Project Description

Conserving species often requires monitoring and or intervening at breeding sites (e.g. nests for birds) as reproduction is a key life-stage driving the persistence of populations. Further, for many protected species, their nest or resting sites are protected features under the Wildlife and Countryside Act (nests of goshawk, osprey and eagle; dens of martens and wild cats; holts of otters; badger sets). Accordingly, it is an offence to disturb these when in use, and land managers expand substantial resources ensuring they comply with the law.

This is a challenge. On one hand, for many of those species, breeding locations are well hidden and localising them requires a large effort by highly skilled staff able to identify the right behavioural patterns or clues. This is typically the case of raptors covering wide landscapes, or cryptic species (nocturnal mammals, wild pollinators). On the other hand, a wealth of data exists documenting the presence of individuals during foraging bouts (e.g., raptor sighting from citizen science, camera traps recording presence of nocturnal mammals, scats), implying a protected feature is present in the vicinity (and even possibly documenting absence where formal surveys are conducted).

Because resources allocated to detected protected features are scarce, it is important to make locating those features as efficient as possible to enable proactive conservation.

Geographic profiling (GP) is a statistical technique originally developed in criminology to prioritise large lists of suspects in cases of serial crime by using the spatial locations of crime sites to make inferences about the offender’s ‘anchor point’ (usually a home, but sometimes a workplace). GP has been successfully applied to conservation (e.g. human wildlife conflicts, invasion biology), and recently (PhD thesis by Kez Armstrong with Dr Caplat) to identify nest locations of the common kestrel in Ireland.

This project will further develop GP by improving integration of ecological data (e.g. habitat suitability or species and sex specific movement patterns), and test its application with iconic species contrasting in their life-history and the scale at which they move (wild pollinators, red kite, pine marten). Data will combine existing datasets from monitoring program and new data acquired in an experimental setting (with bumblebee colonies) and in the field, through citizen-science programs tailored to the project or the student’s own surveys. A possible additional research track will be the investigation of optimal survey methods to inform the GP model.

The student will become proficient with wildlife survey tools, under the supervision of experience conservation officers, and contribute to develop a cutting-edge model in a Bayesian framework, with support from a supervisory team expert in population biology, conservation, and ecological modelling. They will work closely with partners in conservation (Ulster Wildlife, NI Raptor Study Group, Forestry & Land Scotland).


This PhD is suitable for a student enthused by population ecology and the application of state-of-the art statistical techniques to citizen-science data to inform conceptual as well as practical management and conservation issues.

They will have a solid background in statistical modelling, a drivers’ license (for fieldwork), with practice and understanding of Bayesian inference, point pattern analysis, and experience in wildlife survey methods strong assets.

Photo credit: Paul Caplat QUB and Wikimedia Commons


Paul Caplat

Primary Supervisor:

Profile: Paul Caplat
Institution: Queen's University, Belfast
Department/School: School of Biological Sciences

Xavier Lambin

Secondary Supervisor:

Profile: Xavier Lambin
Institution: University of Aberdeen
Department/School: School of Biological Sciences

Additional Supervisor:

Dr Lorraine Scott, QUB, School of Biological Sciences



Dr Deon Roos, University of Aberdeen, School of Medical Sciences, Lecturer in Applied Statistics


Millon, A., Petty, S. J. & Lambin, X. (2010) Pulsed resources affect the timing of first breeding and lifetime reproductive success of tawny owls. Journal of Animal Ecology, 79, 426-435

Stevens, M. C. A., S. C. Faulkner, A. B. B. Wilke, J. C. Beier, C. Vasquez, W. D. Petrie, H. Fry, R. A. Nichols, R. Verity, and S. C. Le Comber. 2021. Spatially clustered count data provide more efficient search strategies in invasion biology and disease control. Ecological Applications 31(5):e02329. 10.1002/eap.2329


  • biodiversity
  • environmental-management


Collaborators will include:

  • NI Raptor Study Group

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