Weegman Mitch D, Wilson Scott, Alisauskas Ray T, Kellett Dana K
School of Natural Resources, University of Missouri, Columbia, MO, USA.
Science and Technology Branch, Pacific Wildlife Research Centre, Environment and Climate Change Canada, Delta, BC, Canada.
PeerJ. 2020 Jun 23;8:e9382. doi: 10.7717/peerj.9382. eCollection 2020.
Joint encounter (JE) models estimate demographic rates using live recapture and dead recovery data. The extent to which limited recapture or recovery data can hinder estimation in JE models is not completely understood. Yet limited data are common in ecological research. We designed a series of simulations using Bayesian multistate JE models that spanned a large range of potential recapture probabilities (0.01-0.90) and two reported mortality probabilities (0.10, 0.19). We calculated bias by comparing estimates against known probabilities of survival, fidelity and reported mortality. We explored whether sparse data (i.e., recapture probabilities <0.02) compromised inference about survival by comparing estimates from dead recovery (DR) and JE models using an 18-year data set from a migratory bird, the lesser snow goose (). Our simulations showed that bias in probabilities of survival, fidelity and reported mortality was relatively low across a large range of recapture probabilities, except when recapture and reported mortality probabilities were both lowest. While bias in fidelity probability was similar across all recapture probabilities, the root mean square error declined substantially with increased recapture probabilities for reported mortality probabilities of 0.10 or 0.19, as expected. In our case study, annual survival probabilities for adult female snow geese were similar whether estimated with JE or DR models, but more precise from JE models than those from DR models. Thus, our simulated and empirical data suggest acceptably minimal bias in survival, fidelity or reported mortality probabilities estimated from JE models. Even a small amount of recapture information provided adequate structure for JE models, except when reported mortality probabilities were <0.10. Thus, practitioners with limited recapture data should not be discouraged from use of JE models. We recommend that ecologists incorporate other data types as frequently as analytically possible, since precision of focal parameters is improved, and additional parameters of interest can be estimated.
联合遭遇(JE)模型利用活体再捕获和死亡个体回收数据来估计种群统计学参数。在JE模型中,有限的再捕获或回收数据对估计的阻碍程度尚未完全明确。然而,有限的数据在生态学研究中很常见。我们使用贝叶斯多状态JE模型设计了一系列模拟,涵盖了广泛的潜在再捕获概率(0.01 - 0.90)和两个报告的死亡率概率(0.10、0.19)。我们通过将估计值与已知的生存、保真度和报告死亡率概率进行比较来计算偏差。我们利用一种候鸟小白额雁18年的数据集,比较死亡回收(DR)模型和JE模型的估计值,探讨稀疏数据(即再捕获概率<0.02)是否会影响生存推断。我们的模拟表明,在广泛的再捕获概率范围内,生存、保真度和报告死亡率概率的偏差相对较低,除非再捕获概率和报告死亡率概率都最低。虽然在所有再捕获概率下保真度概率的偏差相似,但对于报告死亡率概率为0.10或0.19的情况,均方根误差随着再捕获概率的增加而大幅下降,正如预期的那样。在我们的案例研究中,成年雌性雪雁的年生存概率,无论是用JE模型还是DR模型估计,结果都相似,但JE模型的估计比DR模型更精确。因此,我们的模拟和实证数据表明,从JE模型估计的生存、保真度或报告死亡率概率偏差极小,可接受。即使是少量的再捕获信息也为JE模型提供了足够的结构,除非报告死亡率概率<0.10。因此,再捕获数据有限的从业者不应因此而不使用JE模型。我们建议生态学家尽可能频繁地纳入其他数据类型,因为这样可以提高焦点参数的精度,并能估计更多感兴趣的参数。