# Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany

Sophie Peter, Gaëtane Le Provost, Marion Mehring, Thomas Müller, Peter Manning

► To cite this version:

Sophie Peter, Gaëtane Le Provost, Marion Mehring, Thomas Müller, Peter Manning. Cultural world-views consistently explain bundles of ecosystem service prioritisation across rural Germany. People and Nature, 2021, 4 (1), pp.218-230. 10.1002/pan3.10277. hal-05296742

HAL Id: hal-05296742

https://hal.science/hal-05296742v1

Submitted on 3 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany

Sophie Peter<fnref n="1" /> ID
Peter Manning<fnref n="1" /> ID

Gaëtane Le Provost<fnref n="1" /> ID

Marion Mehring<fnref n="1" />,<fnref n="2" /> ID

Thomas Müller<fnref n="1" />,<fnref n="3" /> ID

## Correspondence

Sophie Peter
Email: sophie.peter@senckenberg.de

## Funding information

This research was funded by Senckenberg Society for Nature Research/Senckenberg Biodiversität und Klima- Forschungszentrum (SBiK-F). The PhD is part of the DFG-funded project Biodiversity Exploratories. P.M. acknowledges support from the German Research Foundation (DFG; MA 7144/1-1 and MA 7144/1-2).

Handling Editor: Tobias Plieninger

## Abstract

1. Differences in ecosystem service (ES) priorities often lead to conflicts between stakeholders. While differences in priorities have often been described, the sociocultural factors, including differences in cultural worldview, which drive them have not. We propose that the cultural theory of risk and its 'grid-group' typology, which classifies people as individualists, hierarchists, egalitarians and fatalists, can provide a conceptual framework for doing this.

2. We examined the relationship between ES prioritisation by stakeholders and underlying cultural (cultural worldviews, and related environmental nature and risk perceptions), sociocultural (region, stakeholder group, political party preference) and socio-demographic factors. This was achieved by applying multivariate statistics to data from a survey with 321 respondents, conducted across 14 stakeholder groups in three German regions.

3. Results show that most stakeholders prioritised many services but gave the highest priority to services linked to their stakeholder group. We identified four 'ES priority bundles': cultural services, open-land provisioning services, environmental protection services and forest provisioning services.

4. Each ES priority bundle was consistently associated with particular cultural worldviews, perceptions of nature and sociocultural factors, meaning that we could identify 'cultural types'. Two of these associations were particularly strong: Prioritisation of open-land provisioning ES was high for the agriculture stakeholder group, associated with individualism, a perception of nature as durable but unpredictable, and support for economic liberal, conservative political parties. In contrast, those who prioritised environmental protection tended to hold egalitarian cultural worldviews and perceive nature as tolerant and sensitive. They also often belonged to the research and nature conservation stakeholder groups, with a mostly left-leaning political party preference.

5. The identification of cultural types of stakeholder with consistent ES priorities and cultural worldviews may provide a useful construct in the future ES research. Furthermore, it may allow communications regarding ES to be tailored to improve

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2021 The Authors. *People and Nature* published by John Wiley & Sons Ltd on behalf of British Ecological Society

<figure><img src="image_2.png" /></figure>

<footnote n="1">¹Senckenberg Biodiversity and Climate Research Center BiK-F, Ecosystem Services and Climate, Frankfurt am Main, Germany</footnote>

<footnote n="2">²ISOE - Institute for Social-Ecological Research, Biodiversity and People, Frankfurt am Main, Germany</footnote>

<footnote n="3">³Department of Biological Science, Goethe University Frankfurt, Frankfurt am Main, Germany</footnote>
their effectiveness, potentially aiding the promotion of sustainable management strategies.

KEYWORDS

cultural theory of risk, cultural worldviews, ecosystem service priorities, grid-group typology, stakeholder groups, stakeholders, survey, trade-offs

# 1 | INTRODUCTION

Increasing pressure on the Earth's ecosystems and their capacity to provide multiple ecosystem services (ES) has led to significant interest in understanding how landscapes can be managed to simultaneously provide ES for multiple stakeholder groups (DeFries & Nagendra, 2017; Neyret et al., 2021). Previous work shows that providing high levels of multiple ES within a single landscape is often challenging, due to trade-offs between ES (Castro et al., 2014; Howe et al., 2014; Turkelboom et al., 2018). Within social-ecological systems, two types of trade-offs can emerge (see Table S1 for a glossary of terms). First, there may be biophysical- and management-induced trade-offs between ES. These supply-based trade-offs have been the focus of most research to date, in particular the commonly observed trade-off between agricultural productivity and many other regulating and cultural services (Batary et al., 2017; Turner et al., 2014). The second type of trade-offs are driven by human preferences and have received far less attention. Here, stakeholders may prioritise different and potentially opposing ES that are provided by different components of ecosystems (e.g. a predator species or its prey) or from different management systems (e.g. forests or croplands; García-Llorente et al., 2015; Wang et al., 2019), and demand different levels of them. These two types of trade-offs can interact over time as ES priorities (i.e. the relative importance of ES to stakeholders, based on Chan et al., 2012) and demand (i.e. 'the amount of a service required or desired by society', Villamagna et al., 2013, 116) shape management practices, for example by increasing the species or land use types prioritised by certain groups. Subsequently, the ES supplied by the landscape changes, resulting in winners, losers and conflicts between stakeholder groups (Mehring, Bernard, et al., 2017; Spierenburg, 2012). Resolving such conflicts requires an understanding of not only which management actions will minimise trade-offs in ES supply, but also what shapes the patterns of ES priorities, as actions that shape these could also reduce trade-offs.

To date, most studies considering ES demands and priorities have investigated how these properties differ between stakeholder groups (Iniesta-Arandia et al., 2014; Washbourne et al., 2020). This is a logical starting point as many stakeholder groups form entities with shared ES use, whether as a source of income, recreation or cultural identity (Wolff et al., 2015). While such studies provide useful information, for example by identifying bundles of services that are typically co-prioritised by certain groups (Bennett et al., 2009; Raudsepp-Hearne et al., 2010), they provide limited insight into the deeper underlying causes of variation in ES priorities (Scholte et al., 2015; Zoderer

et al., 2019). While some of these studies have addressed how socio-demographic factors such as age, gender, education, income affect prioritisation, for example the studies by Oteros-Rozas et al. (2013) and Fagerholm et al. (2020), the linking of ES priorities to deeper underlying cultural worldviews, involving perceptions of society, nature and environmental risks, has not been investigated.

Here, we propose that the cultural theory of risk, a theory of sociological risk research, can provide a framework to better understand how cultural worldviews drive ES prioritisation and demand (Douglas & Wildavsky, 1982; Tansey & O'Riordan, 1999). The cultural theory of risk assumes that our perceptions of nature and society, and the risks associated with these perspectives, are socioculturally constructed (Renn, 2008). We extended this assumption to propose that the theories' 'grid-group typology' (Thompson et al., 1990) can be used as a conceptual framework to understand how stakeholders' cultural worldviews drive ES priorities and demand (Peter, 2020). The typology is structured along two axes, the 'group' axis describing the strength with which an individual is bound to a group, and the 'grid' axis which describes how strongly roles are ascribed that constrain individual role choice and behaviour. From the four corners of these two dimensions, 'cultural types' can be defined—fatalists, hierarchists, egalitarians and individualists (Figure 1). These cultural types are hypothesised to be strongly aligned with particular perceptions of, and relationships with, nature, and perceptions of environmental risk (Price et al., 2014). Therefore, the four cultural types described in Figure 1 can be merged with the concept of 'myths of nature', which states, for example, that egalitarians perceive nature as ephemeral, and individualists perceive it as benign (Adams, 1995; Thompson et al., 1990).

