Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific

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.

Data and Resources

This dataset has no data

Cite this as

Uhlenkott, Katja, Vink, Annemiek, Kuhn, Thomas, Martínez Arbizu, Pedro (2020). Dataset: Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific. https://doi.org/10.1594/PANGAEA.912217

DOI retrieved: 2020

Additional Info

Field Value
Imported on November 30, 2024
Last update November 30, 2024
License CC-BY-4.0
Source https://doi.org/10.1594/PANGAEA.912217
Author Uhlenkott, Katja
Given Name Katja
Family Name Uhlenkott
More Authors
Vink, Annemiek
Kuhn, Thomas
Martínez Arbizu, Pedro
Source Creation 2020
Publication Year 2020
Resource Type application/zip - filename: Uhlenkott-etal_2020
Subject Areas
Name: Ecology

Related Identifiers
Title: Predicting meiofauna abundance to define preservation and impact zones in a deep‐sea mining context using random forest modelling
Identifier: https://doi.org/10.1111/1365-2664.13621
Type: DOI
Relation: References
Year: 2020
Source: Journal of Applied Ecology
Authors: Uhlenkott Katja , Vink Annemiek , Kuhn Thomas , Martínez Arbizu Pedro .