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Predicting the variation in detection function in line transect sampling through random parameter model
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DOI：10.1023/A:1009639702036.png)
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Abstract
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A method for calibrating (localizing) detection function models in line transect sampling is proposed. The method is based on a random parameter model which supplies localized predictions of detection function parameters utilizing a few sample data points from the concerned location(s). The method has the clear advantage of being able to provide density estimates based on very few observations from a location which would be impossible through traditional methods. The method is successfully illustrated using census data on sambar (Cervus unicolor) from a set of wildlife sanctuaries in Kerala, India. The need for further research in this direction is indicated.
Keywords:
animal abundance
calibration of detection function
line transect sampling
negative exponential model
wildlife census
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