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

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.