While this grid-group typology has not yet been applied within ES research, it has been used to successfully explain individual variation in the perception of climate change and its management (e.g. McNeeley & Lazrus, 2014; Morris et al., 2020). We also hypothesise that these cultural worldviews and the associated perceptions of nature and environmental risks are linked to sociocultural (region, stakeholder group, political party preference) and socio-demographic factors (e.g. age, gender, education), which also influence ES prioritisation and demand.

Based on the sociological conceptual framework presented above, we explored the following questions in this paper: (a) Which ES are prioritised by which stakeholder groups? (b) What trade-offs and synergies result from stakeholder differences in ES prioritisation? (c) How are cultural, sociocultural and socio-demographic factors related to ES prioritisation? We addressed these questions using
<figure><img src="image_3.png" /><figcaption>FIGURE 1 The four cultural types and the associated cultural worldviews and 'myths of nature', based on the cultural theory of risk (derived and adapted from Peter, 2020, 10)</figcaption></figure>

data from a quantitative survey conducted in three rural regions of Germany.

# 2 METHODS

## 2.1 Study system

The study was conducted as part of the large-scale and long-term Biodiversity Exploratories project, which is conducted in three rural regions of Germany: (a) Schwäbische Alb (South-western Germany), (b) Hainich-Dün (Central Germany) and (c) Schorfheide-Chorin (North-eastern Germany) (Fischer et al., 2010). The project's focus is land use-biodiversity-ecosystem functioning relationships, but it has recently expanded to incorporate ES and the surrounding social system (Neyret et al., 2021; van der Plas, 2019). All three regions are rural, and have an ageing and declining, or almost stable, population (Bundeswahlleiter, 2020a, 2020b, 2020c). All also contain protected areas. The Schwäbische Alb and Schorfheide-Chorin are both UNESCO Biosphere Reserves since 2009 and 1990, respectively, and the Hainich-Dün region contains a National Park. Their regional identity is shaped by different historic events (e.g. the Second World War and the following division of Germany). Today, the interplay of different stakeholder groups (e.g. tourism, forestry, quarrying, policy and administration, and the local residents) shapes ES priority and demand in the regions. All three regions experience a tension between the preservation of old traditions (e.g. the protection of traditional meadows) and the need to adapt in a globalised world (e.g. by providing tourist and economic infrastructure; Peter, in press).

## 2.2 Data collection

In 2018, we first conducted one expert workshop per region in order to identify the main stakeholder groups, and which ES they prioritised. For each, we applied the qualitative Delphi method, which involves inviting local experts who could provide comprehensive information on the regional social network (Döring & Bortz, 2016). The invitees were representatives of different stakeholder groups (e.g. farmer's and tourism associations, regional development programmes, public administration), involved in direct land use and/or management. The workshops were attended by 9 representatives in the Schwäbische Alb, by 11 in the Hainich-Dün and by 10 in the Schorfheide-Chorin. The central question of the workshops was 'What does nature in the region actually provide for us that we can benefit from?'. In it experts were asked to prioritise which ES with direct links to final benefits (as defined in the cascade model, Potschin-Young et al., 2018) are demanded in their regions, based on a pre-selected list of possible terrestrial ecosystem services from CICES (Haines-Young & Potschin, 2018). Therefore, ES supplied solely by aquatic ecosystems or related to air quality were excluded. At each workshop, the list of ES was agreed upon following joint discussion in which unwanted services were omitted and new services were added. These were compiled across workshops to inform a final list of ES that was synchronized with the ES categorisations of CICES and IPBES to ensure comparability in the future research (Haines-Young & Potschin, 2018; Table S2): cultural services (biodiversity protection, landscape aesthetic, regional identity, leisure); provisioning services (food production (from crops), livestock production, timber, energy from raw materials, energy from technology, foraging, hunting); regulating service (climate regulation).
The workshops identified 14 stakeholder groups across the regions: agriculture, landowners, policy & public administration, local economy, local residents, nature conservation associations, regional development programs, tourism, hunting, quarrying, local heritage associations, forestry, local press, scientific research.

The secondary objective of the workshops was to develop a general picture of the stakeholder community. Here, the central question was ‘Which stakeholder groups have collectively shaped land use in the region over the last 10 years and how?’. The Net-Map method (Hauck & Schiffer, 2012) was used to identify the actors and their grouping in a participatory process. The workshops identified 14 stakeholder groups across the regions (see Figure 2 for a full list of the stakeholder groups). The stakeholder group ‘local press’ was only identified in Hainich-Dün, but included in the cross-regional list. The workshops ensured that complete and relevant lists of stakeholder groups and ES were used in the subsequent quantitative survey.

In 2019, 63 representatives of the identified stakeholder groups were invited to qualitative interviews that aimed to provide a broad assessment of the drivers of ES demand. These were structured to include questions related to the cultural theory of risk to the drivers of ES demand and priority (Peter, in press). Based on the findings of these interviews and the expert workshops, a quantitative survey was developed to address our research questions (see Section 1).

The survey was conducted online and by post in autumn/winter 2019. Official channels of organisations and administrations, social media, press and via requests for internal dissemination within larger groups and organisations were used to distribute the survey, and this variety of distribution strategies increases anonymity (Döring & Bortz, 2016). Following this initial distribution, the survey was then specifically targeted at groups with a low response rate to ensure sufficient responses from each stakeholder group. The institutes that employ the researchers who conducted this study did not have an ethics committee for social science research at the time when the data were collected. However, the standards and recommendations of the German Data Forum (2017) were followed and employed. This includes that a written consent for the collection and processing of the anonymised personal survey data was obtained before starting the survey. Participation in the survey was voluntary. At any time, the participants were able to cancel the survey or withdraw their consent (Table S8). The first question of the survey asked which stakeholder group the respondent considered themselves to belong to most closely. We then specified that the respondent should reply to the rest of the survey from the perspective of that stakeholder group. Therefore, while we present these results as individual responses, they should be viewed as individual perspectives on the group they belong to. The survey was divided into four main sections measuring: (i) cultural worldviews that influence the respondents' perception of society and nature; (ii) risk awareness concerning biodiversity loss; (iii) the assessment of ES priority from the perspective of the respective stakeholder group; and (iv) socio-demographic and sociocultural data. Some questions were based on those of previous surveys (e.g. Rubik et al., 2019; Thompson et al., 1990). In addition, only a few socio-demographic details were requested, in order to increase the anonymity (Table S8). The data

were also processed in such a way that traceability to individuals is prevented. Priority, that is which ES should be provided regionally and their relative importance (Chan et al., 2012), was quantified by asking participants to freely assign 20 points among the ES (part iii), that is points could be spread widely or assigned to a single service. These were then converted into percentage priority scores for each individual. We further expressed the relative priority for each ES for the different stakeholder groups as the averaged percentage of priority scores of the individuals belonging to the group. In order to identify which cultural type stakeholders belonged to, four standard statements per cultural type, derived from the existing literature, were used to assess how closely a respondent fit within each of the four cultural types (presented in Figure 1). Here respondents ranked their agreement with the statement on a Likert scale (parts i, see Table S3 for the detailed questions; Kahan, 2012; Rippl, 2002; Zwick & Renn, 2002). These statements concerned various social and individual situations in which the respondents positioned themselves. Statements also queried the affiliation to particular ‘myths of nature’ (e.g. ‘Whatever we humans do, nature always comes back to balance by itself’ Brand, 1998, p. 18; Adams, 1995; Job et al., 2019). The sum of the Likert-scale scores was then used to determine how closely a respondent corresponds to each cultural type and ‘myth of nature’. All respondents agreed to share the collected data anonymously according to data security regulation for this project.

