Abstract
Model results can serve as a basis for adaptation in conservation management. They can help understanding the impact of climate change, and support the formulation of management measures. However, model results rely strongly on the quality and the resolution of the input data; they contain significant uncertainties and need to be interpreted in the context of the modelling assumptions. The perception of models and their results differs between disciplines as well as between science and practice. Part of this gap derives from the long ‘model cascade’ used for the assessment of climate related impacts on biodiversity. For this ‘model cascade’ model results from Global Climate Models are often used to drive Regional Downscaled Climate Models and are transferred to hydrological models or distribution models of plants and animals. In fact, most assessments of potential impacts of climate change on biodiversity rely on habitat modelling of plants and animals. But, only few decision makers are trained to analyse the different outcomes of climate impact modelling. If modelling is integrated into conservation management it must be based on an evaluation of the need for information in protected areas and an assessment of model use in the management process, so as to guarantee maximum usability.
You have full access to this open access chapter, Download chapter PDF
Similar content being viewed by others
Keywords
- Conservation Management
- Climate Change Impact
- Model Cascade
- Climatic Water Balance
- Ecosystem Base Adaptation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
1 Introduction
Even if all political targets are met, climate change is expected to increase temperatures within the next decades globally and to change climate regimes locally, resulting in shifting water regimes and extreme weather events. In Central Europe temperatures are expected to increase and the climatic water balance, especially in summer, to decrease (see Sect. 3.3.1). Consequently, adaptation to climate change will become a necessity. Thus, it is crucial to overcome the notion that accepting the need for adaptation implies admitting the failure of climate policy. In fact, there are some instruments which provide high synergies between adaptation and mitigation, for example, in the area of forest conservation or ecosystem based adaptation as defined by the Convention on Biological Diversity (CBD). In short, adaptation to climate change is becoming increasingly important for nature conservation management. But adaptation to which specific climate change impacts?
One answer is expected to come from the area of modelling. Modelling is increasingly important for understanding and projecting climate change impacts. Hence, model results can serve as a basis for adaptation. However, there is a gap 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.
2 The Long Model Cascade
A major problem in communicating model results is the long ‘model cascade’ with partly hidden assumptions about future development. Results from one model are fed into the next one, which itself relies on assumptions or hypotheses. This results in a chain or cascade of models that starts with projections of the global greenhouse gas emissions, moves on to the impact of the global climate system and on to regional or local impacts on flora and fauna (Fig. 5.1). The relevance and reliability of the final outcome might, thus, be difficult to judge with regard to modelling uncertainties. To have an impression of the usefulness of the specific contribution of models to adapt the management of protected areas with regard to climate change, we follow the course of this model cascade and make various underlying assumptions and models transparent.
2.1 Climate Models
Some prior steps are necessary for regional climate change impact projections. A big challenge for earth system analysis was to understand the interplay between human activities, greenhouse gases, and the climate system of the earth (IPCC 2007). The higher the concentration of greenhouse gases, the higher the energy of the atmosphere is. This results in higher global mean temperatures and subsequently increased global evaporation. Here, the model cascade starts (Fig. 5.1). Each model type builds at least partly on physically based assumptions, hypotheses and input data from prior cascade results (Table 5.1). In the Special Emission Scenarios (SRES; Nakićenović and Swart 2000) projections about the future development of earth’s population with regard to population numbers, economic development, energy sources etc. were developed. According to these scenarios (e.g. B1, A2 etc.) concentrations of greenhouse gases are projected. This provides input for global circulation or climate models (GCMs). These global models normally work on a global grid (e.g. at the resolution of 50 × 50 km2).
For a park manager, a 50 × 50 km2 resolution is far from being useful for management adaptation. For example, in mountainous regions with climate variables differing at a small scale a resolution of several hundred metres is required to be helpful. Currently, there are two alternative model approaches to derive more regional and local resolutions. It is possible to downscale the results from global models dynamically to a resolution of 10 × 10 km (0,08°), an option chosen by the Max Planck Institute for Meteorology in Hamburg with the climate model REMO (Jacob and Podzun 1997). Alternatively, statistical regional climate models scale down trends from global climate models based on data from local weather stations and only include the global temperature trend as a parameter. This is how regional climate models such as STAR or WettReg work (Orlowsky et al. 2008). While the latter reflect the local conditions more precisely, the former has the advantage of also including new climatic conditions and long-term developments.
