Modelling butterfly abundance at varying spatial scales to inform conservation delivery
Modelling butterfly abundance at varying spatial scales to inform conservation delivery
批准号:
2270042
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2019
资助国家:
英国
项目状态:
已结题
起止时间:
2019 至 --
中文摘要
在过去的四十年里,英国四分之三的蝴蝶种类都在减少。蝴蝶对生境和气候变化反应迅速,因此其种群状况是一种有价值的生物多样性指标。对蝴蝶长期监测数据集的分析为气候变化的生物影响提供了一些世界上最好的证据,包括主要物候和分布变化、进化反应和极端事件的影响。人口趋势主要是在国家范围内评估的。该项目将跨空间尺度(例如跨区域、特定栖息地或个别地点)进行更详细的分析,以确定蝴蝶种群对主要变化驱动因素的反应。除了提供高影响力的科学见解外,这将支持更有效的保护,从地方土地管理到跨区域的战略规划,包括制定新的生物多样性指标和站点级别警报。通过完善对濒危物种的调查指导,提高对蝴蝶寿命的认识和进一步评估物种受威胁状况的方法,将加强国家规模的蝴蝶监测。该项目将涉及开发新的统计模型和纳入高分辨率土地覆盖数据。该学生将结合多个复杂的生态数据集,并将产生新的人口指标,考虑天气等外部因素的影响。
英文摘要
Three-quarters of UK butterfly species have declined over the past four decades. Butterflies respond quickly to habitat and climatic change, hence their population status is a valuable biodiversity indicator. Analysis of long-term butterfly monitoring datasets has provided some ofthe world's best evidence of the biological impacts of climate change, including major phenological and distribution shifts, evolutionary responses and the impacts of extreme events.Population trends are primarily assessed at national scales. This project will undertake more detailed analysis across spatial scales (e.g across regions, specific habitats or individual sites) to identify butterfly population responses to major drivers of change. As well as delivering high impact scientific insight, this will underpin more effective conservation, from local land management to strategic planning across regions, including the production of new biodiversity indicators and site level alerts.National-scale butterfly monitoring will be enhanced by refining survey guidance for threatened species, improving knowledge of butterfly lifespans and furthering methods for assessing species threatened status.The project will involve new statistical model developments and incorporation of high-resolution land-cover data. The student will combine multiple complex ecological datasets and will produce new population metrics, accounting for the influence of external factors such as weather.
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