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Cited by 1,157Point processes and geometric inequalitiesSpatial and Panel Data AnalysisEcology and Vegetation Dynamics Studies

Rings, circles and null models for point pattern analysis in ecology.

Wiegand, Thorsten, A. Moloney, Kirk·2004·Oikos
DOI 10.1111/j.0030-1299.2004.12497.x
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Abstract

A large number of methods for the analysis of point pattern data have been developed in a wide range of scientific fields. First‐order statistics describe large‐scale variation in the intensity of points in a study region, whereas second‐order characteristics are summary statistics of all point‐to‐point distances in a mapped area and offer the potential for detecting both different types and scales of patterns. Second‐order analysis based on Ripley's K‐function is increasingly used in ecology to characterize spatial patterns and to develop hypothesis on underlying processes; however, the full range of available methods has seldomly been applied by ecologists. The aim of this paper is to provide guidance to ecologists with limited experience in second‐order analysis to help in the choice of appropriate methods and to point to practical difficulties and pitfalls. We review (1) methods for analytical and numerical implementation of two complementary second‐order statistics, Ripley's K and the O‐ring statistic, (2) methods for edge correction, (3) methods to account for first‐order effects (i.e. heterogeneity) of univariate patterns, and (4) a variety of useful standard and non‐standard null models for univariate and bivariate patterns. For illustrative purpose, we analyze examples that deal with non‐homogeneous univariate point patterns. We demonstrate that large‐scale heterogeneity of a point‐pattern biases Ripley's K‐function at smaller scales. This bias is difficult to detect without explicitly testing for homogeneity, but we show that it can be removed when applying methods that account for first‐order effects. We synthesize our review in a number of step‐by‐step recommendations that guide the reader through the selection of appropriate methods and we provide a software program that implements most of the methods reviewed and developed here.

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  • Termite mounds in the study site exhibit significant over-dispersion (even spacing) at spatial scales less than 100 m.

    “We quantified the spatial pattern of termite mounds using Ripley's K [28], showing that they exhibit significant over-dispersion at spatial scales<100 m (Figure S2).”

    From Spatial Pattern Enhances Ecosystem Functioning in an African Savanna (2010) · cites this paper as [40]

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Authors
Wiegand, T., & A. Moloney, K.
Affiliations
Helmholtz Centre for Environmental Research (DE)
ORCID
Wiegand, Thorsten
Venue
Oikos
Publisher
Wiley
Published
2004-01-16
Volume
104
Issue
2
Pages
209-229
ISSN
  • 0030-1299 (ISSN-L)
  • 1600-0706
DOI
10.1111/j.0030-1299.2004.12497.x
OpenAlex
W2051907667
Cited by
1,157
References
78
Citations by year
2026: 25 · 2025: 38 · 2024: 55 · 2023: 39 · 2022: 65 · 2021: 65 · 2020: 59 · 2019: 65 · 2018: 44 · 2017: 78 · 2016: 62 · 2015: 80 · 2014: 72 · 2013: 62 · 2012: 68
FWCI
45.35
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
Oikos (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Applied Mathematics › Point processes and geometric inequalities
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
Keywords
point pattern analysis, Ripley's K function, second-order statistics, first-order effects, spatial heterogeneity, edge correction, null models, spatial pattern detection

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