Measuring ecosystem multifunctionality across scales

We used 18 ecosystem service indicators of the European MAES dataset (Maes et al 2015) to define ecosystem multifunctionality. The indicators build on various reported and modelled ecosystem services, and present either the actual use or the potential supply of ecosystem services across all 28 EU Member States between 2008 and 2011 (table 1, see S2 for a full description of the ecosystem service indicators). Each indicator was mapped to the lower Local Administrative Units (LAU level 2), representing municipalities or equivalent units (Maes et al 2015). The regional level is represented by NUTS3 regions (Nomenclature of Territorial Units for Statistics), which is a standardized and hierarchical geographical system used across EU Member States. For further analyzes, ecosystem service indicators were corrected for the surface area of the municipalities and standardized between 0 and 1 (see S2 for a correlation analysis between the standardized ecosystem service indicators).

We assessed multifunctionality using two metrics: (i) alpha-multifunctionality, defined as the diversity of ecosystem service supply at the municipality level; and (ii) beta-multifunctionality, defined as the unique ecosystem service contribution of each municipality to the regional ecosystem service supply. To assess alpha-multifunctionality, we calculated the Gini-Simpson diversity index (formula in figure 1) using the function 'diversity' of the vegan package in R (Oksanen et al 2013). This metric accounts for the number of ecosystem services, their supplies, as well as the balance between them and therefore remediates issues with richness or abundance indicators (box 1; Simpson 1949; see S3 for a comparison of multifunctionality indices). As a result, alpha-multifunctionality indicator does not inflate if a municipality supplies only few, but very high levels of ecosystem services (S3).

Beta-multifunctionality was assessed by evaluating the total abundance-based dissimilarities of ecosystem service supply among all municipalities of one NUTS3 region using the 'beta.pair.abund' function of the betapart package in R (Baselga and Orme 2012). This metric again takes into account both service identity and abundance. The dissimilarity matrices were measured by using the Bray Curtis index: $B{C}_{ij}=1-\tfrac{2{C}_{ij}}{{S}_{i}+{S}_{j}},$ where i and j are the two municipalities; S_i is the summed supply of ecosystem services provided on site i; S_j is the summed supply of ecosystem services provided on site j; C_ij is the sum of only the lesser ecosystem service supplies for each ecosystem service found in both sites (Bray and Curtis 1957). The dissimilarity between sites A and B in figure 1 is:

$$\begin{eqnarray*}&&B{C}_{ab}\,=\,1\,-\,\,\displaystyle \frac{2(0.0+0.2+0.2+0.2)}{(0.2+0.2+0.2+0.2)\,+\,(0.0+\,0.5+0.5+0.5)}=\,\mathrm{0.47.}\,\end{eqnarray*}$$

Beta-multifunctionality for each municipality was then calculated as the average dissimilarity between that municipality and all other municipalities within that region. To facilitate comparability, the beta-multifunctionality values within each region were rescaled between 0 and 1, with high values indicating those municipalities with unique services within the region, both in terms of service identity and abundance. Due to this ranking, there are no distinct patterns of beta-multifunctionality beyond the regional borders (figure S3.2). 123 municipalities are LAU2 and NUTS3 units at the same time (e.g. NUTS3 Code 'FR101' = LAU2 Code 'FR75056'), and therefore have no beta-multifunctionality values. In contrast to the original alpha and beta-diversity metrics developed by Whittaker in 1960, alpha and beta as presented in this paper are independent from one another (Whittaker 1960, Jost 2007, Baselga and Orme 2012).

In order to explain the spatial patterns of alpha- and beta-multifunctionality across Europe, we investigated whether alpha- or beta-multifunctionality were correlated to population densities (data source: EEA 2009) using Pearson correlations (see S4 for the results). We also examined the spatial co-occurrence of alpha- and beta-multifunctionality with Europe-wide Land System Archetypes, which are defined as characteristic patterns of land use extent and intensity. Fifteen categories of Land System Archetypes had been mapped at 3*3 km² resolution by Levers et al (2018) using Self-Organizing Maps (Levers et al 2018, data description in S5). We reclassified alpha- and beta-multifunctionality into nine composite classes by quantiles (see figure 3) and calculated the proportion of each class per Land System Archetype. As these had been mapped only for the EU-27 for the year 2006, this analysis could only be performed for the current European Member States, excluding Croatia.

