Climate warming and the risk of frost damage to boreal forest trees: identification of critical ecophysiological traits

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1 Tree Physiology 26, Heron Publishing Victoria, Canada Climate warming and the risk of frost damage to boreal forest trees: identification of critical ecophysiological traits HEIKKI HÄNNINEN 1,2 1 Plant Ecophysiology and Climate Change Group (PECC), Plant Biology, Faculty of Biosciences, University of Helsinki, Finland 2 Corresponding author (heikki.hanninen@helsinki.fi) Received June 1, 2005; accepted September 3, 2005; published online April 3, 2006 Summary According to a hypothesis presented in the mid- 1980s, climate warming will, paradoxically, increase the risk of frost damage to trees in the boreal and temperate zones. Dehardening and even growth onset may occur in trees during mild spells in winter and early spring, resulting in damage during subsequent periods of frost. In the present study, ecophysiological traits critical to the occurrence of frost damage in trees in the boreal zone were identified. Diagnostic computer simulations were performed to examine why one simulation model of frost hardiness in an earlier study predicted heavy frost damage as a consequence of climate warming, whereas another closely related model did not. The modeling comparison revealed that the response of ontogenetic development to air temperature during quiescence is a critical factor determining the risk of frost damage. As the response can be readily determined in growth-chamber experiments, the findings of the present study can be used to guide experimental work on the environmental regulation of the annual cycle of frost hardiness in trees. Keywords: annual cycle, phenology, scenario analysis, simulation modeling, tree traits. Introduction Cannell (1985) presented the paradoxical hypothesis that climate warming in the boreal and temperate zones will cause increased frost damage to trees by causing dehardening or growth onset during mild spells in winter and early spring, leading to frost damage during subsequent cold periods (Cannell and Smith 1986). Early studies addressing this hypothesis indicated that climate warming would result in little, if any, increase in frost damage in temperate conditions (Murray et al. 1989, 1994, Kramer 1994) but would cause a dramatic increase in frost damage in boreal forests (Hänninen 1990a, 1991, Kellomäki et al. 1995). More recent studies have generally suggested less dramatic frost damage in response to climate warming under boreal conditions (Hänninen 1995, Repo et al. 1996, Leinonen 1996a, Hänninen et al. 1996, 2001). Nevertheless, the possibility of severe frost damage to trees in boreal regions as a result of climate warming cannot be ruled out (Saxe et al. 2001). Ecophysiological simulation models of the annual cycle of trees have been used in many studies to address Cannell's hypothesis of climate-warming-induced frost damage (Cannell 1985, Cannell and Smith 1986, Murray et al. 1989, 1994, Hänninen 1990a, 1991, Kramer 1994, Leinonen 1996a, Hänninen et al. 1996, Kramer et al. 1996, 2000). These simulation models are based on a simplified description of the ecophysiology of the environmental regulation of the tree s annual cycle under natural conditions (Hänninen 1990b, 1995). Accordingly, any such model is a collection of conceptual tree traits related to the ecophysiology of the annual cycle (Figure 1). Some of these conceptual traits, e.g., the chilling requirement of rest completion (Perry 1971, Sarvas 1974, Fuchigami et al. 1982, Myking and Heide 1995) are based on direct experimental evidence, whereas other traits are based on theoretical assumptions, e.g., the concept of stationary frost hardiness (Repo et al. 1990, Leinonen 1996a). In trees, there is great variation, often of high heritability, in many of the traits used in the modeling approach (Perry and Wang 1960, Sarvas 1974, Hurme et al. 1997, Aitken and Hannerz 2001, Hannerz et al. 2003, Savolainen et al. 2004). Kellomäki et al. (1992) presented a generalized model for the environmental regulation of the annual cycle of boreal forest trees that was synthesized from a number of earlier empirical and modeling studies. Applying this model for scenario analysis, Kellomäki et al. (1995) found support for the hypothesis of climate-warming-induced frost damage. The model of Kellomäki et al. (1992, 1995) was further developed by Leinonen (1996a), who improved its biological realism. When the improved model was applied to a scenario analysis, however, no great increase in the risk of frost damage in response to climate warming was predicted (Leinonen 1996a). In a study focusing on photosynthetic production of Scots pine (Pinus sylvestris L.), Hänninen et al. (2005) found that the models of Kellomäki et al. (1992, 1995) and Leinonen (1996a) differed in an ecophysiological trait that may be crucial for predicting needle frost damage in response to climate warming; however, no systematic comparison of the two models was carried out. Because ecophysiological traits critical for the risk of frost damage can be identified by a diagnostic modeling comparison (Hänninen 1991), this modeling comparison formed the focus of the present study. In the first phase, the

