Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Dataset Open Access |
| Language: | en |
| Published: |
PANGAEA
2020
|
| Subjects: | |
| Online Access: | https://doi.org/10.1594/PANGAEA.912217 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1867167677671276544 |
|---|---|
| author | Uhlenkott, Katja Vink, Annemiek Kuhn, Thomas Martínez Arbizu, Pedro |
| author_facet | Uhlenkott, Katja Vink, Annemiek Kuhn, Thomas Martínez Arbizu, Pedro |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The dataset contains counts of meiofauna organisms on high taxonomic level and predicted distributions computed for overall meiofauna abundance, diversity (Simpson's Index D and Evenness E), richness (ntax) and individual taxa using random forest regressions. Furthermore, a habitatmap is provided, dividing the area based on k-means clustering of combined predicted distributions, bathymetry and backscatter. The spatial layers are saved as grid-files, being the standard format of the R-package "raster" (https://cran.r-project.org/web/packages/raster/index.html). Study area is an area allocated to the German Federal Institute for Geosciences and Natural Resources for the exploration of polymetallic nodule mining. Deep-sea mining highly endangers the benthic communities; hence the definition of preservation zones, not only for preservation but also to enable the re-settlement of mined areas, is highly important. These datasets on the spatial distribution of meiofauna have been used to account for a modelling approach to find areas with similar environmental conditions and similar benthic communities. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_912217 |
| institution | PANGAEA |
| language | en |
| publishDate | 2020 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific Uhlenkott, Katja Vink, Annemiek Kuhn, Thomas Martínez Arbizu, Pedro distribution model; Habitat Mapping; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; meiofauna; random forest regression The dataset contains counts of meiofauna organisms on high taxonomic level and predicted distributions computed for overall meiofauna abundance, diversity (Simpson's Index D and Evenness E), richness (ntax) and individual taxa using random forest regressions. Furthermore, a habitatmap is provided, dividing the area based on k-means clustering of combined predicted distributions, bathymetry and backscatter. The spatial layers are saved as grid-files, being the standard format of the R-package "raster" (https://cran.r-project.org/web/packages/raster/index.html). Study area is an area allocated to the German Federal Institute for Geosciences and Natural Resources for the exploration of polymetallic nodule mining. Deep-sea mining highly endangers the benthic communities; hence the definition of preservation zones, not only for preservation but also to enable the re-settlement of mined areas, is highly important. These datasets on the spatial distribution of meiofauna have been used to account for a modelling approach to find areas with similar environmental conditions and similar benthic communities. |
| title | Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific |
| topic | distribution model; Habitat Mapping; JPI Oceans - Ecological Aspects of Deep-Sea Mining; JPIO-MiningImpact; meiofauna; random forest regression |
| url | https://doi.org/10.1594/PANGAEA.912217 |