Biological nitrogen fixation represents the largest natural flux of new nitrogen (N) into terrestrial ecosystems, providing a critical N source to support net primary productivity of both natural and agricultural systems. When they are common, symbiotic associations between plants and bacteria can add more than 100 kg N ha(-1) y(-1) to ecosystems. Yet, these associations are uncommon in many terrestrial ecosystems. In most cases, N inputs derive from more cryptic sources, including mutualistic and/or free-living microorganisms in soil, plant litter, decomposing roots and wood, lichens, insects, and mosses, among others. Unfortunately, large gaps remain in the understanding of cryptic N fixation. We conducted a literature review to explore rates, patterns, and controls of cryptic N fixation in both unmanaged and agricultural ecosystems. Our analysis indicates that, as is common with N fixation, rates are highly variable across most cryptic niches, with N inputs in any particular cryptic niche ranging from near zero to more than 20 kg ha(-1) y(-1). Such large variation underscores the need for more comprehensive measurements of N fixation by organisms not in symbiotic relationships with vascular plants in terrestrial ecosystems, as well as identifying the factors that govern cryptic N fixation rates. We highlight several challenges, opportunities, and priorities in this important research area, and we propose a conceptual model that posits an interacting hierarchy of biophysical and biogeochemical controls over N fixation that should generate valuable new hypotheses and research.
生物固氮是新氮(N)进入陆地生态系统的最大自然通量,为支持自然和农业系统的净初级生产力提供了关键的氮源。当植物与细菌的共生关系普遍存在时,可为生态系统增加超过100千克氮/公顷·年。然而,这些共生关系在许多陆地生态系统中并不常见。在大多数情况下,氮输入来自更隐蔽的来源,包括土壤中互利共生和/或自由生活的微生物、植物凋落物、分解的根系和木材、地衣、昆虫和苔藓等。不幸的是,在对隐蔽固氮的理解上仍存在很大差距。我们进行了文献综述,以探索未受管理的生态系统和农业生态系统中隐蔽固氮的速率、模式和调控因素。我们的分析表明,与固氮的常见情况一样,大多数隐蔽生态位的固氮速率差异很大,任何特定隐蔽生态位的氮输入量从近乎零到超过20千克氮/公顷·年不等。如此大的差异凸显了对陆地生态系统中与维管植物无共生关系的生物的固氮进行更全面测量的必要性,以及确定控制隐蔽固氮速率的因素的必要性。我们强调了这一重要研究领域的几个挑战、机遇和重点,并提出了一个概念模型,该模型假定了对固氮的生物物理和生物地球化学控制的相互作用层次结构,这应该会产生有价值的新假设和研究。