Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) — ##plugins.generic.mcfnsReadingTools.readingTools##

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Reverse Causality in Size-Dependent Growth

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1.Title##plugins.generic.mcfnsReadingTools.titleLabel##Reverse Causality in Size-Dependent Growth
2.Creator##plugins.generic.mcfnsReadingTools.creators##Oscar Garcia; Dasometrics, Concon, Chile; Χιλή
3.Subject##plugins.generic.mcfnsReadingTools.keywords##Confounding; bias; consistency; path analysis; structural equation modelling; mixed effects; endogenous variables; instrumental variables; allometry
3.Subject##plugins.generic.mcfnsReadingTools.disciplines##Forest Biometrics; Growth and Yield Modeling; Quantitative Silviculture
4.Description##plugins.generic.mcfnsReadingTools.abstract##Size-dependent growth is likely to be growth-dependent size instead. Larger organisms do not necessarily grow faster, but faster-growing ones always tend to be larger. This fact has been generally ignored. Correct causality structures are essential for plausible predictions outside the range of the data. Some techniques potentially useful for studying these issues are brie y described. In forestry, the relevance of multiple size measures like volume, height, diameter and basal area greatly complicates the picture. Additionally, purely mathematical sources of growth-size correlations arise. Physiological considerations suggest avoiding stem thickness measures as explanatory variables in growth equations.
5.Publisher##plugins.generic.mcfnsReadingTools.publisher##Contemporary Journal Concept Press
6.Contributor##plugins.generic.mcfnsReadingTools.contributors##
7.Date##plugins.generic.mcfnsReadingTools.date##2018-03-30
8.Type##plugins.generic.mcfnsReadingTools.type##Text.Serial.Journal; Reasoning and Derications
9.Format##plugins.generic.mcfnsReadingTools.format##PDF (Αγγλικά); application/pdf
10.Identifier##plugins.generic.mcfnsReadingTools.identifier##https://mcfns.com/index.php/Journal/el/article/view/10.1
11.Source##plugins.generic.mcfnsReadingTools.source##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS); Τόμ. 10 Αρ. 1: MCFNS March 30, 2018; 1-5 (5)
11.Source##plugins.generic.mcfnsReadingTools.onlineIssn##1946-7664
12.Language##plugins.generic.mcfnsReadingTools.language##en
13.Relation##plugins.generic.mcfnsReadingTools.suppFiles##
14.Coverage##plugins.generic.mcfnsReadingTools.coverageGeo##
15.Rights##plugins.generic.mcfnsReadingTools.rights##Mathematical and Computational Forestry & Natural-Resource Sciences (MCFNS) 2018