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Title: Paws for thought : assessing the efficacy of monitoring techniques for rare and elusive species
Author: Moorcroft, E. A.
ISNI:       0000 0004 8498 9572
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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Density estimation is vitally important in the conservation of endangered species. One non-invasive way of observing animals in the wild is camera trapping, a technique that has increased dramatically over the last 20 years. Models applied to the results from camera trapping can produce estimates for density. These techniques are now widespread, so it is now extremely important that the methodology is correctly and consistently used. This thesis reviews the current guidelines for camera trap capture-recapture survey design, and shows that few surveys currently meet these guidelines, thus, many density estimates published in the literature may be systematically biased. However, the guidelines themselves may not be appropriate under realistic movement conditions. A simulation model was developed using a statistically derived movement model for snow leopards, and this was used to explore the effect of survey design on the reliability of camera trap data used in Spatially Explicit Capture Recapture (SECR) analyses. I present evidence that basic assumptions about the movement patterns of the target species affect the accuracy and precision of SECR. As a result, SECR is less accurate when large survey area are used than was previously assumed. In addition, minimum capture numbers are currently used as a guide to the accuracy of density estimates. However, based on the simulation results, other measures such as distance between recaptures, and number of the individuals captured are better guides as to the accuracy of a density estimate. Finally, a possible new method for monitoring animals is introduced, a generalisation of the Random Encounter Model (REM) of density estimation. Whilst this methodology is not precise enough to study snow leopards, it opens up the possibility of applying the model to a wider range of sensors.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available