Simulation of natural capital and ecosystem services in a watershed in Northern Japan focusing on the future underuse of nature: by linking forest landscape model and social scenarios

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This is a post-peer-review, pre-copyedit version of an article published in Sustain Sci 14. The final authenticated version is available online at: https://doi.org/10.1007/s11625-018-0623-9.

A quantitative scenario approach to compare the future state of natural capital and ecosystem services (ESs) plays a key role in facilitating decision-making for the sustainable management of landscapes. In Japan, the shrinking and aging population will likely lead to a situation of underuse of natural resources, resulting in rewilding of terrestrial ecosystems. This study conducted a quantitative scenario analysis of natural capital and ESs by linking model and social scenarios on a local scale. The case study area was the Bekanbeushi River Watershed in Northern Japan. LANDIS-II model (a forest landscape model) was used to simulate the vegetation dynamics in species composition, age structure, and biomass considering impacts of forest and pasture land management. Four “population distribution” and “capital preference” scenarios were translated into forest and pasture land management. The population distribution and capital preference assumptions resulted in different consequences for natural capital and ESs. The population distribution affected the spatial allocation of abandoned pasture land and level of isolation of managed pasture land. The capital preference assumptions largely affected the consequences for ESs. Finally, these simulation results demonstrated the capacity to feed quantitative information to the narrative scenarios. Our process-based approach provides insight into the relationships among social drivers, ecological processes, and the consequences that will affect natural capital and ESs, which can contribute to decision-making and sustainability design of regions, which may face issues associated with underuse in the future.

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