Rushing Clark S
Department of Wildland Resources and the Ecology Center Utah State University Logan Utah.
Smithsonian Conservation Biology Institute Migratory Bird Center Washington District of Columbia.
Ecol Evol. 2019 Feb 5;9(2):849-858. doi: 10.1002/ece3.4826. eCollection 2019 Jan.
Long-distance migration is a common phenomenon across the animal kingdom but the scale of annual migratory movements has made it difficult for researchers to estimate survival rates during these periods of the annual cycle. Estimating migration survival is particularly challenging for small-bodied species that cannot carry satellite tags, a group that includes the vast majority of migratory species. When capture-recapture data are available for linked breeding and non-breeding populations, estimation of overall migration survival is possible but current methods do not allow separate estimation of spring and autumn survival rates. Recent development of a Bayesian integrated survival model has provided a method to separately estimate the latent spring and autumn survival rates using capture-recapture data, though the accuracy and precision of these estimates has not been formally tested. Here, I used simulated data to explore the estimability of migration survival rates using this model. Under a variety of biologically realistic scenarios, I demonstrate that spring and autumn migration survival can be estimated from the integrated survival model, though estimates are biased toward the overall migration survival probability. The direction and magnitude of this bias are influenced by the relative difference in spring and autumn survival rates as well as the degree of annual variation in these rates. The inclusion of covariates can improve the model's performance, especially when annual variation in migration survival rates is low. Migration survival rates can be estimated from relatively short time series (4-5 years), but bias and precision of estimates are improved when longer time series (10-12 years) are available. The ability to estimate seasonal survival rates of small, migratory organisms opens the door to advancing our understanding of the ecology and conservation of these species. Application of this method will enable researchers to better understand when mortality occurs across the annual cycle and how the migratory periods contribute to population dynamics. Integrating summer and winter capture data requires knowledge of the migratory connectivity of sampled populations and therefore efforts to simultaneously collect both survival and tracking data should be a high priority, especially for species of conservation concern.
长途迁徙是动物界的一种普遍现象,但年度迁徙活动的规模使得研究人员难以估计年度周期中这些时期的存活率。对于无法携带卫星标签的小型物种来说,估计迁徙存活率尤其具有挑战性,而这类物种占绝大多数迁徙物种。当有关于繁殖和非繁殖种群的标记重捕数据时,可以估计总体迁徙存活率,但目前的方法无法分别估计春季和秋季的存活率。贝叶斯综合生存模型的最新发展提供了一种利用标记重捕数据分别估计潜在春季和秋季存活率的方法,不过这些估计的准确性和精确性尚未经过正式检验。在此,我使用模拟数据来探索使用该模型估计迁徙存活率的可估计性。在各种生物学现实场景下,我证明可以从综合生存模型中估计春季和秋季的迁徙存活率,尽管估计值偏向于总体迁徙存活概率。这种偏差的方向和大小受春季和秋季存活率的相对差异以及这些率的年度变化程度影响。纳入协变量可以改善模型性能,尤其是在迁徙存活率年度变化较低时。迁徙存活率可以从相对较短的时间序列(4 - 5年)中估计出来,但当有更长的时间序列(10 - 12年)时,估计的偏差和精确性会得到改善。估计小型迁徙生物季节性存活率的能力为增进我们对这些物种的生态学和保护的理解打开了大门。应用这种方法将使研究人员能够更好地了解年度周期中死亡率何时发生,以及迁徙期如何影响种群动态。整合夏季和冬季捕获数据需要了解采样种群的迁徙连通性,因此同时收集生存和追踪数据的工作应成为高度优先事项,特别是对于受保护关注的物种。