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EM: Identifying priority areas for biodiversity conservation based on Marxan and InVEST model (EM-1035)
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Expand AllEM Identity and Description
EM Identification
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EM ID
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EM-1035 |
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EM Short Name
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Marxan-InVEST biodiversity conservation, China |
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EM Full Name
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Identifying priority areas for biodiversity conservation based on Marxan and InVEST model |
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EM Source or Collection
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None |
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EM Source Document ID
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490 |
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Document Author
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Zhang, L. and Li, J. |
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Document Year
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2022 |
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Document Title
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Identifying priority areas for biodiversity conservation based on Marxan and InVEST model |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| https://marxansolutions.org/ | |
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Contact Name
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Liqing Zhang |
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Contact Address
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Department of Landscape Architecture, School of Design, Shanghai Jiao Tong University, Shanghai, 200240, China |
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Contact Email
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liqingzh@sjtu.edu.cn |
EM Description
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Summary Description
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Context According to Global Biodiversity Outlook 5, the Aichi Biodiversity Target regarding protected areas (PAs) has been partially achieved with limited progress in valid performance for biodiversity conservation and effective management. This suggests that not only the spatial planning of PAs but also the spatial prioritization for PAs under different management scenarios need more prudent decision-making. Objectives We aim to develop a comprehensive approach to identify and prioritize PAs to maximize the effectiveness of biodiversity conservation while minimize the conservation costs. Methods Taking Beijing-Tianjin-Hebei region as a case study, we consider the supply and demand of ecosystem services, and landscape connectivity to identify PAs. Systematic conservation planning decision support tool Marxan was employed to identify the priority areas of PAs under multi-target scenarios. The conservation costs, the critical step in the planning process, was estimated using habitat quality evaluation module embedding in InVEST model. Results The results showed that the conservation costs for construction land and unused land were the highest, while the costs for forest land, grassland, and farmland were very low. Sensitivity analysis confirmed the most appropriate conservation goal was 50% of the total area. Therefore, we generated spatial prioritization outcomes based on this target. The highest priority area was mainly located in the northwest of Beijing, the north of Chengde, and the east of Zhangjiakou. Conclusions It is concluded that the proposed approach helps decision-makers to identify spatial prioritization for biodiversity conservation based on different scenarios and also yields insights into systematic conservation planning and management. |
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Specific Policy or Decision Context Cited
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None identified |
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Biophysical Context
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Beijing, Tianjin, and Hebei region of China |
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EM Scenario Drivers
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None provided |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application |
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New or Pre-existing EM?
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New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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None |
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EM ID for related EM
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None |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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2010 |
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EM Time Dependence
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time-stationary |
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EM Time Reference (Future/Past)
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Not applicable |
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EM Time Continuity
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Not applicable |
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EM Temporal Grain Size Value
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Not applicable |
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EM Temporal Grain Size Unit
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Not applicable |
EM Spatial Extent
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Bounding Type
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Geopolitical |
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Spatial Extent Name
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Beijing-Tianjin-Hebei region |
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Spatial Extent Area (Magnitude)
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100,000-1,000,000 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially lumped (in all cases) |
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Spatial Grain Type
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Not applicable |
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Spatial Grain Size
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Not applicable |
EM Structure and Computation Approach
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EM Computational Approach
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Analytic |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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Unclear |
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Model Goodness of Fit Reported?
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Unclear |
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Goodness of Fit (metric| value | unit)
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None |
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Model Operational Validation Reported?
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Unclear |
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Model Uncertainty Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Reported?
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Yes |
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Model Sensitivity Analysis Include Interactions?
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Yes |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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38.86 |
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Centroid Longitude
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116.63 |
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Centroid Datum
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WGS84 |
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Centroid Coordinates Status
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Provided |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Terrestrial Environment (sub-classes not fully specified) |
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Specific Environment Type
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Forest, grassland, farmland, wetland, water bodies |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Not applicable |
Taxonomic level and name of organisms or groups identified
taxonomyHelp
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| None Available |
EnviroAtlas URL
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| GAP Ecological Systems, Ecosystem Markets: Imperiled Species and Habitats, U.S. EPA (Omernik) ecoregions |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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| None |
EM Variable Names (and Units)
Predictor
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Intermediate
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Intermediate (Computed) Variables (and Units)
view details (3 variables)
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Response
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Observed Response Variables (and Units)
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None |
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Computed Response Variables (and Units)
view details (3 variables)
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