Native biodiversity in human-impacted habitats: Applying NGS technology to arthropod assemblages in semi-natural and plantation oak woodlands.

保护和加强林地生物多样性在国际上被公认为可持续林地管理的不可或缺的过程，在英国，《自然环境和农村社区及自然保护法》规定了维持林地生物多样性的行动。然而，由于缺乏可靠、有效的生物多样性评估方法，限制了旨在维持和加强生物多样性的林地管理战略的制定和评价。因此，生物多样性保护措施的有效性，包括纳入森林证书制度的措施，往往缺乏了解，也无法由第三方核查。元编码承诺提供一种快速、高效的生物多样性采样技术，有助于更好地了解林地管理和保护规划。这个项目的目的是：1)学生将领导开发一种快速、廉价、完全可听的和标准化的原型方法来使用DNA元编码技术来评估林地生物多样性的水平，寻求通过以下方式来实现这一目标：a)首先将重点放在a)地面居住的节肢动物作为具有已知的对环境变化快速响应的敏感分类群的超多样化样本组，以及b)在一系列半自然的、人工林和以橡树(Quercus robur和Q.Petraea)为主的林地环境，以及林分结构和组成的复杂性梯度。橡树是高度重视保护的优先半天然林地，也是分布广泛且具有重要商业价值的林地种植类型。2)学生将对生物多样性进行空间-显式分析，以了解半自然状态下地面栖息的节肢动物物种丰富度、功能多样性和冗余度与英吉利群岛的人工橡树林地相比是如何变化的，同时考虑到邻近土地利用、气候、生物多样性和地形的区域差异。对具有保护意义的半天然橡树林地进行采样，将有助于设定橡树林地地面节肢动物多样性的地区特定基准。3)采用区块实验设计，学生将在英格兰西北部的吉斯本实验中，调查不同树种对组成多样化的橡树林地本地地面节肢动物多样性的影响。吉斯伯恩种植试验成立于1955年，最初是为了调查单一栽培和混合种植对森林生产力的影响。4)使用预测模型，学生将把观察到的节肢动物生物多样性模式外推到英国类似橡树林地的未采样区域，并预测未来气候变化下的分布。一种基于地图的方法将为森林从业者和政策制定者提供可视化指导。阿伯丁大学(UofA)将领导该项目，英国林业委员会(FC)下属的森林研究机构将作为其产业合作伙伴。这一伙伴关系将确保与最相关的利益攸关方机构进行有效的知识交流，并确保获得最佳采样点和林地数据集。这项研究的成果将由森林委员会管理人员直接纳入，并传递给其他利益攸关方，以提高林地生物多样性管理的效率和成本效益，并遵守政策。这项研究将直接推进NERC的战略优先领域：支持生物多样性和自然服务，满足社会需求，以及管理环境变化。

The conservation and enhancement of biodiversity in woodlands are recognized internationally as integral processes for sustainable woodland management, and action to sustain woodland biodiversity in Britain is mandated within the Natural Environment and Rural Communities and Nature Conservation acts. However, development and evaluation of woodland management strategies aimed at sustaining and enhancing biodiversity are restricted by a lack of reliable, efficient biodiversity assessment methods. Thus, effectiveness of biodiversity protection measures, including those built into forest certification schemes, is frequently poorly understood and not third-party verifiable. Metabarcoding promises to provide a rapid, efficient biodiversity sampling technique that could contribute to better-informed woodland management and conservation planning. It is cost-efficient because samples comprising many species (eg mixed arthropods from pitfall traps) can be sequenced for less than 200 pounds/sample, providing resolution of constituent species at 85% accuracy.The aims of this project are: 1) The student will lead in developing a rapid, inexpensive, fully auditable, and standardised prototype method to assess levels of woodland biodiversity using DNA metabarcoding techniques, seeking to achieve this by focusing in the first instance on a) ground-dwelling arthropods as a hyper-diverse sample group with sensitive taxa known to respond rapidly to environmental change, and b) generating metabarcoding-based biodiversity estimates in a range of semi-natural, plantation and experimental oak-dominated (Quercus robur and Q. petraea) woodland settings, along gradients of stand structural and compositional complexity. Oak woods are highly valued conservation priority semi-natural woodlands, but also widespread and commercially important woodland plantation types. 2) The student will conduct spatially-explicit analyses of biodiversity to understand how ground-dwelling arthropod species richness, functional diversity and redundancy vary in semi-natural compared with plantation oak woodlands across GB, considering regional variations in adjacent land use, climate, biodiversity, and topography. Sampling of semi-natural oak woodlands of conservation importance will contribute to setting region-specific benchmarks of ground-dwelling arthropod diversity in oak woodland.3) Using a blocked experimental design, the student will investigate the influences of different tree species on native ground-dwelling arthropod biodiversity in compositionally diversified oak woodland plots at the Gisburn Experiment in northwest England. Established in 1955, Gisburn planting trials were originally set up to investigate effects of monoculture and mixed -species plantings on forest productivity. 4) Using predictive modelling, the student will extrapolate observed patterns of arthropod biodiversity to unsampled areas of similar oak woodland in GB and to predict future distributions under climate change. A map-based approach will provide visual guidance for forest practitioners and policy makers.The University of Aberdeen (UofA) will lead the project, with Forest Research, an agency of the British Forestry Commission (FC), as its industrial partner. This partnership will ensure efficient knowledge exchange with the most relevant stakeholder bodies and access to the best sampling sites and woodland datasets. Outputs of the study will be directly incorporated by FC managers and passed on to other stakeholders to improve the efficiency and cost effectiveness of woodland biodiversity management and compliance with policy. The study will directly advance NERC strategic priority areas: supporting biodiversity and nature's services, meeting society's needs, and managing environmental change.