Land System Archetypes and our multifunctionality metrics are partly based on the same input information (in particular CORINE land cover data), which might lead to problems with circular reasoning, also known as data endogeneity (Kümmerle et al 2013). We therefore conducted a sensitivity analysis by recalculating alpha-multifunctionality using only those ecosystem service indicators that are not based on the same information (see S5) and calculating the proportion of each alpha-multifunctionality class per Land System Archetype. The results revealed almost the same ranking of Land System Archetypes as when using the full dataset, with only minor dissimilarities in the proportions of alpha-multifunctionality classes, suggesting that the results based on the complete set of indicators are robust and non-endogenous (see figure S5.1). In the same way as described above, we also assessed the relation between multifunctionality and Archetypical Change Trajectories (land system change between 1990 and 2006; Levers et al 2018) (see figure S5.2).

Pearson correlations between alpha- and beta-multifunctionality within each NUTS3 region were calculated to further explore ecosystem service distributions at the regional scale. Municipalities with unique ecosystem service contributions were identified as outliers of high beta-multifunctionality using correlation plots. The direction of the correlations is positive, if the unique municipalities have a high alpha-multifunctionality, and negative, in case of low alpha-multifuncitonality. Ten case examples were thus selected, representing the NUTS3 regions with the five strongest positive and the five strongest negative correlations between alpha- and beta-multifunctionality (figure 5). NUTS3 regions with four or less municipalities were excluded from this analysis (in total 147 NUTS3 regions, figure S3.2).

All spatial and statistical analyzes were performed using R version 3.4.2 (R Core Team 2013).

The diversity of ecosystem services supplied within municipalities, here defined as alpha-multifunctionality, varied throughout Europe following a latitudinal gradient (figure 2). Larger regions of high alpha-multifunctionality were found in the southern parts of Europe, e.g. Portugal, Spain, Southern France (incl. Corse), Italy (incl. Sardinia), Croatia and Greece. Larger regions of low alpha-multifunctionality were observed in the western and eastern parts of Europe (e.g. Ireland, England, Denmark, Poland, Romania or Bulgaria). In addition, notable patterns of alpha-multifunctionality were observed in areas with strong urban-rural gradients across Europe. Alpha-multifunctionality strongly decreased towards the city centers of metropolitan areas, as exemplified for the cities of London and Paris (figure 2). While this was evident for densely populated areas, a general correlation between population density and alpha-multifunctionality across the whole study region was not apparent (S4).

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As beta-multifunctionality is defined and calculated with reference to each specific NUTS3 region, no clear continuous pattern can be observed across Europe, except for some mountainous regions (e.g. Pyrenees, Carpathians; figure S3.2). Moreover, we found no correlation between population densities and beta-multifunctionality (figure S4.1). The bivariate map (figure 3) provides a composite mapping of both alpha- and beta-multifunctionality, which allows for identification of particular patterns of the diversity and uniqueness of ecosystem service supplies at the municipality level. Some regions which had been identified as highly multifunctional in the alpha-multifunctional map (e.g. Greece), displayed low beta-multifunctionality. Other regions of low alpha-multifunctionality, displayed a high level of beta-multifunctionality (e.g. England). Distinctive combinations of alpha- and beta-multifunctionality and example regions are shown in table 2.

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The overlap analysis with Land System Archetypes showed that areas with high alpha- and beta-multifunctionality generally coincided with low-intensity management systems, including fallow farmland and permanent cropland, with the exception of high-intensity forest (figure 4). Coldspots of both alpha- and beta-multifunctionality, on the other hand, coincided with medium- to high-intensity management systems. Urban built-up areas generally had a low degree of alpha-multifunctionality, while a large proportion of this land use category was characterized by high beta-multifunctionality.

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By comparing alpha- and beta-multifunctionality to the Archetypical Change Trajectories we found hotspots of alpha- and beta-multifunctionality primarily within the trajectories representing major land use conversions (e.g. forest expansion on grassland, loss of permanent cropland, deforestation resulting from agricultural expansion) rather than within trajectories representing changes along intensity gradients (e.g. intensification or extensification of the same land use systems) (figure S5.2). In addition, we identified the largest proportion of coldspots of alpha-multifunctionality in regions characterized by 'Intensification to high-intensity cropland', 'Urban expansion', and 'De-intensification of high-intensity livestock farming' (figure S.5.2).

The correlations between alpha- and beta-multifunctionality within each NUTS3 region were negative for 801 NUTS3 regions (with 297 significant correlations: p < 0.005) and positive for 377 NUTS3 regions (with 75 significant correlations: p < 0.005). Figure 5 shows the ten case examples with the five strongest negative (upper part of figure 5) and the five strongest positive correlations (lower part of figure 5), each presenting one NUTS3 region. Within the example regions, there were up to three municipalities identified as unique municipalities driving the correlation between alpha- and beta-multifunctionality.

