RESEARCH PAPER
Kohonen Artificial Neural Networks and the IndVal Index as Supplementary Tools for the Quantitative Analysis of Palaeoecological Data
1
Department of Invertebrate Zoology and Hydrobiology, Faculty of Biology and Environmental Protection, University of Łódź, 12/16 Banacha Str., 90-237 Łódź, Poland
2
Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Łódź, 12/16 Banacha Str., 90-237 Łódź, Poland
3
GADAM Centre of Excellence, Institute of Physics – CSE, Silesian University of Technology, Konarskiego 22B, 44-100 Gliwice, Poland
4
Department of Biology, University of Bergen, PO Box 7803, N-5020 Bergen, Norway; and Environmental Change Research Centre, University College London, London, WC1E 6BT, UK
Submission date: 2014-08-06
Acceptance date: 2015-10-26
Online publication date: 2015-12-03
Geochronometria 2015;42(1):189-201
KEYWORDS
ABSTRACT
We applied two widely-used methods for data partitioning - constrained incremental sum-of-squares (CONISS) and Optimal Partitioning (OP) along with two supplementary methods, a Kohonen artificial neural network (self-organising map, SOM) and the indicator value (IndVal) index, for the quantitative analysis of subfossil chironomid assemblages from a palaeolake in Central Poland. The samples, taken from 79 core depths, were divided into 5-11 groups (five by SOM, seven by CONISS, 11 by OP), for which different numbers of indicator taxa were determined with the use of the IndVal index (18 for CONISS, 15 for SOM, 11 for OP). Only six indicator taxa were common to all three methods. The number of highly specific (p < 0.001) taxa was highest for SOM. Only the SOM analysis clearly reflected the rate of the changes in chironomid assemblages, which occurred rapidly in the Late Glacial (as a result of greater climate variability) and slowly in the Holocene (as a reflection of slow long-term changes in the local habitat, such as paludification). In summary, we recommend using SOM and the IndVal index in combination with CONISS and/or OP in order to detect different aspects of temporal variability in complex multivariate palaeoecological data.
REFERENCES (62)
1.
Abreu CE and de Ribet B, 2002. Trace shape and multi-attribute seismic facies analysis applied to Paleocene/Eocene reservoirs on deep-water Campos Basin. Revista Brasileira de Geofisica 20: 89-95.
2.
Agterberg FP and Gradstein FM, 1988. Recent developments in quantitative stratigraphy. Earth-Science Reviews 25: 1-73, DOI 10.1016/0012-8252(88)90099-2.
3.
Bedoya D, Novotny V and Manolakos ES, 2009. Instream and off-stream environmental conditions and stream biotic integrity: importance of scale and site similarities for learning and prediction. Ecological Modelling 220: 2393-2406, DOI 10.1016/j.ecolmodel.2009.06.017.
4.
Bennett KD, 1996. Determination of the number of zones in a biostrati-graphical sequence. New Phytologist 132: 155-170, DOI 10.1111/j.1469-8137.1996.tb04521.x.
5.
Birks HJB, 1986. Numerical zonation, comparison and correlation of Quaternary pollen-stratigraphical data. In: Berglund BE, ed., Handbook of Holocene Palaeoecology and Palaeohydrology. Chichester, New York, J. Wiley & Sons Ltd.: 743-774.
6.
Birks HJB, 2012. Analysis of stratigraphical data. In: Birks HJB, Lotter AF, Juggins S and Smol JP, eds., Tracking Environmental Change Using Lake Sediments. Volume 5: Data Handling and Numerical Techniques. Dordrecht, Springer: 355-378.
7.
Birks HJB and Gordon AD, 1985. Numerical Methods in Quaternary Pollen Analysis. London, Academic Press: 317 pp.
8.
Birks HJB, Lotter AF, Juggins S and Smol JP (eds), 2012. Tracking Environmental Change Using Lake Sediments. Data Handling and Numerical Techniques. Dordrecht, Springer: 745 pp.
9.
Bronk Ramsey C, 2009. Bayesian analysis of radiocarbon dates. Radio-carbon 51(1): 337-360.
10.
Brooks SJ, Langdon PG and Heiri O, 2007. The Identification and Use of Palaearctic Chironmidae Larvae in Palaeoecology. QRA Technical Guide No. 10. London, Quaternary Research Association: 276 pp.
11.
Brosse S, Giraudel JL and Lek S, 2001. Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages. Ecological Modelling 146: 159-166, DOI 10.1016/S0304-3800(01)00303-9.
12.
