Coupled human and natural systems exhibit complex feedbacks, thresholds, and tipping points (Liu et al. 2007). Decision-makers are often challenged to ensure sustainability of livelihoods and ecosystem services derived from the natural conditions of the lands and waters they oversee (Millennium Ecosystem Assessment 2005, Nichols et al. 2011, Smit and Skinner 2002). Broad-scale system changes resulting from a shifting climate and increasing resource demands by humans have motivated resource managers to ensure resilient systems that buffer ecosystem services from local to regional scales (Chapin et al. 2010, Tsiafouli et al. 2013). Adaptation, the adjustment of management actions in response to and anticipation of system changes, has been forwarded as a particular means by which to achieve sustainable management in the face of uncertainty about system changes. Within the environmental sciences adaptation is often considered in the context of climate change, but it also applies more generally to management adjustments according to any system change (e.g., increasing resource demand by humans). A general research question is to understand how adaptation varies with respect to multiple dimensions, including political borders, economic sectors within these borders, and affected ecosystem services and human benefits. Examining this complex field of research requires approaches that span disciplines of mathematics, ecology, geography, sociology, psychology, and economics (Adger et al. 2007). Although there have been qualitative studies of adaptation (e.g., Doria et al. 2009, Swart et al. 2009, European Environment Agency 2015), lacking is a quantitative measure to compare the status of adaptation by individuals, organizations, and governments. Such a quantitative index would enable addressing scientific questions regarding adaptation in a robust manner allowing comparisons within and across studies and sampling units within them. Drivers of adaptation have been explored initially at local (e.g., individual households and municipalities) to national levels, with the most in-depth investigations occurring at the national level. Studies range from investigations of drivers at all levels of government across the globe (Berrang-Ford et al. 2011) or within a single country (Tompkins et al. 2010) to a single governmental level for one or more continents (Bauer et al. 2012, Hanger et al. 2013, Ribeiro et al. 2009, Swart et al. 2009). On-ground adaptation (e.g., changing farm-level cropping practices), although guided by organizations working at a national scale, is primarily coordinated and conducted by organizations operating at regional (state/province/territory) and local (municipality to individual household) levels (Berkhout 2012). Further, adaptations at the local level are often closely tied to decisions and guidance made within the corresponding region (Adger et al. 2005, Smit and Skinner 2002). Regional-scale adaptation is therefore a critical lynchpin for linking local-scale adaptation actions to objectives at national to international scales; such vertical integration is a stated goal of international and national policies related to adaptation (Biesbroek et al. 2010). The only study that has included some explicit focus on drivers of regional adaptation raised perhaps more questions than answers on the topic due to the reported early stages of regional adaptation (Ribeiro et al. 2009). Thus, little is known about drivers of adaptation by regional-scale rural stewardship programs (henceforth, programs) and it would be important for adaptation planning and implementation in regional-scale rural stewardship programs which are relevant, and how do they interact, if at all (Fig. 1)?

Figure 1.  

Candidate drivers of adaptation by a program working at a regional scale, partially adapted from Figures 1.1 and 3.1 in Swart et al. (2009). This conceptual framework provides a basis for constructing hypotheses in this project. Each dashed border encapsulates a category of putative drivers. Neither relationships among individual drivers nor feedbacks between categories of drivers and adaptation actions are shown. Bolded boxes represent drivers that will be examined in this study. Underlined drivers can be at least partly informed from literature sources, whereas the remainder will be based solely on surveys and interviews with regional program administrators. (*Communication can also include coordination of adaptation planning/implementation in other regions).

