RESTON, Va.-- The latest tool designed to help manage the threatened piping plover is only a download away; iPlover is the first smartphone data collection application developed by the U.S. Geological Survey and will help those managing plover populations.
iPlover supports a long-established network of partners working to address ongoing impacts on plover populations, such as habitat gain or loss due to storms.
More importantly, data from the app is used to develop models that address long-term management concerns for habitat availability. It also improves the overall quality of coastal geologic information available to effectively manage this species.
The piping plover is a small shorebird that depends on open coastal beaches to breed and raise its young. Listed as threatened along the Atlantic coast in 1986, the piping plover’s conservation has been mandated by the Endangered Species Act. Although Atlantic Coast piping plover numbers have more than doubled since their listing nearly 30 years ago, they are still at risk. Recent estimates place the population at fewer than 2000 pairs, and climate change has introduced new threats to their coastal habitat.
Coastal beaches are dynamic systems and managing them for beach-dependent species like the piping plover requires collecting data on physical and biological characteristics that will be affected by sea level rise. Given the extensive Atlantic breeding range of the piping plover – spanning from North Carolina to Newfoundland – biologists have a lot of ground to cover.
The iPlover app supports the need for coordinated, synchronized data collection. It is a powerful new tool to help scientists and coastal resource managers consistently measure and assess the birds’ response to changes to their habitat. Rather than compiling data from multiple sources and formats, the app gives trained resource managers an easy-to-use platform where they can collect and instantly share data across a diverse community of field technicians, scientists, and managers. iPlover improves scientists’ data gathering and analysis capabilities by simplifying and facilitating consistent data collection and management that interfaces with models of shoreline change and beach geomorphology.
“The data come in from all of our study sites basically in real-time,” said Rob Thieler, USGS scientist and lead developer of the app. “It's already formatted, so data can be quickly plugged into our research models. This should really shorten the time between collecting the data, doing the science, and turning it into actionable information for management.”
“The USGS worked with diverse project partners to incorporate specific data collection needs and enable important stakeholders and partners to contribute data from hundreds of field observations within the plover’s U.S. Atlantic coastal breeding range,” said Andrew Milliken, coordinator of the North Atlantic Landscape Conservation Cooperative. “This included getting inputs from the U.S. Fish and Wildlife Service, National Park Service, state agencies and non-governmental organizations.”
“The app highlights the synergies and benefits of interagency and interdisciplinary science that advances conservation,” Milliken added. “The information collected will not only greatly improve our understanding of impacts from sea level rise, storms and beach management on piping plovers but also how managing for plovers can benefit other beach-dependent species, such as the American oystercatcher.”
Funding for iPlover was provided through the Department of Interior North Atlantic Landscape Conservation Cooperative as part of its Hurricane Sandy response. The app was developed by the USGS’ Woods Hole Coastal and Marine Science Center and the Center for Integrated Data Analytics.
“iPlover is a great example of the USGS’ ability to build and deliver a variety of science applications that use modern technology,” said Nate Booth, USGS Chief of Office of Water Information and former Lead Architect for the USGS Center for Integrated Data Analytics. “It offers research teams great gains in data collection efficiency so that more time can be spent on analyzing the data rather than managing it."