This software estimates the statistical power of population monitoring programs relative to (1) the number of plots monitored, (2) the magnitude of counts per plot, (3) count variation, (4) plot weighting schemes, (5) the duration of monitoring, (6) the interval of monitoring, (7) the magnitude and nature of ongoing population trends, and (8) the significance level associated with trend determination. Plots are considered synonymous with routes, transects, or other monitoring units on which repeated counts of individual plants or animals are made over time. The software can be used to evaluate the statistical power of an existing monitoring program, or to assist in designing a new program. It is appropriate for local- or regional-scale monitoring programs where less than 250 plots are being monitored.
To estimate statistical power, MONITOR generates many, simulated sets of count data based on a monitoring program defined by the user and sample counts drawn at random from distributions defined by the user. Through multiple trials, it evaluates how often a monitoring program detects 11 trends of varying strength. Power of monitoring programs consisting of a single plot is estimated by determining the proportion of trials a trend in sample counts on the plot was significantly different from zero. For monitoring programs consisting of multiple plots, a "route regression" approach is used, whereby trends in sample counts are determined for each plot, and then averaged across plots. The proportion of trials in which these averaged trends differed from zero is used to estimate power. Thus, the power estimate, measured from 0 (low power) to 1 (high power), indicates how effective a monitoring program is at detecting various trends.
To use MONITOR, the user must define the structure of the
monitoring program (number of plots, duration of monitoring,
and interval of monitoring) and provide estimates of the
magnitude and variation in counts on each plot and the
relative weights (influence) to assign to each plot. Much of
this information can be derived easily from preliminary count
data (preferably from plot counts repeated within a single
season) or from data published for populations or species
similar to those targeted for monitoring.
(See
MONITOR main page)
Running Monitor
By clicking 'Run simulations' you can execute the simulation of your
monitoring program. Power calculations are based
on the number of iterations completed.
What is occurring during a simulation is best described
by example. Consider monitoring 3 plots every year over 10
years. During the first iteration of the simulation, the
software projects from the initial abundance on the first plot
one of the 10 deterministic trends expected for a 1, 2, 3, 5,
& 10 % increase or decrease over the 10 year period. It then
generates a random count for each survey occasion drawn from a
distribution whose mean is equal to the deterministic
projection for that year and whose variance is specified by
the user. A least-squares regression of the sample counts
versus year is subsequently performed to determine the plot
trend. The same process is repeated for the two remaining
plots. The slopes are then averaged across the 3 plots.
Finally, the probability that the average slope is different
from zero is determined, and whether this probability is less
than or equal to the significance level declared by the user
is recorded. This process is repeated for each of the
remaining trends. During subsequent iterations, a new set of
3 plots is simulated in exactly the same manner. After many
such iterations, power of the monitoring program is estimated
as the proportion of iterations that detected the expected
trends.
A trend of zero (NO TREND) is included merely to show
that, with adequate replication and sufficiently high initial
counts (and comparable weights and variances in multi-plot
situations), the probability of detecting this "trend" equals
the significance level (type I error rate) specified by the
user.
Results
The results screen presents power estimates for each of 11
trends simulated with your monitoring program. The power
estimate for each trend equals the proportion of trials that
each ongoing trend was actually detected during the
simulation. If trends were detected every time the sample
counts were projected, then power estimates would equal 1.
One should generally seek power estimates that exceed 0.90.
This often is difficult for trends of lower magnitude (1%, 2%,
& 3%). Users will also note that it is generally easier to
detect increasing than decreasing trends in sample counts.
Also included in the results screen is the total number of
counts made during a single, complete execution of your survey
program. This value is included to provide a simple index to
monitoring effort and hence monitoring cost.
Relevant Literature
de la Mare, W. K. 1984. On the power of catch per unit effort series to detect declines in whale stocks. Rep. Int. Whal. Commn. 34:655-61. Edwards, E. F., and P. C. Perkins. 1992. Power to detect linear trends in dolphin abundance: estimates from tuna-vessel observer data, 1975-89. Fishery Bulletin 90:625-631. Gerrodette, T. 1987. A power analysis for detecting trends. Ecology 68:1364-1372. Peterman, R. M., and M. J. Bradford. 1987. Statistical power of trends in fish abundance. Can. J. Fish. Aquat. Sci. 44:1879-1889. Sauer, J. R., and S. Droege, editors. 1990. Survey designs and statistical methods for the estimation of avian population trends. U.S. Fish and Wildlife Service, Biol. Rep. 90(1). 166 pp. Taylor, B. L., and T. Gerrodette. 1993. The uses of statistical power in conservation biology: the vaquita and northern spotted owl. Conservation Biology 7:489-500.
