Optimally managing under imperfect detection: a method for plant invasions.
Abstract
Failing to account for uncertainty in the detection of invasive plants may lead to inefficient management strategies and wasted resources. Smart strategies to manage plant invasions requires consideration of the economic costs and benefits, and plant life-history characteristics as well as imperfect detection. We develop a partially observable Markov decision process (POMDP) to provide optimal management actions when we are uncertain about the presence of invasive plants. The optimal strategy depends on the probability of being in a particular state. We ask the question, 'When is it preferable to use a less efficient, less costly action to a more efficient, more costly action?' We apply the POMDP to branched broomrape Orobanche ramosa, a parasitic plant species at the centre of a national eradication campaign in South Australia. The optimal strategy depends on the ability to detect the invasive species and the location of the infested site. For high detection rates, if the site is a satellite infestation, management should employ the more efficient, more costly action (i.e. soil fumigation) the year the weed is detected followed by monitoring. When the detection probability is low, then it is optimal to employ the less efficient, low cost action (i.e. host denial) in the years the species is not detected. For sites in the centre of the infestation, management should employ the less costly, less efficient action. While the optimal strategy is insensitive to colonization, the likelihood of local eradication diminishes as colonization probability increases, highlighting the importance of limiting colonization if eradication is to be achieved. Synthesis and applications. Providing decision support for managing ecological systems is a key role of applied research. Formulating this support within a decision theory context provides a framework for good decision-making. The POMDP model is a novel decision support tool for optimal sequential decision making when invasive plants are difficult to detect. The model can determine the best management action to employ based on the location of the infestation and can inform when to switch to alternative management actions that buffer against imperfect detection.