Abstract
Abstract Leaf senescence is a major event in a plant's life history as autumn marks the end of the growing season. The optimal timing of leaf senescence is crucial to both minimise risks of low temperature events and to maximise carbon gain during the growing season. As abiotic conditions are currently changing at unprecedented rates, it is important to study how leaf senescence of different species is responding to these changes in order to forecast future growing season length and carbon sequestration potentials. In contrast to flowering phenology, data on autumn events are scarce and even more so for herbaceous than for woody plants, thus more information on this phenological stage is urgently needed. We studied leaf senescence in 632 populations from 17 herbaceous species located along elevational gradients. We focussed on the beginning (5% of the population senesce, LS5) and peak (50% senesce, LS50) of leaf senescence. To see whether we can predict species‐specific changes, we studied the link between LS5 and LS50 and flowering phenology as well as leaf functional traits related to plant performance. We looked at first and last flowering day and flowering duration as well as the traits specific leaf area (SLA), leaf dry matter content (LDMC), area‐based leaf nitrogen and carbon content, carbon isotope discrimination (Δ13C) and the stomatal pore area index. We found species‐specific slopes of the beginning of leaf senescence along the elevational gradient. The peak of leaf senescence was uniformly delayed with increasing elevation across all species. Flowering phenology as well as leaf functional traits had a close relationship with leaf senescence and thus can be used to forecast species‐specific responses to changes in abiotic conditions. High SLA and high leaf nitrogen were related to earlier senescence while high LDMC, high Δ13C and high stomatal pore area index were related to later senescence. Synthesis. The link between senescence, flowering phenology and plant functional traits will help to fine‐tune predictions of future growing season length and ecosystem function. To date, most analyses are based on spring phenology and traits, for which data are more abundant than data on autumn senescence.
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