Budburst
Genetic variation in aspen phenology inferred from satellite data
and common garden experiments
Abstract
Aim: 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. If genotypes are selected for growth traits only, adaptive traits may be
sub-optimal. Better growth may be the result of an extended growing season and associated risks
of late spring and early fall frost damage. Other tradeoffs related to better growth performance
may include higher susceptibility to drought conditions. This study will investigate geographic
patterns of genetic variation in aspen phenology with the aim of guiding seed transfer. In
addition to the classical sample-based approach of provenance testing, we present a novel
approach to seamlessly map geographic patterns of genetic variation in tree populations with
remote sensing.
Location & Methods: We use the 500m resolution MODIS based enhanced vegetation index
(EVI) processed through Timesat software to measure the date of greenup for approximately
70,000 grid cells for western North America that contained at least 40% aspen. Interpolated daily
climate data was subsequently used to calculate the required heatsum for greenup for each grid
cell. The results were validated against data from a provenance trial, where greenup of 43 aspen
provenances from western North America was observed at in a common environment, and
heatsum requirements were calculated using local weather station data.
Results: We find that provenances from the very North, high elevation, and far south have a
earlier bud break than populations from the central boreal plains of Alberta and Saskachewan.
Analysis of corresponding long-term climate data suggest that these patterns are correlated with
precipitation and dryness indices. The latest budbreak is associated with the driest (winter?)
environments, suggesting that early budbreak is not a worthwhile risk strategy because of poor
growing conditions before the summer rains in the boreal plain ecosystems. Analysis of remote
sensing data reveals approximately the same patterns of genetic variation. In addition we observe
very low heatsum requirements in the far North. This can be interpreted as an adaptation to take
maximum advantage of the short available growing season.
Main Conclusion: There is little geographic variation within the boreal plains ecozone,
suggesting that seed may be moved freely within this relatively large area, covering central
Alberta and Saskachewan. While sources from Minnesota grow well when transferred to the
boreal plains ecoszone, the observed increase in productivity is associated with an extended
growth period in fall and spring, potentially resulting in vulnerability to exceptional frost events.
Our analysis also shows that this type of information can be inferred from remote sensing data at
a fraction of the cost and time required in classical common garden studies.
Budburst
Introduction
Phenology is 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; Rathcke and Lacey, 1985). Phenological phases in plants
commonly include the budburst, bud set, onset of frost hardiness, flowering, and fruiting
(Beaubien and Freeland, 2000)(Schwartz 2003). Among all these phenological phases, spring
plant phenology is the most sensitive and direct biological responses to an accumulated heatsum
in temperate regions (explained in the next paragraph). The timing of phenological events, e.g.
budburst or budset, is the result of adaptation that balance the need of avoiding damages due to
extreme climate conditions, such as late spring and early fall frosts, while maximizing the use of
the available growing season (Leinonen and Hanninen, 2002). Plants that cannot respond to
interannual climate variability to sufficiently use the growing season will be at a competition
disadvantage. Mismatches between the spring weather and plant phenological responses could
potentially cause the damage of the plants from failing to produce seeds or fruits to even
resulting in mortality (Billington and Pelham, 1991).
Spring phenology is driven by temperature (e.g. Menzel, 2003; Morin et al., 2009; Penuelas and
Filella, 2001; Sparks and Carey, 1995), commonly measured as heatsum, the daily accumulated
effective temperature for a phenological event in its active period (Beaubien and Freeland, 2000;
McMaster and Wilhelm, 1997; Snyder et al., 1999). The effective temperature was defined as the
temperature above which the phenological event occurs and keeps active. For example, the
active period of budburst is from the end of dormant to the beginning of leaf flush (Ghelardini et
al., 2006; Hunter and Lechowicz, 1992), and the temperature must surpass the effective
temperature in order for it to occur. Because different genotype requires different amount of
heatsum to activate one specific phenological event, such as 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).
Species with large ranges growing under a variety of environmental conditions will likely show
genetic differences through their adaptive traits (Howe et al., 2003). These are usually important
factors to be considered in the movement of planting stock for reforestation and in genetic tree
improvement programs. For example, in forest management, 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, genotypes with high mortality risk due
to susceptibility to frost damage or drought may not be a suitable choice for reforestation.
