Interglacial cycle derived from pollen and insect remains

Palaeogeography, Palaeoclimatology, Palaeoecology, 103 (1993): 73-93 Elsevier Science Publishers B.V., Amsterdam

73

The climate in Western Europe during the last Glacial/Interglacial cycle derived from pollen and insect remains J. G u i o t , J.L. de Beaulieu, R. C h e d d a d i , F. D a v i d , P. P o n e l a n d M. Reille Laboratoire de Botanique Historique et Palynologie, UA CNRS 1152, Facult8 de St-Jkr6me, F-13397 Marseille cedex 13. France (Received April 8, 1993; accepted April 20, 1993)

ABSTRACT Guiot, J., de Beaulieu, J.L., Cheddadi, R., David, F., Ponel, P. and Reille, M., 1993. The climate in Western Europe during the last Glacial/Interglacial cycle derived from pollen and insect remains. Palaeogeogr., Palaeoctimatol., Palaeoecol.. 103: 73 93. Using the pollen sequence of La Grande Pile XX (France), we review problems with the application of transfer functions in paleoclimatic reconstructions. One of them is to find modern analogues for the herbaceous vegetation of the cold periods. We propose a method to distinguish between steppes and tundra vegetations for which the moderns are only partial analogues of the glacial periods. Another method to solve these problems is based on constraining by insect remains. The two methods provide coherent reconstructions. The results are also compared with other paleodata. There is a good correlation with the six cold Heinrich events between 70 and 15 ka B.P. A cooling event during the Eemian period (marked by high percentages of Taxus) at about 125 ka B.P. needs still to be correlated with high resolution ocean and ice cores.

Introduction Plant distributions respond to changes in summer warmth, winter cold and moisture balance (Woodward, 1987; Prentice et al., 1992). Climatic changes during the Late Quaternary have produced continual changes in the distribution of vegetation types. The patterns of these changes can be reconstructed from continent-scale syntheses of pollen data (e.g. Huntley, 1988, 1990a; Prentice et al., 1991). Pollen grains are dispersed each year and accumulate in sediments. Their durable wall is well preserved during hundreds of thousands and even millions of years and their particular form allows palynologists to recognize the plant (sometimes to the species, more frequently to the genus level). The technique has been introduced in 1916 by the Swedish botanist Von Post. Sediment samples are chemically prepared to destroy all what is not 0031-0182/93/$06.00

pollen and their residu is mounted on thin slides. All taxa are identified and counted, usually up to 300-1000 grains in temperate and 1000-3000 grains in tropical vegetation. The percentages of counted taxa provide what is called a pollen spectrum. A succession of pollen spectra through a sediment core is illustrated in a pollen diagram which shows the variations in the floral composition as a function of time (Reille, 1990). Since pollen are sometimes dispersed far away from the source (particularly in open vegetation), macro remains (needles, fruits .... ) found in the sediment help to determine if the plant was really local. The European and North-American continents are extensively covered by Late Quaternary pollen diagrams, providing a wealth of paleoenvironmental information. These data are now collected in two pollen data bases: the European Pollen Database (EPD) in Arles (France) and the North

© 1993 - - Elsevier Science Publishers B.V. All rights reserved.

74

American Pollen Database (NAPD) in Springfield (Illinois, USA). Other continents are also covered by such data, especially East and central Africa, South America, India, West Africa ..., but their density is still to be improved. In long-settled regions, the vegetation patterns have been modified by humans, and changes attributed to human impact are widely registered in European pollen diagrams (Van Zeist and Bottema, 1982; Behre, 1988). This does not prevent the use of pollen data to reconstruct past climate changes at a continental scale. The importance of climate at this scale is indicated by the fact that the European distributions of major taxa are as spatially coherent as they are on other continents. Furthermore the distribution limits of many European species have been related to specific climatic controls on physiological processes (e.g. Piggott and Huntley, 1981; Woodward, 1988). For instance, Huntley et al. (1989) have established the relationship between the Fagus pollen distribution and climate independently on both sides of the Atlantic Ocean. The spatial coherence of past changes in vegetation patterns, reconstructed from pollen data, also strongly argues for the primacy of climatic control in Europe (Huntley, 1988; 1990a,b) as has been demonstrated for eastern North America (Prentice et al., 1991). Two approaches are possible to test the relationships between climate and biosphere. The first approach develops models of vegetation, to simulate the distribution of the vegetation according to a certain state of the climate, and to compare the simulated vegetation with the paleodata (Prentice et al., 1992). This approach is particularly useful to understand the complex effect of climatic change on vegetation. The second approach calculates non-causal statistical equations between climate and modern proxy data (transfer functions) and reconstructs past climate by applying these equations to paleodata. This approach has largely improved our knowledge of the past climates during the last climatic cycle (Guiot et al., 1989). Some problems related to the relatively slow response of the taxa and vegetation to rapid climatic changes or related to the lack of modern analogues for some extreme climates of the past,

