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First published online February 6, 2009; 10.1104/pp.108.133249 Plant Physiology 149:1982-1991 (2009) © 2009 American Society of Plant Biologists OPEN ACCESS ARTICLE
The Control of Autumn Senescence in European Aspen1,[W],[OA]Umeå Plant Science Center, Department of Plant Physiology, Umeå University, SE–90187 Umeå, Sweden
The initiation, progression, and natural variation of autumn senescence in European aspen (Populus tremula) was investigated by monitoring chlorophyll degradation in (1) trees growing in natural stands and (2) cloned trees growing in a greenhouse under various light regimes. The main trigger for the initiation of autumn senescence in aspen is the shortening photoperiod, but there was a large degree of variation in the onset of senescence, both within local populations and among trees originating from different populations, where it correlated with the latitude of their respective origins. The variation for onset of senescence with a population was much larger than the variation of bud set. Once started, autumn senescence was accelerated by low temperature and longer nights, and clones that started to senescence late had a faster senescence. Bud set and autumn senescence appeared to be under the control of two independent critical photoperiods, but senescence could not be initiated until a certain time after bud set, suggesting that bud set and growth arrest are important for the trees to acquire competence to respond to the photoperiodic trigger to undergo autumn senescence. A timetable of events related to bud set and autumn senescence is presented.
Leaf senescence is a highly regulated process that involves the sequential degradation of macromolecules and extensive salvage of nutrients (Gan and Amasino, 1997  ; Noodén et al., 1997; Lim et al., 2003). The nitrogen from chlorophyll seems to remain in the vacuole and is lost with the abscission of leaves (Hörstensteiner, 2006). In contrast, during autumn senescence in deciduous trees like European aspen (Populus tremula), the nitrogen from leaf proteins is transferred to bark storage proteins, stored over the winter, and then remobilized and used for growth in the next spring (Black et al., 2001; Cooke and Weih, 2005). About 90% of the leaf nitrogen is remobilized during autumn senescence in aspen (Keskitalo et al., 2005). The adaptive value of timing leaf senescence appropriately in northern latitudes is obvious: if leaf senescence occurs too early in the season, the growing period will be shortened and the photosynthetic carbon gain reduced, while if senescence occurs too late, green leaves will be killed by frost and leaf nitrogen will be lost, with a negative impact on growth, since nitrogen availability typically limits tree growth in boreal forests (Näsholm et al., 1998). The optimal timing of autumn senescence, therefore, represents a trade-off between conflicting optima for carbon and nitrogen metabolism and is likely to show adaptation to the local environment, like other developmental processes such as bud set (Ingvarsson et al., 2006). However, despite its importance, very little is known about the factors that trigger and modulate autumn senescence, although changes in gene expression (Bhalerao et al., 2003; Andersson et al., 2004) and the main physiological and biochemical changes (Keskitalo et al., 2005) that occur during autumn senescence in aspen have been characterized. The cited studies established (among other things) that autumn senescence starts in a given aspen tree every year at around the same date, suggesting that the main trigger is the reduction in the photoperiod (Keskitalo et al., 2005). Interestingly, the expression of bark storage protein genes during autumn is also regulated by the photoperiod, through the action of phytochrome (Zhu and Coleman, 2001). These findings provide strong support for the hypothesis that the start of autumn senescence in aspen is controlled by photoperiod. The length of the dark period may be the critical factor that controls senescence, although this has not, to our knowledge, been formally proved. However, other factors might have interactive effects on its timing, since stresses like low temperature, shortage of water or nutrients, and pathogen infection can also lead to leaf senescence, even in the absence of photoperiodic cues.
