• latitudinal gradient;
  • genetic variation;
  • flowering phenology;
  • lowland tree species


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

1 The phenology of temperate woody plants is commonly assumed to be locally adapted to climate.

2 However, the high gene flow expected in forest tree species, the high between year variance of thermal conditions at a given place and the high plasticity of phenology regarding temperature, lead us to hypothesize that genetic variation of phenology between populations is likely to be insignificant for many lowland tree species.

3 Using phenological models, we investigated variation in the timing of flowering between locations for four European clonal trees and between different populations of a further five species.

4 Models were also used to study the responses of the different populations to climate change by simulating transfers of each population to different locations.

5 While clinal variations can be observed in the phenological response to temperature between populations, only one species (Corylus avellana) showed significantly different responses between populations and even then only one of three populations could be separated from the others.

6 Hypothetical transfers show that the differences observed between populations depend on the thermal conditions at the location of transfer, and that these differences are less marked in warmer conditions.

7 Our results indicate that local adaptation will probably not be a serious constraint in predicting the phenological responses of temperate lowland tree species to global warming.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Phenology is an important adaptive trait since it determines the duration and timing of the growing season as well as the period of reproduction (Chabot & Hicks 1982; Lechowicz 1984; Kikuzawa 1989; Hänninen 1990; Reich et al. 1992). In the context of the climate global warming, many studies have attempted to predict the consequences of increasing temperatures on the phenology of temperate zone trees to determine whether species would break bud or flower later or earlier, and thus experience increased or decreased risks of frost damage (Cannell & Smith 1986; Prentice et al. 1991; Hänninen et al. 1993; Kramer 1994a; Murray et al. 1994; Kramer 1995; Hänninen 1996; Kramer & Mohren 1996; Kramer et al. 1996). In the last 30 years, many studies have developed and tested predictive models of tree phenology using climatic variables such as temperature and photoperiod (Cannell & Smith 1983; Cannell 1989; Murray et al. 1989; Hunter & Lechowicz 1992; Kramer 1994b; Chuine et al. 1998, 1999). Using such models over the entire range of a species requires, however, that model estimates are valid across this range, i.e that there is no significant genetic variation of phenology between the different populations. Such variation could be due to either local adaptation or drift, but both causes require that the trait considered be under genetic control and show genetic variability.

For many plant species, phenology is known to be a variable character with a high degree of heritability (Billington & Pelham 1991; El-Kassaby & Park 1993; Farmer 1993), and in these cases it can therefore be modified by natural selection. Given the large size of most tree populations, genetic variation (if it exists) is unlikely to be explained by drift, but rather by local adaptation. Local adaptation results from a balance between natural selection and gene flow and will occur if selection is stronger than gene flow. Although, most tree species of temperate and boreal zones show high potential gene flow due to pollen (dispersion up to hundreds of kilometres, see Faegri & Iversen 1989 for a review) and seeds (migration distance over hundreds of metres or even kilometres, reviewed by Huntley & Birks 1983 and Delcourt & Delcourt 1991), gene flow can be low because of the non-overlapping reproductive periods of populations that live at different elevations (Phillips & Brown 1977; El-Kassaby et al. 1984). Climatic variables such as temperature may, however, constitute strong selective forces on phenology, although the plasticity of this trait in response to climate may reduce its selective action. In addition, since temperature and frost are highly variable from one year to another, different genotypes are probably favoured by selection at different times. In conclusion, we would therefore expect that the balance between the factors contributing to local adaptation is unlikely to lead to significant genetic variation in phenology between populations of most lowland tree species.

We analysed genetic variation in the timing of flowering between populations of six European angiosperm tree species using phenological models and flowering dates in different locations. First, using data for one of this species and a further three clonal species at a number of locations, we verified that model estimates for an individual genotype do not vary with environment. Second, by fitting model parameters to the dates of flowering of different populations of the six species, we analysed the pattern of variation between populations. Third, using the fitted models, we studied the response of each population to hypothetical transfers. We address the following questions: (i) do model parameters adequately account for genotype and genotype × environment effects; (ii) do the populations studied show significant genetic variation in their timing of flowering; and (iii) what are the implications of the patterns of genetic variation observed for predicting phenology under a global climate warming scenario?