J. G U I O T ET AL.

could be solved by analysing the insect remains in the sediments. Animal migrations are very rapid when compared to plant migrations. It is especially true for beetle populations--the largest order among insects (more than 200,000 known species). Because they can disappear from an area rapidly when the climatic conditions no longer suit them, beetles do not necessarily have to perish or adapt. Of course, climate is not the only factor controlling the habitat of beetle species: competition, predation, host-plants available .... are others. An adequate selection of species is necessary to minimise these interactions in the data choosen for climatic reconstruction purposes. The British entomologist G.R. Coope and his colleagues F.W. Shotton and P.J. Osborne, are pioneers in this domain. They established a method to identify beetle species from their remains in the sediment and proved that the present ecology of these beetles was the same as in the Late Quaternary so that they are good climatic indicators (Coope, 1977, 1986). Coope established a modern data base for about 350 different species and determined their climatic tolerance, especially related to the temperature of the warmest and coldest month. A particular technique of climate reconstruction, called mutual climatic range method, based on the presence/ absence of the species in relation with the climate has enabled Atkinson and his colleagues (1986, 1987) to reconstruct the temperature variations in the British Isles for the last 50,000 years. Other climatic indicators, such as molluscs, are used to quantitatively reconstruct past climates (Rousseau, 1991; Magnin, 1992). On one hand beetles and molluscs can be identified to the species level and do not suffer from distorting effects like long distance transport by wind or water. They also respond rapidly to climate change. But on the other hand, molluscs are not often preserved in lacustrine or peat bog cores and compared to pollen the insects are rare and one needs large quantities of sediment. This makes it difficult to combine the study of pollen, insects and molluscs. Nevertheless eight cores have been taken at La Grande Pile for the study of beetles (Ponel et al., 1992).

CLIMATE IN WESTERN EUROPE DURING LAST GLACIAL INTERGLACIAL FROM POLLEN AND INSECT REMAINS

The transfer functions

The first attempts to quantitatively reconstruct past climate compared the modern climatic ranges of some important plants of which pollen were preserved in sediments (Iversen, 1944). During the last two decades, multivariate statistical methods were preferably developed. All "modernist" approaches rest on a few hypotheses, namely (Birks, 1981): (1) that climate is the ultimate cause of changes in the paleobiological data: (2) that the ecology of the species considered has not varied between the period analysed and the present time, and consequently that the relationship between the species and the climate was constant in time; (3) that the modern observations contain all the necessary information for interpreting the fossil data. One major consequence of these hypotheses is that the species' present distribution must be at least approximately in equilibrium with climate. Because the response time of the biota to climatic changes is generally uncertain, it is worthwhile to attempt reconstructions using various kinds of data (e.g. pollen, insects) with potentially different response times, and to compare the results. The term transfer funtion was apparently used for the first time in this context by Webb and Bryson (1972). According to Sachs et al. (1977), the transfer function procedure is defined as a method which produces calibrated quantitative estimates of some parameter of a past environment, such as seasonal Or monthly air or ocean surface temperature, from proxy data such as paleobiological assemblages. Transfer function equations are derived by multivariate techniques from an adequate "calibration" sample of modern distributional data, and applied to fossil samples to estimate environmental parameters for past times. The transfer function model is thus defined on the basis of a response function X = R(C,D), which relates the set of biological responses X to a set of climatic and non-climatic factors C and D, respectively. If climate is the major factor, it is possible to approximate the response function by Re(C). The inverse of this function response enables one to calculate climatic parameters from known biological

75

responses: C = T(X). T is called the transfer function. The methods are presented in a more ecological context in Ter Braak and Prentice (1988). The transfer function needs to be calibrated on data. Because the paleodata have a minimum resolution of several decades, it is not possible to use recent climatic time-series for calibration but rather spatial data. This is the cause of some problems such as the effect of the history of the data and the lack of analogues. Two types of methods are now more frequently used with pollen data. The response surface method has been developed by Bartlein and Prentice (1986). The ecological response surfaces are nonlinear functions. They describe the way in which the frequencies of taxa, as measured in surface pollen samples, depend on joint effects of two or three environmental variables. Given a set of response surfaces, the climate estimate of a given pollen spectrum can be derived from the closest analogues among the pollen spectra predicted by the response surfaces. These analogues are obtained using a distance index. Various distance indices have been compared by Overpeck et al. (1985) who recommended the chord-distance, an Euclidian distance on square-root-transformed abundances. The other method is the paleobioclimatic analogues method (Guiot, 1987, 1990). It is also based on the best analogues, but the taxa are weighted by their response to climate estimated by an eigenvector analysis of the fossil pollen data. In spite of the numerous works devoted to the transfer function problem and the many different suggestions that have been made to improve transfer function methods, these methods can be in fact classified into very few categories. The first category contains the extrapolation methods, usually based on regression. The methods of Imbrie and Kipp (1971) or that of Roux et al. (1991), based on correspondence analysis coupled to regression, are included in this category. These methods can provide reconstructions completely outside the range of the calibration data, which is an advantage when they can be controlled but they can be quite unrealistic in some cases. The second category contains interpolation methods, of which many are based on the use of

