a crucial role in enabling communication, collaboration, and consensus
building. We believe that major improvements can be made quickly in this
area if desired. To elevate the role of ecological modeling in the naturalresource-management process, it is critical to teach those involved how
ecological models operate and why they are useful.
Other important research and development investments are necessary if
modeling is to play an integral role in addressing the large-scale, interconnected environmental problems facing humanity, such as global warming,
resource depletion, and biodiversity loss. Addressing these complex problems will require new tools and infrastructure to enable collaborative
modeling across a large, distributed, interdisciplinary group of experts.
Many important modeling applications are intractable without toolkits that
support graphical, modular, and hierarchical model development and integrated visualization of model output. Virtual reality hardware and software
may play an important role in the construction of hypothetical worlds for
scenario analysis. High-performance computing can make important contributions in the simulation of complex environmental systems, but it will
rarely be used unless the modeling infrastructure seamlessly and transparently integrates the investigators into the high-performance domain. Model
sensitivity analysis can determine the most pressing data collection needs.
Further development of data standards and metadata is crucial. Improved
financial support for the development and application of ecological models
and additional funding for basic research leading to new modeling approaches is needed, as well.
The process of environmental modeling should be framed not as an
oracle prescribing a specific solution to a problem but as a learning experience for all involved. This education process develops an understanding of the dynamics of the managed system and its most probable responses
to management interventions. It may also facilitate the exploration of policy
options, the synthesis of disparate knowledge sources, consensus building,
conflict resolution, and scenario generation and evaluation. Enabling this
educational process will require significant investments. Top priority should
be given to investments in targeted educational initiatives to inform managers and stakeholders of the potential and limitations of the modeling
process and to train scientists and modelers in facilitating the educational
process. Opening the educational process to a wider range of participants
and enabling its collaborative aspects will require additional investments in
the modeling tools and toolkits, improved databases and standards, and
modeling methods described in other chapters in this volume.
References
Akçakaya, H.R. and M.G. Raphael. 1998. Assessing human impact despite uncertainty: Viability of the northern spotted owl metapopulation in the northwestern
USA. Biodiversity and Conservation 7:875–894.
282
Thomas P. Maxwell et al.
building. We believe that major improvements can be made quickly in this
area if desired. To elevate the role of ecological modeling in the naturalresource-management process, it is critical to teach those involved how
ecological models operate and why they are useful.
Other important research and development investments are necessary if
modeling is to play an integral role in addressing the large-scale, interconnected environmental problems facing humanity, such as global warming,
resource depletion, and biodiversity loss. Addressing these complex problems will require new tools and infrastructure to enable collaborative
modeling across a large, distributed, interdisciplinary group of experts.
Many important modeling applications are intractable without toolkits that
support graphical, modular, and hierarchical model development and integrated visualization of model output. Virtual reality hardware and software
may play an important role in the construction of hypothetical worlds for
scenario analysis. High-performance computing can make important contributions in the simulation of complex environmental systems, but it will
rarely be used unless the modeling infrastructure seamlessly and transparently integrates the investigators into the high-performance domain. Model
sensitivity analysis can determine the most pressing data collection needs.
Further development of data standards and metadata is crucial. Improved
financial support for the development and application of ecological models
and additional funding for basic research leading to new modeling approaches is needed, as well.
The process of environmental modeling should be framed not as an
oracle prescribing a specific solution to a problem but as a learning experience for all involved. This education process develops an understanding of the dynamics of the managed system and its most probable responses
to management interventions. It may also facilitate the exploration of policy
options, the synthesis of disparate knowledge sources, consensus building,
conflict resolution, and scenario generation and evaluation. Enabling this
educational process will require significant investments. Top priority should
be given to investments in targeted educational initiatives to inform managers and stakeholders of the potential and limitations of the modeling
process and to train scientists and modelers in facilitating the educational
process. Opening the educational process to a wider range of participants
and enabling its collaborative aspects will require additional investments in
the modeling tools and toolkits, improved databases and standards, and
modeling methods described in other chapters in this volume.
References
Akçakaya, H.R. and M.G. Raphael. 1998. Assessing human impact despite uncertainty: Viability of the northern spotted owl metapopulation in the northwestern
USA. Biodiversity and Conservation 7:875–894.
282
Thomas P. Maxwell et al.