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In regions with a negative correlation (figure 5, upper row), unique municipalities are characterized by a low alpha-multifunctionality. They have a more specialized, narrow ecosystem service supply than other municipalities within the same region: the unique municipalities within the two Finish (FI1C3, FI1D4) and one of the German (DE27A) regions supply high levels of provisioning services in otherwise mostly rural and recreational areas; the municipality of Oostende in the Belgian case region (BE255) provides high levels of water abstraction; the unique municipality in the German case region of Wangerooge (DE94A) has the highest recreation potential and represents the part of the region with the highest share of Natura 2000 protected area.

In regions with a positive correlation (figure 5, lower row), unique municipalities are characterized by a high alpha-multifunctionality. They do not have a particularly specialized, but a generalized ecosystem service supply: Most municipalities in the Swedish and the German case regions (SE221, SE121, DEC05) lack a sufficient supply of provisioning services. Those municipalities that do supply timber and other provisioning services were here identified as unique. In the densely populated regions of Southern Athens (EL304) and Portsmouth (UKJ31), there is a lack of many ecosystem services in all municipalities (e.g. food and fodder, timber growing stock, pollination, soil retention) except for those municipalities that are less populated, here characterized by a high beta-multifunctionality.

In line with previous studies (Mouchet et al 2017, Stürck and Verburg 2017), our results demonstrate that the diversity of ecosystem service supplies varies largely across Europe, following climatic and topographic gradients (see figure 2: south–north gradient, mountainous areas versus lowlands). Many ecosystem services (e.g. food and fodder productions, carbon sequestration, net ecosystem productivity) follow a latitudinal gradient as they are largely influenced by the number of growing degree days (Haberman and Bennett 2019) and primary production (Mouchet et al 2017). Increased levels of alpha-multifunctionality are further apparent in mountainous areas due to increased levels of biodiversity and landscape heterogeneity (Grêt-Regamey et al 2012, Crouzat et al 2015), formerly identified as important drivers (Soliveres et al 2016, Birkhofer et al 2018). While such non-anthropogenic factors drive the potential capacity of an area to supply ecosystem services, their actual supply is directly influenced by human land use and management. In this study, we therefore focused on two anthropogenic driving factors: population density (as a proxy that reflects the overall human influence on land and its natural capital) and land use intensity.

While population density could not explain patterns of alpha- or beta-multifunctionality across Europe, it still was a relevant driving factor in highly populated metropolitan areas (see the case of London or Paris). In these examples, the capacity to supply multiple ecosystem services decreased with increasing population densities. A recent study by Haberman and Bennett (2019) yielded very similar findings for metropolitan areas worldwide, confirming that population densities are crucially influencing the supply of ecosystem services. In most cases, only the hinterlands of densely populated areas are capable of producing a high diversity of ecosystem services (see the case of Southern Athens and Portsmouth; Haberman and Bennett 2019). Our results on the spatial co-occurrence of multifunctionality and Land System Archetypes characterized urban areas as coldspots of alpha-multifunctionality across Europe. Nevertheless, most urban areas contributed importantly to the regional multifunctionality by supplying ecosystem services that are unique in these regions (e.g. water abstraction, see BE255). Our results further clearly confirmed the findings of local-scale studies (Allan et al 2015, Queiroz et al 2015, Rodríguez-Loinaz et al 2015, Balzan et al 2018, Le Clec'h et al 2019) that low-intensity management systems support higher ecosystem multifunctionality, also on the continental scale. Regions of high land use intensity are often managed to maximize a specific set of ecosystem services only. However, such areas often contribute importantly to regional multifunctionality, by providing unique services (e.g. food and fodder in a rural area; see FI1C3, FI1D4, DE27A).

We propose a new approach to assess multifunctionality across spatial scales that goes beyond the scope of standard multifunctionality assessments on a single scale. The approach allows identifying areas that provide ecosystem services, which are unique on a larger scale. Until now, multifunctionality assessments often have implied that more diverse or abundant supplies of ecosystem services are 'good per se' and a generally desired management aim. However, there are inherent limits to ecosystem multifunctionality and not all ecosystem services can be maximized at all scales (Turkelboom et al 2018). Consequently, we have to better understand where and at which scale multifunctionality is actually needed (Fischer et al 2017, Turkelboom et al 2018, Plieninger et al 2019). While some ecosystem services are important at all scales (e.g. air quality), other services are primarily demanded and valued at a specific scale where the demand is particularly high (e.g. food production in a densely populated area, flood regulation in a city nearby a river) (Fisher et al 2009, Raudsepp-Hearne and Peterson 2016).

