Pattern detection in plant populations through the analysis of plant-to-all-plants distances

Galiano, E. F.

doi:10.1007/bf00051564

Cited by 54 publications

(23 citation statements)

References 11 publications

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“…a Poisson process, the expected value of K(t) is zrtL K(t) is directly related to the function expressing the probability density of finding a clump at distance t from another clump (e.g. Galiano, 1982), but being a cumulative function it does not require smoothing when estimated from data (Ripley, 1977). To estimate K(t) we used the formula corrected for edge effects, as described e.g.…”

Section: Methodsmentioning

confidence: 99%

Clump spacing in a desert dwarf shrub community

Prentice

Werger

1985

Vegetatio

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A Monte Carlo method based on Ripley's K function -a cumulative function related to the number of plants encountered at different distances from other plants -is used to test the null hypothesis of random distribution of shrub clumps in a desert dwarf shrub community in Namaqualand, South Africa, where Psilocaulon arenosum is the dominant shrub. The method takes into account the apparent regularity of pattern caused by the finite size (up to 2 m diameter) of the clumps. It is shown that the clump centres are significantly aggregated (compared to random expectation) at distances on the order of 1 m. Such aggregation is expected, as a simple result of regeneration near to seed sources, if the time between catastrophic droughts is short in relation to the time required for development of a non-aggregated or regular pattern determined by moisture competition. No significant regularity was detected at distances of 3 m or less. One subplot showed regularity above 3 m, but this pattern was not shown by the other subplot and may not be a competition effect. These results support a hypothesis of aggregation caused by regeneration pattern decaying slowly toward randomness as larger individuals compete.

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Section: Methodsmentioning

confidence: 99%

Clump spacing in a desert dwarf shrub community

Prentice

Werger

1985

Vegetatio

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“…Graphs of interconnections among points, that have been introduced by point pattern analysis, are now widely used also in surface pattern analysis (below), where they serve for instance as basic networks of relationships for constrained clustering, spatial autocorrelation analysis, etc. The methods of point pattern analysis, and in particular the quadrat-density and the nearest-neighbour methods, have been widely used in vegetation science (e.g., Galiano 1982;Carpenter & Chaney 1983) and need not be expounded any further here. These methods have been summarized by a number of authors, including Pielou (1977), Getis & Boots (1978), Cicrri et al (1977 and Ripley (1981Ripley ( , 1987.…”

Section: Sign Of Spatial Autocorrelationmentioning

confidence: 99%

Spatial pattern and ecological analysis

1989

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The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and twodimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.

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“…Our analysis of plant-to-all-plant distances was similar to the method of Galiano, and lacked any devices to avoid an edge effect (Galiano, 1982). All Euclidean distances between individual plants within subpopulations were calculated and summed, so that we obtained both a frequency distribution and a cumulative frequency distribution of distance at 1-m intervals.…”

Section: Distances Between Individualsmentioning

confidence: 99%

Spatial distribution patterns and implications for conservation ofScrophularia takesimensis (Scrophulariaceae), an endangered endemic species on Ulleung Island, Korea

Lim

Lee

et al. 2008

J. Plant Biol.

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As part of an on-going effort to conserve endangered and endemic Scrophularia takesimensis (Scrophulariaceae), we analyzed its spatial distribution patterns by applying an index of dispersion, plant-to-all-plant distances, and the varying quadrat size method. Three indices --Dispersion, Morisita, and Standardized Morisita --all revealed clumping with small aggregates, and distances between aggregates were more or less regular. The asymptote level occurred at a distance of 20 to 30 m; the distance showing 90% of cumulative frequency coincided with 20 to 21 m; 95%, 24 to 25 m; and 99%, 31 to 32 m. The 20 m• 20 m and 40 rex40 m quadrats contained 25 and 40 plants, respectively. We conclude that this number of individuals and size of area are the minimum required for the conservation of this species.

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Pattern detection in plant populations through the analysis of plant-to-all-plants distances

Cited by 54 publications

References 11 publications

Clump spacing in a desert dwarf shrub community

Clump spacing in a desert dwarf shrub community

Spatial pattern and ecological analysis

Spatial distribution patterns and implications for conservation ofScrophularia takesimensis (Scrophulariaceae), an endangered endemic species on Ulleung Island, Korea

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