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

该项目将在最完整的优先（受威胁、稀有）分类群中纳入生物多样性结果的证据，纳入空间明确的土地利用模型，以优化多种生态系统服务（ES）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，即依赖于不适当的代理，而对生物多样性的增强没有效力。研究结果将通过解决如何最好地管理英国的高地栖息地以保护生物多样性和减缓气候变化，直接支持土地利用政策。目前的保护措施未能扭转生物多样性下降的趋势。为了解决这一危机，决策者必须利用强有力的证据来优化干预措施，以确定在哪里采取哪些管理行动。保护政策没有有效地利用现有的关于生物多样性全数分布和需求的大量信息（通常是1000个物种）。土地管理战略必须平衡多种生态环境目标，包括粮食、碳封存、减轻洪水和生物多样性。多个项目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理解。该学生将解决如何最好地管理景观的关键问题，开发新的分析方法，为竞争土地利用的空间优先级提供信息。该学生将在东英吉利大学开发的生物多样性审计方法的基础上，分析植物和无脊椎动物的物种分布和特征数据库，并在r中开发堆叠环境物种分布模型（SDMs）。Multi-SDMs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利用过渡的最佳性，审查林业、农业、沼地管理和再野生化的权衡和附加性。该学生将接受督导团队在大数据、空间建模、研究设计和假设检验、科学写作、数据可视化和科学传播等方面的一对一培训。与皇家鸟类保护协会的自然交付团队和高地地区的私人和公共土地管理人员合作，将加深对土地管理政策的了解。我们将鼓励你在核心项目目标的基础上发展自己的研究想法。申请者须具有生态学、地理学、生物学或相关学科的学位，具有可证明的R和GIS经验，并对自然保育有热情。

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.