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

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