Understanding the past to predict the future of distribution change in British Lepidoptera

了解过去以预测英国鳞翅目分布变化的未来

• 批准号: 2120598
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2120598
• 关键词: Understanding; past; predict; future; distribution

Our knowledge about biodiversity change is severely limited by a dearth of long-term data. The best datasets span only a few decades, providing too little replication of climate change episodes and extreme weather events to estimate their effects precisely. Even in Great Britain, which is unusually well-documented, our knowledge is limited to the period since 1970. This period is too short to reveal whether there are general patterns or whether recent trends are idiosyncratic. Museum collections contain a vast amount of information that can fill this gap, but such data could not be used until now for two reasons. First, natural history collection data could not be modelled robustly because we usually do not know much about how they were collected. Statistical modelling is easiest if everyone collected their specimens in a standardized way, but we know that Museum collections were assembled haphazardly. Fortunately, dynamic occupancy-detection models make it possible to analyse data like these robustly. Second, the databasing of museum specimens has until recently been very incomplete. However, the Natural History Museum (NHM) has just finished digitizing all 500,000 of its UK specimens of butterfly and geometrid moths.In this project, the student will use cutting-edge quantitative methods [1-3] to integrate data from half a million museum specimens of British butterflies and geometrid moths with many millions of observational records from recent decades. Datasets that cover a longer period of time include multiple episodes of climatic warming and cooling (e.g. warming in 1870s, 1890s & 1940s was interspersed with cool periods), as well as more extreme weather events such as droughts.These integrated models make it possible to reconstruct the dynamics of species distributions over a century and to reveal the role of environmental change in driving these changes. Understanding the relationship between climatic events and distribution change makes it possible to derive mechanistic predictions for the impact of long-term climate change on native biodiversity.There are four broad aims:1. Revolutionise the study of long-term range dynamics and distribution change by analysing museum specimen data and observational records in a single new analytical framework.2. Produce historical distribution maps for British butterfly and moth species based on dynamic species distribution models tailored for messy opportunistic data to reconstruct the dynamics of species distributions for British butterflies and geometrid moths over 15 decades.3. Integrate reconstructed trajectories of species distributions with functional trait data and environmental layers to answer key questions about biodiversity change that are unanswerable using the short time series that currently exist, such as:- Does land-use or species' biology limit species' ability to track climate change?- Is the impact of extreme events predictable?- Can we detect early warnings of dramatic range change?4. Based on these relationships, derive projections for future changes in species distributions.The answers will give us a deeper and more precise understanding of range dynamics in space and time than is possible in any other group, and show whether researchers using a shorter-term perspective - usually the best we can do - are being misled.This project will develop new quantitative methods & statistical tools by treating occurrence records and museum specimens as independent realisations of the same ecological state. Analyses will involve 1) high throughput data analysis of large and complex datasets, and 2) integration of external data into ecological models (museum specimens, reconstructed land-use and climate layers).The project will employ Bayesian statistical analysis (dynamic multispecies occupancy models) and involve building efficient workflows for large datasets using supercomputers (NERC JASMIN facility): Python, R, Shell scripting.

由于缺乏长期数据，我们对生物多样性变化的了解受到严重限制。最好的数据集只跨越几十年，提供的气候变化事件和极端天气事件的复制太少，无法准确估计它们的影响。即使在记载异常充分的英国，我们所了解的也仅限于1970年以来的那段时间。这段时间太短，无法揭示是否存在普遍的模式，或者最近的趋势是否具有特殊性。博物馆的收藏品包含了大量可以填补这一空白的信息，但这些数据直到现在才能被使用，原因有两个。首先，自然历史收集数据不能很好地建模，因为我们通常不太了解它们是如何收集的。如果每个人都以标准化的方式收集标本，统计模型是最容易的，但我们知道博物馆的收藏品是随意组装的。幸运的是，动态占有率检测模型使分析此类数据成为可能。其次，直到最近，博物馆标本的数据库一直非常不完整。然而，自然历史博物馆(NHM)刚刚完成了其在英国的全部50万只蝴蝶和尺蛾标本的数字化。在这个项目中，这名学生将使用尖端的量化方法[1-3]将50万份博物馆的英国蝴蝶和尺蛾标本的数据与近几十年来数百万份观察记录整合在一起。覆盖更长时间的数据集包括气候变暖和变冷的多个时期(例如，19世纪70年代、19世纪90年代和40年代的变暖穿插着凉爽的时期)，以及更极端的天气事件，如干旱。这些集成的模型使重建一个世纪以来物种分布的动态成为可能，并揭示环境变化在推动这些变化中的作用。了解气候事件和分布变化之间的关系，就有可能得出长期气候变化对本地生物多样性影响的机械预测。有四个广泛的目标：1.通过在一个单一的新分析框架中分析博物馆标本数据和观测记录，革命性地研究长期范围动态和分布变化。根据为杂乱无章的机会主义数据量身定做的动态物种分布模型，制作英国蝴蝶和飞蛾物种的历史分布图，以重建15年来英国蝴蝶和尺寸蛾的物种分布动态。将重建的物种分布轨迹与功能特征数据和环境层结合起来，以回答有关生物多样性变化的关键问题，这些问题是目前存在的短时间序列无法回答的，例如：-土地利用或物种的生物限制了物种跟踪气候变化的能力吗？-极端事件的影响可预测吗？-我们能否检测到剧烈范围变化的早期预警？4.基于这些关系，得出对物种分布未来变化的预测。这些答案将使我们比任何其他群体更深入和更准确地了解范围动态，这个项目将开发新的量化方法和统计工具，将发生记录和博物馆标本视为同一生态状态的独立实现。分析将涉及1)大型和复杂数据集的高通量数据分析，以及2)将外部数据整合到生态模型(博物馆标本、重建的土地利用和气候层)中。该项目将使用贝叶斯统计分析(动态多物种占用模型)，并涉及使用超级计算机(NERC Jasmin设施)为大型数据集建立高效的工作流程：Python、R、Shell脚本。