## 2.3 | Socio-demographic and sociocultural data

We received 332 responses of which 321 were complete. The majority of the feedback was obtained via the online survey (N = 287), supplemented by the postal survey (N = 34). The median age of respondents was 52 years (Figure S1a) with a male majority (60%; Figure S1b). Most respondents had a net monthly household income ≥2000€ (Figure S1c), which is slightly above the median equalised income in Germany in 2019 (Destatis, 2021). While education level varied from no qualification to university degree, the sample was quite biased towards respondents with a university degree (Figure S2b). All stakeholder groups were represented, but differed in their representation (see Figure 2 for N per group). The respondents also supported a wide range of German political parties: the conservative, market-oriented ‘Christian Democratic Union of Germany’ (CDU), the economic liberal party ‘Free Democrats’ (FDP), the far right-leaning conservative party ‘Alternative for Germany’ (AfD), the left-leaning state-oriented ‘Social Democratic Party’ (SPD), the far left-leaning state-oriented party ‘The Left’ (Die Linke) and the left-leaning liberal ‘Alliance 90/The Greens’ (Bündnis 90/Die Grünen; Figure S2c; Decker, 2018). This suggests a diverse set of respondents, representing a broad socio-demographic and sociocultural range.

## 2.4 | Statistical analyses

All analyses were conducted using R (version 3.5.1 2018-07-02; R Core Team, 2018). We first correlated stakeholders' priority scores for different
ES to detect synergies and trade-offs using Pearson correlation matrices in the R package CORRPLOT (Wei & Simko, 2017). To further understand potential trade-offs in ES prioritisation, we performed a principal component analysis (PCA) on the full set of priority scores using the R package FACTOMINER (Lê et al., 2008) and FACTOEXTRA for the visualisation of the PCA. We used a scree plot to determine the number of PCA components to retain (Cattell, 1966), resulting in three components that were selected for interpretation. These had eigenvalues >1 and accounted for 45.8% total variation. To assess if stakeholders formed groups with consistent priorities, we coupled the results of the PCA with a cluster analysis of the ES priority scores. For this, we used the R package FACTOEXTRA (Kassambara & Mundt, 2017) to perform a K-means clustering (MacQueen, 1967), in which each cluster is summarised by the means of the data points belonging to that cluster. We then projected the identified clusters onto the PCA for interpretative purposes without intervening in the calculation of the eigenvalues. Additionally, one-way analysis of variance tests (ANOVAs) were conducted to compare ES priorities across the pre-defined stakeholder groups.

To understand how societal drivers relate to ES priorities, we performed a multiple factor analysis (MFA). MFA is a multivariate analysis technique that allows information from different datasets to be synthesised, where variables are structured into groups. Several sets of variables (continuous or categorical) can therefore be simultaneously studied (Abdi & Williams, 2010). The MFA computes either a PCA or a multiple correspondence analysis (MCA) depending on the type of variables, and

stores the value from the first eigenvalue of each multivariate analysis to perform a weighted PCA. As input variables, we used the factor scores of each respondent calculated in the ES priority scores PCA. We related these values to the Likert scores for cultural worldviews and myths of nature, their risk awareness for biodiversity loss, their feeling of being threatened by biodiversity loss and also socio-demographic and socio-cultural data (region, stakeholder group and political party preference). For simplicity, other socio-demographic variables such as gender, age, education or income were not included since they do not strongly influence ES priorities (Table S4). Analyses of covariance tests (ANCOVAs) were additionally conducted to test the effects of the drivers considered in the MFA on the ES priority patterns. We used the R packages FACTOMINER and FACTOEXTRA for analysis and visualisation respectively. The results were integrated with understanding from the current state of research, and sources are adequately cited. All authors participated in conducting the work as described and take responsibility for the content.

# 3 | RESULTS

## 3.1 | ES priorities of different stakeholder groups

Stakeholders of all groups prioritised a diverse supply of ES, thus indicating a general tendency to demand multifunctionality, and

<figure><img src="image_4.png" /><figcaption>FIGURE 2 Relative ecosystem service (ES) priority for each stakeholder group given to multiple cultural, provisioning and regulating ES. ES priority was calculated as the proportion of the 20 priority points allocated to the service within the survey (averaged percentage of each individual's 20 points). ES classification follows CICES. Average and standard deviation in ES priorities for each stakeholder group are presented in Figure S3</figcaption></figure>
considerable overlap in the priorities of many groups. Despite this, there were strong differences in the ES priority patterns of certain stakeholder groups (Figure 2; Figure S3), which were consistent across the three regions despite their different historical developments. Certain groups had particularly distinct ES priorities: Nature conservation associations, the tourism sector and local heritage associations mainly prioritised cultural ES (mean priority for all cultural ES for these groups = 35%). Agriculture, hunting and forestry groups mainly prioritised provisioning services (mean priority for all provisioning ES for these groups = 56%). Finally, landowners, regional development programs and scientific researchers prioritised regulating services (mean priority for all regulating ES for these groups = 9%).

The correlation between the ES priority scores of individual respondents was generally weak (-0.30 < r < 0.42, Figure S5), indicating that stakeholders varied greatly in their ES priorities. In general, cultural ES priority scores tended to be positively correlated (e.g. between landscape aesthetic and regional identity), indicating synergies. We found negative correlations between provisioning and regulating ES (e.g. between the prioritisation of livestock production and climate regulation; Figure S5), indicating a trade-off in demand for these ES.

## 3.2 | ES priority bundles: Trade-offs and synergies

The PCA on ES priority identified three main axes of variation which jointly accounted for 45.8% of the total variance (Figure 3; Table S5). The first axis (Figure 3a, 17.4% of variance explained), identified hereafter as ‘Cultural services priority’, is associated with co-prioritisation of cultural ES—landscape aesthetics, regional identity and leisure. The second axis (Figure 3a, 14.6% of explained variance), identified hereafter as ‘Open-land provisioning services priority’, distinguishes between the prioritisation of provisioning ES of open-land ecosystems—that is livestock and food production—and the prioritisation of ES related to environmental protection—biodiversity protection, climate regulation and energy production from technology. The axis therefore represents a trade-off between the prioritisation of open-land provisioning ES and that of environmental protection ES. Finally, the third axis (Figure 3b, 13.8% of explained variance), identified hereafter as ‘Forest provisioning services priority’, is related to a high priority for forest provisioning ES such as timber production, hunting and foraging. The cluster analysis that was projected onto these PCA axes identified four clusters of ES priorities along the three PCA axes (Table S5):

<figure><img src="image_5.png" /><figcaption>**FIGURE 3** Patterns of covariance in ecosystem service (ES) priority, as identified by a principal component analysis (PCA). (a) The first two axes explained 17.4% and 14.6%, respectively, of the total variance; (b) the third axis explained 13.8% of the total variance. Points represent individual respondents. Clusters, representing four ES priority bundles along the three PCA axes, were determined based on the ES priority scores, and projected onto the principal components for interpretative purposes, without intervening in the calculation of the eigenvalues. The number of respondents in each cluster is indicated in the legend. Correlations between ES priority scores and PCA axes are presented in Table S5</figcaption></figure>
the first cluster is positively associated with the first PCA axis and contains respondents who prioritise cultural ES; the second cluster is related to the second PCA axis, and distinguishes respondents prioritising open-land provisioning ES (positively associated with the second axis). At the other end of the second axis is a cluster of respondents that prioritise biodiversity protection and regulating ES (negatively associated with the second axis). Finally, the fourth cluster is positively associated with the third PCA axis and contains respondents who prioritise forest provisioning services. While these four clusters indicate common and consistent patterns of ES prioritisation, there is substantial variation in ES priorities within each cluster (Figure 3; Figure S6). In the subsequent analyses, we used PCA factor scores of each respondent.

