Master Thesis
Introduction of Phenology
Phenology is a term derived from the Greek word phaino meaning to show or appear; hence,
plant phenology could be simply defined as the seasonal timing of life cycle events (Rathcke and
Lacey, 1985). It could be defined more comprehensively: the study of the timing of recurring
biological phases of plant species throughout the year, the biotic and abiotic causes of the timing,
and the interrelation of phases of the same or different species (Leith, 1970). Clearly, the timing
of biological phases and its reaction to the changing environment (e.g. Parmesan, 2006; Walther
et al., 2002) is the major focus of this subject matter. Phenology is usually used to monitor
adaptive traits of the plants and evaluate their relationships to the ecosystem where they grow
(Arora and Boer, 2005; Howe et al., 2003; Walther et al., 2002). It has been widely applied in
agriculture to determine timing of planting and harvesting in order to achieve the maximum crop
yield (Sakamoto et al., 2005), and also used to decide the timings of applying pesticide and
herbicide (Moola and Mallik, 1998). In forest management, tree phenology is commonly used to
predict forest productivity, manage water supply, and improve seed zoon selections (Hamann and
Wang, 2006). In addition, plant phenology is considered the key element that affects the carbon
balance of terrestrial ecosystems (Gill et al., 1998) and characterizes plant competition
capabilities (Rathcke and Lacey, 1985).
Records show that Europe has the longest scientific phenological observation and research
history that could be tracked back as far as the 18th century (Leinonen and Hanninen, 2002;
Luterbacher et al., 2007). In North America, however, Thomas Mikesell started the earliest
systematic phenology observation between 1883 and 1921, about a century later, and recorded
about 25 species during that period of time (Lechowicz, 1995). The modern phenological
recording was started by a Swedish biologist Carolus Linnaeus and a British landowner Robert
Marsham in the 18th century (Lechowicz, 2001). Since then, records of explicit phenological
observations were started, and extensive plant phenological observation networks were
established across the world (Schwartz, 2003). Nowadays, one of the most influential phenology
watch networks, the Plantwatch, is based in Western Canada and is still very active1. This
network has documented phenological observations for decades and covered hundreds of species'
flowering and leave flushing.
Earlier researches noticed the geographic variation of spring phenology (e.g. timings of budburst
or flowering) and attributed this difference to their adaptations to local climates (Lechowicz,
1984). For example, the latitudinal phenology variations were observed for budburst of Scots
pine (Pinus sylvestris) and Norway spruce (Picea abies) that plants from the south usually have
earlier budburst while they are planted in the same environment (Beuker, 1994; Leinonen and
Hanninen, 2002), and similar trends were found in some boreal tree species such as silver birch
(Betula pendula) (Leinonen, 1996) and European elms (Ulmus minor, Ulmus glabra, and Ulmus
laevis) (Santini et al.). The interpretation to all these phenomena is that these phenological
variations are the results of species’ adaptation to their local climates by minimizing their
exposition to frost damage (survival adaptation), while maximizing the duration of growing
1
http://plantwatch.sunsite.ualberta.ca/misc/tracking.php
season (capacity adaptation) (Leinonen and Hanninen, 2002). Mismatches between the spring
weather and plant phenological responses could potentially cause the plants from failing to
produce seeds or fruits to increasing the chance of mortality (Billington and Pelham, 1991). And
plants that cannot respond to inter-annual climate variability to sufficiently use the growing
season will be at a competition disadvantage.
Recent researchers also found that adaptive traits of a species appear to be different through their
life stages, and plants of different ages usually choose different adaptive strategies to the same
environment (???). For example, because the frost damage is negatively correlated with the
timing of budburst, a seedling or sapling usually choose a 'threshold' strategy that it scarifies
taking the advantage of the full growing season, and takes a late budburst before its stem grows
longer and bigger; however, an adult tree may have more endurance to frost damage and may
take more risk to have an earlier budburst (Leinonen and Hanninen, 2002). [The same species
adapting to different environments or same plant at different age adapting the same environment
are overall the embodiment of phenotypic plasticity. Phenotypic plasticity is defined as the
adaptive traits of a plant to the altered environment.] With this concept, the species adaptation
could be better explained…………….
