Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-14T22:24:44.739Z Has data issue: false hasContentIssue false
  • English
  • Français

A statistical approach to evaluating distance metrics and analog assignments for pollen records

Published online by Cambridge University Press:  20 January 2017

Daniel G Gavin,
W.Wyatt Oswald,
Eugene R Wahl and
John W Williams
Daniel G Gavin*
Affiliation:
Department of Plant Biology, University of Illinois, Urbana, IL 61801, USA
W.Wyatt Oswald
Affiliation:
College of Forest Resources, University of Washington, Seattle, WA 98195, USA
Eugene R Wahl
Affiliation:
Environmental and Societal Impacts Group, National Center for Atmospheric Research, Boulder, CO 80301, USA
John W Williams
Affiliation:
National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA 93101, USA
*
*Corresponding author.E-mail address:dgavin@life.uiuc.edu (D.G. Gavin).
Get accessRights & Permissions [Opens in a new window]

Abstract

The modern analog technique typically uses a distance metric to determine the dissimilarity between fossil and modern biological assemblages. Despite this quantitative approach, interpretation of distance metrics is usually qualitative and rules for selection of analogs tend to be ad hoc. We present a statistical tool, the receiver operating characteristic (ROC) curve, which provides a framework for identifying analogs from distance metrics. If modern assemblages are placed into groups (e.g., biomes), this method can (1) evaluate the ability of different distance metrics to distinguish among groups, (2) objectively identify thresholds of the distance metric for determining analogs, and (3) compute a likelihood ratio and a Bayesian probability that a modern group is an analog for an unknown (fossil) assemblage. Applied to a set of 1689 modern pollen assemblages from eastern North America classified into eight biomes, ROC analysis confirmed that the squared-chord distance (SCD) outperforms most other distance metrics. The optimal threshold increased when more dissimilar biomes were compared. The probability of an analog vs no-analog result (a likelihood ratio) increased sharply when SCD decreased below the optimal threshold, indicating a nonlinear relationship between SCD and the probability of analog. Probabilities of analog computed for a postglacial pollen record at Tannersville Bog (Pennsylvania, USA) identified transitions between biomes and periods of no analog.

Keywords

Bayesian statisticseastern North Americamodern analog techniquepollen analysisROC analysissquared-chord distancetest accuracyvegetation type

Information

Type
Research Article
Information
Quaternary Research , Volume 60 , Issue 3 , November 2003, pp. 356 - 367
Copyright
University of Washington

