Berger, W. H.: Deep-Sea Sedimentation, in: The Geology of Continental
Margins, edited by: Burk, C. A. and Drake, C. L., Springer Berlin and
Heidelberg, Germany, 213–241, 1974.
Chen, C., Liaw, A., and Breiman, L.: Using Random Forest to Learn Imbalanced
Data, available at:
https://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf (last access: 7 December 2020),
2004.
Congalton, R. G.: A review of assessing the accuracy of classifications of
remotely sensed data, Remote Sens. Environ., 37, 35–46, 1991.
Cutler, D., Edwards, T., Beards, K., Cutler, A., Hess, K., Gibson, J., and
Lawler, J.: Random Forests for classification in Ecology, Ecology, 88,
2783–2792, 2007.
Danovaro, R., Snelgrove, P. V. R., and Tyler, P.: Challenging the paradigms
of deep-sea ecology, Trends Ecol. Evol., 29, 465–475,
https://doi.org/10.1016/J.TREE.2014.06.002, 2014.
Diesing, M.: Deep-sea sediments of the global ocean mapped with Random
Forest machine learning algorithm, PANGAEA, https://doi.org/10.1594/PANGAEA.911692,
2020.
Diesing, M. and Nüst, D.: Global Deep-Sea Sediments, available at:
https://o2r.uni-muenster.de/#/erc/GWME2voTDb5oeaQFuTWMCEMveKS1MiXm, last access: 7 December 2020.
Diesing, M. and Thorsnes, T.: Mapping of Cold-Water Coral Carbonate Mounds
Based on Geomorphometric Features: An Object-Based Approach, Geosciences,
8, 34, https://doi.org/10.3390/geosciences8020034, 2018.
Diesing, M., Kröger, S., Parker, R., Jenkins, C., Mason, C., and Weston,
K.: Predicting the standing stock of organic carbon in surface sediments of
the North-West European continental shelf, Biogeochemistry, 135,
183–200, https://doi.org/10.1007/s10533-017-0310-4, 2017.
Dutkiewicz, A., Müller, R. D., O'Callaghan, S., and Jónasson, H.:
Census of seafloor sediments in the world's ocean, Geology, 43, 795–798,
https://doi.org/10.1130/G36883.1, 2015.
Dutkiewicz, A., O'Callaghan, S., and Müller, R. D.: Controls on the
distribution of deep-sea sediments, Geochem. Geophys. Geosy.,
17, 3075–3098, https://doi.org/10.1002/2016GC006428, 2016.
ESRI: World Continents, available at:
https://www.arcgis.com/home/item.html?id=a3cb207855b348a297ab85261743351d
(last access: 24 August 2017), 2010.
GEBCO: The GEBCO_2014 Grid, version 20150318, availabe at:
http://www.gebco.net (last access: 24 January 2019), 2015.
Guisan, A. and Zimmermann, N. E.: Predictive habitat distribution models in
ecology, Ecol. Modell., 135, 147–186,
https://doi.org/10.1016/S0304-3800(00)00354-9, 2000.
Harris, P. T., Macmillan-Lawler, M., Rupp, J., and Baker, E. K.:
Geomorphology of the oceans, Mar. Geol., 352, 4–24,
https://doi.org/10.1016/j.margeo.2014.01.011, 2014.
Huang, Z., Siwabessy, J., Nichol, S. L., and Brooke, B. P.: Predictive
mapping of seabed substrata using high-resolution multibeam sonar data: A
case study from a shelf with complex geomorphology, Mar. Geol., 357, 37–52,
2014.
James, G., Witten, D., Hastie, T., and Tibshirani, R.: Tree-Based Methods, in:
An Introduction to Statistical Learning, Springer, New York, USA, 303–335,
2013.
Kuhn, M.: Building Predictive Models in R Using the caret Package, J. Stat.
Software, 1, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008.
Lambeck, K., Rouby, H., Purcell, A., Sun, Y., and Sambridge, M.: Sea level
and global ice volumes from the Last Glacial Maximum to the Holocene, Proc.
Natl. Acad. Sci., 111, 15296–15303, https://doi.org/10.1073/pnas.1411762111, 2014.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R
News, 2, 18–22, https://doi.org/10.1159/000323281, 2002.
Lisitzin, A. P.: Distribution of siliceous microfossils in suspension and in
bottom sediments, in: The Micropaleontology of Oceans, edited by: Funnell, B. M. and Reidel, W. R., Cambridge University Press,
Cambridge, UK, 173–195, 1971.
Luts, J., Ojeda, F., Plas, R., Van De Moor, B., De Huffel, S., and Van
Suykens, J. A. K.: A tutorial on support vector machine-based methods for
classification problems in chemometrics, Anal. Chim. Acta, 665, 129–145,
2010.
Mastrandrea, M. D., Mach, K. J., Plattner, G. K., Edenhofer, O., Stocker, T.
