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Sampling Strategies to Estimate Deer Density by Drive Counts

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Abstract

The best evaluation of deer density can be achieved by accurate drive counts of deer performed in all the suitable wooded patches of the area of interest. This would provide the true density within drive areas which, in turn, should be akin to the true density within the study area. Because the drive of all these areas is prohibitive, only a subset is usually driven. Results are highly dependent on the subjective choice of the areas. In the present study, an objective design-based approach is considered to select the areas to be driven according to some probabilistic sampling schemes, and deer density in the whole collection of drive areas is estimated by means of some criteria. The schemes should be able to achieve samples of areas evenly spread onto the study region. The criteria should be able to exploit the information provided by the area sizes. Four sampling strategies are considered, together with methods to estimate their precision. They are evaluated by means of a simulation study performed on artificial and real populations. Results from artificial populations determine the best strategies to be used. Results from real populations show that precise estimates are achieved at the cost of sampling 20% of the drive areas.

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References

  • Apollonio M, Andersen R, Putman R (2010a) European Ungulates and their Management in the 21st Century. Cambridge University Press, Cambridge.

    Google Scholar 

  • — (2010b) Present status and future challenges for European ungulate management. In Apollonio M, Andersen R, Putman R (eds) European Ungulates and their Management in the 21st Century. Cambridge University Press, Cambridge, pp 578–604

    Google Scholar 

  • Borkowski J, Palmer S C F, Borowski Z (2011) Drive counts as a method of estimating ungulate density in forests: mission impossible? Acta Theriologica 56: 239–253.

    Article  Google Scholar 

  • Collier B A, Ditchkoff S S, Raglin J B, Smith J M (2007) Detection probability and sources of variation in white-tailed deer spotlight surveys. Journal of Wildlife Management 71: 277–281.

    Article  Google Scholar 

  • Deville J C, Tillé Y (2004) Efficient balanced sampling: the cube method. Biometrika 91: 893-912.

    Article  MathSciNet  Google Scholar 

  • Elzinga C L, Salzer D W, Willoughby J W, Gibbs J P (2001) Monitoring plant and animal populations. Blackwell Science, Oxford.

    Google Scholar 

  • Fattorini L (2006) Applying the Horvitz–Thompson criterion in complex designs: a computer-intensive perspective for estimating inclusion probabilities. Biometrika 93: 269-278.

    Article  MathSciNet  Google Scholar 

  • Fattorini L, Ferretti F, Pisani C, Sforzi A (2011) Two-stage estimation of ungulate abundance in Mediterranean areas using pellet group count. Environmental and Ecological Statistics 18: 291-314.

    Article  MathSciNet  Google Scholar 

  • Ferroglio E, Gortázar C, Vicente J (2011) Wild ungulate diseases and the risk for livestock and public health. In Putman R, Apollonio M, Andersen R (eds) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, pp 192-214.

    Chapter  Google Scholar 

  • Focardi S, Isotti R, Raganella-Pelliccioni E and Iannuzzo D (2002) The use of distance sampling and mark-resight to estimate the local density of wildlife populations. Environmetrics 13: 177-186.

    Article  Google Scholar 

  • Grafström A (2012) Spatial correlated Poisson sampling. Journal of Statistical Planning and Inference 142: 139–147.

    Article  MathSciNet  Google Scholar 

  • Grafström A, Tillé Y (2013) Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics 24: 120–131.

    Article  MathSciNet  Google Scholar 

  • Grafström A, Lundström N L P, Schelin L (2012) Spatially Balanced Sampling through the Pivotal Method. Biometrics 68: 514–520.

    Article  MathSciNet  Google Scholar 

  • Hewison M A J, Vincent J P, Reby D (1998) Social organisation of European roe deer. In Duncan P, Linnell J D C (eds) The European roe deer: the biology of success. Scandinavian University Press, Oslo, pp 189–219.

    Google Scholar 

  • Maillard D, Gaillard J-M, Hewison M, Ballon P, Duncan P, Loison A, Toïgo C, Baubet E, Bonenfant C, Garel M, Saint-Andrieux C (2010) Ungulates and their management in France. In Apollonio M, Andersen R, Putman R (eds) European Ungulates and their Management in the 21st Century. Cambridge University Press, Cambridge, pp 441–474.