Hypothesized drivers of regional-scale rural adaptation can be derived from studies at regional to national scales (Bauer et al. 2012, Berrang-Ford et al. 2011, Hanger et al. 2013, Ribeiro et al. 2009, Swart et al. 2009). These identified drivers can be broadly classified under five categories operating at multiple scales (Fig. 1):

1. Scientific information (regional) – communicated projections of ecosystem services as a function of management interventions taking into account uncertainties about system change;
2. Cultural attributes (national) – mode of governance (i.e., unitary vs. federal), flexibility of existing programs to adapt, lobbying/advocacy for or against adaptation, political will, and existence of adaptation policies and legislation;
3. Climatic anomalies (scales: regional to continental) – catastrophic weather events (e.g., floods, droughts, storms) and unusual annual variation in climatic variables;
4. Cross-border social phenomena (scales: interregional to international) – observed adaptation within neighboring regions, and shifting interregional economic demands; and
5. Regional capacity (program): communication or coordination with related decision makers, communication with scientists who can translate costs and benefits of adaptation, and available human or financial resources.

The drivers of these five categories can be hypothesized to work individually or interactively to influence the likelihood of achieving adaptation that yields benefits to ecosystem functions, services, and derived benefits to humans. Remaining unclear then is the relative importance of these drivers, ways they interact, and how they compare between particular economic sectors (e.g., forestry and agriculture) and among adjacent regions for a particular sector.

Europe serves as an excellent focal continent for examining the challenges associated with regional-scale adaptation. Given a high level of cultural diversity along with demonstrated potential for cross-border cooperation and coordination (Perkmann 2003), studying spatial patterns among European countries can provide a model that could be compared with modes of regional adaptation in other parts of the world. Throughout Europe, projected system changes are expected to have spatially heterogeneous impacts on rural landscape condition and the resulting potential for deriving ecosystem goods and services (Alcamo et al. 2007). An example of a spatially heterogeneous impact is that water will become less available in regions experiencing more droughts due to climate change, elevated resource demands due to human population change and decision-making, or a combination of these two (Flörke et al. 2011). The European Union has responded by motivating international cooperation for system-change adaptation through a series of policy documents approved by the European Commission (2007), European Commission (2012a), European Commission (2013), and these have run parallel with developments of national climate strategies within Europe (Hanger et al. 2013). Given the recent international developments, little is known about how these potentially conflicting international policies are interpreted and applied in a region (Bauer et al. 2012, Swart et al. 2009). Many industrialized countries of central Europe have approved a national adaptation strategy going to be implemented on regional level (mostly considered as NUTS 2 regions - Nomenclature of Units for Territorial Statistics - Table 1) in particular.Those regions have their own network for international and interregional cooperation thanks in part to sharing their international borders and for the application of regional policies (European Commission 2020). This network of countries on the one hand exhibits diversity in culture with respect to democratic modes of governance and progress with adaptation that reflects some of the diversity seen across all of Europe (Bauer et al. 2012, Hanger et al. 2013). These central European countries, on the other hand, possess cultural similarities due to a shared history (Johnson 1996). By excluding some of the dimensions of European cultural diversity as a whole, focusing explicitly on central Europe enables a tractable test case of particular scientific challenges associated with regional adaptation.

Table 1. Download as CSV 

Attributes of industrialized countries within central Europe. Geography is based on NUTS-2 regions included within Central Europe Programme (European Commission 2020) plus the nation of Switzerland, which was excluded from the CEP because of its non-EU member status although by many accounts it is part of central Europe (e.g., World Factbook; Encyclopedia Britannica; Brockhaus Enzyklopädie; Columbia Encyclopedia).

Country	Governance	National adaptation strategy approval year	Entire country in central Europe?	Num. NUTS-2 regions in central Europe
Austria	Federal	2012	Yes	9
Czech Republic	Unitary	2015	Yes	7
Germany	Federal	2008	No	8
Hungary	Unitary	2008	Yes	7
Italy	Unitary	2015	No	9
Poland	Unitary	2013	Yes	16
Slovakia	Unitary	2014	Yes	4
Slovenia	Unitary	-	Yes	12
Switzerland	Federal	2012	Yes	26

