae) in a
mixed stand in central coastal California. Americ Jour of Bon, 89: 1792- 1798.
Curtu AL, Gailing O , Finkeldey R (2007). Evidences for hybridization and introgression within a species- rich oak (Quercus spp.)
community. BMC Evol Biol 7: 218.
Cusack DF (2009). Effects of chronic nitrogen additions on carbon and nitrogen cycling in two tropical forests. PhD Dissertation,
202pp. University of California – Berkeley, Berkeley, CA.
Czimczik CI and Trumbore SE (2007). Short-term controls on the age of microbial carbon sources in boreal forest soils. J. Geophys.
Res. 112:GO3001.
Davis MB and Shaw RG (2001). Range shifts and adaptive responses to quaternary climate changes. Science, 292:673–679.
17
Davis MB and Zabinski C (1992). Changes in geographical range resulting from greenhouse warming: effects on biodiversity in forests.
Global warming and biology diversity (eds R.L. Peters and T.E. Lovejoy) pp. 297-308. Yale University Press, New Haven, CT, USA.
Davis MB, KD Woods, SL Webb and RP Futyma (1986). Dispersal versus climate—expansion of Fagus and Tsuga into the upper Great
Lakes region. Vegetation, 67: 93–103.
Delcourt PA and Delcourt HR (1987). Long-term forest dynamids of the temperate zone: a case study of late- quaternary forests in
eastern North America, 439 p. Springer-Verlag, New York.
DeWitt TJ, Sih A and Wilson DS (1998). Costs and limits of phenotypic plasticity. Trends in Ecology and Evolution 13: 77–81.
Diez JM, D’Antonio CM, Dukes JS, Grosholz ED, Olden JD, Sorte CJB et al. (2012). Will extreme climatic events facilitate biological
invasions? Front. Ecol. Environ., 10: 249–257.
Dodd RS and Z Afzal-Rafii (2004). Selection and dispersal in a multispecies oak hybrid zone. Evolution, 58 : 261 – 269.
Donohue K, Hammond-Pyle E, Messiqua D, Heschel MS, Schmitt J (2003). Adaptive divergence in plasticity in natural populations of
Impatiens capensis and its consequences for performance in novel habitats. Evolution, 55: 692–702.
Dudley SA (2004). The functional ecology of phenotypic plasticity in plants. In: DeWittTJ, ScheinerSM, eds. Phenotypic plasticity.
Functional and conceptual approaches. Oxford, UK: Oxford University Press, 151–172.
Duputie A, Massol F, Chuine I, Kirkpatrick M and Ronce O (2012). How do genetic correlations affect species range shifts in a
changing environment? Ecology Letters, 15:251–259.
Elith J and Leathwick JR (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual
Review of Ecology, Evolution, and Systematics 40 : 677-697
Elith, J. et al. (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129–151.
Etterson JR and Shaw RG (2001). Constraint to adaptive evolution in response to global warming. Science, 294:151–154.
Falconer DS and MacKay TFC (1996). Introduction to Quantitative Genetics, Fourth edition, Longman, 464 p.
Finnegan CM, W Berg, GK Lewis and AL De Vico (2002). Antigenic properties of the human immunodeficiency virus transmembrane
glucoprotein during cell-cell fusion. J Virol., 76: 12123-12134.
Franks S and Weiss A (2008). A change in climate causes rapid evolution of multiple life-history traits and their interactions in an
annual plant. Journal of Evolutionary Biology, 21: 321-1334.
Franks SJ, S Sim and AE Weis (2007). Rapid evolution of flowering time by an annual plant in response to a climate fluctuation
Proceedings of the National Academy of Sciences, 104: 1278–1282.
Friedlingstein P, Dufresne J, Cox P and Rayner P (2003). How positive is the feedback between climate change and the carbon cycle?
Tellus B, 55: 692-700.
Gallaway JN, Schlesinger WH, Levy H, Michales JL and Schnoor JL (1995). Nitrogen-fixation: Antropogenic enhancement-environment
response. Global Biochemical Cycles, 9:235-252.
Garcia-Ramos G and Kirkpatrick M (1997). Genetic models of adaptation and gene flow in peripheral populations. Evolution, 51: 21–
28.
Gehring C, D Flores-Rentería, CM Sthultz, TM Leonard, L Flores-Rentería, AV Whipple and TG Whitham (2013). Plant genetics and
interspecific competitive interactions determine ectomycorrhizal fungal community responses to climate change. Molecular Ecology.
doi: 10.1111/mec.12503.
