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Statistical analyses of loess molluscs for paleoecological reconstructions
Quaternary International, Vols. 7/8, pp. 81--89,1990. Printed in Great Britain. All rights reserved.
1040-6182/90 $0.00 + .50 © 1991 INQUAJPergamon Press plc
STATISTICAL ANALYSES OF LOESS MOLLUSCS FOR PALEOECOLOGICAL RECONSTRUCTIONS
D.-D. Rousseau URA CNRS 157, Centre des Sciences de la Terre, 6 Boulevard Gabriel, F-21100 Dijon, France
Molluscs remains are generally numerous in loess sequences and a rich literature deals with them. But since the proposal of Lozek's method which is widely used in Quaternary molluscan studies, no new methods have been proposed apart from some adjustments. It is a fundamental fact that to propose a new approach implies consideration of the studied material with other concepts. Nevertheless to renew existing methods and concepts needs the opportunities of new techniques, new physical supports but also depends on the scientific hard core of scientists. The generalization and the rising power of computers and micro-computers was one opportunity for the geosciences. More precisely, the renewal of concepts in paleontology leads to new hard cores based on multidiseiplinary approaches implying a quantification of data. That is the reason why morphometry appears in evolution studies, quantitative paleoclimatology firstly developed on marine micro-organisms, quantitative paleoecoiogy to characterize the different type of associations, quantitative biostratigraphy - - all these approaches widely using multivariate methods. Concerning Quaternary molluscs, I have attempted to develop new concepts because these organisms allow precise studies dealing among others with evolution, biogeography, climatology, and biostratigraphy. They are generally present in the loess sequences which record several climatic cycles, that is to say variations in geospbere--biosphere relations. Consequently analyses of loess molluscs are able, using statistical analyses, to contribute to the understanding of the geosphere-biosphere system.
INTRODUCTION As observed in the literature, some fossil remains such as rodents, large mammals, and pollen are sometimes recognized in loess sections but molluscs are generally more abundant. In this case, molluscan analyses represent a great contribution to the paleoenvironmental interpretations of the loess sequences, and more generally of Quaternary deposits. If the constant representation through the sequences is important, molluscs when they occur in a section are generally well preserved and specific identification is made which constitutes an advantage compared to pollen. These two conditions constitute the major constraints for a global analysis based on statistics. Why are statistics useful to the study of loess molluscs? In this way statistical studies allow a quantification of the results easily understood by all. This quantification is variable and depends on the philosphy of the approach. Consequently different methods are available. The first is simple and applied by Sparks (1961) for Europe. The mollusc species are referred to four groups which are established with regards to the geographic distribution of the molluscs. The time evolution of these groups allows one to propose a limited climatic sketch for the studied loess sections. A similar method was used for Chinese loess, based on the present-day distribution of characteristic species by Chen Deniu et al. (1982). The second is to consider the ratio of some index species and to examine the course of the index (Krolopp, 1966, 1983; Alexandrowicz, 1986) (Fig. 1). This is the case of the Carychium index for example used in different publications. In this way the interpret81
ation is focused on a precise point which is not representative of all the data. Generally this method is associated with the first (Furhman, 1973; Meijer, 1985). The third is Lozek's method (Lozek, 1964; Puiss6gur 1976). The species are referred to ten groups which correspond to their main ecological characteristics: forest, open land, water environments, etc. Results are presented as following. Two spectra are established based on ecological groups: species (all the species of one group) and individuals (all individuals of one group) spectra. Naturally, the counts are transformed into percentages and each sample is placed in its stratigraphical position. The major problem which emerges is that a same group can concern species representative of different climate: Vallonia costata and Columella columella for instance are in ecological group five although the latter lives in tundra environments. Another problem is the use of percentages which disturbs the information provided by molluscs and more generally by the data. Percentages, by definition, homogenize the data. The total assemblage equals 100. Nevertheless, the strength of each assemblage is ecologically significant. Disregarding this information, other types of data are needed which are often only the author's property, such as for example his scientific experience. Such information is not in this case transmissible to a large audience. Modifying the presentation of the results, Alexandrowicz (1987) proposed other statistical analyses (triangular analyses, circles) of malacofauna. All these methods are based on descriptive statistics, need few computer materials but imply a long and a special reading of the results. Consequently, except for
82
D.-D. Rousseau
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0 10 100 1000
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FIG. 1. An example of the classical approach to loess molluscs: the malacological assemblages in the loess profile at Odonov. On the left are located the malacological samples in the stratigraphy. Then an abundance index is plotted which expresses the total number of shells on a logarithmic scale. Species ranges and strengths are expressed in percentages. Finally letters on the right side are the symbols for molluscan assemblages (modified from Alexandrowicz, 1986).
triangle results, the comparison of the assemblages is not global. It is made from one assemblage to its closest neighbours. The fourth is the method I recommend: numerical analyses (Rousseau, 1985, 1987a,b) (Fig. 2). The many individuals in each assemblages, the many assemblages in a section, or the numerous sections in a given country can be summarized by a large amount of tabulated data where the species are columns while the assemblages, assimilated to stratigraphical levels, are the rows. Such a table implies a special method to enable global analysis• The only available method which can be applied both to malacological assemblages or to specific populations is multivariate analysis. If we want to be more precise for such a proposal, we have to refer to Birks and Birks (1980); "'numerical methods have several advantages in Quaternary paleoecoiogy".
PRINCIPLES OF THE MULTIVARIATE ANALYSES
1. Factor Analyses The principle of factor analyses is to study the variability of a set of data in a multiple dimensionsal space, in an idealistic table, with each column or row
influencing the variability with the same weight. According to this state, the data set can be represented by a sphere where all the n species contributions are equal to 1/n. In fact, this is never the case. A natural assemblage groups together species whose occurrence results from the interaction between limiting factors and ecological valency• The limiting factor conditions the chances an organism has to invade an environment, at least when the conditions are less severe (Fig. 3). It affects the general metabolism of the organism allowing it to subsist effectively in the biotope. In extreme (tundra or desert) environments the limiting factors have a dominant rule. They strongly contribute to the definition of the biological style of the environment determining a limiting value to the plant and animal groups of organisms. In tundra, for example, the limiting factors include temperature and the duration of the warm season suitable for active life. A reduction in the temperature or in the duration of summer implies an alteration in inhabitation, indeed if the intensity is too strong the disappearance of species results. The inverse effect leads to an important change, the immigration of allochthonous species to tundra and the occurrence of new biocenoses. The occurrence of species in an assemblage also depends on its ecological valency which reflects its capacity to colonize different
83
Statistical Analyses of Loess Molluscs
I STATISTICAL ANALYSES OF TEKRESTRIAL MOLLUSCS 1 @
Mulhu~m
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ir
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FIG. 2. Multivariate analyses of terrestrial molluscs for paleoecologic reconstructions. The multivariate cloud, with the first five bending axis, characterizes the variability of the global data set. First, semiquantitative analysis. If the data set comprises measurements as variables, the correspondence analysis (CA) of this table allows shell morphology variations reconstructions. On the contrary, if the data set is established from species counts as variables, CA associated to cluster analysis allows environmental variation reconstructions. Second, quantitative analysis. The data set comprises counts in fossil (Df) and recent (Dr) assemblages. The CA determines a new table defined by the selected factors Ioadings of fossil (BCf) and recent (BCr) assemblages. A stepwise regression (SR) is calculated between recent assemblages Ioadings and the corresponding recent climatic parameters (Pr) which determine estimates of the recent values (P'r). The determined equations are validated (V) by the study of the residuals (actual-estimated values). The final step concerns the estimation of the fossil climatic parameters (P'f).
