Monitoring woodland deer populations in the UK: An imprecise science

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Abstract

1. The need to assess population size and change is central to any population monitoring programme. A range of monitoring techniques is available for deer, but few studies have addressed the performance of these techniques in terms of their accuracy and their power to detect population changes reliably. This study compares the performance of three commonly used techniques to monitor woodland deer populations in terms of their accuracy, precision and statistical power using field data and simulation modelling. 2. Faecal Standing Crop (FSC) was found to provide the most accurate estimates at all deer densities studied, but was outperformed by distance sampling using thermal imaging in terms of precision and statistical power. Faecal Accumulation Rate (FAR) performed worst in terms of precision and statistical power, and was inferior in terms of accuracy to FSC. The accuracy of FAR and distance sampling using thermal imaging was similar. 3. Annual surveying is not the best use of resources, since 10% per annum increases or decreases in a population could not be detected by any of the techniques within a 3-year time period. Even with the best technique under the most favourable conditions of deer density simulated in this study, a population change could only be detected reliably once the population had increased by 33% or decreased by 27%. In the least favourable conditions, the respective figures were a 380% increase or extinction. 4. All the techniques studied are relatively poor at detecting population change, so their use in practical deer management needs to be treated with caution. The consequences of relying on similar techniques to detect population change for rare species could be severe. © 2004 Mammal Society, Mammal Review.

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Smart, J. C. R., Ward, A. I., & White, P. C. L. (2004). Monitoring woodland deer populations in the UK: An imprecise science. Mammal Review, 34(1–2), 99–114. https://doi.org/10.1046/j.0305-1838.2003.00026.x

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