The deep ocean is the largest and least-explored ecosystem on Earth, and a uniquely energy-poor environment. The distribution, drivers and origins of deep-sea biodiversity remain unknown at global scales. Here we analyse a database of more than 165,000 distribution records of Ophiuroidea (brittle stars), a dominant component of sea-floor fauna, and find patterns of biodiversity unlike known terrestrial or coastal marine realms. Both patterns and environmental predictors of deep-sea (2,000-6,500 m) species richness fundamentally differ from those found in coastal (0-20 m), continental shelf (20-200 m), and upper-slope (200-2,000 m) waters. Continental shelf to upper-slope richness consistently peaks in tropical Indo-west Pacific and Caribbean (0-30°) latitudes, and is well explained by variations in water temperature. In contrast, deep-sea species show maximum richness at higher latitudes (30-50°), concentrated in areas of high carbon export flux and regions close to continental margins. We reconcile this structuring of oceanic biodiversity using a species-energy framework, with kinetic energy predicting shallow-water richness, while chemical energy (export productivity) and proximity to slope habitats drive deep-sea diversity. Our findings provide a global baseline for conservation efforts across the sea floor, and demonstrate that deep-sea ecosystems show a biodiversity pattern consistent with ecological theory, despite being different from other planetary-scale habitats.

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Global biodiversity targets have far-reaching implications for nature conservation worldwide. Scenarios and models hold unfulfilled promise for ensuring such targets are well founded and implemented; here, we review how they can and should inform the Aichi Targets of the Strategic Plan for Biodiversity and their reformulation. They offer two clear benefits: providing a scientific basis for the wording and quantitative elements of targets; and identifying synergies and trade-offs by accounting for interactions between targets and the actions needed to achieve them. The capacity of scenarios and models to address complexity makes them invaluable for developing meaningful targets and policy, and improving conservation outcomes. The Potential of Scenarios and Models to Inform Conservation TargetsThe Aichi Targets of the Strategic Plan for Biodiversity 2011-2020 i provide an agreed set of conservation aspirations for the international community, as well as explicit targets that countries have committed to achieve [1]. Justified and compelling targets have the power to shape policy and activity within and beyond the environment sector [2]. Knock-on effects of environmental targets, such as the 2015 global target of less than 2 C warming [3], can be profound, but the ways in which they are realised are complex. Feedbacks and trade-offs between sectors and policies, in particular, are challenging to characterise, understand, and navigate [4,5]. A poor understanding of the potential consequences of conservation targets, the interactions between targets, and the actions needed to achieve them, can lead to unexpectedly poor conservation outcomes, inefficient actions, and lost opportunities for meeting commitments. For example, some of the easiest pathways towards achieving the global target to protect 17% of the terrestrial ecosystems on Earth would not adequately safeguard the biodiversity (see Glossary) this target is intended to conserve [6][7][8][9]. Scenarios depict plausible futures and alternative policies and management strategies that may affect the achievement of conservation goals [10,11]. Models represent simplified and idealised understandings of a system, and can describe or predict (or forecast) conservation outcomes under a range of alternative scenarios. They range from qualitative conceptual models describing relationships between elements of a system, to quantitative models, built from either a principled understanding of the mechanics of a system, or through analysis of the emergent patterns observed in data [11,12]. Here, we focus primarily on quantitative correlative and process-based models dealing with biodiversity and ecosystem services, such as biophysical, ecological, and socio-ecological models. Together, scenarios and models provide a powerful means of characterising, understanding, and projecting the conservation implications of targets, and the positive and negative consequences of actions aimed at achieving them [11,13]; for example, scenarios and models have underpinned climate change target...