Gehring CA and Whitham TG (1994). Comparisons of ectomycorrhizae on pinyon pines Pinus edulis; Pinaceae across extremes of soil
type and herbivory. Am J Bot, 81:1509-1516.
18
Gehring CA and Whitham TG (1995). Duration of herbivore removal and environmental stress affect the ectomycorrhizae of pinyon
pines. Ecology, 76:2118-2123.
Gehring CA, Cobb NS and Whitham TG (1997). Three-way interactions among ectomucorrhizal mutualists, scale insects and resistant
and susceptible pinyon pines. Am Nat, 149: 824- 841.
Gehring CA, Theimer TC, Whitham TG and Kiem P (1998). Ectomycorrhizal fungal community structure of pinyon pine growing in two
environmental extremes. Ecology, 81: 1509-1516.
Ghalambor CK, McKay JK, Carroll SP and Reznick DN (2007). Adaptive versus non-adaptive phenotypic plasticity and the potential for
contemporary adaptation in new environments. Functional Ecology, 21: 394–407.
Gienapp P, Teplitsky C, Alho JS, Mills JA and Merilä J (2008). Climate change and evolution: disentangling environmental and genetic
responses. Mol. Ecol. 17: 167–178.
Gomulkiewicz R, Holt RD and Barfield M (1999). The effects of density-dependence and immigration on local adaptation and niche
evolution in a black-hole sink environment. Theoretical Population Biology, 55 :283–296.
Gomulkiewicz R, RD Holt, M Barfield and SL Nuismer (2010). Genetics, adaptation, and invasion in harsh environments. Evolutionary
Applications, 3: 97–108.
Grandbastien M et al. (2005). Stress activation and genomic impact of Tnt1 retrotransposons in Solanaceae. Cytogenet. Genome Res.
110: 229–241.
Grant PR and Grant BR (1998). Speciation and hybridization in island birds. Pages 142–162 in Grant PR, ed. Evolution on Islands.
Oxford (United Kingdom): Oxford University Press.
Guisan A and Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8: 993–
1009.
Guisan A and Zimmermann NE (2000). Predictive habitat distribution models in ecology. Ecol. Model. 135: 147–186.
Hairston NG Jr, W Lampert, CE Caceres, CL Holt- meier, LJ Weider, U Gaedke, and DM Post (1999). Rapid evolution revealed by
dormant eggs. Nature, 401:466.
Hamrick DL (2004). Response of trees to global environmental changes. Forest ecology and Management 194: 323-335.
Hansen LO and Somme L (1994). Cold hardiness of the elm bark beetle Scolytus laevis Chapuis, 1873 (Col. Scolytidae) and its potential
as Dutch elm disease vector in the northernmost elm forests of Europe. J Appl Entomol, 117: 444-450.
Hanspach J et al. (2011). Geographical patterns in prediction errors of species distribution models. Global Ecol. Biogeography, 20:
779–788.
Hatcher PE (1995). Three-way interactions between plant pathogenic fungi, herbivorous insects and their host plants. Biol Rev
Cambridge Phil Soc, 70: 639-694.
Heinemeyer K, R Tingey, K Ciruna, T Lind, J Pollock, B Butterfield, J Griggs, P Lachetti, C Bode, T Olenicki, E Parkinson, C Rumsey and D
Sizemore (2004). Conservation area design for the Muskwa-Kechika Management Area. Prepared for the BC Ministry of Sustainable
Resource Management. Round River Conservation Studies, Salt Lake City, UT.
Hellmann J and Pineda-Krch M (2007). Constraints and reinforcement on adaptation under climate change: selection of genetically
correlated traits. Biological Conservation, 137: 599–609.
Hendrick MJ, Mahaffey EA, Moore FM, Vos JH and Walder EJ (1998). Histological classification of mesenchymal tumors of skin and
soft tissues of domestic animals, second edition, WHO, Armed Forces Institute of Pathology and American Registry of Pathology,
Washington D.C.
Hendry AP, Farrugia TJ, Kinneson MT (2008). Human influences on rates of phenotypic change in wild animal populations. Molecular
Ecology. 17: 20-29.
19
Herrero A, Zamora R, Castro J and Hódar JA (2012). Limits of pine forest distribution at the treeline: Herbivory matters. Plant Ecology,
213: 459-469.
Herrero M, Gerber P, Vellinga T, Garnett T, Leip A, Opio C, Westhoek HJ, Thornton PK, Olesen J, Hutchings N, Montgomery H,
Soussana J-F, Steinfeld H and McAllister TA (2011). Livestock and greenhouse gas emissions: The importance of getting the numbers
right. Anim. Feed Sci. Technol. 166-167: 779-782.