Rural landscapes comprise >90% of the land area in Europe (Martino and Fritz 2008), and therefore they offer a particularly interesting opportunity for examining and comparing challenges of adaptation between sectors and regions spanning multiple countries. For the purpose of this study, rural landscapes are defined as terrestrial areas that are dominated by vegetative cover and are protected or managed through agriculture or forestry. Through such protection and management, these landscapes can provide diverse ecosystem services and associated benefits to humans (Maes et al. 2012, Schroter 2005). Within the rural landscape, two sectors are distinguished based on the dominant vegetation cover:

1. agriculture sector - dominated by herbaceous plants or shrub over story;
2. forest sector- dominated by trees.

Some landowners may conduct both agricultural and forestry practices on their properties, and so in this project we recognize this overlap while defining separate activities related to agriculture and forestry in our analytical framework. A particular focus of this work will be on forestry and agricultural activities, which are dominant even within so-called protected areas of Europe (Tsiafouli et al. 2013).

A general challenge in rural landscapes is balancing ecosystem services and human benefits derived from forested and agricultural lands (e.g., timber and crop production, carbon sequestration, recreation opportunities) while accounting for anticipated climatic and socioeconomic changes (Vos et al. 2008, West et al. 2009). Calls for improved ways to integrate understanding about adaptation in rural landscapes provide a clear impetus for cross-disciplinary research that considers potential for balancing several ecosystem services (Biesbroek et al. 2010, European Commission 2012b, European Commission 2012a). Focusing on nations of central Europe presents an opportunity to develop and integrate relevant knowledge about how drivers of rural adaptation vary in importance among regions and countries.

Forest management actions have already been augmented from individual-management-unit to landscape scales throughout much of Europe in anticipation of projected climate-induced changes to forest ecosystems and related services (Kolström et al. 2011). Frameworks exist for choosing among silvicultural (i.e., stand-level) adaptation techniques recognizing temporal dynamics of forests and tradeoffs among often competing ecosystem services along with uncertainties about system change (Seidl and Lexer 2013). Indicator systems have also been developed representing multiple European forest ecosystem services (Seidl et al. 2010, Wolfslehner and Vacik 2011). Although there is some knowledge about variation in adaptive capacity in forests among multinational ecoregions within Europe (Lindner et al. 2010), lacking is an understanding of how factors limiting forest adaptation vary among and within individual countries.

Across rural landscapes of Europe, agricultural areas exhibit important contrasts with forested areas. Forests are usually designated as intensively managed for wood production across multiple decades, whereas usage of agricultural landscapes varies annually from intensive crop production to livestock grazing to arable fallows (Harrison et al. 2010). European agricultural lands are increasingly abandoned or managed for short-term profit, while many European forests are expanding and managed for long-term sustainability (Barbati et al. 2011, Stoate et al. 2009, Eurostat 2015). Further afforestation and intensification of remaining agricultural lands are expected to continue losses of European biodiversity (Fahrig et al. 2010). A particular challenge in sustaining agricultural ecosystem services at broad scales is that farms are privately owned, whereas substantial amounts of European forests are publicly owned and managed (Barbati et al. 2011). An expectation applying to both sectors, however, is that climate change will likely lead to elevated primary productivity in central Europe along with increased frequency and severity of droughts and storms that would induce physiological stress of the plants along with soil erosion (Flörke et al. 2011). Forest stands could face an additional threat of pest outbreaks resulting from droughts and storms (Lindner et al. 2010). Another pressure facing both rural sectors is increasing demand for agricultural and forest products (e.g., biofuels, wood, food), while at the same time there is a desire to mitigate intensive management practices to maintain rural landscapes supporting non-consumptive ecosystem services (Rounsevell et al. 2006, Schroter 2005). These salient comparisons between rural sectors give rise to the following question: how do drivers and tradeoffs of adaptation differ between the agriculture and forest sectors among regions within central Europe?