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References


Data required for fitting the models correspond to flowering or bud burst dates that were derived either from phenological observations made on clones (for verifying that model estimates for an individual do not vary with environment) or from pollen concentrations (for studying the genetic variation between populations). Vegetative and reproductive phenology are simulated with the same kind of models (Chuine & Cour 1999; Chuine et al. 1999). To test the stability of model estimates across environments, we therefore also used bud burst dates when flowering dates were not available for the clones.

Observational data

The International Phenological Garden (IPG) consists of a number of recording stations throughout Europe where various clonal tree species are grown. All individuals of a particular species at all sites belong to the same clone. Phenological data for these trees have been published annually in Arboreta Phaenologica since 1958, together with details of site location and instructions for making the observations.

We extracted data from five European IPG stations on the date of flowering of Sambucus nigra L. (black elder) between 1982 and 1990, and on the date of vegetative bud burst of Betula pubescens Ehrh. (downy birch) (data from 1974 to 1990) and Populus nigra L. (black poplar) (data from 1981 to 1990). The location of the sites is shown in Fig. 1 (sites 17–21) and their coordinates given in Table 1. The provenance of the clones is presented in Schnelle & Volkert (1974), Chmielewski (1996) and Chmielewski & Puske (1998).


Figure 1. Location of the pollen sampling stations (nos 1–16) and the IPG stations (nos 17–21). Site numbers correspond to those in Tables 1 & 2. Records for each species were grouped into geographically defined populations (ellipses show allocation for P. xacerifolia as an example).

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Table 1.  Coordinates of the International Phenological Gardens and corresponding meteorological observations: sampling stations code numbers correspond to those in Fig. 1. Two sites (S1 and S2) were compared for each species, with stations allocated to sites as indicated (see text for methods and criteria). Lat, latitude; Lon, longitude; Alt, altitude; n, number of years sampled; Bp, Betula pubescens;Pt, Populus tremula;Sn, Sambucus nigra
No.LocalityLatLonAltnBpPtSnMeteo. stationLatLonAlt
18Munich48.111.154017S2S1 München48.111.3530
19Zürich47.28.560010 S2S2Zürich47.55.83436
20Vienna48.216.415010  S2Wien48.316.4200
21Budapest47.419.222010 S2 Budapest47.419.2138

Pollen data

The dates of flowering of natural populations of six species, Alnus glutinosa Gaertn. (common alder), Carpinus betulus L. (common hornbeam), Corylus avellana L. (hazel), Platanus xacerifolia Willd. (London plane), Olea europaea L. (olive) and Ulmus minor Mill. (smooth-leaved elm) were estimated from airborne pollen data from between 3 and 12 European locations (Table 2). One of these species, Platanus xacerifolia, can be assumed to be a clonal tree, since it has been propagated in nurseries by cuttings since its introduction in Europe in 1670 (Ricaud et al. 1995). Pollen identification is reliable only at the genus level and in order to study genetic variation of phenology at the species level, we considered airborne pollen data from genera represented by a single or largely dominant (in density) species in the areas investigated. From the genera that fulfilled this requirement, we chose species with a sufficiently large geographical range to allow the assessment of different, geographically separated populations. The data consist of weekly recordings of pollen concentrations in the atmosphere at European sampling stations from Stockholm (59.0°N, 18.03°E) to Valencia (38.5°N, 0.47°W) (Fig. 1) over 3–19 years, depending on the station (Table 2). Data were obtained through Cour samplers (Cour 1974) and the mean date of flowering of the populations within a c. 50-km radius was taken to be the middle day of the week when the maximum pollen concentration was recorded.

Table 2.  Sources of the airborne pollen data (locations numbered as in Fig. 1). Abbreviations as in Table 1. Ag, Alnus glutinosa;Ca, Corylus avellana;Cb, Carpinus betulus;Pa, Platanus xacerifolia;Oe, Olea europaea;Um, Ulmus minor. Different populations were defined for each species (N, northern; E, eastern; S, southern; SW, south-western; SE, south-eastern; C, C1 and C2, central populations), so that, for example, P. xacerifolia locations were allocated to four populations (North, Centre 1, Centre 2 and South) (see also Fig. 1). Meteorological data were recorded at each sampling site
2Lille50.353.07603NNNN N
3Rouen49.261.051604NNNN N
4Nancy48.706.202102N NN N
5Tours47.230.411102NNNN N
6Angers47.16 −0.60503NNNN N
7Clermont-Ferrand45.503.102004 SW C1 C
9Grenoble45.205.812102     C
10Bordeaux44.51 −0.34504SWSW   C
11Toulouse43.361.371526SWSW C2 C
12Montpellier43.334.001019   C2N 
13Girona41.902.771437SW    S
14Barcelona41.282.07410   S S
15Tortosa40.820.50449   SS 
16Valencia38.50 −0.47693   SS 