Heuertz M et al. (2006). Multilocus patterns of nucleotide diversity, linkage disequilibrium and demographic history of Norway
spruce (Picea abies (L.) Karst). Genetics, 174: 2095–2105.
Hicker T, Vohland K, Feehan J et al., (2012). Projecting the future distribution of European potential natural vegetation zones with a
generalized, tree species-based dynamic vegetation models. Global Ecology and Biography, 21: 50-53.
Holderegger R, Kamm U and Gugerli F (2006). Adaptive vs. neutral genetic diversity: implications for landscape genetics. Landsc Ecol
21: 797–807.
Houston DR (1998). Beech bark disease. In: Britton K, editor. Exotic pests of eastern forests. USDA Forest Service, 29-41.
Howe GT, Aitken SN, Neale DB, Jermstad KD, Wheeler NC and Chen THH (2003). From genotype to phenotype: unraveling the
complexities of cold adaptation in forest trees. Can J Bot. 81: 1247–1266.
Huntley B and Birks HJB (1983). An Atlas of past and present pollen maps for Europe: 0‐13000 years ago. Cambridge University Press,
Cambridge.
Huntley Ba and Colin Prentice I (1988). July temperatures in Europe from pollen data, 6000 years before present. Science, 241: 687690
Huston MA (2002) Introductory essay: critical issues for improving predictions. In: Predicting species occurrences: issues of accuracy
and scale (eds. Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA & Samson FB), pp. 7-21. Island Press, Covelo,
California.
Ito H. et al.(2011). An siRNA pathway prevents transgenerational retrotransposition in plants subjected to stress. Nature 472, 115–
119.
Ivashuta S, Uchiyama K, Gau M and Shimamoto Y (2002) Linear amplification coupled with controlled extension as a means of probe
amplification in a cDNA array and gene expression analysis during cold acclimation in alfalfa (Medicago sativa L.). J. Exp. Bot, 53:
351–359.
Iwasa YOH and Kubo T (1997). Optimal size of storage for recovery after unpredictable disturbances. Evol. Ecol 11: 41–65.
Jackson ST (2000). Out of the garden and into the cooler? A Quaternary perspective on deep- time paleoecology. Pages 287–308 in A.
Gastaldo and W. A. DiMichele, editors. Evolution of phanerozoic terrestrial ecosystems. Paleontological Society Papers. Volume 6.
New Haven, Connecticut, USA.
Jackson ST and Overpeck JT (2000). Responses of plant populations and communities to environmental changes of the late
Quaternary. Paleobiology, 4:194-220.
Jeddeloh JA, Stokes TL and Richards EJ (1999). Maintenance of genomic methylation requires a SWI2/SNF2-like protein. Nature
Genetics, 22: 94–97.
Johnson T and N Barton (2005). Theoretical models of selection and mutation on quantitative traits. Philos. Trans. R. Soc. Lond. B.
Biol. Sci, 360: 1411–1425.
Jump AS, Hunt JM, Martinez-Izquierdo JA and Peñuelas J (2006). Natural selection and climate change: temperature-linked spatial
and temporal trends in gene frequency in Fagus sylvatica. Molecular Ecology, 15: 3469-3480.
Kareiva PM, JG Kingsolver and RB Huey (1993). Biotic interactions and global change, Sinauer Associates, Sunderland, MA, 75–94.
Kawecki TJ and Ebert D (2004). Conceptual issues in local adaptation. Ecol Lett., 7: 1225–1241.
20
Kelly A E and Goulden ML (2008). Rapid shifts in plant distribution with recent climate change. Proc. Nat. Acad. Sci. USA, 105: 1182311826.
Kerfoot WC, JA Robbins and LJ Weider (1999). A new approach to historical reconstruction: combining descriptive and experimental
paleolimnology. Limnology and Oceanography, 44: 1232-1247.
King GA and Herstrom AA (1997). Holocene tree migration rates objectively determined from fossil pollen data. Past and future rapid
environmental changes: the spatial and evolutionary responses of terrestrial biota (eds B. Huntley, W. Cramer, A.V. Morgan, H.C.
Prentice and J.R.M. Allen), pp. 91-101. Springer, Berlin.
Klanderud K and Birks HJB (2003). Recent increases in species richness and shifts in altitudinal distributions of Norwegian mountain
plants. Holocene, 13: 1-6.
Klironomos FD, Berg J and Collins S (2013). How epigenetic mutations can affect genetic evolution: model and mechanism. BioEssays,
35: 571-578.
