Abstract
The problem of estimating population total of animals from imperfectly characterising animal signs poses a number of interesting statistical questions that are not addressed through conventional methods of estimating animal abundance. A case in point is the estimation of tiger population total from pugmark (footprint) measurements, which has been the traditional mode of tiger census in India for several decades. Usual methods based on distance sampling would not work well because, unlike dung produced by elephants or nests produced by birds, such signs are not produced at a steady rate. On the other hand, these signs may not carry as accurate and reliable characterising information as one expects from fingerprints. Is it still possible to estimate the population total precisely and accurately? If so, what should be the appropriate number of signs to be sampled? How can one cluster the signs so that each group of signs belongs to a distinct animal? Is good clustering a prerequisite for good estimation of population total? Is it possible to account for animals missed in the sample? In this article, we attempt to answer to these questions.
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Sengupta, D. (2015). Estimation of Animal Abundance Through Imperfectly Characterising Signatures. In: Dasgupta, R. (eds) Growth Curve and Structural Equation Modeling. Springer Proceedings in Mathematics & Statistics, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-319-17329-0_4
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DOI: https://doi.org/10.1007/978-3-319-17329-0_4
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