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The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages

Published online by Cambridge University Press:  02 September 2015

Nils Piechaud
Marine Biology & Ecology Research Centre, Marine Institute, Plymouth University, Plymouth PL4 8AA, UK
Anna Downie
Centre for Environment Fisheries and Aquaculture Science, Pakefield Road, Lowestoft NR33 0HT, UK
Heather A. Stewart
British Geological Survey, Murchison House, West Mains Road, Edinburgh EH9 3LA, UK
Kerry L. Howell
Marine Biology & Ecology Research Centre, Marine Institute, Plymouth University, Plymouth PL4 8AA, UK


Predictive modelling of deep-sea species and assemblages with multibeam acoustic datasets as input variables is now a key tool in the provision of maps upon which spatial planning and management of the marine environment can be based. However, with a multitude of methods available, advice is needed on the best methods for the task at hand. In this study, we predictively modelled the distribution and extent of three vulnerable marine ecosystems (VMEs) at the assemblage level (‘Lophelia pertusa reef frameworks’; ‘Stylasterids and lobose sponges’; and ‘Xenophyophore fields’) on the eastern flank of Rockall Bank, using three modelling methods: MaxEnt; RandomForests classification with multiple assemblages (gRF); and RandomForests classification with the presence/absence of a single VME (saRF). Performance metrics indicated that MaxEnt performed the best, but all models were considered valid. All three methods broadly agreed with regard to broad patterns in distribution. However, predicted extent presented a variation of up to 35 % between the different methods, and clear differences in predicted distribution were observed. We conclude that the choice of method is likely to influence the results of predicted maps, potentially impacting political decisions about deep-sea VME conservation.

Copyright © The Royal Society of Edinburgh 2015 

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