However, most park managers focus on living organisms, mainly plants, where temperature and precipitation alone are not sufficient parameters to identify climate change impacts. A more useful integrated indicator is the climatic water balance (CWB). The CWB expresses the difference between precipitation and potential evaporation. In the framework of the HABIT-CHANGE project, climate change projections have been provided for all the investigation areas (Stagl et al. submitted). In Fig. 5.2 the results are displayed via boxplots for two selected investigation areas for three time periods: (a) For the Natural Park Bucegi (Romania) the climate models indicate, despite the existing inter-model uncertainties, a clear trend towards a reduction of potential water availability for the months March to September, but particularly in summer. (b) For the area of the Vessertal – Thuringian Forest Biosphere Reserve (Germany) the model results spread is more pronounced, as indicated by longer boxes and higher variance. More than 50 % of the RCM-GCMs results indicate a trend for a CWB decrease in summer and a slight increase of the CWB in the winter months. The figures do not only show the directions of changes which should be considered but also indicate the uncertainty assigned to the choice of a model.
2.2 Hydrological Models
The CWB is an indicator which is directly calculated from climatic data; it does not consider local physio-geographical parameters like soil conditions or the influence of the vegetation. To consider supplementary parameters, hydrological process-based models support understanding of the behaviour of hydrologic systems as a response to climate change. Such models include evapotranspiration, surface runoff, subsurface and interflow, and river channel flow. For HABIT-CHANGE the hydro-ecological model SWIM (Krysanova et al. 2000; Hattermann et al. 2008) was applied. SWIM is a hydrological model at the scale of a catchment area (for details refer to Chap. 3). It integrates relevant hydrological processes to investigate the impacts of climate changes, such as water percolation, groundwater recharge, plant water uptake, soil evaporation, and river routing. SWIM allows the development of scenarios, for example, with a focus on the habitat type, and it provides information relevant to vegetation dynamics, i.e. the available soil water (Holsten et al. 2009). However, model results rely strongly on the quality and the resolution of the input data (like observed runoff, soil and land use data) for the specific investigation area.
2.3 Modelling Distribution and Occurrence of Plants and Animals
While the projection of potential impacts of climate change on water resources is very helpful and can be linked to specific management options, many protected areas aim to conserve specific plants or animals. Most modelling work to assess the potential impact of climate change on biodiversity relies on habitat modelling of plants and animals, and provides risk assessments for specific species or habitats (Normand et al. 2007; Hickler et al. 2012). Most of these approaches use statistical approaches, such as Bioclimatic Envelopes (BEM), and do not consider functional relationships, as, for example, with dynamic vegetation models (DVMs). Although DVMs are able to analyse changing patterns of competition their disadvantage is that they are resolved either at the basis of plant functional types or selected (tree) species (Kühn et al. 2009; Bellard et al. 2012). An additional uncertainty arises from other model shortcomings, e.g. the adaptive capacity of single species or species associations possibly being underestimated (Thuiller et al. 2008; Fordham et al. 2012).
When focusing on nature conservation issues the impact of climate change alone would not be so extensive if habitat destruction and fragmentation were not so widely advanced permitting species to adapt their distribution area (Opdam and Wascher 2004). The inclusion of land use parameters improves model results significantly.
So far, the major outputs of modelling the impacts on plants and animals (fungi are not yet a focus) are limited to projected changes in probabilities of occurrence. Furthermore, plants, animals, and fungi represent only a specific hierarchy or scale of biodiversity and nature conservation goals. Climate change affects biodiversity on different scales from changing mutation rates of genes and phenology to energy fluxes and ecosystem services (Vohland 2008; Bellard et al. 2012).