F., Field, C. B., Ebi, K. L., and Matschoss, P. R.: The IPCC AR5 guidance
note on consistent treatment of uncertainties: A common approach across the
working groups, Clim. Change, 108, 675, https://doi.org/10.1007/s10584-011-0178-6, 2011.
Millard, K. and Richardson, M.: On the importance of training data sample
selection in random forest image classification: A case study in peatland
ecosystem mapping, Remote Sens., 7, 8489–8515, https://doi.org/10.3390/rs70708489,
2015.
Minasny, B. and McBratney, A. B.: A conditioned Latin hypercube method for
sampling in the presence of ancillary information, Comput. Geosci., 32,
1378–1388, https://doi.org/10.1016/J.CAGEO.2005.12.009, 2006.
Misiuk, B., Diesing, M., Aitken, A., Brown, C. J., Edinger, E. N., and Bell,
T.: A spatially explicit comparison of quantitative and categorical
modelling approaches for mapping seabed sediments using random forest,
Geosciences, 9, 254, https://doi.org/10.3390/geosciences9060254, 2019.
Nilsson, R., Peña, J. M., Björkegren, J., and Tegnér, J.:
Consistent feature selection for pattern recognition in polynomial time, J.
Mach. Learn. Res., 8, 589–612, 2007.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V, Woodcock, C. E., and
Wulder, M. A.: Good practices for estimating area and assessing accuracy of
land change, Remote Sens. Environ., 148, 42–57,
https://doi.org/10.1016/j.rse.2014.02.015, 2014.
Prasad, A. M., Iverson, L. R., and Liaw, A.: Newer classification and
regression tree techniques: Bagging and random forests for ecological
prediction, Ecosystems, 9, 181–199, https://doi.org/10.1007/s10021-005-0054-1, 2006.
Probst, P.: Performance Measures for Statistical Learning, availabe at:
https://cran.r-project.org/web/packages/measures/measures.pdf (last access: 7 December 2020), 2018.
R Core Team: R: A Language and Environment for Statistical Computing, 2018.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J.,
Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder,
B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C.
F.: Cross-validation strategies for data with temporal, spatial,
hierarchical, or phylogenetic structure, Ecography, 40, 913–929,
https://doi.org/10.1111/ecog.02881, 2017.
Seibold, E.: Der Meeresboden Forschungsstand und Zukunftsaufgaben,
Naturwissenschaften, 62, 321–330, https://doi.org/10.1007/BF00608892, 1975.
Snelgrove, P. V. R., Soetaert, K., Solan, M., Thrush, S., Wei, C.-L.,
Danovaro, R., Fulweiler, R. W., Kitazato, H., Ingole, B., Norkko, A.,
Parkes, R. J., and Volkenborn, N.: Global Carbon Cycling on a Heterogeneous
Seafloor, Trends Ecol. Evol., 33, 96–105,
https://doi.org/10.1016/J.TREE.2017.11.004, 2018.
Stehman, S. V. and Foody, G. M.: Key issues in rigorous accuracy assessment
of land cover products, Remote Sens. Environ., 231, 111199,
https://doi.org/10.1016/J.RSE.2019.05.018, 2019.
Stevens Jr., D. L. and Olsen, A. R.: Variance estimation for spatially
balanced samples of environmental resources, Environmetrics, 14,
593–610, https://doi.org/10.1002/env.606, 2003.
Strobl, C. and Zeileis, A.: Danger: High Power! – Exploring the Statistical
Properties of a Test for Random Forest Variable Importance, availabe at:
https://epub.ub.uni-muenchen.de/2111/1/techreport.pdf (last access: 7 December 2020), 2008.
Strobl, C., Boulesteix, A.-L., Zeileis, A., and Hothorn, T.: Bias in random
forest variable importance measures: Illustrations, sources and a solution,
BMC Bioinformatics, 8, 25, https://doi.org/10.1186/1471-2105-8-25, 2007.
Thurman, H. V.: Introductory Oceanography, 8th edn., Prentice-Hall, Upper
Saddle River, New Jersey, USA, 1997.
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., and De
Clerck, O.: Bio-ORACLE: a global environmental dataset for marine species
distribution modelling, Glob. Ecol. Biogeogr., 21, 272–281,
https://doi.org/10.1111/j.1466-8238.2011.00656.x, 2012.
Valavi, R., Elith, J., Lahoz-Monfort, J. J., and Guillera-Arroita, G.:
BLOCKCV: An R package for generating spatially or environmentally separated
folds for k-fold cross-validation of species distribution models, Methods
Ecol. Evol., 10, 225–232, https://doi.org/10.1111/2041-210X.13107, 2018.
van Heteren, S. and Van Lancker, V.: Collaborative Seabed-Habitat Mapping:
Uncertainty in Sediment Data as an Obstacle in Harmonization, in:
Collaborative Knowledge in Scientific Research Networks, edited by: Diviacco, P., Fox, P., Pshenichy, C., and Leadbetter, A., Information
Science Reference, Hershey PA, USA, 154–176, 2015.
