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  5. Characterizing dependence of Irish sitka spruce stands using spatio-temporal sum-metric models
 
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Characterizing dependence of Irish sitka spruce stands using spatio-temporal sum-metric models

Author(s)
O'Rourke, Sarah  
Mac Siúrtáin, Máirtín Pádraig  
Kelly, Gabrielle E.  
Uri
http://hdl.handle.net/10197/8238
Date Issued
2016-10
Date Available
2016-12-21T12:24:46Z
Abstract
Individual tree dependence in forest plots is spatially dependent and changes over time, and the magnitude of spatial dependence may also change over time, particularly in stands subjected to thinning. Models for tree dependence in the literature have been mainly restricted to either spatial models or temporal models. We extend these to spatio-temporal models. The data are from three long-term, repeatedly measured, experimental plots of Sitka spruce (Picea sitchensis [Bong.] Carr.) in Co. Wicklow, Ireland, with thinning treatments of unthinned, 40% thinned, and 50% thinned, respectively. A model for tree by diameter at breast height, over all locations in each plot and all time points, was fitted with fixed covariates and with a sum-metric spatio-temporal variogram for the covariance structure. In the variogram, the spatial correlation component followed a wave function (due to competition at small distances). The correlation over time also followed a wave variogram, whereas the spatio-temporal anisotropy captured the space-time interaction. The models indicate, once fixed effects are accounted for, that spatial variability and correlation are more important than temporal. Models were fitted to plots with three different treatments to demonstrate that model parameters differed by thinning type but were consistent in their interpretation with thinning type. The models show that describing spatial dependence is important for understanding the nature of tree growth and its prediction.
Type of Material
Journal Article
Publisher
Society of American Foresters
Journal
Forest Science
Volume
62
Issue
5
Start Page
490
End Page
502
Subjects

Negative autocorrelat...

Regression-kriging

Spatio-temporal tree ...

Wave covariance funct...

DOI
10.5849/forsci.15-083
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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FS_Blind_Manuscript_17_03_2016.pdf

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738d01d73b6a2768a05b4d38edaab2c7

Owning collection
Mathematics and Statistics Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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