A1 Journal article (refereed)
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests (2022)
Kuronen, M., Särkkä, A., Vihola, M., & Myllymäki, M. (2022). Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests. Environmental and Ecological Statistics, 29(1), 185-205. https://doi.org/10.1007/s10651-021-00514-3
JYU authors or editors
Publication details
All authors or editors: Kuronen, Mikko; Särkkä, Aila; Vihola, Matti; Myllymäki, Mari
Journal or series: Environmental and Ecological Statistics
ISSN: 1352-8505
eISSN: 1573-3009
Publication year: 2022
Publication date: 20/08/2021
Volume: 29
Issue number: 1
Pages range: 185-205
Publisher: Springer Science and Business Media LLC
Publication country: United Kingdom
Publication language: English
DOI: https://doi.org/10.1007/s10651-021-00514-3
Publication open access: Openly available
Publication channel open access: Partially open access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77454
Publication is parallel published: https://arxiv.org/abs/2005.01962
Abstract
We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points xx affects another set of points yy 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 xx 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.
Keywords: mathematical models; Bayesian analysis; Monte Carlo methods; Markov chains; silviculture; tree stand; regeneration (biology)
Free keywords: Bayesian inference; competition kernel; Laplace approximation; MCMC; spatial random effects; tree regeneration
Contributing organizations
Ministry reporting: Yes
Reporting Year: 2022
JUFO rating: 1
