Hidden process models for animal population dynamics
Hidden process models for animal population dynamics
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DOI：
10.1890/04-0592
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发表时间：
2006-02-01
影响因子：
5
通讯作者：
Fernández, C
中科院分区：
文献类型：
--
作者：
Newman, KB;Buckland, ST;Fernández, C
隐藏过程模型是一种概念上有用且实用的方法，可以同时解释动物种群动态中的过程变化以及对种群进行观察和估计时的测量误差。过程变化可以是人口统计学的，也可以是环境统计学的，通过连接一系列随机和确定性的子过程来建模，这些子过程描述了诸如出生、生存、成熟和运动等过程。总体的观测值可以建模为真实丰度的函数，具有现实的概率分布，以描述观测或估计误差。计算机密集的程序，如顺序蒙特卡罗方法或马尔可夫链蒙特卡罗，对观察到的数据进行条件处理，以产生潜在的真实种群丰度和未知种群动态参数的估计。本文对萨克拉门托河越冬大鳞大马哈鱼的隐过程模型进行了建立和拟合。
Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer-intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter-run chinook salmon (Oncorhynchus tshawytsha).