Klironomos JN and Allen MF (1995). UV-b-mediated changes on below-ground communities associated with the roots of Acer
saccharum. Funct Ecol, 9: 923-930.
Klötzli F (1988). On the global position of the evergreen broad-leaved (non- ombrophilous) forest in the subtropical and temperate
zones- Veröff. Geobot. Inst. ETH, Stiftung Rübel, Zürich, 98: 169-196.
Klötzli F, Walther GR, Carraro G and Grundmann A (1996). Anlaufender Biomwandel in Insubrien. Verhandl Ges Okol 26: 537-550.
Knorr M, Frey SD and Curtis PS (2005). Nitrogen additions and litter decomposition- still a topic of debate. Soil Biol and Biochem, 30:
2510-2518.
Knutsen H, Rukke BA, Jorde PE and Ims RA (2000). Genetic differentiation among populations of the beetle Bolitophagus reticulatus
(Coleoptera: Tenebrionidae) in a fragmented and a continuous landscape. Heredity, 84: 667–676.
Konvicka M et al. (2003). Uphill shifts in distribution of butterflies in the Czech Republic: effects of changing climate detected on a
regional scale. Global Ecol. Biogeogr., 12: 403-410.
Kraft NJB and Ackerly DD (2010). Functional trait and phylogenetic tests of community assembly across spatial scales in an
Amazonian forest. Ecological Monographs, 80:401-422.
Kremer A, Kleinshmit J, Cotrell J, Cundall EP, Deans JD, et al. (2002). Is there a correlation between chloroplastic and nuclear
divergence, or what are the roles of history and selection on genetic diversity in European oaks? For. Ecol. Manag, 156:75.
Kremer N, Philipp EER, Carpentier M, Brennan CA, Kraemer L, Altura MA, Augustin R, Hasler R, Heath-Heckman EAC, Peyer S M et al.
(2013). Initial symbiont contact orchestrates host-organ-wide transcriptional changes that prime tissue colonization. Cell host &
microbe, 14: 183–194.
Krouk P Tillard and A Gojon (2006). Regulation of the high-affinity NO3− uptake system by NRT1.1-mediated NO3− demand signaling
in Arabidopsis Plant Physiol., 142: 1075–1086.
Kulmatiski A, Beard KH, Stevens JR and Cobbold SM (2008). “Plant-soil feedbacks: a meta-analytical review.” Ecology Letters, 11: 980992.
Lande R (2009). Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation. J. Evol.
Biol., 22: 1435–1446.
Lande R and Arnold SJ (1983). The measurement of selection on correlated characters. Evolution, 37: 1210–1226.
Landsberg J and Smith MS (1992). A functional scheme for predicting the outbreak potential of herbivorous insects under global
atmospheric change. Aust J Bot, 40: 565-577.
Lang-Mladek C, Popova O, Kiok K, Berlinger M, Rakic B, Aufsatz W, et al. (2010). Transgenerational inheritance and resetting of stressinduced loss of epigenetic gene silencing in Arabidopsis. Mol. Plant, 3: 594–602.
21
Lavoie N, Jeyaraju DV, Peralta MR 3rd, Seress L, Pellegrini L and Toth K (2011). Vesicular zinc regulates the Ca2 sensitivity of a
subpopulation of presynaptic vasicles at hippocampal mossy fiber terminals. J Neurosci, 31: 18251- 18265.
Lenoir J et al. (2008). A significant upward shift in plant species optimum elevation during the 20th century. Science, 320: 1768-1771.
Lenoir J et al. (2009). Differences between tree species seedling and adult altitudinal distribution in mountain forests during the
recent warm period (1986-2006). Ecography, 32: 765-777.
Lewontin RC and Birch LC (1966). Hybridisation as a source of variation for adaptation to new environments. Evolution, 20: 315–336.
Lippman Z, Gendrel AV, Black M, et al (2004). Role of transposable elements in heterochromatin and epigenetic control. Nature, 430:
471–476.
Lisch D (2013). How important are transposons to plant evolution. Nature genetics, 14: 51-61.
Lonsdale D and Gibbs JN (1996). Effects of climate change on fungal diseases. In: Frankland JC, Magan M, Gadd GM (eds.) Fungi and
Developmental change: Symposium of the British Mycological Society, Cranfield, England, pp, 1-19.
Lortie CJ et al (2004). Rethinking plant community theory. Oikos, 107: 433–438.
Lowe AJ, Boshier D, Ward M, Bacles CFE, Navarro C (2005). Genetic resource impacts of habitat loss and degradation; reconciling
empirical evidence and predicted theory for neotropical trees. Heredity, 95: 255–273.