Ideally, genotypes that show lower adaptive risks while maintaining superior growth would be
preferred. It is therefore important to study genetic variation in both adaptive and growth traits.
Genetic variation in adaptive traits is routinely studied with provenance trials (e.g. Crowe and
Parker, 2008; Davis and Shaw, 2001; Hamann et al., 2000; Li, 1995). The purpose of the
provenance trials is to compare growth and adaptive traits of different genotypes within or across
species. To study population differences within species, the provenance trials take plants of one
species from different sites and plant them in a common garden environment where they can be
exposed to identical environmental conditions—soils, climate, water and photoperiod—with a
Budburst
systematic experimental design that accounts for random site variation (???). Because different
genotypes respond to the same environment conditions differently in growth and adaptive traits,
the observed differences may reflect within-species genetic variations. This information can then
be used to creating guidelines of seed transfers and to delineate seed zones. For example,
northern provenances of Norway spruce (Picea abies) showed earlier budburst in the experiment
gardens (Leinonen and Hanninen, 2002) and should therefore not be used in southern planting
environments to avoid late spring frost damage.
The major advantage of provenance trials is that with environmental variables controlled, a
variety of growth and adaptive traits can be evaluated for genetic variations, such as the tree
growth (Lesser et al., 2004), wood properties (Beaulieu et al., 2002), and phenological characters
(Backman, 1991; Li et al., 1997; Lobo et al., 2003). It should be kept in mind, however, that fail
to detect genetic differences among populations in a common garden trial does not mean that
their 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 paper 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.
Because of their significant correlations with standing biomass, various remote sensed data based
vegetation indices have been developed to monitor the biospheric behaviors (Lyon et al., 1998;
Richardson and Everitt, 1992), such as the net primary productivity (NPP) (Running et al., 2004;
Turner et al., 2005) and global carbon budget (Chen et al., 2003; Myneni et al., 1997; Valentini
et al., 2000). Due to its capability of durable and large-scale observations, remote sensing has
been used to study phenology in situ (Fisher and Mustard, 2007; Schwartz and Reed, 1999;
Studer et al., 2007; Zhang et al., 2003). However, it was never used to detect species genetic
differences. Since heatsum is considered the trigger of some phenological events (Reader, 1983),
it is possible to be used in combination with climate data to estimate differences in required
heatsum for budburst. This heatsum map can then be considered as a surrogate for a constant and
comparable environment, which allows us to discriminate genotype variations. 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) and Enhanced Vegetation Index (EVI) are often used to extract
greenup days (Richard and Poccard, 1998; Sakamoto et al., 2005; Wang et al., 2005; White and
Nemani, 2006). According to the data sources, other indices or parameters may also be applied,
such as the, 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).
Budburst
Trembling aspen (Populus tremuloides) is a major commercial tree species that plays a very
important role in the forest industry and forest conservation in North America, especially the
boreal plain of Canada. But studies of the phenological traits of the species are very limited.
Beaubien and Freeland (2000) studied the relationship between aspen flowerings and ocean
temperature, and other studies focus mainly on the relationships between budburst and various
insect population dynamics (e.g. Hunter and Lechowicz, 1992; Parry et al., 1997; Volney and
Mallett, 1998). The phenological traits of the species have never either been studied using
provenance trials, or been used as criteria to differentiate its genotypes. Studying 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 three major objectives:
1) Describe genetic variation of trembling aspen;
2) Interpret how this helps the species to adapt;
3) Test whether the remote sensing/climate data approach works by validating its results against
the provenance trial data.
METHODOLOGY
Study area & plant material
The aspen provenance plants were collected from 43 locations in the western and centre North
America (Figure 1). In this area, the elevation ranges from less than 100 meters at the coastal
lowland in British Columbia to 3959 meters at the Rocky Mountains. Thousands of glacial lakes
spread from the north to southeast, including the Great Slave Lake, Lake Winnipeg, and the
Great Lakes. Due to the wide topographical variations and large spatial scale, this study area is
diversified by climate variations and ecosystems. The climate in this area is regulated by the
warm air from southwest, the Pacific Ocean, and the cold air from north, the Arctic Ocean.