environments, i.e. to survive and to proliferate under various ecological conditions. A species with a low valency tolerates a low variability of the environment and is called a stenoece. On the other hand, if the species is able to populate numerous environments, it is a euryece. All these characteristics which constrain the distribution and the development of the species imply that the multivariate cloud of a data set cannot be spheric but indicate privileged axes of bend that factor analysis will determine and explain (Fig. 2). Various methods allow the analysis of such a table such as canonical analysis, Q or R mode, principal components, and correspondences (Davis, 1973; Ben-
zecri et al., 1973). I recommend (Rousseau, 1987b) the latter, which allows the analysis in the same way both by columns and rows without any a priori consideration of the structure of the data, as opposed to, for instance, principal components analysis. As columns and rows are analyzed symmetrically, their clouds have the same axes of bend so they can be superimposed (Fig. 2). The simultaneous plot of the column and row points facilitates the interpretation of the results by underlining the species and the assemblages which are ecologically significant, by determining relative evaluations of temperature, moisture (temperature and moisture gradients), evolution of environments, and climate. Taking these gradients into account assemblages con-
84
D.-D. Rousseau lower tolerance limit
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the stepwise regression between the factor loading and the thermal parameters proposes statistical equations which allows precise estimation of the original parameters. If the factor analysis of the recent samples has been done with fossil assemblages, and if recent and fossil assemblages are analogous, the statistical equations allow reconstruction of the fossil climatic parameters. MULTIVARIATE ANALYSES OF LOESS MOLLUSC ASSEMBLAGES
®
!
~.~
low values
gradient
v hig/~ value~
FIG. 3. The tolerance law of organisms in relation to their geographical distribution and their abundance (modifiedfrom Sacchi and Testard, 1971).
sidered in their stratigraphical position provide precise variations.
2. Cluster Analyses Cluster analyses allow the determination of a partition in a data set which is decomposed by grouping together the columns (species) or the rows (assemblages) which indicate high similarity in their composition (assemblages) or in their occurrence in assemblages (species) (Davis, 1973). The exclusive use of this method is not adequate because the partitions are determined by environmental variations. Thus, the best way is to combine factor and cluster analyses (Jambu, 1978; Jambu and Lebeaux, 1978). In the first stage, factor analysis determines the variability of the data set and characterizes the elongation axes of the cloud. Then in the second stage the cluster forms groups of species and of assemblages from the factor results, which are called classes, in order to determine a precise environmental partition of the data set and to characterize reworked assemblages (Fig. 2).
3. Multiple Regressions
1. Semi-Quantitative Analysis of the Achenheim (Alsace, France) Sequence The loess sequence of Achenheim is located 5 km westwards of Strasbourg in the Rhine valley (Heim et al., 1982; Lautridou et al., 1985, 1986). There, samples for malacoiogical studies have been done, and 195 samples contain 61 species. Five climatic cycles sensu Kukla (1977) which provided malacofauna have been recognized. Through the multivariate analysis of the total set of data (columns = species and lines = malacological samples, each one corresponding to one stratigraphical level) the environmental evolution of that site can be reconstructed for the last 500 ka BP (Fig. 4). Although thermic factor (first multivate axis) varies between pleniglacial and interglacial estimates, the associated environment varies between "temperate environments' (open forest, open environment with the occurrence of well represented trees and bushes, slightly damp or damper), 'intermediate environments' (open land without any arboreal vegetation but damp or damper or marshy grasslands, same as previously but very dry grassland open grassland with small amounts of Columella columella and Pupilla alpicola) and 'glacial' environments (open ones with sparse vegetation, and low moisture, qualified as a proto-loess steppe: a Pupilla fauna in embryonic state; same as previously but moister qualified as prototundra: a Columella fauna in embryonic state; environment with a sparse vegetation but higher moisture: damp, marshy, loess-steppe: PupiUa fauna; tundralike: Colurnella fauna) (Rousseau, 1987b). The Ioadings of assemblages on the first axis combined with environmental estimation are expressed in Fig. 4. If now we calibrate the last three cycles of the sequence, which are better preserved, with SPECMAP chronology (Imbrie et al., 1984), it appears that the oscillations of the thermic gradient, parallel to the first axis of the multivariate analysis, are similar to those of 6~80 which express the continental ice volume variations (Fig. 5). Again, loadings on the two first axes plotted against (thermic and moisture gradients) indicate that their variations are not identical in one cycle and that their evolution is not identical from one cycle to another (Rousseau and Puissrgur, 1990) (Fig, 5).