2 890 HÄNNINEN logical structures of the model of Kellomäki et al. (1992, 1995) and that of Leinonen (1996a) were analyzed and a common framework for the two models established. In the second phase, diagnostic computer simulations were carried out within the framework developed. Material and methods Modeling framework The two models under study were originally developed and parameterized to predict the annual cycle of frost hardiness of trees in central Finland. Model 1 (Kellomäki et al. 1992, 1995) was originally developed for a theoretical generalized tree species, whereas Model 2 (Leinonen 1996a) was developed and parameterized specifically to predict needle frost hardiness of Scots pine. Because a comprehensive documentation of the models is presented in the original publications, the two models are described only to the extent required by the present modeling comparison. The environmental response of the frost hardening/dehardening process of temperate and boreal trees varies considerably during the annual ontogenetic cycle (Sarvas 1972, 1974, Fuchigami et al. 1982, Repo 1993). This basic phenomenon is included in Model 1 and Model 2 by modeling the annual cycle of frost hardiness as a two-step process. First, the annual ontogenetic development is modeled with air temperature and in Model 1 also with night length, as the driving forces (Figure 2). Second, the rate of frost hardening/dehardening is modeled as a function of air temperature, night length and the prevailing phase of the annual ontogenetic cycle. This is carried out in both models by calculating the rate of change of frost hardiness as a first-order dynamic process (Repo et al. 1990, Repo 1993): dr() t dt = 1 τ ( R () t R() t ) stat (1) Figure 1. Outline of the modeling approach applied in the present study. Although the overall structures of Model 1 (Kellomäki et al. 1992, 1995) and Model 2 (Leinonen 1996a) are similar, they differ in eight conceptual traits related to the environmental regulation of the annual ontogenetic cycle and the frost hardiness of trees (Table 1). These eight differences were examined by the corresponding 2 8 = 16 sub-models indicated in the figure. Because of the analogous modular structure of Model 1 and Model 2, the sub-models can be reciprocally changed between Model 1 and Model 2 with the exception of the sub-models for the conceptual traits Onto and Retain which can be changed in one direction only (see Material and methods). The simulations were carried out with the original overall models and with fourteen modified models that were created by replacing one submodel at a time with the corresponding sub-model from the other overall model. The traits critical for frost damage under climate warming were identified by comparing the predictions of the different models. where R(t) is frost hardiness, R stat (t) is the stationary frost hardiness and τ is the time constant. According to Equation 1, R(t) follows R stat (t) with an inertia determined by the value of τ. The effects of environmental factors (air temperature and night length) and the prevailing phase of the annual ontogenetic cycle are introduced into the value of R stat (t). If these driving factors stay constant for a long period, then according to Equation 1, R(t) will equal R stat (t). Despite the common model structure outlined in Figure 2, there are several differences between Model 1 and Model 2.In the present framework these differences are grouped into eight conceptual tree traits, each of which is specified for each model (Figure 1; Table 1). This grouping of conceptual traits is based on the ecophysiological reasoning that the equations and parameter values affecting the same ecophysiological subphenomenon of frost hardiness are considered to belong to the same conceptual trait. Each conceptual tree trait represents a corresponding difference between two real trees, one function- TREE PHYSIOLOGY VOLUME 26, 2006

3 CLIMATE WARMING AND RISK OF FROST DAMAGE 891 Figure 2. The annual cycle of ontogenetic development included in Model 1 (Kellomäki et al. 1992, 1995) and Model 2 (Leinonen 1996a). In both models, the annual cycle of frost hardiness is modeled on the basis of the prevailing phase of the ontogenetic cycle and the prevailing air temperature and night length. For differences between the two models, see the text and Table 1 (modified from Kellomäki et al. 1992). ing according to Model 1 and the other according to Model 2.To facilitate presentation of the simulations and the results obtained, an abbreviation is assigned to each conceptual trait. Abbreviations without subscripts (e.g., Grow) refer to the trait in general, i.e., to any version of the considered ecophysiological phenomenon potentially occurring in real trees, and abbreviations with subscripts 1 and 2 (e.g., Grow 1 and Grow 2 ) refer to two real trees that differ with respect to the specific trait function according to Model 1 and Model 