Decision Analysis to Help Improve the Effectiveness of Invasive Plants Management

Science Center Objects

Melaleuca is an invasive tree that is highly problematic in the Everglades, threatening native wildlife and habitat. USGS is helping to improve management strategies for the invasive plant. 

The Science Issue and Relevance: Melaleuca quinquenervia is a highly invasive tree in the Greater Everglades that completely displaces native habitats and wildlife. Despite several decades of management efforts, and tens of millions of dollars spent to date, melaleuca is still considered a highly detrimental species to the Everglades ecosystem and its ongoing restoration efforts. Funding for effective management of this invasive species is limited, but if left unmanaged, melaleuca’s impact on the ecosystem could cost the region over a hundred million dollars annually in lost revenues.  Understanding the population dynamics of melaleuca and identifying effective control strategies could benefit the protection of natural resources against this species and help with the control of other invasive plant species.

Melaleuca

Melaleuca

(Public domain.)

 

Methodology for Addressing the Issue: Our project focuses on developing technical products to help improve the effectiveness of invasive plant management. We aim to combine information from multiple sources of data (e.g., field data from aircraft and unmanned aerial systems surveys, data from growth models) with a predictive model linking potential management actions to the future distribution of melaleuca in a landscape. We will use optimization methods to identify effective management strategies.

 

Future Steps: This work intends to help managers better allocate resources to reduce damages caused by melaleuca while considering cost constraints and the population dynamics of the invasive plant. We will combine predictive models with decision analysis to develop decision support tools to help inform managers how to control melaleuca as efficiently as possible.

 

Related Project(s) or Products:

Bonneau, M., Martin, J., Romagosa, C, Johnson, F. (2016). A Hidden Markov Random Field Approach for Control Prioritization Based on Noisy Observations. ESA 2016, Ft Lauderdale, FL.

Bonneau, M., Martin, J., Peyrard, N., Rodgers, L.,Romagosa, C.M., Johnson, F.A. Optimal monitoring and control of invasive in the face of imperfect detection and misclassification. (in review in Ecological Modeling).

Jafari, N., Romagosa, C., Martin, J. (2016). Robust optimization for management of invasive species. 4th International Conference on Computational Sustainability, Cornell University, Ithaca, New York, USA.                                     

Johnson, F. A., Bonneau, B., Martin, J., Romagosa, C., Fackler, P., Smith, B., Jafari, N., Udell, B., Zhang, B. and DeAngelis, D. (2016). Decision analysis for optimal control of invasive species in the Everglades. Ecological Society of America Annual Meeting 2016, Ft Lauderdale, FL.        

Udell, B., Martin, J., Johnson, F.A., Romagosa, C.M. (2017). Decision analysis for optimal control of melaleuca, Greater Everglades Ecosystem Restoration, Coral Springs, Florida, USA.       

Zulqarnain, H., Charkhgard, H., Kwon, C., Martin, J. (2017). A Robust Optimization Approach for Solving Problems in Conservation Planning, Informs conference, Houston, Texas.