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The expansion of large-scale plantations has a major impact on landscapes in the Tropics and Subtropics. Crops like soy bean, oil palm and rubber have led to drastic changes in land cover over the past decades, thereby altering ecosystem functions and services (ESS). Associated shifts in ESS such as climate regulation, erosion and water cycles, biodiversity as well as soil fertility or the provisioning of raw materials have been assessed through several models and software solutions (InVEST, ARIES, MIMES). However, suitable methods for the integration of a range of biodiversity assessments in agricultural landscapes are scarce.
With this study, we introduce a methodology for incorporating multiple levels of species diversity into models to allow an integrated evaluation of ESS. We collected data sets from both published and unpublished sources on the distribution of vascular plants, selected pollinator groups, ground beetles, ungulates as well as amphibians, mammals, reptiles and birds in rubber-dominated landscapes, with a focus on our study sites in Southwest China and Thailand. Based on this information, we developed a common classification scheme that enables the integration of different facets of biodiversity (species diversity and functional diversity) to complement an interdisciplinary ESS assessment.
Species diversity data were normalized against the most divers habitats reported (using habitat scores) to assess the impact of rubber cultivation on multiple levels of biodiversity. This resulted in a comparable matrix of different land use types and their suitability as habitat for the respective species groups allowing the aggregation of very diverse indicators. The findings were applied to two alternative land use scenarios in southern China to highlight the potential effects of land use and management decisions on species and functional diversity. Our results highlighted that the conservation oriented scenario did score higher for habitat suitability in both total species (+5%) as well as IUCN Red List species (+6%) assessments compared to the current state or business as usual scenarios (-2% and −3% compared to current state).
The process presented here allows for an application within established ESS software programs, in our case InVEST, using aggregated indices while additionally providing enhanced opportunities for comparable, spatially explicit assessments of the expected impact of the analyzed scenarios on specific species groups. |
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