Usually, phenological events, such as budburst or flowering, could be statistically described by
timings of their occurrence, duration, and synchrony (Rathcke and Lacey, 1985) . Studies of
these phenological events usually based on onsite observations and recordings (Beaubien and
Freeland, 2000); however, some events, such as budburst, could be observed with the help of
remote sensing technologies (Sellers et al., 1995). Plant phenological phases are usually
categorized as budburst, bud set, flowering, and fruiting (???), although some studies classified
these events somewhat differently (Schwartz, 2003). Biologische Bundesanstalt and Chemical
industry (BBCH) unified the description and coding system by separating growth stages into 10
general phenological phases based on the development stages of bud, leave, stem, flower, and
fruit (Schwartz 2003) (detailed definitions see Appendix table 1), which has been widely adapted
by phenological studies. Following these criteria and standard procedures, plant phenology of
many species, especially those from the temperate zone, have been recorded and studied (e.g. ???
review), these information have provided great help to forest management (??) and agricultural
development (??).
Plant phenology is considered the result of plants adapting to their environment, especially for
those that grow in extreme environments such as the ecosystems u cold temperature or less
moisture (Perry, 1970). Abiotic factors usually have direct impacts on plant phenology. These
factors could be seasonal temperature variations (Beaubien and Freeland, 2000), frost damage in
spring and fall (Leinonen and Hanninen, 2002; Vitasse et al., 2009), or chilling effect in early
spring (Jonsson et al., 2004). However, differences in precipitation and soil moisture (Beaulieu et
al., 2002; Kramer et al., 2000; Reich, 1995) or variations in photoperiod (Partanen et al., 1998)
also have major influences on plant phenology. Biotic factors, on the other hand, are more
influential to plant regenerations. For example, the population dynamics of pollinators could
determine the timing of flowering, and the population variation of seed predators would also
affect whether the fruiting is successful (Elzinga et al., 2007; Kolb et al., 2007). Among all the
abiotic and biotic factors, temperature is the most important driver to plant phenology, especially
for the deciduous plants in the temperate zone (Badeck et al., 2004; Kramer et al., 2000). For
instance, in the moist temperate zone, most dormant trees require winter chilling to end the
dormancy in the beginning of spring and certain amount of heat to start bud bursting (Hunter and
Lechowicz, 1992). In addition, research found that the higher temperature speeds up the
phenology development (Saxe et al., 2001).
- Explain heatsum control of budbreak
Spring phenology is mainly driven by temperature, especially when it is measured as “heatsum”
(e.g. Menzel, 2003; Morin et al., 2009; Penuelas and Filella, 2001; Sparks and Carey, 1995).
Heatsum, also known as heat units, thermal units, degree-days, or day degrees, is defined as the
accumulation of effective temperature for a phenological event in its active period, where
effective temperature is the temperature range within which the phenological event occurs and
keeps active (Beaubien and Freeland, 2000; McMaster and Wilhelm, 1997; Snyder et al., 1999).
For example, budburst, the active period from the cessation of dormant to the beginning of leaf
flush, will not happen until temperature surpasses its active threshold and certain amount of
heatsum is achieved (Ghelardini et al., 2006; Hunter and Lechowicz, 1992). Using heatsum as
predictor, Reaumur (1735) successfully predicted the occurrence of a phenological stage more
than 200 years ago, suggesting heatsum is a constant and could be used to project future or past
phenological event of the same kind. Following the same idea, investigating the relationships
between heatsum and phenological events of various species has become a major topic in
phenological researches (???). Because different genotypes may require different amount of
heatsum to activate one of their specific phenological events (e.g. budburst or flowering)
(Lappalainen, 1994), the distribution of heatsum in the spring may reflect different genotype
ranges of a specific species (Howe et al., 2003).
