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Three-dimensional metrics for the analysis of spatiotemporal data in ecology
Geomatics and Landscape Ecology Lab, Carleton University, Nesbitt, Building, Ottawa, Ontario, Canada.
Complex Systems Laboratory, Département de géographie, Université de Montréal, C.P., Montréal, QC, Canada.
Redpath Museum and Department of Biology, McGill University, Montreal, Quebec, Canada.ORCID iD: 0000-0002-3982-0829
2008 (English)In: Ecological Informatics, ISSN 1574-9541, Vol. 3, no 6, 343-353 p.Article in journal (Refereed) Published
Abstract [en]

A suite of simple metrics that can be used to analyse three-dimensional data sets is presented. We show how these metrics can be applied to raster-based, ecological mosaics sampled over uniform time intervals, such as might be obtained from a series of photographs or from repeated spatial sampling in the field. In these analyses, the concept of a 2D landscape “patch” is replaced by a 3D space–time “blob”. The structure of a dataset can be analysed via the characterisation of blobs, using a number of simple composition and configuration metrics. The use of different metrics, including modified versions of some common landscape metrics such as contagion, that describe the distribution of blobs in space and time, is demonstrated using both model and empirical data. With the increasing availability of spatiotemporal data sets in ecology, such three-dimensional metrics may be indispensable tools for the detection and characterization of landscape change in the context of human and naturally caused disturbances.

Place, publisher, year, edition, pages
Elsevier, 2008. Vol. 3, no 6, 343-353 p.
Keyword [en]
Spatiotemporal analysis, Ecological dynamics, Landscape metrics, Complexity, Heterogeneity, Spatially-explicit models, Landscape ecology, Remote sensing, Patch dynamics
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URN: urn:nbn:se:umu:diva-80812DOI: 10.1016/j.ecoinf.2008.07.001ISI: 000262019000001OAI: diva2:651650
Available from: 2013-09-26 Created: 2013-09-26 Last updated: 2013-10-25Bibliographically approved

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Thibert-Plante, Xavier
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