Collaborative Development of Ecological Forecasting Model and Data Manipulation Software: Everglades National Park, South Florida Natural Resources Center (SFNRC) Active
The goal of the Advanced Applications Team’s partnership with SFNRC is to facilitate the use of scientific research findings in restoration and land management decisions.
The Science Issue and Relevance: Everglades National Park (ENP), the largest subtropical American wilderness, spans 1.5 million acres, and is home to a diverse and abundant plant and wildlife population. Park lands provide habitat to a number of threatened and endangered species, including the Florida panther, the manatee, and several species of birds, sea turtles, and vegetation. Beyond preserving this unique landscape, ENP is actively engaged in a massive cooperative effort to restore the natural ecological patterns and hydrologic flow of the Everglades that were disrupted by economic and agricultural development. ENP’s South Florida Natural Resources Center (SFNRC) conducts research to inform both the management of park lands and the Everglades restoration process. The USGS Advanced Applications Team provides technological support and software development capabilities to SFNRC scientists.
Methodology for Addressing the Issue: The goal of the Advanced Applications Team’s partnership with SFNRC is to facilitate the use of scientific research findings in restoration and land management decisions. Working with SFNRC landscape ecologists, we have developed graphical software to run ecological forecasting models against numerous scenarios, as well as tools to visualize and analyze model outputs. For scientists, these programs enable faster and more complex hypothesis testing, resulting in greater confidence in model logic. For managers, clear and consistent outputs can improve understanding of the effects of proposed actions across a range of concerns, leading to better-informed decision-making.
Future Steps: In response to the need for timely information delivery, a system is under development to automate the regular execution of forecasting models and deliver mapped results online for use in weekly and monthly planning meetings. Research is also being carried out to improve the execution time of complex mathematical models through techniques including distributed computation, graphical processing unit (GPU) utilization, and algorithm optimization.
- Overview
The goal of the Advanced Applications Team’s partnership with SFNRC is to facilitate the use of scientific research findings in restoration and land management decisions.
Sources/Usage: Some content may have restrictions. View Media DetailsGraphical software to run ecological forecasting models The Science Issue and Relevance: Everglades National Park (ENP), the largest subtropical American wilderness, spans 1.5 million acres, and is home to a diverse and abundant plant and wildlife population. Park lands provide habitat to a number of threatened and endangered species, including the Florida panther, the manatee, and several species of birds, sea turtles, and vegetation. Beyond preserving this unique landscape, ENP is actively engaged in a massive cooperative effort to restore the natural ecological patterns and hydrologic flow of the Everglades that were disrupted by economic and agricultural development. ENP’s South Florida Natural Resources Center (SFNRC) conducts research to inform both the management of park lands and the Everglades restoration process. The USGS Advanced Applications Team provides technological support and software development capabilities to SFNRC scientists.
Methodology for Addressing the Issue: The goal of the Advanced Applications Team’s partnership with SFNRC is to facilitate the use of scientific research findings in restoration and land management decisions. Working with SFNRC landscape ecologists, we have developed graphical software to run ecological forecasting models against numerous scenarios, as well as tools to visualize and analyze model outputs. For scientists, these programs enable faster and more complex hypothesis testing, resulting in greater confidence in model logic. For managers, clear and consistent outputs can improve understanding of the effects of proposed actions across a range of concerns, leading to better-informed decision-making.
Sources/Usage: Some content may have restrictions. View Media DetailsEverglades Wood Stork Foraging Potential Model Future Steps: In response to the need for timely information delivery, a system is under development to automate the regular execution of forecasting models and deliver mapped results online for use in weekly and monthly planning meetings. Research is also being carried out to improve the execution time of complex mathematical models through techniques including distributed computation, graphical processing unit (GPU) utilization, and algorithm optimization.