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Analysis of pollen across the surface sediments of Lake Imbradas, Lithuania

Published online by Cambridge University Press:  30 September 2021

Lauras Balakauskas ,
Justina Gaižutytė ,
Vaidotas Valskys  and
Giedrė Vaikutienė
Lauras Balakauskas*
Affiliation:
Institute of Geosciences, Faculty of Chemistry and Geosciences, Vilnius University, M.K. Čiurlionio g. 21/27, LT-03101 Vilnius, Lithuania
Justina Gaižutytė
Affiliation:
Institute of Geosciences, Faculty of Chemistry and Geosciences, Vilnius University, M.K. Čiurlionio g. 21/27, LT-03101 Vilnius, Lithuania
Vaidotas Valskys
Affiliation:
Institute of Biosciences, Life Sciences Center, Vilnius University, Saulėtekio al. 7, LT-10257 Vilnius, Lithuania Nature Research Centre, Akademijos g. 2, LT-08412 Vilnius, Lithuania
Giedrė Vaikutienė
Affiliation:
Institute of Geosciences, Faculty of Chemistry and Geosciences, Vilnius University, M.K. Čiurlionio g. 21/27, LT-03101 Vilnius, Lithuania
*
*Corresponding author email address:lauras.balakauskas@gf.vu.lt
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Abstract

In conventional pollen analysis, usually one sediment core per basin is analyzed to reconstruct past environmental conditions. This approach does not consider spatial heterogeneity of pollen assemblages, and assumes that one analyzed location is representative of the whole basin. To improve the spatial resolution of fossil pollen studies, further knowledge of the factors influencing variations in pollen assemblages throughout a basin is needed. We examined the spatial heterogeneity of pollen assemblages from 45 lacustrine surface samples from a lake with relatively simple hydrology and compared this dense network of surface pollen samples with the Lithuanian State Forest Service arboreal vegetation database. Calculations of pollen productivity at different locations across the lake revealed variations in the behavior of a pollen-vegetation relationship model in different parts of the basin. Our findings suggest that the model underestimated pollen contributions from the lakeshore vegetation. We demonstrate that detailed investigations of surface pollen as a step prior to fossil pollen investigations can provide useful insights, including understanding the influence of sedimentation rate on modelling results and spatial variations in pollen composition, thus providing guidance for site selection for fossil pollen studies.

Keywords

MicropaleontologyPollen analysisPollen productivityPollen-vegetation relationshipSpatial heterogeneityPaleolimnologyEuropePresent
Type
Research Article
Information
Quaternary Research , Volume 106 , March 2022 , pp. 113 - 128
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Copyright
Copyright © University of Washington. Published by Cambridge University Press, 2021

