Experimental Design and Statistical Inference for Cluster Point Processes - With Applications to the Fruit Dispersion of Anemochorous Forest Trees

Biometrical Journal

Volumn 45, Issue 8, 2003, Pages 1006-1022

  Nather W ^a   Walder K ^a  

^a TU BERGAKADEMIE FREIBERG   (Germany)

Author keywords

Choquet integral; Experimental design; Fruit dispersal; Poisson cluster point process

Indexed keywords

DISPERSIONS; FRUITS; INTEGRAL EQUATIONS; STATISTICS;

ANISOTROPIC BEHAVIORS; CHOQUET INTEGRAL; CLUSTER POINT PROCESS; DISPERSION MODELS; FRUIT DISPERSAL; INTERACTIVITY; ORDERED WEIGHTED AVERAGE; POISSON CLUSTER POINT PROCESS; REALISTIC MODEL; STATISTICAL INFERENCE;

DESIGN OF EXPERIMENTS;

EID: 17344377246     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/bimj.200390058     Document Type: Article

References (16)

1. BANDEMER, H. and NÄTHER, W., 1980: Theorie und Anwendung der optimalen Versuchsplanung II. Akademie-Verlag, Berlin.
2. BANDEMER, H. and NÄTHER, W., 1992: Fuzzy Data Analysis. Kluwer Academic Publishers, Dordrecht.
3. CLARK, J. S., MACKLIN, E. and WOOD, L., 1998: Stages and Spatial Scales of Recruitment Limitation in Southern Appalachian Forests. Ecological Monographs 68, 213-235.
4. GRABISCH, M., ORLOVSKI, S. A. and YAGER, R. R., 1998: Fuzzy Aggregation of Numerical Preferences. In: R. SLOWINSKI (ed.): Fuzzy Sets in Decision Analysis, Operations Research and Statistics. Kluwer Academic Publishers, Boston, 31-68.
5. GRABISCH, M., MUROFUSHI, T. and SUGENO, M., 2000: Fuzzy Measures and Integrals. Theory and Applications. Physica-Verlag, Heidelberg.
6. MALANSON, G. P. and ARMSTRONG, M. P., 1996: Dispersal Probability and Forest Diversity in a Fragmented Landscape. Ecological Modelling 87, 103-110.
7. MARICHAL, J.-L., 1999: Aggregation of Interacting Criteria by Means of the Discrete Choquet Integral. Papiers de Recherche/Working Papers. 9910. Faculte D'Economie, de gestion et de sciences sociales. Universite de Liege.
8. NATHAN, R. and MULLER-LANDAU, C., 2000: Spatial Patterns of Seed Dispersals, their Determinants and Consequences for Recruitment. Trends in Ecology & Evolution 15, 278-285.
9. NIELSEN, C. and MACKENTHUN, G., 1991: Die horizontale Variation der Feinwurzelintensität in Waldbö den in Abhängigkeit von der Bestockungsdichte. Allgemeine Forst- und Jagdzeitung 162, 112-119.
10. PACALA, S. W., CANHAM, C. D., SAPONARA, J., SILANDER, J. A., KOBE, and RIBBENS, E., 1996: Forest Models Defined by Field Measurements: Estmation, Error Analysis and Dynamics. Ecological Monographs. 66 1-43.
11. PRESS, W. H., FLANNERY, B. P., TEUKOLSKI, S. A. and VETTERLING, 1990: Numerical Recipes in Pascal. Cambridge University Press, Cambridge.
12. PUKELSHEIM, F., 1993: Optimal design of experiments. John Wiley Sons, New York.
13. RIBBENS, E., SILANDER, J. A. and PACALA, S. W., 1994: Seedling Recruitment in Forests: Calibrating Models to Predict Patterns of Tree Seedling Distribution. Ecology. 75, 1794-1806.
14. STOYAN, D. and STOYAN, H., 1994: Fractals, Random Shapes and Point Fields. John Wiley & Sons, Chichester.
15. STOYAN, D., KENDALL, W. S. and MECKE, J., 1995: Stochastic Geometry and its Applications. John Wiley & Sons, New York.
16. STOYAN, D. and WAGNER, S., 2001: Estimating the fruit dispersion of anemochorous forest trees. Ecological Modelling 145, 35-47.