You are viewing Version 2, the most recent version of this dataset.
2 version(s) available
Date of publication: December 5, 2022

Version 2

Date of publication: December 5, 2022

Type of change: Metadata

Description: Update metric.

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Relative Magnitude of Fragmentation (RMF)

by Babak Naimi

We use an existing spatially contiguous, global remote-sensing data product (i.e. the 27-year annual ESA CCI land cover maps which can be categorized as an EBV ‘Ecosystem Distribution’) to derive an annual (27 year) time-series of the Relative Magnitude of Fragmentation (RMF) at a global scale and with a spatial resolution of 300m. From this derived EBV data product, we can calculate a RMF indicator of ecosystem degradation, i.e. the change, ...(continue reading)

DOI 10.25829/gsgf87
Citation
Naimi, B., Kissling, W. (2022). Relative Magnitude of Fragmentation (RMF) (Version 2) [Dataset]. German Centre for Integrative Biodiversity Research. https://doi.org/10.25829/gsgf87

ESALand covergrid celltime-series

4
The title of the dataset. Relative Magnitude of Fragmentation (RMF)
The date on which this version of the data was created in YYYY-MM-DD format.
A paragraph describing the dataset.
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We use an existing spatially contiguous, global remote-sensing data product (i.e. the 27-year annual ESA CCI land cover maps which can be categorized as an EBV ‘Ecosystem Distribution’) to derive an annual (27 year) time-series of the Relative Magnitude of Fragmentation (RMF) at a global scale and with a spatial resolution of 300m. From this derived EBV data product, we can calculate a RMF indicator of ecosystem degradation, i.e. the change, and rate of change, in fragmentation of ecosystems (e.g. forests) over the last 27 years. This can provide important information for measuring biodiversity change as it directly links to the draft monitoring framework of the zero draft of the post-2020 global biodiversity framework of the Convention on Biological Diversity (CBD), especially Draft 2050 Goal 1 and the related Draft 2030 Target 1 (see Annex of the zero draft).
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N/A

The method of production of the original data.
Hover to see a suggestion for a minimum description. To assess the change in ecosystem fragmentation, we calculate, for every 300 m pixel and its neighbourhood (e.g. all eight adjacent grid cells or all grid cells in a given radius, such as 1 km around a focal cell), a metric that is called the entropy-based local indicator of spatial association (ELSA). This ELSA metric quantifies the relative magnitude of fragmentation of a specific land cover type for each landscape around a 300 m pixel (Naimi et al. 2019, Spatial Statistics 29: 66-88). We focus on forest land cover types by aggregating the 14 tree cover related land cover types from the ESA CCI product into ‘forest’ and by measuring its fragmentation relative to eight other (non-forest) land cover types (following the reclassification of Mousivand & Arsanjani 2019, Applied Geography 106: 82-92). The ELSA metric can be used for both binary and multinomial categorical spatial data and quantifies the degree of fragmentation at each location relative to neighbouring locations, simultaneously incorporating both the spatial composition and the configuration of land cover types. The values of ELSA vary between 0 and 1, denoting lowest and highest fragmentation, respectively. The calculated RMF values of each 300 m pixel can be aggregated (e.g. averaged) at any coarser spatial resolution (e.g. country, national park, region) to summarize trends and the magnitude of ecosystem fragmentation for a specific area. The values are also comparable across regions and national boundaries, and thus scalable, because they are relative on a scale from 0 to 1. Values in the data have been scaled to integer with scale factor 0.0001 to reduce the size of the files.
The name of the Project.
The URL from the project website. N/A
The name of the person or other creator type principally responsible for creating this data.
The email of the person or other creator type principally responsible for creating this data. naimi.b@gmail.com
naimi.b@gmail.com
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Please select the CC license from the list. We recommend the use of CC BY 4.0

Essential Biodiversity Variables

Select the EBV class and the EBV name for the dataset. For cross-cutting use the comment at the bottom of the page for further information.
Genetic composition
Intraspecific genetic diversity
Genetic differentiation
Effective population size
Inbreeding
Other
Species populations
Species distributions
Species abundances
Other
Species traits
Morphology
Physiology
Phenology
Movement
Other
Community composition
Community abundance
Taxonomic and phylogenetic diversity
Trait diversity
Interaction diversity
Other
Ecosystem functioning
Primary productivity
Ecosystem phenology
Ecosystem disturbances
Other
Ecosystem structure
Live cover fraction
Ecosystem distribution
Ecosystem Vertical Profile
Other
Ecosystem services
Pollination
Other
Cross-cutting

Biological entity

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Species
Communities
Ecosystems
Other
None
A description of the range of taxa or ecosystem types addressed in the dataset. E.g. "300 species of mammals”, “Forests”, etc.
The reference as a URL. N/A

Metric

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Scenario

Spatial domain

Global
Continental/Regional
National
Sub-national/Local
degree
southWest lat: -90, lon: -180
northEast lat: 90, lon: 180

Temporal domain

The targeted time period between each value in the dataset.
decadal
annually
monthly
weekly
daily
Other
Irregular
Single time
Select the temporal extent of the dataset.
When the dataset represents a Single time, then use the same start and end date.
__

Environmental domain *

Terrestrial
Marine
Freshwater
Miscellaneous information about the data, not captured elsewhere. We define the 'forest' class by aggregating all 14 tree cover related land cover types from the ESA CCI product into one class. We further define eight non-forest classes (agriculture, grassland, wetland, settlement, sparse vegetation, bare area, water, permanent snow and ice) that we use as multinomial categorical data, or as binary categorical data (to define forest vs. non-forest). This classes follow the reclassification used by Mousivand & Arsanjani 2019 (Applied Geography 106: 82-92). For deriving the RMF, we either calculate ELSA using the binary categorical data (forest vs. non-forest) or the multinomial categorical data (forest vs. the eight non-forest classes).