2, respectively. Grow, Lign, Rest and Quie The four conceptual traits, Grow, Lign, Rest and Quie, describe the environmental regulation of ontogenetic development during the ontogenetic phases of growth, lignification, rest and quiescence, respectively (Figure 2, Table 1). Grow In Model 1, a free growth habit is assumed, so that the cessation of elongation growth and the beginning of the lignification phase take place as a result of the joint effects of night length and air temperature (Koski and Selkäinaho 1982, Koski and Sievänen 1985, Partanen and Beuker 1999, Partanen 2004). In Model 2, a fixed growth habit is assumed, so that the rate of elongation growth depends on air temperature and the onset of lignification occurs when a critical temperature sum (threshold temperature + 5 C) has been accumulated (Raulo and Leikola 1974). Lign A temperature sum model is used for lignification in both Model 1 and Model 2. However, because of differences in the critical temperature sums required to attain the onset of the rest phase, the rate of lignification at any given temperature is higher in Model 1 than in Model 2. Rest In both models, the rest break, i.e., the removal of the physiological growth-arresting conditions in the bud, is caused by chilling. Accordingly, in both models, the rate of rest break depends on air temperature according to a triangular response curve. In Model 2, the rest-breaking temperature range is broader and the rate of rest break peaks at a lower air temperature than in Model 1 (Figure 3A). Quie During quiescence, the rate of ontogenetic development depends in both models on air temperature according to a nonlinear curve. The rate of ontogenetic development is more Table 1. Assumptions of Model 1 (Kellomäki et al. 1992, 1995) and Model 2 (Leinonen 1996a) pertaining to the eight conceptual traits regulating the annual cycle of boreal trees. Acronym Trait Model 1 Model 2 Grow Environmental regulation of growth Night length and air temperature Air temperature cessation Lign Response of the rate of lignification to air Linear with a threshold, comparatively Linear with a threshold, comparatively low temperature high rate rate Rest Response of the rate of rest break to air Triangular, narrow range, high peaking Triangular, broad range, low peaking temptemperature (chilling) temperature (Figure 3A) erature (Figure 3A) Quie Response of the rate of ontogenetic Nonlinear, high sensitivity to air tem- Nonlinear, low sensitivity to air development during quiescence to air perature (Figure 3B) temperature (Figure 3B) temperature (forcing) Slow Hardening/dehardening time constant 12 days 5 days Driv Temperature index used in calculating Daily mean temperature Daily minimum temperature stationary frost hardiness (Equation 1) Onto Response of stationary frost hardiness to Night length regulation during lignifi- Additive effect of night length and air temp the prevailing phase of the annual cation, air temperature regulation during erature on the potential stationary frost ontogenetic cycle rest and quiescence hardiness during the whole cycle Abrupt changes of stationary frost hardiness when a new phase is attained Gradual changes in the realized stationary frost hardiness mediated by hardening competence Retain Retention of the capacity for frost None A decreasing potential during the early part hardening after the onset of elongation of elongation growth is retained TREE PHYSIOLOGY ONLINE at

4 892 HÄNNINEN sensitive to air temperature in Model 1 than in Model 2 (Figure 3B). Slow and Driv These traits describe the environmental regulation of frost hardiness (Table 1). Conceptual trait Slow: The value of the time constant (Equation 1) is higher in Model 1 (12 days) than in Model 2 (5 days), indicating more fluctuation of frost hardiness in real trees responding according to Model 2. Driv In the calculation of the effects of air temperature on stationary frost hardiness, the driving variable is the daily mean temperature in Model 1, whereas in Model 2 that purpose is served by the daily minimum temperature. Onto and Retain These traits describe how the prevailing phase of the annual ontogenetic development (Figure 2) is assumed to affect the environmental regulation of the stationary frost hardiness and thus, the annual cycle of frost hardiness (Equation 1). Conceptual trait Onto is associated with three differences between Model 1 and Model 2 (Table 1). First, in Model 1, night length and air temperature affect the stationary frost hardiness during different phases of the ontogenetic cycle, whereas in Model 2 a potential additive effect is assumed Figure 3. The sub-models for the response to air temperature of (A) the rate of rest break (conceptual trait Rest, Table 1) and (B) the rate of ontogenetic development during quiescence (conceptual trait Quie, Table 1). The sub-models of Rest 1 and Quie 1 (Sarvas 1972, 1974, Hänninen 1990a, 1990b) are included in overall Model 1 (Kellomäki et al. 1992, 1995) and the sub-models Rest 2 and Quie 2 (Kramer 1994) in overall Model 2 (Leinonen 1996a). for the whole cycle (which, however, is not realized