Chang H-C, Kopaska-Merkel DC and Chen H-C, 2002. Identification of lithofacies using Kohonen self-organizing maps. Computers and Geosciences 28: 223-229, DOI 10.1016/S0098-3004(01)00067-X.
13.
Cheng L, Lek S, Lek-Ang S and Li Z, 2012. Predicting fish assemblages and diversity in shallow lakes in the Yangtze River basin. Limnologica 42: 127-136, DOI 10.1016/j.limno.2011.09.007.
14.
Chon TS, 2011. Self-Organizing Maps applied to ecological sciences. Ecological Informatics 6: 50-61, DOI 10.1016/j.ecoinf.2010.11.002.
15.
Conti L, Grenouillet G, Lek S and Scardi M, 2012. Long-term changes and recurrent patterns in fisheries landings from Large Marine Ecosystems (1950-2004). Fisheries Research 119-120: 1-12, DOI 10.1016/j.fishres.2011.12.002.
16.
Dufrêne M and Legendre P, 1997. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecological Monographs 67: 345-366, DOI 10.1890/0012-9615(1997)067[0345:SAAIST]2.0.CO;2.
17.
Dzieduszyńska DA, Kittel P, Petera-Zganiacz J, Brooks SJ, Korzeń K, Krąpiec M, Pawłowski D, Płaza DK, Płóciennik M, Stachowicz-Rybka R and Twardy J, 2014. Environmental influence on forest development and decline in the Warta River valley (Central Poland) during the Late Weichselian. Quaternary International 324: 99-114, DOI 10.1016/j.quaint.2013.07.017.
18.
Forysiak J, 2012. Record of changes in the natural environment of the Late Weichselian and Holocene preserved in the sediments of peatlands of the Łódź region. Acta Geographica Lodziensia 99: 1-164 (in Polish).
19.
Giłka W, 2011. Analysis of faunistic diversity in chironomids of the tribe Tanytarsini in Europe (Diptera: Chironomidae). Dipteron 27: 11-31 (in Polish).
20.
Głowacki Ł, Grzybkowska M, Dukowska M and Penczak T, 2011. Effects of damming a large lowland river on chironomids and fish assessed with the (multiplicative partitioning of) true/Hill biodiversity measure. River Research and Applications 27: 612-629, DOI 10.1002/rra.1380.
21.
Heiri O, Brooks SJ, Birks HJB and Lotter AF, 2011. A 274-lake calibra-tion data-set and inference model for chironomid-based summer air temperature reconstruction in Europe. Quaternary Science Reviews 30: 3445-3456, DOI 10.1016/j.quascirev.2011.09.006.
22.
Juggins S, 2007. C2 Version 1.5 User Guide. Software for Ecological and Palaeoecological Data Analysis and Visualisation. Newcastle University: 73 pp.
23.
Klink AG and Moller Pillot HKM, 2003. Chironomidae Larvae. Key to Higher Taxa and Species of the Lowlands of Northwestern Europe. Amsterdam, ETI. CDROM.
24.
Kloss M and Kucharski L, 2011. History of vegetation of the "Rąbień Bog" reserve based on interdisciplinary research. In: Zieliński A, ed., Interdisciplinary researches in natural sciences. Kielce, Insti-tute of Geography, Jan Kochanowski University: 47-58.
25.
Kohonen T, 1982. Self-organized formation of topologically correct feature maps. Biological Cybernetics 43: 59-69, DOI 10.1007/BF00337288.
26.
Kohonen T, 2001. Self-Organizing Maps. Third Extended Edition. Berlin, Springer: 501 pp.
27.
Kovach WL, 2007. MVSP-A Multivariate Statistical Package for Windows, ver. 3.1. Pentraeth, Kovach Computing Services.
28.
Legendre P and Birks HJB, 2012a. Clustering and partitioning. In: Birks HJB, Lotter AF, Juggins S and Smol JP, eds., Tracking Environ-mental Change Using Lake Sediments. Volume 5: Data Handling and Numerical Techniques. Dordrecht, Springer: 167-200.
29.
Legendre P and Birks HJB, 2012b. From classical to canonical ordination. In: Birks HJB, Lotter AF, Juggins S and Smol JP, eds., Tracking Environmental Change Using Lake Sediments. Volume 5: Data Handling and Numerical Techniques. Dordrecht, Springer: 201-248.
30.
Lek S and Guégan JF, 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 120: 65-73, DOI 10.1016/S0304-3800(99)00092-7.
31.
Lek S, Scardi M, Verdonschot PFM, Descy JP and Park YS, 2005. Modelling Community Structure in Freshwater Ecosystems. Berlin, Springer: 518 pp.