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Anderson, P.M., Bartlein, P.M., Brubaker, L.B., Gajewski, K., and Ritchie, J.C., (1989). Modern analogs of late-Quaternary pollen spectra from the western interior of North America. Journal of Biogeography 16, 573–596.Google Scholar
Birks, H.J.B., and Gordon, A.D., (1985). Numerical Methods in Quaternary Pollen Analysis. Academic Press, London.Google Scholar
Calcote, R., (1998). Identifying forest stand types using pollen from forest hollows. The Holocene 8, 423–432.Google Scholar
Davis, O.K., (1995). Climate and vegetation patterns in surface samples from arid western USA. application to Holocene climatic reconstructions. Palynology 19, 95–117.Google Scholar
Davis, M., Douglas, C., Calcote, R., Cole, K.L., Winkler, M.G., and Flakne, R., (2000). Holocene climate in the western Great Lakes national parks and lakeshores. implications for future climate change. Conservation Biology 14, 968–983.Google Scholar
Guiot, J., (1990). Methodology of the last climatic cycle reconstruction in France from pollen data. Palaeogeography Palaeoclimatology Palaeoecology 80, 49–69.Google Scholar
Guiot, J., Harrison, S.P., and Prentice, I.C., (1993). Reconstruction of Holocene precipitation patterns in Europe using pollen and lake-level data. Quaternary Research 40, 139–149.Google Scholar
Hanley, J.A., and McNeil, B.J., (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36.Google Scholar
Henderson, A.R., (1993). Assessing test accuracy and its clinical consequences. a primer for receiver operating characteristic curve analysis. Annals of Clinical Biochemistry 30, 521–539.Google Scholar
Hilborn, R., and Mangel, M., (1997). The Ecological Detective. Princeton University Press, Princeton, NJ.Google Scholar
Liu, K., and Lam, N.S., (1985). Paleovegetational reconstruction based on modern and fossil pollen data. an application of discriminant analysis. Annals of the Association of American Geographers 75, 115–130.Google Scholar
Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, J., Yang, L., and Merchant, J.W., (2000). Development of a global land cover characteristics database and IGBP DISCover from 1-km AVHRR data. International Journal of Remote Sensing 21, 1303–1330.CrossRefGoogle Scholar
Metz, C.E., (1978). Basic principles of ROC analysis. Seminars in Nuclear Medicine 8, 283–298.CrossRefGoogle ScholarPubMed
Metz, C.E., (1986). Statistical analysis of ROC data in evaluating diagnostic performance. Herbert, D.E., and Myers, R.H. Multiple Regression Analysis. Application in the Health Sciences. American Institute of Physics, Washington, DC. 365–384.Google Scholar
Metz, C.E. (1998). ROCKIT User’s Guide. University of Chicago, . Retrieved from http://www-radiology.uchicago.edu/krl/toppage11.htm, January 2003 Google Scholar
Mossman, D. (1995). Resampling techniques in the analysis of non-binormal ROC data. Medical Decision Making 15, 358–366.Google Scholar
Oswald, W.W., Brubaker, L.B., Hu, F.S., and Gavin, D.G. Pollen-vegetation calibration for tundra communities in the Arctic Foothills, northern Alaska. Journal of Ecology, in press Google Scholar
Overpeck, J.T., Webb, T. III, and Prentice, I.C., (1985). Quantitative interpretation of fossil pollen spectra. dissimilarity coefficients and the method of modern analogs. Quaternary Research 23, 87–108.CrossRefGoogle Scholar
Pflaumann, U., Duprat, J., Pujol, C., and Labeyrie, L.D., (1996). SIMMAX. A modern analog technique to deduce Atlantic sea surface temperatures from planktonic foraminifera in deep-sea sediments. Paleoceanography 11, 15–35.Google Scholar
Prell, W.L., (1985). The Stability of Low-Latitude Sea-Surface Temperatures. An Evaluation of the CLIMAP Reconstruction with Emphasis on the Positive SST Anomalies, Rep. TR 025. U.S. Department of Energy, Washington, D.C.Google Scholar
Prentice, I.C., (1980). Multidimensional scaling as a research tool in Quaternary palynology. a review of theory and methods. Review of Palaeobotany and Palynology 31, 71–104.CrossRefGoogle Scholar
Prentice, I.C., Guiot, J., Huntley, B., Jolly, D., and Cheddadi, R., (1996). Reconstructing biomes from palaeoecological data. a general method and its application to European pollen data at 0 and 6 ka. Climate Dynamics 12, 185–194.Google Scholar
Robertson, I., Lucy, D., Baxter, L., Pollard, A.M., Aykroyd, R.G., Barker, A.C., Carter, A.H.C., Switsur, V.R., and Waterhouse, J.S., (1999). A kernal-based Bayesian approach to climatic reconstruction. The Holocene 9, 495–500.Google Scholar
Sawada, M., Viau, A., and Gajewski, K., (2001). Critical thresholds of dissimilarity in the modern analog technique (MAT) for quantitative paleoclimate reconstruction. Chylek, P., and Lesins, G. 1st Annual Conference on Global Warming and the Next Ice Age. Dalhousie University, Halifax, Nova Scotia, Canada. 149–152. (Compilers) Google Scholar
Schweitzer, P.N., (1999). ANALOG. A Program for Estimating Paleoclimate Parameters Using the Method of Modern Analogs U.S. Geological Survey Open-File Report 94–645. United States Geological Survey, Reston, VA.Google Scholar
Stuiver, M., Reimer, P.J., Bard, E., Beck, J.W., Burr, G.S., Hughen, K.A., Kromer, B., McCormac, G., Van der Plicht, J., and Spurk, M., (1998). INTCAL98 radiocarbon age calibration, 24,000-0 cal BP. Radiocarbon 40, 1041–1083.Google Scholar
Swets, J.A., (1988). Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.Google Scholar
Toivonen, H.T.T., Mannila, H., Korhola, A., and Olander, H., (2001). Applying Bayesian statistics to organism-based environmental reconstruction. Ecological Applications 11, 618–630.Google Scholar
Waelbroeck, C., Labeyrie, L., Duplessy, J.-C., Guiot, J., Labracherie, M., Leclaire, H., and Duprat, J., (1998). Improving past sea surface temperature estimates based on planktonic fossil faunas. Paleoceanography 13, 272–283.Google Scholar
Wahl, E.R. A general framework for determining cutoff values to select pollen analogs with dissimilarity metrics in the modern analog technique. Review of Palaeobotany and Palynology, in press Google Scholar
Watts, W.A., (1979). Late Quaternary vegetation of central Appalachia and the New Jersey coastal plain. Ecological Monographs 49, 427–469.Google Scholar
Williams, J.W., Shuman, B.N., Webb, T. III Dissimilarity analyses of late-Quaternary vegetation and climate in eastern North America. Ecology 82, (2001). 3346–3362.Google Scholar
Williams, J.W., and Jackson, S.T. (2003). Palynological and AVHRR observations of modern vegetational gradients in eastern North America. The Holocene 13, 485–497.Google Scholar
Youden, W., (1950). Index rating for diagnostic tests. Cancer 3, 32–35.Google Scholar
Zweig, M.H., and Campbell, G., (1993). Receiver-operating characteristic (ROC) plots. a fundamental evaluation tool in clinical medicine. Clinical Chemistry 39, 561–577.CrossRefGoogle ScholarPubMed