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simplify the problem (Gerrodette 1987). Because of the complexities involved
in generating power estimates for monitoring programs, the problem may be
most tractable with simulation methods. Accordingly, a conceptually straight-
forward Monte Carlo approach based on linear regression analysis has been
devised (table 7.1; Gibbs and Melvin 1997). With this approach a researcher
defines the basic structure of a monitoring program and provides a variance
estimate for the population index used. Simulations are then run in which
many sets of sample counts are generated based on the structure of the moni-
toring program with trends of varying strength underlying them. The fre-
quency with which trends are detected in the counts, despite the sampling
error imposed by the population index and the structure of the monitoring
program, reflects the power of the monitoring design to detect trends. The
simulation program is particularly useful for evaluating the tradeoffs between
sampling effort, logistical constraints, and power to detect trends. The simula-
tion software (“monitor.exe”) has been adapted for general use on DOS-based
microcomputers, and is available from the author or via the Internet at
http://www.im.nbs.gov/powcase/powcase.html.
VARIABILITY OF INDICES OF ANIMAL ABUNDANCE
A key influence on power to detect a given population trend is the variability
of the population index used. Power to detect trends is inversely related to the
magnitude of index variability and monitoring programs must be designed
around the component of index variability that cannot be controlled (Ger-
rodette 1987). In other words, sufficient numbers of plots must be monitored
frequently enough to capture trends despite the inherent variability of the pop-
ulation index. Without pilot studies, however, researchers often have no esti-
mate of population variability. Lacking estimates of this critical parameter
impairs the ability of animal ecologists to design statistically powerful moni-
toring programs.
A ready source of data on the variability of population indices can be found
in published time series of population counts. Hundreds of long-term popula-
tion studies for a variety of taxa have been published in the last century, albeit
mostly for temperate-zone organisms. Because most of these population series
were generated using population indices, not population censuses, presumably
variation in these count series reflects both environmental variation in the
populations and sampling error associated with the counting methodology. As
long as the time series are of sufficient and comparable duration, significant
trends have been removed from them, and sufficient numbers of studies have
been made, approximations of index variability can be estimated. Further-
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