Acclimation of phenology relieves leaf longevity constraints in deciduous forests

Marqués, Laura; Hufkens, Koen; Bigler, Christof; Crowther, Thomas W.; Zohner, Constantin M.; Stocker, Benjamin D.

doi:10.1038/s41559-022-01946-1

Cited by 8 publications

(12 citation statements)

References 64 publications

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“…Year-to-year differences in the onset and progression of autumn senescence thus emerge as the result of a complex synchronization between trees' developmental states, seasonal variation in the circadian rhythm, and climate fluctuations. This mediation by the annual day-length cycle provides a unifying framework to explain previous results, in which the magnitude and direction of climate warming effects on autumn phenology varied (9,12,22,23,33,(47)(48)(49)(50), largely because studies did not disentangle pre-and post-solstice variables. It now is clear that warmer temperatures and increased vegetation activity before the summer solstice drive an earlier onset of senescence, whereas, in agreement with previous studies (12,47,51), warmer temperatures after the solstice slow down the progression of senescence, predicting that senescence will continue to start earlier but progress more slowly.…”

Section: Discussionmentioning

confidence: 94%

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice

Zohner

Mirzagholi

Renner

et al. 2023

Science

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Climate change is shifting the growing seasons of plants, affecting species performance and biogeochemical cycles. Yet how the timing of autumn leaf senescence in Northern Hemisphere forests will change remains uncertain. Using satellite, ground, carbon flux, and experimental data, we show that early-season and late-season warming have opposite effects on leaf senescence, with a reversal occurring after the year’s longest day (the summer solstice). Across 84% of the northern forest area, increased temperature and vegetation activity before the solstice led to an earlier senescence onset of, on average, 1.9 ± 0.1 days per °C, whereas warmer post-solstice temperatures extended senescence duration by 2.6 ± 0.1 days per °C. The current trajectories toward an earlier onset and slowed progression of senescence affect Northern Hemisphere–wide trends in growing-season length and forest productivity.

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Section: Discussionmentioning

confidence: 94%

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice

Zohner

Mirzagholi

Renner

et al. 2023

Science

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“…The PIA models in projected autumn phenology to advance between the years 2000 and 2100 as a result of increased photosynthetic activity. This result has thus been debated Lu and Keenan, 2022;Marqués et al, 2023) and could not be reproduced in our study. Here, we found positive and negative coefficient estimates, depending on the projection mode, which seems to be partly in line with , but that is actually not the case: First, our estimates refer to changes relative to the reference, which was always positive.…”

Section: Projections Of Autumn Phenologymentioning

confidence: 54%

Climate change-induced shifts in leaf phenology of trees: past and future trends

Meier

2024

Preprint

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Forests and atmosphere interact, for example when trees assimilate and respire CO2 during photosynthesis. Depending on their productivity and net assimilation, forests may function as a carbon sink or source. Further, the amounts of synthesized sugars assigned to growth, reproduction, and defense affect the fitness of the trees and eventually the distribution of species. Climate strongly impacts such forest-atmosphere interactions, which affect forests and in turn feed back to the climate. For deciduous trees, the beginning and end of the photosynthetically active period (i.e., the ‘growing season’) relate to spring and autumn leaf phenology (i.e., leaf unfolding and leaf senescence), respectively. The climatic conditions during the growing season (i.e., the ‘bioclimate’) are directly and indirectly influenced by climate change, as climate change alters the timing and length of the growing season through shifts in leaf unfolding and leaf senescence. While past changes in leaf unfolding and leaf senescence can be analyzed by in-situ observations, the underlying drivers and particularly possible future changes are often studied with process-oriented models. However, these models may suffer from a bias towards the mean (BTM), which causes the simulated values to be closer to the average than the observations and could result in overly flat simulated trends. Here I discuss (1) changes in the growing season and bioclimate in Switzerland during recent decades, (2) impacts of the calibration approach on the performance of 21 leaf senescence models tested with observations from Central Europe, and (3) effects of the BTM in these models on their performance and projections. Growing seasons have predominately lengthened at elevation-specific rates, which was primarily caused by changes in leaf senescence and increased the number of days with a negative atmospheric water balance at low elevations. Calibrations with the Generalized Simulated Annealing algorithm and with systematically balanced or stratified samples yielded the best performing leaf senescence models, while their performance was most influenced by their structure. The BTM caused the performance of current leaf senescence models to be only slightly better than the performance of a null model that constantly simulates the average of the calibration sample. Standard model comparisons favored models with stronger BTM, while models with weaker BTM projected smaller backward shifts in future leaf senescence. The latter is counter-intuitive, since smaller shifts result from flatter trends and are therefore associated with stronger rather than weaker BTM. I conclude that (1) the effects of phenological changes on the bioclimate should be considered when studying past and future forest productivity and species composition, (2) inference from process-oriented models to the underlying processes and drivers of leaf senescence is valid, and (3) current leaf senescence projections are highly uncertain and thus unreliable. While it is likely that current projections of future biosphere behavior under global change are distorted by erroneous state-of-the-art leaf senescence models, there is ample need and potential for the development of more accurate process-oriented models.

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“…The PIA models in Zani et al (2020) projected autumn phenology to advance between the years 2000 and 2100 as a result of increased photosynthetic activity. This result has thus been debated (Norby, 2021;Zani et al, 2021;Lu and Keenan, 2022;Marqués et al, 2023) and could not be reproduced in our study. Here, we found positive and negative coefficient estimates depending on the projection mode, which seems to be partly in line with Zani et al (2020), but that is actually not the case.…”

Section: Projections Of Autumn Phenologymentioning

confidence: 56%

Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

Meier,

Bigler

2023

Geosci. Model Dev.

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Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.

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Acclimation of phenology relieves leaf longevity constraints in deciduous forests

Cited by 8 publications

References 64 publications

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice

Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice

Climate change-induced shifts in leaf phenology of trees: past and future trends

Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections

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