Multiple regression can be a complement to factor analysis for the research of the so-called transfer functions (Fig. 2). The principle is to explain a column (a dependent variable) taking into account other columns (explicative variables) (Scherrer, 1984). But all the columns of a data set do not contribute to the explanation of another, and a simplified solution is often necessary. In fact, the more efficient solution using the lowest number of variables is expected. The stepwise regression is used in this way. It discriminates the columns which provide a statistically significant contribution to the variance of a column which can be predicted (Fig. 2). Consequently this method allows the prediction of the greatest part of the variance of the dependent column from a reduced set of independent columns. If, for example, monthly thermal parameters 2. Quantitative Analysis of the Achenheim Sequence: are obtained from a meterological station close to the Transfer Functions The estimate of February and August temperatures sampling point, after factor analysis of the assemblages
Statistical Analyses of Loess Molluscs
85
loadings of the assemblages on selected axes which explain 90% of the total variability of the set. Then recent climatic parameters are estimated by stepwise regressions which provide different mathematical equa~1 ~ HOLOCENE tions. These equations are validated or rejected by B UPPER analyzing the residuals (true values-estimate values). " PLEISTOCENE Then using these equations, fossil estimates are proposed (Rousseau, 1989, 1991). The first proposal of transfer functions from terrest..z rial molluscs provide results which are in rough agreement with the already published ones, mainly for the last climatic cycle (Fig. 6). Temperature estimates D ,,--, are generally lower than the recent values (1.8°C in Ltl ,.1 February and 18.3°C in August - - Fig. 6). In winter the E ~ values vary between - 1 3 and 2°C while in summer the extreme values are 10 and 17°C (Fig. 6). Concerning precipitation, summer estimates are always deficient 60 + ÷ (between 50 and 78 mm whereas the recent value is 76 m mm) (Fig. 6), in agreement with the results of J. Guiot FIG. 4. Semiquantitative analysis of molluscan assemblages at from pollen analysis of La grande Pile; on the contrary, Achenheim. Temperature variations are determined by plotting loadings of assemblageson the first axis in their stratigraphicposition winter estimates are always higher (between 33 and 76 (t, temperate; c, cold). Environment classes are determined by mm) than the recent value (34 mm) (Fig. 6). cluster analysis (h open forest; 2: semiforest; 3: non-arboreal From a global point of view, transfer functions grassland; 4: dry grassland; 5: cold grassland; 6: scarce vegetated indicate that stage 6 was not so cold that the 6~80 grassland; 7: tundra-likeor loess steppe); (modifiedfrom Rousseau, 1987 a,b). could materialize (Fig. 6). In fact summer temperatures were lower than at present and winter ones vary around -5°C, and precipitation was high during winter. But and precipitations have been attempted using the the important fact is that this glacial stage was preceded analog method. The principle is to establish mathemati- by stage 7 which ends showing higher winter precipitacal equations which relate mollusc assemblages to tion over a cold continent associated with low precipitaclimatic parameters (Fig. 2). In a first step, a large set is tion in summer. In this case the continental ice growth established comprising recent and fossil assemblages as was favoured, implying a greater expansion of Northrows and species as columns. Correspondence analysis, European ice caps during this stage than during stages as previously, provides a new table composed of the 2, 3, 4 (Rousseau, 1991).
Tp
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FIG. 5. Calibration of the Achenheim sequence according to SPECMAP chronology. The mollusc variations corresponding to Ioadings on the first two axes (temperature, axis 1; moisture, axis 2) are scaled for a better comparison with 6 018 oscillations.
86
D.-D. Rousseau
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FIG. 6. Temperature and precipitatnons estimates for February (crosses) and August (black squares) as expressed by transfer functions. The vertical dotted lines correspond to the mean present time value of each parameter (modified from Rousseau,
1991).
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FIG. 7. Morphometry determined for Pupilla genus. Roman letters characterize the necessary reference axes. Letters are the different measured parameters (from Rousseau, 1985, in press b).
87
Statistical Analyses of Loess Molluscs
MULTIVARIATE ANALYSES OF LOESS SPECIES
high size pole while 'interglacial' ones are plotted close to the small size pole, 'intermediary' individuals being in an intermediate position. If loadings on the two first axes of the mean point of each population and of the malacological assemblages (results of the semi-quantitative analysis of assemblages) are plotted against each other, we can conclude that temperature has a great influence on the shell size, whereas moisture contributes little to the shell variations (Fig. 9). It also appears that shape variations are low and can not be correlated with the determined environmental factors (Fig. 9) (Rousseau, 1985, 1989b). These reverse morphological variations which affect a species in stasis are called ecophenotypic because they only correspond to modifications of the phenotype in relation with environment while genotype is not modified (Rousseau, 1985, 1989b). To verify this assumption I have to mention that in recent time, similar variations are observed in European populations of Pupilla muscorum from West to East rather than from North to South. Once more, if extreme recent populations are compared to fossil ones, central European ones are plotted in the high size pole while French ones are plotted in the small size pole (Rousseau, in press b). These variations in size have been interpreted as corresponding to modifications in the ontogenetic sequence in response to environmental
Among all species which constitute the assemblages, Pupilla muscorum, an euryece taxon, is present almost all through the sequence• In this way, a morphological analysis of shells can provide data on the environmental changes already determined by the assemblages. In each population issued from one assemblage, i.e. stratigraphical level, 30 individuals are aleatory selected using random numbers. Then each shell is measured as expressed in Fig. 7 with a Nikon measurescope. A larger table is established as following: row = one individuals and columns -- the 26 measured morphological parameters (Rousseau, 1985, in press b). This table is analyzed using correspondence methods (Fig. 2). First, individuals are distributed according to size and shape in the multispatial cloud (Fig. 8). To summarize, the first axis characterizes a size gradient from large (up to 5.5 whorls) to small (3 whorls) shells in the same category of size (Fig. 8). Once more, populations are mainly distributed with regards to first axis. In each population shape variations are equal: slender and thickset individuals are roughly in similar proportions (Fig. 8). On the other hand populations have a different distribution on the first axis with regards to the climatic conditions of the environment where they lived: 'glacial' ones are plotted close to the
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FIG. 8. Correspondence analysis of Achenheim populations. Individuals (points) distribution according to the expressed morphological characters determined by the first two axes. White stars represent individuals coming from 'pleniglacial' populations. Their loading is concentrated in the high size pole of the first axis. On the contrary, individuals of a "temperate' population (white triangles) are plotted close to the small size pole. The shape variations expressed on the second axis affect all the populations in roughly similar proportions (from Rousseau, 1985, in press h).
88
D.-D. Rousseau
Malacofaunas
VARIATIONS IN
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FIG. 9. Correspondences between mollusc assemblages variations, characterizing temperature and moisture oscillations) and morphological variations of related Pupilla muscorum shells, f: forest (from Rousseau, 1989b).
stress. Such responses are today observed in other European molluscs. CONCLUSION The introduction of statistical analyses into paleoecology has opened up new fields of research which could not be reached in another way, quantification of the results proposing a wide field of scientific cooperation. It is a new method of analysis and approach of the paleontological material, which does not imply the neglect of field work. These methods applied on loess molluscs can also be applied to other kinds of Quaternary deposits (Rousseau, 1986; Rousseau and Puiss6gur, 1989). In fact the use of such approaches depends on the defined objectives of the research. Mollusc remains have the privilege to be often well preserved and in a large quantity so that any kind of paleontological analysis can be designed and accomplished. To conclude, statistical analyses of terrestrial molluscs allow multidisciplinary studies as a fundamental group for Quaternary study. REFERENCES Alexandrowicz, S.W. (1986). Molluscan assemblages from a loess profile at Odonow (Malopolska Upland). Biuletyn Prygla, 31, 715. Alexandrnwicz, S.W. (1987). Analiza malakologiczna w badaniach osadow czwatorzedowych. Geologia, 12, 1-240. Benz~,cri, J.P. et ai. (1973). L'analyse de donn~es. II L'Analyse des Correspondances. Dunod, Paris, 619 pp. Birks, H.J.B. and Birks, H.H. (1980). Quaternary Palaeoecology. Arnold E., London, 289 pp.
