One possible explanation of variation in vegetation is based on the variable Poisson model. In this model, species occurrence is presumed to follow a Poisson distribution, but the value of the Poisson parameter for any species varies from point to point, as a result of environmental variation. As an extreme, this includes dividing the given habitat into areas favourable to a community and areas which are unfavourable, or at least not occupied. The spatial area can then be viewed as a series of patches within which each species follows a Poisson distribution, although different patchesmay have different values for the Poisson parameter for any particular species. In this paper, I use a method of fuzzy clustering (mixture modelling) based on the minimum message length principle to examine the variation in Poisson parameter of individual species. The method uses the difference between the message length for the null, 1-cluster case and the message length for the optimal cluster solution, appropriately normalised, as a measure of the amount of pattern any analysis captures. I also compare the Poisson results with results obtained by assuming the within patch distribution is Gaussian. The Poisson alternative consistently results in a greater capture of pattern than the Gaussian, but at the expense of a much larger number of clusters. Overall, the Gaussian alternative is strongly supported. Other mechanisms that might introduce extra clusters, for example within-cluster correlation or spatial dependency between observations, would presumably apply equally to both models. The variable Poisson model, in the limit, converges on the individualistic model of vegetation, the Gaussian on something like the community unit model. With these data, the individualistic model is strongly rejected. Difficulties with comparing model classes mean this conclusion must remain tentative.
Edwards, R. T. and D. Dowe. 1998. Single factor analysis in MML mixture modelling. Lecture Notes in Artificial Intelligence 1394 Springer Verlag. pp. 96-109.
Single factor analysis in MML mixture modelling , () 96 -109 .
Brokaw, N. and R. T. Busing. 2000. Niche versus chance in tree diversity in forest gaps. TREE 15: 183-188.
'Niche versus chance in tree diversity in forest gaps ' () 15 TREE : 183 -188 .
Dale, M. B. 1987. Knowing when to stop: cluster concept-concept cluster. Coenoses 3: 11-32.
'Knowing when to stop: cluster concept-concept cluster ' () 3 Coenoses : 11 -32 .
Edgoose, T. and L. Allison. 1999. MML Markov classification of sequential data. Statistics and Computing 9:269-278.
'MML Markov classification of sequential data ' () 9 Statistics and Computing : 269 -278 .
Hilderman, R. J. & Hamilton, H. J. 1999. Heuristics for ranking the interestingness of discovered knowledge. Proc. 3rd Pacific-Asia Conf. Knowledge Discovery PKDD'99, Beijing, Springer, Berlin, pp. 204-209.
Heuristics for ranking the interestingness of discovered knowledge. Proc. 3rd Pacific-Asia Conf. Knowledge Discovery PKDD'99, Beijing , () 204 -209 .
Keddy, P. A. 1993. Do ecological communities exist? A reply to Bastow Wilson. J. Veg. Sci. 4: 135-136.
'Do ecological communities exist? A reply to Bastow Wilson ' () 4 J. Veg. Sci : 135 -136 .
Kemp, C. D. and A. W. Kemp. 1956 The analysis of point quadrat data. Austral. J. Bot. 4:167-174.
'The analysis of point quadrat data ' () 4 Austral. J. Bot : 167 -174 .
Kolmogorov, A. N. 1965. Three approaches to the quantitative description of information. Prob. Inform. Transmission 1: 4-7. (translation).
'Three approaches to the quantitative description of information ' () 1 Prob. Inform. Transmission : 4 -7 .
Mackay 1969. Recognition and action. In: S. Watanabe (ed.), Methodologies of Pattern Recognition, Academic Press, London, pp. 409-416.
Recognition and action , () 409 -416 .
Trass, H. and N. Malmer. 1973. North European approaches to classification. In: R. H. Whittaker (ed.), Classification and Ordination of Plant Communities, Dr. W Junk, The Hague, pp. 529-575.
North European approaches to classification , () 529 -575 .
Wallace, C. S. 1995. Multiple factor analysis by MML estimation. Tech. Rep. 95/218, Dept Computer Science, Monash University, Clayton, Victoria 3168, Australia.
Multiple factor analysis by MML estimation. Tech. Rep. 95/218, Dept Computer Science , ().
Wallace C. S. 1998. Intrinsic classification of spatially-correlated data Comput. J. 41: 602-611.
'Intrinsic classification of spatially-correlated data ' () 41 Comput. J. : 602 -611 .
Robinson, P. 1954. The distribution of plant populations. Ann. Bot. 19:59-66.
'The distribution of plant populations ' () 19 Ann. Bot : 59 -66 .
Shipley, B. and P. A. Keddy. 1987. The individualistic and community-unit concepts as falsifiable hypotheses. Vegetatio 69:47-55.