Lundberg DS, Lebeis SL, Paredes SH et al (2012) Defining the core Arabidopsis thaliana root microbiome. Nature, 488: 86–90.
Lush J (1937). Animal breeding plans. Iowa State College Press.
Lynch M and R Lande (1993). Evolution and extinction in response to environmental changein Biotic Interactions and Global Change,
pp. 234–250 edited by P. M. Kareiva, J. G. Kingsolver and R. B. Huey. Sinauer, Sunderland, MA.
Malcolm JR, Markham A, Neilson RP and Garaci M (2002). Estimated migration rates under scenarios of global climate change.
Journal of Biogeography, 29: 835–849.
Manel S, Joost S, Epperson BK, Holderegger R, Storfer A, Rosenberg MS, Scribner KT, Bonin A and Fortin MJ (2010a). Perspectives on
the use of landscape genetics to detect genetic adaptive variation in the field. Mol Ecol, 19: 3760–3772.
Masek JG (2001). Stability of boreal forest stands during recent climate change: Evidence from Landsat satellite imagery. J. Biogeogr.,
28: 967–976.
McGraw JB and N Fetcher (1992). Response of tundra plant populations to climatic change. Pages 359-376 in E Chapin, editor. Arctic
ecosystems in a changing climate. Academic Press, New York, New York, USA.
Merriam J, WH McDowell and WS Currie (1996). A high-temperature catalytic oxidation technique for determining total dissolved
nitrogen. Soil Sci. Soc. Am. J., 60: 1050–1055.
Mhiri C, JB Morel, S Vernhettes, JM Casacuberta, H Lucas and MA Grandbastien (1997). The promoter of the tobacco Tnt1
retrotransposon is induced by wounding and by abiotic stress. Plant Mol. Biol, 33:257–266.
Morgenstern EK (1978). Range-wide variation of black spruce. Canadian Journal of Forest Research, 8: 463–473.
Morgenstern EK (1996) Geographic Variation in Forest Trees: Genetic Basis and Application of Knowledge in Silviculture. UBC Press,
Vancouver.
Morgenstern EK (1996). Geographic variation in forest trees: genetic basis and application of knowledge in silviculture. Univ of British
Columbia Press, Vancouver.
Morin S, M Bottin, N Mazzella, F Macary, F Delmas, P Winterton and M Coste (2009). Linking diatom community structure to
pesticide input as evaluated through a spatial contamination potential (Phytopixal): a case study in the Neste system (South-West
France) Aquat. Toxicol., 94: 28–39.
22
Morjan CL and Rieseberg LH (2004). Hoe species evolve collectively: implications of gene flow and selection for the spread of
advantageous alleles. Mol Ecol, 13: 1341-1356.
Muhameda H, B Touzarda, YL Bagousse-Pinguet and R Michalet (2013). The role of biotic interactions for the early establishment of
oak seedlings in coastal dune forest communities. Forest Ecology and Management, 297: 67–74.
Muir G and Schlotterer (2005). Evidence for share ancestral polymorphism rather than recurrent gene flow at microsatellite loci
differentiating two hybridizing oaks (Quercus spp.) Mol Ecol, 14: 549- 561.
Müller-Starck G (1985). Genetic differentiation between “Tolerant” and “Sensitive” Beeches (Fagus sylvatica L.) in an
Environmentally Stressed Adult Forest Stand. Silvae Genetica, 34: 241-247.
Murray TR, Frank DA and Gehring CA (2010). Ungulate and topographic control of arbuscular mycorrhizal fungal spore community
composition in a temperate grassland. Ecology, 91: 815–827.
Naito K, Zhang F, Tsukiyama T, Saito H, Hancock CN, Richardson AO, Okumoto Y, Tanisaka T and Wessler SR (2009). Unexpected
consequences of a sudden and massive transposon amplification on rice gene expression. Nature, 461: 1130-1134.
Netherer S and Schopf A (2010). Potential effects of climate change on insect herbivores in European forests – General aspects and
the pine processionary moth as specific example. Forest Ecology and Management, 259: 831–838.
Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F, et al.
(2010). Plant phenotypic plasticity in a changing climate. Trends Plant Sci, 15: 684–692.
Ozgul A, DZ Childs, MK Oli, KB Armitage, DT Blumstein, LE Olson, S Tuljapurkar and T Coulson (2010). Coupled dynamics of body mass
and population growth in response to environmental change. Nature, 466:482–485.
Pacala SW, CD Canham, JAJ Silander, RK Kobe and E Ribbens (1996). Forest models defined by field measurements: Estimation, error
analysis and dynamics. Ecol. Monogr., 66: 1-43.