Meanwhile, the south-north extended Rocky Mountains blocks the moist air from coming into
the centre part of the continent. As a result, the mean annul temperature (MAT) decreases with a
latitudinal gradient from south to north, and a topographical gradient from the Rocky Mountains
to the central continent (Appendix 1a). However, the mean annual precipitation (MAP) is highly
influenced by the Rocky Mountains in the west and the lakes in east (Appendix 1b). On the
western side of the study area, MAP of the western side of the Rocky Mountains decreases from
the foothills to northern Alberta and central Saskatchewan and is higher than that on the eastern
side. In the east, however, the precipitation increases in Manitoba and northern Minnesota due to
the surrounding big water bodies including Hudson Bay and other fresh water lakes.
### Figure 1 Study area & provenance trial #####
### Appendix 1 Climatology #####
Budburst
The aspen provenance trial series were established by an industry cooperative consisting of the
Western Boreal Aspen Corporation and Alberta Pacific Forest Industries in 1998, and the
Athabasca common garden is one of the 5 experiment locations in Canada boreal plains 1.
Athabasca common garden locates in central Alberta (54°43′11″N, 113°17′08″W), roughly the
centre location of the aspen range in North America. The local climate is humid continental with
a free-frost period of less than 3 months, mean annual temperature of 2.1°C, and mean annual
precipitation around 381.7 mm with the chief rainfall in summer. Open-pollinated aspen
seedlings of the 43 provenances include 3 from the Taiga Plain of British Columbia (BC), 27
from the Boreal Plain of Alberta (AB), 8 from the Boreal Plain of Saskatchewan (SK), and 5
from the Boreal Shield of Minnesota (MN).
Provenance trial data:
We divided the budburst into 6 stages (figure 2); however, only the 3rd stage, when bud opens at
the top and the leaf comes out, was used in the analysis. The field observations were executed in
the spring of 2009 while the saplings were about 11 years old since they were planted in 1998.
We applied the completely random design in the budburst observations where the treatment is
the provenance type. By that, each sapling was visited and measured 10 times during May 4th to
June 2nd, and budburst stages of 30 trees for each provenance, 1290 trees in total, were observed
and recorded. These observations were later mapped and colored according to their budburst
days (stage 3) to demonstrate differences among plants from various environments. With the
local weather station data, the heatsum values for all provenances were also calculated based on
the daily mean temperatures.
### Figure 2 is approximately here#####
Remote sensing for aspen greenup and heatsum simulation
We employed a MODIS product, the Enhanced Vegetation Index (EVI) (Huete et al., 2002), to
extract the annual greenup dates of the focal species from 2000 to 2005. In order to reduce the
noises from other species, we kept only pixels contain more than 40% crown coverage of
trembling aspen in its range map. We used TIMESAT, a FORTRAN90 software package
(Jonsson and Eklundh, 2004), to extract seasonal parameters such as greenup or green down.
During data processing in this software package, we chose adaptive Savitzky–Golay as the
filtering method. Five maps of aspen greenup dates counted as Julian days were generated.
We obtained the daily temperature observation data during 2001 and 2005 from Meteorological
Service of Canada. Using a SAS G3GRID procedure2, we interpolated these climate data to a
seamless daily mean temperature map at a spatial resolution of 0.25°. Within each of these maps,
every pixel has 365 daily mean temperature values to cover the whole year.
1
The other four common gardens are northern BC, northern Alberta, Foothill Alberta, and center Saskatchewan.
Budburst
Combining the greenup dates and daily mean temperature data, accumulative heatsum maps of
the aspen budburst were generated by using a model developed by Ring et al. (1983). This
remote sensed heatsum map was generated for each of the five years during 2001 ~ 2005. We
calculated the heatsum from January 1st to the greenup date based on the daily mean temperature
data previously produced. The threshold was set at 0°C. To reduce the measurement errors, these
five maps were averaged at the end, and a mean greenup heatsum map was generated. This
averaged heatsum map was later estimated against the heatsum values of different provenances
planted in the common garden, and to determine whether the remote sensing based approach is
sufficient to differentiate genotypes of the focal species. The same heatsum model was also used
to calculate the 43 provenances planted in the Athabasca common garden, but only the local
weather station data of 2009 was used. The greenup dates for each provenance were counted
when budburst at the 3rd stage.