3 Reflection About the Role of Modelling in Conservation Management
Models are by definition simplified descriptions of reality. They have to neglect information and processes that are considered irrelevant for the modelling purpose. It is critical to realise “that model output is not the same as empirical data, and that modelled projections of the future contain significant uncertainties” (Hansen and Hoffmann 2011). These uncertainties derive from lacking or unsuitable data, measurement errors, or systematic mistakes in the data acquisition (Price and Neville 2003; Willis and Birks 2006). But even the simplified structure of models may result in uncertainties of results that have to be illustrated by the modellers. Modelling the complexity of biological systems and their interaction with management is a challenging task (McKenzie et al. 2004). Natural systems are characterised by system inherent variation. This makes it hard to identify signals of relevant effects from background noise of usual fluctuations (Hakonson 2003). An additional source of uncertainty derives from subjective interpretations. In an impressive selection of models developed and applied for environmental management, Pilkey and Pilkey-Jarvis (2007) prove how the selection of input data and interpretation of thresholds, system behaviour, as well as modelling results corrupts their usability in management. In this context transparency of modelling work is a prerequisite for the use of results in the decision-making context.
In consequence, modelling results can include a wide range of sources of uncertainty. This is especially true when results are built on a cascade of coupled models in a ‘model chain’ since all models add their own uncertainty to the overall results.
An important approach to handling uncertainty in model output is the quantification of uncertainty levels in results (see Ayala 1996; Bugmann 2003; Oreskes et al. 1994; Sarewitz and Pielke 1999). Yet, practical work with modelling results shows that quantification of uncertainty is no easy task. This is even more so as the validation of modelling results for complex systems, particularly with regard to predictions about long term future developments, involves major theoretical problems (Harris et al. 2003; Oreskes et al. 1994; Oreskes 2003; Sarewitz and Pielke 1999). Even if models have successfully simulated past or present changes this does not guarantee that they are also able to predict future changes, e.g. if the earth climate system and its biodiversity are pushed into unprecedented conditions in the context of climate change (Hansen and Hoffmann 2011).
As a first consequence, a cautious use of modelling results for decision-making is recommended (Millner 2012). Uncertainty must be considered when using modelling results for management decisions.
The gap between modelling and management issues is attributed to the different objectives of (natural) sciences and decision-oriented management. According to Opdam et al., the analytical and reductionist approach of scientific work is able to provide clues on driving forces and key elements, but lacks the ability to provide solutions and foster decisions if they are not especially tailored to do so (Koomen et al. 2012; Opdam et al. 2009). Further problems arise from the usual procedures of modelling and the integration of stakeholders and decision-makers. Relevance, legitimacy, and transparency are considered key aspects for the acceptance and implementation of scientific results (Meinke et al. 2006). Before modelling efforts are started, existing knowledge should be examined to determine whether it is sufficient to support decisions and how the existing information can be translated to the relevant decision-making context.
Modelling efforts use a lot of valuable resources for data acquisition, processing, model development, calibration, and validation. Usually these resources can be acquired in relation to research projects, but the limitation of resources must be considered if modelling approaches and procedures are transferred to a permanent task like conservation management. In the field of conservation management resources, whether manpower or budget, are chronically scarce and their use must yield the best possible output. Consequently, it is crucial to move modelling work beyond pure prediction (Dawson et al. 2011) and strengthen its integration in risk assessment, protocols of screening and monitoring, and integrated management in protected areas (Bellard et al. 2012). If modelling is to be integrated in management it must be based on an evaluation of the information need in protected areas. An assessment of its use in the management process must be done in order to guarantee maximum usability. The discussion of modelling goals and expected results will also guarantee the relevance and legitimacy of modelling endeavours. There is no doubt that modelling can provide various benefits for conservation management; however, the objectives of nature conservation need to be considered in modelling design, development, and presentation of results. Modelling for conservation management can be useful to:
-
Structure the discussion about problems at hand and help develop hypotheses on consequences.
-
Identify driving forces and hot spots where action is needed.
-
Provide efficient and effective indicators to identify and monitor changes.
-
Identify critical changes and thresholds to trigger action.
