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

将多类群生物多样性保护纳入高地生态系统服务土地利用模型

• 批准号: 2881781
• 金额: --
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2881781
• 关键词: Integrating; multi; taxa; biodiversity; conservation

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.

该学生奖学金将纳入最优先（受威胁的，稀有）分类单元的生物多样性成果的证据，以空间解释的土地利用模型，以优化多个生态系统服务（ES）的战略管理。这解决了现有的ES决策支持工具的关键缺点，该工具依靠不适当的代理而没有生物多样性增强功效。结果将直接通过解决英国高地栖息地如何最好地保护生物多样性并减轻气候变化来直接支持土地利用政策。流动保护未能逆转生物多样性的下降。为了解决这一危机，决策者必须使用强大的证据来最佳地针对干预措施，证明要采取哪些管理行动。保护政策没有有效地使用有关生物多样性（通常为1000种物种）的完整补充的分布和要求的现有大量信息。土地管理策略必须平衡多个ES目标，包括食物，碳固存，减轻洪水和生物多样性。多项举措试图在空间上调和这些结果，但是以前的模型缺乏对生物多样性分配，需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，开发出新颖的分析方法来为空间优先级提供用于竞争土地的空间优先级的问题 - uses。学生将分析跨工厂和无脊椎动物的物种分布和特质数据库，建立在以前在UEA开发的生物多样性审计方法，并在R.多SDMS中开发堆叠的环境物种分布模型（SDMS），最近将与未来的土地利用模型集成由RSPB科学家开发的，可以量化其他ES。这将评估合理的土地利用过渡的最佳性，研究林业，农业，摩尔兰管理和野生境内的权衡和其他领域。该学生将接受主管团队的一对一培训，以大数据，空间建模，研究，研究设计和假设测试，科学写作，数据可视化和科学沟通。与RSPB自然交付团队以及高地地区的私人和公共土地经理合作，将对土地管理政策有深入的了解。鼓励您与核心项目的目标一起发展自己的研究思想。成功的申请人将拥有生态，地理，生物学或相关学科，R和GIS的可证明经验以及对自然保护的热情。