Abstract
Landscapes have been changing at an increasing pace over the past century, with countless consequences for humans and their surrounding environments. Information on past and future land use change and the resulting alteration of landscape service provisioning are valuable inputs for policy making and planning. Land use transitions in Switzerland (2009–2081) were simulated using statistical models informed by past land use changes as well as environmental and socio-economic data (1979–2009). By combining land use types with additional contextual landscape information, eight landscape services, based on both (semi-)natural and artificial landscapes, were quantified and investigated on how they would evolve under projected land use changes. Investigation of land use transitions showed region-dependent trends of urban expansion, loss of agricultural area, and forest regrowth. Landscapes cannot accommodate all services simultaneously, and this study sheds light on some competing landscape services, in particular (i) housing at the expense of agriculture and (ii) vanishing recreation opportunities around cities as city limits, and thus housing and job provisioning, expand. Model projections made it possible to pinpoint potential trade-offs between landscape services in a spatially explicit manner, thereby providing information on service provision losses and supporting planning. While future changes are presented as extrapolations of the patterns quantified in the past, policy changes might cause deviation from the projections presented here. A major challenge is to produce socio-economic and policy scenarios to inform projections that will differ from current landscape management. Given that urban sprawl is affecting many land surfaces globally, the approach used here could be generalized to other countries in similar situations.
Highlights
Global landscapes have been changing at an accelerating pace over the last decades, potentially threatening both the natural environment and human well-being (Vitousek et al, 1997; Foley et al, 2005; Turner et al, 2007; Lambin and Meyfroidt, 2011; Mahmood et al, 2014)
We opted for a hard regionalization over a geographically weighted regression approach because: (i) this approach facilitates comparison with other Swiss studies based on the same categories and enables regional interpretation for policy making, and (ii) we assumed that predictors of land use changes differ more between than within the regions
We considered a set of 22 predictors for the land use change models based on previous studies
Summary
Global landscapes have been changing at an accelerating pace over the last decades, potentially threatening both the natural environment and human well-being (Vitousek et al, 1997; Foley et al, 2005; Turner et al, 2007; Lambin and Meyfroidt, 2011; Mahmood et al, 2014). Such land use changes have reshaped—and will continue to reshape—semi-natural and artificial environments occupied by the human population (Vitousek et al, 1997; Sala et al, 2000; MEA, 2005). Model projections can be used to inform management, which can potentially buffer adverse effects of change through appropriate mitigating policies (Lawler et al, 2014)
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