By Moshe Carmeli
Publication by way of Carmeli, Moshe
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Extra resources for Statistical Theory and Random Matrices
These three steps interact with each other and must be considered together before conducting any sampling of ecological data in a spatial context (Fortin et al. 1989; Dungan et al. 2002). In essence, design of an optimal spatial sampling scheme requires a careful balance between samples that are too close to one another, thus not providing enough new information (data highly autocorrelated), and samples that are too sparse, so that processes at other spatial scales introduce too much variability (Haining 1990).
1988; Baker & Cai 1992; McGarigal & Marks 1995; Gustafson 1998; Leitão et al. 2006). 4). In the simplest case, the combination is additive and the resulting pattern is the sum of patterns generated by the individual processes acting independently. 4; a large-scale trend due to an environmental gradient is augmented by patchiness caused by limited species dispersal, and a random component (‘noise’). Additive spatial pattern can be analysed by removing each contributing pattern, and then quantifying the characteristics of the residual.
For their comments and help on earlier versions of the chapters, we are grateful to: Ferko Csillag, Stewart Fotheringham, Norm Kenkel, Charles Krebs, Pierre Legendre, Stephanie Melles, Evie Merrill, Joe Perry and Mike Rosenberg. We need to thank Joe Perry also for discussion on animal movement analysis. Finally, we acknowledge the financial support that made possible the research that contributed to the material in the book from the Natural Sciences and Engineering Research Council of Canada and from the University of Alberta.
Statistical Theory and Random Matrices by Moshe Carmeli