-
Illustrate consequences of management options.
4 Developments for the Future
Understanding the interrelation between management measures and the impact of climate change is an important task for modelling in conservation management. This should allow the testing of hypotheses and the identification of alternatives in management. As a result, modelling that can be effectively used in the management of protected areas should take management options into account and provide possibility to project different future developments with regard to conservation strategies.
Model development needs to be done in cooperation with local management and stakeholders to guarantee transparency of models and their results. One possibility is a formalised co-production of knowledge. Experiences from visualisations of climate change effects have shown that stakeholder involvement should be considered at least (Pond et al. 2010):
-
1.
when goals and objectives of modelling are discussed;
-
2.
when alternatives are identified that need to be considered and
-
3.
when results are assessed and discussed to derive consequences for management.
This general concept could also give guidance for the use of modelling in conservation management.
4.1 The Use of Models for Scenarios
Peterson et al. (2003) have suggested different methods to handle uncertainty in decision-making. To make decisions and plan for uncontrollable situations characterised by high uncertainty, the use of scenarios is often suggested (Peterson et al. 2003). Consequently, models in earth sciences are often used to simulate scenario results (e.g. GCMs). Several authors have also suggested using scenario approaches to handle uncertainty in conservation management and provide robust adaptation strategies (e.g. Julius and West 2008).
Scenarios as such are meant to project potential alternative developments. They should help managers identifying potential future situations and answer the question ‘What do we do if this event, trend, or change happens?’ Hence, scenarios are not meant to represent the most likely or even preferred development (Sträter 1988). They should prepare managers for a multitude of future situations. In most cases rare and unlikely extreme scenarios can help foster preparation for the unexpected better than mainstream scenarios or business-as-usual projections. In the context of scenario work models can be used to project system behaviour in response to different developments (e.g. increase or decrease of annual precipitation). Identifying relevant developments and transferring them to projections is of high importance for the process. A good scenario needs to be “carefully researched, full of relevant detail, oriented towards real-life decisions, and designed (one hopes) to bring forward surprises and unexpected leaps of understanding” (Schwartz 1992: XIIV).
Godet (2000) has suggested five criteria for a good scenario:
-
It needs to be oriented to the problem at hand.
-
It needs to be relevant for the questions of decision-makers.
-
It needs to be coherent in itself.
-
It needs to build on plausible assumptions.
-
It needs to be transparent to the users.
Scenario development is regarded as supporting decision-making in situations with high uncertainty. Nevertheless, scenarios rarely provide sufficient decision support in complex situations. Decision makers are trained neither to analyse the different outcomes of climate impact modelling nor to develop management scenarios under the conditions of climate change. Hence, the adaptation of management needs to be included in a broader assessment framework to be useful in a decision-making context. This framework should comprise methods to compare scenario results as well as methods to evaluate options and prioritise actions. Risk management is suggested as one tool to provide this framework (e.g. Rannow 2011, 2013; Lorenzoni et al. 2005; Willows and Connell 2003). ‘Stress tests’ that help to identify critical changes and thresholds for resilience of ecosystems might be another tool (Brown and Wilby 2012).
5 Conclusion – How to Deal with Models?
Including the results from modelling climate change impacts can be important and provide a useful instrument to highlight possible negative impacts of climate change, especially when analysed in combination with land use change and degradation, as well as assessing the consequences of different management options. However, modelling results can only support the decision-making process in combination with other methods. Models cannot be better than their input data. If the density of climate stations and temporal resolution is very low or if the functional relationships are unknown, models might show up with numbers but they cannot be interpreted properly. At the local scale the identification of key parameters is complicated and should be done in cooperation with protected area managers as well as local experts. A special challenge in modelling is reflecting user needs and ensuring the applicability of results for decision-making and adaptation of management.