Chen Deniu, Lu Yanchou and An Zhisheng (1982). Snail assemblages in loess strata and their environmental implication. In: Proceedings of the 3rd National Quaternary Conference China (in Chinese), pp. 7-15. Science Press. Davis, J.C. (1973). Statistics and Data Analysis in Geology. J. Wiley and Sons, New York, 550 pp. Fuhrmann, R. (1973). Ule sp~itweichselglaziale und holoz~ne Molluskenfauna Mittel- und Westachsens. Freiberger ForschungschefteH, 278, 1-121. Heim, J., Lautridou, J.P., Maucorps, J., Puiss~gur, J.J. Somme, J. and Th~venin, A. (1982). Achenheim: une s6quence type des loess du PI6istocene moyen et sup~riur. Bulletin de rAssociassion fran¢aise pour I'Etade du quaternaire, 10/11, 147-159. Imbrie, J., Hays, J.D., Martinson, D.G., Mclntyre, A., Mix, A.C., Morley, J.J., Pisias, N.G., Prell, W.L. and Shackleton, N.J. (1984). The orbital theory of Pleistocene climate: support from a revised chronology of the Marine b~aO record. In: Berger, A., Imbrie, J., Hays, J., Kukla G. and Saitzman, B. (eds). Milankovitch and Climate, part 1, pp. 269-305. Reidel, Dordrecht. Jambu, M. (1978). Classification automatique pour Uanalyse des donn~es. 1 M~thodes et algorithmes. Dunod, Paris, 310 pp. Jambu, M. and Lebeaux, M.O. (1978). Classification automatique pour l'analyse des donn(~es. 2 Logiciels. Dunod, Paris, 399 pp. Krolopp, E. (1966). A Mecsek hegys~g k6rny~ki 16szk(~pzOdm~nyek biosztratigr~ifiai vizsgfilata (Biostratigraphic investigation of the loes formations in the environment of the Mecsek Mountains). F61d Int. I~vi Jel. 133-145. Krolopp, E. (1983). Biostratigraphic division of Hungarian Pleistocene formations according to their molluscan fauna. Acta geologica Academiae scientiarum hungaricae, 26, 69-82. Kukla, G. (1977). Pleistocene land-sea correlations. 1. Europe. Earth Science Review, 13, 307-374. Lautridou, J.P., Somm(~, J., Heim, J., Maucorps, J., Puiss~guir, J.J., Rosseau, D.D., Th~venin, A. and Van Vliet-Lano6, B. (1986). Corr61ations entre s(~diments quaternaires continentaux et matins (iittoraux et profonds) darts le domaine France septentrionaleManche. In: O. Cronchon (ed.) Correlations entre s~diments quaternaires continentaux et mains. Revue de g~ologie dynamique et geographie physique, 27, 105-112. Lautridou, J.P., Somme, J., Heim, J., Puiss~gur, J.J. and Rousseau, D.D. (1985). La stratigraphie d e l o e s s et formations fluviatiles d'Achenheim (Alsace): Nouveiles donn~es bioclimatiques et correlations avec les s~quences Pl~istoc~nes de la France du Nord-
Statistical Analyses of Loess Molluscs Quest. In: M. Campy (ed.) Dynamical and Chronological Relations Between Glacial and Periglacial Deposits, Bulletin de rAssociassion fran¢aise pour rEmde du quaternaire, 22-23, 125-132. Lozek, V. (1964). Quart/irmollusken der Tschechoslowakei. Rozpravy Ustredniho ustavu Geologikale, 31, 1-374. Meijer, T. (1985). The pre-Weichselian non-marine molluscan fauna from Maastricht-Belv6d/:re (southern Limburg, The Netherlands). Mededelingen van's Rijks geologischen dienst, 39, 75-103. Puiss6gur, J.J. (1976). Mollusques continentaux quaternaires de Bourgogne. Significations stratigraphiques et climatiques. Rapports avec d'autres faunes bor6ales de France. Mdmoires g~ologique Universit# de Dijon, 3, 241 pp. Rousseau, D.D. (1985). Structure des populations quaternaires de PupiUa muscorum (Gastropode) en Europe du Nord. Relations avec leurs envirinnements. Thdse Doctorat 3dine cycle Institut des Sciences de la Terr Dijon, 133 pp. (unpublished). Rousseau, D.D. (1986). Application de la m6thode d'analyse factorielle des correspondances aux malacofaunes de Tourville (Saalien). Bulletin Centre G~omorph Caen, 31, 5-20. Rousseau, D.D. (1987). New approach to the Pleistocene land snails. In: H.M. French and M. P~csi (eds) Loess and Periglacial Phenomena, pp. 151-163. Hungaricae Academiae Scientarum, Budapest. Rousseau, D.D. (1987). Paleoclimatology of the Achenheim series (Middle and Upper Pleistocene, Alsace, France). A malacological analysis. Palaegeography, Paleoclimatology and Paleocology, 59, 293-314.
89
Rousseau, D.D. (1989a). Estimation quantitative des pal~otemp~ratures hivernales en Alsace depuis 340,000 ans a partir ranalyse des associations malacologiques. Compw rendu de l'Acad#mie des sciences, 309(S6rie II), 1623-1628. Rousseau, D . D (1989b). R6ponses des malacofaunes terrestres Quaternaires aux contraintes climatiques en Europe septentrionale, Palaegeography, Paleoclimatology and Paleoecology 69, 113-124. Rousseau, D.D. Climatic transfer function from Quaternary molluscs in European loess deposits. Quaternary Research, 35 (in press). Rousseau, D.D. Ontogeny-morphology: the weight of constraints in Pupilla muscorum. Palaeobiology (submiued-c). Rousseau, D.D. and Puiss6gur, J.J. (1989), Analyse de la malacofaune continentale. In: A. Tuffreau and J. Somm~ (eds) Le gisement pal~olithique de Blache-St-Vaast (Pas-de-Calais), N6moire Special de la Soci6t6 pr6historique franqaise. 21.89-102. Rousseau, D.D. and Puiss6gur, J.J. A 350,000 years climatic record from the loess sequence of Achenheim (Alsace, France). Boreas, 19, 203-216. Sacchi, C.F. and Testard, F. ( 1971). l~cologie Animale -- Organismes et Milieu. Doin (ed.), Paris, 480 pp. Scherrer, B. (1984). Biostatistique. Gaetan Morin, Chicoutimi CDN, 850 pp. Sparks, B.W. (1961). The ecological interpretation of Quaternary non-marine mollusca. Proceedings of the Linnean Society, London, 172, 71-80.
