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Date of publication: December 5, 2022

Version 1

Date of publication: December 5, 2022

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Historical local species richness (PREDICTS)

by Samantha Hill

Modelled historical local species richness relative to pristine baseline with a spatial resolution of 0.25 degrees for 1980, 2000, 2007, 2009 and 2010. Original dataset produced by Hill, et al. (2018, https://doi.org/10.1101/311787), derived from the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) database for the BES SIM project. This subset is used for analysis in Valdez et al. (2023, https://doi.org/10.1 ...(continue reading)

DOI 10.25829/e3cwb2
Citation
Hill, S., Purvis, A. (2022). Historical local species richness (PREDICTS) (Version 1) [Dataset]. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. https://doi.org/10.25829/e3cwb2

PREDICTSSpecies RichnessBES-SIMTerrestrial

55
The title of the dataset. Historical local species richness (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:
Modelled historical local species richness relative to pristine baseline with a spatial resolution of 0.25 degrees for 1980, 2000, 2007, 2009 and 2010. Original dataset produced by Hill, et al. (2018, https://doi.org/10.1101/311787), derived from the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) database for the BES SIM project. This subset is used for analysis in Valdez et al. (2023, https://doi.org/10.1111/ecog.06604).
Provide the DOI number of associated publications. Click Plus to add DOIs.
https://doi.org/
10.1111/ecog.06604
https://doi.org/
10.1101/311787
The method of production of the original data.
Hover to see a suggestion for a minimum description. A linear mixed-effects model was used to model site-level species richness using the site-level data extracted from PREDICTS (Hudson, et al. 2017, https://doi.org/10.1002/ece3.2579), with historical land use and related pressures (land-use intensity, and human population density) as explanatory variables (Hurtt, et al. 2020, https://doi.org/10.5194/gmd-13-5425-2020). The spatial pattern of the expected site-level species richness was then projected by combining the coefficients of this model with global raster data of these pressures for each focal year (Hill, et al. 2018, https://doi.org/10.1101/311787).
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. Samantha.Hill@unep-wcmc.org
jose_w.valdez@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

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Scenario

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
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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