Although likely differing in magnitude, forests and agriculture provide similar types of ecosystem services including provisioning services (e.g., biofuels), regulating services (e.g., local climate regulation) and cultural services (e.g., outdoor recreation and tourism). The ways in which rural lands are managed will of course modify the levels of ecosystem services and derived benefits provided. Models of forests and of agricultural systems offer quantitative predictions about alternative management strategies, and in some cases climate scenarios (Happe et al. 2006, Lindner et al. 2005). These models can provide a basis for developing quantitative descriptions of scenarios for eliciting value tradeoffs from decision-makers (Task 1.3). We use the concept of a value tradeoff to capture how decision-makers engaged in stewardship of rural landscapes value ecosystem services. Here, a value tradeoff is defined as the value a decision-maker places on combinations of possible outcomes among categories of ecosystem services. This valuation reflects the satisfaction or desirability of the scenario by the decision-maker and can be quantified using the concept of relative importance and hence can be used in multi-criteria decision analysis (Keeney and Raiffa 1993, Mendoza and Martins 2006). Understanding these value tradeoffs is crucial for making science-based policy and management recommendations that are transparent and likely to satisfy objectives of adaptation (Eakin et al. 2009, Nichols et al. 2011). Value tradeoffs among ecosystem services can vary across scales, for example a private rural landowner may place greater value on economic return than on aesthetics, whereas a regional stewardship program would want an outcome that somehow balances aesthetics with landowner profits. Indeed, European governments have subsidized private rural landowners for particular stewardship practices that promote cultural services to offset economic losses from these altered management practices, but none have assessed how decision makers value different combinations of ecosystem services. Some understanding exists regarding ways in which forest managers value tradeoffs among competing ecosystem services but only within a subset of regions of a single country (Wolfslehner and Vacik 2008, Seidl and Lexer 2013). An open question, therefore, is how do decision makers (“rural stewards”) value possible outcomes of categories of ecosystem services derived from rural landscapes under system change, and how do these vary geographically?

The overall goal of this project is to identify the differences in adaptation within rural landscapes among geographies and between sectors within central Europe. This will be followed by two sub-questions:

• Which drivers are important for adaptation planning and implementation within regional-scale rural stewardship programs, how do they interact, and how do they vary among regions or between sectors?
• How do decision-makers engaged in stewardship of rural landscapes perceive value tradeoffs among ecosystem services, how do these vary geographically, and how do they compare between forestry and agricultural sectors?

Three sets of hypotheses can explain variation in drivers of regional-scale rural adaptation with respect to three corresponding dimensions:

1. relative importance among categories of drivers,
2. rural sectors (forests and agriculture), and
3. geography (among regions).

An index representing the state of adaptation within each region will be used to evaluate these hypotheses (compare Tasks 1.1 and 3.2 in the Work Plan description – chapter 3). Example predictions are provided for each hypothesis to illustrate how a hypothesis could be supported by results from this study.

Driver-importance hypotheses H1-3 address the relative importance among categories of candidate drivers of adaptation (Fig. 1), which serves as a basis for examining variation in drivers among regions and between the rural sectors. We indicate this below under the rural and geographic sets of hypotheses.

H1. Unknown drivers: None of the examined drivers are important for explaining the adaptation process. Example prediction: There is no relationship between any of the putative drivers and a regional adaptation index (Fig. 2a). None of the drivers are important and may exhibit little variation among programs.

Figure 2.  

Two hypotheses regarding drivers of adaptation, illustrated by simulated effects of individual drivers on an adaptation index (see below Tasks 1.1, 1.2, 3.2 in the Work Plan). Categories of drivers distinguished by symbols: diamond (u) = science; square (■) = culture; circle (●) = climate; triangle (▲) = cross-border; and × = regional program capacity. Whiskers represent 95% Bayesian credibility intervals; open symbols illustrate significant positive effects. Cx = communication.