Consequently, modelling results on local to regional scales are accompanied by high uncertainties. These uncertainties must be taken into account and treated accordingly when management decisions are based on modelling results. This is best done through the joint development of management scenarios that include the whole spectrum of climate change impacts. The definition of relevant scales for decision-making and the acceptable limits of uncertainty must be considered a prerequisite for this. Uncertainty of facts should not corrupt decisions and needs to be separated from uncertainty of decisions. Models should be included in a wider framework to make them useful for the decision-making process in conservation management. Risk management, no-regret measures, and adaptive management have great potential to foster the profitable application of modelling results in conservation management. However, it should be emphasised that projections based on computer models are not the only option to prepare for climate change and support decisions for adaptation actions. Analysing and mapping sensitivity might be a useful method to identify driving factors, thresholds for resilience and management options even when results from climate models or detailed model based analysis are missing, as illustrated in Chap. 8.
References
Ayala, F. J. (1996). The candle in the darkness. Science, 273, 442–443.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., & Courchamp, F. (2012). Impacts of climate change on the future of biodiversity. Ecology Letters, 15, 365–377.
Biesbroek, G. R., Swart, R. J., & van der Knaap, W. G. M. (2009). The mitigation-adaptation dichotomy and the role of spatial planning. Habitat International, 33(3), 230–237.
Brown, C., & Wilby, R. L. (2012). An alternate approach to assessing climate risks. EOS, 92(41), 401–402.
Bugmann, H. K. M. (2003). Predicting the ecosystem effects of climate change. In C. D. Canham, J. J. Cole, & W. K. Lauenroth (Eds.), Models in ecosystem science (pp. 385–413). Princeton: Princeton University Press.
Christensen, J. H., & Christensen, O. B. (2010). A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Copenhagen: Danish Meteorological Institute.
Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C., & Mace, G. M. (2011). Beyond predictions: Biodiversity conservation in a changing climate. Science, 332, 53–58.
Fordham, D. A., Akcakaya, H. R., Araujo, M., Elith, J., Keith, D. A., Pearson, R., Auld, T. D., Mellin, C., Morgan, J. W., Regan, T. J., Tozer, M., Watts, M. J., White, M., Brendan, A. W., Yates, C., & Brook, B. W. (2012). Plant extinction risk under climate change: Are forecast range shifts alone a good indicator of species vulnerability to global warming? Global Change Biology, 18, 1357–1371.
Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008). Performance metrics for climate models. Journal of Geophysical Research, 113, D06104.
Godet, M. (2000). The art of scenarios and strategic planning: Tools and pitfalls. Technological Forecasting and Social Change, 65, 3–22.
Hakonson, L. (2003). Propagation and analysis of uncertainty in ecosystem models. In C. D. Canham, J. J. Cole, & W. K. Lauenroth (Eds.), Models in ecosystem science (pp. 139–167). Princeton: Princeton University Press.
Hansen, L., & Hoffmann, J. (2011). Climate savvy – Adapting conservation and resource management to a changing world. Washington, DC: Island Press.
Harris, G., Bigelow, S., Cole, J., Cyr, H., Janus, L., Kinzig, A., Kitchell, J., Likens, G., Reckhow, K., Scavia, D., Soto, D., Talbot, L., & Templer, P. (2003). The role of models in ecosystem management. In C. D. Canham, J. J. Cole, & W. K. Lauenroth (Eds.), Models in ecosystem science (pp. 299–307). Princeton: Princeton University Press.
Hattermann, F. F., Krysanova, V., & Hesse, C. (2008). Modelling wetland processes in regional applications. Hydrological Sciences Journal, 53, 1001–1012.
Heller, N. E., & Zavaleta, E. S. (2009). Biodiversity management in the face of climate change: A review of 22 years of recommendations. Biological Conservation, 142, 14–32.
Hickler, T., Vohland, K., Miller, P., Smith, B., Costa, L., Feehan, J., Giesecke, T., Fronzek, S., Carter, T., Cramer, W., Kuehn, I., & Sykes, M. (2012). Climate-driven changes in European potential natural vegetation in relation to the Natura 2000 protected area network. Global Ecology and Biogeography, 21, 50–63.
Holsten, A., Vetter, T., Vohland, K., & Krysanova, V. (2009). Impact of climate change on soil moisture dynamics in Brandenburg with a focus on nature conservation areas. Ecological Modelling, 220, 2076–2087.
