2020
DOI: 10.1073/pnas.2007058117
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Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers

Abstract: Wood formation consumes around 15% of the anthropogenic CO2 emissions per year and plays a critical role in long-term sequestration of carbon on Earth. However, the exogenous factors driving wood formation onset and the underlying cellular mechanisms are still poorly understood and quantified, and this hampers an effective assessment of terrestrial forest productivity and carbon budget under global warming. Here, we used an extensive collection of unique datasets of weekly xylem tissue formation (wood formatio… Show more

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Cited by 137 publications
(95 citation statements)
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References 57 publications
(117 reference statements)
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“…Besides water, temperature is known to influence wood formation, both in terms of phenology and growth rate. Growth start has been found to be largely temperature‐dependent (Delpierre et al., 2018; Huang et al., 2020; Oribe & Funada, 2017; Rossi et al., 2016) and using the temperature‐based CLM5.0 GDD‐model to simulate growth start worked well in confirming a likely t1 for our site. This implies that temperature may be involved in growth onset at our site to some degree, even though the daily‐resolution correlation analysis did not find a significant temperature‐effect on TRWi early in the year.…”
Section: Discussionsupporting
confidence: 73%
“…Besides water, temperature is known to influence wood formation, both in terms of phenology and growth rate. Growth start has been found to be largely temperature‐dependent (Delpierre et al., 2018; Huang et al., 2020; Oribe & Funada, 2017; Rossi et al., 2016) and using the temperature‐based CLM5.0 GDD‐model to simulate growth start worked well in confirming a likely t1 for our site. This implies that temperature may be involved in growth onset at our site to some degree, even though the daily‐resolution correlation analysis did not find a significant temperature‐effect on TRWi early in the year.…”
Section: Discussionsupporting
confidence: 73%
“…Moreover, simulated and observed proportions of formed cells are tightly synchronized mainly at the beginning of the tree-ring ( Figure 6 ). This is not surprising, because onset of cambial activity ( Rossi et al, 2008 , 2016 ; Treml et al, 2015 ; Delpierre et al, 2019 ; Huang et al, 2020 ) and regulation of first phases of xylogenesis ( Deslauriers and Morin, 2005 ) are strictly temperature-controlled in cold environments. The algorithm of the VS-model uses the cumulative temperature threshold to estimate the date of spring onset of cambial cell growth and, in the next step, calculates daily resolved kinetics of cell growth and differentiation based on the cell position in the radial file and climatically driven external growth rates ( Vaganov et al, 2006 ).…”
Section: Discussionmentioning
confidence: 99%
“…Northern and high-elevation forests experience prominent year-to-year changes in cambial dynamics (process of xylem tissue development), phenology (timing of cambial activity) and kinetics (speed of xylem cell differentiation). The initiation of cambial activity in cold regions is driven mainly by temperature ( Rossi et al, 2008 ; Delpierre et al, 2019 ; Huang et al, 2020 ). Consequently, the date of cambial activity onset tightly follows the variability of spring temperature around specific thresholds ( Rossi et al, 2007 ; Treml et al, 2015 ).…”
Section: Introductionmentioning
confidence: 99%
“…In their Letter, Elmendorf and Ettinger (1) question the dominant role of photoperiod in driving secondary growth resumption (hereafter referred to as xylem formation onset) of the Northern Hemisphere conifers, recently reported by Huang et al (2). Their opinions are grounded on the following three aspects, including 1) the seasonality of the photoperiod, 2) the dependence of the predictor variables (e.g., photoperiod, forcing, and chilling) on the response variable (the date of onset of xylem formation, day of the year [DOY]), and 3) the limited value of the obtained models for interannual forecasting.…”
mentioning
confidence: 98%