between the kind of information models can provide and what managers of natural
areas need. The perception of models differs between disciplines and between
science and practice. Models range from semantic descriptions of assumed interrelations to computer based mathematical models; the term ‘model’ is often used
for computer based models only.
During recent decades, model-based prediction of biological responses to climate change has become a very active field of research. In the field of climate
change modelling, models primarily enable understanding of global climate cycles.
The development of models could demonstrate the impact of human activities on
the earth climate system. Models serve to structure knowledge, to formulate
hypotheses and to illustrate future developments. In the field of nature conservation
management, the development of alternative scenarios is of special interest.
Different management options may lead to different futures. Ex-ante assessment
of the outcome of specific management measures in conjunction with different
climate change impact scenarios may support management – but may also lead to
deep frustration because of the high degree of uncertainty about future developments. Especially the necessary combination of different model types, such as
climate models, vegetation models, and hydrological models, including all their
underlying assumptions and uncertainties, renders it difficult for managers to
identify useful options for decision making.
Even though there have been extensive studies to model the impacts of climate
change on biodiversity, the results are still sobering. In 2002 the IPCC found that
“most models of ecosystem changes are not well suited to projecting changes in
regional biodiversity” (IPCC 2002: 15). More and more authors have picked up this
critical attitude towards the modelling of climate effects (e.g. Biesbroek et al. 2009;
Opdam et al. 2009; Pyke et al. 2007). After a far-reaching survey of literature on
conservation issues, Heller and Zavaleta (2009) concluded that “many articles
based on concrete modeling work or empirical studies of species responses to
climate change tended either to not elaborate their results to management directives, or to present recommendations in vague terms such as, ‘restoration should be
considered’” (Heller and Zavaleta 2009: 17). Therefore, Heller and Zavaleta
highlighted the need to pay more attention of transferring modelling results into
the decision context of management issues (Heller and Zavaleta 2009). This
problem is not only limited to conservation management but to adaptation to
climate change in general (Millner 2012).
In this chapter we are describing concrete model approaches used to support
decision-making within the HABIT-CHANGE project context. Facing climate
change, climate impact models are currently seen as a big support for the development of alternative management scenarios. Handling uncertainties and understanding the ‘model cascade’ will help in judging where models will supply useful
information. A short overview of model approaches and their assumptions is
presented as a basis for adaptive management and their benefits as well as limitations are discussed.
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K. Vohland et al.
areas need. The perception of models differs between disciplines and between
science and practice. Models range from semantic descriptions of assumed interrelations to computer based mathematical models; the term ‘model’ is often used
for computer based models only.
During recent decades, model-based prediction of biological responses to climate change has become a very active field of research. In the field of climate
change modelling, models primarily enable understanding of global climate cycles.
The development of models could demonstrate the impact of human activities on
the earth climate system. Models serve to structure knowledge, to formulate
hypotheses and to illustrate future developments. In the field of nature conservation
management, the development of alternative scenarios is of special interest.
Different management options may lead to different futures. Ex-ante assessment
of the outcome of specific management measures in conjunction with different
climate change impact scenarios may support management – but may also lead to
deep frustration because of the high degree of uncertainty about future developments. Especially the necessary combination of different model types, such as
climate models, vegetation models, and hydrological models, including all their
underlying assumptions and uncertainties, renders it difficult for managers to
identify useful options for decision making.
Even though there have been extensive studies to model the impacts of climate
change on biodiversity, the results are still sobering. In 2002 the IPCC found that
“most models of ecosystem changes are not well suited to projecting changes in
regional biodiversity” (IPCC 2002: 15). More and more authors have picked up this
critical attitude towards the modelling of climate effects (e.g. Biesbroek et al. 2009;
Opdam et al. 2009; Pyke et al. 2007). After a far-reaching survey of literature on
conservation issues, Heller and Zavaleta (2009) concluded that “many articles
based on concrete modeling work or empirical studies of species responses to
climate change tended either to not elaborate their results to management directives, or to present recommendations in vague terms such as, ‘restoration should be
considered’” (Heller and Zavaleta 2009: 17). Therefore, Heller and Zavaleta
highlighted the need to pay more attention of transferring modelling results into
the decision context of management issues (Heller and Zavaleta 2009). This
problem is not only limited to conservation management but to adaptation to
climate change in general (Millner 2012).
In this chapter we are describing concrete model approaches used to support
decision-making within the HABIT-CHANGE project context. Facing climate
change, climate impact models are currently seen as a big support for the development of alternative management scenarios. Handling uncertainties and understanding the ‘model cascade’ will help in judging where models will supply useful
information. A short overview of model approaches and their assumptions is
presented as a basis for adaptive management and their benefits as well as limitations are discussed.
64
K. Vohland et al.