Forest management/Description and Terrestrial biodiversity/Data uncertainties limitations: Difference between pages

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{{ComponentDescriptionTemplate
{{ComponentDataUncertaintyAndLimitationsTemplate
|Description==== Timber demand ===
|Reference=Bartholome et al., 2004; DMA, 1992; Newbold et al., 2013;
In IMAGE 3.0, the extent of forest harvest is driven by timber demand per region. This timber demand is the sum of domestic/regional demand and timber claims by other regions (= export/trade). Trade assumptions for sawlogs and paper/pulp wood are adopted from  external models, such as EFI-GTM {Kallio, 2004 #1004}, where for fuel wood they are based on the TIMER model.  
}}<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
GLOBIO builds on data from literature reviews to construct relationships between biodiversity metrics ({{abbrTemplate|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.  


=== Timber supply ===
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]]).
In the model, felling in a region continues, until the timber demand has been met, using a stepwise procedure to attribute shares of the total demand to different management systems. First, the demand for timber will be met by the wood harvested in forests that have been converted to agriculture. Second, mature forest plantations (which are at the end of their rotation cycle) within that region can be harvested. The regional establishment of such plantations is scenario driven (see also Table 4.2.2.1). If the timber harvested from these plantations is still insufficient to meet the timber  demand, trees  from natural forests are harvested, applying either clearcut or selective cut.  


=== Forest management types ===
===Uncertainties===
The share of each management system was derived from forest inventories taken for different world region (Arets et al., 2011), and used as model input (Table 4.2.2.1). Under selective cut, only a fraction of the trees (and thus stems and other tree pools) is logged (fraction is regional and time specific). The other trees remain in the forest. After logging only a fraction of the harvested wood can actually become removed (=fraction take away). Also this fraction is given by input/is scenario driven, i.e. regional specified and can vary over time (Table 4.2.2.1). In addition it depends on the overall wood demand (it equals 1 for wood fuel and the default value to industrial round wood). What is left behind in the forest represents losses/residues during tree harvesting (from tree damage and unusable tree parts) or left in the forest by purpose because of environmental concerns (e.g. biodiversity and nutrient supply). Trees can only be harvested in any forest management type once the rotation cycle of forest regrowth  has been completed (Table 4.2.2.1).  
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.  


=== Fuel Wood ===
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.  
Part of the global energy demand is met by fuel wood, depending on the world region. Some of the fuel wood is harvested through formal forestry activities and can be coupled to the management systems described above. In addition, fuel wood is produced and collected from non-forest areas; for example, from thinning orchards and along roadsides (FAO, 2001; FAO, 2008). Reliable data on fuel wood production are scarce; therefore, assumptions have been made in IMAGE3.0. For the developed regions, it is assumed that fuel wood is produced on a large scale and, therefore, in IMAGE, all fuel wood demand is added to timber demand. In the transitional regions as well as in the developing regions, smaller fractions of the fuel wood demand are assumed to be met from forestry operations: 50% and 32%.


=== Establishing new plantations ===
===Limitations===
Forest plantations are established with the purpose of growing wood in an efficient way. The expectation is that, in the future, more and more wood will be produced on plantations, as sustainability criteria may complicate the harvest from natural forests (Form, 2013). For this purpose, plantation planting rate scenarios have constructed by the FAO (Brown 2000; Carle and Holmgren 2008). In the IMAGE 3.0 model, forest plantations are assumed firstly to be established on abandoned agricultural land. Only when there is not enough abandoned land available forest plantations will be established on cleared forest areas.  Once a forest plantation has been established, the land cannot be used for other purposes or be changed back to natural vegetation until the trees’ rotation cycle has been completed.
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).  


=== Additional Deforestation ===
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.  
As aforementioned, land-use change is the major driver of global forest clearing. Apart from this process, however, there are also other causes of deforestation, not related to changes in food demand. In order for IMAGE 3.0 to be consistent with the deforestation rates per world region as reported by the FAO (2010), the category ‘additional deforestation’ has been introduced in the model, as an additional type of forest use.  


The wood from forests cleared as a result of ‘additional deforestation’ is not used to fulfil the timber demand. Instead, in certain regions, mainly at higher latitudes, the wood is simply left behind;  in other -more tropical- regions it is burnt. The model assumes no recovery of natural vegetation for these areas, and no agricultural activities. In the model, additional deforestation for any particular region is allocated only after the timber demand has been met.
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.
 
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|Flowchart=ForestManagementModel.png
|AltText=Component flow chart forest management
|CaptionText=Flow diagram of forest management
|ExternalModel=EFIGTM
}}

Latest revision as of 19:56, 15 November 2018

Link to framework components overview

Parts of Terrestrial biodiversity/Data uncertainties limitations

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
Component is implemented in:
Components:
Projects/Applications
Key publications
References
GLOBIO model for terrestrial biodiversity in IMAGE 3.0
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.

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