76

a similarity index between fossil and modern biological assemblages to determine the modern analogues of the fossil data. The reconstruction is given by a weighted average of the climate values for the modern analogues. The techniques differ in the way they compute the similarities. They include weighted averaging of analogues, response surfaces (Bartlein and Prentice, 1986), the mutual climatic range method (Atkinson et al., 1987) and the paleobioclimatic analogues method (Guiot, 1987). The mutual climatic range method is slightly different from the others as the similarities between assemblages do not depend upon the abundances of the species but only on their presence/absence. Because extrapolation methods can provide estimates beyond the limits of the calibration data, there is a great risk of obtaining unrealistic estimates. This problem does not arise with interpolation methods which are unable, in return, to guess what happens outside the reference domain and for this reason interpolation methods are strongly recommended over extrapolation methods. Some methods recontruct one climatic variable at a time (e.g. regression). These methods are unable to take into account the interaction of the climate components and afortiori to isolate the variable best suited for its reconstruction. Such methods perfectly reproduce the correlations between the climatic variables in the reference data set (Elovicheva and Bogdel, 1987). It is generally important (a) to collect diversified modern data where the various climate components are as little correlated as possible and (b) to use a method that can separate the effects of the different components, even in the presence of some intercorrelations among components. Interpolation methods satisfy this last requirement, as they are not based on correlations between variables but rather between samples. They have the advantage that they consider the climate (at least the variables that are available) as a multivariate entity. Particular questions arise when the defects of the univariate approach are amplified by a restricted reference data set. These are the methods based on species distributions along a modern altitudinal gradient (Adam and West, 1983). The present-day gradients of temperature and precipi-

J. GUIOT ET AL.

tation are known so it is easy to obtain climatic estimates. But because precipitation and temperature are both assumed to be directly correlated with elevation, Adam and West (1983), for example, have reconstructed perfectly parallel precipitation and temperature curves, whereas, over the same period (140,000 yr), Guiot et al. (1989) have shown that the two variables have a complex and distinct history.

Coherency of long pollen sequences in Western Europe Transects

The last glacial-interglacial cycle is covered i: Europe by several long pollen sequences. All these terrestrial records show three warm periods correlated with the warm isotopic stages 5e, 5c and 5a (Mangerud, 1989). The La Grande Pile section (Woillard, 1978; Woillard and Mook, 1982) has been extensively studied and correlated with marine data. Several other long sequences have been obtained in Western Europe (Fig. 1) and more particularly along a south-north transect: Padul in southern Spain (Pons and Reille, 1988), le Lac du Bouchet and the Velay Maars in the French Massif Central (Reille and Beaulieu, 1988, 1990; Beaulieu and Reille, 1992b), les Echets near Lyon (Beaulieu and Reille, 1984a,b) and La Grande Pile in the French Vosges (Woillard, 1978; Beaulieu and Reille, 1992). A west-east transect is also available from Spain to Greece, with Padul (Pons and Reille, 1988), Monticchio (Watts, 1985), Valle di Castiglione (Follieri et al., 1988) both in Italy, with Tenaghi Philippon (Wijmstra, 1969; Wijmstra and Young, 1992) and Ioaninna (Tzedakis, 1991). All these sequences present the same main climatic variations. This has often been summarized by the variations of the arboreal pollen sum. Obviously, the boreal forest does not have the same climatic meaning as the warm deciduous forest. To differentiate between such ecosystems we used an index based on the multivariate analysis of the pollen diagrams. The coherency between the pollen diagrams is illustrated with a south-north transect of five sequences: Padul (Pons and Reille,

CLIMAFE IN WESTERN EUROPE DURING LAST GLACIAL/INTERGLACIAL FROM POLLEN AND INSECT REMAINS

7"7

:~¢ Very long record ~" More than one climatic cycle

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Fig. I. Map showing the location of the main long pollen sequences in Europe. The Bouchet sequence is included in the Velay point and the Taphanel sequence (used in the text but which is not a long sequence) is represented by a T. 1988), Grande Pile XX (Beaulieu and Reille, 1992a), Lac du Bouchet/Ribains (Reille and Beaulieu, 1990; Beaulieu and Reille, 1992b), les Echets (Beaulieu and Reille, 1984a) and Oerel (Behre, 1986). To obtain a common time scale, the correlation of these sequences is based on several ~4C dates for the upper parts. The lower parts are correlated with the isotopic marine chronologies.

Time-scale Pollen correlations of the French sites with data of Woillard (1978) are obvious for the Eemian-Lower Pleniglacial interval, all the episodes of this period being recorded. According to Woillard and Mook (1982), and taking into account the SPECMAP updating (Martinson et al., 1987), the following chronology can be established:

- - t h e Eemian interglacial from 128 (beginning of Betula phase) to 112 ka B.P. (beginning of a steppic phase) is correlated with isotopic Sub-stage 5e. According to Turon (1984), we adopt 115 ka B.P. for the transition Carpinus-Picea; - - t h e Melisey I stadial from 112 to 104 ka B.P. (the beginning of a Betula phase) is correlated with Sub-stage 5d; - - t h e Saint-Germain I interstadial from 104 to 96 ka B.P. (beginning of a steppic phase) corresponds to Sub-stage 5c; - - t h e Melisey II stadial from 96 to 84 ka B.P. (beginning of a Betula phase) is correlated with Sub-stage 5b; - - t h e Saint-Germain II interstadial from 84 to 72 ka B.P. (the beginning of a steppic phase) is correlated with Sub-stage 5a. These dates are not crucial and could be easily

78

modified. They are given to provide a common basis for the comparison. The beginning of the isotopic Stage 3 is difficult to identify, as no clear variations of the vegetation appear during the last glacial period. After the end of isotopic Stage 3, the 14C dates allow a good correlation between the sequences.