    Google Scholar 

  • Mayle B A, Staines B W (1998) An overview of methods used for estimating the size of deer populations in Great Britain. In Goldspink CR, King S, Putman R J (eds) Population Ecology, Management and Welfare of Deer. British Deer Society/Universities’ Federation for Animal Welfare, Manchester, pp 19–31.

    Google Scholar 

  • Mayle B A, Peace A J, Gill R M A (1999) How Many Deer? A Field Guide to Estimating Deer Population Size. Field Book 18, Forestry Commission, London, UK.

  • Meriggi A, Lamberti P, Sotti F, Gilio N (2008) A review of the methods for monitoring Roe deer European populations with particular reference to Italy. Hystrix – the Italian Journal of Mammalogy 19: 103-120.

    Google Scholar 

  • Morellet N, Klein F, Solberg E, Andersen R (2011) The census and management of populations of ungulates in Europe. In Putman R, Apollonio M, Andersen R (eds). Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, pp 106-143.

    Chapter  Google Scholar 

  • Pellerin M, Bessière A, Maillard D, Capron G, Gaillard J-M, Michallet J, Bonenfant C (2017) Saving time and money by using diurnal vehicle counts to monitor roe deer abundance. Wildlife Biology 1-10.

  • Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe. Problem and practice. Cambridge University Press, Cambridge.

    Book  Google Scholar 

  • Reimoser F, Putman R (2011) Impacts of wild ungulates on vegetation: costs and benefits. In Putman R, Apollonio M, Andersen R (eds) Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge, pp 144-191.

    Chapter  Google Scholar 

  • Särndal C E, Swensson B, Wretman J (1992) Model Assisted Survey Sampling. Springer-Verlag, New York.

    Book  Google Scholar 

  • Sinclair A R E, Fryxell J M, Caughley G (2006) Wildlife ecology conservation and management, Second edition. Blackwell Publishing Ltd, Oxford.

    Google Scholar 

  • 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: 99–114

    Article  Google Scholar 

  • Staines B W, Ratcliffe P R (1987) Estimating the abundance of red deer (Cervus elaphus L.) and roe deer (Capreolus capreolus L.) and their current status in Great Britain. Symposia of the Zoological Society of London 58: 131-152.

    Google Scholar 

  • Stevens D J, Olsen A R (2004) Spatially Balanced Sampling of Natural Resources. Journal of the American Statistical Association 99: 262-278.

    Article  MathSciNet  Google Scholar 

  • Takeshita K, Ikeda T, Takahashi H, Yoshida T, Igota H, Matsuura Y, Kaji K (2016) Comparison of Drive Counts and Mark-Resight As Methods of Population Size Estimation of Highly Dense Sika Deer (Cervus nippon) Populations. PLoS ONE 11: 1-14.

    Article  Google Scholar 

  • Wäber K, Dolman M P (2015) Deer abundance estimation at landscape-scales in heterogeneous forests. Basic and Applied Ecology 16: 610-620.

    Article  Google Scholar 

  • Williams B K, Nichols J D, Conroy M J (2002) Analysis and Management of Animal Populations. Academic Press, San Diego.

    Google Scholar 

  • Wolter KM (2007) Introduction to Variance Estimation (2nd edn). Springer-Verlag, New York.

    MATH  Google Scholar 

  • Wotschikowsky U (2010) Ungulates and their management in Germany. In Apollonio M, Andersen R, Putman R (eds) European Ungulates and their Management in the 21st Century. Cambridge University Press, Cambridge, pp 201-222.

    Google Scholar 

  • Viganò R, Demartini E, Riccardi F, Corradini A, Besozzi M, Lanfranchi P, Chiappini P L, Cottini A, Gaviglio A. (2019) Quality parameters of hunted game meat: Sensory analysis and pH monitoring. Italian Journal of Food Safety 8: 55-59.

    Article  Google Scholar 

  • Zaccaroni M, Dell’Agnello F, Ponti G, Riga F, Vescovini C, Fattorini L (2017) Vantage point counts and monitoring roe deer. Journal of Wildlife Management 82: 354–361.

    Article  Google Scholar 

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Acknowledgements

We thank M. Lombardini for her comments on an earlier draft of the manuscript.

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Correspondence to Lorenzo Fattorini.

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Fattorini, L., Meriggi, A., Merli, E. et al. Sampling Strategies to Estimate Deer Density by Drive Counts. JABES 25, 168–185 (2020). https://doi.org/10.1007/s13253-020-00386-3

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