Large scale wildlife monitoring studies: statistical methods for design and analysis

Pollock, K.H.; Nichols, J.D.; Simons, T. R.; Farnsworth, G.L.; Bailey, L.L.; Sauer, J.R.

Abstract:

Techniques for estimation of absolute abundance of wildlife populations have received a lot of attention in recent years. The statistical research has been focused on intensive small-scale studies. Recently, however, wildlife biologists have desired to study populations of animals at very large scales for monitoring purposes. Population indices are widely used in these extensive monitoring programs because they are inexpensive compared to estimates of absolute abundance. A crucial underlying assumption is that the population index (C) is directly proportional to the population density (D). The proportionality constant, b, is simply the probability of ‘detection‘ for animals in the survey. As spatial and temporal comparisons of indices are crucial, it is necessary to also assume that the probability of detection is constant over space and time. Biologists intuitively recognize this when they design rigid protocols for the studies where the indices are collected. Unfortunately, however in many field studios the assumption is clearly invalid. We believe that the estimation of detection probability should be built into the monitoring design through a double sampling approach. A large sample of points provides an abundance index, and a smaller sub-sample of the same points is used to estimate detection probability. There is an important need for statistical research on the design and analysis of these complex studies. Some basic concepts based on actual avian, amphibian, and fish monitoring studies are presented in this article.

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Article

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5224113

Citation Author:

Pollock, K.H.; Nichols, J.D.; Simons, T. R.; Farnsworth, G.L.; Bailey, L.L.; Sauer, J.R.

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Patuxent Wildlife Research Center

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119

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English

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Environmetrics

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105-119

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5831_Pollock.pdf

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Large scale wildlife monitoring studies: statistical methods for design and analysis; 2002; Article; Journal; Environmetrics; Pollock, K.H.; Nichols, J.D.; Simons, T. R.; Farnsworth, G.L.; Bailey, L.L.; Sauer, J.R.