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.

该奖学金将在最完整的优先（受威胁，稀有）类群中纳入生物多样性成果的证据，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，而没有有效的生物多样性增强。研究结果将直接支持土地利用政策，解决如何在英国高地栖息地可以得到最好的管理，以保护生物多样性和减缓气候变化。为了解决这一危机，决策者必须利用强有力的证据来确定干预措施的最佳目标，这些证据表明应在何处采取哪些管理行动。保护政策没有有效地利用现有的大量关于生物多样性的分布和要求的信息（通常是1000个物种）。土地管理战略必须平衡多个环境服务目标，包括粮食、碳固存、减轻洪水和生物多样性。多项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，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，开发新的分析方法，为竞争性土地使用的空间优先级提供信息。学生将分析植物和无脊椎动物的物种分布和特征数据库，建立在以前在UEA开发的生物多样性审计方法的基础上，并在R中开发堆叠的环境物种分布模型（SDM）。多空间数据模型将与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利用过渡的最优性，检查林业，农业，高沼地管理和重新野生化的权衡和附加性。学生将接受来自大数据，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的监督团队的一对一培训。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。你会被鼓励发展自己的研究思路旁边的核心项目目标。成功的申请人将拥有生态学，地理学，生物学或相关学科的学位，R和GIS的可证明经验，以及对自然保护的热情。