An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics

Although forest tree pattern-dynamics has long been a focus for ecological theory, many aspects of basic analysis remain problematic.This paper describes, examines and illustrates an 'extreme-value' approach to clump detection. Simulations demonstrate that the approach, though simple, is sensitive a...

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Main Authors: Sheil, Douglas, Ducey, M.J.
Format: Journal Article
Language:Inglés
Published: 2002
Subjects:
Online Access:https://hdl.handle.net/10568/18572
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author Sheil, Douglas
Ducey, M.J.
author_browse Ducey, M.J.
Sheil, Douglas
author_facet Sheil, Douglas
Ducey, M.J.
author_sort Sheil, Douglas
collection Repository of Agricultural Research Outputs (CGSpace)
description Although forest tree pattern-dynamics has long been a focus for ecological theory, many aspects of basic analysis remain problematic.This paper describes, examines and illustrates an 'extreme-value' approach to clump detection. Simulations demonstrate that the approach, though simple, is sensitive and well suited to identifying aggregation, even in small data sets.Though powerful, the extreme-value tests are slightly conservative.The approach is adaptable to other null distributions and applications. An illustration uses tree data from a Ugandan forest plot with records from 1939 to 1992. One plausible explanation for observed stem increases in this plot is an unusually high incidence of large tree-fall events. Evidence for this is sought through spatial localization of various stem populations. Various technical and ecological aspects of the extreme-value approach and tree spatial analyses are discussed.
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spelling CGSpace185722025-01-24T14:20:30Z An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics Sheil, Douglas Ducey, M.J. forest trees distribution patterns aggregation experimental plots rain forests Although forest tree pattern-dynamics has long been a focus for ecological theory, many aspects of basic analysis remain problematic.This paper describes, examines and illustrates an 'extreme-value' approach to clump detection. Simulations demonstrate that the approach, though simple, is sensitive and well suited to identifying aggregation, even in small data sets.Though powerful, the extreme-value tests are slightly conservative.The approach is adaptable to other null distributions and applications. An illustration uses tree data from a Ugandan forest plot with records from 1939 to 1992. One plausible explanation for observed stem increases in this plot is an unusually high incidence of large tree-fall events. Evidence for this is sought through spatial localization of various stem populations. Various technical and ecological aspects of the extreme-value approach and tree spatial analyses are discussed. 2002 2012-06-04T09:08:34Z 2012-06-04T09:08:34Z Journal Article https://hdl.handle.net/10568/18572 en Sheil, D., Ducey, M.J. 2002. An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics . Journal of Tropical Ecology 18 :671-686. ISSN: 0564-3295.
spellingShingle forest trees
distribution
patterns
aggregation
experimental plots
rain forests
Sheil, Douglas
Ducey, M.J.
An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title_full An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title_fullStr An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title_full_unstemmed An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title_short An extreme-value approach to detect clumping and an application to tropical forest gap-mosaic dynamics
title_sort extreme value approach to detect clumping and an application to tropical forest gap mosaic dynamics
topic forest trees
distribution
patterns
aggregation
experimental plots
rain forests
url https://hdl.handle.net/10568/18572
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