## 3.3 | Sociocultural and cultural drivers of ES priority

We found both strong and moderate associations between ES priorities and sociocultural factors, including the cultural worldviews of the grid-group typology (Figure 4; Table S6). The first MFA axis (10.3% of variance explained) is positively associated with respondents who prioritised open-land provisioning ES, demonstrated a high level of 'Individualism', and tended to perceive nature as unpredictable but also durable (Figure 4a). These were associated with the 'Agriculture' and 'Quarrying' groups (Figure 4b), as well as support for right-leaning conservative (AfD), economic liberal (FDP) and conservative liberal (CDU) parties (see also the results of the ANCOVA in Table S7). In contrast, respondents who prioritised biodiversity

<figure><img src="image_6.png" /></figure>

**FIGURE 4** Association between (a) cultural and ecosystem service (ES) priority and (b) sociocultural variables in multiple factor analysis (MFA). The two orthogonal axes explained 10.3% and 5.8% of the total variance respectively. R = risk perception: R1, biodiversity loss is a greater risk for economy than for nature, R2, biodiversity loss is a greater risk for society than for nature, R3, biodiversity loss is a greater risk for the economy than for society. The colours in (a) differentiate the quantitative variables: ecosystem service priorities (yellow), cultural worldviews (blue-green), myths of nature (light blue), awareness of biodiversity loss (red), risks linked to biodiversity loss (grey) and the feeling of being threatened by biodiversity loss (green). The colours in (b) differentiate the sociocultural variables: region (dark green), stakeholder group (red) and political party supported (blue). Correlations between variables and MFA axes are presented in Table S5. Factors associated with each ES demand cluster are summarised in (c)
protection and climate regulation (negatively associated with the first MFA axis) scored highly for 'Egalitarianism' and perceived nature as tolerant but also sensitive (Figure 4a). This second type of respondent also felt threatened by biodiversity loss and was aware of risks linked to biodiversity loss. These perceptions were common within the 'Local press', 'Scientific research', 'Regional development programs', 'Nature conservation associations' and 'Policy and public administration' groups, and with those who supported left-leaning liberal (Bündnis 90/Die Grünen), far left-leaning state-oriented (die Linke) and left-leaning state-oriented (SPD) parties.

The second axis of the MFA (5.8% of variance explained) was positively associated with respondents who prioritise forest provisioning services (Figure 4a), and who tend to perceive biodiversity loss as a greater risk to the economy and society than for nature. They typically belong to the 'Forestry', 'Landowners' and 'Hunting' groups. At the other end of the second axis are respondents who prioritise cultural ES (Figure 4a), and tend to think that biodiversity loss is a greater risk for nature itself (Figure 4a). They are disproportionately members of the 'Tourism' and 'Local residents' groups (Figure 4b). In total, the MFA explained only 16.1% of the variability in the first two axes, indicating a wide variability in views beyond the cultural types identified (see Figure S7 for further details).

It is notable that no major differences in ES priorities were observed between the three study regions (p-values >0.05, see the results of the ANCOVA in Table S7). This indicates that the different cultural types identified were evenly and consistently represented across regions, and far more strongly associated with stakeholder groups than geography (Figure 4b). In the ANCOVA, far more variance in stakeholder priorities was explained by sociocultural factors than that by region (Table S7).

4 | DISCUSSION

Our study reveals that the ES priorities of stakeholders are associated with cultural worldviews, as described by the grid-group typology, and other sociocultural factors. These associations were also consistent across all three study regions, indicating they are general, for German rural protected areas at least. In the discussion below, we examine the observed patterns of ES priority and explore their relation to these drivers.

Respondents differed greatly in their prioritisation of ES, both within and between stakeholder groups. However, stakeholder groups clearly prioritised certain ES above others, and usually those related to their income or leisure pursuits. Nevertheless, most respondents assigned points to a wide range of services, thus indicating a general tendency to demand multifunctional ES provision (sensu Manning et al., 2018). This result indicates that stakeholders either have similar priorities, or that they have adjusted their own priorities to accommodate the needs of others. The heterogeneity in prioritisation within stakeholder groups warrants further attention, because it may play an important role in determining group behaviour and ES management, as suggested by Tauro et al. (2018).

While ES priorities differed between stakeholder groups, differences between the regions were marginal despite their large ecological and historic differences (described in Section 2). The stakeholder groups prioritised the same ES in each region, thus indicating common, possibly universal, patterns of prioritisation across Germany, at least within high biodiversity cultural landscapes such as these. The patterns of priority observed here differ from those in most previous studies (Linders et al., 2021; Martin-Lopez et al., 2012; Quintas-Soriano et al., 2018; Washbourne et al., 2020) in that stakeholders are less specialised and less polarised (e.g. each group exclusively prioritising just a few favoured ES). This may be due to methodological differences, for example the large number of ES and stakeholder groups, or the 'points-based' prioritisation, which in contrast to other methods used to identify ES preferences (Haida et al., 2015; Walz et al., 2016), may prompt stakeholders to consider the need for multifunctionality more deeply. These differences could also be driven by fundamental differences in the social systems studied. We hypothesise that the regional social systems we studied, and the stakeholder groups they contain, have a history of compromises that has been encouraged by National Park and Biosphere reserve policies. This may have has led to a more harmonious pattern of prioritisation than in regions where changes to social structure, land use and governance are controlled by a few powerful individuals or groups, who use different ES to those without power (Peter, in press). Such regions often display strong ES trade-offs and conflicts (Turkelboom et al., 2018). In contrast, our regions may have involved what Turkelboom et al. (2018) term 'non-influential' stakeholders into the decision-making process. This could result in alliances and understanding between groups, and more even patterns of prioritisation between groups.

Within the cultural landscapes considered in this study, we identified ES priority bundles, which were consistently related to particular stakeholder groups, cultural worldviews and sociocultural factors. Based on these associations and their consistency, we suggest that the grid-group typology and 'myths of nature' of the cultural theory of risk (Bellamy & Hulme, 2011; Douglas, 2007; Kahan, 2012; Price et al., 2014) can be extended to ES priorities, and that such types may be common features of social-ecological systems. While the types we found were consistent across regions, additional or more pronounced types may also be found across broader society, for example in cities or more intensive agricultural areas without a recent history of compromises between stakeholder groups. Hereafter, we refer to these associations of cultural worldviews, sociocultural factors (political party preference) and ES priority bundles as 'cultural types of ES priority'.

The integration of sociological cultural theory of risk represents a new approach in ES research that may enlighten how ES priorities and demand are shaped by cultural worldviews (Peter, 2020). In this study, a closer look at the identified cultural types provides valuable information about what motivates ES prioritisation. For example, egalitarians dominate the group that prioritises ES related to environmental protection. They are hypothesised to be politically left leaning (Oltedal et al., 2004), which was supported by the sociocultural
data on political party preferences. Furthermore, they perceive en-
vironmental risks, view nature as sensitive and threatened by human
overuse. They therefore consider it to require protection from hu-
mans, for example via the precautionary principle (Adams, 1995).
Our study also found that members of this group perceive nature as
tolerant. However, despite a broad consistency between our results
and theory, there are several caveats to consider with regard to cul-
tural theory of risk and its application to ES research. Firstly, we did
not always find the theoretically predicted correlation between the
cultural types and the 'myths of nature'. While in some cases, our
findings correspond with theoretical expectations, for example egal-
itarians saw nature as fragile, in other cases they did not, for example
individualists perceived nature as both durable and unpredictable,
when only durable is predicted by theory (Price et al., 2014). These
results indicate that the group axis of the grid-group typology better
explains ES priorities, and is more theoretically consistent with the
'myths of nature', than the grid axis. Another important caveat re-
lated to this is that the cultural type of fatalists was rarely observed.
Other studies have also shown a lack of empirical evidence of all
four cultural types (e.g. Marris et al., 1998; Price et al., 2014; Tansey
& O'Riordan, 1999). While this might indicate that the grid-group
typology needs revision, in our case, a lack of fatalists may also be
driven by their rarity within the study system, or their disinclination
to respond to the survey request.