during the latter part of elongation growth). Second, in Model 2, the gradually changing effect of the annual ontogenetic cycle on the hardening potential is taken into account by the specific variable, hardening competence, whereas in Model 1 the hardening potential changes abruptly at the onset of elongation growth, the onset of lignification and the onset of rest (Figure 2). Third, in addition to these qualitative differences, there are differences between the models in the values of the parameters describing the effects of air temperature and night length on stationary frost hardiness. Conceptual trait Retain describes the last qualitative difference between Model 1 and Model 2 (Table 1). In Model 1, the hardening potential is lost at the onset of elongation growth, whereas in Model 2 that potential decreases, but is retained during the early part of the elongation growth phase. The latter assumption is realistic for the original scope of Model 2, i.e., for modeling the frost hardiness of previous-year needles of Scots pine (Leinonen 1996a, Leinonen et al. 1997). Air temperature and night length data For air temperature data, we used the measurements of the Finnish Meteorological Institute in Jyväskylä (62 14 N, E), central Finland, over the period The temperature measurements were made in standard meteorological screens 2 m above the ground. In the simulations, the daily mean temperature, determined as the mean of three measurements carried out at 0800, 1600 and 2400 h, and the observed daily minimum were used. Because of missing observations, the data from September 1, 1911 to August 31, 1916 were rejected. In the simulations, August 1911 was considered to have been followed by September In this way, apparently uninterrupted data of daily mean and daily minimum air temperatures for a period of 93 years were obtained. To simulate climate warming, the original values of the daily mean and minimum temperatures were increased according to the scenario presented by Bach (1987) and Kettunen et al. (1987), i.e., according to the scenario also used in the original studies with Model 1 (Kellomäki et al. 1995) and Model 2 (Leinonen 1996a). This was a nonuniform scenario of a 4.1 C rise in annual mean temperature, allocated more to winter months (maximum rise 6.2 C in January) than to summer months (minimum rise 1.6 C in July). The night lengths prevailing in Jyväskylä were calculated with a standard astronomical model. Simulations The starting date for the simulations was June 1, 1883, the assumption being that by that day the tree had attained the phase of elongation growth and minimum frost hardiness. The elongation phase of 1883 was used for initializing the simulation, i.e., the only simulation required was the date of onset of lignification. This was the starting point for the reported full-scale simulations which comprised a total of 92 annual cycles from the onset of the lignification phase to the onset of the next lignification phase (Figure 2). TREE PHYSIOLOGY VOLUME 26, 2006

5 CLIMATE WARMING AND RISK OF FROST DAMAGE 893 On the basis of the simulations of frost hardiness, two indices of risk of frost damage were calculated: the number of critical annual cycles and the mean annual needle loss percentage (calculated as the arithmetic mean of the annual values). The number of critical annual cycles was determined as the number of cycles during which the daily minimum temperature dropped at least once below the simulated daily frost hardiness threshold. This index, even though it gives a good overall idea of the risk of frost damage during the whole simulation period, does not quantify the extent of injury caused by frost during any given annual cycle. For this purpose, annual needle loss percentage was calculated according to the method of Leinonen (1996a), in which a daily minimum temperature equal to the simulated frost hardiness threshold indicates 50% needle loss, so that some needle loss takes place at temperatures above, but close to, the predicted frost hardiness. For this reason, some needle loss was predicted for cases where the number of critical annual cycles was zero. The simulations were carried out without considering the effect of simulated frost damage on the future performance of the trees. That is, a new annual cycle was initiated at the onset of lignification (Figure 2) without any consideration of simulated frost damage, no matter how critical, having occurred in the past. carried out to identify the critical traits: two simulations with the original models, 2 6 = 12 simulations with the models obtained by the reciprocal exchanges and two simulations where the exchange of sub-models was carried out with Onto and Retain in one direction only (Figure 1). Results Predictions of the original models The predictions of the original models are shown in Figures 4 and 5 with the bars labeled None. These results were as expected on the basis of previous studies with Model 1 (Kellomäki et al. 1995) and Model 2 (Leinonen 1996a): Model 1 predicted considerable frost damage in response to climate warming, indicated by 37 critical