Data integration for large scale ecological models

生态模型正在变得更大、更复杂，并被用于越来越广泛的应用，从描述趋势和绘制分布到理解机制关系和预测未来情景的影响。作为回应，大规模生态模型的统计方法有了巨大的增长。然而，大多数这样的方法没有考虑到这样一个事实，即生态数据本质上是异质的，并且大数据集通常包含多种形式的偏差。最近，一组层次贝叶斯模型(HBMS)被认为是处理有偏差数据的有效方法，特别是对于发生记录和其他非结构化数据。数以百万计的非结构化事件记录存在，因此这些新方法的潜力是巨大的。然而，并不是所有的数据都包含偏见。少数生物多样性数据是按照采样地点、固定协议和定期采样高度结构化的。理想情况下，我们希望在模型中保留有关这方面的信息，但将其与非结构化数据集的大得多的样本量结合起来。集成模型提供了一种实现这一点的方法。它们是HBM的一个子类，通过将具有不同观测过程的数据集视为相同基础状态的独立实现，对数据异质性进行显式建模。例如，关于GBIF的因果观察和繁殖鸟类调查都包含关于特定物种的种群在特定的时空点是否存在的信息。目前，这些综合模型是高能力统计学家的专属领域。它们很难具体说明，也很难匹配和诊断。这一伙伴关系的目标之一是建立一个可扩展的框架，以适应综合模型，使广大生态模型师社区能够接触到这些模型。这个框架以开源工具的形式，将使生态学家在解决关于生物多样性的大规模问题时更容易处理有偏见的数据。尽管从概念的角度来看很有吸引力，但尚不清楚集成模型的复杂性是否比简单模型带来真正的好处。特别是，当结构化和非结构化数据源都可用时，迫切需要一些关于如何继续进行的一般性原则。关键问题包括：第一季度。我们应该在什么时候以及如何组合具有不同属性的数据集？在什么情况下，简单的聚合(即忽略不同的观察过程)比集成更好？第三季度。如果我们怀疑这些数据包含偏见，我们能检测到它们并对它们进行充分处理吗？信息内容和模型匹配的最合适指标是什么？这些一般性问题存在于Pi Isaac、Co-I Henrys和项目合作伙伴O‘Hara的研究兴趣的交集处。每个国家在解决这些问题的具体方面方面都取得了一些进展。合作伙伴关系将使现有研究超越特定背景，朝着这些大问题的一般答案迈进，从而为每个人增加显著的价值。它将允许协调努力，并建立一个具有国际意义的工作计划。这一泵启动奖将为这种伙伴关系提供一个平台。总体目标是在物种分布的大规模模型中建立一个推理框架，并使用计算机模拟对其进行测试。

Ecological models are becoming larger, more complicated, and being used for an increasingly wide range of applications, from describing trends and mapping distributions to understanding mechanistic relationships and predicting the impact of future scenarios. In response, there has been a huge growth in statistical methods for large-scale ecological models. However, most such methods do not account for the fact that ecological data is inherently heterogeneous, and large datasets typically contain many forms of bias.Recently, a set of hierarchical Bayesian models (HBMs) have emerged as promising ways for dealing with biased data, particularly for occurrence records and other unstructured data. Many millions of unstructured occurrence records exist, so the potential of these new methods is enormous. Not all data contain biases, though. A minority of biodiversity data is highly structured in terms of the sample locations, fixed protocols and regular sampling. Ideally, we'd like to retain the information about this in our models, but combine it with the much larger sample sizes of unstructured datasets.Integrated models provide a way to do this. They are a subclass of HBM in which data heterogeneity is modelled explicitly, by treating datasets with different observation processes as independent realisations of the same underlying state. For example, causal observations on GBIF and the Breeding Bird Survey both contain information about whether the population of a particular species was extant at a particular point in space and time.At present, these integrated models are the preserve of highly competent statisticians. They are hard to specify and difficult to fit and diagnose. One goal of this partnership is to build an extensible framework for fitting integrated models that will make them accessible to a broad community of ecological modellers. This framework, in the form of open source tools, will make it easier for ecologists to handle biased data when addressing large-scale questions about biodiversity.Although attractive from a conceptual standpoint, it is unclear whether the sophistication of integrated models deliver real benefits over simple ones. In particular there is an urgent need for some general principles about how to proceed when both structured and unstructured data sources are available. Critical questions include:Q1. When and how should we combine datasets with different properties?Q2. Under what circumstances is simple aggregation (i.e. ignoring the different observation processes) better than integration? Q3. If we suspect the data contain biases, can we detect them and handle them adequately?Q4. What are the most appropriate metrics for information content and model fit?These general questions lie at the intersection of the research interests of PI Isaac, Co-I Henrys and Project Partner O'Hara. Each has made some progress towards addressing specific aspects of these questions. Working in partnership would add significant value to each, by taking existing research beyond the specific context and toward general answers to these big questions. It would permit a co-ordinated effort and build a work program of international significance. This pump-priming award would provide a platform for this partnership. The overall aim is to build a framework for inference in large-scale models of species' distribution, and to test it using computer simulations.