Because an accurate effective temperature and starting date for a phenological event might have
some impacts on the precision of heatsum computations, different approaches were adopted to
estimate these values. Yang et al. (1995) summarized four most common used approaches
including the smallest stantard deivation method, linear regression model, iteration method, and
the triangle method. The main idea of these approaches was to approximate an effective
temperature threshold with regression or iteration method based on the field-measured
phenological data (such as rate of development). Snyder et al. (1999) found that the results from
iteration method usually provid the smallest root mean square error (RMSE) in most cases,
indicating this is a better approach in effective temperature estimates. Although these thresholds
are theoretical rather than realistic which are based on biological tests or field observations, these
thresholds are considered close enough in practice (Snyder et al., 1999). Normally, temperature
between 0°C and 10°C, optimum at about 5°C, is considered sufficient for dormancy release for
most species based on experiments (Perry, 1971), and many studies suggested to set 0C or 5C for
budburst effective temperature in predict models (Snyder et al., 1999). After the effective
temperature is set, the heatsum could be calculated by adding up all the effective temperatures
from the starting to the end of a phenological event, this computation is also called thermal time
model (Delahaut, 2003; Reader, 1983) 2.
2
For a particular species, their geographical variation of heatsum requirements could be summarized as
the Linsser’s law (Ref. Reader 1983): the fractions of the heatsum required for a particular phenological
stage divided by the total annual heatsum for a plant at its site of origin are the same for plants from all
locations.
- What does it mean to the forest management?
Forest management is to guide forests toward a society's goals: preserving the environment,
meeting the current and future forest products needs of human society, or the combinations of
these former goals. Besides growing trees, forest management deals with other benefits provided
by forested land, the non-wood forest products, such as habitats for wildlife, food resources,
biodiversity, agroforestry, or recreation (Zeide, 2008) . Forest management is a long-range
viewpoint of a planner (Davis et al., 2005), it considers the predictable changes (such as human
population and climates) in the future and finds the resolutions, for example, to answer the
question about how we can share benefits from forests (e.g. forest service) with our descendants.
A sustainable human-forest ecosystem is desirable under this context and the core of modern
forest management (Davis et al., 2005).
Among all the factors affecting forest management, climate change is one of the biggest threats
for the forest industry. Climate change impacts forest reproductions by failing tree flowering and
fruiting. Kudo (2004), for example, found that bee-pollinated species had less seed-set in 2000 in
Japan because of a shortage of pollinators for the earlier flowering in the warm spring.
Considerable climate change has been observed around the world (Parmesan and Yohe, 2003),
and the trend is predicted to be continuous for the next century (Mbogga et al., 2009)(IPCC,
2007). Studies showed that the temperature has been increased dramatically since the 1980's
(Karl et al 2005); however, climate change also increased the frequencies of storms, fire,
precipitation, flood, snow, and other extreme events (Groisman et al., 2005; Saxe et al., 2001).
These changes have major impacts on species abundance, biological process, organic matter
decomposition, species range shift, as well as species adaptations (e.g. plant phenology) (Badeck
et al., 2004; Parmesan and Yohe, 2003; Saxe et al., 2001; Walther et al., 2002). Species change
their phenology to cope with the changing environments have been widely observed, such as the
changes of budburst and flowering timings (Beaubien and Freeland, 2000), leaf coloring (Estrella
and Menzel, 2006), and length of growing season (???). However, the climate change is too fast
for some species to keep up with, the physical migrations and gene flow from warm-adapted
population will be more important than species' evolution for maintaining the level of forest
ecosystem services (Billington 2008). Therefore, assistant plant migrations are necessary and the
major tasks for the future forest management.
