Optimizing Presence–Absence Surveys For Detecting Population Trends

Rhodes, J. R., Tyre, A. J., Jonzen, N., McAlpine, C. A. and Possingham, H. P. (2006) Optimizing Presence–Absence Surveys For Detecting Population Trends. Journal of Wildlife Management, 70 1: 8-18. doi:10.2193/0022-541X(2006)70[8:OPSFDP]2.0.CO;2

Author Rhodes, J. R.
Tyre, A. J.
Jonzen, N.
McAlpine, C. A.
Possingham, H. P.
Title Optimizing Presence–Absence Surveys For Detecting Population Trends
Journal name Journal of Wildlife Management   Check publisher's open access policy
ISSN 0022-541X
Publication date 2006-01
Sub-type Article (original research)
DOI 10.2193/0022-541X(2006)70[8:OPSFDP]2.0.CO;2
Volume 70
Issue 1
Start page 8
End page 18
Total pages 11
Editor M. Morrison
Place of publication Hoboken, NJ, United States
Publisher John Wiley & Sons
Collection year 2006
Language eng
Abstract Presence-absence surveys are a commonly used method for monitoring broad-scale changes in wildlife distributions. However, the lack of power of these surveys for detecting population trends is problematic for their application in wildlife management. Options for improving power include increasing the sampling effort or arbitrarily relaxing the type I error rate. We present an alternative, whereby targeted sampling of particular habitats in the landscape using information from a habitat model increases power. The advantage of this approach is that it does not require a trade-off with either cost or the Pr(type I error) to achieve greater power. We use a demographic model of koala (Phascolarctos cinereus) population dynamics and simulations of the monitoring process to estimate the power to detect a trend in occupancy for a range of strategies, thereby demonstrating that targeting particular habitat qualities can improve power substantially. If the objective is to detect a decline in occupancy, the optimal strategy is to sample high-quality habitats. Alternatively, if the objective is to detect an increase in occupancy, the optimal strategy is to sample intermediate-quality habitats. The strategies with the highest power remained the same under a range of parameter assumptions, although observation error had a strong influence on the optimal strategy. Our approach specifically applies to monitoring for detecting long-term trends in occupancy or abundance. This is a common and important monitoring objective for wildlife managers, and we provide guidelines for more effectively achieving it.
Keyword Ecology
Markov model
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

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Created: Wed, 15 Aug 2007, 10:26:15 EST