74
4 StatistIcal Models and Tcchniques
of reality, which is to be described or explained by the model, might be more complex or of a different kind than covered by the statistical model.
The positive co-operation of statisticians and other natural scientists realized in
ELA W A T suggested further common developments. Thus, there is a demand for
analysis techniques applicable if variables are not spatially or temporally independent. Also, the statistical properties of dissimilarity measures and multivariate
non-parametric tests are mostly unknown. Therefore, a combination of ecological
questions and appropriate statistical techniques, including software development, is
necessary.
Acknowledgements
This chapter owes much to the work of many people in ecosystem research. I especially acknowledge the contribution of the statistical working group: D. Pfeifer,
H.-P. Baumer and H. Ortleb. I am grateful to A. Hild, G. Liebezeit, V. Niese!, J.
Steuwer and A. Tecklenborg for permission to use their data for the case studies. I
thank H.-P. Baumer, a reviewer and S. Dittmann for many helpful comments and
suggestions on the manuscript. The project was funded by the German Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF)
under grant number 03F0112 A and B. The responsibility for the contents of the
publication rests with the author.
References
Baumcr H-P (1994) Stochastic simulation of dynamic point patterns. In: Faulbaum F (Ed) SoftStat '93: Advances in Statistical Software 4. The 7th Conference on the Scientific Use of
Statistical Software. Heidelberg, March, 14-18. 1993. G. Fischer. Stuttgart, pp 145-152
Bickel PJ (1969) A distribution free version of the Smirnov two-sample test in the multivariate
case. Ann Math Statist 40: 1-23
Blome D. Schlcier U. van Bernem K-H (1999) Analysis of the small-scale spatial patterns of
free-living marine nematodes from tidal flats in the East Frisian Wadden Sea. Marine Biology 133: 717-726
Bock HH (1980) Explorative Datenanalyse. In: Victor N. Lehmacher W & van Eimeren W
(Eds). Explorative Datenanalyse. Springer. Berlin Heidelberg New York. pp 6-37
Bohle-Carbonell M (1992) Empirische Variabilitatsabschatzung und Methodik der Datenerhebung. AbschluBbericht. Bundesamt fUr Seeschiffahrt und Hydrographic. Hamburg
Borovkov K. Pfeifer D. Baumer H-P (1996) Modeling dynamics and spatial aggregation of
biological populations by stochastic networks. Senckenbergiana marit 27: 129-136
Bortz J. Lienert GA. Boehnke K (1990) Vertcilungsfreie Methoden in der Biostatistik. Springer.
Berlin Heidelberg New York
BOning H. Trenkler G (1994) Nichtparametrische statistische Methoden. de Gruyter. Berlin
Cerioli A (1997) Modified tests of independence in 2 x 2 tables with spatial data. Biometrics 53:
619-628
Chatfield C (1975) The Analysis of Time Series: Theory and Practice. Chapman & Hall. London
Clarke K R (1993) Non-parametric multivariate analyses of changes in community structure.
Austr J Eco118: 117-143
Cox TF. Cox MAA ( 1994) Multidimensional Scaling. Chapman & Hall. London
4 StatistIcal Models and Tcchniques
of reality, which is to be described or explained by the model, might be more complex or of a different kind than covered by the statistical model.
The positive co-operation of statisticians and other natural scientists realized in
ELA W A T suggested further common developments. Thus, there is a demand for
analysis techniques applicable if variables are not spatially or temporally independent. Also, the statistical properties of dissimilarity measures and multivariate
non-parametric tests are mostly unknown. Therefore, a combination of ecological
questions and appropriate statistical techniques, including software development, is
necessary.
Acknowledgements
This chapter owes much to the work of many people in ecosystem research. I especially acknowledge the contribution of the statistical working group: D. Pfeifer,
H.-P. Baumer and H. Ortleb. I am grateful to A. Hild, G. Liebezeit, V. Niese!, J.
Steuwer and A. Tecklenborg for permission to use their data for the case studies. I
thank H.-P. Baumer, a reviewer and S. Dittmann for many helpful comments and
suggestions on the manuscript. The project was funded by the German Bundesministerium fur Bildung, Wissenschaft, Forschung und Technologie (BMBF)
under grant number 03F0112 A and B. The responsibility for the contents of the
publication rests with the author.
References
Baumcr H-P (1994) Stochastic simulation of dynamic point patterns. In: Faulbaum F (Ed) SoftStat '93: Advances in Statistical Software 4. The 7th Conference on the Scientific Use of
Statistical Software. Heidelberg, March, 14-18. 1993. G. Fischer. Stuttgart, pp 145-152
Bickel PJ (1969) A distribution free version of the Smirnov two-sample test in the multivariate
case. Ann Math Statist 40: 1-23
Blome D. Schlcier U. van Bernem K-H (1999) Analysis of the small-scale spatial patterns of
free-living marine nematodes from tidal flats in the East Frisian Wadden Sea. Marine Biology 133: 717-726
Bock HH (1980) Explorative Datenanalyse. In: Victor N. Lehmacher W & van Eimeren W
(Eds). Explorative Datenanalyse. Springer. Berlin Heidelberg New York. pp 6-37
Bohle-Carbonell M (1992) Empirische Variabilitatsabschatzung und Methodik der Datenerhebung. AbschluBbericht. Bundesamt fUr Seeschiffahrt und Hydrographic. Hamburg
Borovkov K. Pfeifer D. Baumer H-P (1996) Modeling dynamics and spatial aggregation of
biological populations by stochastic networks. Senckenbergiana marit 27: 129-136
Bortz J. Lienert GA. Boehnke K (1990) Vertcilungsfreie Methoden in der Biostatistik. Springer.
Berlin Heidelberg New York
BOning H. Trenkler G (1994) Nichtparametrische statistische Methoden. de Gruyter. Berlin
Cerioli A (1997) Modified tests of independence in 2 x 2 tables with spatial data. Biometrics 53:
619-628
Chatfield C (1975) The Analysis of Time Series: Theory and Practice. Chapman & Hall. London
Clarke K R (1993) Non-parametric multivariate analyses of changes in community structure.
Austr J Eco118: 117-143
Cox TF. Cox MAA ( 1994) Multidimensional Scaling. Chapman & Hall. London