1040-6182/90 $0.00 + .50 © 1991 INQUAJPergamon Press plc
STATISTICAL ANALYSES OF LOESS MOLLUSCS FOR PALEOECOLOGICAL RECONSTRUCTIONS
D.-D. Rousseau URA CNRS 157, Centre des Sciences de la Terre, 6 Boulevard Gabriel, F-21100 Dijon, France
Molluscs remains are generally numerous in loess sequences and a rich literature deals with them. But since the proposal of Lozek's method which is widely used in Quaternary molluscan studies, no new methods have been proposed apart from some adjustments. It is a fundamental fact that to propose a new approach implies consideration of the studied material with other concepts. Nevertheless to renew existing methods and concepts needs the opportunities of new techniques, new physical supports but also depends on the scientific hard core of scientists. The generalization and the rising power of computers and micro-computers was one opportunity for the geosciences. More precisely, the renewal of concepts in paleontology leads to new hard cores based on multidiseiplinary approaches implying a quantification of data. That is the reason why morphometry appears in evolution studies, quantitative paleoclimatology firstly developed on marine micro-organisms, quantitative paleoecoiogy to characterize the different type of associations, quantitative biostratigraphy - - all these approaches widely using multivariate methods. Concerning Quaternary molluscs, I have attempted to develop new concepts because these organisms allow precise studies dealing among others with evolution, biogeography, climatology, and biostratigraphy. They are generally present in the loess sequences which record several climatic cycles, that is to say variations in geospbere--biosphere relations. Consequently analyses of loess molluscs are able, using statistical analyses, to contribute to the understanding of the geosphere-biosphere system.
INTRODUCTION As observed in the literature, some fossil remains such as rodents, large mammals, and pollen are sometimes recognized in loess sections but molluscs are generally more abundant. In this case, molluscan analyses represent a great contribution to the paleoenvironmental interpretations of the loess sequences, and more generally of Quaternary deposits. If the constant representation through the sequences is important, molluscs when they occur in a section are generally well preserved and specific identification is made which constitutes an advantage compared to pollen. These two conditions constitute the major constraints for a global analysis based on statistics. Why are statistics useful to the study of loess molluscs? In this way statistical studies allow a quantification of the results easily understood by all. This quantification is variable and depends on the philosphy of the approach. Consequently different methods are available. The first is simple and applied by Sparks (1961) for Europe. The mollusc species are referred to four groups which are established with regards to the geographic distribution of the molluscs. The time evolution of these groups allows one to propose a limited climatic sketch for the studied loess sections. A similar method was used for Chinese loess, based on the present-day distribution of characteristic species by Chen Deniu et al. (1982). The second is to consider the ratio of some index species and to examine the course of the index (Krolopp, 1966, 1983; Alexandrowicz, 1986) (Fig. 1). This is the case of the Carychium index for example used in different publications. In this way the interpret81
ation is focused on a precise point which is not representative of all the data. Generally this method is associated with the first (Furhman, 1973; Meijer, 1985). The third is Lozek's method (Lozek, 1964; Puiss6gur 1976). The species are referred to ten groups which correspond to their main ecological characteristics: forest, open land, water environments, etc. Results are presented as following. Two spectra are established based on ecological groups: species (all the species of one group) and individuals (all individuals of one group) spectra. Naturally, the counts are transformed into percentages and each sample is placed in its stratigraphical position. The major problem which emerges is that a same group can concern species representative of different climate: Vallonia costata and Columella columella for instance are in ecological group five although the latter lives in tundra environments. Another problem is the use of percentages which disturbs the information provided by molluscs and more generally by the data. Percentages, by definition, homogenize the data. The total assemblage equals 100. Nevertheless, the strength of each assemblage is ecologically significant. Disregarding this information, other types of data are needed which are often only the author's property, such as for example his scientific experience. Such information is not in this case transmissible to a large audience. Modifying the presentation of the results, Alexandrowicz (1987) proposed other statistical analyses (triangular analyses, circles) of malacofauna. All these methods are based on descriptive statistics, need few computer materials but imply a long and a special reading of the results. Consequently, except for
82
D.-D. Rousseau
=,
I
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I I
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I
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I
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0 10 100 1000
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FIG. 1. An example of the classical approach to loess molluscs: the malacological assemblages in the loess profile at Odonov. On the left are located the malacological samples in the stratigraphy. Then an abundance index is plotted which expresses the total number of shells on a logarithmic scale. Species ranges and strengths are expressed in percentages. Finally letters on the right side are the symbols for molluscan assemblages (modified from Alexandrowicz, 1986).
triangle results, the comparison of the assemblages is not global. It is made from one assemblage to its closest neighbours. The fourth is the method I recommend: numerical analyses (Rousseau, 1985, 1987a,b) (Fig. 2). The many individuals in each assemblages, the many assemblages in a section, or the numerous sections in a given country can be summarized by a large amount of tabulated data where the species are columns while the assemblages, assimilated to stratigraphical levels, are the rows. Such a table implies a special method to enable global analysis• The only available method which can be applied both to malacological assemblages or to specific populations is multivariate analysis. If we want to be more precise for such a proposal, we have to refer to Birks and Birks (1980); "'numerical methods have several advantages in Quaternary paleoecoiogy".
