Merriam (1985)] and the spatially structured RAMAS Metapop (Akçakaya
1994).
Another integration involved habitat models and demographic models.
Habitat models aim to predict a species’ response to its environment or its
habitat requirements (Verner et al. 1986), whereas demographic models aim
to predict the changes in its abundance or its risks of decline and extinction (Burgman et al. 1993). Models that integrate habitat and demographic
models have used different approaches, including individual-based models
(Lamberson et al. 1994), grid-based models (Price and Gilpin 1996), and
habitat-based metapopulation models (Akçakaya et al. 1995).
A third example of integration aims to link landscape models with
metapopulation models. Landscape models predict changes in landscape
and land use, based on modeling the dynamics of vegetation, natural
processes [such as disturbances (fire, floods, wind, etc.) and succession],
and human impacts (such as timber harvest and pollution). A new approach
aims to integrate the landscape model LANDIS with the habitat-based
metapopulation model RAMAS GIS. LANDIS (Mladenoff et al. 1996) predicts changes in forest stand structure, including species composition,
dominant tree species, and age distribution. The RAMAS GIS (Akçakaya
1998) simulates the dynamics of species that inhabit distinct habitat patches.
The integrated model will allow risk assessments for species and populations based on expected habitat changes. It will simulate the dynamics of
the metapopulation in a landscape in which the underlying habitat variables
(and thus the number, size, and spatial structure of the habitat patches) are
changing (Akçakaya 2001). Incorporating landscape dynamics in the spatial
structure of metapopulation models will allow evaluating effects of landscape management options on the viability of key species.
We believe the trend of integrating existing models will persist in the near
future and that developing models through the integration of existing types
will continue to be more efficient than creating entirely new models.
Existing models and approaches have several potential links. One of
these possibilities involves linking simple (scalar) population models to
allometric relationships. The resulting models can be used in screening
assessments with minimum or no field data. An important research issue
for this development is testing whether the level of conservatism (precaution) of this approach is comparable to the level required in a screening
test.
Another type of integration may involve linking fate-and-transport
models to ecological models. Although this approach has been used in
specific cases, a general modeling platform is needed that links physical/
chemical models (e.g., hydrological models), dose–response models, and
population or metapopulation models (to assess ecological affects).
General models are also needed to integrate food-web and metapopulation models. Such an integration would allow modeling trophic interactions
in a spatially structured habitat, with different metapopulation structures
13. Science and Management Investments
255
1994).
Another integration involved habitat models and demographic models.
Habitat models aim to predict a species’ response to its environment or its
habitat requirements (Verner et al. 1986), whereas demographic models aim
to predict the changes in its abundance or its risks of decline and extinction (Burgman et al. 1993). Models that integrate habitat and demographic
models have used different approaches, including individual-based models
(Lamberson et al. 1994), grid-based models (Price and Gilpin 1996), and
habitat-based metapopulation models (Akçakaya et al. 1995).
A third example of integration aims to link landscape models with
metapopulation models. Landscape models predict changes in landscape
and land use, based on modeling the dynamics of vegetation, natural
processes [such as disturbances (fire, floods, wind, etc.) and succession],
and human impacts (such as timber harvest and pollution). A new approach
aims to integrate the landscape model LANDIS with the habitat-based
metapopulation model RAMAS GIS. LANDIS (Mladenoff et al. 1996) predicts changes in forest stand structure, including species composition,
dominant tree species, and age distribution. The RAMAS GIS (Akçakaya
1998) simulates the dynamics of species that inhabit distinct habitat patches.
The integrated model will allow risk assessments for species and populations based on expected habitat changes. It will simulate the dynamics of
the metapopulation in a landscape in which the underlying habitat variables
(and thus the number, size, and spatial structure of the habitat patches) are
changing (Akçakaya 2001). Incorporating landscape dynamics in the spatial
structure of metapopulation models will allow evaluating effects of landscape management options on the viability of key species.
We believe the trend of integrating existing models will persist in the near
future and that developing models through the integration of existing types
will continue to be more efficient than creating entirely new models.
Existing models and approaches have several potential links. One of
these possibilities involves linking simple (scalar) population models to
allometric relationships. The resulting models can be used in screening
assessments with minimum or no field data. An important research issue
for this development is testing whether the level of conservatism (precaution) of this approach is comparable to the level required in a screening
test.
Another type of integration may involve linking fate-and-transport
models to ecological models. Although this approach has been used in
specific cases, a general modeling platform is needed that links physical/
chemical models (e.g., hydrological models), dose–response models, and
population or metapopulation models (to assess ecological affects).
General models are also needed to integrate food-web and metapopulation models. Such an integration would allow modeling trophic interactions
in a spatially structured habitat, with different metapopulation structures
13. Science and Management Investments
255