IMAGEIntegrated Model to Assess the Global Environment.

Terrestrial biodiversity


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GLOBIO model for terrestrial biodiversity in IMAGE 3.0
Flowchart Terrestrial biodiversity. See also the Input/Output Table on the introduction page.

Key policy issues

  • What is the future rate of terrestrial biodiversity loss in the absence of additional policies and measures?
  • What are the key pressure factors causing biodiversity loss?
  • How will nature conservation policies and measures to reduce the key pressure factors of biodiversity loss contribute to meeting the targets of the UN Convention on Biological Diversity (CBD)?


Biodiversity is declining rapidly with consequences for human well-being and ultimately even for the existence of humankind (MA, 2005). The Conference of Parties to the Convention on Biological Diversity (CBD) formulated the long-term vision: ‘By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people’. For the period up to 2020, five strategic goals comprising 20 biodiversity targets have been adopted, referred to as the Aichi targets (sCBD, 2010).

IMAGE assesses the impacts of socio-economic drivers on the physical environment, such as climate change, land-use change and pollution, and these are input to the GLOBIO model to evaluate their impacts on biodiversity. GLOBIO was developed to provide information to policymakers at international level on current and future biodiversity, (Alkemade et al., 2009). The model delivers quantified results on the impact of environmental drivers and potential policy options on biodiversity. Potential trends in biodiversity are addressed in future scenarios, including the expected outcome in the absence of additional policies to prevent biodiversity loss.

GLOBIO builds on a series of cause–effect relationships between environmental drivers and biodiversity, based on state-of-the-art knowledge.

The key measure of biodiversity in GLOBIO is the mean abundance of original species relative to their abundance in undisturbed ecosystems. Referred to as the mean species abundance (MSA), this measure reflects the degree to which the ecosystem is intact and is similar to the Biodiversity Intactness Index (Scholes and Biggs, 2005). New methods combine MSA estimates and species area relationships to estimate species loss at different geographical levels (Faith et al., 2008; Musters et al., submitted). The resulting Species Richness Index (SRI) is calculated as one of the end points of GLOBIO. The current version of SRI only covers vertebrate species. In addition, natural areas with high MSA values, defined as wilderness areas, are identified by their extent, landcover type and regional spread. The drivers of biodiversity loss considered are land-cover change, land-use intensity, fragmentation, climate change, atmospheric nitrogen deposition and infrastructural development.

Input/Output Table

Input Terrestrial biodiversity component

IMAGE model drivers and variablesDescriptionSource
Protected area - grid Map of protected nature areas, limiting use of this area. Drivers
Global mean temperature Average global temperature. Atmospheric composition and climate
Land cover, land use - grid Multi-dimensional map describing all aspects of land cover and land use per grid cell, such as type of natural vegetation, crop and grass fraction, crop management, fertiliser and manure input, livestock density. Land cover and land use
Management intensity crops Management intensity crops, expressing actual yield level compared to potential yield. While potential yield is calculated for each grid cell, this parameter is expressed at the regional level. This parameter is based on data and exogenous assumptions - current practice and technological change in agriculture - and is endogenously adapted in the agro-economic model. Agricultural economy
Management intensity livestock Management intensity of livestock, expressed at the regional level. This parameter is based on data and exogenous assumptions, i.e. current practice and technological change in livestock sectors, and is endogenously adapted within the Agricultural economy component. Agricultural economy
Nitrogen deposition - grid Deposition of nitrogen. Emissions
External datasetsDescriptionSource
Biome and eco-region - grid Biomes are groups of plants and animals, often referred as ecosystems. Their spatial distribution on Earth is defined by climatic and geographical conditions defined as contiguous areas with similar climatic conditions. Biomes are often referred to by climatic conditions (such as, tropical, temperate, boreal) and physiological characteristic (such as, grassland, deciduous trees, coniferous trees).
High resolution land cover (GLC2000) - grid The database contains two levels of land cover information—detailed, regionally optimized land cover legends for each continent and a less thematically detailed global legend that harmonizes regional legends into one consistent product. The land cover maps are all based on daily data from the VEGETATION sensor on-board SPOT 4. EC-JRC
MSA effect values Database on empirical relationships between environmental pressures and reduction in mean species abundance for terrestrial ecosystems.
Nitrogen critical load Level of N deposition or concentration that should not be exceeded.
Road-map - grid Global road map.
Species-area relationships Number of species in relation to the size of an ecosystem.

Output Terrestrial biodiversity component

IMAGE model variablesDescriptionUse
MSA (mean species abundance) - grid Mean Species Abundance (MSA) relative to the natural state of original species.
Land use and land-use intensity - grid High resolution land use and land use intensity based on GLC2000 and IMAGE land cover and land use.
Wilderness area - grid Non-agricultural areas close to their natural state, with MSA values above 0.8. Final output
SRI (species richness index) - grid Species richness calculated from MSA and species area curves. Final output