用贝叶斯分层状态空间模型估计不可观测驼鹿种群规模转换指数
Estimating the unobservable moose - converting index to population size using a Bayesian Hierarchical state space model
摘要：
关于种群大小的间接信息，如颗粒计数或志愿者计数，是大多数生态研究和应用种群管理情况的主要信息来源。通常，这样的观察被当作是对种群规模的实际测量。这种假设导致了关于种群规模及其动态的错误结论。我们提出了一个模型的时间变化的联系，表示可数性，间接观测和实际人口规模之间。我们表明，当间接测量具有高精度（例如许多观察小时）的假设随时间变化的可数性估计的人口动态可能会产生至关重要的影响。我们将该模型应用于瑞典当地的驼鹿种群。估计的人口动态被发现解释30- 50%的总变异的观测数据，因此，可数占大部分的变化。这种估计动态的不可靠性对管理种群的能力有很大的负面影响;例如，减少（增加）需要收获的动物数量，以维持种群高于（低于）固定水平。最后，两个研究区之间的可数性差异很大，这意味着可数性的空间变化很大，这种变化本身就很值得研究。
英文摘要：
Indirect information on population size, like pellet counts or volunteer counts, is the main source of information in most ecological studies and applied population management situations. Often, such observations are treaded as if they were actual measurements of population size. This assumption results in incorrect conclusions about a population's size and its dynamics. We propose a model with a temporal varying link, denoted countability, between indirect observations and actual population size. We show that, when indirect measurement has high precision (for instance many observation hours) the assumption of temporal varying countability can have a crucial effect on the estimated population dynamic. We apply the model on two local moose populations in Sweden. The estimated population dynamics is found to explain 30-50 percent of the total variability in the observation data; thus, countability accounts for most of the variation. This unreliability of the estimated dynamics has a substantial negative impact on the ability to manage populations; for example, reducing (increasing) the number of animals that needs to be harvested in order to sustain the population above (below) a fixed level. Finally, large difference in countability between two study areas implies a substantial spatial variation in the countability; this variation in itself is highly worthy of study.
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