PBO analysis We consider the pollen diagram as a multivariate series of taxa recorded over successive periods. An isolated pollen spectrum at period t represents a more or less distorted image of vegetation. To simplify, we considered two arboreal taxa (A)" representative of a temperate climate and two herbaceous taxa (H) representative of a glacial climate before and after a climate transition (Fig. 2). The dynamics of these taxa are schematized by temporal coherence (i.e. correlation between state t and state t + l ) and by direct correlations (A', versus At as H', vs 11,) or indirect correlations (At versus H , A t vs H't, A', vs Ht, A't vs H't). This double coherency can be summarized by positive (raa', rnn') and negative (ran, r a,n,r an,,r A,n, ) cross-correlations. We obtain thus a matrix of dimension m (m being the number of taxa analysed, here m = 28). This large array may be represented by a few eigenvectors using the same principle as the principal component analysis. The first eigenvector containing the greatest part of those dynamics is termed PBO (Paleo-Bioclimatic Operator). This vector has positive elements corresponding to thermophilous arboreal taxa and negative ones corresponding to cold herbaceous taxa (Fig. 3). The coherency between the PBO proves a common forcing which must be related to climate. We just note the negligeable coefficient of Poaceae for Padul, because the taxon is present during glacial as well as interglacial periods. When this PBO operator is applied to the pollen diagram, it provides a time component, named Paleo-Bioclimatic Component (PBC), which is positive for temperate episodes and negative for glacial ones. Figure 4, which considers only the three complete sequences for the entire last climatic cycle, shows a difference in the amplitude of the

J. G U I O T ET AL.

warm periods. For example, around 104-96 ka and 84-72 ka B.P., the Echets PBC seems to indicate warmer conditions than elsewhere. In order to understand the climatic meaning of these curves, we have to refer to modern data. The PBO of the five pollen sequences (Fig. 3) are averaged and applied to the modern pollen spectra available, providing the space distribution of Fig. 5a. We calculated for the same points the actual evapotranspiration (Fig. 5b) according to the method described in Harrison et al. (1993). Its distribution is very similar to the mean PBO with high values in western Europe and low values in northern Europe, eastern Europe and Morocco (no data are available elsewhere in North Africa). The PBO is then related to actual evapotranspiration, which, however, can be low either because of low temperature (in northern Europe) or because of low precipitation and high summer temperature (in Morocco or southeast Europe). Consequently the modern analogues for the Glacial period are heterogeneous with respect to summer temperature. The climatic reconstruction at La Grande Pile

Method and results The coefficients of the PBO provide the weights wj in the distance operator used to obtain the best analogues i of each fossil spectra t (Guiot, 1990):

d 2= ~"wj2"_f~,)21,jij tk, ,

(1)

j=o

where f~j and ftj are the relative frequencies of pollen taxon j (out of m taxa) in modern pollen spectrum i and fossil pollen spectrum t, respectively. The exponents k~ and k~ are given by: ki= 0.3

mi/m 0.5

if ml/m ~<0.3 if 0.3 < mi/m < 0.5 if ml/m >~ 0.5

(and similarly for kt) where m j m is the proportion of the 28 taxa present in the spectrum i, i.e. an index of taxonomic diversity. In Europe during non-glacial periods generally, m j m > 0.5 so Eq.

CLIMAIF IN WESTERN EUROPE DURING LAST GLACIAL INTERGLACIALFROM POLLEN AND INSECT REMAINS

79

THE CLIMATIC RECONSTRUCTIONS THE PALEOBIOCLIMATIC OPERATOR ineJunoph~lou~ oJtbore~u~ t a x a

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(1) gives the chord distance of Overpeck et al. (1985). Equation (1) is used to find a set of closest modern analogues of the fossil spectra. Variability among the climates represented by this set of analogues is estimated by bootstrapping (as explained in GuioL 1990). The reconstructed cli-

mate value for each fossil spectrum t is the distance-weighted mean of the climate values C~ associated with the s (usually 50) bootstrapped best analogues:

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J. GUIOT ET AL.

Paleobioclimatic analysis of 5 cores

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Fig. 3. Cumulated loadings of the taxa obtained from the paleobioclimatic analysis of 5 pollen long sequences. See Fig. I for core locations. Abbreviations: Abi(es), Aln(us), Bet(ula), Bux(us), Car(pinus), Ced(rus), Cor(ylus), Fag(us), Fra(xinus), Hip(pophae), ll(e)x, Jun(iperus), Lar(ix), O/e(a), Pic(ea), Pin(us), Pis(tacia), Qu(ercus) d(eciduus), Q(uercus) e(ver)g(reen), Sal(ix), Til(ia), Ulm(us),

Eph(edra), Eri(caceae), Hed(era), Art(emisia), Che(nopodiaceae), Gra(mineae).

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Instead of a unique standard deviation around °Rt, the lower and upper limits of this mean estimate were computed. The lower limit LLt is given by the distance-weighted mean of the analogues (out of

the total s = 50) with Ci < °Rt and the upper limit ULt is given by the distance-weighted mean of these analogues with Ci > °Rt. These confidence limits implicitly include errors in the modern climate

CLIMAI'E IN WESTERN EUROPE DURING LAST GLACIAL NTERGLACIAL FROM POLLEN AND INSECT REMAINS

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J. GUIOT ET AL.

82

observations (usually small, a few tenths of a °C or a few mm/month), the natural variability of the assemblages for a given value of the climatic variable, and the influence of non-climatic factors. Figures 6a and 7a give the results for C = annual temperature and C = annual actual evapotranspiration (AET), the latter being motivated

by Fig. 5. The method can be validated by estimating the modern climate from the modern pollen data. The correlations obtained between estimated and actual values were about 0.80 for temperature and AET. This validation is more significant for the temperate periods than for periods very different from the present time.

