PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2009 | 78 | 4 |

Tytuł artykułu

Self-organizing feature maps and selected conventional numerical methods for assessment of environmental quality

Autorzy

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The investigations concerned sites of Acer platanoides L. infected or not by Rhytisma aceriniu (Pers.) Fr. The aim of the study was to check the occurrence of R. acerinium, and whether it reflects the environmental status. Furthermore, an analysis was carried out to find out whether the applied SOFM offers additional advantages to solve problems in relation to conventional methods. Concentrations of selected elements in soils and leaves, and leaf and "tar-spot" morphometric traits were also measured. A significant differentiation was found between sites in relation to the analyzed traits. It appeared, that sites showing lower concentrations of chemical elements and proper developmental habitat conditions massive infections take place. The study showed that R. acerinium is a good biological indicator for assessment of environmental status. The applied, conventional statistical methods, SOFM and image techniques showed similar, but not identical results for assessment of environmental quality using R. acerinium. SOFM appeared to be more useful for ordination of results and ought to be taken into account as a proper tool of estimation of various plants and their biotopes.

Wydawca

-

Rocznik

Tom

78

Numer

4

Opis fizyczny

p.335-343,fig.,ref.

Twórcy

autor
  • Wroclaw University, Kanonia 6/8, 50-328 Wroclaw, Poland

Bibliografia

  • BARANOWSKA-MOREK A. 2003. Roślinne mechanizmy tolerancji na toksyczne działanie metali ciężkich. Kosmos, 52, 2-3: 283-298. (in Polish with English summary)
  • BREJ T., FABISZEWSKI J. 2003. Rośliny akumulujące metale ciężkie we florze Sudetów. Ann. Silesiae, 32: 155-163. (in Polish with English summary)
  • BROWER J.E., ZAR J.H., VON ENDE C.N. 1998. Field and laboratory methods for general ecology. WCB/McGraw-Hill, Boston, Massechusetts Burr Ridge, Illinois Dubuque, Iowa Madison, Wisconsin New York, New York San Francisco, California St. Louis, Missouri.
  • CHON T., PARK Y.S., MOON K.H., CHA E.Y. 1996. Patternizing communities by using an artificial neural network. Ecol. Model., 90: 69-78.
  • CORTEX NOVA. 2005. DigiShape. Program do automatycznej morfometrii. Cortex Nova, Bydgoszcz, www.cortex.nova.pro.wp.pl. (in Polish and English)
  • DAHMANI-MULLER H., VAN OORT F., GELIE B., BALA- BANE M. 2000. Strategies of heavy metal uptake by three plant species growing near a metal smelter. Environ. Pollut., 109: 231-238.
  • EMEP Status Report. 2004. Heavy metals: trans-boundary pollution of the environment. Status Report 2/2004., MCS-E & CCC. GEVREY M., DIMOPOULOS L., LEK S. 2003. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model., 160: 249-264.
  • GIRAUDEL J.L., LEK S. 2001. A comparison of self-organizing map algorithm and some conventional statistical methods for ecological community ordination. Ecol. Model., 146: 329-339.
  • GOLDEN SOFTWARE, INC. 2004. Surfer Version 8.03, Surfer Mapping System, Contouring and 3D Surface Mapping for Scientists and Engineers. Golden, Colorado, USA, (www.goldensoftware.com).
  • HARRISON R.M., CHIRGAWI M.B. 1989. The assessment of air and soil as contributors of some trace metals to vegetable plants. I. Use of a filtered air growth cabinet. Sci. Tot. Environ., 83, 1-2: 13-34.
  • HELIOS-RYBICKA E. 1996. Impact of mining and metallurgical industries on the environment in Poland. Appl. Geochem., 11: 3-9.
  • KABATA-PENDIAS A. 2001. Trace Elements in Soils and Plants. CRC Press, Boca Raton, FL.
  • KLEIN R., PAULUS M. 1997. Biometric sample characterization. Part II. The relation between size of needles and concentrations of airborn pollutants. Chemosphere, 34, 9/10: 2015-2021.
  • KOHONEN T. 2001. Self-Organizing Maps. Springer-Verlag, Berlin, Heidelberg Series in Information Sciences, Vol. 30, Berlin, Springer-Verlag.
  • KOSIBA P. 2007. Impact of air pollution on the occurrence of Rhytisma acerinium “tar-spot” on maple leaves. Acta Soc. Bot. Pol., 76, 4: 333-343.
  • KOSIBA P., STANKIEWICZ A. 2007. Water trophicity of Utricularia microhabitats identified by means of SOFM as a tool in ecological modelling. Acta Soc. Bot. Pol., 76, 3: 255-261.
  • KWAPULINSKI J., SAROSIEK J. 1988. Radioecotoxicological influence of a power station determined by investigation mosses. Sci. Total Environ., 68: 173-180.
