State-space models for the dynamics of wild animal populations
State-space models for the dynamics of wild animal populations
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DOI：
10.1016/j.ecolmodel.2003.08.002
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发表时间：
2004-01-01
影响因子：
3.1
通讯作者：
Koesters, NB
中科院分区：
文献类型：
--
作者：
Buckland, ST;Newman, KB;Koesters, NB
我们开发了一个统一的框架，用于共同定义人口动态模型和对人口的测量。该框架是一个状态空间模型，其中人口过程由状态过程建模，测量由观测过程建模。在许多情况下，状态过程的期望值可以表示为标准总体投影矩阵的推广：状态过程中的每个子过程可以由单独的矩阵建模，这些矩阵的乘积是广义Leslie矩阵。通过选择适当的矩阵及其排序，可以指定各种各样的模型。该方法是完全灵活的过程中允许随机变化。工艺参数本身可以作为协变量的函数进行建模。结构容纳的影响，如密度依赖，竞争和捕食者-猎物的关系，和集合种群很容易建模。对人口的观察进入一个观察过程模型，我们展示了如何建立似然函数，反映人口统计随机性（出现在状态过程）和随机误差的观察。参数估计和状态过程变量的估计可以使用顺序蒙特卡罗程序进行。(C)2003 Elsevier B. V.保留所有权利。
We develop a unified framework for jointly defining population dynamics models and measurements taken on a population. The framework is a state-space model where the population processes are modelled by the state process and measurements are modelled by the observation process. In many cases, the expected value for the state process can be represented as a generalisation of the standard population projection matrix: each sub-process within the state process may be modelled by a separate matrix and the product of these matrices is a generalised Leslie matrix. By selecting appropriate matrices and their ordering, a wide range of models may be specified. The method is fully flexible for allowing stochastic variation in the processes. Process parameters may themselves be modelled as functions of covariates. The structure accommodates effects such as density dependence, competition and predator-prey relationships, and metapopulations are readily modelled. Observations on the population enter through an observation process model, and we show how likelihood functions can be built that reflect both demographic stochasticity (which appears in the state process) and stochastic errors in the observations. Parameter estimation and estimation of state process variables can be conducted using sequential Monte Carlo procedures. (C) 2003 Elsevier B.V. All rights reserved.