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83

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Fig. 7. Reconstructed range of the annual actual evapotranspiration (AET in ram) at La Grande Pile by three methods: (a) using pollen alone, (b) using pollen constrained by biomes, (c) using pollen constrained by beetles (see Fig. 6 for the time-scale).

Glacial analogues problem Figure 6a shows clearly the analogue problem for the Glacial periods. The error bars are very large and the high temperatures indicate that the steppe analogues come from dry climate areas because of the high potential evapotranspiration.

In return, the actual evapotranspiration (AET) (Fig. 7a) is low with narrow error bars. As a matter of fact the glacial vegetation is dominated by Poaceae and Artemisia. The tundra analogues have weak values of Artemisia but high values of Poaceae and reversely for the warm steppic analogues. Our pollen references show the quasi

84

absence of Artemisia (less than 1% in the pollen spectra) north of 50°N, except in the steppes of Central Asia, while Poaceae is distributed everywhere. Following Quezel et al. (1980), these Artemisia have a xeric rather than thermic determinism, contrary to the glacial ones. The length of the day has likely an influence. Thus perfect analogues for the glacial periods do not exist in our modern references. There are two solutions to obtain adequate analogues for the Glacial periods: either to extend the present set of modern data to other areas than Europe or to constrain those already available. Better analogues for the glacial period could be found on the Tibetan Plateau or the Canadian prairies. This assertion is reinforced by the fact that some beetle species found during glacial periods at La Grande Pile live today on the Tibetan Plateau (Ponel et al., 1992). The climate is cold and continental in the two regions, both located south of 50°N. Preliminary tests have shown that the pollen spectra from these areas are good analogues of the European glacial period, but we still have to check the coherency of their Climatic tolerance with Eurasian spectra. We will use here the second approach that attenuates the influence of Artemisia by constraining the modern analogues to come from either tundra vegetation or cold steppic vegetation (and not warm steppes). This approach was applied earlier (Guiot et al., 1989; Guiot, 1990; Guiot et al., 1992), but the analogues were simply constrained to be located north of 40°N and west of 30°E. These geographical limitations are only possible for pollen sequences themselves located within these limits and not for mediterranean sequences like Padul. In this paper, we present this constraining by biomes to be applicable to pollen sequences regardless of their geographical location. Biome constraints The idea of the method is to set up thresholds based on sums of pollen assemblages which will allow separating different ecosystems such as tundra, cold and warm steppic spectra. The first step is to calculate the arboreal pollen sum (AP) for a given spectrum. If this sum is below 50%,

J. GUIOT ET AL.

then we consider that the landscape corresponds to an open vegetation. We then calculate the following functional types (corresponding to the types defined by Prentice et al. (1992) in their biome model): - - t s (temperate summergreen)= sum of the temperate deciduous trees (if taxa like Quercus, Fagus, ... represent more than 5%, Alnus and Salix are added in this group, otherwise they are added in the last group); - - e l s (cold shrub and grass) = Poaceae + Cyperaceae + Ericaceae + Juniperus, if was < 5%; otherwise cls is set to zero; - - c o s (cool shrub and grass) = Hippophae + Ericaceae + Helianthemum; - - w a s (warm shrub and grass) = Artemisia + Chenopodiaceae; - - b s (boreal summergreen)= Betula + Larix + (Alnus + Salix if ts < 5). From the other types defined by Prentice et al. (1992). From these types, we apply hierarchical rules: - - i f A P > 5 0 % , the spectrum comes from a woodland biome, otherwise; - - i f cls > 2 0 % and ts < 5 % , it comes from tundra, otherwise; - - i f was > 20% and (cos + bs) < 1%, it comes from warm steppes; --otherwise, it comes from cool steppes. We applied this scheme to the 372 fossil samples of La Grande Pile and to the 1274 available modern spectra. At La Grande Pile, we find that all the glacial spectra are well classified in the tundra or cool steppe category. The Late Glacial spectra are preferably classified in the cool steppes. To check the validity of the approach, we use the environmental constraints of the Prentice et al. (1992) biome model: mean temperature of the coldest T~; mean temperature of warmest month Tw; sum of the growing day temperatures >0°C, GDDo; > 5°C, GDDs; ratio ~ = actual over potential evapotranspiration. They are interpolated at the location of the spectra and their frequency distribution is used to establish a most probable value with a confidence interval for the three biomes. Prentice et al. (1992) defined: (1) The warm steppe by the absence of tree and sclerophyll bush types and the possibility of life of