Species with large ranges growing under a variety of environmental conditions will likely show
phenotypes differences in their adaptive traits (Howe et al., 2003), which are usually important
factors to be considered in the movement of planting stock for reforestation and in genetic tree
improvement programs. For example, if genotypes are selected for growth traits in short-term
experiments, adaptive traits may be sub-optimal. Consequently, the better growth may be the
result of risking late spring and early fall frost damage for an extended growing season (Brissette
and Barnes, 1984). Therefore, phenotypes with high mortality risk due to susceptibility to frost
damage or drought may not be a suitable choice for reforestation. Ideally, we would like to
choose phenotypes that show lower adaptive risks while maintaining superior growth.
- Review of genetic variation adapted trial.
Genetic variation is an evolutionary result of plant adaption to the environmental heterogeneity
(Jelinski, 1997), and can be maintained through reproduction if the diversity was acquired
through recombination, introgression, or somatic mutation (Rasmussen and Kollmann, 2007).
The genetic variations are regulated by forces such as mutation, genetic drift, gene flow, and
natural selection (Ohsawa and Ide, 2008). Studying of genetic variation can help us to identify
species, assess their spatial distribution, examine the genetic structure, or probe their phenotypes.
Moreover, it has been used to select the high-quality timber resources or seed zones for forest
industry. [Add a few sentences]
Genetic variations can be detected by many methods such as field observation, molecular genetic
marker, or quantitative traits locus (QTL) mapping (Gonzalez-Martinez et al., 2006). Molecular
genetic markers can be used to directly detect the genetic variations of a species because they
could be found at a known location on a chromosome and associated with a particular gene or
phenology trait (Hall et al., 2007). However, researches also found that genetic variations do not
alway march to the phenological or growth variations (Hall et al., 2007), and genetic variations
detected by these markers do not always associate with the suitable growth or phenological traits
that are desirable for the forest industry. For example, research found that the genetic variations
in the conifer species are much less than their adaptive traits (Gonzalez-Martinez et al., 2004).
QTL mapping is relatively straightforward than the other methods (Damerval et al., 1994). With
this method, many adaptive traits of tree species have been successfully identified, such as poplar
(Ferris et al., 2002) and Douglas fir (Wheeler et al., 2005). However, because this method
requires a large sample size, it is very time-consuming and expensive to construct.
Field observation of phenological traits is the most traditional way to reveal the genetic
variations of a species; however, these observed differences might be confounded by
environmental variations. To eliminate the environmental factors and protrude the genetic
variations, the provenance trial, which is also called common garden, progeny test, or clonal test,
has been routinely performed (e.g. Hamann et al., 2000; Kleinschmit et al., 2004; Savva et al.,
2007). The essence of the provenance trials is to compare phenological and growth traits of
different genotypes within or among species from different sites and transplant them in the same
experimental site, where they can be exposed to the same environmental conditions—soils,
climate, water, and photoperiod—with a systematic experimental design that accounts for
random site variation (Bower and Aitken, 2008). Because different genotypes may respond to the
same environment conditions differently in phenology and growth traits, the observed
differences can reflect within-species genetic variations. This information can be used to create
guidelines of seed transfers and to delineate seed zones. The objective of limiting seed movement
in reforestation is to ensure that planting stock is not mal-adapted to environmental conditions of
the planting site. For example, northern provenances of Norway spruce (Picea abies), have
earlier budburst and should therefore not be used in southern planting environments to avoid late
spring frost damage (Leinonen and Hanninen, 2002).