Meteorological data

For the pollen data, we used the daily minimum and maximum temperatures recorded by the meteorological station installed in each sampling station. For the IPG data, daily minimum and maximum temperatures of the closest meteorological stations (Table 1) were used. The average temperature of each day was estimated to be the mean of the daily minimum and maximum temperatures.

The phenological models and their fit

The phenological models used in this study are termed the Spring Warming, ForcSar, SeqSar and Par1Sar models. The Spring Warming and ForcSar models assume that flowering occurs when a critical state of forcing has been reached, and this is determined either by a sum of daily rate of forcing, which is a function of the temperature (ForcSar model) (Chuine et al. 1999), or by a sum of degree-days (Spring Warming model) (Sarvas 1974 in Hänninen 1990; Hunter & Lechowicz 1992; Chuine et al. 1998). The SeqSar and Par1Sar models consider additionally the effect of chilling on the break of bud dormancy and on the acceleration of bud growth during forcing in the spring (Cannell & Smith 1983; Murray et al. 1989; Hänninen 1990; Kramer 1994b; Chuine et al. 1999). All four models use a single climatic variable, daily mean temperature.

For each species for which models had already been defined and tested (Chuine et al. 1999) we used the best predictor model. In other cases (Sambucus nigra and Populus tremula), each model proposed in Chuine et al. (1999) was fitted to the data and the best model found was chosen for the analysis (Table 3). The external validity of the models used has already been estimated and is higher than that of the other phenological models (Chuine et al. 1999). The number of parameters in a particular model varies from two (ForcSar) to four (SeqSar and Par1Sar), and each parameter is fitted to specific data by minimizing the residual sums of squares using a simulated annealing method (see Chuine et al. 1998 for details). The number of years sampled in a single station was usually insufficient to allow a robust estimation of the parameters. Thus, we were forced to pool together several sampling stations to fit the models. Stations were grouped according to proximity, following broadly latitudinal or continental gradients, in order to obtain 14–17 years of data points per group (comprising the observations indicated in Tables 1 & 2). An approximately constant number of data points per group is needed for the statistical comparison of the estimates. For the naturally occurring species, data from groups of stations are called populations, for the IPG clones they are called sites.

Table 3.  Percentage of variance explained (R2) by the chosen model when adjusted over all populations or all sites (TOT), or on each population or each site separately (codes as defined in Tables 1 & 2). SW, Spring Warming model
  ModelTOTEach site or population
Clonal species
Betula pubescensSW0.750.71S10.86S2  
Platanus xacerifoliaSW0.920.81N0.88C10.83C20.34S
Populus tremulaSW0.590.63S10.42S2  
Sambucus nigraForcSar0.450.05S10.82S2  
Non-clonal species
Alnus glutinosaPar1Sar0.860.93N0.49E0.78SW 
Corylus avellanaPar1Sar0.620.95N0.57SW0.91SE 
Carpinus betulusSeqSar0.840.78N0.92S  
Olea europaeaSeqSar0.950.91N0.84S  
Ulmus minorPar1Sar0.810.93N0.64C0.54S 

Stability of model estimates across environments

The dates of flowering of the P. xacerifolia populations and of the IPG clones at different sites were used to test the hypothesis that estimates from the phenological models fitted using data from genetically identical (or very similar) individuals will not differ with location. The IPG clones are true clones made by cuttings of a single individual originating from Germany. P. xacerifolia, which was introduced to Europe in 1670 for ornamental use, is only reproduced in nurseries by cuttings (Ricaud et al. 1995) and we therefore consider it equivalent to a single clone. Models were fitted using the dates of bud burst or flowering observed in all locations for each species (model A), and then by using the dates observed at each defined group of locations separately (model B). An F value was calculated as

  • F     =     [(SSA  -  SSB)/(dfB - dfA)] / [SSB/(dfTOT  -  dfB)],(eqn 1)

where SSA and SSB are the residual sum of squares of model A and B, respectively, dfA and dfB are the degrees of freedom of model A and B, and dfTOT is the total degrees of freedom. Model estimates of one clone were said to vary with environment if the F value was significantly higher than the critical value of F(dfB–dfA, dfTOT–dfB).

