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

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|Reference=Bartholome et al., 2004; DMA, 1992; Newbold et al., 2013;
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|Reference=Kallio et al., 2004; Arets et al., 2010; FAO, 2001; FAO, 2009; FAO, 2010;  Brown 1990; Carle and Holmgren 2008
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|Description=Forest harvest  in IMAGE is driven by the timber demand per region. This demand is the sum of the domestic demand and the net import of timber. Trade is accounted for by either external models, such as EFI-GTM ([[Kallio et al., 2004]]). Logging in a region continues until the timber demand is met. A stepwise procedure is designed to attribute shares of the total demand to the different management systems. Part of the demand for timber is fulfilled by the harvest of wood from the conversion of forests to agriculture. Next, all full-grown wood plantations (at the end of their rotation cycle) of a region are harvested. Plantations are used first, as these have been established on purpose and significant investments have been made. When this harvested amount is not enough to supply the demand, other management systems are used. This can be either done by applying clear-cut cycles in semi-natural forests or by selective logging of heterogeneous forests. The share of each system is derived from inventories in different world region.
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==Data, uncertainty and limitations==
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[[File:ForestManagementModel.png|Flow diagram of forest management|alt=Component flow chart forest management|thumbnail|right|200px]]
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===Data===
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<h3>Rotation cycle logging</h3>
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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.  
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Per year, region and vegetation type is defined what fraction of the forests is logged by selective cut and what part is logged by clear cut (parameters from inventory by [[Arets et al., 2010]]). Selective cut only takes place on vegetation types with a high forest coverage (>75%). After logging only a fraction of the harvested wood is actually removed as timber for the market. What is left behind in the forest represents the losses during tree harvesting, either from unintended collateral tree damage or by removing unusable tree parts. Harvest in any forest management type can only take place when the rotation cycle of regrowth of a forest is completed (see table below).
 
  
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[[File:CycleLogging.png|thumb|200px|right|alt=Table describing the rotation cycles for forest management types in different forests|Table: Rotation cycles for forest management types in different forest biomes.]]
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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]]).
  
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===Uncertainties===
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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.
  
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<h3>Fuel wood</h3>
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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.  
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Part of the energy needs of the regional population is fulfilled with wood, called fuelwood. Only part of the fuelwood is harvested by industrial forestry activities, and can be coupled to the described management systems above.  There are several informal ways to produce and collect fuelwood, next to industrial production: orchards, roadsides, forest management residues after timber removal, etc ([[FAO, 2001]]; [[FAO, 2009]]). Exact data on informal fuelwood production are missing, and therefore assumptions have been made.  In the developed regions, it is assumed that fuelwood is produced on industrial scales and therefore all fuel wood demand is added to the timber demand. In the transitional regions 50% of the fuel wood demand is coming from timber and in the developing regions 32%.  
 
  
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<h3>Establishing wood plantations</h3>
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===Limitations===
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Wood plantations are established on purpose to provide specific wood qualities in an efficient way. The expectation is that in future, more and more wood will be produced from plantations. For this, plantation planting rate scenarios have been drawn up by FAO ([[Brown 1990]]; [[Carle and Holmgren 2008]]). In the IMAGE model wood plantations are established on abandoned agricultural land, and this process can be called reforestation. If there is not enough abandoned land, they will be established on clear-cut forest areas, where the forest coverage of the original natural vegetation type should be more than 75%.  A wood plantation is specifically planted for roundwood or for pulpwood. Once a wood plantation is established this area cannot be used for other purposes, and it cannot change back to natural vegetation until after the rotation cycle and subsequent wood removal.
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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).  
  
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<h3>Additional deforestation</h3>
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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.  
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A special type of forest use in IMAGE is the so-called “additional” deforestation.  
 
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With this additional deforestation extra areas of forest are converted in addition to the deforestation in IMAGE caused by agricultural expansion. This process is included as a correction factor, and is due to unmodelled conversion. Additional deforestation is not a forest management type because the wood is not used for timber but the process  is part of the forest management module. The areas are slashed and the wood is left behind in regions with higher latitudes and burned down at the lower latitudes. No recovery to natural vegetation takes place in these areas and no agricultural activities can be started. Logging for additional deforestation in a region only starts after the demand for timber is satisfied.
 
  
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The additional deforestation is driven by the difference of the regional deforestation data of the ([[FAO, 2010]]) and the agricultural expansion data of the IMAGE land use model .
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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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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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