Population Viability Analysis
305
jected, then genetic variation should be considered. I would expect the population to change as selection takes place. Even if no selection is operating,
genetic drift is expected for small population sizes. However, the importance
of genetic effects is still an issue in question; see Joopouborg and Van Groenendael (1996). Lande (1988, 1995) suggested that demographic variation or
genetic effects can be lethal to a small population.
• For long-term persistence, we must be willing to assume that the system
will not change, that is, that the levels of stochasticity will not change through
time, the species will not evolve through selection, and the supporting capacity of the environment (the species habitat) remains static. We must assume
that natural processes such as long-term succession and climatic change do not
affect persistence and that human activity will cease (given that humans have
been responsible for most recent extinctions). To believe the results, we have to
assume that the model and all its parameters stay the same across inordinately
long time periods.
After examining this list, I am sure you will agree with Boyce (1992:482):
“Collecting sufficient data to derive reliable estimates for all the parameters
necessary to determine MVP is simply not practical in most cases.” Of course,
limitations of the data seldom slow down modelers of population dynamics.
Furthermore, managers are forced to make decisions, so modelers attempt to
make reasonable guesses. In the next three sections, I explore statistical methods to obtain the necessary data to develop a reasonable PVA model and suggest modeling techniques to incorporate empirical data into the persistence
model.
Direct Estimation of Variance Components
The implication of the list of requirements in the previous section is that population parameters or their distributions are known without error; that is,
exact parameter values are observed, not estimated. In reality, we may be fortunate and have a series of survival or reproduction estimates across time that
provides information about the temporal variation of the process. However,
the variance of this series is not the proper estimate of the temporal variation
of the process. This is because each of our estimates includes sampling variation; that is we have only an estimate of the true parameter, not its exact value.
To properly estimate the temporal variation of the series, the sampling variance
of the estimates must be removed. In this section, I demonstrate a procedure
305
jected, then genetic variation should be considered. I would expect the population to change as selection takes place. Even if no selection is operating,
genetic drift is expected for small population sizes. However, the importance
of genetic effects is still an issue in question; see Joopouborg and Van Groenendael (1996). Lande (1988, 1995) suggested that demographic variation or
genetic effects can be lethal to a small population.
• For long-term persistence, we must be willing to assume that the system
will not change, that is, that the levels of stochasticity will not change through
time, the species will not evolve through selection, and the supporting capacity of the environment (the species habitat) remains static. We must assume
that natural processes such as long-term succession and climatic change do not
affect persistence and that human activity will cease (given that humans have
been responsible for most recent extinctions). To believe the results, we have to
assume that the model and all its parameters stay the same across inordinately
long time periods.
After examining this list, I am sure you will agree with Boyce (1992:482):
“Collecting sufficient data to derive reliable estimates for all the parameters
necessary to determine MVP is simply not practical in most cases.” Of course,
limitations of the data seldom slow down modelers of population dynamics.
Furthermore, managers are forced to make decisions, so modelers attempt to
make reasonable guesses. In the next three sections, I explore statistical methods to obtain the necessary data to develop a reasonable PVA model and suggest modeling techniques to incorporate empirical data into the persistence
model.
Direct Estimation of Variance Components
The implication of the list of requirements in the previous section is that population parameters or their distributions are known without error; that is,
exact parameter values are observed, not estimated. In reality, we may be fortunate and have a series of survival or reproduction estimates across time that
provides information about the temporal variation of the process. However,
the variance of this series is not the proper estimate of the temporal variation
of the process. This is because each of our estimates includes sampling variation; that is we have only an estimate of the true parameter, not its exact value.
To properly estimate the temporal variation of the series, the sampling variance
of the estimates must be removed. In this section, I demonstrate a procedure