期刊
ECOSYSTEMS
卷 21, 期 3, 页码 567-581出版社
SPRINGER
DOI: 10.1007/s10021-017-0169-1
关键词
fungal biomass; riparian vegetation; invertebrate communities; scraper; shredder; detritivores; multiple scales
类别
资金
- DPP/UnB
- CAPES
- CNPq [471572/2012-8]
The decomposition of plant litter plays a fundamental role in the cycling of carbon and nutrients and is driven by complex interactions of biological and physical controls, yet little is known about its variability and controls across spatial scales. Here we address the indirect effects of riparian canopy cover on litter decomposition and decomposers and their variability within a set of hierarchical scales (watershed, stream segments and reaches) controlling for confounding factors that could co-vary with canopy cover (for example, temperature and nutrients), in high-altitude subtropical streams. Total, microbial and invertebrate-driven decomposition rates were approximately 1.4-6.6 times higher in closed-canopy than in open-canopy watersheds. Riparian canopy cover accounted for 62-69% of total variability of decomposition rates and indirectly (via light availability and litter inputs) promoted fungal facilitation of shredders through leaf litter conditioning. In contrast to what we expected, much of the spatial variability in the decomposition occurred at smaller scale (4-20% of total variability among reaches versus < 1% among watersheds) and coincided with the greatest variability in shredder abundance and fungal biomass (70 and 17% among reaches, respectively). We conclude that riparian canopy cover may be an important control of natural variability of litter decomposition at the watershed scale through its effects on fungal decomposers and shredder consumption. We also provide evidence of higher reach and minor watershed variability of litter decomposition in stream networks. Our results point to the importance of identifying the sources of natural variability of decomposition and how they interact within and among spatial scales.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据