Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon

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dc.contributor.authorPearman, T.R.R.
dc.contributor.authorRobert, K.
dc.contributor.authorCallaway, Alex
dc.contributor.authorHall, R.
dc.contributor.authorLo lacono, C.
dc.contributor.authorHuvenne, V.A.I.
dc.date.accessioned2020-10-20T09:16:18Z
dc.date.available2020-10-20T09:16:18Z
dc.date.issued2020-04-21
dc.descriptionPublication history: Accepted - 17 April 2020; Published online - 21 April 2020en_US
dc.description.abstractSubmarine canyons are associated with increased biodiversity, including cold-water coral (CWC) colonies and reefs which are features of high conservation value that are under increasing anthropogenic pressure. Effective spatial management and conservation of these features requires accurate distribution maps and a deeper understanding of the processes that generate the observed distribution patterns. Predictive distribution modelling offers a powerful tool in the deep sea, where surveys are constrained by cost and technological capabilities. To date, predictive distribution modelling in canyons has focussed on integrating groundtruthed acoustically acquired datasets as proxies for environmental variables thought to influence faunal patterns. Physical oceanography is known to influence faunal patterns but has rarely been explicitly included in predictive distribution models of canyon fauna, thereby omitting key information required to adequately capture the species-environment relationships that form the basis of predictive distribution modelling. In this study, acoustic, oceanographic and biological datasets were integrated to undertake high-resolution predictions of benthic megafaunal diversity and CWC distribution within Whittard Canyon, North-East Atlantic. The main aim was to investigate which environmental variables best predict faunal patterns in canyons and to assess whether including oceanographic data improves predictive modelling. General additive models, random forests and boosted regression trees were used to build predictive maps for CWC occurrence, megafaunal abundance, species richness and biodiversity. To provide more robust predictions, ensemble techniques that summarise the variation in predictions and uncertainties between modelling approaches were applied to build final maps. Model performance improved with the inclusion of oceanographic data. Ensemble maps identified areas of elevated current speed that coincided with steep ridges and escarpment walls as the areas most likely to harbour CWCs and increased biodiversity, probably linked to local hydrodynamics interacting with topography to concentrate food resources. This study shows how incorporating oceanographic data into canyon models can broaden our understanding of processes generating faunal patterns and improve the mapping of features of conservation, supporting effective procedures for spatial ecosystem management.en_US
dc.description.sponsorshipThis work was funded by the NERC MAREMAP programme, the ERC CODEMAP project (Grant no 258482), the EC FP7 IP HERMIONE, DEFRA and the NERC CLASS programme (Grant No NE/R015953/1). Tabitha Pearman is a PhD student in the NERC-funded SPITFIRE Doctoral Training Programme (Grant number NE/L002531/1). Veerle Huvenne currently receives funding from the NERC CLASS programme and from the European Union’s Horizon2020 research and innovation programme iAtlantic project (grant agreement No 818123).en_US
dc.identifierhttp://hdl.handle.net/20.500.12518/181
dc.identifier.citationPearman, T. R. R., Robert, K., Callaway, A., Hall, R., Lo Iacono, C. and Huvenne, V. A. I. (2020) ‘Improving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyon’, Progress in Oceanography. Elsevier BV, 184, p. 102338. doi: 10.1016/j.pocean.2020.102338.en_US
dc.identifier.issn0079-6611
dc.identifier.urihttps://doi.org/10.1016/j.pocean.2020.102338
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).Ten_US
dc.subjectSpecies distribution models, Submarine canyons, Cold-water corals, Internal tideen_US
dc.titleImproving the predictive capability of benthic species distribution models by incorporating oceanographic data – Towards holistic ecological modelling of a submarine canyonen_US
dc.typeArticleen_US
dcterms.dateAccepted2020-04-17
dcterms.dateSubmitted2020-04-21

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