Ecosystem services that are unique within a larger area might therefore have a higher value to society than abundant ecosystem services. In this way less diverse, but specialized municipalities might be undervalued if they are not viewed within a larger spatial context. Our new indicator of beta-multifunctionality evaluates the 'uniqueness' of services supplied by municipalities on a regional scale. We argue that, in order to actually increase the benefits that people derive from nature, the promotion of ecosystem multifunctionality (i.e. the diversity or richness of ecosystem services) should be complemented by the reflection of the actual values of the particular area to society. In this context, uniqueness is an important indicator when analyzing the demand for ecosystem services (Quintas-Soriano et al 2019).

Our proposed approach can be applied at any spatial scale. We have chosen the municipality versus regional scale, because landscapes have traditionally been shaped by human land use at the municipality level (Vos and Meekes 1999, Reyers et al 2013). Municipalities are therefore expected to supply a distinct set of ecosystem services and are often regarded as the relevant spatial units to assess the amounts and interactions of ecosystem services (Raudsepp-Hearne et al 2010, Queiroz et al 2015, Rodríguez-Loinaz et al 2015). Many policies target—or are implemented by—municipal decision structures (e.g. responsibilities for regulating land use, management of drinking water, maintenance of restoration infrastructures, regulation of tourism operations; Raudsepp-Hearne and Peterson 2016). Moreover, municipalities within one region are not isolated, but important ecosystem service flows (defined as spatial movements of ecosystem-derived material, energy and information; Schröter et al 2018) exist between municipalities on the regional scale (Spake et al 2017). Finally, fostering socio-ecological resilience requires increasing multifunctionality for a diverse set of stakeholders (e.g. recreationists, rural population, farmers and other land managers, environmental organizations, municipal administration), which come together at regional scale (Howe et al 2014, Holt et al 2015, Turkelboom et al 2018).

Choosing spatial scales always creates strong boundaries. Future research on multifunctionality should therefore apply this method at other spatial scales (e.g. focus on unique regions at national scale), depending on the ecosystem services considered and the research questions at stake. In a similar reasoning, alpha- and beta-multifunctionality might be applied to assess regional versus national multifunctionality, or even parcel versus municipality multifunctionality. This choice will ultimately be guided by the policy application of interest, or the specific management goal (Holt et al 2016, Raudsepp-Hearne and Peterson 2016). If an analysis would for instance be performed to inform national fiscal redistribution over municipalities, a municipality versus nation scale is the relevant scale. Likewise, if an analysis would be performed to demonstrate how multifunctional mountainous areas contribute to downstream or lowland areas (Grêt-Regamey et al 2012), non-administrative scales, such as transboundary watersheds, could be chosen.

Land management options to increase ecosystem multifunctionality are evident: we need to adapt to low-intensity management systems that are capable of supplying a more diverse set of ecosystem services. Our findings clearly point in this direction and confirm the results of numerous local studies (e.g. Allan et al 2015, Queiroz et al 2015, Rodríguez-Loinaz et al 2015, Balzan et al 2018, Le Clec'h et al 2019). However, in order to avoid scale mismatches (defined as problems that occur when the scale of environmental variation and the scale of use or management of ecosystem services are not aligned; Raudsepp-Hearne and Peterson 2016) and to design policies at the right scale, we need to better understand (i) the varying capacities of landscapes to supply certain services (as well as inherent limits to multifunctionality) and (ii) the varying importance of ecosystem services for different areas and scales (as explained above).

While sustainable management aims at promoting as many ecosystem services as possible and in large amounts, this might not always be possible at the local scale. Ecosystems have varying capacities to supply ecosystem services, and trade-offs exist that are inherent to the system (Felipe-Lucia et al 2018), such as those between organic matter turnover and soil carbon stocks (Lavorel et al 2017), or timber production and habitat quality (Felipe-Lucia et al 2014). If the supply of multiple services within one area cannot be maximized at a single time (Holt et al 2016, Cord et al 2017, Turkelboom et al 2018), achieving multifunctionality at the landscape scale (i.e. high beta-multifunctionality) would be the goal. However, the fact that multifunctionality can be purposefully maximized at the landscape scale should not justify reducing multifunctionality at the local scale. Some ecosystem services cannot be replaced by a supply of this service elsewhere, especially regulating ecosystem services (e.g. flood regulation, air quality) (Fisher et al 2009, Raudsepp-Hearne and Peterson 2016). Beta-multifunctionality can hence be seen as a way to accommodate competing land uses when maximum alpha-multifunctionality is not possible.