  • LEE B-H., SCHOLZ M. 2006. A comparative study: Prediction of constructed treatment wetland performance with k-nearest neighbors and neural networks. Water, Air Soil Poll., 174: 279-301.
  • LEGENDRE P., LEGENDRE L. 1998. Numerical ecology. Elsevier Science BV, Amsterdam.
  • MARKERT B.A. 1992. Presence and significance of naturally occurring chemical elements of the periodic system in the plant organism and consequences for future investigations on inorganic environmental chemistry in ecosystems. Vegetatio, 103: 1-30.
  • MARKERT B.A, BREURE A.M., ZECHMEISTER H.G. 2003. Definitions, strategies and principles for bioindication/biomonitoring of the environment. In: Bioindicators and biomonitors, Markert, B.A., Breure A.M., Zechmeister H.G. (eds). Elsevier, Oxford, pp. 3-39.
  • MARKERT B., HERPIN U., SIEWERS U., BERLEKAMP J., LIETH H. 1996. The German heavy metal survey by means of mosses. Sci. Total Environ., 182: 159-168.
  • MARSCHNER H. 1995. Mineral nutrition of higher plants. Academic Press, London.
  • MENGEL K., KIRKBY E.A. 2001. Principles of plant nutrition. Kluwer Academic Publisher, Dordrecht, The Netherlands.
  • MORENO-SANCHEZ M. 2004. Graphic approach for morphometric analysis of Archaeopteris leaves. Ann. Paleontologie, 90: 161-173.
  • NIINEMETS U., KULL K. 2003. Lear structure vs. nutrient relationships vary with soli condition in temperate shrubs and trees. Acta Oecol., 24: 209-219.
  • NORDÉ B., APPELQVIST T. 2001. Conceptual problems of ecological continuity and its bioindicators. Biodivers. Conserv., 10: 779-791.
  • PARUELO J.M., TOMASEL F. 1997. Prediction of functional characteristics of ecosystems: a comparision of artificial neural networks and regression models. Ecol. Model., 98, 2-3: 173-186.
  • PASTOR-BARCENAS O., SORIA-OLIVAS E., MARTIN-GU- ERRERO J.D., CAMPAS-VALLS G., CARRASCO-RODRIGUEZ J.L., DEL VALLE-TASCÓN S. 2005. Unbiased sensitivity analysis and pruning techniques in neural networks for surface ozone modelling. Ecol. Model., 182: 149-158.
  • RAY C., KLINDWORTH K.K. 2000. Neural Networks for agrichemical vulnerability assessment of rural private wells. J. Hydrol. Eng., 5, 2: 162-171.
  • RECKNAGEL F. 2001. Applications of machine learning to ecological modelling. Ecol. Model., 146: 303-310.
  • SALEMAA M., DEROM J., HELMISAARI H. S., NIEMINEN T., VANHA-MAJAMAA I. 2004. Element accumulation in boreal bryophytes, lichenes and vascular plants exposed to heavy metal and sulfur deposition in Finland. Sci. Total Environ., 324: 141-160.
  • SAMECKA-CYMERMAN A., STANKIEWICZ. A., KOLON K., KEMPERS A.J. 2007. Self-organizing feature map (neural networks) as a tool in classification of the relations between chemical composition if aquatic bryophytes and type of stre- ambeds in the Tatra national park in Poland. Chemosphere, 67, 5: 954-960.
  • SAWICKA-KAPUSTA K., ZAKRZEWSKA M., BAJOREK K., GDULA-ARGASIŃSKA J. 2003. Input of heavy metals to the forest floor as a result of Cracow Urban pollution. Environ. Int., 28: 691-698.
  • SIEDLECKA A., TUKENDORF A., SKORZYŃSKA-POLIT E., MAKSYMIEC W., WÓJCIK M., BASZYŃSKI T., KRUPA Z. 2001. Angiosperms. In: Metals in the environment. Analysis by biodiversity, Prasad M.N.V. (ed.). Marcel Dekker, Inc., New York, Hyderabad, India, pp. 171-217.
  • SOKAL R.R., ROHLF F.J. 2003. Biometry. The principles and practice if statistics in biological research. W. H. Freeman & Company, New York.
  • SOKOŁOWSKI A. 2002. Metody stosowane w data mining. In: Data mining - metody i przykłady. StatSoft Polska Sp. z o.o., Kraków, pp. 5-12. (in Polish)
  • SPENCER S. 2001. Effects of coal dust on species composition of mosses an lichenes in an arid environment. J. Arid Environ., 49: 843-853.
  • STANKIEWICZ A., KOSIBA P. 2009. Advances in ecological modelling of soil properties by self-organizing feature maps of natural environment of Lower Silesia (Poland). Acta Soc. Bot. Pol., 78, 2: 167-174.
  • STATSOFT, INC. 2007. Statistica (data analysis software system), version 8.0 - www.statsoft.com.
  • TADEUSIEWICZ R. 2000. The application of neural networks in biotechnology and biomaterials. Prace Mineralogiczne, 89: 9-17.
  • TRETYAKOVA I.N., NOSKOVA N.E. 2004. Scotch pine pollen under condition of environmental stress. Russ. J. Ecol., 35, 1: 20-26.
  • WALKER C.H., HOPKIN S.P., SILBY R.M., PEAKALL D. 2006. Principles of Ecotoxicology. Taylor & Francis, London.
  • ZAR J.H. 1999. Biostatistical analysis. Prentice Hall, New Jersey.

Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

bwmeta1.element.agro-article-b9cd2102-34b5-4683-9637-189db8b36658
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.