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Yiannis G. Matsinos, Wilfried F. Wolff, and Donald L. DeAngelis
successfully decreased almost simultaneously for all three nestlings. Therefore, if
one nestling fledges successfully, in general at least one of its siblings is successful as well.
For contest competition we still had threshold effects. However, the oldest
nestling (i.e., the better competitor) may still fledge successfully when its younger
siblings had little chance to do so. Only when food becomes very scarce did the
older nestling have a decreased probability of fledging.
Conclusions
There are a number of advantages that an individual-based approach can offer
when applied to assessing the prospects for a rare or endangered species.
1. It allows a characterization of the spatial and temporal detail of the landscape
and resources.
2. It can incorporate individual differences among members of a population. In
the above example, these were merely the incidental differences of pursuing
different daily patterns. But more generally, genetic information specific to
each organism can be included and the genetic structure of the population
followed through time (e.g., Heuch 1978; Rice 1984).
3. It can use detailed behavioral and physiological information for the species.
4. It allows one to conduct detailed model management “experiments.” As an
illustration outside the scope of the example above, one could use such models
to plan animal releases from a captive breeding program to determine if there
are scenarios that provide consistently higher population survival and genetic
diversity.
5. It provides output in the form of probability distributions, and thus predictions
can be interpreted in a risk-analysis format.
In summary, the individual-based model allows one to calculate population
variability that may come from a number of sources that are difficult to incorporate in state variable models. These sources include landscape complexity, details
of internal states of organisms, and behavioral rules such as the competition
between sibling nestlings.
However, the amount of detail that can be incorporated in individual-based
models leads critics to ask some questions: How much detail is it reasonable to
include? Are there any guidelines to say at what stage the model is finished in
some sense? How can one be sure that the mechanisms included at the level of
individuals produce correct results at the population level?
These questions, and others that can be raised, do not have absolute answers.
Concerning the first question, many modelers using the individual-based approach (e.g., Fahrig 1991; Scheffer et al. 1994) have advocated using very simple
models that have as few parameters and assumptions as possible. Such models can
be used as research models, to try to determine whether the details of individual
differences can, in principle, make a difference in population dynamics. The case
Yiannis G. Matsinos, Wilfried F. Wolff, and Donald L. DeAngelis
successfully decreased almost simultaneously for all three nestlings. Therefore, if
one nestling fledges successfully, in general at least one of its siblings is successful as well.
For contest competition we still had threshold effects. However, the oldest
nestling (i.e., the better competitor) may still fledge successfully when its younger
siblings had little chance to do so. Only when food becomes very scarce did the
older nestling have a decreased probability of fledging.
Conclusions
There are a number of advantages that an individual-based approach can offer
when applied to assessing the prospects for a rare or endangered species.
1. It allows a characterization of the spatial and temporal detail of the landscape
and resources.
2. It can incorporate individual differences among members of a population. In
the above example, these were merely the incidental differences of pursuing
different daily patterns. But more generally, genetic information specific to
each organism can be included and the genetic structure of the population
followed through time (e.g., Heuch 1978; Rice 1984).
3. It can use detailed behavioral and physiological information for the species.
4. It allows one to conduct detailed model management “experiments.” As an
illustration outside the scope of the example above, one could use such models
to plan animal releases from a captive breeding program to determine if there
are scenarios that provide consistently higher population survival and genetic
diversity.
5. It provides output in the form of probability distributions, and thus predictions
can be interpreted in a risk-analysis format.
In summary, the individual-based model allows one to calculate population
variability that may come from a number of sources that are difficult to incorporate in state variable models. These sources include landscape complexity, details
of internal states of organisms, and behavioral rules such as the competition
between sibling nestlings.
However, the amount of detail that can be incorporated in individual-based
models leads critics to ask some questions: How much detail is it reasonable to
include? Are there any guidelines to say at what stage the model is finished in
some sense? How can one be sure that the mechanisms included at the level of
individuals produce correct results at the population level?
These questions, and others that can be raised, do not have absolute answers.
Concerning the first question, many modelers using the individual-based approach (e.g., Fahrig 1991; Scheffer et al. 1994) have advocated using very simple
models that have as few parameters and assumptions as possible. Such models can
be used as research models, to try to determine whether the details of individual
differences can, in principle, make a difference in population dynamics. The case