PRINCIPLES OF THE MULTIVARIATE ANALYSES
1. Factor Analyses The principle of factor analyses is to study the variability of a set of data in a multiple dimensionsal space, in an idealistic table, with each column or row
influencing the variability with the same weight. According to this state, the data set can be represented by a sphere where all the n species contributions are equal to 1/n. In fact, this is never the case. A natural assemblage groups together species whose occurrence results from the interaction between limiting factors and ecological valency• The limiting factor conditions the chances an organism has to invade an environment, at least when the conditions are less severe (Fig. 3). It affects the general metabolism of the organism allowing it to subsist effectively in the biotope. In extreme (tundra or desert) environments the limiting factors have a dominant rule. They strongly contribute to the definition of the biological style of the environment determining a limiting value to the plant and animal groups of organisms. In tundra, for example, the limiting factors include temperature and the duration of the warm season suitable for active life. A reduction in the temperature or in the duration of summer implies an alteration in inhabitation, indeed if the intensity is too strong the disappearance of species results. The inverse effect leads to an important change, the immigration of allochthonous species to tundra and the occurrence of new biocenoses. The occurrence of species in an assemblage also depends on its ecological valency which reflects its capacity to colonize different
83
Statistical Analyses of Loess Molluscs
I STATISTICAL ANALYSES OF TEKRESTRIAL MOLLUSCS 1 @
Mulhu~m
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FIG. 2. Multivariate analyses of terrestrial molluscs for paleoecologic reconstructions. The multivariate cloud, with the first five bending axis, characterizes the variability of the global data set. First, semiquantitative analysis. If the data set comprises measurements as variables, the correspondence analysis (CA) of this table allows shell morphology variations reconstructions. On the contrary, if the data set is established from species counts as variables, CA associated to cluster analysis allows environmental variation reconstructions. Second, quantitative analysis. The data set comprises counts in fossil (Df) and recent (Dr) assemblages. The CA determines a new table defined by the selected factors Ioadings of fossil (BCf) and recent (BCr) assemblages. A stepwise regression (SR) is calculated between recent assemblages Ioadings and the corresponding recent climatic parameters (Pr) which determine estimates of the recent values (P'r). The determined equations are validated (V) by the study of the residuals (actual-estimated values). The final step concerns the estimation of the fossil climatic parameters (P'f).
environments, i.e. to survive and to proliferate under various ecological conditions. A species with a low valency tolerates a low variability of the environment and is called a stenoece. On the other hand, if the species is able to populate numerous environments, it is a euryece. All these characteristics which constrain the distribution and the development of the species imply that the multivariate cloud of a data set cannot be spheric but indicate privileged axes of bend that factor analysis will determine and explain (Fig. 2). Various methods allow the analysis of such a table such as canonical analysis, Q or R mode, principal components, and correspondences (Davis, 1973; Ben-
zecri et al., 1973). I recommend (Rousseau, 1987b) the latter, which allows the analysis in the same way both by columns and rows without any a priori consideration of the structure of the data, as opposed to, for instance, principal components analysis. As columns and rows are analyzed symmetrically, their clouds have the same axes of bend so they can be superimposed (Fig. 2). The simultaneous plot of the column and row points facilitates the interpretation of the results by underlining the species and the assemblages which are ecologically significant, by determining relative evaluations of temperature, moisture (temperature and moisture gradients), evolution of environments, and climate. Taking these gradients into account assemblages con-
84
D.-D. Rousseau lower tolerance limit
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the stepwise regression between the factor loading and the thermal parameters proposes statistical equations which allows precise estimation of the original parameters. If the factor analysis of the recent samples has been done with fossil assemblages, and if recent and fossil assemblages are analogous, the statistical equations allow reconstruction of the fossil climatic parameters. MULTIVARIATE ANALYSES OF LOESS MOLLUSC ASSEMBLAGES
®
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~.~
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FIG. 3. The tolerance law of organisms in relation to their geographical distribution and their abundance (modifiedfrom Sacchi and Testard, 1971).
sidered in their stratigraphical position provide precise variations.
2. Cluster Analyses Cluster analyses allow the determination of a partition in a data set which is decomposed by grouping together the columns (species) or the rows (assemblages) which indicate high similarity in their composition (assemblages) or in their occurrence in assemblages (species) (Davis, 1973). The exclusive use of this method is not adequate because the partitions are determined by environmental variations. Thus, the best way is to combine factor and cluster analyses (Jambu, 1978; Jambu and Lebeaux, 1978). In the first stage, factor analysis determines the variability of the data set and characterizes the elongation axes of the cloud. Then in the second stage the cluster forms groups of species and of assemblages from the factor results, which are called classes, in order to determine a precise environmental partition of the data set and to characterize reworked assemblages (Fig. 2).
3. Multiple Regressions
1. Semi-Quantitative Analysis of the Achenheim (Alsace, France) Sequence The loess sequence of Achenheim is located 5 km westwards of Strasbourg in the Rhine valley (Heim et al., 1982; Lautridou et al., 1985, 1986). There, samples for malacoiogical studies have been done, and 195 samples contain 61 species. Five climatic cycles sensu Kukla (1977) which provided malacofauna have been recognized. Through the multivariate analysis of the total set of data (columns = species and lines = malacological samples, each one corresponding to one stratigraphical level) the environmental evolution of that site can be reconstructed for the last 500 ka BP (Fig. 4). Although thermic factor (first multivate axis) varies between pleniglacial and interglacial estimates, the associated environment varies between "temperate environments' (open forest, open environment with the occurrence of well represented trees and bushes, slightly damp or damper), 'intermediate environments' (open land without any arboreal vegetation but damp or damper or marshy grasslands, same as previously but very dry grassland open grassland with small amounts of Columella columella and Pupilla alpicola) and 'glacial' environments (open ones with sparse vegetation, and low moisture, qualified as a proto-loess steppe: a Pupilla fauna in embryonic state; same as previously but moister qualified as prototundra: a Columella fauna in embryonic state; environment with a sparse vegetation but higher moisture: damp, marshy, loess-steppe: PupiUa fauna; tundralike: Colurnella fauna) (Rousseau, 1987b). The Ioadings of assemblages on the first axis combined with environmental estimation are expressed in Fig. 4. If now we calibrate the last three cycles of the sequence, which are better preserved, with SPECMAP chronology (Imbrie et al., 1984), it appears that the oscillations of the thermic gradient, parallel to the first axis of the multivariate analysis, are similar to those of 6~80 which express the continental ice volume variations (Fig. 5). Again, loadings on the two first axes plotted against (thermic and moisture gradients) indicate that their variations are not identical in one cycle and that their evolution is not identical from one cycle to another (Rousseau and Puissrgur, 1990) (Fig, 5).