Because cultural types can be extended to ES priorities, we gain
insights into the cultural context and associated perceptions of nature
that may influence management actions and ES prioritisation within a
stakeholder group. For example, conservationists tend to work with
the fragile parts of nature and species which are vulnerable to inten-
sive land use systems, leading to a cautious approach to environmen-
tal management. In contrast, farmers have created ecosystems that
consistently deliver the provisioning ES they prioritise, despite man-
agement intensification, thus leading to their perception of nature as
being robust. The fact that this group considers nature to be unpre-
dictable could result from the fact that the services they prioritise are
threatened by environmental risks (e.g. disease outbreaks, weather
events). However, food production typically returns after these
events, which is also in line with a perception of nature as robust.

Reaching beyond the current study, we propose that the identifi-
cation of cultural types of ES priority and further application of the cul-
tural theory of risk has the potential to increase the operational impact
of the ES concept in policy and management, a necessity highlighted by
Dick et al. (2018). The identification of cultural types may enable us to
better understand trade-offs, and synergies between the priorities of
different stakeholder groups, and conflicts of interest between them.
In our study, cultural types who prefer open-land provisioning ES dif-
fered in their perception of society and nature to those who promote
environmental protection ES. While the associated trade-off in priori-
ties has already been described in several studies (Batary et al., 2017;
García-Llorente et al., 2012; Turner et al., 2014), our findings allow us
to draw new conclusions about related behavioural patterns, that may
aid identification of environmental management and communication
strategies that manage this trade-off. Although some trade-offs are

unavoidable (Rounsevell et al., 2019), future research must address
the question of how communication to and between cultural types
can be achieved and how an appreciation of each other's needs and
views could be created. The identification of cultural types may be the
first step in identifying communication strategies that would foster
understanding between these groups. Similarly, detailed knowledge
about stakeholder group diversity and the predominant cultural types
can help to create tailored communication strategies that promote
sustainable land use. For example, individualist farmers who are com-
petition focused and regard nature as durable will respond to different
communications to egalitarians who regard nature as sensitive. This
potential application of the cultural theory of risk in communication
strategies is already receiving attention in climate change research
(McNeeley & Lazrus, 2014; Morris et al., 2020; Price et al., 2014).
For example, by showing that 'low group' individuals respond more
strongly to negative, fear based, climate change messaging than 'high
group' individuals and vice versa (Morris et al., 2020). Finally, for better
or worse, political parties and local governors may use information on
cultural types to help identify which stakeholder groups would benefit
from policies that promote particular ES (e.g. greater or lesser environ-
mental protection), and which communication strategies they could
be reached by. Theory may also find application at the science-policy
interface of ES research, for example IPBES (Diaz et al., 2018; Pascual
et al., 2017), where it has been argued that sociocultural factors need
greater attention.

While our results show clear links between ES priorities, group
affiliation and cultural worldviews, the causal linkages between
these factors are less easy to determine. In reality, it is likely that
holding certain views may influence stakeholder group affiliation,
and subsequently ES priorities, demand and use, which can feed-
back to shape these views further in a dynamic, nonlinear social-
ecological coevolution, like that described by Reyers et al. (2018).
For example, a farmer prioritises ES related to agricultural yields,
but may also develop a view of robust nature and an individualistic
outlook due to resilience of their yields and the small-scale, family-
based nature of their industry. Meanwhile, a person with an egal-
tarian outlook may be attracted to work in the conservation sector,
where views of nature as fragile and the prioritisation of ES related
to environmental protection are fostered. As a future research ap-
proach, longitudinal studies may represent a potential approach to
better understand these interactions, for example by monitoring
how cultural worldviews and ES priorities change when different
stakeholder groups are left and joined.

5 | CONCLUSION

In order to study social-ecological systems, it is necessary to un-
derstand all components and dynamics (Chan et al., 2012; Mehring,
Zajonz, et al., 2017). However, most previous work has been per-
formed on the ES supply-side, and the underlying drivers of ES de-
mand and priority have not been studied in detail. Here we show
that the incorporation of sociological theory into ES research,
specifically the cultural theory of risk, can help illuminate the drivers of ES priorities, making it a promising avenue for ES research. In particular, the identification of cultural types, if general, may be a useful tool for classifying ES users and managers, thus making stakeholder-based ES studies comparable. However, the causal relationships between ES priority, cultural worldviews, sociocultural and socio-demographic properties are difficult to disentangle at present. Understanding these linkages should be part of a broader goal of achieving a better understanding of how the drivers of ES priority and demand shape ecosystem management and use, thus further shaping ES supply demand and ES priorities (Bretagnolle et al., 2018; Fagerholm et al., 2020; Hanna et al., 2020; Hölting et al., 2019; Manning et al., 2018). The inclusion of cultural types into such comprehensive studies is recommended, as understanding societal drivers may help to find solutions to conflicts driven by trade-offs in ES priorities, thus promoting the sustainable and multifunctional use of landscapes.

## ACKNOWLEDGEMENTS

The authors thank all survey respondents and the Local Management Teams of the Biodiversity Exploratories for helping with survey planning. They also thank Prof. Dr. Birgit Blättel-Mink (Department of Social Sciences at Goethe University) for constructive comments on the work.

## CONFLICT OF INTEREST

The authors declare no conflict of interest. They confirm that this is their own work, which has not yet been published in any other journal. Furthermore, the manuscript has not been submitted to any other journal. The manuscript contains ideas from their own research project and the data collection and analysis meet scientific quality standards.

## AUTHORS' CONTRIBUTIONS

P.M., S.P., M.M. and T.M. conceived the study; S.P., G.L.P., P.M. and M.M. designed the study; S.P., G.L.P. and P.M. performed the analyses and wrote the manuscript with significant inputs from all authors. Data were collected by S.P.

## DATA AVAILABILITY STATEMENT

This work is based on data from the Biodiversity Exploratories programme (DFG Priority Program 1374). All data used for analyses are archived in the Biodiversity Exploratories Information System (https://doi.org/10.17616/R32P9Q) at https://www.bexis.uni-jena.de/. The dataset used has the ID 31110 and can be requested via the platform.

## ORCID

Sophie Peter ID https://orcid.org/0000-0002-5262-6780
Gaëtane Le Provost ID https://orcid.org/0000-0002-1643-6023
Marion Mehring ID https://orcid.org/0000-0002-9606-7554
Thomas Müller ID https://orcid.org/0000-0001-9305-7716
Peter Manning ID https://orcid.org/0000-0002-7940-2023

## REFERENCES

Abdi, H., & Williams, L. J. (2010). Principal component analysis. *Wiley Interdisciplinary Reviews: Computational Statistics*, 2(4), 433-459.
https://doi.org/10.1002/wics.101

Adams, J. (1995). *Risk*. Routledge.

