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      Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason

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          Influences of species, latitudes and methodologies on estimates of phenological response to global warming

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            Chilling of dormant buds hyperinduces FLOWERING LOCUS T and recruits GA-inducible 1,3-beta-glucanases to reopen signal conduits and release dormancy in Populus.

            In trees, production of intercellular signals and accessibility of signal conduits jointly govern dormancy cycling at the shoot apex. We identified 10 putative cell wall 1,3-β-glucanase genes (glucan hydrolase family 17 [GH17]) in Populus that could turn over 1,3-β-glucan (callose) at pores and plasmodesmata (PD) and investigated their regulation in relation to FT and CENL1 expression. The 10 genes encode orthologs of Arabidopsis thaliana BG_ppap, a PD-associated glycosylphosphatidylinositol (GPI) lipid-anchored protein, the Arabidopsis PD callose binding protein PDCB, and a birch (Betula pendula) putative lipid body (LB) protein. We found that these genes were differentially regulated by photoperiod, by chilling (5°C), and by feeding of gibberellins GA(3) and GA(4). GA(3) feeding upregulated all LB-associated GH17s, whereas GA(4) upregulated most GH17s with a GPI anchor and/or callose binding motif, but only GA(4) induced true bud burst. Chilling upregulated a number of GA biosynthesis and signaling genes as well as FT, but not CENL1, while the reverse was true for both GA(3) and GA(4). Collectively, the results suggest a model for dormancy release in which chilling induces FT and both GPI lipid-anchored and GA(3)-inducible GH17s to reopen signaling conduits in the embryonic shoot. When temperatures rise, the reopened conduits enable movement of FT and CENL1 to their targets, where they drive bud burst, shoot elongation, and morphogenesis.
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              A unified model for budburst of trees.

              I Chuine (2000)
              Accurate plant phenology (seasonal plant activity driven by environmental factors) models are vital tools for ecosystem simulation models and for predicting the response of ecosystems to climate change. Since the early 1970s, efforts have concentrated on predicting phenology of the temperate and boreal forests because they represent one-third of the carbon captured in plant ecosystems and they are the principal ecosystems with seasonal patterns of growth on Earth (one-fifth of the plant ecosystems area). Numerous phenological models have been developed to predict the growth timing of temperate or boreal trees. They are in general empirical, nonlinear and non-nested. For these reasons they are particularly difficult to fit, to test and to compare with each other. The methodological difficulties as well as the diversity of models used have greatly slowed down their improvement. The aim of this study was to show that the most widely used models simulating vegetative or reproductive phenology of trees are particular cases of a more general model. This unified model has three main advantages. First, it allows for a direct estimation of (i) the response of bud growth to either chilling or forcing temperatures and (ii) the periods when these temperatures affect the bud growth. Second, it can be simplified according to standard statistical tests for any particular species. Third, it provides a standardized framework for phenological models, which is essential for comparative studies as well as for robust model identification. Copyright 2000 Academic Press.
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                Author and article information

                Journal
                Global Change Biology
                Glob Change Biol
                Wiley
                13541013
                December 2017
                December 2017
                July 06 2017
                : 23
                : 12
                : 5189-5202
                Affiliations
                [1 ]Plant Ecology; Institute of Integrative Biology; ETH Zurich; Zurich Switzerland
                [2 ]Department of Mathematics and Department of Computational Science; University of Zurich; Zurich Switzerland
                [3 ]MeteoSwiss; Zurich-Airport; Zurich Switzerland
                Article
                10.1111/gcb.13781
                28586135
                68233e76-5fea-44c2-9f24-50d2426962b8
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1.1

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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