Measure of the genetic variation between populations

First, each model was fitted using the data of all sampling stations together for each species, i.e. all populations (model A). Second, models were fitted using the data for each population separately (model B). Model A is nested within model B since this latter model takes into account the interaction between temperature and phenology. The percentage of variation between populations is defined as

  • 100  ×  [(SSA  -  SSB)/(dfB -  dfA)] / (SSTOT/dfTOT),(eqn 2)

where SSTOT is the total variance. The population effect was significant if eqn 1 was significantly higher than the critical value of F(dfB–dfA, dfTOT–dfB).

Hypothetical transfers

Models were used to predict the date of flowering of each population of Alnus glutinosa, Carpinus betulus, Corylus avellana, Platanus xacerifolia and Ulmus minor if it was transferred to the locations of each of the other populations of that species. Olea europea was not considered in this analysis because of the limited number of locations available to perform the analysis. The daily mean temperatures of the locations and the model estimates of each population were used to make the predictions. Such hypothetical transfers allow the estimation and comparison of the responses of different populations to temperature when transferred to environments with different climates.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Individual model estimates do not vary with environment

The percentages of variance explained by the models were high for almost all clonal species, varying from 45 to 92% when fitted using every location and from 42 to 88% when fitted using each group separately. The only exception was S. nigra (Table 3) where the goodness-of-fit of the ForcSar model was very low for one site (5%) so that estimation of the variation between the two sites was impossible. We could not reject the hypothesis that model estimates do not vary with environment for any of the other clonal species (Table 4). We therefore conclude that model estimates of response to temperature of genetically identical individuals cultivated in different environments are not significantly different, and thus that phenological models can be used to study the genetic variation of phenology between different populations.

Table 4.  Probability (p) of wrongly rejecting the hypothesis that all populations or sites have the same estimates and percentages of variation between natural populations
SpeciespPercentage of variation
Clonal species
Betula pubescens0.18 
Populus tremula0.08 
Platanus xacerifolia0.890.6%
Non-clonal species
Alnus glutinosa0.104.1%
Corylus avellana< 10−4 (0.32 N–SW; < 10−3SW–SE)24.7% (4.3% N–SW; 47.8% SW–SE)
Carpinus betulus0.353.0%
Olea europaea0.600.6%
Ulmus minor0.543.4%

Genetic variation between populations

Parameters of the best predictive model of each species were fitted over all populations together and on each population separately. The percentages of variance explained by the model in both cases were high (Table 3). This indicates that the selected models are effective and that a comparison between populations is possible. Only C. avellana showed a significant genetic variation in time of flowering between populations (Table 4), with the south-eastern population having a markedly earlier date than that of the northern and south-western populations.

Hypothetical transfers

The predicted dates of flowering for each population of P. xacerifolia, A. glutinosa, C. betulus, C. avellana and U. minor if transferred to the other locations of that species are presented in Fig. 2. Flowering dates predicted at a particular site were very similar for all populations of P. xacerifolia whatever their origin (less than 12 days separate the earliest to the latest population on average over all locations). In addition, the populations showed no consistent trend in their order of flowering. Nevertheless, each population would flower earlier if it were transferred southward.


Figure 2. Observed dates of flowering (observation, day of the year) of Platanus xacerifolia, Alnus glutinosa, Carpinus betulus, Corylus avellana and Ulmus minor in different European locations (represented by their latitude) compared to the dates of flowering as predicted by the models if each of the other populations of that species were transplanted to these locations (population codes as in Table 2).