Batáry, P., Gallé, R., Riesch, F., Fischer, C., Dormann, C. F., Mußhoff, O., Császár, P., Fusaro, S., Gayer, C., Happe, A.-K., Kurucz, K., Molnár, D., Rösch, V., Wietzke, A., & Tscharntke, T. (2017). The former Iron Curtain still drives biodiversity-profit trade-offs in German agriculture. *Nature Ecology & Evolution*, 1(9), 1279-1284. https://doi.org/10.1038/s41559-017-0272-x

Bellamy, R., & Hulme, M. (2011). Beyond the tipping point: Understanding perceptions of abrupt climate change and their implications. *Weather, Climate, and Society*, 3(1), 48-60. https://doi.org/10.1175/2011wcas1081.1

Bennett, E. M., Peterson, G. D., & Gordon, L. J. (2009). Understanding relationships among multiple ecosystem services. *Ecology Letters*, 12(12), 1394-1404. https://doi.org/10.1111/j.1461-0248.2009.01387.x

Brand, K.-W. (1998). Soziologie und Natur - eine schwierige Beziehung. Zur Einführung. In K.-W. Brand (Ed.), *Soziologie und Natur. Theoretische Perspektiven*. Springer Fachmedien.

Bretagnolle, V., Berthet, E., Gross, N., Gauffre, B., Plumejeaud, C., Houte, S., Badenhausser, I., Monceau, K., Allier, F., Monestiez, P., & Gaba, S. (2018). Towards sustainable and multifunctional agriculture in farmland landscapes: Lessons from the integrative approach of a French LTSER platform. *Science of the Total Environment*, 627, 822-834. https://doi.org/10.1016/j.scitotenv.2018.01.142

Bundesumweltministerium and Umweltbundesamt. (2017). *Umweltbewusstsein in Deutschland 2016. Ergebnisse einer repräsentativen Bevölkerungsumfrage*. Umweltbundesamt.

Bundeswahlleiter. (2020a). *Strukturdaten Reutlingen*. Statistisches Bundesamt. Retrieved from https://www.bundeswahlleiter.de/europawahlen/2019/strukturdaten/bund-99/land-8/kreis-8415.html

Bundeswahlleiter. (2020b). *Strukturdaten Uckermark*. Statistisches Bundesamt. Retrieved from https://www.bundeswahlleiter.de/europawahlen/2019/strukturdaten/bund-99/land-12/kreis-12073.html

Bundeswahlleiter. (2020c). *Strukturdaten Unstrut-Hainich-Kreis*. Statistisches Bundesamt. Retrieved from https://www.bundeswahlleiter.de/europawahlen/2019/strukturdaten/bund-99/land-16/kreis-16064.html

Castro, A. J., Verburg, P. H., Martín-López, B., Garcia-Llorente, M., Cabello, J., Vaughn, C. C., & López, E. (2014). Ecosystem service trade-offs from supply to social demand: A landscape-scale spatial analysis. *Landscape and Urban Planning*, 132, 102-110. https://doi.org/10.1016/j.landurbplan.2014.08.009

Cattell, R. B. (1966). The scree test for the number of factors. *Multivariate Behavioral Research*, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10

Chan, K. M. A., Guerry, A. D., Balvanera, P., Klain, S., Satterfield, T., Basurto, X., Bostrom, A., Chuenpagdee, R., Gould, R., Halpern, B. S., Hannahs, N., Levine, J., Norton, B., Ruckelshaus, M., Russell, R., Tam, J., & Woodside, U. (2012). Where are cultural and social in ecosystem services? A framework for constructive engagement. *BioScience*, 62(8), 744-756. https://doi.org/10.1525/bio.2012.62.8.7

Decker, F. (2018). *Jenseits von links und rechts. Lassen sich Parteien noch klassifizieren? Bundeszentrale für Politische Bildung*. Retrieved from https://www.bpb.de/apuz/279819/lassen-sich-parteien-noch-klassifizieren

DeFries, R., & Nagendra, H. (2017). Ecosystem management as a wicked problem. *Science*, 356(6335), 265-270. https://doi.org/10.1126/science.aal1950
Destatis. (2021). *Einkommensverteilung (Nettoäquivalenzeinkommen) in Deutschland*. Statistisches Bundesamt. Retrieved from https://www destatis de/DE/Themen/Gesellschaft-Umwelt/Einkommen-Konsum-Lebensbedingungen/Lebensbedingungen-Armutsgefaehr dung/Tabellen/einkommensverteilung-silc.html

Diaz, S., Pascual, U., Stenseke, M., Martín-López, B., Watson, R. T., Molnár, Z., Hill, R., Chan, K. M. A., Baste, I. A., Brauman, K. A., Polasky, S., Church, A., Lonsdale, M., Larigauderie, A., Leadley, P. W., van Oudenhoven, A. P. E., van der Plaat, F., Schröter, M., Lavorel, S., ... Shirayama, Y. (2018). Assessing nature's contributions to people. *Science*, 359(6373), 270–272. https://doi.org/10.1126/science.aap8826

Dick, J., Turkelboom, F., Woods, H., Iniesta-Arandia, I., Primmer, E., Saarela, S.-R., Bezák, P., Mederly, P., Leone, M., Verheyden, W., Kelemen, E., Hauck, J., Andrews, C., Antunes, P., Aszalós, R., Baró, F., Barton, D. N., Berry, P., Bugter, R., ... Zulian, G. (2018). Stakeholders' perspectives on the operationalisation of the ecosystem service concept: Results from 27 case studies. *Ecosystem Services*, 29, 552–565. https://doi.org/10.1016/j.ecoser.2017.09.015

Döring, N., & Bortz, J. (2016). *Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften*. Springer-Verlag.

Douglas, M. (1970). *Natural symbols: Explorations in cosmology*. Barrie & Jenkins, Cresset Press.

Douglas, M. (2007). *A history of grid and group cultural theory*. University of Toronto. Retrieved from http://projects.chass.utoronto.ca/semiotics/cyber/douglas1.pdf

Douglas, M., & Wildavsky, A. B. (1982). *Risk and culture: An essay on the selection of technical and environmental dangers*. University of California Press.

Fagerholm, N., Martín-López, B., Torralba, M., Oteros-Rozas, E., Lechner, A. M., Bieling, C., Stahl Olafsson, A., Albert, C., Raymond, C. M., Garcia-Martin, M., Gulsrud, N., & Plieninger, T. (2020). Perceived contributions of multifunctional landscapes to human well-being: Evidence from 13 European sites. *People and Nature*, 2(1), 217–234. https://doi.org/10.1002/pan3.10067

Fischer, M., Bossdorf, O., Gockel, S., Hänsel, F., Hemp, A., Hessenmöller, D., Korte, G., Nieschulze, J., Pfeiffer, S., Prati, D., Renner, S., Schöning, I., Schumacher, U., Wells, K., Buscot, F., Kalko, E. K. V., Linsenmair, K. E., Schulze, E.-D., & Weisser, W. W. (2010). Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. *Basic and Applied Ecology*, 11(6), 473–485. https://doi.org/10.1016/j.baae.2010.07.009

García-Llorente, M., Iniesta-Arandia, I., Willaarts, B. A., Harrison, P. A., Berry, P., Bayo, M. D. M., Castro, A. J., Montes, C., & Martín-López, B. (2015). Biophysical and sociocultural factors underlying spatial trade-offs of ecosystem services in semiarid watersheds. *Ecology and Society*, 20(3). https://doi.org/10.5751/ES-07785-200339

García-Llorente, M., Martín-López, B., Iniesta-Arandia, I., López-Santiago, C. A., Aguilera, P. A., & Montes, C. (2012). The role of multi-functionality in social preferences toward semi-arid rural landscapes: An ecosystem service approach. *Environmental Science & Policy*, 19–20, 136–146. https://doi.org/10.1016/j.envsci.2012.01.006

German Data Forum. (2017). *Forschungsethische Grundsätze und Prüfverfahren in den Sozial- und Wirtschaftswissenschaften*. In *9 Output, 5. Berufungsperiode*. Rat für Sozial- und Wirtschaftsdaten (RatSWD).

