Area interaction point processes for bivariate point patterns in a Bayesian context

Nightingale, G. F., Illian, J. B. , King, R. and Nightingale, P. (2019) Area interaction point processes for bivariate point patterns in a Bayesian context. Journal of Environmental Statistics, 9(2),

Abstract

In this paper we consider bivariate point patterns which may contain both attractive and inhibitive interactions. The two subpatterns may depend on each other with both intra- and interspecific interactions possible. We use area interaction point processes for quantifying both attractive and inhibitive interactions in contrast to pairwise interaction point processes, typically model regular point patterns. The ability to permit both attraction and repulsion is a valuable feature and allows for the modelling of different forms of interactions in a given community. The differentiation between intra- and interspecific interactions in one model accounts for the fact that the presence of a second species may “mask” or “magnify” existing intraspecific interactions. A Bayesian approach has been applied for estimating interaction parameters and for discriminating between eight competing research hypotheses. For the particular application to modelling the interactions of species in a highly biodiverse forest, this study reveals posterior support for an interspecific interaction of attraction between the two species considered and may serve to inform forest rehabilitation schemes relating to this forest. Overall, knowledge of the interactions of key species in any given forest would be invaluable to reforestation efforts if this forest is later ravaged by wildfires.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Illian, Professor Janine
• Authors: Nightingale, G. F., Illian, J. B., King, R., and Nightingale, P.
• College/School: College of Science and Engineering > School of Mathematics and Statistics > Statistics
• Journal Name: Journal of Environmental Statistics
• Publisher: UCLA Department of Statistics
• ISSN: 1945-1296
• ISSN (Online): 1945-1296