CLIMATE IN WESTERN E[2ROPE DI.'RING LAST GLACIAL/INTERGLACIAL FROM POLLEN AND INSECT REMAINS

the warm grass/shrub type. It then needs c~ < 0.65 (and ~ >0.18) and Tw >22°C. The 22 spectra affected to the warm steppes biome have a most probable Tw = 23°C (96% of the spectra have a T,.> 21°C) and all have a e <0.60 (most probable value = 0.40). This biome is then well characterized by pollen. (2) The tundra biome by the absence of boreal tree types and the possibility of life of the cold grass/shrub type, i.e. either GDD 5 <350 degrees days or ~ < 0.65 (and > 0.33). As estimated from pollen, this biome has a most probable GDDs = 300 degrees days, 65% (of 97 spectra) having a GDD5 < 1000 degrees days; 78% have an ~ >0.95. In return, 69% of them have a Tw ranging from 9 to 18'-'C. This biome is less well characterized than the warm steppes because of the pollen transport from close boreal forests. (3) The cool steppe biome as an intermediate between the two others as cool and cold grass/shrubs are able to live, i.e. GDD5 >500 degrees days, Tw < 22°C and c~ < 0.65 (and > 0.33). As estimated from pollen, we found that 93% of the 234 spectra have well a Tw < 23°C (the most probable value is 17':C). 90% of them have a GDD5 < 2500 degrees days but only 25% of them have an ~ <0.65. It is evidently difficult to have a perfect matching with the modern vegetation strongly disturbed by man, but the main characteristics of the three biomes can be retrieved from pollen: a very cold summer for tundra, a cool summer for cool steppes and a warm summer for the warm steppes. The water stress is also found to be proportionnal to this temperature. The method described at the beginning of this section is again applied to La Grande Pile, but the selection of the modern analogues has been constrained. A tundra fossil spectrum is constrained to have not a steppic analogue. A cool steppic spectrum is constrained to have neither a tundra nor a warm steppic analogue, but they can have a woodland analogue. Thus, we use a "light" constraining method. If the fossil spectrum does not come from a steppic or tundra biome, the modern analogues can come from any of the non-steppic or tundra biomes. As no fossil spectra are found to come from a warm steppe, we are then sure

~5

that we will not obtain a glacial summer temperature higher than 22°C. In order to minimize m a n ' s disturbance, we reconstructed the biomes of the modern spectra using the climate defined by the model of Prentice et al. (1992) instead of the pollen assemblages. The results are given in Figs. 6b and 7b. The comparison of Fig. 6a,b shows that the temperature of the temperate periods did not change while that of the Glacial period is much reduced. The four driest periods were located around 130, 60, 35-30, 13 ka B.P. in Fig. 6b and 7b as well. This selection of analogues does not significantly increase the distance of the closest analogues (minim u m distance) except at about 60 ka B.P. and after 20 ka B.P. (Fig. 10c). In fact the tundra analogues are almost as close analogues as the warm steppic ones, so that they are as legitimate as the others. We have just additional good reasons to reject the latter ones. Given the principle that modern analogues are more likely found in tundra or cool steppes than in warm steppes and that the common climate to all these biomes is the drought (and then low AET), the selection of the analogues must result in a change in the temperature reconstruction but not in the AET, as remarked in Fig. 6 and 7. An additional test of the relevance of this approach will be presented now with the use of beetles.

The combination of pollen and beetle data Introduction

It is more and more clear that the combination of several types of proxy data is very promising to deconvolute the various climatic components found in the biological data or to solve the problem of lack of modern analogues to some past environments. This is true as well at the historical time scale (Guiot, !992) as at the Late Quaternary timescale (Seret et al., 1992; Guiot et al., 1993). Here, we want to illustrate this approach with two examples and compare it with the approach described in the previous section. At the Late Quaternary time-scale, a first approach has previously been adopted in order to improve the temperature and precipitation esti-

86

J. G U I O T ET AL.

mates obtained from glacial pollen data in La Grande Pile using organic carbon and mineral clay measurements respectively (Seret et al., 1992). The method has then been refined in reconstructing precipitation from pollen data combined to lakelevel data (Guiot et al., 1993).

Methodology (application to deglaciation ) The method is illustrated with a pollen sequence located at La Taphanel, French Massif Central, which covers the Late Glacial and the Holocene. Ponel and Coope (1990) have studied beetles with ages approximately from 16 to 8.5 ka B.P. The mutual climatic range method (Atkinson et al., 1986, 1987) infers temperature of the coldest and warmest months from the presence of some beetles species, corresponding to a given climate acceptable for all of them. This climate is deduced from the modern distribution of these species. For the temperature reconstructions of La Taphanel (Ponel and Coope, 1990), we consider here that the warmest month is July. Here, we retain only the fact that the July temperature i s > or < 12°C. For each fossil pollen spectrum, the distance with modern spectra are sorted in ascending order. Only the few analogues with a July temperature in agreement with the conditions of Table 1 ( > or < 12°C) are kept. The Older and Younger Dryas July temperature reconstructed from pollen alone disagrees completely from the beetle-based one. I f

we select the analogues according to beetles, we increase the distance of the best analogues by less than 2%. The method is controlled by the comparison of the January temperature reconstructed using the pollen analogues constrained by beetles with the range provided by pollen alone. The latter contains (almost) always the mean reconstruction from the associated data, because vegetation and beetles indicate an equivalent degree of continentality. While the B611ing period appeared warm in July as well from pollen as from beetles (Fig. 8), the Aller6d remained mild when reconstructed from beetles but not from pollen. The reason is that the a large percentage of Betula pollen in the tundra - - s u c h as during the Aller6d p e r i o d - - g i v e s a low temperature. When the analogues are selected according to beetles, the minimum distance (Table 1) is not really increased. This illustrates the ambiguity of the climate reconstruction from a single data source. Even if the beetles constraints do not push out the January temperature estimates outside the range based on pollen alone (Fig. 8), they clearly increase the upper limit for the Aller6d reconstruction.

