Breeding site fidelity of blue-winged teal in South Dakota

Assumptions about breeding site fidelity (i.e., fidelity) in blue-winged teal (Spatula discors) are based on limited recapture data and analytic techniques. We banded female blue-winged teal (n = 12,543) from 2003 to 2014 in a 3,800-ha sample area in north-central South Dakota, USA, and used a Bayesian hierarchical modeling approach combining live recapture and dead recovery data to predict probabilities of fidelity, survival, recapture, and reporting. We explored sources of variation including time, annual wet area on the landscape, age, and nest survival, and compared our results to other dabbling ducks that nest in the Prairie Pothole Region, a critically important breeding area for waterfowl in central North America. We found annual estimates of fidelity ranging from 0.20 to 0.91, with mean values of 0.62 and 0.67 for hatch year birds and after hatch year birds, respectively. Our findings indicate that environmental factors may cause blue-winged teal to return to breeding sites more frequently than previously assumed. © The Wildlife Society, 2019     

动物移动网络研究对景观生态学的贡献

景观生态学在其发展之初的20世纪80年代, 提出了关于景观网络研究(包括景观网络概念、网络结构指数和景观连接度)的基本构想, 这些构想需要在景观过程的研究中逐渐被落实和发展。动物移动过程因动物在斑块或廊道上有着独特丰富的属性特征、与周围资源环境之间存在复杂反馈而区别于无机物运移的景观过程, 则动物移动网络研究在实现关于景观网络研究的基本构想、推动景观生态学发展中贡献独特。因此, 总结动物移动网络研究的来源脉络及其对景观生态学的理论贡献对于景观网络领域和景观生态学学科的发展都具有重要意义。本文抓住景观生态学发展之初提出的关于景观网络研究的基本构想, 寻找和剖析其中所蕴含的景观生态学思想, 追踪这些思想如何被落实、发展、并形成目前的三个热点方向: 动物移动网络模拟、重要值评价和景观连接度分析; 总结这三个方向的研究进展, 指出整合动物的空间行为特征是必然发展趋势; 揭示出动物移动网络研究始终都以发掘斑块或廊道的动物有机体的属性特征(如种群数量)、以及描述这种属性在不同斑块或廊道之间的差异和联系为方向, 正是这种属性的发掘有效地落实、发展和丰富了关于景观网络研究的最初构想, 对景观生态学的贡献比其他过程更为独特。文章还总结了我国动物移动网络研究与国际研究相比较为滞后的现状, 指出其暂时尚未显示出对我国景观生态学的独特贡献; 强调发展源于跟踪定位数据的动物空间行为生态学研究是减小差距的重要、必要前提。期望本文能引发关于景观网络乃至景观生态学理论发展的方向性思考, 为研究者提供参考。     

Bayesian estimation in animal breeding using the Dirichlet process prior for correlated random effects

In the case of the mixed linear model the random effects are usually assumed to be normally distributed in both the Bayesian and classical frameworks. In this paper, the Dirichlet process prior was used to provide nonparametric Bayesian estimates for correlated random effects. This goal was achieved by providing a Gibbs sampler algorithm that allows these correlated random effects to have a nonparametric prior distribution. A sampling based method is illustrated. This method which is employed by transforming the genetic covariance matrix to an identity matrix so that the random effects are uncorrelated, is an extension of the theory and the results of previous researchers. Also by using Gibbs sampling and data augmentation a simulation procedure was derived for estimating the precision parameter M associated with the Dirichlet process prior. All needed conditional posterior distributions are given. To illustrate the application, data from the Elsenburg Dormer sheep stud were analysed. A total of 3325 weaning weight records from the progeny of 101 sires were used.