中文摘要
在生物多样性空前丧失的背景下，一个紧迫问题是：如何在资源和信息有限的情况下提高保护效率。针对小种群物种收集数据成本高昂且耗时，而许多高风险决策需基于有限数据作出。我们构建了一个参数未知的部分可观测马尔可夫决策过程模型，以联合优化受威胁物种的信息收集与保护努力。该模型考虑了物种状态、可探测性及动态过程的不确定性，并实时自适应地调整调查与保护努力。尽管标准表述难以求解，我们利用生态问题的结构识别出一个低维的混合信念状态，并将该随机动态规划重新表述为分段确定性的最优控制问题。这使我们能够获得关于最优策略的结构性洞见（部分为闭式解），并找到具有性能保证的近似最优策略。在某些情形下，从未发现过该物种的区域可能比先前发现过该物种的区域更有可能存在该物种。我们还针对最稀有灵长类物种海南长臂猿的保护开展了案例研究，其中我们将模型扩展以优化有限资源的时空配置。
Abstract
Amid an unprecedented loss of biodiversity, a pressing issue is how to improve the efficiency of conservation with limited resources and information. Collecting data on species with a small population is costly and time consuming, and many high-stakes decisions need to be made based on limited data. We develop a partially observable Markov decision processes model with unknown parameters to jointly optimize the information collection and protection efforts for threatened species. The model takes into account uncertainties about the state, detectability, and dynamics of the species, and it adaptively adjusts the efforts of surveying and protection in real time. Although the standard formulation is intractable, we exploit the structure of ecological problems to identify a hybrid belief state in low dimensions, and we reformulate the stochastic dynamic program as a piecewise deterministic optimal control problem. This enables us to obtain structural insights into the optimal policy (some are in closed form) and find a near-optimal approximate policy with performance guarantee. In certain situations, areas where the species has never been found may be more likely to contain the species than areas where it has been previously found. We also conduct a case study on the conservation of the Hainan gibbon, the rarest primate species, in which we extend the model to optimize the spatiotemporal allocation of limited resources.