The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

Understanding 150 years 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 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. 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: Q1: Does land-use or species’ biology limit species' ability to track climate change? Q2: Is the impact of extreme events predictable? Q3: Can we detect early warnings of dramatic range change? 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.
Nick Isaac
Andy Purvis
Cristina Bank-Leite (Imperial: c.banks@imperial.ac.uk); Ian Kitching (NHM: i.kitching@nhm.ac.uk); Bob O'Hara (Univ Trondheim: bob.ohara@ntnu.no)
Computing, Quantitative data analysis, Ecological observations / data collection
Nick Isaac
The student will gain a high degree of competence in four skill areas identified as ‘most wanted’ by the 2012 NERC review: Modelling, Data management, Numeracy and dealing with risk & uncertainty. Specifically, the student will develop skills in: -Bayesian statistical analysis (BUGS, STAN, INLA, R). -Building efficient workflows for large datasets (Python, R, Shell scripting).
This project aims to revolutionise the study of long-term range dynamics and distribution change by analysing museum specimen data alongside observational records in a single new analytical framework. The student will produce historical distribution maps for British butterfly and moth species reconstructing the dynamics of species distributions over an unprecedented length of time - 15 decades.
The student will address big questions about biodiversity change that are unanswerable using the short time series that currently exist: Q1: What limits species' ability to track climate change? Q2: Is the impact of extreme events predictable? Q3: Can we detect early warnings of dramatic range change? Q4: Are assemblage changes more or less than the sum of species changes?
The impacts of climate change are poorly understood, as few climatic events have been studied in detail. This project will quantify the impacts of multiple instances of a) periodic warming and b) seasonal drought spanning more than 100 years. Looking into the deeper past will contribute to our understanding of climate risks, placing future projections on a firmer footing.
The combination of new data and new models will show whether researchers using a shorter-term perspective - usually the best we can do - are being misled. It will demonstrate how museum specimens can be used to address big questions in biodiversity science, thus unlocking the potential of this vast yet untapped data source.
This project combines statistics with multiple sub-disciplines of ecology. Isaac develops statistical approaches to estimate trends for British biodiversity (especially insects) from messy and biased data. Banks-Leite (community ecologist) and Purvis (macroecologist) have complementary experience in modelling biodiversity change. O’Hara is a statistician who develops tools for ecologists.
Climate and climate change, Conservation ecology, Ecological/Evolutionary tools, technology & methods
- Bayesian statistical analysis (BUGS, STAN, INLA) - programming and building workflows (R, Python, Shell scripting) - Personal effectiveness, communication skills, teamwork, research conduct. Advanced quantitative skills will be taught through a combination of in-house training and specialist courses. General skills training will be provided through existing courses at CEH and Imperial.
CEH, NHM, Imperial & external courses
No
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