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Empirically simulated study to compare and validate sampling methods used in aerial surveys of wildlife populations
Author(s) -
Khaemba Wilson Mwale,
Stein Alfred,
Rasch Dieter,
De Leeuw Jan,
Georgiadis Nick
Publication year - 2001
Publication title -
african journal of ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.499
H-Index - 54
eISSN - 1365-2028
pISSN - 0141-6707
DOI - 10.1046/j.0141-6707.2001.00329.x
Subject(s) - sampling (signal processing) , sampling design , wildlife , statistics , transect , systematic sampling , sample size determination , population , mean squared error , abundance estimation , abundance (ecology) , environmental science , computer science , ecology , mathematics , biology , demography , filter (signal processing) , sociology , computer vision
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.