Spatial Point Pattern Analysis

point pattern

Over the last decade or so, there has been increasing interest in the study of spatial patterns in ecology. Ecologists study spatial patterns to better understand the processes that may have caused the observed patterns. A particularly important class of spatial data in ecology is given by the location of individuals in space. Such “point-pattern data” are given by the coordinates of the ecological objects of interest and may also include additional properties (so called “marks”). The study of spatial patterns at the Department of Ecological Modelling is embedded within the broader framework of combined use of spatially-explicit simulation models and spatial pattern analysis (i.e., pattern-oriented modelling; Grimm et al. 2005). The spatial data generated by simulation models are compared with observed spatial data, and application of inverse techniques may allow for an identification of processes through patterns (e.g., Cipriotti et al. 2012, 2014; May et al. 2015, 2016).

Thorsten Wiegand from the Department of Ecological Modelling developed the software Programita for conducting point-pattern analysis with several summary functions such as the pair-correlation function, mark correlation functions or multivariate summary functions. Programita contains many standard and non-standard null models that cover most practical applications of point pattern analysis in ecology. Since its launch in January 2014 the Programita software has been downloaded by more than 1000 scientists from all over the world. Google scholar lists more than 300 articles in ISI journals that used it.

point pattern book cover
An important product is the publication of the “Handbook of spatial point pattern analysis in Ecology” by Thorsten Wiegand. Although a broad array of statistical methods for analyzing spatial point patterns have been available for several decades, they haven’t been extensively applied in an ecological context. Addressing this gap, the Handbook shows how the techniques of point pattern analysis are useful for tackling ecological problems. Within an ecological framework, the book guides readers through a variety of methods for different data types and aids in the interpretation of the results obtained by point pattern analysis.

Features of the book

  • Focuses on the application of spatial point pattern analysis in an ecological context
  • Helps ecologists unfamiliar with advanced statistics select the proper analysis method
  • Emphasizes the formulation of appropriate null models and point processes for describing the features of point patterns and testing ecological hypotheses of spatial dependence
  • Provides the Programita software package on the first author’s website, enabling readers to perform analyses with their own point pattern data
  • Includes a collection of real-world examples
  • Offers suggestions on how to use the book for teaching graduate students
  • Wiegand, T., and K.A. Moloney. 2014. A handbook of spatial point pattern analysis in ecology. Chapman and Hall/CRC press, Boca Raton, FL

Selected Publications

  • May, F., T. Wiegand, S. Lehmann & A. Huth (2016).
    Do abundance distributions and species aggregation correctly predict spatial biodiversity patterns in tropical forests?
    Global Ecology and Biogeography. in press
  • Wang, X., T. Wiegand, N.J.B. Kraft, N.G. Swenson, S.J. Davies, Z. Hao, R. Howe, Y. Lin, K. Ma, X. Mi, S-H Su, I-F Sun & A Wolf (2016).
    Stochastic dilution effects weaken deterministic effects of niche-based processes on the spatial distribution of large trees in species rich forests.
    Ecology. in press.
  • Velázquez, E., I. Martínez, S. Getzin, K.A. Moloney & T. Wiegand (2016).
    An evaluation of the state of spatial point pattern analysis in ecology.
    Ecography. in press.
  • Wang, X., Wiegand, T., Swenson, N.G., Wolf, A.T., Howe, R.W., Hao, Z., Lin, F., Ye, J., Yuan, Z., (2015):
    Mechanisms underlying local functional and phylogenetic beta diversity in two temperate forests
    Ecology 96 (4), 1062 - 1073
    full text (pdf)
  • Punchi-Manage, R., Wiegand, T., Wiegand, K., Getzin, S., Huth, A., Gunatilleke, C.V.S., Gunatilleke, I.A.U.N., (2015):
    Neighborhood diversity of large trees shows independent species patterns in a mixed dipterocarp forest in Sri Lanka
    Ecology 96 (7), 1823 - 1834
    full text (pdf)
  • May, F., Huth, A., Wiegand, T., (2015):
    Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest
    Proc. R. Soc. B-Biol. Sci. 282 (1802), art. 20141657
    full text (pdf)
  • May, F., Huth, A., Wiegand, T., (2015):
    Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest
    Proc. R. Soc. B-Biol. Sci. 282 (1802), art. 20141657
    full text (url)
  • Fedriani, J.M., Wiegand, T., Calvo, G., Suárez-Esteban, A., Jácome, M., Żywiec, M., Delibes, M., (2015):
    Unravelling conflicting density- and distance-dependent effects on plant reproduction using a spatially explicit approach
    J. Ecol. 103 (5), 1344 - 1353
    full text (url)
  • Getzin, S., Wiegand, K., Wiegand, T., Yizhaq, H., von Hardenberg, J., Meron, E., (2015):
    Adopting a spatially explicit perspective to study the mysterious fairy circles of Namibia
    Ecography 38 (1), 1 - 11
    full text (url)
  • Getzin, S., Wiegand, T., Hubbell, S.P., (2014):
    Stochastically driven adult–recruit associations of tree species on Barro Colorado Island
    Proc. R. Soc. B-Biol. Sci. 281 (1790), art. 20140922
    full text (url)
  • Wiegand, T., He, F., Hubbell, S.P., (2013):
    A systematic comparison of summary characteristics for quantifying point patterns in ecology
    Ecography 36 (1), 92 - 103
    full text (url)
  • Shen, G., Wiegand, T., Mi, X., He, F., (2013):
    Quantifying spatial phylogenetic structures of fully stem-mapped plant communities
    Methods Ecol. Evol. 4 (12), 1132 - 1141
    full text (url)
  • Wiegand, T., Huth, A., Getzin, S., Wang, X., Hao, Z., Gunatilleke, C.V.S., Gunatilleke, I.A.U.N., (2012):
    Testing the independent species' arrangement assertion made by theories of stochastic geometry of biodiversity
    Proc. R. Soc. B-Biol. Sci. 279 (1741), 3312 - 3320
    full text (url)
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