12. Individual-Based Models and Assessment of Population Variability
197
study described above (Wolff 1994), however, attempts to gain insight into a
particular environmental situation. Thus, it uses a level of detail appropriate to
determining how certain types of behaviors and food availabilities for Wood
Storks might affect population dynamics. If one were going to use Wolff ’s (1994)
model to make specific management decisions, however, even greater detail, such
as a more specific landscape, might be needed. Thus, the second question above is
answered; the problem being addressed will determine the degree of detail
needed.
The third question is more difficult. Of course, we need to improve our knowledge in identifying critical aspects of various behavioral and physiological traits
before we can include them explicitly in models. Our ability to include the crucial
mechanisms at the individual level depends on the understanding that physiological and behavioral ecologists and other life scientists have gained concerning the
species in question. But the individual-based simulation approach at least provides a framework for inclusion of details at the individual level.
The great potential of individual-based modeling in no way obviates the need
for state variable models. Because state variable models require far less information and are far easier to analyze, these models will continue to be the standard
approach for many years.
Acknowledgments. This chapter was written with assistance from the National
Park Service, U.S. Department of the Interior (Cooperative Agreement
CA-5460-0-9001). The statements, findings, conclusions, recommendations, and
other data in this chapter are solely those of the authors and do not necessarily
reflect the views of the U.S. Department of the Interior, National Park Service.
Literature Cited
Cain ML (1991) When do treatment differences in movement behaviors produce observable differences in long-term displacements? Ecology 72:2137–2142
Caswell H (1989) Matrix population models: construction, analysis, and interpretation.
Sinauer Associates, Sunderland, MA
Fahrig L (1991) Simulation methods for developing general landscape level-hypotheses for
single-species dynamics. In: Turner MG, Gardner RH (eds) Quantitative methods in
landscape ecology. Ecological Studies 82. Springer Verlag, Berlin, pp 417– 442
Fleming DM, Wolff WF, DeAngelis DL (1994) The importance of landscape heterogeneity
to Wood Storks in the Florida Everglades. Environmental Management 18:743–758
Fujioka M (1985) Food delivery and sibling competition in experimentally even-aged
broods of the Cattle Egret. Behavioral Ecology and Sociobiology 17:67–74
Heuch I (1978) Maintenance of butterfly populations with all female broods under recurrent extinction and recolonization. Journal of Theoretical Biology 75:115–122
Hyman JB, McAninch JB, DeAngelis DL (1991) An individual-based simulation model of
herbivory in a heterogeneous landscape. In: Turner MG, Gardner RH (eds) Quantitative
methods in landscape ecology. Ecological Studies 82. Springer Verlag, Berlin, pp 443–
475
May RM, MacArthur RH (1972) Niche overlap as a function of environmental variability.
Proceedings of the National Academy of Sciences USA 69:1109–1113
197
study described above (Wolff 1994), however, attempts to gain insight into a
particular environmental situation. Thus, it uses a level of detail appropriate to
determining how certain types of behaviors and food availabilities for Wood
Storks might affect population dynamics. If one were going to use Wolff ’s (1994)
model to make specific management decisions, however, even greater detail, such
as a more specific landscape, might be needed. Thus, the second question above is
answered; the problem being addressed will determine the degree of detail
needed.
The third question is more difficult. Of course, we need to improve our knowledge in identifying critical aspects of various behavioral and physiological traits
before we can include them explicitly in models. Our ability to include the crucial
mechanisms at the individual level depends on the understanding that physiological and behavioral ecologists and other life scientists have gained concerning the
species in question. But the individual-based simulation approach at least provides a framework for inclusion of details at the individual level.
The great potential of individual-based modeling in no way obviates the need
for state variable models. Because state variable models require far less information and are far easier to analyze, these models will continue to be the standard
approach for many years.
Acknowledgments. This chapter was written with assistance from the National
Park Service, U.S. Department of the Interior (Cooperative Agreement
CA-5460-0-9001). The statements, findings, conclusions, recommendations, and
other data in this chapter are solely those of the authors and do not necessarily
reflect the views of the U.S. Department of the Interior, National Park Service.
Literature Cited
Cain ML (1991) When do treatment differences in movement behaviors produce observable differences in long-term displacements? Ecology 72:2137–2142
Caswell H (1989) Matrix population models: construction, analysis, and interpretation.
Sinauer Associates, Sunderland, MA
Fahrig L (1991) Simulation methods for developing general landscape level-hypotheses for
single-species dynamics. In: Turner MG, Gardner RH (eds) Quantitative methods in
landscape ecology. Ecological Studies 82. Springer Verlag, Berlin, pp 417– 442
Fleming DM, Wolff WF, DeAngelis DL (1994) The importance of landscape heterogeneity
to Wood Storks in the Florida Everglades. Environmental Management 18:743–758
Fujioka M (1985) Food delivery and sibling competition in experimentally even-aged
broods of the Cattle Egret. Behavioral Ecology and Sociobiology 17:67–74
Heuch I (1978) Maintenance of butterfly populations with all female broods under recurrent extinction and recolonization. Journal of Theoretical Biology 75:115–122
Hyman JB, McAninch JB, DeAngelis DL (1991) An individual-based simulation model of
herbivory in a heterogeneous landscape. In: Turner MG, Gardner RH (eds) Quantitative
methods in landscape ecology. Ecological Studies 82. Springer Verlag, Berlin, pp 443–
475
May RM, MacArthur RH (1972) Niche overlap as a function of environmental variability.
Proceedings of the National Academy of Sciences USA 69:1109–1113