Budburst
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 3 rd 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 4 th
to
June 2 nd
, 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 procedure
2
, 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 1 st
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 3 rd
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 km
2
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
Definition mean annual temperature (°C), mean warmest month temperature (°C), mean coldest month temperature (°C),
TD
MAP
MSP
AHM
SHM 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 LAT LONG ELEV REGION Heatsumday(Julian Day) Heatsum(°C)
769
770
771
772
773
774
775
776
777
778
762
763
764
765
766
767
768
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
751
752
753
754
755
756
757
758
759
760
761
55.03 -118.73 649
56.41 -117.76 739
57.12 -117.74 606
56.61 -117.98 709
57.78 -117.96 459
58.56 -115.64 343
ABf nAB nAB nAB nAB nAB
55.19 -114.61 726
55.34 -115.01 646 cAB cAB
58.2 -123.33 1177 BC
58.6 -122.33 335 BC
55.6 -116.67 632 cAB
55.6 -116.67 632
55.6 -116.67 632
55.64 -114.69 709
54.18 -115.78 731 cAB cAB cAB
ABf
54.1 -116.5 1018 ABf
54.14 -116.58 868 ABf
54.21 -116.44 803 ABf
54.21 -116.44 803
54.21 -116.59 914
53.2 -115.6 939
53.31 -115.46 939
53.3 -115.43 927
53.08 -115.26 912
55.6 -113.41 762
54.93 -112.74 545
54.94 -112.86 546
55.14 -113.02 601
55.06 -112.11 624
54.2 -105.7 490
54.2 -106.8 513
54 -106.9 519
53.9 -105.8 517 cAB
SK
SK
SK
SK
ABf
ABf
ABf
ABf
ABf
ABf cAB cAB cAB cAB
53.8 -106.7 583
54.03 -108 530
53.8 -108.5 710
53.9 -107.5 570
47 -93
47.2 -93.8
384
405
47.6 -93.4
47.5 -93.6
47.2 -93.4
58.4 -123
424
433
395
511
SK
SK
SK
SK
MN
MN
MN
MN
MN
BC
137
145
144
149
142
143
148
146
142
147
145
145
152
143
146
144
148
146
149
144
149
143
149
146
147
150
141
143
140
143
137
130
142
143
143
145
141
136
148
151
130
129
145
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.
W1
Budburst -0.0478
Budset -0.9158
MAT
V1
0.6369
MWMT 0.8005
MCMT 0.0215
TD
MAP
MSP
AHM
0.3126
0.3795
0.2604
0.0492
SHM 0.0776
PAS -0.3459
EXT_Cold -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

Budburst
-51 -50 -49 -48
Canonical Correlation
-47
Figure 3. Budburst of various provenances
-51 -50 -49 -48
Canonical Correlation
-47

Budburst
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