Stochastic structure and individual-tree growth models

Fox, Julian C., Ades, Peter K. and Bi, Huiquan (2001) Stochastic structure and individual-tree growth models. Forest Ecology And Management, 154 1-2: 261-276. doi:10.1016/S0378-1127(00)00632-0

Author Fox, Julian C.
Ades, Peter K.
Bi, Huiquan
Title Stochastic structure and individual-tree growth models
Journal name Forest Ecology And Management   Check publisher's open access policy
ISSN 0378-1127
Publication date 2001-11-15
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1016/S0378-1127(00)00632-0
Open Access Status Not yet assessed
Volume 154
Issue 1-2
Start page 261
End page 276
Total pages 16
Place of publication Amsterdam
Publisher Elsevier Academic Press Inc
Language eng
Abstract The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword Forestry
Growth Modelling
Spatial Auto-correlation
Linear-regression Model
Field Experiments
Diameter Growth
Stand Management
Yield Trials
Scots Pine
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown
Additional Notes This document is a journal review.

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: School of Geography, Planning and Environmental Management Publications
School of Architecture Publications
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Citation counts: TR Web of Science Citation Count  Cited 97 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 13 Aug 2007, 22:38:56 EST