Meta-Analyses and Cause–Effect Relationships
To construct cause–effect relationships for each driver we conducted meta-analyses of peer-reviewed literature. Meta-analysis is the quantitative synthesis, analysis, and summary of a collection of studies and requires that the results be summarized in an estimate of the ‘effect size’ (Osenberg and others 1999). MSA is considered to be the effect size in our analyses. Meta-analyses were performed by first scanning the peer-reviewed literature using a relevant search profile in tools, such as the SCI—Web of Science. Secondly, we selected papers that present data on species composition in disturbed and undisturbed situations. Thirdly, these data were extracted from the paper and MSA values and their variances were calculated. MSA values were calculated for each study by first dividing the abundance of each species, recorded as density, numbers, or relative cover, found in disturbed situations by its abundance found in undisturbed situations, then truncate these values at 1, and finally calculate the mean over all species considered in that study. Species not found in undisturbed vegetations were omitted. Finally, a statistical analysis was carried out by using S-PLUS 7.1 (Insightful Corp 2005).
To find relevant papers for land use, land-use intensity, and harvesting (including forestry), SCI—Web of Science was queried in April 2008 using the key words species
diversity, biodiversity, richness, or abundance; land use, or habitat conversion; and pristine, primary, undisturbed, or original. The land-use types were categorized into 10 classes: primary vegetation, lightly used forests, secondary forests, forest plantations, livestock grazing, man-made pastures, agroforestry, low-input agriculture, intensive agriculture, and built-up areas (Table 2). A linear mixed effect model was fitted to the data (Venables and Ripley 1999).
Table 2 Proportions (%) of Low-Input and Intensively Used Agricultural Land for Selected World Regions, Based on Farming System Descriptions (Dixon and others 2001) and GLC2000
The analysis for N deposition in excess of critical loads (N exceedance) was based on data from empirical N critical-load studies (Bobbink and others 2003). Additional data were obtained from SCI—Web of Science queries in 2007. Data were analyzed for separate biomes using linear or log-linear regression.
In addition to papers collected for GLOBIO2 (UNEP 2001), Scopus and Omega (Utrecht University Digital Publications Search Machine) were queried using the key words: road impact, infrastructure development, road effect, road disturbance, and road avoidance in August 2008. For each impact zone derived from UNEP/RIVM (2004) we estimated MSA using generalized linear mixed models (Pinheiro and Bates 2000). The impact zones include effects of disturbance on wildlife, increased hunting activities, and small-scale land-use change along roads.
The relationship between MSA and patch size was built upon data on the minimum area requirement of animal species defined as the area needed to support at least a minimum viable population (Verboom and others 2007). The proportion of species for which a certain area is sufficient for their MVP is calculated and considered a proxy for MSA. A linear mixed effect model was fitted to the data (Venables and Ripley 1999).
The cause–effect relationships for climate change are based on model studies. Species Distribution Models from the EUROMOVE model (Bakkenes and others 2002) were used to estimate species distributions for the situation in 1995 and the forecasted situation in 2050 for three different climate scenarios. For each grid cell the proportion of remaining species were calculated by comparing the species distribution maps for 1995 and for 2050 (Bakkenes and others 2006). For each biome, a linear regression equation was estimated between the proportion of remaining species and the global mean temperature increase (relative to pre-industrial) (GMTI), corresponding to the different climate scenarios. Additionally, the expected stable area for each biome calculated for different GMTIs was derived from Leemans and Eickhout (2004). They presented percentages of stable area of biomes at 1, 2, 3, and 4°C GMTI. Linear regression analysis was used to relate the percentages and GMTI. Stable areas for each biome (IMAGE), or group of plant species occurring within a biome (EUROMOVE) are considered proxies for MSA.