annual cycles (i.e., cycles where the minimum air temperature fell below the simulated frost hardiness at least once (Figure 4A) and by a mean annual needle loss of 49% (Figure 4B). In contrast, Model 2 predicted little frost damage, indicated by no critical cycles (Figure 5A) and a mean annual needle loss of 7% (Figure 5B). All cases of predicted frost damage were associated with premature onset of elongation growth (Figures 6A and 6C). Identification of critical traits In the earlier studies, Model 1 predicted severe frost damage in response to climate warming (Kellomäki et al. 1995), unlike Model 2 (Leinonen 1996a). The reason for this discrepancy was examined by simulating frost hardiness and frost damage for the 92 annual cycles: first with the two original models; and then with a series of pairs of models that were modified by reciprocally exchanging, between the original models, the submodels corresponding to one conceptual trait at a time (Figure 1). These exchanges were possible because Model 1 and Model 2 share the same basic structure, being composed of analogical sub-models, i.e., the conceptual traits presented in Table 1 (except in the cases of conceptual traits Onto and Retain, see below). If the conceptual trait considered is critical for frost damage in response to climate warming, then the reciprocal exchange of the corresponding sub-models should remove the prediction of severe frost damage from the predictions of the modified Model 1 and introduce it into the predictions of the correspondingly modified Model 2. Two of the conceptual traits, i.e., Onto and Retain, are interconnected so that the Model 1 version of Onto does not allow a combination with the Model 2 version of Retain. This is because in the logical structure of Model 1, the hardening potential is lost abruptly at the time of the onset of elongation growth (Kellomäki et al. 1992, 1995), so that there is no hardening competence variable (Leinonen 1996a) to carry any decreasing hardening potential over to the early part of the elongation phase (Table 1). Therefore, the Model 2 version of Onto was introduced into Model 1 and the Model 1 version of Retain was introduced into Model 2, but the corresponding inverse exchanges were not possible. In all, a set of 16 simulations was Figure 4. Frost damage to trees following climate warming in central Finland as predicted for 92 annual cycles by Model 1 ( None ) and by the seven modified models created by replacing one sub-model at a time in Model 1 with the corresponding sub-model from Model 2 (Figure 1). For an explanation of the models and the conceptual traits, see the text and Table 1. (A) The number of critical annual cycles, i.e., annual cycles in which the air temperature dropped below the predicted frost hardiness threshold at least once. (B) Mean annual needle loss percentage. The dotted lines indicate the baselines obtained with Model 1 ( None ). TREE PHYSIOLOGY ONLINE at

6 894 HÄNNINEN midwinter (Figures 6A and 6D). With the more gently sloping sub-model originally included in Model 2 (Quie 2, Figure 3B), the onset of elongation growth occurred considerably later (Figures 6B and 6C). Figure 5. Frost damage to trees following climate warming in central Finland as predicted for 92 annual cycles by Model 2 ( None ) and by the seven modified models created by replacing one sub-model at a time in Model 2 with the corresponding sub-model from Model 1 (Figure 1). For an explanation of the models and the conceptual traits, see the text and Table 1. (A) The number of critical annual cycles, i.e., annual cycles in which the air temperature dropped below the predicted frost hardiness threshold at least once. (B) Mean annual needle loss percentage. In (B) the dotted line indicates the baseline obtained with Model 2 ( None ), whereas in (A) the baseline is superimposed on the horizontal axis. Identification of the critical traits The results show that trait Quie (response of the rate of ontogenetic development during quiescence to air temperature, Figure 3B) is a critical factor determining the risk of frost damage following climate warming (Figures 4 and 5). With Model 1, the reciprocal exchange of sub-models for Quie decreased the number of critical annual cycles from 37 to three (Figure 4A) and mean annual needle loss from 49 to 17% (Figure 4B). Correspondingly with Model 2, as a result of the reciprocal exchange of Quie, the number of critical annual cycles increased from zero to 51 (Figure 5A) and the mean annual needle loss increased from seven to 59% (Figure 5B). As in the simulations with the original models, all cases of frost damage were associated with premature onset of growth. The steep air temperature response originally included in Model 1 (Quie 1, Figure 3B) caused comparatively rapid ontogenetic development during the mild spells (air temperatures between 0 and 10 C) that were assumed to occur during the scenario winters in the present study. Thus the steep air temperature response, whether used in Model 1 or in Model 2, predicted a markedly hastened onset of elongation growth under climate warming, i.e., the onset of growth often occurred in Figure 6. Frequency distributions of the timing of the onset of elongation growth in trees subject to climate warming in central Finland as predicted for 92 years by the models of the annual cycle. (A) Model 1 ; (B) a modified Model 1 created by replacing the sub-model for conceptual trait Quie with the corresponding sub-model from Model 2 ; (C) Model 2 ; (D) a modified Model 2 created by replacing the submodel for conceptual trait Quie with the corresponding sub-model from Model 1. For explanation of Model 1, Model 2 and conceptual trait Quie, see Material and methods and Table 1. TREE PHYSIOLOGY VOLUME 26, 2006