Multiple regression can be a complement to factor analysis for the research of the so-called transfer functions (Fig. 2). The principle is to explain a column (a dependent variable) taking into account other columns (explicative variables) (Scherrer, 1984). But all the columns of a data set do not contribute to the explanation of another, and a simplified solution is often necessary. In fact, the more efficient solution using the lowest number of variables is expected. The stepwise regression is used in this way. It discriminates the columns which provide a statistically significant contribution to the variance of a column which can be predicted (Fig. 2). Consequently this method allows the prediction of the greatest part of the variance of the dependent column from a reduced set of independent columns. If, for example, monthly thermal parameters 2. Quantitative Analysis of the Achenheim Sequence: are obtained from a meterological station close to the Transfer Functions The estimate of February and August temperatures sampling point, after factor analysis of the assemblages
Statistical Analyses of Loess Molluscs
85
loadings of the assemblages on selected axes which explain 90% of the total variability of the set. Then recent climatic parameters are estimated by stepwise regressions which provide different mathematical equa~1 ~ HOLOCENE tions. These equations are validated or rejected by B UPPER analyzing the residuals (true values-estimate values). " PLEISTOCENE Then using these equations, fossil estimates are proposed (Rousseau, 1989, 1991). The first proposal of transfer functions from terrest..z rial molluscs provide results which are in rough agreement with the already published ones, mainly for the last climatic cycle (Fig. 6). Temperature estimates D ,,--, are generally lower than the recent values (1.8°C in Ltl ,.1 February and 18.3°C in August - - Fig. 6). In winter the E ~ values vary between - 1 3 and 2°C while in summer the extreme values are 10 and 17°C (Fig. 6). Concerning precipitation, summer estimates are always deficient 60 + ÷ (between 50 and 78 mm whereas the recent value is 76 m mm) (Fig. 6), in agreement with the results of J. Guiot FIG. 4. Semiquantitative analysis of molluscan assemblages at from pollen analysis of La grande Pile; on the contrary, Achenheim. Temperature variations are determined by plotting loadings of assemblageson the first axis in their stratigraphicposition winter estimates are always higher (between 33 and 76 (t, temperate; c, cold). Environment classes are determined by mm) than the recent value (34 mm) (Fig. 6). cluster analysis (h open forest; 2: semiforest; 3: non-arboreal From a global point of view, transfer functions grassland; 4: dry grassland; 5: cold grassland; 6: scarce vegetated indicate that stage 6 was not so cold that the 6~80 grassland; 7: tundra-likeor loess steppe); (modifiedfrom Rousseau, 1987 a,b). could materialize (Fig. 6). In fact summer temperatures were lower than at present and winter ones vary around -5°C, and precipitation was high during winter. But and precipitations have been attempted using the the important fact is that this glacial stage was preceded analog method. The principle is to establish mathemati- by stage 7 which ends showing higher winter precipitacal equations which relate mollusc assemblages to tion over a cold continent associated with low precipitaclimatic parameters (Fig. 2). In a first step, a large set is tion in summer. In this case the continental ice growth established comprising recent and fossil assemblages as was favoured, implying a greater expansion of Northrows and species as columns. Correspondence analysis, European ice caps during this stage than during stages as previously, provides a new table composed of the 2, 3, 4 (Rousseau, 1991).
Tp
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86
D.-D. Rousseau
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87
Statistical Analyses of Loess Molluscs
MULTIVARIATE ANALYSES OF LOESS SPECIES
high size pole while 'interglacial' ones are plotted close to the small size pole, 'intermediary' individuals being in an intermediate position. If loadings on the two first axes of the mean point of each population and of the malacological assemblages (results of the semi-quantitative analysis of assemblages) are plotted against each other, we can conclude that temperature has a great influence on the shell size, whereas moisture contributes little to the shell variations (Fig. 9). It also appears that shape variations are low and can not be correlated with the determined environmental factors (Fig. 9) (Rousseau, 1985, 1989b). These reverse morphological variations which affect a species in stasis are called ecophenotypic because they only correspond to modifications of the phenotype in relation with environment while genotype is not modified (Rousseau, 1985, 1989b). To verify this assumption I have to mention that in recent time, similar variations are observed in European populations of Pupilla muscorum from West to East rather than from North to South. Once more, if extreme recent populations are compared to fossil ones, central European ones are plotted in the high size pole while French ones are plotted in the small size pole (Rousseau, in press b). These variations in size have been interpreted as corresponding to modifications in the ontogenetic sequence in response to environmental
Among all species which constitute the assemblages, Pupilla muscorum, an euryece taxon, is present almost all through the sequence• In this way, a morphological analysis of shells can provide data on the environmental changes already determined by the assemblages. In each population issued from one assemblage, i.e. stratigraphical level, 30 individuals are aleatory selected using random numbers. Then each shell is measured as expressed in Fig. 7 with a Nikon measurescope. A larger table is established as following: row = one individuals and columns -- the 26 measured morphological parameters (Rousseau, 1985, in press b). This table is analyzed using correspondence methods (Fig. 2). First, individuals are distributed according to size and shape in the multispatial cloud (Fig. 8). To summarize, the first axis characterizes a size gradient from large (up to 5.5 whorls) to small (3 whorls) shells in the same category of size (Fig. 8). Once more, populations are mainly distributed with regards to first axis. In each population shape variations are equal: slender and thickset individuals are roughly in similar proportions (Fig. 8). On the other hand populations have a different distribution on the first axis with regards to the climatic conditions of the environment where they lived: 'glacial' ones are plotted close to the
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FIG. 8. Correspondence analysis of Achenheim populations. Individuals (points) distribution according to the expressed morphological characters determined by the first two axes. White stars represent individuals coming from 'pleniglacial' populations. Their loading is concentrated in the high size pole of the first axis. On the contrary, individuals of a "temperate' population (white triangles) are plotted close to the small size pole. The shape variations expressed on the second axis affect all the populations in roughly similar proportions (from Rousseau, 1985, in press h).
88
D.-D. Rousseau
Malacofaunas
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FIG. 9. Correspondences between mollusc assemblages variations, characterizing temperature and moisture oscillations) and morphological variations of related Pupilla muscorum shells, f: forest (from Rousseau, 1989b).
stress. Such responses are today observed in other European molluscs. CONCLUSION The introduction of statistical analyses into paleoecology has opened up new fields of research which could not be reached in another way, quantification of the results proposing a wide field of scientific cooperation. It is a new method of analysis and approach of the paleontological material, which does not imply the neglect of field work. These methods applied on loess molluscs can also be applied to other kinds of Quaternary deposits (Rousseau, 1986; Rousseau and Puiss6gur, 1989). In fact the use of such approaches depends on the defined objectives of the research. Mollusc remains have the privilege to be often well preserved and in a large quantity so that any kind of paleontological analysis can be designed and accomplished. To conclude, statistical analyses of terrestrial molluscs allow multidisciplinary studies as a fundamental group for Quaternary study. REFERENCES Alexandrowicz, S.W. (1986). Molluscan assemblages from a loess profile at Odonow (Malopolska Upland). Biuletyn Prygla, 31, 715. Alexandrnwicz, S.W. (1987). Analiza malakologiczna w badaniach osadow czwatorzedowych. Geologia, 12, 1-240. Benz~,cri, J.P. et ai. (1973). L'analyse de donn~es. II L'Analyse des Correspondances. Dunod, Paris, 619 pp. Birks, H.J.B. and Birks, H.H. (1980). Quaternary Palaeoecology. Arnold E., London, 289 pp.