- The advantage and disadvantage of provenance trials
Studying genetic variation through provenance trials has advantages and disadvantages. Major
advantage of provenance trials is with environmental variables controlled, a variety of growth
and adaptive traits can be evaluated for genetic variation, e.g.: growth traits (Lesser et al., 2004),
wood properties (Beaulieu et al., 2002), and adaptive traits (i.e. phenological characters)
(Backman, 1991; Li et al., 1997; Lobo et al., 2003). It should be kept in mind, however, that the
failure to detect genetic differences among populations in a common garden trial does not mean
that genotypes are identical. Genetic differences may be revealed under one set of environmental
conditions, but not under another. Therefore, provenance trials are typically replicated over
several environments. Testing multiple genotypes over multiple environments makes provenance
trial series expensive research efforts. To evaluate growth traits in trees at rotation age, they are
also very time-consuming. Studying budburst, however, is simpler. The trait can be observed
early on in seedlings or saplings (assuming that there is no change in phenology between
juvenile and adult trees), and environmental factors such as soil conditions and soil moisture are
thought to play a minor role (Backman, 1991). Therefore, results from a single provenance trial
observed in a single year should provide sufficient information. This offers the opportunity to
abandon the provenance trial approach entirely and attempt to study genetic variation in situ: this
study proposed a new approach of using remote sensed data to differentiate genotypes. This
would allow for the first time to generate seamless maps of genetic variation in populations
rather than obtaining information for a very limited set of samples.
- Remote sensing:
Remote sensing approach has previously been used to study phenology in situ (e.g. Delbart et al.,
2005; Kobayashi et al., 2007), but not to detect genetic differences because it requires
environment being hold constant. However, since heatsum is considered the trigger of some
phenological events, such as budburst or flowering (Reader, 1983), it is possible to interpolate
observed daily climate data to generate a seamless heatsum map according to the greenup days
extracted from remote sensing data. Greenup date is measured as the Julian day when the
satellite imagery pixel starts to green up. This heatsum map can then be considered as a surrogate
for a constant and comparable environment, which allows us to discriminate genotype variations
of the focal species across the general study area. Using remote sensing data to identify greenup
days has been a common practice in remote sensing studies (Delbart et al., 2008; Fisher et al.,
2006; Schwartz et al., 2006). Normalized Difference Vegetation index (NDVI) was often used as
the indicator to identify greenups of an individual species or a species group (Richard and
Poccard, 1998; Wang et al., 2005; White and Nemani, 2006). However, according to the data
sources, other vegetation indices may also be applied, such as the Enhanced Vegetation Index
(EVI) (Sakamoto et al., 2005), Normalized Difference Water Index (NDWI) (Delbart et al.,
2005), Normalized Difference Snow Index (NDSI) (Salomonson and Appel, 2004), and f-PAR
(Ahl et al., 2006).
Typical remote sensing is the acquisition of information by using electromagnet radiation
without physically contacting the objects (Elachi et al. 2006). With this technology, ground
information of the whole electromagnetic spectrum from the low-frequency radio waves through
microwave, far infrared, near infrared, and visible lights could be extracted (Elachi et al. 2006).
The advantages of remote sensing include its ability to repetitively acquire large scale and
synoptic information from the surface of the Earth; meanwhile, its information acquisition is not
limited by geographical locations. Remote sensing enhances and increases the reliability in fieldbased investigation and monitoring (Body & Danson, 2005). Because of these advantages,
remote sensing applications have been extended especially to forestry, ecology, and agriculture
research (Lass et al., 2005; Leblon, 2005). After 40 years of development, satellite imageries
with high spatial resolution, spectral resolution, and temporal resolution become available; these
images are mainly from Landsat ETM+, MODIS, RADARSAT, Hyperion, or QuickBird and
cover the whole world periodically (Cohen et al., 2003; Raney et al., 1991; Thenkabail et al.,
2004; Toutin, 2004). Moreover, some of these data, such as MODIS and Landsat ETM+, are free
for the public and have been extensively used in various research projects.