H2. Unlinked driver(s): One or more categories of drivers work independently to affect adaptation. Example prediction: Neighbor adaptation (cross-border driver) and national adaptation (cultural driver) increase with an index of regional adaptation (henceforth ‘are important’; Fig. 2b and Fig. 3a), but these factors show no interaction and none of the other drivers are important. Cross-border and cultural drivers independently drive adaptation.

Figure 3.  

Two hypotheses regarding drivers of adaptation, illustrated by simulated values representing absence (A) or presence (B) of interactions between effects on an adaptation index (see below Tasks 1.1, 1.2, 3.2 in Work Plan). Simulated effects include progress toward adaptation by countries of focal regions and by neighbors of these regions. Categories of progress toward adaptation defined as ‘more advanced’ (at or above median index value) or ‘less advanced’ (below median index value). Whiskers represent 95% Bayesian credibility intervals; non-overlapping whiskers illustrate statistically significant contrasts.

H3. Interacting drivers: Pairs of drivers from separate categories interact to affect adaptation. Example prediction: Adaptation of a focal regional program is only advanced when both national adaptation of the focal region and regional adaptation of neighboring regions are advanced (Fig. 3b). This hypothesis has support from existing adaptation literature (Bauer et al. 2012, Berrang-Ford et al. 2011, Hanger et al. 2013, Ribeiro et al. 2009, Swart et al. 2009).

Intersectoral-drivers hypotheses H4-5 compare importance of drivers between rural sectors. These hypotheses refer to the previous set of hypotheses regarding the relative importance of drivers (H1-3).

H4. Transferable drivers: Importance of drivers is similar between the forestry and agricultural sectors. Example prediction: Neighbor adaptation and national adaptation are important for both agriculture and forestry (Fig. 2b).

H5. Nontransferable drivers: Relative importance of drivers differs between sectors in that an important driver for one sector is not important for the other sector. Example prediction: Only neighbor adaptation is important in the forestry sector, whereas only national adaptation is important in the agriculture sector.

Spatial-drivers hypotheses H6-10 address how drivers of regional adaptation may vary geographically among regions and countries. This set of hypotheses refers to the previous set of hypotheses regarding variation in the importance of drivers among categories of drivers (H1-3).

H6. Spatially homogenous drivers: Importance of drivers (H1-3) does not vary geographically. Example prediction: Only national culture is important across all of the examined regions.

H7. Spatially unpredictable drivers: Relative importance of drivers varies without any predictable spatial pattern. Example prediction: In randomly distributed regions only national adaptation is important, whereas in the remaining regions none of the drivers are important.

H8. Spatially clustered drivers: Relative importance of drivers aggregates geographically. Example prediction: As the importance of extreme weather for adaptation in neighboring regions increases, the importance of extreme weather for adaptation within the focal region increases.

Synergistic dimensions of drivers: Although the dimensions have been presented as independent, synergies among the three dimensions will be examined through the statistical analysis such that one hypothesis per set could be supported by the data (compare Task 3.2 in the Work Plan description – chapter 3). If it were found for example that in both rural sectors importance of national adaptation for a focal region increases with the importance of national adaptation for neighboring regions but none of the other drivers were important, then this would support three hypotheses: H2 “Unlinked driver(s)”, H4 “Transferable drivers”, and H8 “Spatially clustered drivers”.

As with the class of hypotheses regarding drivers of adaptation, three sets of non-mutually exclusive hypotheses can explain the variation among value tradeoffs associated with regional-scale rural adaptation. Rather than focusing on the variation in drivers, the issue here is resolving variation in the relative values assigned by regional programs toward alternative ecosystem services that could be supported by adaptation. The three focal dimensions of value tradeoffs regarding adaptation correspond with the three categories of candidate drivers of adaptation: relative importance among categories of ecosystem services, rural sectors (forests and agriculture), and geography (among regions).