Analysis
[Adaptation analysis]
In order to identify the climate variables that dominate a genotype for its adaptive traits, we ran a
canonical correlation analysis between the greenup days and a group of climate variables that
were averaged from a period of 30 years during 1961~1990 using ClimateWNA_v4.32 (see
Wang et al., 2006). Eight annual climate variables were included in the analysis, e.g. mean
annual temperature (MAT), mean warmest month temperature (MWMT), and mean annual
precipitation (MAP) (definitions see Table 1). Using the same variables, we also performed the
canonical discriminate analysis to validate the result of canonical correlation analysis.
######Table 1 approximately here######
[Validation of genotype distributions with common garden provenance trials]
By the end, we tested whether the genotype distributions derived from remote sensing data
match with the common garden provenance trials. First, from the greenup heatsum map, we
sampled pixels within a 10×10 km2 square that surround each of the provenance locations. Then
we performed the t-test (α = 0.05) to compare whether the observed phenological traits (i.e.
greenup days) are different from the remote sensing originated greenup days. Altogether, 43
provenances were tested. We mapped the t-test p-values for all provenances to show explicitly
which provenances failed to be represented by the remote sensing originated approach.
RESULT AND DISCUSSION:
Map of provance trial results
- Comparing growth traits with phenological traits
- The basic statistics and spatial pattern of budburst days of different aspen
genotypes/provenances from various locations
About 90% of all provenance Aspen saplings survived since they were planted in the Athabasca
common garden about 10 years ago. As measured in 2006 (unpublished data from Hamann’s
lab), the growth traits of this species showed some interesting spatial patterns. In comparison to
provenances from the south, provenances from areas northern to the common garden usually
grow shorter in height and smaller in DBH. For example, the average height and DBH of the
Budburst
provenances from northern British Columbia were 2.53 m and 2.25 cm, are all smaller than those
from Minnesota, 3.97 m and 3.14 cm, respectively. The budburst timings (days) of different
provenances, however, showed a very different spatial distribution pattern. We found that
provenances from northern British Columbia and Minnesota have the earliest budburst (Figure
1), 130 and 136 days respectively; budburst of foothill provenance ranks the second, about 140
days; the centre Alberta and Saskatchewan provenances have the latest budburst, both are about
151 days (Table 2). Budburst of various provenances varies along the latitude: increases first
from northern British Columbia to central Saskatchewan, and then decreases in northern
Minnesota. These observations also showed a trend along altitude that with the increase of
elevation, the budburst days decreased. For example, the foothill provenances have less budburst
days than that of the Saskatchewan provenances at the similar latitude (Figure 2).
#### #Figure 2 approximate here: Map of provenance trial
-
Canonical correlation analysis: climate variables that control the timings of
phenological events:
Figure 3 illustrated the budburst pattern in more details. According to Figure 3a, if the
provenances from Minnesota are excluded, the timings of budburst are earlier for the
provenances from higher latitudes and elevations, i.e. provenances from Northern British
Columbia, Northern Alberta, and the rocky mountain foothills have earlier budburst than
provenances from central Alberta and Saskatchewan, which follow roughly the latitude gradient
except for the foothill. This pattern is clearer if we combined together the provenances from
central Alberta and Saskatchewan and removed Foothill provenances from the graph (Figure 3b).
Following this trend, budburst of various provenances should be in an order from northern
British Columbia, to northern Alberta, centre Alberta and Saskatchewan, and to Minnesota
(locations of the provenances see Figure 1). However, we found that the budburst of provenances
from Minnesota are much earlier than the provenances from north Alberta, only slightly later
than provenances from northern British Columbia (Figure 3a and b), indicating these
provenances are dominated by factors more than latitude, or heatsum, only. Therefore, we need
to look into some more relevant biological and non-biological elements in order to understand
this phenomenon.
Explain the patterns with climates;
As it is well known, variation of budburst timings of different provenances is the evidence of
plant adaptations to their local habitats. Among all environmental variables, climates are the
major forces shaping the adaptive traits of plants from different provenances. Any phenological
differences caused by variations of elevation, latitude, and longitude are ultimately the results of
climate variants. The canonical correlation analysis demonstrated that the budburst is highly
correlated with climate variables (r = 0.729, see Table 2). We found that budburst timing is
positively correlated with MWMT, MAP, AHM, and EXT_COLD; but it is more significantly
associated with AHM, MAP, and PAS, the moisture variables (Table 2).