Input Data
The data for land cover and/or land use—and changes therein—come from the IMAGE model at a 0.5 by 0.5° resolution. To increase the spatial detail within each IMAGE grid cell, we calculated the proportion of each type of land cover and/or land use from the Global Land Cover 2000 (GLC2000) map, representing the year 2000 (Bartholome and others 2004). GLC2000 distinguishes 10 forest classes, 5 classes of low vegetation (grasslands and scrublands), 3 cultivated land classes, ice and snow, bare areas and artificial surfaces (Bartholome and others 2004). To translate these classes to the land-use categories considered here we first aggregated the GLC2000 classes into broader land-cover classes (Table 4). Secondly we assigned different land-use intensity classes to these broad classes. ‘Cultivated and managed areas’ was divided into ‘intensive agriculture’ and ‘low input agriculture’ based on estimates of the distribution of intensive and low-input agriculture in different regions of the world, from Dixon and others (2001). We assumed 100% intensive agriculture in regions not covered by these estimates (Table 2). ‘Mosaic of cropland and forest,’ was treated as a 50–50% mixture of ‘low input agriculture’ and ‘lightly used forest’.
‘Scrublands and grasslands’ were divided into ‘pristine vegetations,’ ‘livestock grazing areas,’ and ‘man-made pastures’. ‘Livestock grazing areas’ were estimated by IMAGE for current and future years and distributed, proportionally, to all GLC2000 classes containing low vegetation. ‘Man-made pastures’ were assigned to the GLC2000 class of ‘herbaceous cover’ if found in originally forested areas according to the potential vegetation map generated by IMAGE (based on the BIOME model, Prentice and others 1992). For future scenarios, the change in agricultural land and grazing areas calculated by IMAGE for each world region, was added to current land use and, proportionally, distributed over all grid cells.
Similarly, we assigned the land-use categories ‘lightly used forest,’ ‘secondary forest,’ and ‘forest plantations’ to forest classes of GLC2000. We used national data on forest use from FAO (2001) and assigned the derived fractions for each region, proportionally, to all grid cells that contain one or more GLC2000 forest classes (Table 3). For future scenarios, we used calculations of future timber demands to obtain the areas needed to produce the timber, and proportionally distributed the new fraction to each grid cell.
Table 3 Proportions (%) of Forest-Use Classes Derived from FAO (2001) and GLC2000 for Different World Regions
Water bodies are excluded from the analyses and ‘artificial surfaces’ are all considered to be built-up areas. Bare areas are considered to be areas of primary vegetation if the potential vegetation is ice, snow, tundra, or desert, according to the BIOME model. Scrub classes are considered to be secondary vegetation if the potential vegetation is forest, except for boreal forests, where scrub vegetation is assumed to be part of the natural ecosystem.
IMAGE simulates global atmospheric N deposition, based on data on agricultural and live stock production (MNP 2006). A critical-load map for major ecosystems was derived from the soil map of the world and from the sensitivity of ecosystems to N inputs (Bouwman and others 2002). The exceedance of N deposition was defined to be the amount of N in excess of the critical load and obtained by subtracting the critical load from N deposition. The N-exceedance is input for GLOBIO3.
A global map of linear infrastructure, containing roads, railroads, power lines, and pipe lines, was derived from the Digital Chart of the World (DCW) database (DMA 1992). Buffers of different width, varying between biomes, were calculated and assigned to impact zones according to UNEP/RIVM (2004). The impact zones were summarized at 0.5° grid resolution.
Patch sizes were calculated by first reclassifying GLC2000 into two classes: man-made land (including croplands and urban areas) and natural land, all the rest. An overlay with the main roads derived from the infrastructural map resulted in a map of patches of natural areas. For future scenarios, the patch sizes were adapted as a result of land-use change, by adding or subtracting the amount of natural area assigned to each grid cell.
Global mean temperature change was directly derived from IMAGE.