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References

REFERENCES

Baker, A.G., Zimny, M., Keczyński, A., Bhagwat, S.A., Willis, K.J., Latałowa, M., 2015. Pollen productivity estimates from old-growth forest strongly differ from those obtained in cultural landscapes: Evidence from the Białowieża National Park, Poland. Holocene 26, 80–92.CrossRefGoogle Scholar
Balakauskas, L., 2012. Development of the Late Glacial and Holocene forest vegetation in Lithuania, according to LRA (Landscape Reconstruction Algorithm) modelling data. PhD dissertation, Vilnius University, Vilnius, Lithuania. [in Lithuanian]Google Scholar
Balakauskas, L., Vaikutienė, G., Paškevičiūtė, M., Valskys, V, Spiridonov, A., 2021. Effects of spatial heterogeneity on the estimation of diatom assemblage composition: an example of Lake Imbradas (NE Lithuania). Baltica 34, 123–134. https://doi.org/10.5200/baltica.2021.1.10.CrossRefGoogle Scholar
Beaudoin, A.B., Reasoner, M.A., 1992. Evaluation of differential pollen deposition and pollen focussing from three Holocene intervals in sediments from Lake O'Hara, Yoho National Park, British Columbia, Canada: intra-lake variability in pollen percentages, concentrations and influx. Review of Palaeobotany and Palynology 75, 103–131. https://doi.org/10.1016/0034-6667(92)90152-7.CrossRefGoogle Scholar
Berglund, B.E., Ralska-Jasiewiczowa, M., 1986. Pollen analysis and pollen diagrams. In: Berglund, B.E. (Ed.), Handbook of Holocene Palaeoecology and Palaeohydrology. Wiley, Chichester, pp. 455–484.Google Scholar
Broström, A., Nielsen, A.B., Gaillard, M.-J., Hjelle, K., Mazier, F., Binney, H., Bunting, J., et al. , 2008. Pollen productivity estimates of key European plant taxa for quantitative reconstruction of past vegetation: a review. Vegetation History and Archaeobotany 17, 461–478. https://doi.org/10.1007/s00334-008-0148-8.CrossRefGoogle Scholar
Bunting, M.J., Farrell, M., Broström, A., Hjelle, K., Mazier, F., Middleton, R., Nielsen, A.-B., Rushton, E., Shaw, H., Twiddle, L.T., 2013. Palynological perspectives on vegetation survey: A critical step for model-based reconstruction of Quaternary land cover. Quaternary Science Reviews 82, 41–55. https://doi.org/10.1016/j.quascirev.2013.10.006.CrossRefGoogle Scholar
da Luz, C.F.P., Barth, O.M., Silva, C.G., 2005. Spatial distribution of palynomorphs in the surface sediments of the Lagoa do Campelo lake, North region of Rio de Janeiro State, Brazil. Acta Botanica Brasillica 19, 741–752. https://doi.org/10.1590/S0102-33062005000400009.CrossRefGoogle Scholar
Davis, M.B., Brubaker, L., Beiswenger, J., 1971. Pollen grains in lake sediments: pollen percentages in surface sediments from southern Michigan. Quaternary Research 1, 450–467. https://doi.org/10.1016/0033-5894(71)90058-5.CrossRefGoogle Scholar
Davis, M.B., Ford, M.S., 1982. Sediment focusing in Mirror Lake, New Hampshire. Limnology and Oceanography 27, 137–150. https://doi.org/10.4319/lo.1982.27.1.0137.CrossRefGoogle Scholar
Dodson, J.R., 1977. Pollen deposition in a small closed drainage basin lake. Review of Palaeobotany and Palynology 24, 179–193. https://doi.org/10.1016/0034-6667(77)90046-X.CrossRefGoogle Scholar
Eisenhut, G., 1961. Untersuchungen über die Morphologie und Ökologie der Pollenkörner heimischer und fremdländischer Waldbäume. Paul Parley, Hamburg. 68 pp.Google Scholar
Esri, 2020. ArcGIS Pro: Release 2.6. Redlands, CA.Google Scholar
Faegri, K., Iversen, J., 1964. Textbook of Pollen Analysis, 2nd ed. Munksgaard, Copenhagen.Google Scholar
Fagerlind, F., 1952. The real significance of pollen diagrams. Botaniska Notiser 105, 185–224.Google Scholar
Frazer, H., Prieto, A.R., Carbonella, J.C., 2020. Modern pollen source and spatial distribution from surface lake sediments in the southwestern Pampa grasslands, Argentina: implications to interpret Holocene pollen records. Review of Palaeobotany and Palynology 277, 104207. https://doi.org/10.1016/j.revpalbo.2020.104207.CrossRefGoogle Scholar
Gaillard, M.-J., Sugita, S., Bunting, M.J., Middleton, R., Broström, A., Caseldine, C., Giesecke, T., et al. , 2008. The use of modelling and simulation approach in reconstructing past landscapes from fossil pollen data: A review and results from the POLLANDCAL network. Vegetation History and Archaeobotany 17, 419–443. https://doi.org/10.1007/s00334-008-0169-3.CrossRefGoogle Scholar