### #Table 2 approximate here: Map of provenance trial
Budburst
The gradient of budburst timings across levels of latitude showed that the higher the latitude
where the provenances are from, the earlier the bud bursts (Figure 3). This implies that heat is
the major factor influencing budburst. However, other climate variables also play important roles
by constraining these adaptive traits being realized. For example, according to the canonical
correlation analysis, we found timing of trembling aspen budburst is constrained by the moisture
regimes (Table 2). Therefore, provenances from the center region of our study area where spring
is usually dry (Howe et al., 2003), won’t gain any advantage through an earlier budburst even
when the required heatsum is reached. Consequently, they have developed a strategy of waiting
until the moisture regime is appropriate before initiating their budburst. Similarly, provenances
from the south, i.e. Minnesota, bud burst earlier and require less heatsum. Because these plants
have adapted to the wetter environment (Appendix 1), and it would be less competitive with
other species or genotypes if they don’t take the advantage of a longer growing season. The
earlier budburst for provenances from the far north (e.g. northern British Columbia), on the other
hand, could be a reflection of the strategy of sufficiently using the limited growing season. As a
sign, most of these plants developed certain mechanisms to resist frost damage in the early spring
(Howe et al., 2003).
Results from Remote Sensing approach;
Comparing with the mean annual precipitation map (Appendix 1), the distribution of pure aspen
stands is usually associated with wetter environment. The species range map showed that most
trembling aspen communities of higher crown coverage distribute in the boreal plain (Figure 4),
and usually conform with the distribution of lakes and rivers.
The averaged aspen greenup heatsum map showed a gradient from southwest to northeast
(Figure 5). Trembling aspen that grows around the Peace River and Athabasca River in northern
Alberta requires less heatsum for budburst than those growing in central Alberta and
Saskatchewan and the rocky mountain foothills in Alberta (figure 5). However, this pattern of
latitude gradient was broken at Minnesota where less heatsum was required for the species to
green up. By overlapping the measured heatsum values of each provenance from the common
garden on top of their original sources in the heatsum map (Figure 5), we found the heatsum
values from these two different sources (observed and remote sensing based) match up nicely,
especially those that were from the northern Alberta, foothills, and Minnesota. We found that the
common garden heatsum measurements are higher than their remote sensing based estimates in
the central Alberta and Saskatchewan area. Based on the canonical correlation analysis result
(see Table 3), it is suggested that this adaptive trait was shaped by the insufficient moisture in the
spring in this area (see also Appendix 1: map of moisture).
[[[[[[NEW ANALYSIS??????]]]]
The t-test between heatsum obtained from the observed provenance trials and the remote sensing
data images (Table 4) confirmed what we have found by visual comparisons in Figure 5. For the
provenances from Minnesota, Foothill, and northern Alberta, heatsum values estimated based on
the remotely sensed data are not significantly different from the ones calculated based on the
local weather station observations in the common garden; however, for the provenances from
Northern British Columbia and central Alberta and Saskatchewan, the majority of the heatsum
values do not match each other very well, only xxx% agree with each other, the rest are
Budburst
significantly different. Our interpretation of these discrepancies in central Alberta and
Saskatchewan is that when moisture is insufficient, heatsum is no longer the only factor
determining when the bud would burst, and moisture becomes the constrain and determine
whether the bud should burst even though heatsum is already sufficient (have been said
somewhere else). However, for the provenances from Northern British Columbia, the smaller
heatsum values observed in the common garden provenance trials might be a reflection of when
sufficient moisture regime is reached how quickly the plants from far north would respond; the
remote sensing based estimate of heatsum were sampled mainly from the low crown coverage
stands in Northern British Columbia and relatively drier climates (see Appendix xxx) comparing
with the common garden.