7 CLIMATE WARMING AND RISK OF FROST DAMAGE 895 A typical prediction of frost damage is shown in Figure 7. As a result of mild periods in winter, the onset of elongation growth took place according to the steep air temperature response Quie 1 (Figure 3B) in January with Model 1 (Figure 7A) and Model 2 (Figure 7B). As a result of this premature onset of growth, the hardening potential was lost, immediately with Model 1 and gradually with Model 2 (see Retain in Table 1), so that a full dehardening followed the onset of growth and heavy frost damage occurred in February and March (Figures 7A and 7B). With the more gently sloping air temperature response Quie 2 (Figure 3B), dehardening was predicted to take place during mild spells in January with Model 1 (Figure 7A) and Model 2 (Figure 7B). However, because the onset of elongation growth did not take place in these cases until April, the trees were able to reharden during cold spells in February and no frost damage was predicted by either Model 1 (Figure 7A) or Figure 7. Daily minimum air temperature (uppermost lines in A and B) predicted frost hardiness of trees (smooth lines) and the predicted dates of the onset of elongation growth (circles and squares) during one annual cycle in a central-finland climate scenario. The scenario air temperature data were generated by increasing the corresponding values of the daily mean and minimum temperatures of according to the warming scenario of Bach (1987) and Kettunen et al. (1987). (A) Simulations with Model 1 ( Quie 1, thick line, circle) and with a modified Model 1 created by replacing the sub-model for the conceptual trait Quie with the corresponding submodel from Model 2 ( Quie 2, thin line, square). (B) Simulations with Model 2 ( Quie 2, thin line, square) and with a modified Model 2 created by replacing the sub-model for the conceptual trait Quie with the corresponding sub-model from Model 1 ( Quie 1, thick line, circle). In the left-hand part of the figures, the predictions of the two models for frost hardiness overlap. For explanations of Model 1, Model 2 and conceptual trait Quie, see the text and Table 1; for the reciprocal changes of the sub-models, see Figure 1. Model 2 (Figure 7B). The results also show that Retain (ability to retain a decreasing capacity for frost hardening after the onset of elongation growth) is another critical factor. Here a reciprocal exchange of the sub-models was not possible (see Material and methods), but the introduction of the sub-model of Retain from Model 1 (no ability to retain frost hardening capacity) into Model 2 increased the number of critical years from 0 to 46 (Figure 5A) and the mean annual needle loss from 7 to 55% (Figure 5B). The retention of some capacity for frost hardening after the onset of elongation growth often resulted in a sufficient delay in the loss of hardiness to avoid the frost damage that would have occurred otherwise (results not shown). In the reciprocal exchanges of sub-models corresponding to conceptual traits other than Quie and Retain, no exchange caused a considerable decrease in the values of the two frost damage indices with Model 1 (Figures 4A and 4B) or a considerable increase of these values with Model 2 (Figures 5A and 5B). Thus, among the conceptual traits considered, Quie and Retain were the only ones critical for the occurrence of frost damage under climate warming. In the case of the traits Rest and Onto, however, the introduction of the sub-models of Model 2 into Model 1 further increased the values of both frost damage indices (Figures 4A and 4B). In the case of conceptual trait Rest, this is because Rest 2 sets the threshold temperatures for effectiveness in rest break higher than in Rest 1 (Figure 3A). Thus, when the air temperature dropped during late summer and early autumn, rest break started earlier with Rest 2 than with Rest 1 and rest was also completed earlier with the former than with the latter. The hastening of rest completion was reflected in the hastening of the onset of elongation growth, and a further increase in frost damage (Figures 4A and 4B). In the case of trait Onto, there was a further increase in frost damage because, in Onto 2, part of the hardening potential is already gradually lost during the phase of quiescence, whereas in Onto 1 the full potential is retained until the onset of elongation growth when it is lost abruptly. Therefore, in some cases, Model 1 predicts the trees to be more frost