Stratifying ocean sampling globally and with depth to account for environmental variability.

With increasing depth, the ocean is less sampled for physical, chemical and biological variables. Using the Global Marine Environmental Datasets (GMED) and Ecological Marine Units (EMUs), we show that spatial variation in environmental variables decreases with depth. This is also the case over temporal scales because seasonal change, surface weather conditions, and biological activity are highest in shallow depths. A stratified sampling approach to ocean sampling is therefore proposed whereby deeper environments, both pelagic and benthic, would be sampled with relatively lower spatial and temporal resolutions. Sampling should combine measurements of physical and chemical parameters with biological species distributions, even though species identification is difficult to automate. Species distribution data are essential to infer ecosystem structure and function from environmental data. We conclude that a globally comprehensive, stratification-based ocean sampling program would be both scientifically justifiable and cost-effective.

The emergence of spatial cyberinfrastructure.

Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.