December 22, 2017 | PAMF program staff
The Phragmites Adaptive Management Framework (PAMF) is a decision-support tool for anyone managing Phragmites in the Great Lakes basin. As a PAMF participant, you receive decision support that is tailored to the conditions at each of your management units, and that support becomes smarter with each passing year. This annual treatment guidance is based on the cumulative knowledge gained from the reported outcomes of all the Phragmites management efforts that have been enrolled in the PAMF program.
The PAMF team converts your monitoring data into treatment guidance using a decision framework designed specifically for this purpose. The framework is made up of two distinct parts. The first, a state-transition model, organizes our knowledge about how Phragmites responds to different treatments. The second, an optimization procedure, uses this and other information to choose the most efficient treatment option for each site.
Learning how Phragmites responds to treatment
The PAMF state-transition model uses monitoring and treatment data to describe the possible futures of each management unit. In the model, states are snapshots of a unit’s condition at the time of monitoring: how dense the Phragmites is, and how much of the site it covers. Transitions are the pathways between the state observed in one year and the resulting state in the following years. When you enroll a management unit into the PAMF program, the unit will be assigned to one of six states, based on your measurements. As the Phragmites infestation changes in response to management, the state that represents your unit will also change. Although transitions between any two states are possible in the model, some transitions are more likely than others. For example, were you to leave a unit unmanaged, it would be more likely to transition to a more-infested state than to a less-infested state.
The chance that a management unit will change between any two states in a year’s time–or even remain in its current state–is known as a transition probability. In its first year, the model will use probabilities based on expert opinion. These initial probabilities encapsulate what we know so far about Phragmites management. Then, as data are submitted into the system, these probabilities are updated based on the real-life outcomes of various treatment alternatives used in the field. This is where collective learning comes in. Each set of treatment reports and monitoring data contributes to improving transition probabilities within the model, which leads to improved decision support for anyone facing a similar state, with the same array of treatment options in the future.
Getting optimal guidance
Presumably, we all want to control Phragmites for the least monetary cost, using the treatments that will most likely give us the best results, but there may be trade-offs between effectiveness and cost. The state-transition model organizes what we know about the uncertain outcomes associated with each treatment alternative, but it doesn’t take monetary cost into account or tell us which option is the best. For that, the decision framework incorporates an optimization step. This data crunching exercise navigates the trade-offs between state desirability, uncertainty, and cost of treatment, to choose and recommend the most effective and efficient management option for each unit enrolled in the program.
The PAMF model is designed to learn from the outcomes of Phragmites management taking place across the entire basin and to provide you with data-driven treatment guidance that improves over time. Using this model, we are ready to start the first ever science-based, basin-wide program to fight Phragmites.