Fyllas Nikolaos M, Bentley Lisa Patrick, Shenkin Alexander, Asner Gregory P, Atkin Owen K, Díaz Sandra, Enquist Brian J, Farfan-Rios William, Gloor Emanuel, Guerrieri Rossella, Huasco Walter Huaraca, Ishida Yoko, Martin Roberta E, Meir Patrick, Phillips Oliver, Salinas Norma, Silman Miles, Weerasinghe Lasantha K, Zaragoza-Castells Joana, Malhi Yadvinder
Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.
Department of Global Ecology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA.
Ecol Lett. 2017 Jun;20(6):730-740. doi: 10.1111/ele.12771. Epub 2017 May 2.
One of the major challenges in ecology is to understand how ecosystems respond to changes in environmental conditions, and how taxonomic and functional diversity mediate these changes. In this study, we use a trait-spectra and individual-based model, to analyse variation in forest primary productivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accurately predicted the magnitude and trends in forest productivity with elevation, with solar radiation and plant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, and wood density) collectively accounting for productivity variation. Remarkably, explicit representation of temperature variation with elevation was not required to achieve accurate predictions of forest productivity, as trait variation driven by species turnover appears to capture the effect of temperature. Our semi-mechanistic model suggests that spatial variation in traits can potentially be used to estimate spatial variation in productivity at the landscape scale.
生态学面临的主要挑战之一是了解生态系统如何响应环境条件的变化，以及分类和功能多样性如何调节这些变化。在本研究中，我们使用基于性状谱和个体的模型，分析了亚马逊-安第斯山脉3.3公里海拔梯度上森林初级生产力的变化。该模型准确预测了森林生产力随海拔的变化幅度和趋势，太阳辐射和植物功能性状（单位面积叶干质量、叶氮和磷浓度以及木材密度）共同解释了生产力的变化。值得注意的是，要准确预测森林生产力并不需要明确表示海拔引起的温度变化，因为物种更替驱动的性状变化似乎捕捉到了温度的影响。我们的半机理模型表明，性状的空间变化有可能用于估计景观尺度上生产力的空间变化。
