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Harvest-based Bayesian estimation of sika deer populations using state-space models

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Population Ecology

Abstract

We have estimated the number of sika deer, Cervus nippon, in Hokkaido, Japan, with the aim of developing a management program that will reduce the level of agricultural damage caused by these deer. A population index that is defined by the population divided by the population of 1993 is first estimated from the data obtained during a spotlight survey. A generalized linear mixed model (GLMM) with corner point constraints is used in this estimation. We then estimate the population from the index by evaluating the response of index to the known amount of harvest, including hunting. A stage-structured model is used in this harvest-based estimation. It is well-known that estimates of indices suffer from large observation errors when the probability of the observation fluctuates widely; therefore, we apply state-space modeling to the harvest-based estimation to remove the observation errors. We propose the use of Bayesian estimation with uniform prior-distributions as an approximation of the maximum likelihood estimation, without permitting an arbitrary assumption that the parameters fluctuate following prior-distributions. We are able to demonstrate that the harvest-based Bayesian estimation is effective in reducing the observation errors in sika deer populations, but the stage-structured model requires many demographic parameters to be known prior to running the analyses. These parameters cannot be estimated from the observed time-series of the index if there is insufficient data. We then construct a univariate model by simplifying the stage-structured model and show that the simplified model yields estimates that are nearly identical to those obtained from the stage-structured model. This simplification of the model simultaneously clarifies which parameter is important in estimating the population.

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Acknowledgments

We thank Dr. Y. Iwasa for reviewing the manuscript. We also thank anonymous reviewers for their comments that improved the manuscript greatly. This work has been supported by a Grant-in-Aid for Scientific Research of JSPS to H.Y.

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Authors and Affiliations

  1. Laboratory of Population Ecology, National Institute for Agro-Environmental Sciences, 3-1-3 Kannondai, Tsukuba, 305-8604, Japan

    Kohji Yamamura

  2. Department of Environmental Management, Yokohama National University, Yokohama, 239-8501, Japan

    Hiroyuki Matsuda

  3. CSIRO Sustainable Ecosystems, 306 Carmody Road, St Lucia, QLD, 4067, Australia

    Hiroyuki Yokomizo

  4. The Ecology Centre, School of Integrative Biology, The University of Queensland, St Lucia, QLD, 4072, Australia

    Hiroyuki Yokomizo

  5. Tokyo University of Agriculture and Technology, Fuchu, 183-8509, Japan

    Koichi Kaji

  6. Hokkaido Institute of Environmental Sciences, Sapporo, 060-0819, Japan

    Hiroyuki Uno & Katsumi Tamada

  7. Eastern Hokkaido Wildlife Research Station, Hokkaido Institute of Environmental Sciences, Kushiro, 085-8588, Japan

    Toshio Kurumada

  8. Field Science Center, Hokkaido University, Sapporo, 060-0811, Japan

    Takashi Saitoh

  9. Forestry and Forest Products Research Institute, Sapporo, 062-8516, Japan

    Hirofumi Hirakawa

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  1. Kohji Yamamura
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  2. Hiroyuki Matsuda
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  9. Hirofumi Hirakawa
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Correspondence to Kohji Yamamura.

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Yamamura, K., Matsuda, H., Yokomizo, H. et al. Harvest-based Bayesian estimation of sika deer populations using state-space models. Popul Ecol 50, 131–144 (2008). https://doi.org/10.1007/s10144-007-0069-x

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  • DOI: https://doi.org/10.1007/s10144-007-0069-x

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