Drivers of terrestrial plant production across broad geographical gradients

生物量(生态学) 初级生产 生产力 生长季节 气候变化 环境科学 生态学 纬度 自然地理学 大气科学 陆地生态系统 气候学 生态系统 地理 生物 地质学 大地测量学 宏观经济学 经济
作者
Sean T. Michaletz,Andrew J. Kerkhoff,Brian J. Enquist
出处
期刊:Global Ecology and Biogeography [Wiley]
卷期号:27 (2): 166-174 被引量:68
标识
DOI:10.1111/geb.12685
摘要

Abstract Terrestrial net primary production (NPP) varies across global climate gradients, but the mechanisms through which climate drives this variation remain subject to debate. Specifically, it is debatable whether NPP is primarily influenced by ‘direct’ effects of climate on the kinetics of plant metabolism or ‘indirect’ effects of climate on plant size, stand biomass, stand age structure and growing season length. We clarify several issues in this debate by presenting multiple lines of evidence that support a primarily indirect influence of climate on global variation in NPP across broad geographical gradients. First, we highlight > 60 years of research that suggests leaf area, growing season length, plant biomass and/or plant age are better predictors of NPP than climate or latitude. Second, we refute recent claims that using biomass and age as predictors of NPP represents circular reasoning. Third, we illustrate why effects of climate on the kinetics of plant production must be evaluated using instantaneous (not annualized) rates of productivity. Fourth, we review recent analyses showing that the effects of biomass and age on NPP are much stronger than the effects of climate. Fifth, we present new analyses of a high‐quality NPP dataset that demonstrate further that biomass, age and growing season length are better predictors of global variation in NPP than climate variables. Our results are consistent with the hypothesis that variation in NPP across global climate gradients primarily reflects the influence of climate on growing season length and stand biomass, as well as stand age, rather than the effects of temperature and precipitation on the kinetics of metabolism. However, this hypothesis should be evaluated further using larger, high‐quality observational and experimental datasets spanning multiple geographical scales.
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