Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data

By J. Perry, Andrew M. Liebhold, M. Rosenberg, Jennifer L. Dungan, M. Miriti, A. Jakomulska, S. Citron-Pousty. 2002

474 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/37abdbc9fa98bd27fd20b18e37ba39a5073e90e0

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses (2005, methodology): “The influences of different quantification techniques and data types on the assessment of scales of pattern must also be considered (Dale et al., 2002; Perry et al., 2002) We examined three different quantitative techniques (spectral, wavelet, and lacunarity analyses) used to extract information on ”
Improving generalized regression analysis for the spatial prediction of forest communities (2006, background): “Statistical solutions exist for correcting this bias (Dutilleul et al., 1993; Keitt et al., 2002; Legendre et al., 2002; Perry et al., 2002), but these are not yet commonly applied in species distribution modelling.”
Rings, circles, and null-models for point pattern analysis in ecology (2004, background): “Ecol- ogists study spatial pattern to infer the existence of underlying processes (Perry et al. 2002).”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, methodology): “Established procedures include (Isaaks and Shrivastava 1989, Perry et al. 2002): Moran’s I plots (also termed Moran’s I correlogram by Legendre and Legendre 1998), Geary’s c correlograms and semi-variograms.”
Scent-marking behaviour of the honey badger, Mellivora capensis (Mustelidae), in the southern Kalahari (2003, methodology): “We used the program Sadie to investigate whether scent marks were significantly random, regular or aggregated by using counts of scent-marking events per grid cell (Perry et al. 2002).”
A balanced view of scale in spatial statistical analysis (2002, background): “This definition, based strictly on the spatial aspects of the sampling unit, ignores the crucial element of the attribute (z) measurement scale (Perry et al. 2002).”
Beyond description: the active and effective way to infer processes from spatial patterns. (2009, background): “Recent changes in our understanding of causality (Pearl 2000, Shipley 2000), the continued development of analytical tools for spatial data (Perry et al. 2002, Borcard et al. 2004, Wiegand and Moloney 2004, Fortin and Dale 2005, Keitt and Urban 2005, Ives and Zhu 2006), and our awareness that a…”
Functional biodiversity in the agricultural landscape: relationships between weeds and arthropod fauna. (2010, methodology): “Space-time geostatistical approaches have been used in both weed research (Kyriakidis & Journel, 1999) and entomology (Perry et al., 2002).”
Integrating the statistical analysis of spatial data in ecology (2002, unknown): “geological or geographical applications, but as Ripley (1981) showed, for example, point locations can be aggregated into counts in quadrats which can then be analyzed for spatial autocorrelation.”