Collaborative Research on Bayesian Semiparametric Population Dynamics Modeling

人口调控的机制很少得到很好的理解，也经常引起激烈的辩论。原则上，聪明的实验可以揭示种群波动的原因，但对于大多数物种来说，这样的实验是不切实际的，如果不是不可能的话。因此，确定这些机制的间接方法是必要的。将开发使用半参数贝叶斯方法的统计模型，允许从观测到的丰度波动推断未知机制。种群动力学模型经常被用来在保护和管理中做出重要决策。然而，目前使用的所有模型都有一个重大缺陷，即它们需要所有模型组件的规范，无论这些机制是否已知。型号说明中的错误可能会导致收割机损失数百万美元或濒危物种灭绝。根据该奖项将制定的方法将使保护和管理界能够产生强有力的人口预测，并将极大地增加他们成功的机会。这项工作将是生态学建模和贝叶斯非参数学思想的新颖综合。为了促进这些方法的应用，私人投资机构将开发易于使用的软件，并通过保护和管理会议及其专业网站积极分发。

The mechanisms of population regulation are rarely well understood and often hotly debated. In principle, clever experiments could reveal why populations fluctuate but, for most species, such experiments are impractical, if not impossible. Indirect methods for identifying these mechanisms are therefore needed. Statistical models that use semiparametric Bayesian methods will be developed that allow unknown mechanisms to be inferred from observed fluctuations in abundance. Population dynamics models are routinely used to make important decisions in conservation and management. However, all models currently in use have the substantial drawback that they require the specification of all model components whether or not these mechanisms are known. Mistakes in model specification may result in millions of dollars lost to harvesters or the extinction of threatened species. The methods that will be developed under this award will enable the conservation and management communities to generate robust population projections and will greatly increase their chances of success. This work will be a novel synthesis of ideas from ecological modeling and Bayesian nonparametrics. To foster applications of these methods the PIs will develop easy to use software that will be actively distributed through conservation and management meetings and their professional websites.