
We would like to propose a manuscript submission to the *Methods* section of Ecology Letters, discussing the use of Partially Observed Markov Decision Processes (POMDP) in ecological research questions.  Markov Decision Process (MDP) methods have a long history in ecology, particularly in natural resource management such as fisheries or forestry, and in behavioral ecology (where they are commonly called SDP problems, referring to the stochastic dynamic programming method used to solve them.) POMDPs have seen limited application owing to the complexity of solving them and the lack of appropriate software to do so. Recent breakthroughs in the robotics & artificial intelligence community have made POMDPs for large problems tractable, opening the door to a wide array of ecological problems previously considered intractable in an optimal control framework.  

Our proposed methods paper would present a high quality R package we have developed over several years which leverages a recently developed algorithm for solving POMDP problems.  We would illustrate the use of the package on both a classic decision problem from fisheries and in extending a recent Ecology Letters paper of ecosystem services (Dee et al, 2017; which received ESA's Innovation in Sustainability Science award) to account for imperfect observations.  We believe that these examples coupled with the user friendly R package would catalyze significant breakthroughs in this critical and timely area of conservation decision making.

Authors qualifications: Carl Boettiger, is a theoretical ecologist & assistant professor at UC Berkeley's department Environmental Science, Policy, and Management. Jeroen Ooms is a well-recognized professional software developer with rOpenSci; several of his packages are downloaded over 100,000 times each week. Milad Memarzadeh is a post-doc in Civil & Environmental engineering at UC Berkeley with a PhD from Carnegie Mellon in POMDP methods.  
