Modelling and Inference for the Movement of Interacting Individuals
Lead Research Organisation:
University of Sheffield
Department Name: Mathematics and Statistics
Abstract
This project will look at continuous-time movement models for multiple tracked wild animals, e.g. using GPS, where tracking overlaps in time, allowing inference about interactions between individuals. The emphasis is likely to be on relatively small numbers of individuals. A key tool will be a recently developed inference technique for state-switching diffusion models - representing movement and behaviour - where the probabilities of switching between states depend on location. The method, known as "uniformization", is based on relating the process of interest to a well-understood simpler process which is uniform in space. The project will extend this methodology by looking at diffusion in a higher-dimensional space, to represent multiple individuals simultaneously, and at state-spaces with more complex structure, to represent more sophisticated behaviour. Algorithms to speed up the fitting of these models will also be developed. There are no formal external partners, but there is informal collaboration with ecologists at the Animal and Plant Health Agency, including access to GPS data on wild boar.
Organisations
People |
ORCID iD |
Paul Blackwell (Primary Supervisor) | |
Jordan Milner (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/N509735/1 | 01/10/2016 | 30/09/2021 | |||
1949142 | Studentship | EP/N509735/1 | 01/10/2017 | 31/03/2021 | Jordan Milner |
Title | Modelling and inference for the movement of interacting animals |
Description | A statistical model, and accompanying inference algorithm, have been developed in order to capture the social interaction within animal movement data. Modelling the movement of a group of animals jointly, as opposed to treating the animals as independent, provides richer information into their movement behaviours. The social framework in this model is based on the idea of social hierarchies and so we can learn about the groups social structure, which animals have a high level of incluence etc. Such insights have potential applications in conservation and management decisions. |
Type Of Material | Data analysis technique |
Year Produced | 2020 |
Provided To Others? | Yes |
Impact | A journal article has been published which details the model and inference techniques, as well as containing an example of its use. In the example, we use a data set of baboon movement that appears to contain some directional conflict within the group. Through fitting the model to this data, we are able to obtain information on how the baboons interacted with each other through this conflict. |
Title | Spatial heterogeneity extension to our collective movement model |
Description | Our collective movement model was previously developed for the spatially homogeneous case. We have extended it to the spatially heterogeneous case in order to gain more insight (e.g. how the collective behaviours change in different environments). We have developed a new algorithm with which to fit this model to data, one which is more efficient than current alternatives. We have also extended our model to include a radius of interaction (thus making use of the above extension). That is, animals can only interact if they are proximate to one another. Doing so adds some biological realism into the model (e.g. animals distant to one another can't interact) and it reduces the state space that needs exploring. We infer the radius during the model fitting process, proving more insight into the animals' social behaviours. |
Type Of Material | Data analysis technique |
Year Produced | 2020 |
Provided To Others? | No |
Impact | Both of these extensions increase the insight we can gain in to collective movement behaviours. |
Description | UGRI - Undergraduate Research Internship |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Undergraduate students |
Results and Impact | I supervised an undergraduate student for four weeks, who undertook a research project related to this award. The aim was to provide them with experience of what postgraduate research entailed, particularly in this research area, with the hope they would pursue it further. They subsequently chose this research area for their 4th year dissertation. |
Year(s) Of Engagement Activity | 2019 |