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Title: Estimating population densities and testing for pathogens in deer
Author: Hogg, Kayleigh Grace
ISNI:       0000 0004 6061 1460
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2016
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Deer, both native and non-native, numbers in Great Britain and Ireland are rising, as a result of woodland expansion, increased food availability and a lack of natural predators. At high densities, deer pose a threat to biodiversity and can have negative impacts on forestry and agriculture. However, in certain areas deer are considered to be a resource and provide economic benefits to otherwise fragile rural communities. Estimating deer densities accurately is therefore essential to effectively inform deer management practices. For example, setting cull targets in order to meet different stakeholders’ objectives, such as crop protection, sustainable deer hunting, or to prevent the establishment of an invasive species. Various methods exist to estimate deer numbers, however, in dense forestry these methods can be challenging. Nevertheless, new technologies such as camera traps have helped to facilitate data collection in these areas. This thesis is the first body of research to show that a newly developed deer counting method, the Random Encounter Model, used in conjunction with camera trap technology could be used effectively to monitor the size and distribution of native and invasive deer populations, and provide a step forward in establishing deer management plans. Through the use of citizen science coupled with the development of the REM, this research served to confirm the presence and abundance of a newly invasive deer species. Furthermore, through utilizing molecular diagnostic methods, this thesis describes for the first time, novel pathogens within invasive deer populations, which may pose a disease transmission risk to other wildlife and livestock.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available