Biodiversity–ecosystem functioning research: Brief history, major trends and perspectives

生物多样性 生态系统 生态学 生态系统服务 物种丰富度 地理 生态系统多样性 环境资源管理 生物量(生态学) 生物 环境科学
作者
Arshad Ali
出处
期刊:Biological Conservation [Elsevier BV]
卷期号:285: 110210-110210 被引量:67
标识
DOI:10.1016/j.biocon.2023.110210
摘要

Since Grime's 1973 study on the competitive exclusion of herbaceous plants, ecologists have intensively debated the Biodiversity–Ecosystem Functioning (BEF) relationships for more than 50 years (1973 – the present). Following brief history, I synthesize the major BEF trends, i.e., (1) the shift from quick-growing to long-lasting systems or from grasslands to forests; (2) the shift from species richness to multiple facets of biodiversity; (3) the shift from single ecosystem functions to multifunctionality and stability; (4) the scaling up of BEF relationships across spatiotemporal scales and environmental gradients; (5) the shift from traditional bivariate to advanced multiple multivariate statistical modelling; and (6) approximating BEF-dependent biodiversity conservation and ecosystem services. Although the question of how species diversity affects biomass productivity remains unresolved, functional trait identity (i.e., the mass ratio effect) is often considered more important than species and trait diversity (i.e., the niche complementarity effect) under the selection effect. Yet, little attention has been paid to exploring how different trophic levels affect BEF outcomes within and across ecosystems under global change which can have important implications for biodiversity conservation that underpins human well-being through ecosystem services. Thus, I summarize the key ecological theories and debated questions, and outline future research directions that can guide us on what is known, unknown known, and unknown in the current global BEF literature. Despite 50 years of debates, several unexplored geographical regions and concepts of ambiguity in BEF research and implications for biodiversity conservation remain to be explored through experiments, natural observations, and remote sensing approaches.
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