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Title:Modelling seasonal dynamics of secondary growth in R
Authors:ID Jevšenak, Jernej (Author)
ID Gričar, Jožica (Author)
ID Rossi, Sergio (Author)
ID Prislan, Peter (Author)
Files:URL URL - Source URL, visit https://onlinelibrary.wiley.com/doi/10.1111/ecog.06030
 
.pdf PDF - Presentation file, download (1,26 MB)
MD5: 4A22D0099883D42840822B0DBC30D3AD
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo SciVie - Slovenian Forestry Institute
Abstract:The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.
Keywords:artificial neural networks, cambium, generalized additive model, Gompertz function, growing season, intra-annual time series
Publication status:Published
Publication version:Version of Record
Year of publishing:2022
Number of pages:8 str.
Numbering:Vol. 2022, iss. 9, e06030
PID:20.500.12556/DiRROS-15308 New window
UDC:630*811
ISSN on article:1600-0587
DOI:10.1111/ecog.06030 New window
COBISS.SI-ID:116105475 New window
Note:Nasl. z nasl. zaslona; Opis vira z dne 21. 7. 2022;
Publication date in DiRROS:21.07.2022
Views:990
Downloads:619
Metadata:XML DC-XML DC-RDF
:
JEVŠENAK, Jernej, GRIČAR, Jožica, ROSSI, Sergio and PRISLAN, Peter, 2022, Modelling seasonal dynamics of secondary growth in R. Ecography [online]. 2022. Vol. 2022, no. 9, e06030. [Accessed 21 April 2025]. DOI 10.1111/ecog.06030. Retrieved from: https://dirros.openscience.si/IzpisGradiva.php?lang=eng&id=15308
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Record is a part of a journal

Title:Ecography
Publisher:Munksgaard International Publishers
ISSN:1600-0587
COBISS.SI-ID:517696537 New window

Document is financed by a project

Funder:ARRS - Slovenian Research Agency
Project number:J4-9297-2018
Name:Skladnost in časovno ujemanje med ogljikom vezanim v lesno biomaso in "eddy covariance" oceno neto ekosistemske produkcije za presvetljen gozdnat ekosistem" oceno neto ekosistemske produkcije za presvetljen gozdnat ekosistem
Funder:ARRS - Slovenian Research Agency
Project number:J4-7203-2016
Name:Kratkoročni in dolgoročni odzivi hrastov v submediteranu na ekstremne vremenske dogodke s pomočjo drevesno-anatomskih analiz in eko-fizioloških meritev
Funder:ARRS - Slovenian Research Agency
Project number:Z4-7318-2016
Name:Vpliv klimatskih sprememb na kambijevo aktivnosti, nastajanje in zgradbo lesa ter produktivnost bukve (Fagus sylvatica L.) in smreke (Picea abies L.) v Sloveniji
Funder:ARRS - Slovenian Research Agency
Project number:P4-0430-2022
Name:Gozdno-lesna veriga in podnebne spremembe: prehod v krožno biogospodarstvo
Funder:ARRS - Slovenian Research Agency
Project number:J4-2541-2020
Name:Vpliv podnebnih sprememb na dinamiko akumulacije lesne biomase bukve in smreke v Sloveniji in ovrednotenje s tem povezanih potencialov rasti biogospodarstva

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Undetermined
Keywords:umetne nevronske mreže, kambij, generalizirani linearni modeli, Gompertzova funkcija, rastna sezona, znotraj-letne časovne vrste


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