'The individualistic and community-unit concepts as falsifiable hypotheses ' () 69 Vegetatio : 47 -55 .
Hastie, T. and W Stuetzle. 1989. Principal curves. Amer. Statist. Assoc.J. 84: 502-516.
'Principal curves ' () 84 Amer. Statist. Assoc. J. : 502 -516 .
Singh, B. N. and K. Das. 1938. Distribution of weed species on arable land J. Ecol. 26: 455-466.
'Distribution of weed species on arable land ' () 26 J. Ecol. : 455 -466 .
Stanford, D. and A. E. Raftery. 1997. Principal curve clustering with noise. Tech. Rep. 317, Dept. Statistics, University of Washington.
Principal curve clustering with noise. Tech. Rep. 317, Dept. Statistics , ().
Simberloff, D. 1980. A succession of paradigms in ecology: Essentialism to materialism and probabilism Synthese 43:3-29.
'A succession of paradigms in ecology: Essentialism to materialism and probabilism ' () 43 Synthese : 3 -29 .
Wallace, C. S. and D. L. Dowe. 2000. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10: 73-83.
'MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions ' () 10 Statistics and Computing : 73 -83 .
Westhoff, V. and E. vand der Maarel 1973. The Braun-Blanquet approach. In: R. H. Whittaker (ed.), Classification and Ordination of Plant Communities, Dr. W Junk, The Hague, pp. 617-707.
The Braun-Blanquet approach , () 617 -707 .
Wilson, J. B. 1991. Does vegetation science exist? J. Veg. Sci. 2:289-290.
'Does vegetation science exist ' () 2 J. Veg. Sci : 289 -290 .
Ashby, E. 1935. The quantitative analysis of vegetation. Ann. Bot. 49: 779-802.
'The quantitative analysis of vegetation ' () 49 Ann. Bot : 779 -802 .
Banfield, J. D. and A. E. Raftery 1993. Model-based Gaussian and non-Gaussian clustering. Biometrics 49:803-821.
'Model-based Gaussian and non-Gaussian clustering ' () 49 Biometrics : 803 -821 .
Barsalou, L. W. 1995. Deriving categories to achieve goals. In:. A. Ram and D. B. Leake (eds.), Goal Directed Learning. MIT Press, Cambridge MA. pp. 121-176.
Deriving categories to achieve goals , () 121 -176 .
Bensmail, H., G. Celeux, A. E. Raftery and C. P. Robert. 1997. Inference in model-based cluster analysis. Statistics and Computing 7:1-10.
'Inference in model-based cluster analysis ' () 7 Statistics and Computing : 1 -10 .
Boerlijst, M. and P. Hogeweg. 1991. Spiral wave structure in prebiotic evolution: hypercycles stable against parasites. Physica D. 48: 17-28.
'Spiral wave structure in prebiotic evolution: hypercycles stable against parasites ' () 48 Physica D. : 17 -28 .
Pólya, G. 1930. Sur quelques points de la théorie des probabilityés. Ann. Inst. Poincaré 1: 117-161.
'Sur quelques points de la théorie des probabilityés ' () 1 Ann. Inst. Poincaré : 117 -161 .
Rissanen, J. 1999. Hypothesis selection and testing by the MDL principle. Comput. J. 42:260-269.
'Hypothesis selection and testing by the MDL principle ' () 42 Comput. J. : 260 -269 .
Stevens, W L. 1937. Significance of grouping. Ann. Eug. London. 8: 57-69.
'Significance of grouping ' () 8 Ann. Eug. London : 57 -69 .
Greig-Smith, P. 1983. Quantitative Plant Ecology, 3rd Edition, Blackwell, Oxford.
Quantitative Plant Ecology , ().
Goodall, D. W. 1953. Objective methods for the classification of vegetation 1. The use of positive interspecific correlation. Austral. J. Bot. 1:39-63.
'Objective methods for the classification of vegetation 1. The use of positive interspecific correlation ' () 1 Austral. J. Bot : 39 -63 .
Fraley C. and A. E. Raftery 1998. How many clusters? Which clustering method? - Answers via Model-Based Cluster Analysis. Technical Report no. 329, Department of Statistics, University of Washington.
Erickson, R. O. and J. R. Stehn. 1945. A technique for analysis of population density data. Amer. midl. Nat. 33:781-787.
'A technique for analysis of population density data ' () 33 Amer. midl. Nat. : 781 -787 .
Feller, W. 1943. On a general class of 'contagious' distributions. Ann. Math. Statist. 14:389-400.
'On a general class of 'contagious' distributions ' () 14 Ann. Math. Statist : 389 -400 .