Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
Journal article, 2022

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.

Competition kernel


Laplace approximation

Tree regeneration

Spatial random effects

Bayesian inference


Mikko Kuronen

Natural Resources Institute Finland (Luke)

Aila Särkkä

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Matti Vihola

University of Jyväskylä

Mari Myllymäki

Natural Resources Institute Finland (Luke)

Environmental and Ecological Statistics

1352-8505 (ISSN) 1573-3009 (eISSN)

Vol. 29 1 185-205

Subject Categories

Applied Mechanics

Bioinformatics (Computational Biology)

Probability Theory and Statistics



More information

Latest update

4/5/2022 5