Ultimately, we would like to identify the genes that regulate autumn senescence in aspen and the alleles that are responsible for their adaptation to northern climates. To achieve this goal, we need better understanding of the mechanisms that initiate senescence in aspen. In particular, we need to elucidate (1) the interactive effects of photoperiod and temperature changes on the induction and progression of senescence and (2) the relationship between the timing of bud set/growth cessation and the initiation of autumn senescence. Useful materials to meet these objectives include appropriate germplasm resources for phenotypic evaluations of the trait(s) of interest and a reliable set of candidate genes, since we have shown that association mapping is a powerful technique for identifying the genetic bases, down to single nucleotides, that may be responsible for phenotypic variations in aspen (Ingvarsson et al., 2008
The Onset of Senescence Is Controlled by Photoperiod, But Low Temperatures Accelerate Chlorophyll Degradation
Common gardens have been established at Ekebo and Sävar in southern and northern Sweden, respectively, to examine the performance of members of the SwAsp collection in contrasting environments. However, some of the aspen genotypes from northern Sweden grow poorly in the common garden in southern Sweden, since the maximum daylength at this latitude is below their critical value for bud set (Luquez et al., 2008
We wanted to see if the late-senescing genotypes had a more rapid senescence only as a consequence of the lower temperature during the later part of the scoring period. Overall, the mean temperature gradually decreased during the whole scoring period from approximately 16°C to 4°C in 2006 and from 12°C to 2°C in 2007 (Fig. 1). Therefore, we examined the possibility that the rate of senescence progression was directly correlated to temperature by subjecting the phenological and environmental data to ANOVA. However, the results indicated that the time to complete senescence was more strongly correlated with the date of appearance of first yellow leaves (Supplemental Table S1, model 1) than with the mean temperature during the time to complete senescence (Supplemental Table S1, model 2). Furthermore, model 3, based on both parameters, gave a considerably better fit, with the combined effects of date of first yellow leaves and temperature explaining 85% and 67% of the variation in the time to complete senescence in 2006 and 2007, respectively. Both the date of appearance of yellow color and the time to complete senescence were significantly correlated between the 2 years, with correlation coefficients of 0.636 and 0.406, respectively (data not shown). Further support for the increased speed of senescence in lower temperatures came from our measurements of the onset and rate of senescence in tree 201, which we have followed from 1999 to 2007 by monitoring its chlorophyll contents. Here, starting date and rate of progression of autumn senescence in relation to temperature have been quantified more accurately and quantitatively since chlorophyll levels have been measured directly, not just estimated by visual scoring. The start of autumn senescence, as we have shown before, was remarkably constant in the monitored years (Table I ), occurring within a 4-d period (September 9–12) in 7 of the 8 years studied. The date for start of senescence did not correlate to prior temperature conditions (data not shown). In contrast, the rate of subsequent chlorophyll degradation varied substantially among years. The possibility that variations in temperature may have been responsible for some of the variation in the rate of chlorophyll degradation was tested by examining the strength of linear relationships between the chlorophyll estimates and both calendar date and temperature (Supplemental Table S2). Calendar date alone explained approximately 82% of the variation in chlorophyll content over all 7 years (model 1). However, chlorophyll degradation was slowest in the warmest year, 2006, and two to three times faster in the coldest years (2003, 2004, and 2007). Consequently, including cumulated mean daily temperatures in the statistical model resulted in a significant improvement (Supplemental Table S2, model 2), with cumulated temperatures showing a positive correlation with chlorophyll content. This shows that chlorophyll degradation occurred more slowly in warm years than in cold years and explains why visual scoring detected the appearance of yellow color on this tree already on September 13 in the cold year 2007 but not until September 28 in the warm year 2006, despite the fact that onset of chlorophyll degradation occurred almost the same day. Taken together, onset of senescence was temperature independent and speed of senescence was temperature dependent.
However, since temperatures decreased during the scoring, it was not possible to study in the field if late-senescing trees were fast only due to the lower temperature during their senescence or if they had a faster senescence due to some genotypic property. To study this, we analyzed data from direct measurements of onset and speed of senescence in the trees of the SwAsp collection in the greenhouse in Umeå. These measurements were performed in 2006 under natural photoperiods but in controlled temperature. Since the changes in photoperiod in this experiment were identical to those experienced by the trees of the UmAsp collection, this allowed the effects of low temperature and photoperiod on the initiation of senescence to be distinguished. All clones started to senesce as the photoperiod gradually decreased (Fig. 2 ), demonstrating that photoperiodic cues were sufficient to induce autumn senescence. Moreover, the mean date of the onset of senescence in the greenhouse for the SwAsp clones sampled from the Umeå region was September 15, 2006, close to the date at which senescence of the model tree began (September 11, 2006). This also correlated well with the mean date on which yellow color appeared (score 3, corresponding to approximately 40% chlorophyll loss) on the trees from the UmAsp collection (September 21 in 2006 and September 14 in 2007). These observations strongly suggest that the senescence was induced under natural photoperiods in the greenhouse in the same way as in the field. Also in the greenhouse, the rate of senescence was weakly but significantly correlated with the starting date of senescence under natural photoperiods (Fig. 3A ), suggesting that late-senescing trees also were fast-senescing trees.