Paine TD, Raffa KF and Harrington TC (1997). Interactions among scolytid bark beetles, their associated fungi, and live host conifers.
Annu Rev Entomol, 42: 179-206.
Parmesan C and Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421: 37-42.
Parolo G and Rossi G (2008). Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl. Ecol, 9:
100-107.
Pauli H et al (2007). Signals 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 Biol., 13: 147-156.
Pavet V, Quintero C, Cecchini NM, Rosa AL and Alvarez ME (2006). Arabidopsis displays centromeric DNA hypomethylation and
cytological alterations of heterochromatin upon attack by pseudomonas syringae. Mol Plant Microbe Interact, 19: 577-87.
Payette S and Gagnon R (1985). Late Holocene deforestation and tree regeneration in the forest-tundra Quebec Nature, 2: 313-570.
Pearson RG and Dawson TP (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope
models useful? Glob. Ecol. Biogeogr, 12: 361–371.
Pearson RG and Dawson TP (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope
models useful? Global Ecology and Biogeography, 12: 361–371.
Pease CM, R Lande and JJ Bull (1989). A model of population growth, dispersal, and evolution in a changing environment. Ecology 70:
1657-1664.
Pecinka A, Dinh HQ, Baubec T, Rosa M, Lettner N and Mittelsten Scheid O (2010). Epigenetic regulation of repetitive elements is
attenuated by prolonged heat stress in Arabidopsis. Plant Cell, 22: 3118–3129.
Peñuelas C and Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature, 421: 37-42.
23
Peñuelas J, Ogaya R, Boada M and Jump A (2007). Migration, invasion and decline: Changes in recruitment and forest structure in a
warming-linked shift of European beech forest in Catalonia. Ecography, 30: 830-38.
Petit RJ, Aguinagalde I, de Beaulieu J-L, Bittkau C, Brewer S, et al (2003). Glacial refugia: hotspots but not melting pots of genetic
diversity. Science, 300: 1563–65.
Petit RJ, Kremer A and Wagner DB (1993). Geographic structure of chloroplast DNA polymorphisms in European oaks. Theoretical
and Applied Genetics, 87: 122-128.
Pigliucci M, Murren CJ and Schlichting CD (2006). Phenotypic plasticity and evolution by genetic assimilation. Journal of Experimental
Biology, 209: 2362–2367.
Pinder RW, Bettez ND, Bonan GB, Greaver TL, WR Wieder, WH Schlesinger and EA Davidson (2012). Impacts of human alteration of
the nitrogen cycle in the US on radiative forcing. Biochem, DOI 10.1007/s10533-012-9787-z.
Polechova J, Barton NH and Marion G (2009). Species’ range: adaptation in space and time. Am. Nat, 174: E186–E204.
Poorter H and Lambers H (1986). Growth and competitive ability of a highly plastic and marginally plastic genotype of Plantago major
in a fluctuating environment. Physiologia Plantarum, 67: 217–222.
Porter JH, Parry ML and Carter TR (1991). The potential effects of climate change on agriculture insect pets. Arg For Meteorol, 57:
221-240.
Power SA and Ashmore MR (1996). Nutrient relations and root mycorrhizal status of healthy and declining beech Fagus sylvatica L. in
southern Britain. Water Air Soil Pollut, 86: 317-333.
Premoli AC, Raffaele E and Mathiasen P (2007). Morphological and phenological differences in Nothofagus pumilio from contrasting
elevations: evidence from a common garden. Austral Ecology, 32: 515–523.
Probst AV, Fransz PF, Paszkowski J and Mittelsten Scheid O (2003). Two means of transcriptional reactivation within hetrochromatin.
The Plant Journal, 33: 743–749.
Rapp RA and Wendel JF (2005). Epigenetics and plant evolution. New Phytol, 168: 81-91.
Raxworthy CJ et al (2008). Extinction vulnerability of tropical montane endemism from warming and upslope displacement: a
preliminary appraisal for the highest massif in Madagascar. Global Change Biol., 14: 1703-1720.
Rayment MB and Jarvis PG (2000). Temporal and spatial variation of soil CO 2 efflux in a Canadian boreal forest. Soil Biology and
Biochemistry, 32: 35–45.
Reekie EG and FA Bazzaz (1989). Competition and patterns of resource use among seedling of five tropical trees grown at ambient
and elevated CO2. Oecologia, 79: 212-222.
Rehfeldt GE, Wykoff WR and Ying CC (2001). Physiologic plasticity, evolution, and impacts of a changing climate on Pinus contorta.