Throughout most of the year, black bears in Dachigam are scattered throughout the sanctuary and are very difficult to count.
At certain periods of the year, however, during the peak fruiting period for local mast-bearing trees, most bears in the sanctuary travel to a large, central grove of masting trees to forage. From a particular observation point, it is possible to get repeatable counts of the number of bears travelling to and from the grove on any given day. Not all the bears in the sanctuary visit the grove, and some are occasionally double counted, so the daily counts from the observation point represent an index of the bear population's size, not a true census of the population.
During a single season, 15 separate, day-long counts of the bears were made, which yielded a average of 15.6 bears observed per day and an accompanying standard deviation of 3.6 bears. The following question arose among individuals concerned about the bear population's status: would allocating the time of one park warden to count bears on 3 different days during peak of masting periods each season produce data useful for monitoring Dachigam's bear population? Specifically, would this intensity of monitoring effort exerted over a 10 year period be sufficient to detect annual trends (positive and negative) of at least 3% in the bear population at a probability of > 0.90?
The inputs for MONITOR are fairly straightforward in this case. Only a single plot is being monitored, so Number of Plots is one. Surveys would be conducted every year for 10 years, so the Number of Surveys would be 10, and the survey occasions would be 0, 1, 2, ..., 7, 8, 9. [Note that in all simulations, projections start at time 0, not at time 1]. The guard would devote 3 days per year (= 3 counts per survey occasion because each year represents one survey occasion) to day-long bear counts, so Counts/plot/survey is three. Under Rounding, random counts should be modelled as whole numbers because integer, not decimal, numbers of bears are counted each day. A significance of level of 0.05 for trend detection is chosen for this analysis as a compromise between using an overly liberal value (e.g., 0.1) and an overly stringent one (e.g., 0.001). These inputs have been included in the sample file "BEARS".
With Replications set at 500 (to ensure repeatable results) it is apparent that conducting three counts each year is inadequate to detect 3% increases or decreases in the bear population should they occur. With three counts per year, the probability of detecting a 3% population increase, when it was actually occurring in the sample counts, was just 0.65. The probability of detecting a 3% decline when it was actually occurring in the counts was even lower (0.42).
How can this monitoring program be improved to achieve the probability desired (> 0.90) for detecting 3% or greater annual changes in the bear population? Because the bears can only be counted from one, particular observation point, the number of "plots" is unfortunately limited to one. With no great increase in monitoring effort, however, more days can be devoted to counting bears each year. Increasing the Counts/plot/year by just two (from three to five), while leaving all other elements of the monitoring program unchanged, nearly achieves the monitoring goals for population increases (power estimate = 0.87), but still fails to detect 3% population declines with sufficient confidence (0.61). Finally, increasing the monitoring intensity to 10 counts per year yields a power estimate nearly adequate for detecting 3% or greater declines (> 0.88) and more than adequate for detecting 3% of greater population increases (> 0.99).
MONITOR version 7.0 (web version) 07 June 2001 based on version 6.2 15 April 1995 copyright 1995 James P. GibbsThis version (web version) was converted by Jim Hines, USGS-Patuxent Wildlife Research Center. Click
to send comments/suggestions.
or snail mail to:
Jim Hines USGS-Patuxent Wildlife Research Center 12100 Beech Forest Rd. # 206A Laurel, Md 20708-4030
This software was designed and programmed by James P. Gibbs. It benefited greatly from the technical comments of Clinton T. Moore of the U.S. Fish and Wildlife Service. The software was programmed in Turbo Pascal 7.0, with numerical algorithms taken from: Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, 1990, Numerical recipes in Pascal, Cambridge University Press, New York. If you have any comments or suggestions for improving the software, send them to Gibbs at: http://www.esf.edu/efb/gibbs/
Department of Forest and Environmental Biology SUNY-ESF 404 Illick Hall Syracuse, NY 13210 USA