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Populations of the naturally dispersed species show much higher variability between their dates of flowering at a particular location of transfer. However, differences decreased going southward, and were almost absent in Spain. The order of flowering of the different populations was generally consistent between locations with northern populations generally flowering the latest and southern or eastern populations the earliest. C. avellana did not however follow this pattern, and U. minor did so only at certain locations.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Stability of model estimates

The stability of model estimates across environments was verified in three different species. It is particularly noteworthy that the estimates of such empirical models are so consistent, and thus these traits are apparently under strong and consistent genetic control. This result emphasizes the reliability of the phenological models and this provides a consistent method for verifying that estimates are not environment-dependent, prior to using models for predictions over a broad geographical scale.

Genetic variation between populations

The timing of flowering of four of the five species studied was not significantly different between populations. Of these, O. europaea constitutes a particular case as it has been consistently affected by human activities, but the others (A. glutinosa, C. betulus and U. minor) are not dispersed by other than natural agents and none support the hypothesis that phenology is locally adapted to climate.

Two factors that are likely to play a major role in explaining the lack of genetic variation in the species studied are a high rate of gene flow (A. glutinosa, U. minor and to lesser extent C. betulus) and the fact that frost damage, one of the most important selective pressures on the timing of flowering, exerts only a weak effect (C. betulus, O. europaea and to a lesser extent U. minor). Gene flow is expected to be particularly high in A. glutinosa because this species produces a large amount of wind-dispersed pollen and because seeds are transported over long distances by water (Birks 1980; Huntley & Birks 1983; Suszka et al. 1994). Extremely high gene flow in Alnus species is supported by allozyme studies on A. crispa (Bousquet et al. 1987b). Ulmus minor also produces large amounts of pollen, and seeds are dispersed over long distances by wind (populations can expand at a rate of 1 km yr−1). Although C. betulus is not expected to have a high level of gene flow, selective pressures by frost are also low since it almost never experiences frost damage in any part of its natural geographical range. The very late flowering of C. betulus allows it to avoid frost damage (to which it is particularly sensitive, Huntley & Birks 1983) but seriously compromises its ability to produce mature fruits, which ripen only during unusually warm and long summers, such as those that occur in western France. Populations of O. europaea also have a very low probability of suffering frost damage because of their southerly (Mediterranean) distribution. Other factors may reinforce or replace the effects of a high gene flow and a weak selection. These include a lack of a founding effect due to the existence of a continuous migration front during the Holocene recolonization of Europe, as seen in the rapid (1 km yr−1) expansion of U. minor and the continuous progress of its migration front from glacial refuges (Huntley & Birks 1983), or a lack of isolated populations before this recolonization.

Contrary to the other species, we found a significant genetic variation of phenology in Corylus avellana, where one (SE) of the three populations studied is different from the other two (N and SW). Different hypotheses can account for this observation. First, extant populations of C. avellana are very likely to suffer frost damage because of its very early flowering. The probability that temperature will drop below −4 °C during the flowering period varies from 0.9 at Lyon (SE France) to 0.2 at Toulouse (SW France). Although this early flowering may appear maladaptive in terms of susceptibility to frost injury, it is necessary to allow for the long period of fruit maturation (Huntley & Birks 1983). Hazel nuts require major inputs of energy during the long period of lipid accumulation. Later flowering, even if affording greater safety from frost injuries, might not allow C. avellana to complete fruit maturation. The evolutionary history of the genus suggests that fruit maturation requirements have exerted very strong selective pressures on Corylus species since ancestral types with bigger fruits than that of C. avellana now have a restricted and relict distribution (Huntley & Birks 1983). Frost risks may vary on a very local scale and phenology in populations of C. avellana may therefore have evolved locally. Second, C. avellana is the only species among those studied here that had small isolated populations all over Europe during the last glacial maximum, and these may have diverged for some highly adaptive traits such as the timing of flowering. However, the variation observed does not necessarily reflect a local adaptation in response to frost injuries as hazel is also cultivated for its fruits and may be subject to artificial selection. A number of cultivars have been planted and coexist with the wild type, but since their pollen cannot be distinguished from that of the wild type, their contribution can not be evaluated. Further information on the phenology and ecology of C. avellana would be necessary to confirm the possibility of local adaptation of phenology in some of its populations.