Climatic Change. 50: 355–376.
Rehfeldt GE, Ying CC, Spittlehouse DL and Hamilton DA (1999). Genetic responses to climate in Pinus contorta: Niche breadth,
climate change, and reforestation. Ecological Monographs 69: 375–407.
Rieseberg LH and Willis (2007). Plant speciation. Science, 317: 910-914.
Rieseberg LH, Archer MA and Wayne RK (1999). Transgressive segregation, adaptation and speciation. Heredity. 83: 363–372.
Ritchie JC and MacDonald GM (1986). The patterns of post glacial spread of white spruce. Journal of Biogeography, 13: 527-540.
Robertson C, Nelson TA, Jelinski DE, Wulder MA and Boots B (2009). Spatial–temporal analysis of species range expansion: the case
of the mountain pine beetle, Dendroctonus ponderosae. Journal of Biogeography, 36: 1446–1458.
Roth-Nebelsick A (2001). Heat transfer of rhyniophytic plant taxes. Review of Palaeobotany and Palynology, 116:109–122.
24
Russo SE, Davies SJ, King DA and Tan S(2005). Soil-related performance variation and distributions of tree species in a Bornean rain
forest. Journal of Ecology, 93: 879-889.
Salvini D, P Bruschi, S Fineschi, P Grossoni , ED Kjaer and GVendramin (2009). Natural hybridization between Quercus petraea (Matt.)
Liebl. and Quercus pubescens Willd within an Italian stand as revealed by microsatellite fingerprinting. Plant Biology 11 : 758 – 765 .
Savolainen O, Pyhäjärvi T and Knurr T (2007). Gene flow and local adaptation in forest trees. Annu Rev Ecol Evol Syst., 37: 595–619.
Schmid E (1956). Flora des Südens. Rascher, Zürich.
Schröter C (1936). Flora des Südens. Rascher, Zürich.
Sharpe PJH and DW DeMichele (1977).Reaction kinetics of poikilotherm development. J. Theor. BioI. 64: 649-670.
Simberloff D and B Von Holle (1999). Synergistic interactions of nonindigenous species: Invasional meltdown? Biological Invasions
X:XX.
Sinervo B et al (2010). Erosion of Lizard Diversity by Climate Change and Altered Thermal Niches Science, 328: 894-899.
Soppe WJ, Jasencakova Z, Houben A, Kakutani T, Meister A, Huang MS, Jacobsen S, Schubert I and Fransz PF (2002). DNA methylation
controls histone H3 lysine 9 methylation an heterochromatin assembly in Arabidopsis. The EMBO Journal, 21: 6549–6559.
Sork VL and Waits L (2010). Contributions of landscape genetics—approaches, insights, and future potential. Mol Ecol, 19: 3489–
3495.
Sork VL, Aitken SN, Dyer RJ, Ecker AJ, Legendre P and Neale DB (2013). Putting the landscape into the genomics of trees: approaches
for understanding local adaptation and population responses to changing climate. Tree Genetics & Genomes DOI 10.1007/s11295013-0596-x.
Stearns T, Evans L and Kirschner M (1991). -Tubulin is a highly conserved component of the centrosome. Cell, 65: 825–836.
Stebbins GL (1959). The role of hybridisation in evolution. Proc. Am. Philos. Soc., 103: 231–251.
Streiff R, Ducousso A, Lexer C, Steinkellner H, Gloessl J and Kremer A (1999). Pollen dispersal inferred from paternity analysis in a
mixed oak stand of Quercus robur L. and Q. petraea (Matt.) Liebl. Mol Ecol, 8: 831- 841.
Suarez F, BIinkley D and Kaye MW (1999). Expansion of forest stands into tundra in the Noatak National Preserve, northwest Alaska
Écoscience, 6: 465–70.
Sultan SE (2004). Promising directions in plant phenotypic plasticity. Perspectives in Plant Ecology Evolution and Systematics, 6: 227–
233.
Thompson JD (1991). Phenotypic plasticity as a component of evolutionary change. Trends in Ecology and Evolution, 6: 246–249.
Thuiller W, S Lavorel M, B Araujo, MT Sykes and IC Prentice (2005). Climate change threats to plant diversity in Europe. Proceedings
of the National Academy of Sciences USA, 102: 8245 – 8250.
Tittel-Elmer M, Bucher E, Broger L, Mathieu O, Paszkowski J and Vaillant I (2010). Stress-induced activation of heterochromatic
transcription. PLoS Genet 6:e1001175.
Turesson G (1922). Species and the variety as ecological units. Hereditas 3: 100-113.
