Understanding how landscape structure, the composition and configuration of land use/land cover (LULC) types, affects the relative supply of ecosystem services (ES), is critical to improving landscape management. While there is a long history of studies on landscape composition, the importance of landscape configuration has only recently become apparent. To understand the role of landscape structure in the provision of multiple ES, we must understand how ES respond to different measures of both composition and configuration of LULC. We used a multivariate framework to quantify the role of landscape configuration and composition in the provision of ten ES in 130 municipalities in an agricultural region in Southern Québec. We identified the relative influence of composition and configuration in the provision of these ES using multiple regression, and on bundles of ES using canonical redundancy analysis. We found that both configuration and composition play a role in explaining variation in the supply of ES, but the relative contribution of composition and configuration varies significantly among ES. We also identified three distinct ES bundles (sets of ES that regularly appear together on the landscape) and found that each bundle was associated with a unique area in the landscape, that mapped to a gradient in the composition and configuration of forest and agricultural LULC. These results show that the distribution of ES on the landscape depends upon both the overall composition of LULC types and their configuration on the landscape. As ES become more widely used to steer land use decision-making, quantifying the roles of configuration and composition in the provision of ES bundles can improve landscape management by helping us understand when and where the spatial pattern of land cover is important for multiple services.
Research quantifying ecosystem services (ES) – collectively, the benefits that society obtains from ecosystems –is rapidly increasing. Despite the seemingly straightforward definition, a wide variety of methods are used to measure ES. This methodological variability has largely been ignored, and standard protocols to select measures that capture ES provision have yet to be established. Furthermore, most published papers do not include explicit definitions of individual ES. We surveyed the literature on pollination ES to assess the range of measurement approaches, focusing on three essential steps: (1) definition of the ES, (2) identification of components contributing to ES delivery, and (3) selection of metrics to represent these components. We found considerable variation in how pollination as an ES – a relatively well‐defined service – is measured. We discuss potential causes of this variability and provide suggestions to address this issue. Consistency in ES measurement, or a clear explanation of selected definitions and metrics, is critical to facilitate comparisons among studies and inform ecosystem management.
ABSTRACT. To maximize specific ecosystem services (ES) such as food production, people alter landscape structure, i.e., the types of ecosystems present, their relative proportions, and their spatial arrangement across landscapes. This can have significant, and sometimes unexpected, effects on biodiversity and ES. Communities need information about how land-use activities and changes to landscape structure are likely to affect biodiversity and ES, but current scientific understanding of these effects is incomplete. The Montérégie Connection (MC) project has used the rapidly suburbanizing agricultural Montérégien landscape just east of Montreal, Québec, Canada, to investigate how current and historic landscape structure influences ES provision. Our results highlight the importance of forest connectivity and functional diversity on ES provision, and show that ES provision can vary significantly even within single landuse types in response to changes in landscape structure. Our historical analysis reveals that levels of ES provision, as well as relationships among individual ES, can change dramatically through time. We are using these results to build quantitative ES-landscape structure models to assess four future landscape scenarios for the region: Periurban Development, Demand for Energy, Whole-System Crisis, and Green Development. These scenarios integrate empirical and historical data on ES provision with local stakeholder input about global and local social and ecological drivers to explore how land-use decisions could affect ES provision and human well-being across the region to the year 2045. By integrating empirical data, quantitative models, and scenarios we have achieved the central goals of the MC project: (1) increasing understanding of the effects of landscape structure on biodiversity and ES provision, (2) effectively linking this knowledge to decision making to better manage for biodiversity and ES, and (3) creating a vision for a more sustainable socialecological system in the region.
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