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.
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
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.
Abstract
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.
Abstract
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 Ecology96 (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 Ecology96 (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 Ecography38 (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 Ecography36 (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)
We use cookies, which are necessary for the basic functionality of our website, so that it can be continuously optimised for you and its user-friendliness improved. In addition, we use the web analysis tool Matomo, which tracks data anonymously. This enables us to statistically evaluate the use of our website. Your consent to the use of Matomo can be revoked at any time via the privacy policy.
