Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.695342
Title: Development of camera trap methodology in monitoring deer distribution and abundance
Author: Freeman, Marianne Sarah
ISNI:       0000 0004 5995 0262
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2015
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Abstract:
Camera traps have taken off one of the most popular tools in ecology. This thesis aims to develop existing camera trap methodology in order to better assess the distribution and abundance of deer in the UK. Particular focus was made on the invasion history of muntjac to help elucidate their invasion pattern. The number of founding females was estimated to be 4 or 5 individuals. The effect of covariates on the camera detection zones were considered to help improve density estimates resulting from camera trap research. Flash type and individual passing speed proved to be two important covariates adding weight to the recommendation that camera detection zones should be survey specific and that activity patterns should be considered when determining detection zones. Eight deer population densities were estimated from across the UK using both thermal imaging distance sampling and random encounter model (REM) techniques. A higher density was found with the REM, thought the two methods appeared more comparable in open woodlands. A low quality thermal imagine camera may have bias the results, but this study also emphasises the need to ensure other parameters, such as daily travel distance are site specific and as accurate as possible. Muntjac sightings, within Northern Ireland, were collated and verified using a scoring system and survey combination. The REM was trialled in one site, finding a minimum population of 5 muntjac deer. This baseline result can be used in any future population monitoring. These verified sightings alongside others from Ireland were used to test a muntjac species distribution model with different sampling bias approaches. The random background model was the most parsimonious model suggesting, in this case, that the additional bias controlling techniques may not always be necessary.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.695342  DOI: Not available
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