What is the PoPS Forecasting Platform?
The PoPS model is just one piece of
the PoPS Forecasting Platform. Users may download the R or GRASS GIS version of PoPS, or
users can use the PoPS Forecasting Platform to run PoPS in the cloud (available for public use
The PoPS Forecasting Platform features an easy-to-use PoPS Dashboard Interface, the PoPS
Database, and the PoPS model running in the cloud. With the PoPS Dashboard Interface, users can
run PoPS, compare scenarios, and save data without any programming experience.
How is the PoPS Forecasting Platform structured?
The PoPS Forecasting Platform consists of a series of interconnected open-source packages (available on
GitHub) and tools
End-users may download the R or GRASS GIS
packages to run PoPS on their own computer, or they may use the interactive PoPS Dashboard
Interface (coming soon).
How did we create the PoPS Forecasting Platform?
We started PoPS in 2018 as a collaboration between the Center for Geospatial Analytics at North Carolina
State University and the USDA Animal and Plant Health Inspection Service (APHIS).
PoPS was developed, and continues to be refined, through what we call an “iterative modeling cycle,” a
process that we suggest may help guide ecological forecasters in other contexts. Meeting with the
stakeholders who will use the forecasts comes first, to discuss their needs and set mutual objectives.
After initial data are gathered to feed into the model, four revisionary loops follow:
(1) The Calibration Loop
occurs anytime new occurrence data are acquired or new biological information about the pest or pathogen
is discovered; new data require that model parameters be re-calibrated, validated, and updated in the
(2) The Scenario Modeling
Loop involves stakeholders defining a management scenario that they want to test together,
using a fully calibrated and validated version of the forecast model, to experiment with strategies that
they can then implement on the ground as part of real-world adaptive management; the process repeats as
stakeholders compare strategies possible under a realistic monetary budget and decide on the optimal
(3) The Field Observation and
Scientific Feedback Loop is engaged when stakeholders use the forecast to determine
management and surveillance priorities; these new surveys and management actions are recorded in the
database, triggering another iteration of the Calibration Loop. The Field Observation and Scientific
Feedback Loop can also be triggered when new scientific studies reveal information about species
characteristics (e.g., environmental tolerances or host preferences); this new information is added to
the database and can lead to new insights that change model assumptions; such a change triggers another
iteration of the Calibration Loop, and the results of forecasts (and hindcasts) with and without the new
knowledge are compared.
(4) The Participatory Feedback
Loop consists of iterative back-and-forth discussion with stakeholders, to ensure that
research progress matches their needs and vision; stakeholders test and provide input on not only the
forecast model but also ways in which they prefer to interact with it.