Intergovernmental Panel on Climate Change (IPCC). (2002). Climate change and biodiversity (IPCC Technical Paper, V). Cambridge: Cambridge University Press.
IPCC-SR-LULUCF. (2000). Special report on land use, land-use change, and forestry. In Watson, R. T., Noble, I. R., Bolin, B., Ravindranath, D. J., Verardo, D. J., & Dokken, D. J. (Eds.), IPCC special reports (pp. 1–377). Cambridge: UK7 Cambridge University Press.
IPCC. (2007). Climate change 2007: Synthesis report. Contribution of Working Groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change (Core Writing Team, R. K. Pachauri, A. Reisinger, Eds.). Geneva: IPCC.
Jacob, D., & Podzun, R. (1997). Sensitivity studies with the regional climate model REMO. Meteorology and Atmospheric Physics, 63, 119–129.
Julius, S. H., & West, J. M. (Eds.). (2008). Preliminary review of adaptation options for climate-sensitive ecosystems and resources. Washington, DC: U.S. Environ-mental Protection Agency.
Koomen, E., Opdam, P., & Steingröver, E. (2012). Adapting complex multi-level landscape systems to climate change. Landscape Ecology, 27, 469–471.
Krysanova, V., Wechsung, F., Arnold, J., Srinivasan, R., & Williams, J. (2000). SWIM (Soil and Water Integrated Model), User manual. In PIK report no. 69 (pp. 1–239). Potsdam: Potsdam Institute of Climate Impact Research.
Kühn, I., Vohland, K., Badeck, F., Hanspach, J., Pompe, S., & Klotz, S. (2009). Aktuelle Ansätze zur Modellierung der Auswirkungen von Klimaveränderungen auf die biologische Vielfalt. Natur und Landschaft, 84, 8–12.
Lawler, J. J., Shafer, S. L., White, D., & Kareiva, P. (2009). Projected climate-induced faunal change in the Western Hemisphere. Ecology, 90, 588–597.
Lorenzoni, I., Pidgeon, N. F., & O’Connor, R. E. (2005). Dangerous climate change: The role for risk research. Risk Analysis, 25(6), 1387–1398.
McKenzie, D., Gedalof, Z., Peterson, D. L., & Mote, P. (2004). Climatic change, wildfire and conservation. Conservation Biology, 18(4), 890–902.
Meinke, H., Nelson, R., Kokic, P., Stone, R., Selvaraju, R., & Baethgen, W. (2006). Actionable climate knowledge: From analysis to synthesis. Climate Research, 33, 101–110.
Millner, A. (2012). Climate prediction for adaptation: Who needs what? Climatic Change, 110, 143–167.
Nakićenović, N., & Swart, R. (2000). Special report on emissions scenarios: A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
Normand, S., Svenning, J.-C., & Skov, F. (2007). National and European perspectives on climate change sensitivity of the habitats directive characteristic plant species. Journal for Nature Conservation, 15, 41–53.
Opdam, P., & Wascher, D. (2004). Climate change meets habitat fragmentation: Linking landscape and biogeographical scale levels in research and conservation. Biological Conservation, 117, 285–297.
Opdam, P., Luque, S., & Jones, K. B. (2009). Changing landscapes to accommodate for climate change impacts: A call for landscape ecology. Landscape Ecology, 24(6), 715–721.
Oreskes, N. (2003). The role of quantitative models in science. In C. D. Canham, J. J. Cole, & W. K. Lauenroth (Eds.), Models in ecosystem science (pp. 13–31). Princeton: Princeton University Press.
Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, confirmation of numerical models in the earth sciences. Science, 263, 641–646.
Orlowsky, B., Gerstengarbe, F.-W., & Werner, P. C. (2008). A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM. Theoretical and Applied Climatology, 92, 209–223.
Peterson, G. D., Cumming, G. S., & Carpenter, S. R. (2003). Scenario planning: A tool for conservation in an uncertain world. Conservation Biology, 17(2), 358–366.
