Integrating new statistical frameworks into eDNA survey and analysis at the landscape scale

将新的统计框架整合到景观尺度的 eDNA 调查和分析中

• 批准号: NE/T010045/1
• 负责人: Eleni Matechou
• 金额: $ 38.63万
• 依托单位: University of Kent
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT010045%2F1
• 关键词: Integrating; new; statistical; frameworks; into

In recent years, three major innovations have occurred in ecology. (1) The emergence of new statistical methods for analysing community data; (2) the rapid detection of species and whole communities from environmental DNA (eDNA) and bulk-sample DNA; and (3) the wide availability of remotely sensed environmental covariates. The efficiency gains are such that hundreds or even thousands of species can now be detected and, to an extent, quantified in hundreds or even thousands of samples. Collectively, these three innovations have the potential to relieve the problems of data limitation and analysis that environmental management has been struggling with, opening the way to near-real-time tracking of state and change in biodiversity and its functions and services over whole landscapes. The aim of our project is to develop an integrated statistical framework for DNA-based surveys of biodiversity. The framework will allow the estimation of community compositions and the identification of the landscape characteristics that drive them. We will develop a Bayesian hierarchical model accounting for the probabilistic nature of DNA-based data due to observation error and taxonomic uncertainty and for model uncertainty due to the unknown strength and direction of landscape effects on the system. We will build sophisticated and efficient algorithms within a Bayesian framework for identifying the important landscape covariates that predict community structure and provide guidelines on optimal allocation of resources in DNA-based surveys for achieving the required power to infer species distributions and to link them to landscape covariates. The huge potential contribution of DNA-based data to landscape decision-making is demonstrated by how Natural England, Local Planning Authorities, and the NatureSpace Partnership use eDNA to create a biodiversity-offset market ('District Licensing') for the protected Great Crested Newt (GCN). Water samples from 500 ponds across the South Midlands (spanning ~3320 sq km) were tested for GCN and used to create a distribution map, which was then zoned into four 'impact risk' levels. Builders pay a known, sliding-scale fee, and a portion of the fee is used to build and manage new habitat. District Licensing is only feasible with eDNA's greater efficiency. GCN District Licensing expands to at least 16 LPAs in 2020, aiming to go nationwide, which would make it the largest biodiversity-focused, land-use decision scheme in the UK, if not the world.The natural-and highly desirable-extension to the GCN scheme would be to map 'all biodiversity' and to make land-use decisions (e.g. impact risk maps, offset markets, habitat creation) on this broader basis. In fact, samples originally collected for GCN can be repurposed for this larger goal by using 'metabarcoding,' meaning that the eDNA is PCR-amplified for a larger range of taxa. Given the District-Licensing expansion plans, pond eDNA metabarcoding alone could provide an efficient way to map biodiversity across much of the UK. This is far from the only such programme. Ecologists in industry and academia around the world are plunging ahead with large-scale DNA-sampling campaigns, and there is, as yet, no comprehensive set of statistical methods for modelling the individual steps of the new observation processes, quantifying the resulting uncertainty, and assessing how it affects decision-making at the landscape level. Our proposed modelling framework will provide such tools by explicitly capturing measurement bias within biodiversity models as a set of observation processes, and not merely as error. Improving sampling designs and workflows as a result of our proposed models will profoundly increase the efficiency and credibility of inference and therefore reduce the risk of biodiversity loss during the political process of allocating land to different uses.

近年来，生态学领域出现了三大创新。(1)分析群落数据的新统计方法的出现;（2）从环境DNA和大量样本DNA中快速检测物种和整个群落;（3）广泛提供遥感环境协变量。效率的提高使得现在可以检测到数百甚至数千个物种，并且在一定程度上可以在数百甚至数千个样本中进行量化。总的来说，这三项创新有可能缓解环境管理一直在努力解决的数据限制和分析问题，为近实时跟踪整个景观的生物多样性及其功能和服务的状态和变化开辟了道路。我们项目的目的是为基于DNA的生物多样性调查制定一个综合统计框架。该框架将允许社区组成的估计和驱动他们的景观特征的识别。我们将开发一个贝叶斯分层模型占基于DNA的数据的概率性质，由于观测误差和分类的不确定性和模型的不确定性，由于未知的强度和方向的景观对系统的影响。我们将建立一个贝叶斯框架内的复杂和有效的算法，以确定重要的景观协变量，预测社区结构，并提供指导方针，在基于DNA的调查资源的最佳分配，以实现所需的权力，推断物种分布，并将它们链接到景观协变量。基于DNA的数据对景观决策的巨大潜在贡献是由自然英格兰，地方规划局和自然空间合作伙伴关系如何使用eDNA为受保护的大凤头蝾螈（GCN）创建生物多样性抵消市场（“地区许可”）来证明的。来自南米德兰兹郡500个池塘（跨度约3320平方公里）的水样进行了GCN测试，并用于创建分布图，然后将其划分为四个“影响风险”级别。建筑商支付一个已知的，滑动规模的费用，和费用的一部分是用来建立和管理新的栖息地。只有在eDNA效率更高的情况下，地区许可才是可行的。到2020年，GCN地区许可证计划将扩展到至少16个土地保护区，目标是在全国范围内推广，这将使其成为英国最大的以生物多样性为重点的土地使用决策计划，如果不是世界的话。GCN计划的自然和高度可行的扩展将是绘制“所有生物多样性”，并在此更广泛的基础上做出土地使用决策（例如影响风险地图，抵消市场，栖息地创造）。事实上，最初为GCN收集的样本可以通过使用“元条形码”来重新用于这个更大的目标，这意味着eDNA被PCR扩增用于更大范围的分类群。考虑到地区许可扩张计划，仅池塘eDNA元条形码就可以提供一种有效的方法来绘制英国大部分地区的生物多样性地图。这远非唯一的此类方案。世界各地的工业界和学术界的生态学家正在进行大规模的DNA采样活动，到目前为止，还没有一套全面的统计方法来模拟新观测过程的各个步骤，量化由此产生的不确定性，并评估它如何影响景观层面的决策。我们提出的建模框架将提供这样的工具，明确捕捉生物多样性模型中的测量偏差作为一组观察过程，而不仅仅是错误。由于我们提出的模型，改进采样设计和工作流程将大大提高推理的效率和可信度，从而降低在将土地分配给不同用途的政治过程中生物多样性丧失的风险。

项目成果

Reliability of environmental DNA surveys to detect pond occupancy by newts at a national scale.

• DOI: 10.1038/s41598-022-05442-1
• 发表时间: 2022-01-25
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Buxton A;Diana A;Matechou E;Griffin J;Griffiths RA
• 通讯作者: Griffiths RA

An RShiny app for modelling environmental DNA data: accounting for false positive and false negative observation error

• DOI: 10.1111/ecog.05718
• 发表时间: 2021-10-20
• 期刊: ECOGRAPHY
• 影响因子: 5.9
• 作者: Diana, Alex;Matechou, Eleni;Griffiths, Richard A.
• 通讯作者: Griffiths, Richard A.