Gaillard, M.-J., Sugita, S., Mazier, F., Trondman, A.-K., Broström, A., Hickler, T., Kaplan, J.O., et al. , 2010. Holocene land-cover reconstructions for studies on land cover-climate feedbacks. Climate of the Past 6, 483–499. https://doi.org/10.5194/cp-6-483-2010.CrossRefGoogle Scholar
Godwin, H., 1934. Pollen analysis: an outline of the problems and potentialities of the method. Part I: Technique and interpretation. New Phytologist 33, 278–305. https://doi.org/10.1111/j.1469-8137.1934.tb06815.x.CrossRefGoogle Scholar
Grimm, E.C., 2011. TILIA software version 1.5.12. Illinois State Museum Research and Collection Center, Springfield, USA.Google Scholar
Hellman, S., Gaillard, M.-J., Broström, A., Sugita, S., 2008. The REVEALS model, a new tool to estimate past regional plant abundance from pollen data in large lakes: validation in southern Sweden. Journal of Quaternary Science 23, 21–42. https://doi.org/10.1002/jqs.1126.CrossRefGoogle Scholar
Hicks, S., 2001. The use of annual arboreal pollen deposition values for delimiting tree-lines in the landscape and exploring models of pollen dispersal. Review of Palaeobotany and Palynology 117, 1–29. https://doi.org/10.1016/S0034-6667(01)00074-4.CrossRefGoogle Scholar
Huang, X., Zhou, G., Ma, Y., Xu, Q., Chen, P., 2010. Pollen distribution in large freshwater lake of arid region: a case study on the surface sediments from Bosten Lake, Xinjiang, China. Frontiers of Earth Science in China 4, 174–180. https://doi.org/10.1007/s11707-009-0060-2.CrossRefGoogle Scholar
Jacobson, G.L. Jr., Bradshaw, R.H.W., 1981. The selection of sites for paleovegetational studies. Quaternary Research 16, 80–96. https://doi.org/10.1016/0033-5894(81)90129-0.CrossRefGoogle Scholar
Kilkus, K., 2000. Dimiktinių Ežerų Terminės Struktūros [Thermal Structures of Dimictic Lakes]. Vilnius University Press, Vilnius, Lithuania. 200 pp. [in Lithuanian]Google Scholar
Larsen, C.P.S, MacDonald, G.M., 1993. Lake morphometry, sediment mixing and the selection of sites for fine resolution palaeoecological studies. Quaternary Science Reviews 12, 781–792. https://doi.org/10.1016/0277-3791(93)90017-G.CrossRefGoogle Scholar
Lehman, J., 1975. Reconstructing the rate of accumulation of lake sediments: the effect of sediment focusing. Quaternary Research 5, 541–550. https://doi.org/10.1016/0033-5894(75)90015-0.CrossRefGoogle Scholar
Marquer, L., Gaillard, M.-J., Sugita, S., Trondman, A.-K., Mazier, F., Nielsen, A.-B., Fyfe, R., et al. , 2014. Holocene changes in vegetation composition in northern Europe: why quantitative pollen-based vegetation reconstructions matter. Quaternary Science Reviews 90, 199–216. https://doi.org/10.1016/j.quascirev.2014.02.013.CrossRefGoogle Scholar
Mazier, F., Gaillard, M.J., Kuneš, P., Sugita, S., Trondman, A.-K., Broström, A., 2012. Testing the effect of site selection and parameter setting on REVEALS-model estimates of plant abundance using the Czech Quaternary Palynological Database. Review of Palaeobotany and Palynology 187, 38–49. https://doi.org/10.1016/j.revpalbo.2012.07.017.CrossRefGoogle Scholar
Moore, P.D., Webb, J.A., Collinson, M.E., 1991. Pollen Analysis. 2nd Edition. Blackwell Scientific Publications, Oxford.Google Scholar
Overpeck, J.T., Webb, T., Prentice, I.C., 1985. Quantitative interpretation of fossil pollen spectra: dissimilarity coefficients and the method of modern analogs. Quaternary Research 23, 87–108. https://doi.org/10.1016/0033-5894(85)90074-2.CrossRefGoogle Scholar
Parsons, R.W., Prentice, I.C., 1981. Statistical approaches to R-values and pollen—vegetation relationship. Review of Palaeobotany and Palynology 32, 127–152. https://doi.org/10.1016/0034-6667(81)90001-4.CrossRefGoogle Scholar
Prentice, I.C., 1988. Records of vegetation in time and space: the principles of pollen analysis. In: Huntley, B., Webb, T. (Eds.), Vegetation History. Kluwer Academic Publishers, Dordrecht, pp. 17–42. https://doi.org/10.1007/978-94-009-3081-0_2.CrossRefGoogle Scholar
