Terrestrial biodiversity/Data uncertainties limitations

Component is implemented in:

GLOBIO model (version GLOBIO 3.0) Components: Aquatic biodiversity Ecosystem services Land degradation Terrestrial biodiversity

Projects/Applications

Millennium Ecosystem Assessment - MA (2005) project Rethinking Biodiversity Strategies (2010) project Roads from Rio+20 (2012) project

Key publications

Alkemade et al., 2009 PBL, 2010

References

Bartholome et al., 2004 DMA, 1992 Newbold et al., 2013

Flowchart Terrestrial biodiversity. See also the Input/Output Table on the introduction page.

Data, uncertainty and limitations

Data

GLOBIO builds on data from literature reviews to construct relationships between biodiversity metrics (MSA) and environmental factors, such as land use, climate, and infrastructure. These are mainly local data on a large variety of ecosystems. Although systematically reviewed, representativeness is not guaranteed and bias may occur towards well-studied species groups, such as birds, and biodiversity-rich regions, such as tropical forests.

GLOBIO input used for assessing the impact of scenarios on biodiversity stems from various IMAGE modules. This includes data on main drivers, such as land-use change (including cropland, grazing land and forests), climate change, and nitrogen deposition. Higher resolution data on land cover are derived from GLC2000 (Bartholome et al., 2004), and data on infrastructure from the Digital Chart of the World (DMA, 1992).

Uncertainties

Uncertainties in GLOBIO outcomes arise from parameterisation of cause–effect relationships, and uncertainties about the input data. Preliminary results from an ongoing sensitivity analysis indicate the largest uncertainties are about land use and land-use intensity parameters, even though these impacts are relatively well studied. In addition, the spatial resolution of land use and landscape composition is still rather coarse, and biodiversity patterns often strongly depend on small landscape elements.

Furthermore, the effect of climate change on biodiversity is based on a limited set of species distribution models and climate change scenarios. As the patterns of climate change are uncertain, and differ strongly between global climate models, the local impact of climate change on biodiversity is also subject to substantial uncertainty.

Limitations

Biodiversity is a complex concept that cannot be measured by a single indicator. CBD agreed on a set of five indicator categories to represent the state and changes in the state of biodiversity: extent of ecosystems; abundance and distribution of species; status of threatened species; genetic diversity; and coverage of protected areas (UNEP, 2004).

GLOBIO has indicators for species abundance (MSA), for the status of threatened species (SRI), and the natural and wilderness area is an indicator for the extent of relatively intact ecosystems. In principle, the GLOBIO model handles all ecosystems in the same way, reporting the relative reduction in MSA in relation to the natural state. Thus, the loss of natural area in a desert is awarded equal weight as the loss of a biodiversity hotspot in the tropics, although results can be presented per biome. This may be a controversial assumption, but there is no straight-forward method to weight ecosystems differently and it allows to assess a broad range of drivers and their effects on biodiversity in a consistent framework and on a global scale.

To broaden the scope of GLOBIO, additional aspects, such as information on ecological traits of the species in the GLOBIO database, are used to address genetic diversity (Newbold et al., 2013). A methodology for projecting Red List Indices is now being developed. The strength of GLOBIO is that a broad range of drivers and their effects on biodiversity can be assessed in a consistent framework and on a global scale.