Hierarchical spatial point process analysis for a plant community with high biodiversity

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36 Citations (Scopus)

Abstract

A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species' interactions and to generate new biological questions and hypotheses on species' interactions.
Original languageEnglish
JournalEnvironmental and Ecological Statistics
Volume16
Issue number3
Pages (from-to)389-405
ISSN1352-8505
DOIs
Publication statusPublished - 2009

Fingerprint

Spatial Point Process
Biodiversity
plant community
biodiversity
Interaction
regeneration
Radius
Spatial Point Pattern
Regeneration
Prior Information
Point Process
Bayesian Approach
Process Model
Maximum Likelihood
Community
process analysis
Process analysis
Point process
Unknown
Demonstrate

Cite this

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title = "Hierarchical spatial point process analysis for a plant community with high biodiversity",
abstract = "A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species' interactions and to generate new biological questions and hypotheses on species' interactions.",
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Hierarchical spatial point process analysis for a plant community with high biodiversity. / Illian, Janine B.; Møller, Jesper; Waagepetersen, Rasmus.

In: Environmental and Ecological Statistics, Vol. 16, No. 3, 2009, p. 389-405.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Hierarchical spatial point process analysis for a plant community with high biodiversity

AU - Illian, Janine B.

AU - Møller, Jesper

AU - Waagepetersen, Rasmus

PY - 2009

Y1 - 2009

N2 - A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species' interactions and to generate new biological questions and hypotheses on species' interactions.

AB - A complex multivariate spatial point pattern of a plant community with high biodiversity is modelled using a hierarchical multivariate point process model. In the model, interactions between plants with different post-fire regeneration strategies are of key interest. We consider initially a maximum likelihood approach to inference where problems arise due to unknown interaction radii for the plants. We next demonstrate that a Bayesian approach provides a flexible framework for incorporating prior information concerning the interaction radii. From an ecological perspective, we are able both to confirm existing knowledge on species' interactions and to generate new biological questions and hypotheses on species' interactions.

U2 - 10.1007/s10651-007-0070-8

DO - 10.1007/s10651-007-0070-8

M3 - Journal article

VL - 16

SP - 389

EP - 405

JO - Environmental and Ecological Statistics

JF - Environmental and Ecological Statistics

SN - 1352-8505

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ER -