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• W3019280147 abstract "Autumnal leaf senescence signals the end of photosynthetic activities in temperate deciduous trees and consequently exerts a strong control on various ecological processes. Predicting leaf senescence dates (LSD) with high accuracy is thus a prerequisite for better understanding the climate-ecosystem interactions. However, modeling LSD at large spatial and temporal scales is challenging. In this study, first, we used 19972 site-year records (848 sites and four deciduous tree species) from the PAN European Phenology network to calibrate and evaluate six leaf senescence models during the period 1980-2013. Second, we extended the spatial analysis by repeating the procedure across Europe using satellite-derived end of growing season and a forest map. Overall, we found that models that considered photoperiod and temperature interactions outperformed models using simple temperature or photoperiod thresholds for Betula pendula, Fagus sylvatica and Quercus robur. On the contrary, no model displayed reasonable predictions for Aesculus hippocastanum. This inter-model comparison indicates that, contrary to expectation, photoperiod does not significantly modulate the accumulation of cooling degree days (CDD). On the other hand, considering the carryover effect of leaf unfolding date could promote the models' predictability. The CDD models generally matched the observed LSD at species level and its interannual variation, but were limited in explaining the inter-site variations, indicating that other environmental cues need to be considered in future model development. The discrepancies remaining between model simulations and observations highlight the need of manipulation studies to elucidate the mechanisms behind the leaf senescence process and to make current models more realistic." @default.
• W3019280147 created "2020-05-01" @default.
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• W3019280147 date "2020-05-16" @default.
• W3019280147 modified "2023-10-14" @default.
• W3019280147 title "Modeling leaf senescence of deciduous tree species in Europe" @default.
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• W3019280147 doi "https://doi.org/10.1111/gcb.15132" @default.
• W3019280147 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32329935" @default.
• W3019280147 hasPublicationYear "2020" @default.
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