GLobal Insect Threat-Response Synthesis (GLiTRS): a comprehensive and predictive assessment of the pattern and consequences of insect declines

全球昆虫威胁响应综合（GLiTRS）：对昆虫衰退模式和后果的全面预测评估

• 批准号: NE/V006800/1
• 负责人: Andy Purvis
• 金额: $ 56.71万
• 依托单位: Natural History Museum
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FV006800%2F1
• 关键词: GLobal; Insect; Threat; Response; Synthesis

With increasing recognition of the importance of insects, there are growing concerns that insect biodiversity has declined globally, with serious consequences for ecosystem function and services. Yet, gaps in knowledge limit progress in understanding the magnitude and direction of change. Information about insect trends is fragmented, and time-series data are restricted and unrepresentative, both taxonomically and spatially. Moreover, causal links between insect trends and anthropogenic pressures are not well-established. It is, therefore, difficult to evaluate stories about "insectageddon", to understand the ecosystem consequences, to devise mitigation strategies, or predict future trends. To address the shortfalls, we will bring together diverse sources of information, such as meta-analyses, correlative relationships and expert judgement. GLiTRS will collate these diverse lines of evidence on how insect biodiversity has changed in response to anthropogenic pressures, how responses vary according to functional traits, over space, and across biodiversity metrics (e.g. species abundance, occupancy, richness and biomass), and how insect trends drive further changes (e.g. mediated by interaction networks).We will integrate these lines of evidence into a Threat-Response model describing trends in insect biodiversity across the globe. The model will be represented in the form of a series of probabilistic statements (a Bayesian belief network) describing relationships between insect biodiversity and anthropogenic pressures. By challenging this "Threat-Response model" to predict trends for taxa and places where high-quality time series data exist, we will identify insect groups and regions for which indirect data sources are a) sufficient for predicting recent trends, b) inadequate, or c) too uncertain. Knowledge about the predictability of threat-response relationships will allow projections - with uncertainty estimates - of how insect biodiversity has changed globally, across all major taxa, functional groups and biomes.This global perspective on recent trends will provide the basis for an exploration of the consequences of insect decline for a range of ecosystem functions and services, as well as how biodiversity and ecosystem properties might be affected by plausible scenarios of future environmental change.GLiTRS is an ambitious and innovative research program: two features are particularly ground-breaking. First, the collation of multiple forms of evidence will permit a truly global perspective on insect declines that is unachievable using conventional approaches. Second, by validating "prior knowledge" (from evidence synthesis) with recent trends, we will assess the degree to which insect declines are predictable, and at what scales.

随着人们越来越认识到昆虫的重要性，人们越来越担心全球昆虫生物多样性下降，对生态系统功能和服务造成严重后果。然而，知识差距限制了理解变革的幅度和方向的进展。有关昆虫趋势的信息是支离破碎的，时间序列数据在分类学和空间上都受到限制且不具代表性。此外，昆虫趋势与人为压力之间的因果关系尚未明确。因此，很难评估有关“昆虫袭击”的故事、了解生态系统的后果、制定缓解策略或预测未来趋势。为了解决这些不足，我们将汇集不同来源的信息，例如荟萃分析、相关关系和专家判断。 GLiTRS 将整理这些不同的证据，说明昆虫生物多样性如何因人为压力而变化，反应如何根据功能特征、空间和生物多样性指标（例如物种丰度、占有率、丰富度和生物量）而变化，以及昆虫趋势如何推动进一步的变化（例如通过相互作用网络介导）。我们将把这些证据整合到一个威胁响应模型中，描述 全球昆虫生物多样性的趋势。该模型将以一系列概率陈述（贝叶斯信念网络）的形式表示，描述昆虫生物多样性与人为压力之间的关系。通过挑战这个“威胁-响应模型”来预测类群的趋势和存在高质量时间序列数据的地方，我们将确定间接数据源a）足以预测最近趋势、b）不足或c）太不确定的昆虫类群和区域。关于威胁-响应关系的可预测性的知识将允许通过不确定性估计来预测昆虫生物多样性在全球所有主要类群、功能群和生物群落中如何变化。这种对近期趋势的全球视角将为探索昆虫衰退对一系列生态系统功能和服务的影响以及未来可能的情景如何影响生物多样性和生态系统特性提供基础。 环境变化。GLiTRS 是一项雄心勃勃且创新的研究计划：有两个特点尤其具有开创性。首先，对多种形式证据的整理将允许对昆虫减少有一个真正的全球视角，这是使用传统方法无法实现的。其次，通过用最近的趋势验证“先验知识”（来自证据综合），我们将评估昆虫减少的可预测程度以及可预测的规模。

项目成果

Dynameta: A dynamic platform for ecological meta-analyses in R Shiny

Dynameta：R Shiny 中生态荟萃分析的动态平台

• DOI: 10.1016/j.softx.2023.101439
• 发表时间: 2023
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Skinner G

The direct drivers of recent global anthropogenic biodiversity loss.

• DOI: 10.1126/sciadv.abm9982
• 发表时间: 2022-11-11
• 期刊: Science advances
• 影响因子: 13.6

Key tropical crops at risk from pollinator loss due to climate change and land use.

• DOI: 10.1126/sciadv.adh0756
• 发表时间: 2023-10-13
• 期刊: SCIENCE ADVANCES
• 影响因子: 13.6
• 作者: Millard, Joseph;Outhwaite, Charlotte L.;Ceausu, Silvia;Carvalheiro, Luisa G.;Silva, Felipe Deodato da Silva e;Dicks, Lynn V.;Ollerton, Jeff;Newbold, Tim
• 通讯作者: Newbold, Tim

Achieving a real-time online monitoring system for conservation culturomics.

实现保护培养组学实时在线监测系统。

• DOI: 10.1111/cobi.14096
• 发表时间: 2023
• 期刊: the journal of the Society for Conservation Biology
• 作者: Johnson TF

Adopt digital tools to monitor social dimensions of the global biodiversity framework

采用数字工具监测全球生物多样性框架的社会层面

• DOI: 10.1111/conl.12991
• 发表时间: 2023
• 期刊: Conservation Letters
• 影响因子: 8.5
• 作者: Veríssimo D