Abstract

Point process statistics provides valuable tools for many ecological studies, where 'points' are commonly determined to represent the locations of plants or animals and 'marks' are additional items such as species or size. In the statistical analysis of marked point patterns, various correlation functions are used such as the mark variogram or the mark correlation function. Often the interpretation of these functions is not easy and the nonspatial ecologist is in need of support. In order to make the analysis of spatial point patterns more accessible to ecologists, we introduced and tested a new graphical method, the mark-mark scatterplot. This plot visualises the marks of point pairs of inter-point distances r smaller than some small distance r(max). We tested the application of the mark-mark scatterplot by reconsidering three quite different tree patterns: a pattern of longleaf pine trees from the southern US which was strongly influenced by fires, a tropical tree pattern of the species Shorea congestiflora from Sri Lanka and a Scots pine pattern from Siberia (Russia). The new method yielded previously undetected cause-effect information on mark behaviour at short inter-point distances and thus improved the analysis with mark correlation functions as well as complemented the information they provided. We discovered important new correlations in clusters of trees at close proximity. The application of the mark-mark scatterplot will facilitate the interpretation of point process summary statistics and will make point process analysis more accessible to ecologists not specialized in point process statistics.

Keywords

Spatial ecological patterns; Longleaf pine; Shorea congestiflora; Scots pine; Plant interaction; Fire ecology; Mark variogram; Mark correlation function; Spatial scales

Published in

Ecological Informatics
2019, Volume: 49, pages: 13-21
Publisher: ELSEVIER SCIENCE BV

SLU Authors

• Pommerening, Arne

  • Department of Forest Ecology and Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics
Ecology


Publication identifier

DOI: https://doi.org/10.1016/j.ecoinf.2018.11.002

Permanent link to this page (URI)

https://res.slu.se/id/publ/98123