The Science of the total environment · 2022
El Niño-Southern Oscillation affects the species-level temporal variation in seed and leaf fall in a mixed temperate forest.
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Zhu Meihui, Ester González de Andrés, Wang Yunyun, Xu Zhichao, Ye Ji, Yuan Zuoqiang, Lin Fei, Fang Shuai, Mao Zikun, Wang Xugao, Hao Zhanqing
Аннотация
El Niño-Southern Oscillation (ENSO), the variation between anomalously cold (La Niña) and warm conditions (El Niño), is one of the most prominent large-scale climate patterns with worldwide effects. Elevated seed and leaf fall has been found at the positive phase of ENSO (El Niño) in tropical forests. However, how seed and leaf fall respond to ENSO at species level is understudied, especially in temperate forests. In this study, we monitored seed and leaf fall at the species-level at 150 points across a 25-ha temperate forest in northeastern China over a span of 12 years. Using time series and wavelet analyses, we assessed three hypotheses: 1) temperate tree species' seed and leaf fall are strongly, but differently, correlated with ENSO and, 2) community synchrony in seed and leaf occurred both at seasonal and ENSO scales; finally, 3) local climatic modulated the effects of ENSO on seed and leaf fall. We found that ENSO was significantly correlated with seed and leaf fall of all species, although correlation strength varied across species (r = 0.206-0.658). Specifically, ENSO indices (ENSO12 or ENSO34) accounted for the most variation in seed and leaf fall of Acer pseudo-sieboldianum (40 % and 34 %, respectively) and ranged 4 %-31 % in all other species. Leaf fall was synchronous with ENSO cycles with a period of 2-7 years, but community synchrony of seed fall was only detected at seasonal scales. ENSO influenced seed fall of Fraxinus mandshurica and Tilla amurensis by mediating rainfall and relative humidity, respectively, highlighting the interactive effects of local climate and ENSO. Our findings highlight the potential effects of ENSO on ecosystems outside of tropical regions and improve our ability to predict regeneration dynamics and nutrient cycling of temperate forests under the context of global change.