Application to the glacial/interglacial cycle The mutual climatic range method is applied to the beetle sequence of La Grande Pile dating from 140 to about 15 ka B.P. (Ponel et al., 1992). The reconstruction of the July temperature is displayed

TABLE I Main steps of the climatic reconstruction from pollen constrained by beetles at la Taphanel. Six samples are selected to illustrate the method. Tjul(b) gives the beetles-basedconstraints (> or < 12°C); Tjul(p) gives the reconstructed July temperature from pollen alone and Tjul(p + b) from pollen constrained by beetles. Dist(p) gives the minimum distance from pollen alone (in % of a threshold calculated on 100 pairs of spectra randomly taken) and Dist(p+ b) gives the minimum distance if we take into account the beetles constraints. Tjan(p) gives the climatic range of January temperature calculated from pollen alone and Tjan(p+ b) gives the corresponding mean value obtained from the same analogues selected for Tjul(p + b) Period/sample

Tjul(b)

Tjul(p)

Tjul(p + b)

Dist(p)

Dist(p + b)

Tjan(p)

Tjan(p + b)

Preboreal (20) Younger Dryas (15) Aller6d (11) Older Dryas (8) Boiling (6b) Oldest Dryas (3)

> 12 < 12 >12 < 12 > 12 < 12

14 to 15 12 to 15 12 to 13 12 to 15 12.5 to 19 11.7 to 13

14.4 I1 12.6 12 20 10.7

1.3 6.3 1.3 1.9 5.4 1.9

1.3 7.8 1.3 2.1 5.4 1.9

2.5 to 3.5 - 8 to - 6 -11 to - 8 -8.5 to - 7 -7.5 to 2.5 - 8 to - 5

2.5 -7.5 -11 -8.8 2.3 - 6.7

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at Fig. 9a. There is an abrupt jump at about 128 ka B.P., but the range is relatively large, so that it is difficult to distinguish cold from warm periods until 72 ka B.P. Afterwards, the temperature remains low (with a narrow range) until 15 ka B.P., with an exception at about 40-35 ka B.P. Figure 9b, inferred from pollen alone, shows a good agreement during the temperate periods, but not the glacial periods where the warm steppic analogues provide a very hot summer reconstruction. This problem has already been discussed at the previous section, where it was solved by constraining with biomes.

The selection of the only analogues falling in the range given in Fig. 9a is an alternative method to distinguish between cold steppes/tundra and warm steppes. The reconstruction from combined pollen and beetle remains evidently compatible with the reconstruction from beetles alone, but the confidence intervals are much narrower (Fig. 9c). Around 125 ka, July temperature decreases because of the high percentages of Taxus at this period which has modern analogues in Atlantic regions. The annual temperature decreases much less (Fig. 6c). This could be spurious due to anthropogenic disturbances but a better time control is

88

J. G U I O T ET AL.

La Grande Pile Reconstruction from beetles (MCR) 30III

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necessary for comparison with other data such as deep-sea cores (Sarnthein and Tiedemann, 1990). The cold episodes of the annual curve, just before 104 and 84 ka B.P., were not in July. They were then characterized by an increase of the continentality. The comparison of Fig. 6b,c proves a remark-

able coherency of the two approaches, even for the warm episode of 40-35 ka B.P. Figure 10a shows well that the biome and the beetle approaches lead to the same correction of the annual temperature reconstruction. We have also the same coherency for AET (Fig. 7b, c). Because AET is the most limiting climatic parameter for

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the vegetation, the three a p p r o a c h e s (pollen alone, pollen c o n s t r a i n e d by biomes, pollen c o n s t r a i n e d by beetles) m u s t converge, as clearly indicated by Fig. 10b. The c o m p a r i s o n of the m i n i m u m distances (Fig. 10c) shows that the c o n s t r a i n t s by beetles lead us to a more drastic selection t h a n the con-

straints by biomes, as the m i n i m u m distance increases significantly d u r i n g the glacial periods. At these periods, July t e m p e r a t u r e inferred from beetles was a b o u t 10°C lower t h a n today. These cool s u m m e r s are f o u n d today only in Atlantic t u n d r a , which c o n s i d e r a b l y restricts the choice for m o d e r n analogues.

90

J. GUIOT ET AL.

Discussion

Seret et al. (1992) have done a similar reconstruction of the annual temperature using pollen constrained by organic matter. The two reconstructions (Fig. 11) have some disagreement mainly at about 100 ka B.P. where the warming appears more abrupt from beetles and the oscillations are not synchronous between 70 and 30 ka B.P. The warming is equally reconstructed from all sources of data at about 55 ka B.P. (interstadial Goulotte in Woillard, 1978). The peak between 40 and 30 ka B.P. marked by the presence of warm species of beetles (Ponel et al., 1992) is in fact a set of several peaks in the pollen + organic matter curve (interstadials Charbon and Grand Bois in Woillard, 1978). The main disagreement is the period 50-40 ka B.P., warm as reconstructed from pollen combined with organic matter (interstadial Pile in Woillard, 1978). The Ognon complex claimed, by Woillard (1978), as a set of 3 warm peaks at the beginning of stage 4 was absent in the organic matter curve but the July temperatures reconstructed from beetles, with a minor maximum at 68-66 ka B.P., are compatible with the pollen data. Obviously the difference of resolution between beetle data and pollen data does not allow a perfect match. In conclusion, the main remaining problem is the absence of the interstadial Pile in beetle data (50-40 ka B.P.)