Objectives-importance hypotheses (H9-11) address the variation among programs regarding importance they place among three categories of ecosystem services and corresponding program objectives regarding rural adaptation (see Task 1.3 under Work Plan below). A set of importance weights summing to 100% across categories will be used to evaluate this set of hypotheses (Fig. 4). The importance weights reflect tradeoffs among values placed on each category by regional programs and will be determined (compare Task 1.3 and 3.2 in the Work Plan description – chapter 3). It is possible that individual programs place 100% importance on a single objective and 0% on the remaining two objectives.

Figure 4.  

Example classifications of tradeoffs from the perspective of regional stewardship programs: Distributed: each ecosystem service category is 20-30% (exclusive); Emphasized: ≥ 1 category is 30-50% (exclusive); Dominant: one categories is >50%.

H9. Indistinguishable value tradeoffs: Equal value of importance among categories of ecosystem services. Prediction: Likelihoods of dominance among classifications of value tradeoffs are indistinguishable.

H10. Dominant value tradeoffs: One category of services dominates the remainder. Example prediction: Likelihood of regulating services being dominant exceeds that of the other classifications.

H11.Diverse value tradeoffs: Multiple categories of services are consistently important. Example prediction: Likelihood of cultural and regulating services being emphasized exceeds that of the other classifications (Fig. 5).

Figure 5.  

Hypothetical result of an emphasis on regulating and cultural services consistent with the diverse value tradeoffs hypothesis. General classes of value tradeoffs distinguished by shapes: distributed (u), dominant (●) and double emphasis (■).Whiskers represent 95% Bayesian credibility intervals; open symbol illustrates a significant difference.

Intersectoral-value tradeoffs hypotheses (H12-13) address comparisons in value tradeoffs between rural sectors. This set of hypotheses refers to the previous set of hypotheses regarding importance among categories of ecosystem services (i.e. value tradeoffs; H9-12).

H12. Transferable value tradeoffs: Value tradeoffs (H1-3) are similar between the forestry and agricultural sectors. Example prediction: Regulating and cultural services are emphasized by both agriculture and forestry.

H13. Nontransferable value tradeoffs: Value tradeoffs differ between the forestry and agricultural sectors. Example prediction: Cultural services dominate in importance for agriculture, whereas regulating services dominate in the forestry sector.

Spatial-value tradeoffs hypotheses (H14-16) address how value tradeoffs associated with rural adaptation may vary geographically among regions and countries. This set of hypotheses refers to the previous set of hypotheses regarding importance among categories of ecosystem services (i.e. value tradeoffs; H9-12).

H14. Spatially homogenous value tradeoffs: Value tradeoffs do not vary geographically. Example prediction: For example regulating services dominate across all of the examined regions.

H15. Spatially unpredictable value tradeoffs: Value tradeoffs vary without any predictable spatial pattern. Example prediction: In randomly distributed regions regulating services dominate, whereas in the remaining regions relative importance among ecosystem services is similar.

H16. Spatially clustered value tradeoffs: Value tradeoffs aggregate geographically. Example prediction: When regulating services dominate in neighboring regions, the focal region has a high likelihood of this value tradeoff. In contrast, when ecosystem-service categories have similar importance in neighboring regions, then the focal region has a high likelihood of having such diverse interests.

Synergistic dimensions of value tradeoffs: As with the hypotheses regarding drivers of adaptation, synergies among the three dimensions will be examined through the statistical analysis such that one hypothesis per set could be supported by the data. If it were found for example that for both sectors in randomly distributed regions regulating services dominate whereas in the remaining regions cultural services dominate, then this would support three hypotheses: H11 “Dominant services”, H13 “Transferable value tradeoffs”, and H15 “Spatially unpredictable value tradeoffs”.

