Abstract
In this paper a three-step procedure is proposed to deal with ecological data, usually very complex in their treatment. The three steps – exploratory, confirmatory, and modelling phases – reflect the different methodological approaches necessary in each phase of the study. To illustrate the methodology, a case study is proposed, concerning the suitability of plants as pollution bioindicators. Samples of differently aged Pinus pinea L. needles were collected throughout 1 year in three different locations, whose human disturbance was known to be different. In the samples some morphological and functional parameters were measured, whose relation with the stress was already known. The exploratory analysis suggested pollution with human origin, the needle’s age, and the environmental conditions as the main factors of influence of damage. The confirmatory analysis confirmed both site and age as main factors and occasionally the sampling date. On this basis, some models were estimated separately for each site: models that best described the damage as function of age resulted non-linear and some of them with seasonal fluctuations. As a result, whereas the models described well enough the pollution temporal variation, the difference of pollution in the sites was best described by the different values of the models parameters in the different sites. In short, different pollution conditions are described better by the damage trend than by the individual measures. The three-step procedure resulted of high utility in outlining the most interesting relations to investigate through the modelling, the opportunity to model the indicators variation along time separately for each site, and to introduce the seasonal variation in some models.
Similar content being viewed by others
Notes
The climate data were kindly supplied by UCEA, Collegio Romano, Roma; the air pollution data were supplied for Roma by ISS – Laboratorio Igiene Ambientale and for Civitavecchia by ENEL – Tor Valdaliga Nord.
References
Altieri, A., Del Caldo, L., & Manes, F. (1994). Micromorphology of epicuticular waxes in Pinus pinea L. needles in relation to season and pollution climate. European Journal of Forest Pathology, 24(2), 79–91.
Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic.
Angelini, R., Manes, F., & Federico, R. (1990). Spatial and functional correlation between diamine-oxidase and peroxidase activities and their dependence upon de-etiolation and wounding in chick-pea stems. Planta, 182, 89–96.
Arabie, P., & Hubert, L. (1994). Cluster analysis in marketing research. In R. J. Bagozzi (Ed.) Advanced methods of marketing research (pp. 160–189). London: Blackwell.
Baig, M. N., & Tranquillini, W. (1976). Studies on upper timberline: morphology and anatomy of Norway spruce (Picea abies) and stone pine (Pinus cembra) needles from various habitat conditions. Canadian Journal of Botany, 54, 1622–1632.
Benzécri, J. P. (1973–1982). L'analyse des données (2 volumes). Paris: Dunod.
Birecka, H., Chaskes, M. J., & Goldstein, J. (1979). Peroxidase and senescence. Journal of Experimental Botany, 30(116), 565–573.
Calinski, T., & Harabász, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1–27.
Camiz, S. (1991). Reflections on spaces relationships in ecological data analysis: Effects, problems, possible solutions. Coenoses, 6(1), 3–13.
Camiz, S. (1993a). STATIS ordinations vs. the Juhász–Nagy models: The predictability of an exploratory tool. Abstracta Botanica, 17(1–2), 29–36.
Camiz, S. (1993b). Computer assisted procedures for structuring community data. Coenoses, 8(2), 97–104.
Camiz, S. (2001). Exploratory 2- and 3-way data analysis and applications. Lecture Notes of TICMI. http://www.emis.de/journals/TICMI/lnt/vol2/lecture.htm. Tbilisi University Press, vol. 2.
Camiz, S. (2005). The Guttman effect: Its interpretation and a new redressing method. Data Analysis Bulletin, 5, 7–34.
Camiz, S., Altieri, A., & Manes, F. (1993). Effetti degli inquinanti atmosferici su aghi di Pinus pinea L. in ambiente naturale. In Statchem93 – Atti del Convegno su Statistica e Chemiometria per lo studio dell'ambiente. Università di Venezia, Società Italiana di Statistica.
Camussi, A., Möller, F., Ottaviano, E., & Sari Gorla, M. (1986). Metodi statistici per la sperimentazione biologica. Bologna: Zanichelli.
Cape, J. N. (1988). Air pollutant effects on conifer leaf surfaces. In J. N. Cape, & P. Mathy (Eds.) Scientific basis of forest decline symptomatology. Air pollution report series of the environmental research, no. 15 (pp. 149–159). Bruxelles: CEC.
Castillo, F. J. (1986). Extracellular peroxidases as markers of stress? In H. Greppin, C. Penel, & T. H. Gaspar (Eds.) Molecular and physiological aspects of plant peroxidases (pp. 419–430). Switzerland: Geneva.
Crossley, A., & Fowler, D. (1986). The weathering of scots pine epicuticular wax in polluted and clean air. New Phytologist, 103, 207–218.
Diggle, P. J., Liang, K. Y., & Zieger, S. L. (1994). Analysis of longitudinal data. Oxford: Clarendon.
Einot, I., & Gabriel, K. R. (1975). A study of the powers of several methods of multiple comparison. Journal of the American Statistical Association, 70, 351.
Escudero, A., Del Arco, J. M., Sanz, I. C., & Ayala, J. (1992). Effects of leaf longevity and retranslocation efficiency on the retention of nutrients in the leaf biomass of different woody species. Oecologia, 90, 80–87.
Federico, R., Bruno, F., & Manes, F. (1986). Ion distribution in maize seedlings. Annali di Botanica, 44, 155–161.
