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

Abstract

Pousty, S. 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. -Ecography 25: 578-600. This paper aims to provide guidance to ecologists with limited experience in spatial analysis to help in their choice of techniques. It uses examples to compare methods of spatial analysis for ecological field data. A taxonomy of different data types is presented, including point-and area-referenced data, with and without attributes. Spatially and non-spatially explicit data are distinguished. The effects of sampling and other transformations that convert one data type to another are discussed; the possible loss of spatial information is considered. Techniques for analyzing spatial pattern, developed in plant ecology, animal ecology, landscape ecology, geostatistics and applied statistics are reviewed briefly and their overlap in methodology and philosophy noted. The techniques are categorized according to their output and the inferences that may be drawn from them, in a discursive style without formulae. Methods are compared for four case studies with field data covering a range of types. These are: 1) percentage cover of three shrubs along a line transect; 2) locations and volume of a desert plant in a 1 ha area; 3) a remotely-sensed spectral index and elevation from 10 5 km 2 of a mountainous region; and 4) land cover from three rangeland types within 800 km 2 of a coastal region. Initial approaches utilize mapping, frequency distributions and variance-mean indices. Analysis techniques we compare include: local quadrat variance, block quadrat variance, correlograms, variograms, angular correlation, directional variograms, wavelets, SADIE, nearest neighbour methods, Ripley's L . (t), and various landscape ecology metrics. Our advice to ecologists is to use simple visualization techniques for initial analysis, and subsequently to select methods that are appropriate for the data type and that answer their specific questions of interest. It is usually prudent to employ several different techniques.

Analysis of trends in fused AVHRR and MODIS NDVI data for 1982–2006: Indication for a CO2 fertilization effect in global vegetation

“…Some correlations between the RVI and leaf-out date are positive ( Figure 1); the correlations between spring temperature and leaf-out day was positive for these sites as well (not shown). The RVI 0.5 ∘ Â 0.5 ∘ cells and site phenological data are observed at different spatial scales; however, both NDVI and temperature anomalies (closely linked to the RVI anomalies in temperate latitudes) have in general high spatial correlations at distances <60 km [Perry et al, 2002;Gunst, 1995], and this allows for a meaningful comparison.…”

Estrutura demográfica e padrão espacial de uma população natural de Araucaria angustifolia (Bertol.) Kuntze (araucariaceae), na Reserva Genética Florestal de Caçador, Estado de Santa Catarina

“…O estudo do padrão espacial, além de ser o primeiro passo para espécies que não tenham estudos detalhados, permite gerar hipóteses sobre processos que dão origem a determinado padrão (DALE, 1999). Um padrão pode ser resposta à heterogeneidade ambiental, interações intra e interespecíficas, como competição e predação, dispersão, crescimento, senescência, mortalidade e reprodução (JANZEN, 1970;BAROT et al, 1999;DALE, 1999;PERRY et al, 2002;AIBA et al, 2004;PALMIOTTO et al, 2004;STOLL e BERGIUS, 2005;RODRIGUES et al, 2007).…”

Geostatistical tools for the study of insect spatial distribution: practical implications in the integrated management of orchard and vineyard pests

“…When attractive devices are used for a spatial monitoring scheme, some geostatistical properties must be taken into account (Perry et al 2002): the extent, describing the dimension of the study area; the support, i.e. the sampling unit size corresponding to the attractive range of the trap; the lag, i.e.…”