We also performed another greenhouse experiment in 2005, when the trees were first grown under a controlled photoperiod of 23 h, sufficient to keep all clones actively growing, then on September 21 the photoperiod was suddenly changed to natural photoperiod (12 h) and clones subsequently experienced natural decline of photoperiod, while the other environmental conditions were very similar to those experienced by the trees in 2006. The two light regimes are compared in Supplemental Figure S1. This experiment will be further denoted "sudden shift." Under natural photoperiods, the earliest tree started to senesce on August 24, when the daylength was 15.1 h, while the latest tree started on October 22, when the daylength was only 8.6 h. After the sudden shift, senescence of the trees occurred under shorter and less variable photoperiods (4.1–8.7 h of light at the starting date of senescence). Onset of senescence in the sudden-shift experiment has a complex control (see below); therefore, late-senescing trees in this experiment do not correlate well with those that senesce late under natural photoperiod (data not shown). In the sudden-shift experiment, there was no significant correlation between the starting date and the rate of senescence (Fig. 3B). Taken together, the greenhouse experiments suggested that clones that were late in onset of senescence had a tendency to senesce faster. It also appeared that photoperiod during senescence influenced the rate: on average, the rate of chlorophyll degradation was greater after the sudden shift (0.97 relative chlorophyll content index [CCI] per day), when the nights were longer, than under natural photoperiods (0.67 CCI per day). Therefore, we conclude that (1) onset of autumn senescence at a specific photoperiod is a genotype property that is not significantly influenced by temperature and (2) low temperature accelerated the rate of autumn senescence, after it had been initiated by photoperiodic cue. The data also suggest that (3) clones that have a late onset of senescence tended to senescence faster and (4) the rate of senescence was also influenced by the photoperiod, longer nights leading to faster senescence.
During the two greenhouse experiments with the SwAsp collection (the natural-photoperiod and the sudden-shift experiments), we also scored bud set (Fig. 4 ). When the bud set and senescence data under natural photoperiod were compared (Figs. 2 and 4), the within-population variation in onset of senescence was much greater than the within-population variation in bud set. The repeatability within populations for the starting date of senescence under natural photoperiods was 0.41, implying that only 41% of the variation was attributed to differences between populations. This is significantly lower than the within-population repeatability for bud set (0.86). Second, the correlation with geographical origin was substantially lower for the start date of senescence (r2 = 0.45) than for the date of bud set (r2 = 0.82). The extreme populations differed by 50 d in bud set (Fig. 4) but only by 20 d in onset of senescence (Fig. 2), and there was an overlap in the timing of autumn senescence between clones from the far north and the far south, whereas for bud set all clones obtained from southern populations differed from northern clones.
Senescence Is Triggered by a Different Critical Photoperiod Than Bud Set, But Trees Have to Set Buds before They Can Undergo Autumn Senescence Since growth arrest and bud set are under strong photoperiodic control and sometimes occur well before autumn senescence, there are three possible explanations for the temporal relationship between these two events. First, it is possible that they are under independent photoperiodic control. Second, it is possible that only growth arrest and bud set are under photoperiodic control and autumn senescence is initiated after a certain lag phase, which may differ between genotypes. A third possibility is that after induction of growth arrest and bud set by short days the tree has to acquire competence to senesce and then senescence occurs when a second critical photoperiod is reached. In order to assess which of these possibilities is most likely to be correct, we compared the relationship between bud set and senescence under different conditions.
