Ecol Appl. 2014 Jul;24(5):1155-66. doi: 10.1890/13-0558.1.
Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard survival parameter estimates toward the terminus of the times series (terminal bias). Under random temporary emigration, unbiased parameters can be estimated with CJS models. However, unmodeled Markovian temporary emigration causes bias in parameter estimates, and an unobservable state is required to model this type of emigration. The robust design is most flexible when modeling temporary emigration, and partial solutions to mitigate bias have been identified; nonetheless, there are conditions were terminal bias prevails. Long-lived species with high adult survival and highly variable nonrandom temporary emigration present terminal bias in survival estimates, despite being modeled with the robust design and suggested constraints. Because this bias is due to uncertainty about the fate of individuals that are undetected toward the end of the time series, solutions should involve using additional information on survival status or location of these individuals at that time. Using simulation, we evaluated the performance of models that jointly analyze robust design data and an additional source of ancillary data (predictive covariate on temporary emigration, telemetry, dead recovery, or auxiliary resightings) in reducing terminal bias in survival estimates. The auxiliary resighting and predictive covariate models reduced terminal bias the most. Additional telemetry data were effective at reducing terminal bias only when individuals were tracked for a minimum of two years. High adult survival of long-lived species made the joint model with recovery data ineffective at reducing terminal bias because of small-sample bias. The naive constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, although still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several sources of data improved parameter estimates and reduced terminal bias. Efforts to incorporate or acquire such data should be considered by researchers and wildlife managers, especially in the years leading up to status assessments of species of interest. Simulation modeling is a very cost-effective method to explore the potential impacts of using different sources of data to produce high-quality demographic data to inform management.
尽管进行了密集监测，临时迁出采样区仍可能导致严重的偏差，使管理者在时间序列末期丢弃生存参数估计值（末端偏差）。在随机临时迁出的情况下，可以使用 CJS 模型估计无偏参数。然而，未建模的马尔可夫临时迁出会导致参数估计的偏差，并且需要一个不可观测的状态来模拟这种类型的迁出。在建模临时迁出时，稳健设计最为灵活，已经确定了减轻偏差的部分解决方案；尽管如此，在某些情况下仍会出现末端偏差。具有高成年存活率和高度可变的非随机临时迁出的长寿命物种，尽管使用稳健设计和建议的约束条件进行建模，但在生存估计中仍存在末端偏差。由于这种偏差是由于对时间序列末期未检测到的个体命运的不确定性引起的，因此解决方案应涉及使用有关这些个体在该时间的生存状态或位置的其他信息。通过模拟，我们评估了联合分析稳健设计数据和附加辅助数据（临时迁出的预测协变量、遥测、死亡回收或辅助重见）的模型在减少生存估计中末端偏差的性能。辅助重见和预测协变量模型减少末端偏差的效果最为明显。只有当个体被追踪至少两年时，额外的遥测数据才能有效地减少末端偏差。由于小样本偏差，长寿命物种的高成年存活率使得与回收数据的联合模型无法有效减少末端偏差。幼稚约束模型（最后和倒数第二个临时迁出参数相等）效率最低，尽管与无约束模型相比，它仍然能够减少末端偏差。联合分析多种数据来源可以改善参数估计并减少末端偏差。研究人员和野生动物管理者应考虑努力纳入或获取此类数据，特别是在对感兴趣物种进行状况评估之前的几年。模拟建模是一种非常具有成本效益的方法，可以探索使用不同数据源产生高质量人口统计数据以提供管理信息的潜在影响。
