Terrestrial biodiversity/Description

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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.

Model description of Terrestrial biodiversity

The GLOBIO model calculates changes in biodiversity in terrestrial ecosystems, based on seven drivers of biodiversity change: land-use change, land-use intensity, climate change, atmospheric nitrogen deposition, infrastructural development, encroachment and fragmentation. Four steps in the model are distinguished:

  1. Drivers of biodiversity change derived from IMAGE results are combined with additional data;
  2. Mean Species Abundance (MSA) is calculated for each driver and year, using empirical relationships between driver and change in MSA (Alkemade et al., 2009);
  3. MSA values for each driver are aggregated to obtain one MSA values;
  4. Two additional indicators are calculated: Wilderness area, and Species Richness Index (Figure Flowchart).

MSA expresses the relationship of mean species abundance between a disturbed or managed ecosystem and an undisturbed ecosystem, on a scale from 1 (undisturbed or pristine) to 0 (complete loss). This concept is applicable for most ecosystems and dynamics of biodiversity loss, and allows to compare and aggregate across ecosystems and drivers. However, it ignores possible increase in species abundance due to natural processes or in certain agricultural systems, such as European high nature value farmland.

Land use and land-use intensity

Changes in land use and land cover are major drivers of biodiversity change. Land use includes all human activities with a spatial component, such as forestry, agriculture, infrastructure and urban development. The impact of land use on biodiversity ranges from small (where the habitat quality is too poor for a limited subset of species) to large (where complete conversion of ecosystems results in habitat loss for a large number of species).

GLOBIO calculates maps of land-use categories and intensities for the year 2000. The starting point is land-cover data from GLC2000 (Bartholome et al., 2004) on the major types of forests, rangelands and agricultural land areas, at around 30 arc seconds resolution (1x1km near the equator). These data are combined with the World Database on Protected Areas (WDPA database; UNEP-WCMC, 2005) that distinguishes protected and non-protected areas. The land-cover classes obtained are summarised as proportions of cropland, forest and pasture for IMAGE grid cells of 5x5 minutes resolution.

For the period after 2000, changes in land use and land-use intensity from IMAGE are used as regional totals and allocated to the starting map. Data on cropland areas derived from the land-use allocation model are used as a total claim for each region. Three intensity classes are distinguished on the basis of management intensity (land-use allocation) for each region, calibrated with areas of irrigated, extensive and intensive croplands from the farming system typology from the FAO (Dixon et al., 2001). Data on three forestry management types are derived from the forest management module, and data on two grazing intensities from the livestock systems module.

The pastoral grassland areas are allocated in natural rangelands. Grazing in mixed systems is assumed on managed pastures, where the natural vegetation would be densely forested biomes. The remaining grassland areas (e.g., semi-arid and arid grasslands, tundra) are considered natural areas. All regional cropland, forests and grazing areas are geographically distributed per land-use intensity class by adjusting the proportion per grid cell, avoiding protected areas (Visconti et al., 2011).

MSA values for all land-use types are derived from the literature (Alkemade et al., 2009; Alkemade et al., 2012) and applied to the land-use map, with proportions of each land-use intensity class to yield the MSA land-use map for the year considered.


Climate is a key determinant of ecosystems and biodiversity. Climate change causes shifts in species occurrence and abundance, and ultimately may lead to local species extinction. Species distribution models (SDM) are used to describe relationships between climate variables and species distribution.

Regression equations are derived for each biome by applying a large number of SDMs to a series of climate scenarios, and calculating the proportion of remaining species per grid cell (0.5x0.5 degrees). The average proportion of remaining species per grid cell is related to the global mean temperature increase (GMTI) from IMAGE for the scenario considered (Alkemade et al., 2011a). The regression equation between GMTI and the proportion of remaining species is used to derive the map of MSA levels related to climate change for a given year.


Nitrogen is a plant nutrient that stimulates growth, but some species benefit more than others and become more dominant with higher nitrogen availability. Thus, nitrogen deposition affects the species composition, mainly of plant and invertebrate species. Ecosystems can take up nitrogen without observable effects up to the level at which the assimilative capacity of the ecosystem is exceeded. This level of N input is defined as the critical load (CL).

Deposition rates of atmospheric nitrogen for current and future years are derived from IMAGE (Components Emissions and Nutrients), and the map of critical loads is based on Bouwman et al. (2002b). The nitrogen exceedance is calculated by subtracting the critical load from the estimated deposition. For forested and grassland ecosystems, the MSA map for nitrogen is derived from the regression equation between nitrogen exceedance and the proportion of remaining species. Regression equations are derived from published impact studies on the effects of a nitrogen surplus on species composition (Bobbink et al., 2010).

Infrastructure and Encroachment

The construction and use of infrastructure, such as roads, railroads and built-up area, may have multiple impacts on biodiversity. Roads have a direct impact on species, for example as the result of traffic disturbance, road kills and habitat fragmentation (see below). There are also indirect impacts, such as increased human access to natural areas, increasing hunting, gathering and tourism. Traffic disturbance reduces the breeding success of bird and mammal species, reducing their abundance close to infrastructure. Hunting and gathering reduce populations when intensity exceeds threshold values.

Data on infrastructure are derived from globally available road maps, such as the Digital Chart of the World (DMA, 1992) and the GRIP, Global Roads Inventory Project (Meijer and Klein Goldewijk, 2009). Direct impacts occur in a 500 m zone on both sides of roads and an MSA value is derived from a meta-analysis on disturbance effects (Benitez-Lopez et al., 2010).

Human settlements are the major access points to natural areas, and are likely to correlate with agricultural areas. Thus, 20 km impact zones are calculated around cropland areas and assigned as encroachment areas. Based on literature review of hunting activities, an MSA value of 0.7 is attributed to such zones. The MSA map for infrastructure and encroachment is obtained by combining the MSA map for direct (infrastructure) and indirect (encroachment) effects. In projections, the impact zone of direct effects is broadened according to the GLOBIO2.0 procedure (UNEP, 2001). Future impact zones for indirect effects are determined by the projections for agricultural areas.

Ecosystem fragmentation

Conversion of natural land to intensive cropping and road construction change vast areas of contiguous wilderness into a fragmented landscape with remnants of natural areas remaining as isolated islands. These relatively small patches are likely to house fewer species than could be expected from their habitat quality, because the individual patches may be too small to sustain viable populations of some species. Based on literature data on minimum area requirements of species, a relationship is constructed between patch size and relative number of species compared to a non-fragmented situation, known as the minimum area requirement (MAR) curve (Verboom et al., 2014). The relative number of species in a certain patch according to this MAR curve is used as a proxy for mean species abundance (MSA).

The area of natural vegetation patches is calculated by reclassifying the GLC2000 Global Land Cover data into two classes: human-dominated land (including croplands and urban areas) and natural land. Contiguous cells of natural land are grouped together and with an overlay of main roads (see above) are used to produce a map of natural land patches.

In scenario projections, patch sizes change as agricultural land use expands and as new roads emerge (Verboom et al., 2014). Changes in patch sizes also change the relative number of species and the MSA biodiversity indicator.


Total MSA values per area unit are calculated by multiplying the individual MSA values related to the separate drivers of biodiversity change (Figure Flowchart) to arrive at the total effect of all drivers. The contribution of individual drivers to biodiversity loss is also calculated. Wilderness areas are defined as natural areas with high (>0.8) MSA values. The Species Richness Index (SRI) is calculated by applying species–area relationships according to Faith et al. (2008), and using MSA values as a proxy for their intactness parameter. Aggregation from regional to global species richness is based on species lists in the Wildfinder database to avoid double counting (Musters et al., submitted).