Vegetation detection is always the primary interests of remote sensing, and the key for this
detection is the reflectance characteristic of the plant leaves. In order to detect the vegetation,
two major remote sensed measurements are usually needed: the reflectance characteristics of
chlorophyll and water. Chlorophyll absorbs solar radiation of 0.43—0.45 µm (blue) and 0.65—
0.66 µm (red) to process photosynthesis in a healthy green leaf, and reflects the radiation at 0.55
µm; meanwhile, water in leaf cells has a higher reflectance at 0.74—1.00 mm (near-infrared)
(Hunt and Rock, 1989; Woolley, 1971).The canopy therefore appears relatively dark in the
absorption bands and relatively bright in near-infrared in remotely sensing data. However, soil
and water do not absorb solar radiation at the same blue and red band as the canopy does; clouds
and snow have high reflectance at visible light and near-infrared, and absorb infrared radiation at
around 1.50 µm, they are rather bright in the red and quite dark in the near-infrared band. The
ratio between near-red and infrared red band is considerably high for vegetation and low for soil,
water, and clouds—the sources of background noises. This ratio therefore can be used to
distinguish the vegetation from the background. This becomes the fundamental idea for all other
vegetation indexes (VI) (see Jensen, 2005), and rather than reflectance, this ratio is more
commonly used in remote sensing. Furthermore, this ratio can also be used to identify drought
stress and plant healthiness according to the reflectance change at near-infrared and infrared
(Asner et al., 2004; Zarco-Tejada et al., 2003).
Normalized Difference Vegetation Index (NDVI) is one of the important VIs we previously
introduced. This index involves only two parameters: the reflectance of near infrared band (NIR)
and the reflectance of red band. Clearly, NDVI value varies between -1.0 to 1.0. NDVI has been
proved to perform better in distinguishing vegetation from soil (Richardson and Wiegand, 1977).
In summary, vegetation covered area has positive NDVI value that ranges from 0.3 to 0.8; clouds
and snow fields are characterized by negative values of this index; clear water (e.g., oceans, seas,
lakes and rivers) has a low reflectance in both NIR and red bands and thus result in very low
positive or even slightly negative NDVI values; soils generally exhibit a NIR spectral reflectance
somewhat larger than the red, and tend to also generate rather small positive NDVI values
(around 0.1 to 0.2) (????). Synchronized with the leave color variations, photosynthesis activity
changes seasonally with the change of chloroplast content levels, and these changes could be
measured through NDVI values (???). In addition, NDVI is often used to detect crop yields and
end-of-season above-ground dry biomass (???). Although NDVI is widely used to detect the
vegetation phenology, research also shows that NDVI is affected by the local seasonal
atmospheric conditions as well as the aerosols and clouds (Kobayashi and Dye, 2005), which in
turn affect the quality of phenology detections.
In order to correct this problem, Enhanced Vegetation Index (EVI) was introduced. MODIS
Science Team3 used blue band to calibrate the reflectance of red and near infrared band, and
developed a new index to enhance the quality of NDVI production. Because the wavelength
between 0.45–.0495 µm (blue band) is sensitive to atmospheric conditions (e.g. aerosol) and is
often used for atmospheric correction. EVI was developed to optimize the vegetation signal by
reducing atmosphere influences and enhancing its sensitivity to high biomass area (Huete et al.,
2002). EVI uses three spectrum bands: the red, the near-infrared, and the blue. It requires three
3
http://modis.gsfc.nasa.gov/sci_team/
sets of coefficients: a soil adjustment coefficient L, two aerosol calibration coefficients (C1 and
C2) derived by using the blue band to correct the atmospheric influences in the red and nearinfrared band, and the last one is called the “gain factor” (G) which is used to modify the whole
EVI computation equation (equation see Huete et al., 2002). It is widely accepted that the values
of these coefficients are L=1, C1=6, C2 = 7.5, and G = 2.5 which were empirically determined
by Huete et al (1997). EVI is sensitive to canopy structural variations, including leaf area index
(LAI), canopy type, plant physiognomy, and canopy architecture (Gao et al., 2000). Comparing
with NDVI, EVI is not easy to reach the saturation point when LAI is high. Assuming there is a
time-series remotely sensed data set, and NDVI and EVI are separately used to detect when the
vegetation produces the most biomass in a year; as a result, it is certain that the NDVI will result
in a earlier date than EVI does because NDVI value saturates at a lower biomass level than EVI
does (???). Moreover, EVI was found to perform better in the heavy aerosol conditions and
soil/vegetation background (Miura et al., 2001; Xiao et al., 2003).
