Abstract

Ecosystem Services (ESs) are the goods and services supplied by ecosystems. In order to fully understand their contribution to human wellbeing, there is a need to identify them, assess their supply, recognise areas where they appear together repeatedly and analyse the interactions that may exist among them. Most of these tasks are also specifically required by the European Biodiversity Strategy for 2020, which asks Member States, by 2014, to identify key ESs and to spatially assess their supply and demand (European Commission, 2011). Nevertheless, these are difficult tasks and to date they have been only partly performed: existing studies in fact have typically focused on a small sub-set of ESs and made use of information that poorly reflects the actual variability of the ESs distribution across a region. The present research aims to fill these gaps, by developing methods involving a wide set of ESs and providing a detailed ESs assessment, based on spatial and statistical analyses. The methods have been tested on an Alpine region of Italy, Trentino. The Alps present a heterogeneous landscape, resulting from the combination of natural and urbanized environments, that allows the supply of a wide range of ESs. The research has four specific objectives. The first objective focuses on the selection and the representation over specific spatial units of the real supply of multiple ESs. Operatively, 51 experts from the local administrative offices and research institutes have been involved in the selection of the most important ESs and spatial indicators for the case study. The experts identified 25 ESs and 57 representative spatial indicators (1 to 5 indicators for each service), and provided data for indicators mapping. To consider the heterogeneity of the ESs supply across the region, indicators were mapped over 20 different spatial units, including: land cover classes, cadastral parcels, fishing zones and catchments.The second objective is to develop and test a statistical method for identifying key indicators that are spatially-explicit and able to measure the biophysical, socio-cultural and economic values of ESs (both in terms of stock and flow). Spearman pairwise correlation analysis was performed among the indicators of the same service in order to identify the highly correlated ones, hence deemed to provide redundant information. Key indicators were selected among the lowly correlated ones. 35 indicators were selected for the case study (out of the 57 initial indicators). The analysis showed that there is a minimum number of key indicators for each ES. Accordingly, three general rules were identified for the selection: (i) if the supply of an ES is regulated, both its biophysical-stock and biophysical-flow indicators must be selected, (ii) if multiple stock (flow) biophysical indicators for a single ES are mapped over different spatial units, all stock (flow) indicators must be maintained, (iii) socio-cultural or economic indicators are always selected as key indicators. The third objective is to develop and test a statistical method for defining bundles of ESs, as sets of spatially correlated services. Principal Component Analysis was used to summarize the information of the 35 indicators, while hierarchical clustering was applied to identify 11 ESs clusters. Clusters were turned into bundles by analyzing the spatial variability of the services due to biophysical (e.g. morphological conditions) and human (e.g. land use) factors. The results of the analysis show that in Trentino multiple ESs can be grouped in a few number of bundles with a complex shape. In particular, areas with poor ESs supply are grouped in one single bundle and the largest bundle follows the spatial distribution of a single land cover class: i.e. forest.The fourth objective is to develop a method to study interactions among ESs, by combining statistical and spatial analyses. In fact, the supply of a given ES is correlated with the supply of other ESs and it is affected by multiple external factors. Correlations may be positive when an increase in the supply of one service corresponds to higher supplies of other services (i.e. synergies), or negative when an increase in the supply of one service corresponds to lower supplies of other services (i.e. tradeoffs). The degree of interactions among 35 key indicators is determined by performing a Spearman pairwise correlation analysis. The latter enabled to identify six patterns of ESs interactions, one pattern of tradeoffs and five of synergies. The analysis showed that the local land use management has not compromised the capacity of ecosystems to provide regulating services while supplying the provisioning ones. The external factors causing the variability of the services across the region were identified and explained by means of spatial and Spearman correlation analyses among the ESs principal components. Principal components were turned into drivers of change by analyzing the spatial variability of the ESs due to biophysical (e.g. forest density) and human (e.g. land use) factors. Land use management was found as the external factor that causes the greatest variability of the ESs distribution across the region. Within forest areas, forest management activities that involve loss of vegetation were found as the main drivers of ESs change. This research aimed to consider a wide set of ESs and information able to reflect the actual variability of the services distribution across a region. It proposed a scientifically sound methodology to deal with the main issues of the ESs spatial assessment, that may reveal efficiently applicable in other geographical areas where ESs are heterogeneously supplied.