USGS Patuxent Wildlife Research Center, 12100 Beech Forest Road, Laurel, Maryland 20708, USA.
Ecology. 2010 Jun;91(6):1598-604. doi: 10.1890/09-1633.1.
Multistate mark-recapture models with unobservable states can yield unbiased estimators of survival probabilities in the presence of temporary emigration (i.e., in cases where some individuals are temporarily unavailable for capture). In addition, these models permit the estimation of transition probabilities between states, which may themselves be of interest; for example, when only breeding animals are available for capture. However, parameter redundancy is frequently a problem in these models, yielding biased parameter estimates and influencing model selection. Using numerical methods, we examine complex multistate mark-recapture models involving two observable and two unobservable states. This model structure was motivated by two different biological systems: one involving island-nesting albatross, and another involving pond-breeding amphibians. We found that, while many models are theoretically identifiable given appropriate constraints, obtaining accurate and precise parameter estimates in practice can be difficult. Practitioners should consider ways to increase detection probabilities or adopt robust design sampling in order to improve the properties of estimates obtained from these models. We suggest that investigators interested in using these models explore both theoretical identifiability and possible near-singularity for likely parameter values using a combination of available methods.
多状态标记重捕模型中若存在无法观测的状态，在存在临时迁出（即某些个体暂时无法被捕到）的情况下，仍可以得到生存概率的无偏估计量。此外，这些模型还允许估计状态之间的转移概率，这本身可能也是人们感兴趣的；例如，只有繁殖动物可被捕到的情况下。然而，参数冗余在这些模型中经常是一个问题，会产生有偏的参数估计值，并影响模型选择。我们使用数值方法研究了涉及两个可观测状态和两个不可观测状态的复杂多状态标记重捕模型。这种模型结构的灵感来自于两个不同的生物学系统：一个涉及岛屿筑巢的信天翁，另一个涉及池塘繁殖的两栖动物。我们发现，虽然许多模型在理论上是可以识别的，但在实践中获得准确和精确的参数估计可能很困难。从业者应该考虑增加检测概率或采用稳健设计抽样的方法，以提高从这些模型中获得的估计值的性能。我们建议有兴趣使用这些模型的研究人员，使用现有的多种方法，探索理论可识别性和可能的参数值接近奇异的情况。
