Literature DB >> 19193570

A comparison of phenological models of leaf bud burst and flowering of boreal trees using independent observations.

Tapio Linkosalo1, Hanna K Lappalainen, Pertti Hari.   

Abstract

We compared and tested Thermal Time, Sequential, Parallel and Flexible phenological models of leaf bud burst in birch (Betula pendula Roth. and B. pubescens Ehrh.) and flowering in bird cherry (Prunus padus L.) and rowan (Sorbus aucuparia L.). We used phenological records from Oulainen-Ohineva (64 degrees 13' N, 24 degrees 53' E) in central Finland from 1953 to 2002 to estimate model parameters. We tested the models with data collected in all but six years between 1896 and 2002 in southern and central Finland; we divided this dataset into two 50-year datasets. The use of three datasets enabled us to test the models with data that were independent of the parameter fitting data, facilitating robust evaluation of model performance. Several models that fitted the parameterization data well showed poorer performance when tested with the independent data. This may be because the models were over-parameterized and able to adapt to noise in the data in addition to the phenological phenomenon itself. Simple Thermal Time models performed best with independent data, and Sequential and Parallel models were similar in prediction accuracy. Although Thermal Time models simulated boreal phenological events under current climatic conditions, some precautions are needed with simulations of climatic warming. For example, changed conditions may increase the relative importance of chilling in the timing of bud burst under elevated temperature conditions, which could alter the performance of phenological models.

Entities:  

Mesh:

Year:  2008        PMID: 19193570     DOI: 10.1093/treephys/28.12.1873

Source DB:  PubMed          Journal:  Tree Physiol        ISSN: 0829-318X            Impact factor:   4.196


  17 in total

1.  A global analysis of the comparability of winter chill models for fruit and nut trees.

Authors:  Eike Luedeling; Patrick H Brown
Journal:  Int J Biometeorol       Date:  2010-08-22       Impact factor: 3.787

2.  Winter and spring warming result in delayed spring phenology on the Tibetan Plateau.

Authors:  Haiying Yu; Eike Luedeling; Jianchu Xu
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-29       Impact factor: 11.205

3.  Environmental controls on the phenology of moths: predicting plasticity and constraint under climate change.

Authors:  Anu Valtonen; Matthew P Ayres; Heikki Roininen; Juha Pöyry; Reima Leinonen
Journal:  Oecologia       Date:  2010-09-30       Impact factor: 3.225

4.  Nonlinear flowering responses to climate: are species approaching their limits of phenological change?

Authors:  Amy M Iler; Toke T Høye; David W Inouye; Niels M Schmidt
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2013-07-08       Impact factor: 6.237

5.  Estimation of the base temperature and growth phase duration in terms of thermal time for four grapevine cultivars.

Authors:  D Zapata; M Salazar; B Chaves; M Keller; G Hoogenboom
Journal:  Int J Biometeorol       Date:  2015-04-23       Impact factor: 3.787

6.  Bayesian calibration of the Unified budburst model in six temperate tree species.

Authors:  Yongshuo H Fu; Matteo Campioli; Gaston Demarée; Alex Deckmyn; Rafiq Hamdi; Ivan A Janssens; Gaby Deckmyn
Journal:  Int J Biometeorol       Date:  2011-02-06       Impact factor: 3.787

7.  Comparison of regression methods for phenology.

Authors:  Adrian Mark Ikin Roberts
Journal:  Int J Biometeorol       Date:  2011-07-26       Impact factor: 3.787

8.  From observations to experiments in phenology research: investigating climate change impacts on trees and shrubs using dormant twigs.

Authors:  Richard B Primack; Julia Laube; Amanda S Gallinat; Annette Menzel
Journal:  Ann Bot       Date:  2015-04-07       Impact factor: 4.357

9.  Models for the beginning of sour cherry blossom.

Authors:  Philipp Matzneller; Klaus Blümel; Frank-M Chmielewski
Journal:  Int J Biometeorol       Date:  2013-03-02       Impact factor: 3.787

10.  Models to predict the start of the airborne pollen season.

Authors:  Consolata Siniscalco; Rosanna Caramiello; Mirco Migliavacca; Lorenzo Busetto; Luca Mercalli; Roberto Colombo; Andrew D Richardson
Journal:  Int J Biometeorol       Date:  2014-09-19       Impact factor: 3.787

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