5679642883_24a2e905e0_zEarth’s climate is changing. The rising global temperature, shrinking ice sheets, and increase in extreme weather events all serve as evidence that the phenomenon is real. These direct effects are relatively easy to measure due to their global scale, but identifying the indirect effects happening at smaller scales is more challenging. 

In nature, ecosystems emerge from complex interactions between plants and animals. These interactions happen in ways that have evolved to form stable life-cycles over vast periods of time. Climate patterns serve as the shared clock that synchronizes the process. As the climate changes, so does the clock and with it an ecosystem’s interactions, sequences, and life-cycles.

Phenology is the science of how climate change affects the seasonal timings of plant and animal life-cycles. Scientists are concerned that life-cycles will adapt differently in response to climate change. This may alter when species interact, threatening an ecosystem’s stability. Given the complexity of ecosystems, even a small change in timing could have a substantial impact.

Assessing this threat is tremendously difficult. A wide array of data is needed to evaluate and model the situation. While automated means exist for collecting weather and vegetation data, documenting life-cycle timings (“phenophases”), requires field observations over large geographic regions. To manage the scale, scientific researchers rely on citizen scientists to make field observations on their behalf.

Multiple organizations exist to coordinate citizen science efforts, each typically focused on a particular domain such as bird migration. The data collected and curated by these organizations is invaluable. However, bringing all of this data together in one place can be a daunting task. Until this can be done reliably in a comprehensive analytics environment, scientists will be hampered in connecting the dots between climate and phenophase changes.

The EMC Federation’s comprehensive portfolio of Big Data technologies puts us in a unique position to help this important scientific endeavor. To that end, EMC has partnered with the Earthwatch Institute, Acadia National Park, and the Schoodic Institute to start the Whenology project. The goal is to create the above described analytics environment using the Federation Business Data Lake.whenology-project-diagram-twitter-1024x500As a pilot project, the team is studying phenophase changes related to bird migrations at Acadia National Park.  Acadia is an important waypoint along the Eastern Seaboard migration route and scientists are worried that phenophase changes there may prevent birds from getting the nutrition needed to complete their journey. This project relies heavily on data collected by NOAA, NASA, and the citizen science organizations eBird, Hawk Migration Association of North America, and USA National Phenology Network.

The Whenology project is a great example of the many ways that the EMC Federation uses technology for social good. If you agree, please show your support by visiting the Whenology website and finding a way to participate!

John Cardente

John Cardente

Distinguished Engineer at EMC
John Cardente is a Big Data and Analytics technologist within the Corporate CTO Office of EMC, a global leader in enabling businesses and service providers to transform their operations and deliver information technology as a service. In this role, John helps define EMC's Big Data strategy and leads joint proof-of-concept projects across divisions, federation companies (VMWare and Pivotal), and industry partners. During his eighteen years with the company, John has helped pioneer, as an individual contributor and leader of advanced development teams, the adoption of notable innovations including solid-state disks, automated data tiering, storage virtualization technologies, and embedded operating system emulators. John holds Bachelor and Master degrees in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Northeastern University. He recently earned a Graduate Certificate in Mining Massive Data Sets from Stanford University, a four course program that covers the theory and practice of many big data and machine learning technologies. John is a named inventor on ten granted U.S. patents with additional patents pending. In 2011, John was named an EMC Distinguished Engineer, a title held by less than 1% of EMC's global engineering population.
John Cardente

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