We calculated the time lag between bud set and onset of senescence when the SwAsp collection was grown under natural photoperiods in the greenhouse in 2006 (Fig. 5A
). The exact relationship between growth arrest in the cambium and in the apical meristems and bud set in aspen is not known, but since bud set is easy to score it can be used as a good proxy for growth arrest in general. It has to be kept in mind that bud set, measured as development of bud scales, happens rather late in the process, when the trees have already stopped growing both apically and laterally. When scored in the greenhouse under natural photoperiod, some trees from southern populations started to senesce shortly after bud set: the average lag phase between bud set and senescence was about 10 d for the southernmost populations, while the time lag for trees from northern populations was much longer, up to about 40 d (the most extreme, clone 105, set buds on July 15 and started to senesce 61 d later, on September 14). In the sudden-shift experiment, photoperiod was switched from 23 to 12 h, a value below the critical limits for bud set of almost all clones (Fig. 4), and buds were set on average after 26.5 d (Fig. 5B), consistent with reports regarding the lag between perception of critical photoperiods and bud set (Ruttink et al., 2007
The Scottish surgeon John Hunter (1728–1793) wrote, "When I was a boy, I wanted to know all about the clouds and grasses, and why leaves changed color in the Autumn; ... I pestered people with questions about what nobody knew or cared anything about" (cited by Ellis, 2001 ). Since then, science has made immense progress, and we now have abundant information about clouds and grasses. Nevertheless, research about changes in leaf color during the autumn is still in its infancy; very little is known, for example, about the genes that regulate the process. However, with the recent development of Populus as a model system for tree genetics and genomics, better tools are now available to address the question pondered by Hunter more than 200 years ago.
There is enormous within-species variation in Populus due to their very large populations, dioecious nature, wind pollination, and wind-dispersed seeds. For instance, the average nucleotide diversity within genes in aspen is approximately 1% (Ingvarsson, 2005 First, we have shown that although the timing of onset of autumn senescence is a genotypically governed property, the natural variation in onset of senescence among aspen trees growing at a given site is larger than the variation in bud set, and the effect of latitude of origin is weaker. The reasons for this are not obvious. However, it is possible that the timing of growth arrest and bud set is more closely related to winter survival, so it is under more uniform selection pressure at a given latitude. Alternatively, since the timing of autumn senescence involves a trade-off between carbon acquisition and nitrogen loss, local variations in microclimate and soil nutrient levels may enhance natural variation in senescence but not in bud set. A third possible explanation is linked to biotic interactions. Since autumn senescence phenology is likely to be important for many pathogens and herbivores, a tree that differs phenologically from the others in its local environment is more likely to escape attack by pathogens and herbivores, which develop local adaptation. It is also possible that all of these selective pressures have interactive effects.
Second, we have shown that the speed of senescence varies between genotypes, with those starting senescence later senescing more rapidly than earlier starters. In addition, low temperature accelerates senescence. Senescence is probably accompanied by oxidative stress due to imbalances between light capture and carbon assimilation (Kar et al., 1993
Third, our data on the relationship between bud set and the onset of senescence allow a model of the events that occur during autumn in aspen to be formulated, integrating data from the recently published molecular timetable of apical bud set (Ruttink et al., 2007
The time between bud set and the date when the critical photoperiod for senescence is reached is longer for the northern clones than for the southern clones (Fig. 5A). This may reflect adaptation to northern latitudes, where ensuring winter survival and safeguarding carbon gains may be more important, relative to maximizing nitrogen saving, than it is farther south due to the short growing seasons and highly fluctuating temperatures. It has not been established if there is a lag phase between perception of the critical photoperiod and onset of senescence, but it is likely that this would be shorter than the lag between the critical photoperiod for bud set and bud set, since other factors that induce leaf senescence, such as ethylene treatment, pathogen infection, and drought, do so very rapidly, most likely because the catabolic machinery seems to be, at least in part, already present in green leaves (Zelisko et al., 2005 ; Garcia-Lorenzo et al., 2006). We believe that this is also true for autumn senescence, and we have a model to test this prediction. Our data show that nitrogen remobilization typically takes 2 weeks but is highly dependent on temperature. It is likely that the timing of the other senescence-related events is also temperature dependent, but this has not been tested, to our knowledge. Once the remobilization process is complete, abscission occurs. Our mechanistic model and collection of genotypes allow experiments to be designed to test whether gene expression is important in the induction of autumn senescence. If trees of different genotypes are exposed to sufficiently short days to induce bud set, but not senescence, then left until competence to senesce has developed and subsequently exposed to an even shorter photoperiod that induces senescence in some genotypes but not others, it may be possible to determine whether and which transcriptional changes are specifically involved in the transition to autumn senescence, which has not been possible so far (Jansson and Thomas, 2008).
The strict photoperiodic control of the onset of autumn senescence in aspen might be rather unusual. In sugar maple (Acer saccharum), American beech (Fagus grandifolia), and yellow birch (Betula alleghaniensis), 90% of the variation in autumn canopy senescence has been attributed to variations in temperature (Richardson et al., 2006
We have found that the initiation of autumn senescence in European aspen is under photoperiodic control but senescence is not provoked until the leaves are competent to senesce, a process that may be related to the carbohydrate status of the tree and influenced by growth arrest and dormancy. With our germplasm resources (the SwAsp and UmAsp collections) and a set of candidate genes, we can now attempt to correlate nucleotide polymorphisms in these genes to the autumn-related traits dissected in this contribution.
The UmAsp Collection
A set of 180 European aspen (Populus tremula) trees was selected during the summer of 2006 along roads in the region of Umeå, Sweden (20° 15' E, 63° 50' N). The single tree we have studied previously (Bhalerao et al., 2003
All trees in the UmAsp collection were visually scored for autumn senescence using an autumn senescence score card (Supplemental Fig. S2) on August 23, September 4, 7, 11, 14, 18, 21, 25, and 28, and October 2, 5, 9, 12, 16, 19, and 23 in 2006 and August 20, 23, 27, and 30, September 3, 6, 10, 13, 17, 20, 24, and 27, and October 1, 4, 8, 12, 16, and 19 in 2007. The start of autumn senescence was defined as the date when yellow color appeared (score 3), and senescence was considered complete when more than 90% of the leaves had fallen (score 7). The duration of senescence was determined as the number of days between these two dates. Three (2006) and two (2007) trees that already had yellow leaves in mid-August were excluded from the analysis because senescence in these cases was probably induced by other factors (e.g. water stress during the unusually dry summer of 2006).
The SwAsp collection, comprising 116 clones, was obtained by sampling 12 populations across Sweden (from 55° to 67° N) as described in detail by Luquez et al. (2008) In both the 2005 and 2006 experiments, the trees were sprayed with a commercial fungicide and insecticide (Baymat; Bayer Crop Science) to avoid rust infection and insect damage. Spider mite infections were treated by biological control using spider mite predators (Ambluseius swiskii; provided by Lindesro). The temperature in the greenhouse could briefly rise on sunny days until the ventilators opened.
The trees' chlorophyll contents were estimated twice per week by measuring the relative CCI of three (2005) or five (2006) leaves per tree using a CCM-200 chlorophyll meter (Opti-Sciences). The average values of the three or five measurements were used to calculate the senescence parameters. Chlorophyll degradation occurred in two almost linear phases: a first phase with no or slow chlorophyll degradation and a second phase with rapid chlorophyll decay (Supplemental Fig. S3). Data from both phases were fitted to linear models, and the intercept of the two lines was defined as the starting date of senescence. The negative value of the slope of the second regression was used as an estimate of the rate of senescence. Bud set was scored according to Luquez et al. (2008)
Chlorophyll content was measured during each of seven autumns in a tree growing on the university campus. From 1999 to 2003, chlorophyll was determined from leaf extracts as described by Keskitalo et al. (2005)
Statistical analyses were performed using functions of the "base" and the "stat" packages of the Windows version of R software (R Development Core Team, 2006
The following materials are available in the online version of this article.
Lena Wallheim and Frank Klimmek are acknowledged for participation in the creation of the UmAsp collection. Received November 27, 2008; accepted February 2, 2009; published February 6, 2009.
1 This work was supported by grants from the Swedish Research Council, the Swedish Research Council for the Environment, Agricultural Sciences, and Spatial Planning, the Swedish Foundation for Strategic Research, and the Kempe Foundation.  The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantphysiol.org) is: Stefan Jansson (stefan.jansson{at}plantphys.umu.se).
[W] The online version of this article contains Web-only data.
[OA] Open Access articles can be viewed online without a subscription. www.plantphysiol.org/cgi/doi/10.1104/pp.108.133249 * Corresponding author; e-mail stefan.jansson{at}plantphys.umu.se.
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