The applicant Harald Vacik (in-kind contribution) has proven his expertise on the evaluation of natural resource management in the context of sustainability, biodiversity and climate change. Geographical information sciences and decision-support systems for multipurpose natural resource management are part of his research activities. For a period of almost 15 years he is actively involved in the development and application of criteria and indicators for evaluating sustainable forest management within national and international contexts. In this context he is providing expert advice in the implementation of scientifically based practical applications of multi-criteria decision making techniques in Europe and Asia. In collaboration with more than 65 co-authors all over the world including graduate students, Harald Vacik has published >200 articles, comprising an h-index of 12. Harald Vacik will use the project activities to intensify the existing collaboration with research institutions in Central Europe, contribute to the scientific output of the Institute of Silviculture and further develop the capacity of human resources regarding drivers and value tradeoffs of regional-scale adaptation:

The principal worker Werner Toth (the project embraces a PhD-program) will be supervised by the applicant Harald Vacik and the Post-Doc researcher Brady Mattsson. Werner Toth has a good knowledge of qualitative research methods, statistical analysis, and has shown his capability to analyze complex problems in his master studies. The project aims to support Werner Toth to further intensify research on decision-making associated with natural resource management.

The project will allow Brady Mattsson to further engage in natural resource management communities of central Europe, building upon his experience with developing climate adaptation decision support systems in North America. Currently he is providing decision support for sustainable forest management on cross-border national parks of central Europe, which will support the identification of important drivers for adaption.

Pia Kieninger and Marianne Penker (in-kind contribution) will be regularly involved in discussions regarding the project activities in WP1 and WP3. Pia Kieninger is currently a lecturer at BOKU. She was formerly a university assistant at the Institute of Integrative Nature Conservation Research and a post-doc at the University of Vienna. She was involved in several interdisciplinary research projects on sustainable land use. Marianne Penker is Associated Professor for Regional Development at BOKU and her current research focusses on collective management and sustainable use of rural resources (e.g. landscapes, local food products, biodiversity, traditional knowledge and skills) and the governance of socio-ecological systems (e.g. geographical indications, protected areas, agri-environmental schemes). This project will allow for intensive collaboration with forest scientists and work on joint publications.

The project is supported by two master-theses (N.N.), linked to the development of the index of adaptation (Task 1.1) and to the development of a set of metrics for drivers of adaptation (Task 1.2). This will support the master students in their scientific careers by providing contacts to European research groups and working on a highly innovative topic. Additionally the continuing education program at BOKU comprises various courses and workshops that will be used to improve professional skills in project management. This will improve the impact of the outreach activities planned in course of the project.

Colleagues from the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria will be involved (as noted in Task 1.4, c), as a research emphasis at IIASA is investigating drivers of global transformations in response to emerging broad-scale stressors, especially climate change. Additionally, several international partners from central European countries have committed themselves to support the project regarding the identification of the sample population and reviewing the survey inWP2: Technical University in Zvolen (Slovakia), The Slovenian Forestry Institute, Global Change Research Centre, Academy of Sciences of the Czech Republic, Institute of Dendrology (Poland), Center for Development Research (ZEF) (Germany) and the Swiss Federal Institute for Forest, Snow and Landscape Research). More details on the partners are provided in the annex.

DRIVER will be implemented with the contribution of one PhD (Werner Toth - 75%, 30 months), partly two post doc positions (Brady Mattsson, Pia Kieninger,10%, 30 months) and two short term contracts (GB) for the two master students (N.N. 6 months). A total amount of 135.299.60 € is needed to cover the staff costs for the activities described in the workplan (chapter 3). A total amount of 4.400 € is needed to cover the travel costs according to the consultation with the program officers in WP 2 (1 person travelling to 8 countries for 3 days each), the validation of the results (1 person travelling to 8 countries for 1-2 days each – see WP4) and the participation in scientific conferences. Additional short term service contracts will be made to cover the costs for the online survey and the translations services (3.350 €). The catering for the kick off and final workshop will require additional financial support (1.800 €) to cover the costs for the 30 participants at each workshop. This sums up to a total amount of 152.092,08 € (including the overheads) that are asked for funding. The full financial contribution is shown in a table in the annex.