Similarly, indices such as Normalized Difference Water Index (NDWI) (Delbart et al., 2005),
Normalized Difference Snow Index (NDSI) (Salomonson and Appel, 2004), and f-PAR (Ahl et
al., 2006) were developed to study especially the plant phenology, and showed great strength in
reflecting plant phenology variations. However, these indices were limited to extreme
environments such as the far north in the north hemisphere, and are different from the more
universal indices, i.e. NDVI and EVI, as we introduced previously.
- Biological background
In this study we work with trembling aspen (Populus tremuloides), a widely distributed species
ranges from Alaska in the north to Mexico in the south in northern America. Trembling aspen is a
soft-wooded and fast growing tree, grows best on moist, well-drained soils. Aspen grows
especially well on soils with rich calcium, such as those derived from limestone (Stettler et al.,
1996). Aspen sprout from root suckers and form clones of many individual stems, which can be
distinguished in spring or fall when groups of stems leaf out or change color all at once.
Individual aspen stems are relatively short-lived and often succumb to disease at 50 years or so
(??), which provide homes for cavity-nesting birds. Aspen dominated or co-dominated forest
ecosystems are usually key habitats for wildlife species from song birds (Harrison et al., 2005;
Schieck and Song, 2006) to large mammals such as wolf, elk, and deer (Stettler et al., 1996;
White et al., 1998). Protecting and preserving aspen forests are major goals in many forest
management plans in North America especially in the Canadian boreal forests4.
-
Commercial background (importance in western Canada & AB, describe commercial
tree improvement programs, therefore important to understand ecological genetics of
adaptive traits as well)
Because of its fast growth, trembling aspen has been used as a source of fuel, fiber, lumber, and
animal feed (Steller et al. 1996). Aspen has been considered the renewable resource of biomass
for energy and source of pulp industry (Ondro, 1991). When managed in short rotations and
under intensive cultivation, this species showed impressive productivity and could be considered
a highly promising crop option (Morley and Balatinecz, 1993). As a major planting species,
trembling aspen has drawn much attention recently, and the tree improvement programs have
4
http://www.srd.alberta.ca/forests/pdf/forest_management_fact_sheets.pdf
been developed in order to select the best genotype for plantation in order to balance between
optimal tree growth while lower risks of stock movement(Li, 1995). Therefore it is important to
understand its adaptive as well as its growth traits. Only a few studies of the phenological traits
of this species, especially in North America, can be found, and most importantly, they are all
targeting at issues other than studying adaptive traits of various genotypes. For example,
Beaubien and Freeland (2000) studied the relationship between aspen flowerings and ocean
temperature, and other studies are focus on the relationships between budburst and various insect
population dynamics (e.g. Hunter and Lechowicz, 1992; Parry et al., 1997; Volney and Mallett,
1998). In addition, aspen phenological traits have never either been studied using provenance
trials, or been used as criteria to differentiate its genotypes.
Overall, the timing of leaf flush and leaf abscission are important adaptive traits in forest trees
that need to be considered in movement of planting stock for reforestation and in genetic tree
improvement programs. Trembling aspen is a major commercial tree species plays a very
important role in the forest industry and forest ecosystem conservation in North America,
especially the boreal plain of Canada. Study aspen phenology with the traditional provenance
trials could reveal genotype differences of the species; however, it would be more efficient and
economical if the vast and easily renewed remote sensed data is used to achieve the same goals.
Hence, this study will focus on two major objectives:
1) Describe genetic variation & interpret how that helps the species to adapt.
2) Test whether the remote sensing/climate data approach works by validating its results
against the provenance trial data.
In the past two centuries, many prediction models were developed to predict timings of budburst;
however, all these models are inseparable from the same variable, the heatsum. Some
sophisticated models may include chilling temperature, photoperiod, or moisture as covariates
(???).
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