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iPlover: Piping plover habitat suitability in a changing climate

iPlover

Designed by scientists to simplify consistent data collection and management, the iPlover smartphone application gives trained resource managers an easy-to-use platform where they can collect and share data about coastal habitat utilization across a diverse community of field technicians, scientists, and managers. With the click of a button, users can contribute biological and geomorphological data to regional models designed to forecast the habitat outlook for piping plover, and other species that depend upon sandy beach habitat.

Scientists at U.S. Geological Survey developed a smartphone application called iPlover to assess nesting habitat for the federally-listed piping plover (Charadrius melodus) and other beach-dwelling species on Atlantic coastal beaches and to forecast future habitat under accelerating sea level rise. This project engages a broad community of stakeholders along 1500 km of the U.S. Atlantic breeding range from North Carolina to Maine to address a shared problem in species and landscape management and increases collaboration and collective 'ownership' of the problem.

The project can be divided into three parts:

1) Application development. Using agile software development approaches, the application was conceived, developed and deployed in just a few months. Within two years iPlover has provided robust, consistent data that informs highly skilled predictive habitat models. Methods used here to develop and deploy a distributed data collection system have broad applicability to interdisciplinary environmental monitoring, modeling and management.

2) Habitat suitability. The project is testing use of iPlover and other data (e.g., lidar, imagery) to understand and predict the probability that specific combinations of habitat variables are associated with nesting sites. Habitat preferences across a significant portion of the species’ Atlantic coast breeding range will become available to guide management by mapping areas with a high probability of suitability for nesting. In collaboration with a network of scientists and practitioners, this approach facilitates the collation of evidence-based information from many locations and sources, and promotes the development and communication of actionable scientific information.

3) Habitat evolution. Understanding how sea-level rise and coastal storms affect early successional habitat for piping plovers along the U.S. Atlantic Coast is a multivariate problem in space and time. The project is using multiple, linked Bayesian networks to describe the probability that a given location will be used for piping plover nesting – and how that probability will be altered given dynamic response of beaches to sea-level rise and changes to coastal storm regimes in the coming decades.

Hurricane Sandy Disaster Mitigation Funds

U.S. breeding range of the Piping Plover (Charadrius melodus) from North Carolina to Maine.

 

Webinar from NA LCC science seminar series (July 2016)

https://www.usgs.gov/news/shorebird-science-iplover-app

https://itunes.apple.com/us/app/iplover/id975620593

Gieder KD, Karpanty SM, Fraser JD, Catlin DH, Gutierrez BT, Plant NG, et al. A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features. Ecological Modelling. 2014;276:38-50. doi: 10.1016/j.ecolmodel.2014.01.005.

Gutierrez BT, Plant NG, Thieler ER, Turecek A. Using a Bayesian network to predict barrier island geomorphologic characteristics. Journal of Geophysical Research: Earth Surface. 2015;120(12):2452-75. doi: 10.1002/2015JF003671.

Thieler ER, Zeigler SL, Winslow LA, Hines MK, Read JS, Walker JI. Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability. In review.

Zeigler SL, Thieler ER, Gutierrez BT, Plant NG, Hines M, Fraser JD, et al. Use of mobile smartphone technologies and Bayesian networks to assess habitat preferences for shorebirds over broad spatial scales. In review

 

Webinar from NA LCC science seminar series (July 2016)

https://www.usgs.gov/news/shorebird-science-iplover-app

https://itunes.apple.com/us/app/iplover/id975620593

Gieder KD, Karpanty SM, Fraser JD, Catlin DH, Gutierrez BT, Plant NG, et al. A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features. Ecological Modelling. 2014;276:38-50. doi: 10.1016/j.ecolmodel.2014.01.005.

Gutierrez BT, Plant NG, Thieler ER, Turecek A. Using a Bayesian network to predict barrier island geomorphologic characteristics. Journal of Geophysical Research: Earth Surface. 2015;120(12):2452-75. doi: 10.1002/2015JF003671.

Thieler ER, Zeigler SL, Winslow LA, Hines MK, Read JS, Walker JI. Smartphone-based distributed data collection enables rapid assessment of shorebird habitat suitability. In review.

Zeigler SL, Thieler ER, Gutierrez BT, Plant NG, Hines M, Fraser JD, et al. Use of mobile smartphone technologies and Bayesian networks to assess habitat preferences for shorebirds over broad spatial scales. In review