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

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{{ComponentDescriptionTemplate
{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Kallio et al., 2004; FAO, 2001a; FAO, 2008; Brown, 2000; Carle and Holmgren, 2008; FAO, 2012a; FAO, 2012b; FAO, 2010;
|Reference=Bartholome et al., 2004; DMA, 1992; Newbold et al., 2013;
|Description=The forest management module describes regional timber demand and the production of timber in the three different management systems clear felling, selective felling and forest plantations. Deforestation rates reported by {{abbrTemplate|FAO}} are used to calibrate deforestation rates in IMAGE, using a so called additional deforestion.  
}}<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 demand===
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 IMAGE 3.0, the driver for forest harvest is timber demand per region. Timber demand is the sum of domestic and/or regional demand and timber claims by other regions (export/trade). Production and trade assumptions for saw logs and paper/pulp wood are adopted from external models, such as [[EFIGTM model|EFI-GTM]] ([[Kallio et al., 2004]]), and domestic demand for fuelwood is based on the [[TIMER model]] (See Component [[Energy supply and demand]]).


Part of the global energy supply is met by fuelwood and charcoal, in particular in less developed world regions. Not all wood involved is produced from formal forestry activities, as it is also collected from non-forest areas, for example from thinning orchards and along roadsides ([[FAO, 2001a]]; [[FAO, 2008]]). As few reliable data are available on fuelwood production, own assumptions have been made in IMAGE. While fuelwood production in industrialized regions is dominated by large-scale, commercial operations, in transitional and developing regions smaller proportions of fuelwood volumes are assumed to come from forestry operations: 50% and 32% respectively.  
===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.  


===Timber supply & production in forests===
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.  
In IMAGE, felling in each region follows a stepwise procedure until timber demand is met, attributed to the three aforementioned management systems. The proportion for each management system is derived from forest inventories for different world regions ([[Arets et al., 2011]]) and used as model input (Figure Flowchart). Firstly, timber is harvested in forests that have been converted to agriculture. Secondly, timber from forest plantations at the end of their rotation cycle are harvested. Finally, trees from natural forests are harvested, applying clear felling and/or selective felling. In all management systems, trees can only be harvested when the rotation cycle of forest regrowth has been completed.


===Selective logging===
===Limitations===
Under selective felling, only a - regional and time specific- fraction of the trees is logged and the other trees remain in the forest. After logging, a fraction of the harvested wood is removed from the forest to fulfil the demand. Biomass left behind in the forest represents losses/residues during tree harvesting (from tree damage and unusable tree parts) or left in the forest because of environmental concerns (biodiversity and nutrient supply). This fraction take-away is derived from literature, defined for industrial roundwood (see [[Arets et al., 2011]]) It is further adjusted to account for the demand for wood fuel, for which it equals unity.
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).  


===Forest plantations===
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.  
Forest plantations are established for efficient, commercially viable wood production. Their regional establishment in IMAGE 3 is scenario driven (see also Input/Output Table at [[Forest management|Introduction part]]), based on FAO. The expectation is that increasingly more wood will be produced in plantations because sustainability criteria may limit harvest from natural forests ([[Brown, 2000]]; [[Carle and Holmgren, 2008]]; [[FAO, 2012a]]). The development of forest plantations in IMAGE and [[LPJmL model|LPJmL]] is still under development, but expected to be available soon. Forest plantations are assumed to be established firstly on abandoned agricultural land. When sufficient abandoned land is not available, forest plantations are established on cleared forest areas. When a forest plantation has been established, the land cannot be used for other purposes or converted to natural vegetation until the tree rotation cycle has been completed.


===Additional deforestation ===
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.
Globally, conversion to agricultural land is the major driver of forest clearing, and timber harvest does not result in deforestation, if natural vegetation is regrowing. But there are other causes of deforestation not related to food demand and timber production, such as urbanisation, mining and illegal logging. These activities contribute to loss of forest area, increased degradation risks and a decline in the supply of forest services. To be consistent with the total deforestation rates per world region reported by the FAO ([[FAO, 2010|2010]]), IMAGE 3.0 introduces a category ‘additional deforestation’. IMAGE assumes no recovery of natural vegetation in these areas, and no agricultural activities.
</div>
|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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