Final remarks:
There is little geographic variation within the boreal plains ecozone, suggesting that seed may be
moved freely within this relatively large area, covering central Alberta and Saskachewan. While
sources from Minnesota grow well when transferred to the boreal plains ecoszone, the observed
increase in productivity is associated with an extended growth period in fall and spring,
potentially resulting in vulnerability to exceptional frost events. Our analysis also shows that this
type of information can be inferred from remote sensing data at a fraction of the cost and time
required in classical common garden studies.
[Implications of this study: forest management, genotypes, phenological traits…….]
Budburst
Acknowledgment
Budburst
Table 1. Climate variables used ……
Climate variables
MAT
MWMT
MCMT
TD
MAP
MSP
AHM
SHM
Definition
mean annual temperature (°C),
mean warmest month temperature (°C),
mean coldest month temperature (°C),
temperature difference between MWMT and MCMT, or
continentality (°C),
mean annual precipitation (mm),
mean annual summer (May to Sept.) precipitation (mm),
annual heat:moisture index (MAT+10)/(MAP/1000))
Budburst
Table 2.
SEEDLOT
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
LAT
55.03
56.41
57.12
56.61
57.78
58.56
55.19
55.34
58.2
58.6
55.6
55.6
55.6
55.64
54.18
54.1
54.14
54.21
54.21
54.21
53.2
53.31
53.3
53.08
55.6
54.93
54.94
55.14
55.06
54.2
54.2
54
53.9
53.8
54.03
53.8
53.9
47
47.2
47.6
47.5
47.2
58.4
LONG
-118.73
-117.76
-117.74
-117.98
-117.96
-115.64
-114.61
-115.01
-123.33
-122.33
-116.67
-116.67
-116.67
-114.69
-115.78
-116.5
-116.58
-116.44
-116.44
-116.59
-115.6
-115.46
-115.43
-115.26
-113.41
-112.74
-112.86
-113.02
-112.11
-105.7
-106.8
-106.9
-105.8
-106.7
-108
-108.5
-107.5
-93
-93.8
-93.4
-93.6
-93.4
-123
ELEV
649
739
606
709
459
343
726
646
1177
335
632
632
632
709
731
1018
868
803
803
914
939
939
927
912
762
545
546
601
624
490
513
519
517
583
530
710
570
384
405
424
433
395
511
REGION
ABf
nAB
nAB
nAB
nAB
nAB
cAB
cAB
BC
BC
cAB
cAB
cAB
cAB
ABf
ABf
ABf
ABf
ABf
ABf
ABf
ABf
ABf
ABf
cAB
cAB
cAB
cAB
cAB
SK
SK
SK
SK
SK
SK
SK
SK
MN
MN
MN
MN
MN
BC
Heatsumday(Julian Day)
142
143
143
145
141
136
148
151
130
129
145
145
145
152
143
146
144
148
137
145
144
149
142
143
148
146
142
147
146
149
144
149
143
149
146
147
150
141
143
140
143
137
130
Heatsum(°C)
190.2914
202.8289
202.8289
227.2956
178.2664
170.6206
262.2081
294.0289
155.7539
145.1248
227.2956
227.2956
227.2956
304.7373
202.8289
237.6831
215.0123
262.2081
170.6206
227.2956
215.0123
275.6748
190.2914
202.8289
262.2081
237.6831
190.2914
248.2789
237.6831
275.6748
215.0123
275.6748
202.8289
275.6748
237.6831
248.2789
284.6998
178.2664
202.8289
170.6206
202.8289
170.6206
155.7539
Budburst
Table 3.
Budburst
Budset
MAT
MWMT
MCMT
TD
MAP
MSP
AHM
SHM
PAS
EXT_Cold
W1
-0.0478
-0.9158
V1
0.6369
0.8005
0.0215
0.3126
0.3795
0.2604
0.0492
0.0776
-0.3459
-0.1397
W2
0.7283
-0.0108
V2
-0.0431
0.0650
0.0716
-0.0289
0.3415
0.2686
-0.5326
-0.1927
0.3590
0.1167
Budburst
Figure 1 Distribution of samples plants of aspen
Figure 2. Stages of budburst
Index of Budburst
2
1
2
1
Index of Budburst
3
3
4
4
Budburst
-51
-50
-49
-48
-47
Canonical Correlation
Figure 3. Budburst of various provenances
-51
-50
-49
-48
Canonical Correlation
-47
Budburst
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