Stages 4, 3 and 2 are marked by a great number of climatic oscillations. The warm peaks of these periods are well known, as the DansgaardOeschger events, since the 180 record of the Camp Century ice core in Greenland (Dansgaard et al., 1971). The cold peaks are the consequence of ice rafting in the northeast Atlantic ocean (Heinrich, 1988), but these episodes still need to be detected on the continent. Figure 11 shows the 6 Heinrich events (Bond et al., 1992) between 70 ka and 13 ka B.P. The first one, noted H1 and dated between 14.6 and 13.5 (during the Oldest Dryas), is correlated with a phase when the local vegetation (dominated by Artemisia, Cyperaceae and Graminae) increased significantly. This episode is reconstructed as the last cool event before the deglaciation (the Younger Dryas is absent in the core). The second and the third Heinrich events (H2 and H3) dated at about 21-20 ka and 29-26 ka B.P. are the two coldest phases of the Last Glacial Maximum, as indicated by the three sources of data (pollen, beetles and organic matter). The three other Heinrich events are less accurately dated. The H4 event (at about 42 ka B.P.) is located between Charbon and Grand Bois interstadials (Woillard, 1978) and corresponds to a cold episode as evidenced by organic matter and by beetles as well. It is even clearer from organic matter because beetles seem to place the minimum

LA GRANDE PILE: ANNUAL TEMPERATURE Reconstructed from pollen constrained 14

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TIME in Ka BP Fig. 11. Comparison of the annual temperature reconstruction obtained from pollen constrained by organic matter content (Seret et al., 1992) and obtained from pollen constrained by beetles. Time scale is indicated in 14C ka B.P. Correlation with Heinrich events (Bond et al., 1992) noted by H1, H2 .... H6. (see Fig. 6 for the time-scale).

CLIMATE IN WESTERN EUROPE DURING LAST GLACIAL/INTERGLACIAL FROM POLLEN AND INSECT REMAINS

at m i d p o i n t between H5 a n d H4. T h e H5 event seems to c o r r e s p o n d to the stadial between G o u l o t t e a n d Pile. H 6 falls within the O g n o n c o m p l e x which a p p e a r s to be a set o f 3 mild p e a k s as r e c o n s t r u c t e d by p o l l e n a n d beetles b u t which a p p e a r e d t o t a l l y cold f r o m o r g a n i c m a t t e r (Seret et al., 1992). The conclusion is t h a t the use o f p o l l e n a l o n e is not a d e q u a t e to p e r m i t the r e c o n s t r u c t i o n o f extremely cold events d u r i n g a glacial period. W e need to c o m b i n e several signals such as those f r o m s e d i m e n t o l o g y a n d beetles. H o w e v e r , n o n e o f these signals is perfect. T h e origin o f the o r g a n i c m a t t e r c a n n o t a l w a y s be f o u n d in significant vegetation changes, beetles d i s a p p e a r c o m p l e t e l y d u r i n g e x t r e m e l y cold events a n d the r e s o l u t i o n o f the beetle sequences is n o t a l w a y s high e n o u g h for such s h o r t events (in the m e a n t i m e , it is possible to seperate a cold p h a s e f r o m a w a r m e r one in the s a m e s e d i m e n t s a m p l e when i n c o m p a t i b l e assemblages are mixed). Efforts on d a t a t i o n a n d verification with o t h e r c o n t i n e n t a l cores are still to be d o n e to d e m o n s t r a t e the occurrence o f these H e i n r i c h events. Here the beetles h a v e the a d v a n tage to be d a t a b l e themselves by A M S ( A t k i n s o n et al., 1987). The s h o r t cold event (especially) in s u m m e r in the first p a r t o f stage 5e r e c o n s t r u c t e d in this p a p e r (here at a b o u t 125 k a B.P., b u t there is no real d a t a t i o n a v a i l a b l e for this p e r i o d ) is m a r k e d by the d e v e l o p m e n t o f a Taxus forest. F u r t h e r a n a l y ses are necessary for c o n f i r m a t i o n . In o u r p r e v i o u s r e c o n s t r u c t i o n s ( G u i o t et al., 1989, 1992), this event was n o t seen because Taxus was n o t used in the pollen assemblages. We have tried, in this p a p e r , to show t h a t c l i m a t e r e c o n s t r u c t i o n in a n a l o g y to m o d e r n v e g e t a t i o n s is i m p e d e d by the lack o f g o o d a n a l o g u e s . M e t h o d s based on regression can suffer also f r o m this p r o b l e m in a d d i t i o n to their i n h e r e n t possible unrealistic e x t r a p o l a t i o n s . The s o l u t i o n is the use o f a d d i t i o n n a l sources o f d a t a such as beetles. W h e n they are n o t available, we have tried to define s o m e rules to d e t e r m i n e if, in the glacial period, o u r p a r t i a l a n a l o g u e s c o m e f r o m w a r m , cool steppes or t u n d r a , as defined in the b i o m e m o d e l o f Prentice et al. (1992). In the t r a n s i t i o n periods, such as the d e g l a c i a t i o n , the p r o b l e m

9[

c a n n o t be solved by pollen alone, b e c a u s e o f the inerty o f the vegetation. In this case beetles and s e d i m e n t o l o g i c a l d a t a are really helpful.

Acknowledgments We are grateful to F. Saadi, J. Belmonte, V. Ruis-Vasquez a n d S. B o t t e m a who have k i n d l y p r o v i d e d m o d e r n pollen d a t a for Spain, M o r o c c o a n d the N e a r East. The E P O C H p r o g r a m o f the European Community and the French " C o m m i s s a r i a t ~, l'Energie A t o m i q u e " have g r a n t e d this study. F r u i t f u l discussions with I.C. Prentice, S.P. H a r r i s o n a n d B. H u n t l e y have enabled us to i m p r o v e some p a r t s o f the m e t h o d o l o g y .

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