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Estimating red deer abundance using the pellet-based distance sampling method

Author

Listed:
  • R.T. Torres

    (CESAM, Department of Biology, University of Aveiro, Aveiro, Portugal)

  • A.M. Valente

    (CESAM, Department of Biology, University of Aveiro, Aveiro, Portugal)

  • T.A. Marques

    (Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, Scotland)

  • C. Fonseca

    (CESAM, Department of Biology, University of Aveiro, Aveiro, Portugal)

Abstract

Many European agricultural landscapes have been abandoned facilitating the comeback of large ungulates. In Portugal, the increase in red deer numbers caused local conflicts with landowners reporting economic losses in forest and agricultural plantations. A great effort is needed to mitigate human-red deer conflicts through management strategies. Successful management strategies require reliable information on population trends. Here we propose an easy and readily applied method to estimate an increasing ungulate population. We estimated the red deer population density in a Mediterranean environment located in northeastern Portugal: Lombada National Hunting Area (LNHA) and Serra de Montesinho (SM), using pellet group counts coupled with distance sampling to account for pellet detectability. The estimated red deer density using a stratified detection function was 5.81 indd per 100 ha for LNHA and 1.34 indd per 100 ha for SM (95% CI: 3.65-9.25 and 0.74-2.42, respectively). For the entire area, the estimated density was 3.38 deer per 100 ha (95% CI: 2.18-5.24). Monitoring population trends is crucial to assess the impact of methods aimed at reducing the population size or impact and here we provided an example of a robust method that can be implemented to continuously monitor expanding populations.

Suggested Citation

  • R.T. Torres & A.M. Valente & T.A. Marques & C. Fonseca, 2015. "Estimating red deer abundance using the pellet-based distance sampling method," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 61(10), pages 422-430.
    • Handle: RePEc:caa:jnljfs:v:61:y:2015:i:10:id:52-2015-jfs
    DOI: 10.17221/52/2015-JFS
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    References listed on IDEAS

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    1. Tiago A. Marques, 2004. "Predicting and Correcting Bias Caused by Measurement Error in Line Transect Sampling Using Multiplicative Error Models," Biometrics, The International Biometric Society, vol. 60(3), pages 757-763, September.
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