Pilkey, O. H., & Pilkey-Jarvis, L. (2007). Useless arithmetic. Why environmental scientists can’t predict the future. New York: Columbia University Press.
Pond, E., Schroth, O., Sheppard, S. R. J., Muir-Owen, S., Liepa, I., Campbell, C., Flanders, D., & Tatebe, K. (2010). Local climate change visioning and landscape visualizations: Guidance manual. Retrieved September 20, 2011, from University of BC, Collaborative for Advanced Landscape Planning Website: http://www.calp.forestry.ubc.ca/wp-content/uploads/2010/02/CALP-Visioning-Guidance-Manual-Version-1.1.pdf
Price, M. F., & Neville, G. R. (2003). Designing strategies to increase the resilience of alpine/montane systems to climate change. In WWF (Ed.), Buying time: A user’s manual for building resistance and resilience to climate change in natural systems (pp. 73–94). Berlin: WWF.
Pyke, C. R., Bierwagen, B. G., Furlow, J., Gamble, J., Johnson, T., Julius, S., & West, J. (2007). A decision inventory approach for improving decision support for climate change impact assessment and adaptation. Environmental Science and Policy, 10, 610–621.
Rannow, S. (2011). Naturschutzmanagement in Zeiten des Klimawandels – Probleme und Lösungsansätze am Beispiel des Nationalparks Hardangervidda, Norwegen. Dissertation, TU Dortmund.
Rannow, S. (2013). Climate proofing conservation: How to identify robust adaptation strategies for the management of protected species. Regional Environmental Change. doi:10.1007/s10113-013-0449-z.
Sarewitz, D., & Pielke, R. J. (1999). Prediction in science and policy. Technology in Society, 21, 121–133.
Schwartz, P. (1992). Art of the long view: Scenario planning – Protect your company against an uncertain future. London: Century Business.
Stagl, J., Hattermann, F. F., & Vohland, K. (submitted). What information about the regional impacts of climate change on nature parks can be extracted from a climate multi-model set? Regional Environmental Change (Submitted).
Sträter, D. (1988). Szenarien als Instrument der Vorausschau in der räumlichen Planung. In Akademie für Raumforschung und Landesplanung (Ed.), Regionalprognose. Methoden und ihre Anwendung (Veröffentlichungen der ARL: Forschungs- und Sitzungsberichte, Vol. 175, pp. 417–440). Hannover: ARL.
Thuiller, W., Albert, C., Araujo, M. B., Berry, P. M., Cabeza, M., Guisan, A., Hickler, T., Midgely, G. F., Paterson, J., Schurr, F. M., Sykes, M. T., & Zimmermann, N. E. (2008). Predicting global change impacts on plant species’ distributions: Future challenges perspectives in plant ecology. Evolution, and Systematics, 9, 137–152.
Van Der Linden, P., & Mitchell, J. F. B. (Ed.). (2009). ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project. Exeter: Met Office Habley Centre.
Vohland, K. (2008). Impacts of climate change on biodiversity – Consolidated knowledge and research gaps. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 86, 1–11.
Wilby, R. L., & Wigley, T. M. L. (1997). Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, 21, 530–548.
Willis, K. J., & Birks, H. J. B. (2006). What is natural? The need for a long-term perspective in biodiversity conservation. Science, 314(24), 1261–1265.
Willows, R. I., & Connell, R. K. (Eds.). (2003). Climate adaptation: Risk, uncertainty and decision-making. Oxford: UKCIP.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Copyright information
© 2014 The Author(s)
About this chapter
Cite this chapter
Vohland, K., Rannow, S., Stagl, J. (2014). Climate Change Impact Modelling Cascade – Benefits and Limitations for Conservation Management. In: Rannow, S., Neubert, M. (eds) Managing Protected Areas in Central and Eastern Europe Under Climate Change. Advances in Global Change Research, vol 58. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7960-0_5
Download citation
DOI: https://doi.org/10.1007/978-94-007-7960-0_5
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7959-4
Online ISBN: 978-94-007-7960-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)