Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, United States of America.
PLoS One. 2012;7(2):e28993. doi: 10.1371/journal.pone.0028993. Epub 2012 Feb 17.
When managing populations of threatened species, conservation managers seek to make the best conservation decisions to avoid extinction. Making the best decision is difficult because the true population size and the effects of management are uncertain. Managers must allocate limited resources between actively protecting the species and monitoring. Resources spent on monitoring reduce expenditure on management that could be used to directly improve species persistence. However monitoring may prevent sub-optimal management actions being taken as a result of observation error. Partially observable Markov decision processes (POMDPs) can optimize management for populations with partial detectability, but the solution methods can only be applied when there are few discrete states. We use the Continuous U-Tree (CU-Tree) algorithm to discretely represent a continuous state space by using only the states that are necessary to maintain an optimal management policy. We exploit the compact discretization created by CU-Tree to solve a POMDP on the original continuous state space. We apply our method to a population of sea otters and explore the trade-off between allocating resources to management and monitoring. We show that accurately discovering the population size is less important than management for the long term survival of our otter population.
在管理受威胁物种的种群时，保护管理者力求做出最佳的保护决策以避免物种灭绝。然而，要做出最佳决策却非常困难，因为真实的种群规模和管理的效果具有不确定性。管理者必须在积极保护物种和监测之间分配有限的资源。用于监测的资源减少了可用于直接提高物种存续能力的管理支出。但是，监测可以防止由于观测误差而采取次优的管理措施。部分可观测马尔可夫决策过程（POMDP）可以针对部分可检测性的种群进行管理优化，但只有当离散状态较少时，才可以应用解决方案方法。我们使用连续 U 树（CU-Tree）算法通过仅使用维持最优管理策略所需的状态来离散表示连续状态空间。我们利用 CU-Tree 生成的紧凑离散化来解决原始连续状态空间上的 POMDP。我们将该方法应用于海獭种群，并探讨了在管理和监测之间分配资源的权衡。结果表明，对于我们的海獭种群的长期生存，准确发现种群规模的重要性不如管理。
