WP 2 – Indexing multifunctionality in agricultural landscapes: regional quantification of biodiversity and ecosystem services

Lead: Agroscope, participating UFZ, UPM, IMDEA, VU, BOKU

WP 2 aims at establishing a modelling tool box that allows for evaluating impacts of environmental and management changes on the provision of ecosystem services and biodiversity in the five different case studies investigated in TALE. With that an important basis is set for investigating impacts of land use and management scenarios established in WP3, thus helping to explore possibilities to promote synergies between different ecosystem services and biodiversity in the future. Comparisons of case study results will disclose influences of regional characteristics on trade-offs and synergies, which may allow to derive general conclusions for the agricultural landscapes under study regarding the applicability of best management solutions.

Quantification of ecosystem services

A set of indicators for major agricultural ESS has been developed (Table 1). The selection of these indicators reflects the main services of concern in the respective case studies investigated in TALE. According to the Common International Classification of Ecosystem Services (CICES V4.3; http://cices.eu/) these indicators cover the sections of provisioning, regulation & maintenance and cultural services. By aligning indicator definitions with the CICES standard a basis for comparability between case study results within TALE and beyond is provided.

Selected set of ecosystem services indicators Table 1: Selected set of ecosystem services indicators for the five case study regions of TALE sorted by CICES sections.

Different biophysical models are employed for quantifying crop yields, soil loss, nutrient leaching, water quantity, water quality and soil organic carbon (i.e. EPIC, SWAT, CropSyst, FAMOS or PASMA). All models quantifying ecosystem services are calibrated and validated based on statistical data records, field experimental data, discharge and water quality information. In addition, information from the national emission inventory can be utilized to quantify greenhouse gas emissions and an empirical bee pollination model is used to quantify bee pollination potential in the Dutch case study. For the quantification of cultural services in the Austrian case study, an empirically derived indicator based on landscape metrics is used. The share of extensive land use, which is used as an indicator to represent an important service of lifecycle maintenance, habitat and gene pool protection, is quantified based on spatial land use and management data.

Quantification of biodiversity

Since bioclimatic conditions as well as landscape structure differ between the case study regions, it can be expected that land-use and management impacts on biodiversity differ between case studies. While empirical relationships between land-use/management patterns and diversity of different species groups can be established based on case study-specific field survey data (where such data is available), landscape metrics, which are commonly used as proxies for biodiversity at the landscape scale are used to facilitate comparability between case study regions.

The set of landscape metrics is selected based on the consideration that land use categories providing the basis for the case study intercomparison of biodiversity value should

  • be represented in all case study areas,
  • provide a broad indication of biodiversity potential, and
  • imply links to ecosystem service indicators listed in Table 1 (allowing to quantify possible trade-offs).

The indices commonly utilized in landscape diversity measurement combine the evaluation of two separate aspects of diversity: richness and evenness. Landscape richness refers to the number of different land cover types present in the landscape; the greater the number of land cover types, the more diverse the landscape in terms of richness. On the other hand, landscape evenness refers to the relative percentage of land distributed amongst these different cover types. The more equitable this distribution, the more diverse the landscape in terms of evenness. The Landscape Shannon Diversity Index (SHDI) is proposed as the metric to quantify these aspects in all case study regions of TALE.

While aspects of structural diversity can be captured based on SHDI, class-type metrics will be required to represent aspects of hemeroby and naturalness. To account for differences in biodiversity value between land use categories, class-type metrics such as Class Area (CA), Patch Density (PD), Edge Density (ED) and Effective Mesh Size (MESH) will be calculated for specific high-value or low-value land use types.