Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions

Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the "Four Vs" axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code- and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting.