Integrating multi-taxa biodiversity conservation into upland ecosystem service land-use models

该项目将把最完整的优先(受威胁的、稀有的)分类群的生物多样性结果的证据纳入空间明确的土地利用模型，以优化对多种生态系统服务的战略管理。这解决了现有ES决策支持工具的一个关键缺陷，即依赖不适当的代理而没有提高生物多样性的有效性。结果将直接支持土地利用政策，解决如何最好地管理英国的旱地栖息地，以保护生物多样性和减缓气候变化。目前的保护未能扭转生物多样性的下降。为了解决这场危机，决策者必须使用强有力的证据，证明在哪里采取哪些管理行动，从而优化干预措施。保护政策没有有效地利用现有的大量关于全部生物多样性(通常是数千个物种)的分布和需求的信息。土地管理战略必须平衡包括粮食、碳固存、缓解洪水和生物多样性在内的多个可持续发展目标。许多计划试图在空间上协调这些结果，但以前的模型缺乏对生物多样性分布、要求和管理结果的全面了解。这个项目将解决如何最好地管理景观这一关键问题，开发新的分析方法，为相互竞争的土地使用提供空间优先顺序。学生将分析植物和无脊椎动物的物种分布和特征数据库，建立在UEA以前开发的生物多样性审计方法的基础上，并在R开发堆叠环境物种分布模型(SDMS)。多环境物种分布模型将与RSPB科学家最近开发的未来土地利用模型相结合，量化其他ES。这将评估合理的土地使用转变的最佳性，检查林业、农业、沼地管理和重新开垦的权衡和附加因素。学生将从监督团队接受大数据、空间建模、研究设计和假设检验、科学写作、数据可视化和科学交流的一对一培训。与RSPB自然交付团队以及高地地区的私人和公共土地管理者合作，将深入了解土地管理政策。你将被鼓励在核心项目目标的同时发展你自己的研究想法。成功的申请者将拥有生态学、地理学、生物学或相关学科的学位，在研究和地理信息系统方面有明显的经验，并对自然保护充满热情。

This studentship will incorporate evidence for biodiversity outcomes across the fullest suite of priority (threatened, rare) taxa, into spatially-explicit land-use models, to optimise strategic management for multiple ecosystem services (ES). This addresses a key shortcoming of existing ES decision-support tools, that rely on inappropriate proxies without efficacy for biodiversity enhancement. Results will directly support land-use policy by addressing how upland habitats in the UK can be best managed to protect biodiversity and mitigate climate change.Current conservation has failed to reverse biodiversity declines. To resolve this crisis, decision makers must optimally target interventions using robust evidence of which management actions to undertake, where. Conservation policy does not effectively use existing large bodies of information on the distribution and requirements of the full complement of biodiversity (typically 1000s of species). Land management strategies must balance multiple ES objectives including food, carbon sequestration, flood mitigation and biodiversity. Multiple initiatives attempt to spatially reconcile these outcomes, but previous models lacked comprehensive understanding of biodiversity distribution, requirements, and management outcomes.This studentship will address the crucial question of how to best manage landscapes, developing novel analytical approaches to inform spatial prioritisation for competing land-uses. The student will analyse species distribution and trait databases across plants and invertebrates, building on biodiversity auditing methodologies previously developed at UEA, and develop stacked environmental species distribution models (SDMs) in R. Multi-SDMs will be integrated with future land-use models recently developed by RSPB scientists, that quantify other ES. This will evaluate the optimality of plausible land-use transitions, examining trade-offs and additionalities across forestry, agriculture, moorland management and rewilding.The student will receive one-to-one training from the supervisory team in Big Data, spatial modelling, study design and hypothesis testing, scientific writing, data visualisation and science communication. Working with RSPB nature delivery teams and private and public land managers in upland regions, will give in-depth understanding of land management policy. You will be encouraged to develop your own research ideas alongside the core project aims. The successful applicant will have a degree in ecology, geography, biology or related discipline, demonstrable experience of R and GIS, and an enthusiasm for nature conservation.