Master Thesis
Introduction to 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 (Perez-Pastor et al., 2004), 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).
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.
(Observation method: Field observation, digital camera, and remote sensing in large scale. )
Environmental control of budburst
Temperature, thermal time,
Chilling requirement, photoperiod
1
http://plantwatch.sunsite.ualberta.ca/misc/tracking.php
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 is the accumulation of degree-days for a phenological event in its active period.
Degree-days also known as heat units, thermal units, or day degrees and defined as the
accumulation of effective temperature. Generally, it is measured as the algebraic average of daily
temperature within a range (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.
Threshold 0-10, 5 (Perry 1970), 0 for based temperature and 1 for aspen (Heide 1993), 0
(Elisabeth), -3 to 15 effective as chilling (Santini 2004)
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.
Spring phenology as an adaptive trait
Geographic patterns of budburst
(latitudinal, altitudinal, and coastal Davis 2001 )
Earlier researches noticed the geographic variation of spring phenology 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., 2003).
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 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 interannual 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 (??).
Interpretation
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).
Genetic differentiation with respect to quantitative aspects of the phenotypes are not reflected in
patterning of enzyme variation, indicating that populations diverged in relation to local climate
despite gene flow (Davis and Shaw, 2001).
Phenology and 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.
Provenance
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
What it’s been used for
Remotely sensed phenology is land surface phenology
New application
Objectives
Spatial patterns of genetic variations;
Remote sensing approach;
Reason for spatial patterns: understand the mechanic of adaptation to climate, application for
forest management; and predict the shift in the future responded to the climate change.
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