Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 110990
Title Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations
Author(s) Khaemba, W.M.; Stein, A.; Rasch, D.; Leeuw, J. de; Georgiadis, N.
Source African Journal of Ecology 4 (2001)39. - ISSN 0141-6707 - p. 374 - 382.
DOI https://doi.org/10.1046/j.0141-6707.2001.00329.x
Department(s) Mathematical and Statistical Methods - Biometris
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2001
Abstract This paper compares the distribution, sampling and estimation of abundance for two animal species in an African ecosystem by means of an intensive simulation of the sampling process under a geographical information system (GIS) environment. It focuses on systematic and random sampling designs, commonly used in wildlife surveys, comparing their performance to an adaptive design at three increasing sampling intensities, using the root mean square errors (RMSE). It further assesses the impact of sampling designs and intensities on estimates of population parameters. The simulation is based on data collected during a prior survey, in which geographical locations of all observed animals were recorded. This provides more detailed data than that usually available from transect surveys. The results show precision of estimates to increase with increasing sampling intensity, while no significant differences are observed between estimates obtained under random and systematic designs. An increase in precision is observed for the adaptive design, thereby validating the use of this design for sampling clustered populations. The study illustrates the benefits of combining statistical methods with GIS techniques to increase insight into wildlife population dynamics.
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