Haida, C., Rüdisser, J., & Tappeiner, U. (2015). Ecosystem services in mountain regions: Experts' perceptions and research intensity. *Regional Environmental Change*, 16(7), 1989–2004. https://doi.org/10.1007/s10113-015-0759-4

Haines-Young, R. H., & Potschin, M. B. (2018). Common International Classification of Ecosystem Services (CICES).V5.1 and guidance on the application of the revised structure.

Hanna, D. E. L., Roux, D. J., Currie, B., & Bennett, E. M. (2020). Identifying pathways to reduce discrepancies between desired and provided ecosystem services. *Ecosystem Services*, 43, 101119. https://doi.org/10.1016/j.ecoser.2020.10119

Hauck, J., & Schiffer, E. (2012). Between intuition and indicators – Using Net-Map for visual and qualitative social network analysis. In M. Gamper, L. Reschke, & M. Schönhuth (Eds.), *Knoten und Kanten 2.0. Soziale Netzwerkanalyse in der Medienforschung und der Kulturanthropologie* (pp. 231–257). Transcript.

Hölting, L., Jacobs, S., Felipe-Lucia, M. R., Maes, J., Norström, A. V., Plieninger, T., & Cord, A. F. (2019). Measuring ecosystem multifunctionality across scales. *Environmental Research Letters*, 14(12), 124083. https://doi.org/10.1088/1748-9326/ab5ccb

Howe, C., Suich, H., Vira, B., & Mace, G. M. (2014). Creating win-wins from trade-offs? Ecosystem services for human well-being: A meta-analysis of ecosystem service trade-offs and synergies in the real world. *Global Environmental Change*, 28, 263–275. https://doi.org/10.1016/j.gloenvcha.2014.07.005

Iniesta-Arandia, I., Garcia-Llorente, M., Aguilera, P. A., Montes, C., & Martín-López, B. (2014). Socio-cultural valuation of ecosystem services: Uncovering the links between values, drivers of change, and human well-being. *Ecological Economics*, 108, 36–48. https://doi.org/10.1016/j.ecolecon.2014.09.028

Job, H., Fließbach-Schendzielzer, M., Bittlingmaier, S., Herling, A., & Woltering, M. (2019). Akzeptanz der bayerischen Nationalparks. Ein Beitrag zum sozioökonomischen Monitoring in den Nationalparks Bayerischer Wald und Berchtesgaden. Würzburg: Institut für Geographie und Geologie der Universität Würzburg in Verbindung mit der Geographischen Gesellschaft Würzburg.

Kahan, D. M. (2012). Cultural Cognition as a Conception of the Cultural Theory of Risk. In S. Roeser, R. Hillerbrand, P. Sandin, & M. Peterson (Eds.), *Handbook of risk theory. Epistemology, decision theory, ethics, and social implications of risk* (pp. 726–753). Springer.

Kassambara, A., & Mundt, F. (2017). *Factoextra: Extract and visualize the results of multivariate data analyses*. R package version 1.0.4.

Lê, S., Josse, J., & Husson, F. (2008). *FactoMineR: An R package for multivariate analysis*. *Journal of Statistical Software*, 25(1). https://doi.org/10.18637/jss.v025.i01

Linders, T. E. W., Schaffner, U., Alamirew, T., Allan, E., Choge, S. K., Eschen, R., Shiferaw, H., & Manning, P. (2021). Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. *People and Nature*, 3(3), 658–672. https://doi.org/10.1002/pan3.10197

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.), *Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability* (pp. 281–297). University of California Press.

Manning, P., van der Plas, F., Soliveres, S., Allan, E., Maestre, F. T., Mace, G., Whittingham, M. J., & Fischer, M. (2018). Redefining ecosystem multifunctionality. *Nature Ecology & Evolution*, 2(3), 427–436. https://doi.org/10.1038/s41559-017-0461-7

Marris, C., Langford, I. H., & O'Riordan, T. (1998). A quantitative test of the cultural theory of risk perceptions: Comparison with the psychometric paradigm. *Risk Analysis*, 18(5), 635–647. https://doi.org/10.1111/j.1539-6924.1998.tb00376.x

Martín-López, B., Iniesta-Arandia, I., García-Llorente, M., Palomo, I., Casado-Arzuaga, I., Amo, D. G. D., Gómez-Baggethun, E., Oteros-Rozas, E., Palacios-Agundez, I., Willaarts, B., González, J. A., Santos-Martín, F., Onaindia, M., López-Santiago, C., & Montes, C. (2012). Uncovering ecosystem service bundles through social preferences. *PLoS ONE*, 7(6), e38970. https://doi.org/10.1371/journ.al.pone.0038970

McNeeley, S. M., & Lazrus, H. (2014). The cultural theory of risk for climate change adaptation. *Weather, Climate, and Society*, 6(4), 506–519. https://doi.org/10.1175/wcas-d-13-00027.1
Mehring, M., Bernard, B., Hummel, D., Liehr, S., & Lux, A. (2017). Halting biodiversity loss: How social-ecological biodiversity research makes a difference. *International Journal of Biodiversity Science, Ecosystem Services & Management*, 13(1), 172–180. https://doi.org/10.1080/21513732.2017.1289246

Mehring, M., Zajonz, U., & Hummel, D. (2017). Social-ecological dynamics of ecosystem services: Livelihoods and the functional relation between ecosystem service supply and demand—Evidence from Socotra Archipelago, Yemen and the Sahel Region, West Africa. *Sustainability*, 9(7), 1037. https://doi.org/10.3390/su9071037

Morris, B. S., Chrysochoou, P., Karg, S. T., & Mitkidis, P. (2020). Optimistic vs. pessimistic endings in climate change appeals. *Humanities and Social Sciences Communications*, 7(1). https://doi.org/10.1057/s41599-020-00574-z

Neyret, M., Fischer, M., Allan, E., Hölzel, N., Klaus, V. H., Kleinebecker, T., Krauss, J., Le Provost, G., Peter, S., Schenk, N., Simons, N. K., van der Plas, F., Binkenstein, J., Börschig, C., Jung, K., Prati, D., Schäfer, D., Schäfer, M., Schöning, I., ... Manning, P. (2021). Assessing the impact of grassland management on landscape multifunctionality. *Ecosystem Services*. https://doi.org/10.1016/j.ecoser.2021.101366

Oltedal, S., Moen, B.-E., Klempe, H., & Rundmo, T. (2004). *Explaining risk perception: An evaluation of cultural theory*. Edited by Torbjørn Rundmo (85th ed.). Norwegian University of Science and Technology, Department of Psychology.

Oteros-Rozas, E., Martín-López, B., González, J. A., Plieninger, T., López, C. A., & Montes, C. (2013). Socio-cultural valuation of ecosystem services in a transhumance social-ecological network. *Regional Environmental Change*, 14(4), 1269–1289. https://doi.org/10.1007/s10113-013-0571-y

Pascual, U., Balvanera, P., Díaz, S., Pataki, G., Roth, E., Stenseke, M., Watson, R. T., Başak Dessane, E., Islar, M., Kelemen, E., Maris, V., Quaas, M., Subramanian, S. M., Wittmer, H., Adlan, A., Ahn, S. E., Al-Hafedh, Y. S., Amankwah, E., Asah, S. T., ... Yagi, N. (2017). Valuing nature's contributions to people: The IPBES approach. *Current Opinion in Environmental Sustainability*, 26–27, 7–16. https://doi.org/10.1016/j.cosust.2016.12.006

Peter, S. (in press). What determines individual demand for ecosystem services? – Insights from a social science study of three German regions. *Nature and Culture*.

