Conservation decisions should be made considering the information available. The quality of information can vary, depending on how the data is collected. High quality (expensive) information could be obtained from detailed field inventories, or lower quality (inexpensive / free) information could be obtained from remotely sensed information or previously acquired information. From a Bayesian statistics perspective, the value of collecting better information can be evaluated. The remotely sensed or previously acquired information could serve as prior information while the detailed field inventories could be the posterior information. For a simple one stand decision, the value of information can be examined through a Bayesian decision framework [1, 2]. This provides the expected benefits of obtaining additional information that can be evaluated in comparison with the expected costs of obtaining the information. When multiple stands are considered for conservation simultaneously, concisely describing the value of information become difficult, as the value of information is linked to the decision maker’s preferences and the potential impact the additional information has on decision. In this presentation, we describe how to evaluate the value of information for multiple criteria on a single stand. We then present how this concept can be applied to a simple forest conservation problem with four stands. For each stand, a decision can be made to inventory the site (with a specific cost) or not. If an inventory is conducted the decision to conserve the site (with a specific cost) or not can be made with perfect information, otherwise the decision will be made with imperfect information.
The results are demonstrated through a variety of visualization techniques including the empirical achievement function which describes the probabilistic outcomes of each decision alternative . By comparing the general decisions we highlight the potential value of information from different conservation and inventory strategies. With this simple case, the benefits of collecting more information occurs when the desired conservation value is near the mean of the conservation value range.
 Canessa, S., Guillera‐Arroita, G., Lahoz‐Monfort, J. J., Southwell, D. M., Armstrong, D. P., Chadès, I., Lacy, R.C, Converse, S. J. (2015). When do we need more data? A primer on calculating the value of information for applied ecologists. Methods in Ecology and Evolution, 6(10), 1219-1228.
 Raiffa, H. and Schlaifer, R. (1961). Applied Statistical Decision Theory. Harvard University, Boston.
 López-Ibáñez, M., Paquete L., Stützle,T. (2010). Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. Eds. Experimental Methods for the Analysis of Optimization Algorithms, pages 209–222. Springer, Berlin.