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

Piechaud, Nils; Downie, Anna; Stewart, Heather A.; Howell, Kerry L.. 2015 The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages. Earth and Environmental Science Transactions of the Royal Society of Edinburgh, 105 (4). 251-261. https://doi.org/10.1017/S1755691015000122

Before downloading, please read NORA policies.

Preview

Text
Piechaud_et_al_2015_The impact of modelling method selection on predicted extent and distribution of deep-sea benthic assemblages.pdf - Accepted Version
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1017/S1755691015000122

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1017/S1755691015000122
ISSN:	1755-6910
Date made live:	14 Apr 2016 14:01 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/513445

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1017/S1755691015000122

ISSN:

1755-6910

Date made live:

14 Apr 2016 14:01 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/513445