You are viewing Version 2, the most recent version of this dataset.
2 version(s) available
Date of publication: November 21, 2022

Version 2

Date of publication: November 21, 2022

Type of change: Metadata

Description: Updated metadata for several fields. Hackaton 21.11.2022.

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Local terrestrial diversity (PREDICTS)

by Andy Purvis

Changes in average local terrestrial diversity for each one degree grid cell caused by land-use from 1900 to 2050, as estimated by the PREDICTS model based on historical reconstructions and SSP/RCP scenarios. It reports the number of species in each cell relative to a pristine baseline and the number of species in each cell relative to 1900. Produced for the BES-SIM project. ...(continue reading)

DOI 10.25829/5yq9t7
Citation
Purvis, A., Hill, S. (2022). Local terrestrial diversity (PREDICTS) (Version 2) [Dataset]. German Centre for Integrative Biodiversity Research. https://doi.org/10.25829/5yq9t7

PREDICTS

6
The title of the dataset. Local terrestrial diversity (PREDICTS)
The date on which this version of the data was created in YYYY-MM-DD format.
A paragraph describing the dataset.
Hover to see a suggestion for a good description. Allowed:
Changes in average local terrestrial diversity for each one degree grid cell caused by land-use from 1900 to 2050, as estimated by the PREDICTS model based on historical reconstructions and SSP/RCP scenarios. It reports the number of species in each cell relative to a pristine baseline and the number of species in each cell relative to 1900. Produced for the BES-SIM project.
Provide the DOI number of associated publications. Click Plus to add DOIs.
https://doi.org/
10.1016/bs.aecr.2017.12.003
https://doi.org/
10.1101/311787v1
The method of production of the original data.
Hover to see a suggestion for a minimum description. Uses the LUH 2.0 projections for historical reconstruction (1900-2015) and SSP/RCP for future (2015-2050). for land-use and PREDICTS - Projecting Responses of Ecological Diversity In Changing Terrestrial Systems (Purvis et al., 2018). The PREDICTS database includes 767 studies from over 32 000 sites on over 51 000 species from a range of taxa. PREDICTS estimates the impacts of land-use, land-use intensity, and human population density, on biodiversity. This dataset was developed for the BES-SIM project.
The name of the Project.
The URL from the project website. https://www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html
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. Andy.Purvis@nhm.ac.uk
hpereira@idiv.de
The names of the co-creators responsible for creating this data. Click Plus to add co-creators.
Select between Creative Commons (CC) or Non-CC license.
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

Select the entity type of the dataset.
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

Provide the name, description, units of minimum 1 metric. Click Plus to add metrics.

Scenario

If applicable, name the scenario's exercise, the version and provide a URL to the reference.

Spatial domain

Global
Continental/Regional
National
Sub-national/Local
degree
southWest lat: -58, lon: -180
northEast lat: 83.8, 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. N/A