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 or more. When combined with data on repeated climatic events and the longer history of land-use change, it becomes possible to answer a range of important questions about the drivers of biodiversity change with unprecedented power.
• 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.
• 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.
• 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:
o Q1: Does land-use or species’ biology limit species’ ability to track climate change?
o Q2: Is the impact of extreme events predictable?
o Q3: Can we detect early warnings of dramatic range change?
o Q4: Are assemblage changes more or less than the sum of species changes?
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.
Training and Skills
The project will employ the following techniques and tools:
1. Bayesian statistical analysis (dynamic multispecies occupancy models): BUGS, STAN, INLA, R.
2. Building efficient workflows for large datasets using supercomputers (NERC JASMIN facility): Python, R, Shell scripting.
The student will gain a high degree of competence in four skill areas identified as ‘most wanted’ by a recent review: Modelling, Data management, Numeracy and dealing with risk & uncertainty.
Logistics & Application
The project will be primarily based at the Centre for Ecology & Hydrology (CEH) in Wallingford. It will be jointly supervised by Dr Nick Isaac at CEH, Prof Andy Purvis & Dr Ian Kitching at the Natural History Museum and Dr Cristina Banks-Leite at Imperial College.
Applicants for a studentship must have obtained, or be about to obtain, a 2.1 degree or higher. If you have a 2.2 degree, but have also obtained a masters qualification, you are also eligible.
To apply please send your CV, cover letter and academic references to Dr Nick Isaac ([email protected]
) by 5pm on 19 January 2017. The cover letter should explain your interest in and suitability for the project and the QMEE College of Doctoral Training. Informal enquiries are welcome.