Environmental changes in recent years have been linked to phenological shifts which in turn are linked to the survival of species. The work in this paper is motivated by capture-recapture data on blackcaps collected by the British Trust for Ornithology as part of the Constant Effort Sites monitoring scheme. Blackcaps overwinter abroad and migrate to the UK annually for breeding purposes. We propose a novel Bayesian nonparametric approach for expressing the bivariate density of individual arrival and departure times at different sites across a number of years as a mixture model. The new model combines the ideas of the hierarchical and the dependent Dirichlet process, allowing the estimation of site-specific weights and year-specific mixture locations, which are modelled as functions of environmental covariates using a multivariate extension of the Gaussian process. The proposed modelling framework is extremely general and can be used in any context where multivariate density estimation is performed jointly across different groups and in the presence of a continuous covariate.

中文翻译：

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在英国为鸟类迁徙模式建模之前，依赖于层次的Dirichlet过程

近年来的环境变化与物候变化有关，而物候变化又与物种的生存有关。本文的工作是由英国鸟类学会信托基金（作为“持续努力站点”监视计划的一部分）收集的有关黑cap的捕获-捕获数据的推动。黑cap越冬越冬，每年出于繁殖目的移居英国。我们提出了一种新颖的贝叶斯非参数方法，以混合模型的形式表示多年来不同地点的个体到达和离开时间的双变量密度。新模型结合了分层方法和从属Dirichlet过程的思想，可以估算特定地点的权重和特定年份的混合物位置，使用高斯过程的多元扩展将其建模为环境协变量的函数。所提出的建模框架非常笼统，可用于在不同组之间共同执行多变量密度估计并且存在连续协变量的任何情况。