Chen Deniu, Lu Yanchou and An Zhisheng (1982). Snail assemblages in loess strata and their environmental implication. In: Proceedings of the 3rd National Quaternary Conference China (in Chinese), pp. 7-15. Science Press. Davis, J.C. (1973). Statistics and Data Analysis in Geology. J. Wiley and Sons, New York, 550 pp. Fuhrmann, R. (1973). Ule sp~itweichselglaziale und holoz~ne Molluskenfauna Mittel- und Westachsens. Freiberger ForschungschefteH, 278, 1-121. Heim, J., Lautridou, J.P., Maucorps, J., Puiss~gur, J.J. Somme, J. and Th~venin, A. (1982). Achenheim: une s6quence type des loess du PI6istocene moyen et sup~riur. Bulletin de rAssociassion fran¢aise pour I'Etade du quaternaire, 10/11, 147-159. Imbrie, J., Hays, J.D., Martinson, D.G., Mclntyre, A., Mix, A.C., Morley, J.J., Pisias, N.G., Prell, W.L. and Shackleton, N.J. (1984). The orbital theory of Pleistocene climate: support from a revised chronology of the Marine b~aO record. In: Berger, A., Imbrie, J., Hays, J., Kukla G. and Saitzman, B. (eds). Milankovitch and Climate, part 1, pp. 269-305. Reidel, Dordrecht. Jambu, M. (1978). Classification automatique pour Uanalyse des donn~es. 1 M~thodes et algorithmes. Dunod, Paris, 310 pp. Jambu, M. and Lebeaux, M.O. (1978). Classification automatique pour l'analyse des donn(~es. 2 Logiciels. Dunod, Paris, 399 pp. Krolopp, E. (1966). A Mecsek hegys~g k6rny~ki 16szk(~pzOdm~nyek biosztratigr~ifiai vizsgfilata (Biostratigraphic investigation of the loes formations in the environment of the Mecsek Mountains). F61d Int. I~vi Jel. 133-145. Krolopp, E. (1983). Biostratigraphic division of Hungarian Pleistocene formations according to their molluscan fauna. Acta geologica Academiae scientiarum hungaricae, 26, 69-82. Kukla, G. (1977). Pleistocene land-sea correlations. 1. Europe. Earth Science Review, 13, 307-374. Lautridou, J.P., Somm(~, J., Heim, J., Maucorps, J., Puiss~guir, J.J., Rosseau, D.D., Th~venin, A. and Van Vliet-Lano6, B. (1986). Corr61ations entre s(~diments quaternaires continentaux et matins (iittoraux et profonds) darts le domaine France septentrionaleManche. In: O. Cronchon (ed.) Correlations entre s~diments quaternaires continentaux et mains. Revue de g~ologie dynamique et geographie physique, 27, 105-112. Lautridou, J.P., Somme, J., Heim, J., Puiss~gur, J.J. and Rousseau, D.D. (1985). La stratigraphie d e l o e s s et formations fluviatiles d'Achenheim (Alsace): Nouveiles donn~es bioclimatiques et correlations avec les s~quences Pl~istoc~nes de la France du Nord-
Statistical Analyses of Loess Molluscs Quest. In: M. Campy (ed.) Dynamical and Chronological Relations Between Glacial and Periglacial Deposits, Bulletin de rAssociassion fran¢aise pour rEmde du quaternaire, 22-23, 125-132. Lozek, V. (1964). Quart/irmollusken der Tschechoslowakei. Rozpravy Ustredniho ustavu Geologikale, 31, 1-374. Meijer, T. (1985). The pre-Weichselian non-marine molluscan fauna from Maastricht-Belv6d/:re (southern Limburg, The Netherlands). Mededelingen van's Rijks geologischen dienst, 39, 75-103. Puiss6gur, J.J. (1976). Mollusques continentaux quaternaires de Bourgogne. Significations stratigraphiques et climatiques. Rapports avec d'autres faunes bor6ales de France. Mdmoires g~ologique Universit# de Dijon, 3, 241 pp. Rousseau, D.D. (1985). Structure des populations quaternaires de PupiUa muscorum (Gastropode) en Europe du Nord. Relations avec leurs envirinnements. Thdse Doctorat 3dine cycle Institut des Sciences de la Terr Dijon, 133 pp. (unpublished). Rousseau, D.D. (1986). Application de la m6thode d'analyse factorielle des correspondances aux malacofaunes de Tourville (Saalien). Bulletin Centre G~omorph Caen, 31, 5-20. Rousseau, D.D. (1987). New approach to the Pleistocene land snails. In: H.M. French and M. P~csi (eds) Loess and Periglacial Phenomena, pp. 151-163. Hungaricae Academiae Scientarum, Budapest. Rousseau, D.D. (1987). Paleoclimatology of the Achenheim series (Middle and Upper Pleistocene, Alsace, France). A malacological analysis. Palaegeography, Paleoclimatology and Paleocology, 59, 293-314.
89
Rousseau, D.D. (1989a). Estimation quantitative des pal~otemp~ratures hivernales en Alsace depuis 340,000 ans a partir ranalyse des associations malacologiques. Compw rendu de l'Acad#mie des sciences, 309(S6rie II), 1623-1628. Rousseau, D . D (1989b). R6ponses des malacofaunes terrestres Quaternaires aux contraintes climatiques en Europe septentrionale, Palaegeography, Paleoclimatology and Paleoecology 69, 113-124. Rousseau, D.D. Climatic transfer function from Quaternary molluscs in European loess deposits. Quaternary Research, 35 (in press). Rousseau, D.D. Ontogeny-morphology: the weight of constraints in Pupilla muscorum. Palaeobiology (submiued-c). Rousseau, D.D. and Puiss6gur, J.J. (1989), Analyse de la malacofaune continentale. In: A. Tuffreau and J. Somm~ (eds) Le gisement pal~olithique de Blache-St-Vaast (Pas-de-Calais), N6moire Special de la Soci6t6 pr6historique franqaise. 21.89-102. Rousseau, D.D. and Puiss6gur, J.J. A 350,000 years climatic record from the loess sequence of Achenheim (Alsace, France). Boreas, 19, 203-216. Sacchi, C.F. and Testard, F. ( 1971). l~cologie Animale -- Organismes et Milieu. Doin (ed.), Paris, 480 pp. Scherrer, B. (1984). Biostatistique. Gaetan Morin, Chicoutimi CDN, 850 pp. Sparks, B.W. (1961). The ecological interpretation of Quaternary non-marine mollusca. Proceedings of the Linnean Society, London, 172, 71-80.