Evaluation and parameterisation of individual-based models of animal populations
基于个体的动物种群模型的评估和参数化
基本信息
- 批准号:NE/K006282/1
- 负责人:
- 金额:$ 39.2万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2013
- 资助国家:英国
- 起止时间:2013 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Ecosystems are populated by autonomous, adaptive individuals, each figuring out its own ways of achieving its goals. It is a widely shared hope that the general principles governing such complex systems will eventually be understood from analysis of computer simulations known collectively as individual-based models (IBMs). IBMs are dynamical systems containing many autonomous interacting agents which are used where, broadly, the factors influencing the behaviour of individual agents are known, but interest centres on what happens at the population level. Will the population increase or decrease? How fast will be the response? Where practical management of ecosystems is required, many consider this can only be realistically performed with IBMs. Examples include conservation management of nature reserves and shell fisheries, assessment of environmental impacts of building proposals including wind farms and highways, management of fish stocks and assessment of the effects on non-target organisms of new chemicals for the control of agricultural pests. Articles in scientific journals have suggested IBMs are the only realistic way forward in diverse fields including economic analysis where the recent global 'credit crunch' might have been avoided with the use of such models. Thus IBMs are the only practicable method of modelling many complex systems where prediction is of vital importance to all. Despite the widely-appreciated importance of IBMs, the evaluation of these very complex systems still leaves much to be desired. Clearly, the purpose of a model is to explain the world that we see around us. From a statistical point of view we wish to 'fit' the model to data. How can we do this? Recent advances in statistical theory, known as Approximate Bayesian Computation, ABC, suggest how this might be done. Implementation of ABC requires development of practical methods that will allow users to fit their IBM models to real data in an efficient manner. This Bayesian approach should allow calculation of distributions of possible parameter values in IBMs, given observations, and evaluation of whether one model is better than another. In this project we devise practical methods that will allow all makers of IBMs to validate their models properly by reference to relevant data. Provision of such methods is crucial if we are to have robust and reliable bases for making crucial decisions about environmental impacts, nature conservation, and the licensing of new chemicals for the control of agricultural pests.
生态系统由自主的、适应性强的个体组成,每个个体都有自己的方式来实现自己的目标。人们普遍希望,通过对计算机模拟(统称为基于个体的模型(IBM))的分析,最终能够理解管理此类复杂系统的一般原理。IBM是动态系统,包含许多自主互动的代理人,使用的地方,广泛地说,影响个别代理人的行为的因素是已知的,但兴趣集中在人口水平上发生了什么。人口会增加还是减少?反应会有多快?在需要对生态系统进行实际管理的地方,许多人认为这只能通过IBM来实现。这方面的例子包括自然保护区和贝类渔业的养护管理、评估包括风力发电场和高速公路在内的建筑提案对环境的影响、鱼类种群的管理以及评估用于控制农业害虫的新化学品对非目标生物的影响。科学期刊上的文章表明,IBM是在包括经济分析在内的各个领域取得进展的唯一现实途径,在这些领域,使用这种模型可能会避免最近的全球“信贷紧缩”。因此,IBM是模拟许多复杂系统的唯一可行方法,其中预测对所有人都至关重要。 尽管IBM的重要性得到了广泛的认可,但对这些非常复杂的系统的评估仍有许多不足之处。显然,模型的目的是解释我们周围的世界。从统计学的角度来看,我们希望将模型与数据“拟合”。我们怎样才能做到这一点?统计理论的最新进展,被称为近似贝叶斯计算,ABC,表明这可能是如何做到的。ABC的实现需要开发实用的方法,这些方法将允许用户以有效的方式将他们的IBM模型与真实的数据相匹配。这种贝叶斯方法应该允许计算IBM中可能的参数值的分布,给定观察结果,并评估一个模型是否优于另一个模型。在这个项目中,我们设计了实用的方法,将允许IBM的所有制造商,以验证他们的模型正确参考相关数据。如果我们要在环境影响、自然保护和农业害虫防治新化学品的许可证发放等关键决策方面拥有坚实可靠的基础,提供此类方法至关重要。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Predicting how many animals will be where: How to build, calibrate and evaluate individual-based models
预测有多少动物将在哪里:如何构建、校准和评估基于个体的模型
- DOI:10.1016/j.ecolmodel.2015.08.012
- 发表时间:2016
- 期刊:
- 影响因子:3.1
- 作者:Van Der Vaart E
- 通讯作者:Van Der Vaart E
Calibration and evaluation of individual-based models using Approximate Bayesian Computation
使用近似贝叶斯计算校准和评估基于个体的模型
- DOI:10.1016/j.ecolmodel.2015.05.020
- 发表时间:2015
- 期刊:
- 影响因子:3.1
- 作者:Van Der Vaart E
- 通讯作者:Van Der Vaart E
Taking error into account when fitting models using Approximate Bayesian Computation.
使用近似贝叶斯计算拟合模型时考虑误差。
- DOI:10.1002/eap.1656
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Van Der Vaart E
- 通讯作者:Van Der Vaart E
Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass?
将环境变化纳入欧洲鲈鱼的空间明确的个体模型中?
- DOI:10.1016/j.ecolmodel.2022.109878
- 发表时间:2022
- 期刊:
- 影响因子:3.1
- 作者:Watson J
- 通讯作者:Watson J
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Richard Sibly其他文献
‘Unitary drives’ revisited
- DOI:
10.1016/s0003-3472(72)80020-4 - 发表时间:
1972-08-01 - 期刊:
- 影响因子:
- 作者:
David McFarland;Richard Sibly - 通讯作者:
Richard Sibly
Richard Sibly的其他文献
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{{ truncateString('Richard Sibly', 18)}}的其他基金
Quantifying uncertainty in the predictions of complex process-based models
量化基于复杂过程的模型预测的不确定性
- 批准号:
NE/T004010/1 - 财政年份:2019
- 资助金额:
$ 39.2万 - 项目类别:
Research Grant
BBSRC Industrial CASE Partnership Grant.
BBSRC 工业案例合作伙伴补助金。
- 批准号:
BB/I532429/1 - 财政年份:2010
- 资助金额:
$ 39.2万 - 项目类别:
Training Grant
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