Prentice, I.C., Parsons, R.W., 1983. Maximum likelihood linear calibration of pollen spectra in terms of forest composition. Biometrics 39, 1051–1057. https://doi.org/10.2307/2531338.CrossRefGoogle Scholar
R Core Team, 2020. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org.Google Scholar
Seddon, A.W.R., Mackay, A.W., Baker, A.G., Birks, H.J.B., Breman, E., Buck, C.E., Ellis, E.C., et al. , 2014. Looking forward through the past: identification of 50 priority research questions in palaeoecology. Journal of Ecology 102, 256–267. https://doi.org/10.1111/1365-2745.12195.CrossRefGoogle Scholar
Šeirienė, V., Kabailienė, M., Kasperovičienė, J., Mažeika, J., Petrošius, R., Paškauskas, R., 2009. Reconstruction of postglacial paleoenvironmental changes in eastern Lithuania: Evidence from lacustrine sediment data. Quaternary International 207, 58–68. https://doi.org/10.1016/j.quaint.2008.12.005.CrossRefGoogle Scholar
Sly, P.G., 1978. Sedimentary processes in lakes. In: Lerman, A. (Ed.), Lakes. Springer, New York, pp. 65–80. https://doi.org/10.1007%2F978-1-4757-1152-3_3.CrossRefGoogle Scholar
Stockmarr, J., 1971. Tablets with spores used in absolute pollen analysis. Pollen et Spores 13, 615–621.Google Scholar
Sugita, S., 1993. A model of pollen source area for an entire lake surface. Quaternary Research 39, 239–244. https://doi.org/10.1006/qres.1993.1027.CrossRefGoogle Scholar
Sugita, S., 1994. Pollen representation of vegetation in Quaternary sediments: theory and method in patchy vegetation. Journal of Ecology 82, 881–897. https://doi.org/10.2307/2261452.CrossRefGoogle Scholar
Sugita, S., 2007. Theory of quantitative reconstruction of vegetation I: pollen from large sites REVEALS regional vegetation composition. The Holocene 17, 229–241. https://doi.org/10.1177/0959683607075837.CrossRefGoogle Scholar
Sugita, S., Parshall, T., Calcote, R., Walker, K., 2010. Testing the Landscape Reconstruction Algorithm for spatially explicit reconstruction of vegetation in northern Michigan and Wisconsin. Quaternary Research 74, 289–300. https://doi.org/10.1016/j.yqres.2010.07.008.CrossRefGoogle Scholar
Tauber, H., 1965. Differential pollen dispersion and the interpretation of pollen diagrams. Danmarks Geologiske Undersøegelse II 89, 1–69.CrossRefGoogle Scholar
Theuerkauf, M., Kuparinen, A., Joosten, H., 2012. Pollen productivity estimates strongly depend on assumed pollen dispersal. The Holocene 23, 14–24. https://doi.org/10.1177/0959683612450194.CrossRefGoogle Scholar
Trondman, A.-K., Gaillard, M.-J., Mazier, F., Sugita, S., Fyfe, R., Nielsen, A.-B., Twiddle, C., et al. , 2015. First pollen-based quantitative reconstructions of Holocene regional vegetation cover (plant functional types and land-cover types) in Europe suitable for climate modelling. Global Change Biology 21, 676–697. https://doi.org/10.1111/gcb.12737.CrossRefGoogle Scholar
van Rossum, G., 1995. Python Tutorial. Python Software Foundation Technical Report CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, May 1995. https://www.python.org.Google Scholar
von Stedingk, H., Fyfe, R., 2009. The use of pollen analysis to reveal Holocene treeline dynamics: a modelling approach. The Holocene 19, 273–283. https://doi.org/10.1177/0959683608100572.CrossRefGoogle Scholar
Vu, V.Q., 2011. ggbiplot: A ggplot2 based biplot. R package version 0.55. http://github.com/vqv/ggbiplot.Google Scholar
Wieczorek, M., Herzschuh, U., 2020. Compilation of relative pollen productivity (RPP) estimates and a taxonomically harmonised RPP datasets for single continents from Northern Hemisphere extratropics. Earth System Science Data 12, 3515–3528. https://doi.org/10.5194/essd-12-3515-2020.CrossRefGoogle Scholar
Wilmshurst, J.M., McGlone, M.S., 2005. Origin of pollen and spores in surface lake sediments: Comparison of modern palynomorph assemblages in moss cushions, surface soils and surface lake sediments. Review of Palaeobotany and Palynology 136, 1–15. https://doi.org/10.1016/j.revpalbo.2005.03.007.CrossRefGoogle Scholar
Xu, Q., Tian, F., Bunting, M.J., Li, Y., Ding, W., Cao, X., He, Z., 2017. Pollen source areas of lakes with inflowing rivers: modern pollen influx data from Lake Baiyangdian, China. Quaternary Science Reviews 37, 81–91.CrossRefGoogle Scholar
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