Response to transplantation

Genetic variation in tree phenology has primarily been studied using data on vegetative or reproductive phenology derived from experiments involving transplantation of seedlings from natural populations. Differences in phenological traits between populations have been shown for some species (Perry & Wang 1960; Kuser & Ching 1980; Van Niejenhuis & Parker 1996; Li et al. 1997a, 1997b) but not for others (Farmer 1993; Li et al. 1993; Von Wuehlisch et al. 1995). The patterns of response to transplantation varies with origin, some species showing earlier bud burst when seedlings are of southern origin (Perry & Wang 1960; Ducousso et al. 1996), others showing the reverse (Mergen 1963; Beuker 1994; Falusi & Calamassi 1996), and some showing no differences (see Lieth 1974 for a review).

Hypothetical transfers show that, even if there is no significant difference in phenology between populations, some clinal patterns do exist. Phenological models are thus precise enough to detect small genetic differences and could therefore be used to examine the potential value of a particular provenance for transfer and to assess the response of populations to environmental change (Campbell 1974; Matyas 1994). This could be a useful alternative to costly provenance tests when setting guidelines for seed transfer in reforestation programs.

Hypothetical transfers also show that differences in phenology between populations is primarily determined by temperature. If trees are grown outside, the differences observed will depend on where the comparison is conducted. This result is particularly consistent with the findings of Perry & Wang (1960) who found that Florida populations of Acer rubrum burst bud either much earlier than more northern populations or at the same time, depending on the temperature. Differences between populations of species studied here were usually weaker in warmer than in cooler climates and the order of flowering was sometimes reversed (Fig. 2). Under natural conditions, a lack of chilling temperatures therefore appears less important than a lack of forcing temperature to hasten bud burst, confirming the findings of earlier modelling studies (Hunter & Lechowicz 1992; Chuine et al. 1998). However, in artificial conditions, a lack of chilling temperatures may increase differences between populations irrespective of prevailing forcing conditions (Perry & Wang 1960).

Implications for global change studies

Phenological models, which have been used since the late 1980s to assess the response of boreal and temperate forests to climate warming, have predicted a range of different responses (Cannell & Smith 1986; Murray et al. 1989, 1994; Hänninen et al. 1993; Kramer 1994a, 1995; Hänninen 1996; Kramer et al. 1996). Differences may arise from the use of different models and study species, or from the lack of external validity (i.e. accuracy of predictions using cross-validation) of these models (Kramer 1994b), as well as from the different assumptions made on the intensity or the geographical pattern of the warming. Predictions of tree phenology under global climate change will be reliable only if models and their estimates are reliable, which implies a need for tests by cross-validation, such as those reported in Chuine et al. (1999).

Both models and experiments show that the response of phenology to climate change, and in particular to global warming, will depend on the species, the latitude at which the populations are observed and the intensity of the change. Responses will also depend on the relative warming of autumn/winter compared to spring and the magnitude of each species' response to the environment (its plasticity). Moreover, even if some species within a genus show similar phenology, others may not, and small differences in the observed response between species may be increased under conditions of climatic warming. Thus, predictions of the response of vegetation to global climate change must consider each species separately and, if local adaptation is strong, it may even be necessary to treat individual populations independently. Such local adaptation of tree phenology would be a serious constraint on our ability to predict the general response of phenology to global climate changes. However, if we can assume that there is little or no genetic variation between populations, as is suggested from the results for most of the lowland tree species studied here, model parameters that are adjusted according to the data obtained for a particular population of a species could be used to predict the response of that species to global climate change.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The authors are particularly grateful to T. Lenormand, J. D. Shykoff, S. N. Aitken and M. Kirkpatrick for their constructive criticisms, and to L. Haddon, Managing Editor, for extensive revisions that greatly improved the manuscript. We are also very grateful to F.-M. Chmielewski for having provided the IPG data, P. Cour for his assistance in analysing the pollen data, and D. Duzer, J. Ferrier, L. Quet, G. Sare and D. Vernier for the pollen analyses. Meteorological data were provided by MeteoFrance and the Centre Commun de Recherche, Commission des Communautés Européennes, établissement d'Ispra, Italy. Support was provided to I. Chuine by a Bourse de Docteur Ingenieur du Centre National de la Recherche Scientifique. This is an ISEM contribution 00-032.


  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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Received 6 May 1999 revisionaccepted 4 January 2000