32.
Li F, Bae MJ, Kwon YS, Chung N, Hwang SJ, Park SJ, Park HK, Kong DS and Park YS, 2013. Ecological exergy as an indicator of land-use impacts on functional guilds in river ecosystems. Ecological Modelling 252: 53-62, DOI 10.1016/j.ecolmodel.2012.09.006.
33.
Lotter AF and Juggins S, 1991. POLPROF, TRAN and ZONE. Pro-grams for Plotting, Editing and Zoning of Pollen and Diatom Data. INQUA Commission for the study of Holocene. Working Group on Data-Handing Methods, Newsletter 6.
34.
MacArthur RH, 1957. On the relative abundance of bird species. Proceedings of the National Academy of Sciences USA 43: 293-295.
35.
Malmgren BA and Nordlund U, 1997. Application of artificial neural networks to paleoceanographic data. Palaeogeography, Palaeo-climatology, Palaeoecology 136: 359-373, DOI 10.1016/S0031-0182(97)00031-X.
36.
McCune B and Mefford MS, 2011. PcOrd Multivariate Analysis of Ecological Data, Version 6.06. Gleneden Beach, Oregon, MjM Software Design.
37.
Michczyńska DJ, Forysiak J, Pawłowski D, Płóciennik M, Borówka RK, Witkowski A, Obremska M, Słowiński M, Żurek S, Brooks SJ and Michczyński A, 2013. The environment changes and chronology of the Late Vistulian (Weichselian) sediments in the Rabień mire. In: Piotrowska N, ed., 11th International Conference "Methods of Absolute Chronology", 15-18th May 2013, Podlesice, Poland. Abstracts and Programme: 79.
38.
Michczyńska DJ, Borówka RK, Okupny D, Obremska M, Forysiak J, Pawłowski D, Płóciennik M, Słowiński M, Żurek S, Brooks SJ, Michczyński A and Witkowski A, 2014. The environment changes and chronology of the Late Vistulian (Weichselian) and Early Holocene sediments in the Rąbień mire, Central Poland. In: Abstract Volume and Programme, INTIMATE Open Workshop and COST Action ES0907 Final Event, Zaragoza, 15th‐ 21st June 2014: 53.
39.
Michczyńska DJ, Forysiak J, Borówka RK, Brooks SJ, Luoto TP, Michczyński A, Nevalainen L, Obremska M, Okupny D, Pawłowski D, Payron O, Płóciennik M, Self A, Słowiński M, Witkowski A, Żurek S, in preparation. Response of bio- and geo-chemical proxies to Late Glacial and early-Holocene climatic fluctuations recorded in Rąbień paleolake sediment (Poland).
40.
Moller Pillot HKM, 2009a. Chironomidae Larvae. Biology and Ecology of the Chironomini. Zeist, KNNV Publishing: 270 pp.
41.
Moller Pillot HKM, 2009b. A Key to the Larvae of the Aquatic Chiro-nomidae of the North-west European Lowlands. Private print, not published: 77 pp.
42.
Ney JJ, 1993. Practical use of biological statistics. In: Kohler CC and Hubert WA, eds., Inland Fisheries Management in North America. Bethesda, MD, American Fisheries Society: 137-158.
43.
Park YS, Tison J, Lek S, Giraudel JL, Coste M and Delmas F, 2006. Application of a self-organizing map to select representative species in multivariate analysis: A case study determining diatom distribution patterns across France. Ecological Informatics 1: 247-257, DOI 10.1016/j.ecoinf.2006.03.005.
44.
Penczak T, 2011. Usefulness of the SOM algorithm for estimation of species distribution and significance in comparing habitats. Journal of Applied Ichthyology 27: 1371-1374, DOI 10.1111/j.1439-0426.2011.01867.x.
45.
Penczak T, Lek S, Godinho F and Agostinho AA, 2004. Patterns of fish assemblages in tropical streamlets using SOM algorithm and conventional statistical methods. Ecohydrology and Hydrobiology 4: 139-146.
46.
Penczak T, Agostinho AA, Gomes LC and Latini JD, 2009. Impacts of a reservoir on fish assemblages of small tributaries of the Corumbá River, Brazil. River Research and Applications 25: 1013-1024, DOI 10.1002/rra.1200.
47.
Płóciennik M, Self A, Birks HJB and Brooks SJ, 2011. Chironomidae (Insecta: Diptera) succession in Żabieniec bog and its palaeo-lake (Central Poland) through the Late Weichselian and Holocene. Palaeogeography, Palaeoclimatology, Palaeoecology 307: 150-167, DOI 10.1016/j.palaeo.2011.05.010.