Gallant, A. R. (1975). Nonlinear regression. The American Statistician, 29, 73–81.
Gordon, A. D. (1999). Classification. London, UK: Chapman and Hall.
Guttman, L. (1953). A note on Sir Cyril Burt’s factorial analysis of qualitative data. British Journal of Statistical Psychology, 6, 21–24.
Huisman, J., Olff, H., & Fresco, L. F. M. (1993). A hierarchical set of models for species response analysis. Vegetatio, 4(1), 37–46.
Kennedy, W. J., & Gentle, J. E. (1980). Statistical computing. New York: Marcel Dekker.
Kolmogorov, A. N. (1933). Grundbegriffe der Wahrscheinlichkeitsrechnung. Berlin: Springer.
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistics Associaiton, 47, 583–621.
Lebart, L., Morineau, A., & Warwick, K. M. (1984). Multivariate descriptive statistical analysis: correspondence analysis and related techniques for large matrices. New York: Wiley.
Lebart, L., Morineau, A., Lambert, T., & Pleuvret, P. (1991). SPAD.N – Manuel de référence. Paris: CISIA.
Legendre, L., & Legendre, P. (1983). Numerical ecology. Amsterdam: Elsevier.
Legge, A. H., Bogner, J. C., & Krupa, S. V. (1988). Foliar sulphur species in tine: A new indicator of a forest ecosystem under air pollution stress. Environmental Pollution, 55, 15–27.
Levene, H. (1960). Robust tests for the equality of variance. In I. Olkin (Ed.) Contributions to probability and statistics (pp. 278–292). Palo Alto, CA: Stanford University Press.
Lichtenthaler, H. K. (1998). The stress concept in plants: An introduction. Annals of the New York Academy of Sciences, 851(1), 187–198.
Lindman, H. R. (1974). Analysis of variance in complex experimental designs. San Francisco: Freeman.
Manes, F., Altieri, A., Angelini, R., Bruno, F., Cortiello, M., Del Caldo, L., et al. (1988). Micromorphological and biochemical changes in Pinus pinea L., Pinus pinaster Aiton, Nicotiana tabacum L. in relation to atmospheric pollutants. In J. N. Cape, & P. Mathy (Eds.) Scientific basis of forest decline symptomatology. Air pollution report series of the environmental research, no. 15 (pp. 342–353). Bruxelles: CEC.
Manes, F., Altieri, A., Boffa, A., Bruno, F., & Federico, R. (1987). Early diagnosis of injuries in Pinus pinaster Aiton treated with simulated acid rain. Annali di Botanica, 45, 17–25.
Manes, F., Federico, R., Cortiello, M., & Angelini, R. (1990). Ozone induced increase of peroxidase activity in tobacco (Nicotiana tabacum L. cv. Burley) leaves. Phytopathologia Mediterranea, 29, 101–106.
Manes, F., Grignetti, A., Tinelli, A., Lenz, R., & Ciccioli, P. (1997). General features of the Castelporziano test site. Atmospheric Environment, 31(51), 19–25.
Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50–60.
McQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. Berkeley: University of California.
Miller Jr., R. G. (1981). Simultaneous statistical inference. New York: Springer Verlag.
Milligan, G. W., & Cooper, M. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159–179.
Mood, A. M., Graybill, F. A., & Boes, D. C. (1974). Introduction to the theory of statistics. New York: McGraw-Hill.
Orlóci, L. (1978). Multivariate analysis in vegetation research. The Hague: Junk.
Pillar, V. D. (1999). How sharp are classifications? Ecology, 80(8), 2508–2516.
Pillar, V. D., & Orlóci, L. (1996). On randomization testing in vegetation science: Multifactor comparisons of Relevé groups. Journal of Vegetation Science, 7, 585–592.
SAS (1987). SAS/STAT guide for personal computers. Cary, NC: SAS Institute.
Scheffé, H. (1959). The analysis of variance. New York: Wiley.
Treshow, M. (1984). Air pollution and plant life. Chichester: Wiley.
Tukey, J. W. (1953). The problem of multiple comparisons. Princeton: Princeton University Press.
Turunen, M., & Huttunen, S. (1990). A revue of the response of epicuticular wax of conifer needles to air pollution. Journal of Environmental Quality, 19, 35–45.
Van Loon, L. C. (1986). The significance of changes in peroxidase in diseased plants. In H. Greppin, C. Penel, & TH. Gaspar (Eds.) Molecular and physiological aspects of plant peroxidase (pp. 405–418). Genêve: Imprimierie Nationale.
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–144.
Whittaker, R. H. (Ed.) (1967). Handbook of vegetation science – Part V: Ordination and Classification of Vegetation. The Hague: Junk.
Acknowledgements
Both development and revision of this paper were carried out by the first author in the frame of his grant by the Facoltà d’Architettura ValleGiulia of Sapienza Università di Roma. In addition, he wishes to acknowledge the encouragement and the suggestions given by Valério D. Pillar during a visit to his Departmento de Ecología, in the framework of a bilateral agreement between Sapienza and Universidade Federal do Rio Grande do Sul (Porto Alegre, Brazil).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Camiz, S., Altieri, A. & Manes, F. Pollution Bioindicators: Statistical Analysis of a Case Study. Water Air Soil Pollut 194, 111–139 (2008). https://doi.org/10.1007/s11270-008-9702-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11270-008-9702-3