Calculation of MSA and Relative Contributions of Each Driver
For each driver X a MSA
X
map is calculated by applying the cause–effect relationships to the appropriate input map. Little quantitative information exists on the interaction between drivers. To assess possible interactions assumptions can be made, ranging from ‘complete interaction’ (only the worst impact is allocated to each grid cell) to ‘no interaction’ (the impacts of each driver are cumulative). In the no-interaction case, for each IMAGE grid cell, GLOBIO3 calculates the overall MSA
i
value by multiplying the individual MSA
X
maps derived from the relationships for each driver:
$$ {\text{MSA}}_{i} = {\text{MSA}}_{{{\text{LU}}_{i} }} *{\text{MSA}}_{{{\text{N}}_{i} }} *{\text{MSA}}_{{{\text{I}}_{i} }} *{\text{MSA}}_{{{\text{F}}_{i} }} *{\text{MSA}}_{{{\text{CC}}_{i} }} $$
(1)
where i is a grid cell, MSA
i
is the overall value for grid cell i, MSA
Xi
is the relative mean species abundance corresponding to the drivers LU (land cover/land use), N (atmospheric N deposition), I (infrastructural development), F (fragmentation), and CC (climate change).
As the area of land within each IMAGE grid cell is not equal, the MSA
r
of a region is the area weighted mean of MSA
i
values of all relevant grid cells.
$$ {\text{MSA}}_{r} = \sum\limits_{i} {{\text{MSA}}_{i} } *A_{i} /\sum\limits_{i} {A_{i} } $$
(2)
where A
i
is the land area of grid cell i.
The relative contribution of each driver to a loss in MSA may be calculated from formulas 1 and 2.
We assumed that N deposition does not affect MSA in croplands, because the addition of N in agricultural systems was expected to be much higher than the atmospheric N deposition, and should have already been accounted for in the estimation of agricultural impacts. Furthermore, climate change and infrastructure were assumed to affect only natural and semi-natural areas, and effects of infrastructure were reduced in protected areas.
Scenario and Policy Options
Reference Scenario
A moderate socio-economic reference scenario has been used as a reference to evaluate the effects of the options (OECD 2008). The key indirect drivers, such as global population and economic activity, increase under this scenario. Between 2000 and 2050, the global population is projected to grow by 50% and the global economy to quadruple. This reference scenario is comparable with the B2 scenario in the Special Report on Emissions Scenarios (SRES) (Nakicenovic and others 2000) and the ‘Adaptive Mosaic’ scenario of the Millennium Ecosystem Assessment (MA 2005).
Option: Climate Change Mitigation Through Energy Policy
The implementation of an ambitious and bioenergy-intensive climate change mitigation policy option would require substantial changes in the world energy system (Metz and Van Vuuren 2006). The mitigation option studied here involves stabilizing CO2-equivalent concentrations at a level of 450 ppmv, which is in line with keeping the global temperature increase below 2°C. One of the more promising possibilities for reducing emissions (in particular, from transport and electric power generation) is the use of bioenergy. A scenario has been explored in which bioenergy plays an important role in reducing emissions. In this scenario, major energy-consumption savings are achieved, and 23% of the remaining global energy supply, in 2050, will be produced from bioenergy.
Option: Plantation Forests
The demand for wood is expected to increase by 30%, by 2050, leading to an increased use of (semi-)natural forests under the reference scenario. The option comprises a gradual shift of wood production toward sustainable managed plantations, aiming for a complete supply by plantations by 2050.
Option: Protected Areas
Protecting 10% of all biomes, a target of the CBD Programme of Work on Protected Areas, has nearly been achieved in the baseline scenario. Therefore, we analyzed the implementation of effectively conserving 20% of all biomes and all known single-site endemics. Conceptual maps of an extended protected-areas network were developed by using the October 2005 version of the World Database of Protected Areas (UNEP/WCMC 2005), the WWF terrestrial biome map (14 categories; Olson and others 2001), and a set of existing prioritization schemes (WWF global 200 terrestrial and freshwater priority ecoregions, Olson and Dinerstein 1998; amphibian diversity areas, Duellman 1999; endemic bird areas, Stattersfield and others 1998; and conservation international hotspots, Myers and others 2000). All sites of alliance for zero extinction (single-site endemics, Ricketts and others 2005) were selected for addition to the network. To achieve the 20% target for each biome, based on a consensual view on biodiversity value, potential new, protected cells were ranked for inclusion, based on the number of prioritization schemes that included that cell, with random selection in the case of a tie.