Warm springs alter timing but not total growth of temperate deciduous trees

Dow, Cameron; Kim, Albert Y.; D’Orangeville, Loïc; Gonzalez‐Akre, Erika; Helcoski, Ryan; Herrmann, Valentine; Harley, Grant L.; Maxwell, Justin T.; McGregor, Ian R.; McShea, William J.; McMahon, Sean M.; Pederson, Neil; Tepley, Alan J.; Anderson‐Teixeira, Kristina J.

doi:10.1038/s41586-022-05092-3

Cited by 57 publications

(35 citation statements)

References 89 publications

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“…All bands are measured biannually ( n = 549 in 2021): once at the beginning of the growing season (in the spring, around March) and once at the end (in the fall, around November). A subset of these bands are further measured biweekly throughout the growing season starting ( n = 146 in 2021; Dow et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

et al. 2022

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Accurate field data are essential to understanding ecological systems and forecasting their responses to global change. Yet, data collection errors are common, and data analysis often lags far enough behind its collection that many errors can no longer be corrected, nor can anomalous observations be revisited. Needed is a system in which data quality assurance and control (QA/QC), along with the production of basic data summaries, can be automated immediately following data collection. Here, we implement and test a system to satisfy these needs. For two annual tree mortality censuses and a dendrometer band survey at two forest research sites, we used GitHub Actions continuous integration (CI) to automate data QA/QC and run routine data wrangling scripts to produce cleaned datasets ready for analysis. This system automation had numerous benefits, including (1) the production of near real‐time information on data collection status and errors requiring correction, resulting in final datasets free of detectable errors, (2) an apparent learning effect among field technicians, wherein original error rates in field data collection declined significantly following implementation of the system, and (3) an assurance of computational reproducibility—that is, robustness of the system to changes in code, data and software. By implementing CI, researchers can ensure that datasets are free of any errors for which a test can be coded. The result is dramatically improved data quality, increased skill among field technicians, and reduced need for expert oversight. Furthermore, we view CI implementation as a first step towards a data collection and analysis pipeline that is also more responsive to rapidly changing ecological dynamics, making it better suited to study ecological systems in the current era of rapid environmental change.

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Section: Methodsmentioning

confidence: 99%

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

et al. 2022

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“…For instance, models would be likely to overestimate the sensitivity of ANPP woody to relatively short droughts (e.g. Martínez‐Sancho et al ., 2022) but underestimate its sensitivity to longer droughts (Kannenberg et al ., 2022), while overestimating ANPP woody of temperate deciduous forests under warming spring temperatures (Dow et al ., 2022). To reduce uncertainty regarding the future impact of global change on forest C sequestration, we need to develop comprehensive understanding regarding: (1) when, where and why decoupling occurs; (2) the spatial and temporal scales at which it operates; and (3) the implications for how we understand and model the forest C cycle.…”

Section: Figmentioning

confidence: 99%

“…New Phytologist is quite responsive to drought or temperature stress during the peak season for woody growth (D'Orangeville et al, 2018;Dow et al, 2022;Kannenberg et al, 2022). This is because cellular enlargement requires adequate turgor pressure and is therefore particularly sensitive to aridity (Delpierre et al, 2016a;Zweifel et al, 2021;Etzold et al, 2022;Kannenberg et al, 2022).…”

Section: Forummentioning

confidence: 99%

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What drives forest carbon storage? The ramifications of source–sink decoupling

Anderson‐Teixeira

Kannenberg

2022

New Phytologist

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“…These findings indicate that climatic warminginduced increases in TS do not necessarily translate to a linear extension of the actual vegetation green season extensions (GS). In other words, the thermal requirement of plants is likely to change under warmer environments, introducing large uncertainties and possibly errors in the predictions of future vegetation productivity and species distributions when constant thermal sums are used or when the growing season length is only limited by the climatic conditions without explicitly accounting for tree physiological processes (Chuine, 2010;Chuine & Beaubien, 2001;de Sauvage et al, 2022;Dow et al, 2022;Etzold et al, 2022). Investigating the temporal overlap and potential lag between vegetation phenology and favorable climatic conditions is critical to improving our understanding of vegetation responses to the ongoing climate change (Linderholm, 2006;Piao et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees

Geng

Chen

et al. 2022

Global Change Biology

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Over the past decades, global warming has led to a lengthening of the time window during which temperatures remain favorable for carbon assimilation and tree growth, resulting in a lengthening of the green season. The extent to which forest green seasons have tracked the lengthening of this favorable period under climate warming, however, has not been quantified to date. Here, we used remote sensing data and long-term ground observations of leaf-out and coloration for six dominant species of European trees at 1773 sites, for a total of 6060 species-site combinations, during 1980-2016 and found that actual green season extensions (GS: 3.1 ± 0.1 day decade −1 ) lag four times behind extensions of the potential thermal season (TS: 12.6 ± 0.1 day decade −1 ). Similar but less pronounced differences were obtained using satellitederived vegetation phenology observations, that is, a lengthening of 4.4 ± 0.13 and 7.5 ± 0.13 day decade −1 for GS and TS, respectively. This difference was mainly driven by the larger advance in the onset of the thermal season compared to the actual advance of leaf-out dates (spring mismatch: 7.2 ± 0.1 day decade −1 ), but to a less extent caused by a phenological mismatch between GS and TS in autumn (2.4 ± 0.1 day decade −1 ). Our results showed that forest trees do not linearly track the new thermal window extension, indicating more complex interactions between winter and spring temperatures and photoperiod and a justification of demonstrating that using more sophisticated models that include the influence of chilling and photoperiod is needed to accurately predict spring phenological changes under warmer climate. They urge

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Warm springs alter timing but not total growth of temperate deciduous trees

Cited by 57 publications

References 89 publications

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

Implementing GitHub Actions continuous integration to reduce error rates in ecological data collection

What drives forest carbon storage? The ramifications of source–sink decoupling

Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees

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