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

Preliminary JUFO rating: 1