hardy during the late part of quiescence than does Model 2 (results not shown). In conclusion, even though the traits Rest and Onto were not identified as critical factors in the strict sense of the present study, in some cases they may affect the occurrence of frost damage under climate warming. Discussion Theoretical analysis of the critical traits In this study, the simulations were carried out without considering the effect of the simulated frost damage on the subsequent performance of the trees. This was because the aim was to identify traits that are critical in determining the risk of frost damage as a result of climate warming, rather than to examine quantitatively the growth and survival of the trees in response to climate warming (Hänninen et al. 2005). Examination of the risk of frost damage was based on mean values, rather than the corresponding maxima, of needle dam- TREE PHYSIOLOGY ONLINE at

8 896 HÄNNINEN age over the 92 annual cycles examined (Figures 4B and 5B). With Model 1, maximum needle damage was at, or close to, 100% in most cases (results not shown), which implies tree mortality. However, because the state of the art in modeling the annual cycle of frost hardiness does not yet facilitate a quantitative analysis of frost damage consequent on climate warming, consideration of these rare extreme cases did not serve the purpose of the present study. Therefore, the overall tendencies revealed by the mean values were the criteria for identifying critical traits in this study. Air temperature and the rate of ontogenetic development during quiescence The results of this study, as well as those of Hänninen et al. (2005), show that the response of the rate of ontogenetic development during quiescence to air temperature (Figure 3B) is a critical trait for the risk of frost damage following climate warming (Figures 4 and 5). This response can be readily determined in growth-chamber experiments. For any given constant experimental temperature, the rate of development is obtained as the reciprocal of the time required for the onset of growth at that specific temperature (Sarvas 1972, Campbell and Sugano 1975, 1979). However, the effects of fluctuating temperatures should also be addressed (Campbell and Sugano 1975, Hänninen 1990b, Partanen et al. 1998) which makes experimental work less straightforward. The sub-model of Quie 1 (Figure 3B) is based on detailed experimental studies of flower buds by Sarvas (1972) who noticed that the response of the rate of ontogenetic development to air temperature was, up to a scaling constant, exactly the same in several boreal tree species, both coniferous and broad-leaved. This sub-model includes high sensitivity to a rise in air temperature including the temperature range between zero and 10 C (Figure 3B), i.e., at temperatures whose frequencies in winter were much higher in the modeling scenario than in the present climate (results not shown). For this reason, Quie 1 predicts frost damage following climate warming (Figures 4, 5, 7). The sub-model of Quie 2 (Figure 3B) was obtained by Kramer (1994) by parameterizing the sigmoidal response function for vegetative buds of Scots pine on the basis of long-term historic phenological and air temperature records from central Europe. Hence the applicability of this air temperature response for boreal conditions can be questioned. However, when using this response as a sub-model in his overall model of frost hardiness (i.e., Model 2 in the present study), Leinonen (1996a) obtained an excellent fit between the predicted and the measured frost hardiness of Scots pine in central Finnish conditions, especially during the critical phases of hardening in the autumn and dehardening in the spring. The sub-model of Quie 2 is quite insensitive to increases in air temperature including in the important temperature range between zero and 10 C (Figure 3B). For this reason, it predicts little, if any, frost damage following climate warming (Figures 4, 5 and 7). Relatively few experimental data are available for the trait Quie in vegetative buds. Campbell and Sugano (1975, 1979) determined the response experimentally for several provenances of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) seedlings. In the temperature range C, they found values between 0.01 and 0.05 day 1 for the rate of ontogenetic development. Thus, the responses they found approximate the response Quie 2 (Figure 3B) determined by indirect methods by Kramer (1994) for Scots pine. Furthermore, when determining the high-temperature requirement for the onset of growth in 2-year old seedlings of Scots pine and Norway spruce (Picea abies (L.) Karst.) at temperatures ranging from 12 to 22 C, Hänninen (1990b, p 33) found that the number of required high-temperature units increased with increasing air temperature when the accumulation of the units was calculated according to the response curve of Sarvas (1972), i.e., with Quie 1 (Figure 3B). By definition, with a proper air temperature response, the number of required units should be constant regardless of the experimental temperature applied. The