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Mowing and warming effects on grassland species richness and harvested biomass: meta-analyses

物种丰富度 草原 生物量(生态学) 生物多样性 生态学 全球变暖 气候变化 环境科学 扰动(地质) 物种多样性 农学 生物
作者
Francesca Piseddu,Gianni Bellocchi,Catherine Picon-Cochard
出处
期刊:Agronomy for Sustainable Development [Springer Science+Business Media]
卷期号:41 (6)
标识
DOI:10.1007/s13593-021-00722-y
摘要

Climate and management affect grassland plant diversity but studies vary regarding the magnitude of changes in plant species richness. Here we develop a comprehensive understanding of species richness modification due to management (mowing) and climate (warming) variations worldwide, and present the results of two meta-analyses from 999 and 1793 records (articles). Recorded articles had at least one experiment with a case-control design. The results show that both mowing (43 articles) and warming (34 articles) modify species richness, which on average increased by c. 32% with once-a-year mowing (against no mowing) and declined by c. 13% with warming (against ambient temperature). Our meta-analysis on the mowing regime supports the humped-back model, with one or two cuts per year being the level of disturbance optimising species richness. We also observed that warming-induced reduction in species richness is lower in dry climates (< 300 mm yr-1) and at low elevations (< 1000 m a.s.l.). Where available, we accounted for harvested biomass as a concomitant variable and we found that overall it decreased by c. 21% (mowing) and increased by c. 11% (warming). The evidence provided of an opposite response of species richness and harvested biomass to disturbance is consistent with the competitive-exclusion hypothesis of negatively correlated patterns between the two outcomes (high taxonomic diversity with low biomass production, and vice versa). The reported difficulties in finding representative studies in previous meta-analyses and in the present ones highlight the need to orient future research towards long-term experiments on the combined effects of mowing and warming for a more robust inference of environmental and management constraints on grassland performance.
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