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摘要翻译：
对环境的压力越来越大，越来越需要可持续地管理动植物种群，保护和重建濒危种群。有效的管理需要可靠的数学模型，以便能够预测管理行为的效果，并量化这些预测中的不确定性。这些模型必须能够预测人口对人为变化的反应，同时处理不确定性的主要来源。我们描述了一个简单的“构建块”方法来建立离散时间模型。我们展示了如何从数据的时间序列中估计这些模型的参数，以及如何使用计算机密集的贝叶斯方法量化这些估计和种群中不同类型的个体数量的不确定性。我们还讨论了这种方法的优点和缺陷，并给出了一个英国灰海豹种群的例子。
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英文标题：
《Embedding Population Dynamics Models in Inference》
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作者：
Stephen T. Buckland, Ken B. Newman, Carmen Fern\'andez, Len Thomas,
John Harwood
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple ``building block'' approach to formulating discrete-time models. We show how to estimate the parameters of such models from time series of data, and how to quantify uncertainty in those estimates and in numbers of individuals of different types in populations, using computer-intensive Bayesian methods. We also discuss advantages and pitfalls of the approach, and give an example using the British grey seal population.
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PDF链接：
https://arxiv.org/pdf/708.3796
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