Chilling and forcing requirements for foliage bud burst of European beech ( Fagus sylvatica L.) differ between provenances and are phenotypically plastic

Krämer, K.; Ducousso, Alexis; Gömöry, Dušan; Hansen, Jon Kehlet; Ioniţă, L.; Liesebach, Mirko; Lorenţ, Adrian; Schüler, Silvio; Sułkowska, Małgorzata; Vries, Sven de; Wühlisch, Georg von

doi:10.1016/j.agrformet.2016.12.002

Cited by 66 publications

(57 citation statements)

References 76 publications

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“…Although this average response is strikingly consistent when considered across diverse datasets (Wolkovich et al ., ), it masks considerable variation. Variation is extreme when examined across species (Wolkovich et al ., ), but additional variation can be seen within species over space (Vitasse et al ., ; Kramer et al ., ) and time (Yu et al ., ; Fu et al ., ). Understanding this variation has been the goal of much recent work (Rutishauser et al ., ; Laube et al ., ; Donnelly et al ., ; Zohner et al ., ), with research focusing on two major linked aims: (1) identifying and quantifying the environmental cues that drive spring phenology (i.e.…”

Section: Introductionmentioning

confidence: 99%

Temperature and photoperiod drive spring phenology across all species in a temperate forest community

2018

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Accurate predictions of spring plant phenology with climate change are critical for projections of growing seasons, plant communities and a number of ecosystem services, including carbon storage. Progress towards prediction, however, has been slow because the major cues known to drive phenology - temperature (including winter chilling and spring forcing) and photoperiod - generally covary in nature and may interact, making accurate predictions of plant responses to climate change complex and nonlinear. Alternatively, recent work suggests many species may be dominated by one cue, which would make predictions much simpler. Here, we manipulated all three cues across 28 woody species from two North American forests. All species responded to all cues examined. Chilling exerted a strong effect, especially on budburst (-15.8 d), with responses to forcing and photoperiod greatest for leafout (-19.1 and -11.2 d, respectively). Interactions between chilling and forcing suggest that each cue may compensate somewhat for the other. Cues varied across species, leading to staggered leafout within each community and supporting the idea that phenology is a critical aspect of species' temporal niches. Our results suggest that predicting the spring phenology of communities will be difficult, as all species we studied could have complex, nonlinear responses to future warming.

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Section: Introductionmentioning

confidence: 99%

Temperature and photoperiod drive spring phenology across all species in a temperate forest community

2018

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“…Provenances of Norway spruce, adapted to different environmental conditions, differ in the temperature sums required to trigger bud development, and tree breeders have for a long time been selecting trees with high growth capacity and low risk of frost damage (Hannerz, 1999). The lack of provenance-specific requirements in phenology models may impose bias and uncertainty in simulations across geographical and climatic gradients (Chuine, Belmonte, & Mignot, 2000;Kramer et al, 2017;Olsson, Bolmgren, Lindström, & Jönsson, 2013;Olsson & Jönsson, 2015). Adding model parameters may however lead to increased uncertainty as more factors have to be taken into account (Beven, 2009).…”

Section: Introductionmentioning

confidence: 99%

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe

Olsson

Olin

Lindström

et al. 2017

Ecology and Evolution

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Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051–2080 than in 1971–2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

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“…Many environmental and modelling studies are focused on longterm climatic averages (e.g., climatic normal over a 30 years period) to determine the effects of climate change on forest populations (Isaac-Renton et al 2014, Marchi et al 2016. However, an increasing degree of representativeness is needed in studies that require a higher temporal-resolution, such as dendrochronology (Amodei et al 2012, Marchi et al 2015, or studies based on seasonal effects on plant growth (Rathgeber et al 2011, Savi & Fares 2014, Kramer et al 2017). The more detailed the analysis is, the more accurate and continuous the database should be.…”

Section: Discussionmentioning

confidence: 99%

Sampling strategies for high quality time-series of climatic variables in forest resource assessment

Ferrara¹,

Marchi²,

Fares³

et al. 2017

iForest

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Many ecological studies require long-term time series of high quality. Missing data may represent a serious problem since they can affect the reliability of measured variables in specific locations. To which extent and according to which methodology a gap in time series should be filled is a major research challenge. In this study, the time-series of meteorological data relative to 13 monitoring sites from the ICP-Forest network in Italy were analysed with the aim to define the minimum number of site-specific observations, which can be considered adequate for further analysis on forest resource management. Three main climatic variables were taken into account in the analysis: air temperature, relative humidity and total precipitation. By using an increasing proportion of available data, descriptive and inferential statistic methods were applied to evaluate the amount of variability along the period of analysis (1998-2013) and associated error of estimation at seasonal level. The relative importance of each factor accounted in our analysis (season, year, variable, plot, sampling proportion) was investigated fitting a Random Forest model on the results of the bootstrapping procedure. Air temperature was the variable with a marked seasonal profile and the easiest to be represented at monthly level on a specific time period. Humidity and precipitation were more stable across the analysed time period. Trends in precipitation showed that a high amount of variability could be detected only when > 80% of valid observations were available. Humidity showed an intermediate pattern, with an exponential increase in the amount of explained variability when using an increased proportion of sampled observations. Random Forest Regression models indicated sampling proportion (i.e., number of available observations) as an important factor for trend analysis of relative air humidity and precipitation. We conclude that monthly or seasonal statistics can be proficiently estimated for both air temperature and relative humidity with a proportion of missing values higher than 50%. Conversely, a reliable analysis of intra-seasonal or intramonthly precipitation variability requires a much higher amount of observations. In the latter case gap filling represents the only feasible solution.

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Chilling and forcing requirements for foliage bud burst of European beech ( Fagus sylvatica L.) differ between provenances and are phenotypically plastic

Cited by 66 publications

References 76 publications

Temperature and photoperiod drive spring phenology across all species in a temperate forest community

Temperature and photoperiod drive spring phenology across all species in a temperate forest community

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe

Sampling strategies for high quality time-series of climatic variables in forest resource assessment

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