Peter, S. (2020). Integrating key insights of sociological risk theory into the ecosystem services framework. *Sustainability*, 12(16), 6437. https://doi.org/10.3390/su12166437

Potschin-Young, M., Haines-Young, R. H., Görg, C., Heink, U., Jax, K., & Schleyer, C. (2018). Understanding the role of conceptual frameworks: Reading the ecosystem services cascade. *Ecosystem Services*, 29, 428–440. https://doi.org/10.1016/j.ecoser.2017.05.015

Price, J. C., Walker, I. A., & Boschetti, F. (2014). Measuring cultural values and beliefs about environment to identify their role in climate change responses. *Journal of Environmental Psychology*, 37, 8–20. https://doi.org/10.1016/j.jenvp.2013.10.001

Quintas-Soriano, C., Brandt, J. S., Running, K., Baxter, C. V., Gibson, D. M., Narducci, J., & Castro, A. J. (2018). Social-ecological systems influence ecosystem service perception: A Programme on Ecosystem Change and Society (PECS) analysis. *Ecology and Society*, 23(3). https://doi.org/10.5751/es-10226-230303

R Core Team. (2018). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.

Raudsepp-Hearne, C., Peterson, G. D., & Bennett, E. M. (2010). Ecosystem service bundles for analyzing tradeoffs in diverse landscapes. *Proceedings of the National Academy of Sciences of the United States of America*, 107(11), 5242–5247. https://doi.org/10.1073/pnas.0907284107

Renn, O. (2008). Concepts of risk: An interdisciplinary review. Part 1: Disciplinary risk concepts. GAIA - *Ecological Perspectives for Science and Society*, 17(1), 50–66. https://doi.org/10.14512/gaia.17.1.13

Reyers, B., Folke, C., Moore, M.-L., Biggs, R., & Galaz, V. (2018). Social-ecological systems insights for navigating the dynamics of the Anthropocene. *Annual Review of Environment and Resources*, 43(1), 267–289. https://doi.org/10.1146/annurev-environ-110615-085349

Rippl, S. (2002). Cultural theory and risk perception: A proposal for a better measurement. *Journal of Risk Research*, 5(2), 147–165. https://doi.org/10.1080/13669870110042598

Rounsevell, M. D. A., Metzger, M. J., & Walz, A. (2019). Operationalising ecosystem services in Europe. *Regional Environmental Change*, 19(8), 2143–2149. https://doi.org/10.1007/s10113-019-01560-1

Rubik, F., Müller, R., Harnisch, R., Holzhauer, B., Schipperges, M., & Geiger, S. (2019). *Umweltbewusstsein in Deutschland 2018*. Umweltbundesamt; Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit.

Scholte, S. S. K., van Teefelen, A. J. A., & Verburg, P. H. (2015). Integrating socio-cultural perspectives into ecosystem service valuation: A review of concepts and methods. *Ecological Economics*, 114, 67–78. https://doi.org/10.1016/j.ecolecon.2015.03.007

Soscisurvey. (2019). *Online survey tool*. Retrieved from www.soscisurvey.de

Spierenburg, M. (2012). Getting the message across biodiversity science and policy interfaces – A review. *GAIA - Ecological Perspectives for Science and Society*, 21(2), 125–134. https://doi.org/10.14512/gaia.21.2.11

Tansey, J., & O'r iordan, T. (1999). *Cultural theory and risk: A review. Health, Risk & Society*, 1(1), 71–90. https://doi.org/10.1080/13698579908407008

Tauro, A., Gómez-Baggethun, E., García-Frapolli, E., Chavero, E. L., & Balvanera, P. (2018). Unraveling heterogeneity in the importance of ecosystem services: Individual views of smallholders. *Ecology and Society*, 23(4). https://doi.org/10.5751/es-10457-230411

Thompson, M., Ellis, R., & Wildavsky, A. B. (1990). *Cultural theory*. Westview Press.

Turkelboom, F., Leone, M., Jacobs, S., Kelemen, E., García-Llorente, M., Baró, F., Termansen, M., Barton, D. N., Berry, P., Stange, E., Thoonen, M., Kalóczkai, Á., Vadineanu, A., Castro, A. J., Czúcz, B., Röckmann, C., Wurbs, D., Odee, D., Preda, E., ... Rusch, V. (2018). When we cannot have it all: Ecosystem services trade-offs in the context of spatial planning. *Ecosystem Services*, 29, 566–578. https://doi.org/10.1016/j.ecoser.2017.10.011

Turner, K. G., Odgaard, M. V., Bøcher, P. K., Dalgaard, T., & Svenning, J.-C. (2014). Bundling ecosystem services in Denmark: Trade-offs and synergies in a cultural landscape. *Landscape and Urban Planning*, 125, 89–104. https://doi.org/10.1016/j.landurbplan.2014.02.007

van der Plas, F. (2019). Biodiversity and ecosystem functioning in naturally assembled communities. *Biological Reviews of the Cambridge Philosophical Society*, 94(4), 1220–1245. https://doi.org/10.1111/brv.12499

Villamagna, A. M., Angermeier, P. L., & Bennett, E. M. (2013). Capacity, pressure, demand, and flow: A conceptual framework for analyzing ecosystem service provision and delivery. *Ecological Complexity*, 15, 114–121. https://doi.org/10.1016/j.ecocom.2013.07.004

Walz, A., Grêt-Regamey, A., & Lavorel, S. (2016). Social valuation of ecosystem services in mountain regions. *Regional Environmental Change*, 16(7), 1985–1987. https://doi.org/10.1007/s10113-016-1028-x

Wang, L., Zheng, H., Wen, Z., Liu, L., Robinson, B. E., Li, R., Li, C., & Kong, L. (2019). Ecosystem service synergies/trade-offs informing the supply-demand match of ecosystem services: Framework and application. *Ecosystem Services*, 37, 100939. https://doi.org/10.1016/j.ecoser.2019.100939

Washbourne, C.-L., Goddard, M. A., Le Provost, G., Manning, D. A. C., & Manning, P. (2020). Trade-offs and synergies in the ecosystem service demand of urban brownfield stakeholders. *Ecosystem Services*, 42, 101074. https://doi.org/10.1016/j.ecoser.2020.101074

Wei, T., & Simko, V. (2017). *R package "corrplot": Visualization of a Correlation Matrix (Version 0.84)*. Retrieved from https://github.com/taiyun/corrplot
Wolff, S., Schulp, C. J. E., & Verburg, P. H. (2015). Mapping ecosystem services demand: A review of current research and future perspectives. *Ecological Indicators*, 55, 159-171. https://doi.org/10.1016/j.ecolind.2015.03.016

Zoderer, B. M., Tasser, E., Carver, S., & Tappeiner, U. (2019). Stakeholder perspectives on ecosystem service supply and ecosystem service demand bundles. *Ecosystem Services*, 37, 100938. https://doi.org/10.1016/j.ecoser.2019.100938

Zwick, M., & Renn, O. (2002). *Wahrnehmung und Bewertung von Risiken. Ergebnisse des "Risikosurvey Baden-Württemberg 2001*. Akademie für Technikfolgenabschätzung; Universität Stuttgart, Lehrstuhl für Technik- und Umweltsoziologie.

## SUPPORTING INFORMATION

Additional supporting information may be found in the online version of the article at the publisher's website.

**How to cite this article:** Peter, S., Le Provost, G., Mehring, M., Müller, T., & Manning, P. (2022). Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany. *People and Nature*, 4, 218-230. https://doi.org/10.1002/pan3.10277