Wings of change: using museum collections to forecast insect pollinator responses to climate change

昆虫在为农作物和野花授粉方面发挥着关键作用，这意味着了解它们对气候变化的反应对于预测粮食安全和生态系统恢复力至关重要。种群可以通过多种方式对环境变化做出反应，包括它们的行为，如物候（出现等生活史事件的时间）和形态（如翅膀形状）。但这种反应可能对生态系统产生重要的连锁后果，导致植物开花和传粉者出现等时间的不匹配。然而，尽管生态学家明白这些后果的重要性，但他们的工作主要局限于过去30-40年的数据。缺乏气候变化前人口的历史数据限制了我们机械地模拟反应的能力，使我们没有长期的恢复背景，特别是在“异常”年份之后。因此，如果我们要了解最近主要气候和土地利用变化前后的种群物候和形态变化，我们迫切需要上个世纪早期的基线数据。在这个博士学位课程中，你将通过研究过去150年来收集的自然历史标本来解决这一差距。该项目将主要评估蝴蝶和蜜蜂对气候变化的反应，但可能会研究其他昆虫传粉者分类群。为此，您将使用一个独特而庞大的数据集，包括来自英国各地的数万只数字化蜜蜂和数十万只蝴蝶，以及使用Symbiota数据平台的全球bbb750自然历史收藏的数据。您将使用标本标签信息和形态计量学方法来理解功能性状反应，同时帮助开发生物信息学工具来收集这些信息。你将建立昆虫在不改变生存范围的情况下何时以及如何适应气候变化的机械模型，并将其与已知的物种分布变化进行比较。使用这些模型，您将建立物种分布的准确预测，以及至关重要的生态系统服务提供，以帮助制定气候变化减缓规划。该项目将利用Pearse实验室已经开发的工具，该工具以前用于从拼凑的收集数据中准确估计物候观测日期，并使用机器学习从图像中自动提取形态信息。你也将得到吉尔实验室的支持，他们一直在整理英国蜜蜂数据集，可以为许多蜜蜂和蝴蝶标本提供特征数据。吉尔在研究环境压力对昆虫传粉者生态的影响，特别是了解蜜蜂的生活史方面有丰富的经验。该学生还将与其他昆虫传粉者研究人员合作，包括Andres Arce博士、Jeff Ollerton教授、Phillip Fenberg博士和Ian Barnes教授

The critical role insects play in pollinating crops and wildflowers means understanding their responses to climate change is vital for predicting food security and ecosystem resilience. Populations can respond to environment variation in a number of ways, including their behaviour such as phenology (the timing of life-history events like emergence) and morphology (e.g., wing shape). But such responses can have important, cascading consequences for ecosystems, inducing mismatches in timing such as plant flowering and pollinator emergence. Yet whilst ecologists understand the significance of these consequences, work has been constrained to mostly data from the last 30-40 years. Lacking historic data about populations before climate change limits our ability to mechanistically model responses, leaving us without a longer-term context of recovery especially after 'outlier' years. Hence, we urgently require baseline data from the earlier part of the last century if we are to understand populations' phenological and morphological variation before and after the recent major climate and land-use changes.In this PhD studentship you will address this gap by studying natural history specimens collected over the past 150 years. The project will primarily assess butterfly and bee responses to climate change, but other insect pollinator taxa may be studied. To do this, you will be working with a unique and large dataset, including tens of thousands of digitised bees and hundreds of thousands of butterflies from across the UK, as well as data from the >750 natural history collections worldwide that use the Symbiota data platform. You will use specimen label information and morphometric approaches to understand functional trait responses, whilst helping to develop bioinformatic tools to gather this information. You will build mechanistic models of when and how insects can adapt to climate change without necessarily having to shift their ranges, and compare this to known species distribution changes. Using these models, you will build accurate forecasts of species' distributions and, critically, ecosystem service delivery, in order to help climate change mitigation planning.This project will leverage tools already developed by the Pearse lab previously used to accurately estimate phenological observation dates from patchy collections data, and to automatically extract morphological information from images using machine learning. You will also be supported by the Gill lab, who has been putting together the UK bee dataset and can provide trait data for many of the bee and butterfly specimens. Gill has experience in studying the effects of environmental stressors on insect pollinator ecology, and especially understanding bee life histories. The student will also get to collaborate with other insect pollinator researchers including Dr Andres Arce, Prof. Jeff Ollerton, Dr Phillip Fenberg, and Prof. Ian Barnes