Uvarov BP (1931). Uvarov Insects and climate Trans Entomol Soc London, 79: 1–247.
Valladares F and Pearcy RW (1998). The functional ecology of shoot architecture in sun and shade plants of Heteromeles arbutifolia
m. Roem., a Californian chaparral shrub. Oecologia, 114: 1–10.
Valladares F, I Dobarro, D Sánchez-Gómez and RW Percy (2005). Photoinhibition and drought in Mediterranean woody saplings:
scaling effects and interactions in sun and shade phenotypes. J.Exp. Bot, 56:483–494.
25
Valladares F, Sanchez-Gomez D and Zavala MA (2006). Quantitative estimation of phenotypic plasticity: bridging the gap between the
evolutionary concept and its ecological applications. Journal of Ecology, 94: 1103–1116.
Van der Putten WH et al. (2013). "Plant–soil Feedbacks: The Past, the Present and Future Challenges." Journal of Ecology, 101: 26576.
Van Zanten M et al., (2011). Seed maturation in Arabidopsis thaliana is characterized by nuclear size reduction and increased
chromatin condensation. Proc. Natl. Acad. Sci. U. S. A., 108: 20219–20224.
Vitasse Y, Porte AJ, Kremer A, Michalet R and Delzon S (2009c). Responses of canopy duration to temperature changes in four
temperate tree species: relative contributions of spring and autumn leaf phenology. Oecologia, 161, 187–198.
Vittoz P et al (2008). One century of vegetation change on Isla Persa, a nunatak in the Bernina massif in the Swiss Alps. J. Veg. Sci, 6:
671-680.
Waldrop MP and Firestone MK (2004). Altered utilization patterns of young and old soil carbon by microorganisms caused by
temperature shifts and nitrogen additions. Biogeochemistry, 67: 235–248.
Walther GR (1997). Longterm changes in species composition of Swiss beech forests. Annali di Bonatica, 55: 77-84.
Walther GR (1999). Distribution and limits of the evergreen broad-leaved (laurophyllous) species in Switzerland. Botanica Helvetica,
109: 153-167.
Walther GR et al (2005). Trends in the upward shift of alpine plants. J. Veg. Sci., 16: 541-548.
Walther GR et al (2009). Alien species in a warmer world: risks and opportunities. Trends Ecol. Evol. 24, 686–693.
Wang T, Hamann A, Yanchuk A, O’Neill GA and Aitken SN (2006). Use of response functions in selecting lodgepole pine plantations
for future climates. Glob Change Biol, 12: 2404–2416.
Wang XH , PiaoSL, CiaisP, LiJS, FredlingsteinP, KovenC and ChenAP (2011). Spring temperature change and its implication in the
change of vegetation growth in North America from 1982 to 2006. Proceedings of the National Academy of Sciences of the United
States of America, 108: 1240–1245.
Webb TI (1997). Spatial response of plant taxa to climate change: a palaeoecological perspective. Pages 57–72 in B. Huntley, W.
Cramer, A. V. Morgan, H. C. Prentice, and J. Allen, editors. Past and future rapid environmental changes: the spatial and evolutionary
responses of terrestrial biota. Springer-Verlag, Berlin, Germany.
Whitham TG, PA Morrow, and BM Potts (1994). Plant hybrid zones as centers of Biodiversity: The herbivore community of two
endemic Tasmanian eucalypts. Oecologia, 97: 481- 490.
Whitney ER, Kemper TL, Bauman ML, Rosene DL and Blatt GJ (2008). Cerebellar Purkinje Cells are reduced in a Subpopulation of
Autistic Brains: A Stereological Experiment Using Calbindin-D28k. Cerebellum. 2008
Wilkinson DM (1998). Mycorrhizal fungi and Quaternary plant migrations. Global Ecology and Biogeography, 7: 137- 140.
Williams, B.L., Brawn, J.D. & Paige, K.N. (2003). Landscape scale genetic effects of habitat fragmentation on a high gene flow species:
Speyeria idalia (Nymphalidae). Mol. Ecol., 12, 11–20.
Willis KJ and Van Andel TH (2004). Trees or no trees? The environments of central and eastern Europe during the Last Glaciation.
Quaternary Science Reviews, 23:2369‐2387.
Woodwell GM (1995). Biotic feedbacks from the warming of the Earth. In Woodwell GM, Mackenzie FT Biotic Feedbacks in the Global
Climate System. Oxford University Press, New York, 3-21.
Young, A., Boyle, T. & Brown, T. (1996). The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol.,
11, 413–418.
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