finding of Hänninen (1990b) suggests that the relative efficiency of air temperature in driving ontogenetic development does not increase between 12 and 22 C by as much as assumed in the empirical curve for generative buds constructed by Sarvas (1972), i.e., the curve is less steep for vegetative buds than for generative buds. Thus, besides the findings of Campbell and Sugano (1975, 1979) with Douglas-fir, those of Hänninen (1990b) with Scots pine and Norway spruce also accord with the findings of Kramer (1994), who presented the gently sloping air temperature response for the rate of ontogenetic development (Quie 2 ; Figure 3B). On the basis of the empirical background and support of the two sub-models for the conceptual trait Quie, it appears that, in response to climate warming, flower buds (Quie 1 ; Figure 3B) may be at a higher risk of frost damage than vegetative buds (Quie 2 ; Figure 3B). This is in accordance with the findings of Linkosalo (2000a) who suggested that tree organs with a pattern of early phenological development in the present climate are more susceptible to frost damage under climate warming than organs with a pattern of later phenological development in the present climate. Organs such as the previous-years needles of Scots pine, which retain part of their frost hardiness after the growth of the apical meristems has begun (Leinonen et al. 1997), would be especially well protected against frost damage in a warming climate. In contrast, current-year developing needles are especially susceptible to frost. This is in accordance with the findings of the present study where removing retention of the capacity for frost hardening from Model 2 increased the predicted frost damage (Figures 5A and 5B). Many studies have adopted piece-wise linear models to predict the response of ontogenetic development during quiescence to air temperature, i.e., traditional degree-day models have been used without explicit consideration of the form of the response (Arnold 1959, Richardson et al. 1974, Cannell and Smith 1983, Nizinski and Saugier 1988). The results of the present study show that in assessing the effects of climate warming it is crucial to determine the form of the nonlinear response, which necessitates further experimental work to characterize the response function (see also Wang 1960). TREE PHYSIOLOGY VOLUME 26, 2006

9 CLIMATE WARMING AND RISK OF FROST DAMAGE 897 Rest completion According to the prevailing view, exposure to chilling temperatures causes rest completion in boreal forest trees (Coville 1920, Perry 1971, Sarvas 1972, 1974, Fuchigami et al. 1982). This concept is included in Model 1 and Model 2 (Figures 2 and 3). According to most experimental studies, carried out mainly with seedlings, the chilling requirement of rest completion is met in late autumn (Hänninen 1990b, Leinonen 1996b, Hannerz et al. 2003). Recently, however, increasing evidence has accumulated for the contrary view that in natural stands of mature trees rest completion takes place much later, i.e., in early spring (Häkkinen et al. 1998, Häkkinen 1999a, 1999b, Hannerz 1999, Linkosalo 2000b, Linkosalo et al. 2000). Furthermore, it has been suggested that besides chilling, additional environmental cues are required for rest completion (Nizinski and Saugier 1988, Häkkinen et al. 1998, Partanen et al. 1998, 2001, Linkosalo 2000b, Linkosalo et al. 2000). If rest completion is delayed until early spring because no such additional environmental cues are forthcoming it would effectively protect the trees from premature dehardening and frost damage following climate warming. In conclusion, the response of ontogenetic development during quiescence to air temperature was identified as a critical trait affecting the risk of frost damage in boreal trees subject to climate warming. This response can be readily determined experimentally, although few experimental data are available on the form of the response. These findings highlight the need for further experimental work with a variety of tree species, both coniferous and broad-leaved. Furthermore, several recent studies suggest that the environmental regulation of the rest break in boreal trees is more complex than assumed in the classic view, which emphasized only the role of chilling. Considering all the uncertainties about the environmental regulation of the annual cycle of boreal trees, predictions of the risk of increased frost damage to trees in response to climate warming remains equivocal. Acknowledgments I thank the Finnish Meteorological Institute and the Finnish Forest Research Institute for providing the air temperature data; Risto Häkkinen, Veikko Koski, Tapio Linkosalo, Robin Lundell, Jouni Partanen, Tapani Repo, Timo Saarinen, Outi Savolainen and two anonymous referees for constructive criticism; Pekka Hirvonen for checking language of the text; and the Academy of Finland for funding the study (Project ). References Aitken, S.N. and M. Hannerz Genecology and gene resource management strategies for conifer cold hardiness. In Conifer Cold Hardiness. 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