48.
Płóciennik M, Kruk A, Forysiak J, Pawłowski D, Mianowicz K, Elias S, Borówka RK, Kloss M, Obremska M, Coope R, Krąpiec M, Kittel P and Żurek S, 2015. Fen ecosystem responses to water-level fluctuations during the early and middle Holocene in central Europe: a case study from Wilczków, Poland. Boreas 44: 721-740, DOI 10.1111/bor.12129.
49.
Quinn GP and Keough MJ, 2002. Experimental Design and Data Anal-ysis for Biologists. Cambridge, Cambridge University Press: 537 pp.
50.
Reimer PJ, Bard E, Bayliss A, Beck JW, Blackwell PG, Bronk Ramsey C, Buck CE, Cheng H, Edwards RL, Friedrich M, Grootes PM, Guilderson TP, Haflidason H, Hajdas I, Hatté C, Heaton TJ. Hoffmann DL, Hogg AG, Hughen KA, Kaiser KF, Kromer B, Manning SW, Niu M, Reimer RW, Richards DA, Scott EM, Southon JR, Staff RA, Turney CSM and van der Plicht J, 2013. IntCal13 and Marine13 radiocarbon age calibration curves 0-50,000 years cal BP. Radiocarbon 55(4): 1869-1887.
51.
Remin Z, 2008. Artificial Kohonen neural networks as a tool in paleontological taxonomy - An introduction and application to Late Cretaceous belemnites. Przegląd Geologiczny 56: 58-66 (in Polish with English abstract).
52.
Remin Z, 2012. The Belemnella stratigraphy of the Campanian-Maastrichtian boundary; a new methodological and taxonomic approach. Acta Geologica Polonica 62: 495-533.
53.
Rolland N and Larocque I, 2007. The efficiency of kerosene flotation for extraction of chironomid head capsules from lake sediments samples. Journal of Paleolimnology 37: 565-572, DOI 10.1007/s10933-006-9037-2.
54.
Simpson GL and Birks HJB, 2012. Statistical learning in palaeolimnol-ogy. In: Birks HJB, Lotter AF, Juggins S and Smol JP, eds., Tracking Environmental Change Using Lake Sediments. Volume 5: Data Handling and Numerical Techniques. Dordrecht, Springer: 249-327.
55.
Starkel L, Michczyńska D, Krąpiec M, Margielewski W, Nalepka D and Pazdur A, 2013. Progress in the holocene chrono-climatostratigraphy of Polish territory. Geochronometria 40(1): 1-21, DOI 10.2478/s13386-012-0024-2.
56.
Stojković M, Simić V, Milošević D, Mancev D and Penczak T, 2013. Visualization of fish community distribution patterns using the self-organizing map: A case study of the Great Morava River system (Serbia). Ecological Modelling 248: 20-29, DOI 10.1016/j.ecolmodel.2012.09.014.
57.
Sun X, Deng J, Gong Q, Wang Q, Yang L and Zhao Z, 2009. Kohonen neural network and factor analysis based approach to geochemical data pattern recognition. Journal of Geochemical Exploration 103: 6-16, DOI 10.1016/j.gexplo.2009.04.002.
58.
Szczerkowska-Majchrzak E, Grzybkowska M and Dukowska M, 2010. Effect of flow fluctuations on patch dynamics and chironomid distribution in a medium-sized lowland river. Journal of Freshwater Ecology 25: 437-448, DOI 10.1080/02705060.2010.9664387.
59.
Vallenduuk HJ and Moller Pillot HKM, 2007. Chironomidae larvae of the Netherlands and adjacent lowlands. General ecology and Tanypodinae. Zeist, KNNV Publishing: 143 pp.
60.
Vesanto J and Alhoniemi E, 2000. Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11: 586-600, DOI 10.1109/72.846731.
61.
Vesanto J, Himberg J, Alhoniemi E and Parhankangas J, 2000. SOM Toolbox for Matlab 5. Report A57. Helsinki, Helsinki University of Technology. WEB site: <http://www.cis.hut.fi/somtoolb...>. Accessed 2015 March 5. Ward Jr JH, 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58: 236-244.
62.
Zhang Q, Zhang JS, Zhang B, Cheng J and Tian S, 2011. Self-organizing feature map classification and ordination of Larix principis-rupprechtii forest in Pangquangou Nature Reserve. Acta Ecologica Sinica 31: 2990-2998 (in Chinese with English summary).
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